OPEN-SOURCE SCRIPT
AetherEdge - Quantum-Inspired Entanglement Detector
🖊️ Overview
A detector built on a "quantum entanglement" analogy. In entanglement, the states of several particles are so correlated that measuring one instantly informs the others. This engine transposes that onto markets: it measures the synchrony (entanglement strength) among several market variables — price, volume, and one or two correlated assets / global indices — and watches for decoherence, the moment that synchrony breaks down (a decoupling). A NeuraLib map weighs the pairwise couplings into a single Entanglement score, while a self-learning baseline distinguishes "normal" coupling from genuine breaks. Decoupling events — when the system falls out of its entangled state — are flagged as opportunity (divergence, regime shift). Visualization: a circular radar of Entanglement strength radiating from a center node to each variable, with alerts when synchrony collapses.
🔶 Key Features
Quantum-entanglement analogy — quantifies multi-variable synchrony as "entanglement strength"
Pairwise coupling detection — measures four links: price×volume, price×alt, price×index, alt×index
NeuraLib entanglement map — nonlinearly weights the couplings into a unified Entanglement score
Self-learning baseline — learns the "normal" synchrony level to judge genuine collapses by deviation
Decoherence detection — flags decoupling (opportunity) when entanglement drops sharply below baseline
Coherence/decoherence state — distinguishes synchronized (coherent) from collapsed (decoherent)
Circular entanglement radar — radial spokes from a center node to each coupling (length/color by strength) plus concentric rings
Intelligence panel — entanglement, baseline, decoupling magnitude, and each coupling strength at a glance
🧠 Technical Architecture
The engine pulls in a correlated asset and a global index via request.security and forms the "states" of four variables (price return, volume anomaly, alt return, index return).
As pairwise couplings, it computes the absolute rolling correlation |corr|∈[0,1] over four pairs (PX·VOL, PX·ALT, PX·IDX, ALT·IDX) — the strength of each "entanglement link." When all links are strong, the system is synchronized (high entanglement).
The NeuraLib entanglement map (couplings(4) → hidden(tanh) → entanglement strength(1, squashed to [0,1])) nonlinearly weights the couplings into a unified score. The target is the system-wide mean coupling (a self-supervised synchrony measure), and the network learns a smoothed, weighted estimate generalizing the raw mean.
For decoherence, it measures the drop from entanglement's self-learning baseline (a slow EMA tracking the "normal" level): baseline − current. When this clears the threshold, it flags decoupling. The key is catching deviation from the synchrony the system usually holds — not just low correlation.
The circular radar computes coordinates via math.cos/math.sin, drawing spokes from the center node to each coupling link with length proportional to coupling strength. Spoke color shifts with strength (red = collapse → cyan = sync), and concentric rings (25/50/75/100%) mark the scale.
⚙️ Recommended Settings & Tuning Guide
BTC (main) + ETH + TOTAL (1H–4H): Entanglement Window 40, Coherence 0.65, Decoherence 0.35 — a crypto setup
ETH (main) + BTC + TOTAL (1H–4H): BTC as the alt, the broad crypto market as the index
Stock (main) + peer + SPX/index: for single-stock coupling and decoupling detection
SOL (15m–1H): High volatility favors Entanglement Smoothing 5–6 to smooth, Decoherence Threshold 0.4 to be strict
Entanglement Window: longer is a steadier synchrony estimate; shorter is nimbler — ~40 is practical
Coherence Level: above this is "strong sync" — set to the group's typical correlation
Decoherence Threshold: a drop below baseline beyond this is decoupling; smaller is more sensitive (earlier warning)
Choice of alt/index: pick economically coupled assets/indices for detection accuracy
💡 How to Use in Practice
Reacting to decoupling alerts: a ⚡DECOUPLING marker is the moment usually-synced variables break apart — an opportunity for divergence trades or regime shifts; the broken variable may be "leading"
Reading the circular radar: a long, bright (cyan) spoke = strong coupling (synced); short, red = weak (collapsed). All spokes long = system-wide high entanglement; some short = partial decoupling
Using the coherence state: in COHERENT conditions the market moves as one (risk-on/off) and trends propagate; in DECOHERENT conditions idiosyncratic factors dominate, favoring selection and pair trades
Comparing to baseline: when panel entanglement is far below baseline, that's an abnormal break — judge deviation against the usual synchrony
Which coupling broke: the per-coupling rows (PX·VOL, etc.) identify which pair's sync broke (e.g. a broken PX·IDX means the symbol decoupled from the broad market)
Multi-timeframe usage: read the big-picture coherence state on the higher timeframe (4H), then catch opportunities on decoupling alerts on the lower one (15m–1H)
⚠️ Important Notes
Alt/index must be set: this requires proper correlated-asset/index symbols; unrelated variables break synchrony detection
On the quantum analogy: "entanglement" and "coherence" are metaphors from quantum mechanics, not actual quantum effects; the essence is detecting statistical synchrony among variables
Initial learning period: right after launch the network buffer and baseline are immature; treat entanglement estimates as low-confidence until it learns (several hundred bars)
Decoupling ≠ direction: a break signals an opportunity exists, not a direction (buy/sell); which variable leads needs separate analysis
request.security constraints: alt/index are fetched on the same timeframe; liquidity or timeframe mismatch can make synchrony estimates inaccurate
Adaptive-system nature: historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The entanglement detection, coupling analysis, decoherence judgment, and circular radar are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Synchrony-break detection can produce false positives. Always combine any tool with your own analysis and disciplined risk management.
A detector built on a "quantum entanglement" analogy. In entanglement, the states of several particles are so correlated that measuring one instantly informs the others. This engine transposes that onto markets: it measures the synchrony (entanglement strength) among several market variables — price, volume, and one or two correlated assets / global indices — and watches for decoherence, the moment that synchrony breaks down (a decoupling). A NeuraLib map weighs the pairwise couplings into a single Entanglement score, while a self-learning baseline distinguishes "normal" coupling from genuine breaks. Decoupling events — when the system falls out of its entangled state — are flagged as opportunity (divergence, regime shift). Visualization: a circular radar of Entanglement strength radiating from a center node to each variable, with alerts when synchrony collapses.
🔶 Key Features
Quantum-entanglement analogy — quantifies multi-variable synchrony as "entanglement strength"
Pairwise coupling detection — measures four links: price×volume, price×alt, price×index, alt×index
NeuraLib entanglement map — nonlinearly weights the couplings into a unified Entanglement score
Self-learning baseline — learns the "normal" synchrony level to judge genuine collapses by deviation
Decoherence detection — flags decoupling (opportunity) when entanglement drops sharply below baseline
Coherence/decoherence state — distinguishes synchronized (coherent) from collapsed (decoherent)
Circular entanglement radar — radial spokes from a center node to each coupling (length/color by strength) plus concentric rings
Intelligence panel — entanglement, baseline, decoupling magnitude, and each coupling strength at a glance
🧠 Technical Architecture
The engine pulls in a correlated asset and a global index via request.security and forms the "states" of four variables (price return, volume anomaly, alt return, index return).
As pairwise couplings, it computes the absolute rolling correlation |corr|∈[0,1] over four pairs (PX·VOL, PX·ALT, PX·IDX, ALT·IDX) — the strength of each "entanglement link." When all links are strong, the system is synchronized (high entanglement).
The NeuraLib entanglement map (couplings(4) → hidden(tanh) → entanglement strength(1, squashed to [0,1])) nonlinearly weights the couplings into a unified score. The target is the system-wide mean coupling (a self-supervised synchrony measure), and the network learns a smoothed, weighted estimate generalizing the raw mean.
For decoherence, it measures the drop from entanglement's self-learning baseline (a slow EMA tracking the "normal" level): baseline − current. When this clears the threshold, it flags decoupling. The key is catching deviation from the synchrony the system usually holds — not just low correlation.
The circular radar computes coordinates via math.cos/math.sin, drawing spokes from the center node to each coupling link with length proportional to coupling strength. Spoke color shifts with strength (red = collapse → cyan = sync), and concentric rings (25/50/75/100%) mark the scale.
⚙️ Recommended Settings & Tuning Guide
BTC (main) + ETH + TOTAL (1H–4H): Entanglement Window 40, Coherence 0.65, Decoherence 0.35 — a crypto setup
ETH (main) + BTC + TOTAL (1H–4H): BTC as the alt, the broad crypto market as the index
Stock (main) + peer + SPX/index: for single-stock coupling and decoupling detection
SOL (15m–1H): High volatility favors Entanglement Smoothing 5–6 to smooth, Decoherence Threshold 0.4 to be strict
Entanglement Window: longer is a steadier synchrony estimate; shorter is nimbler — ~40 is practical
Coherence Level: above this is "strong sync" — set to the group's typical correlation
Decoherence Threshold: a drop below baseline beyond this is decoupling; smaller is more sensitive (earlier warning)
Choice of alt/index: pick economically coupled assets/indices for detection accuracy
💡 How to Use in Practice
Reacting to decoupling alerts: a ⚡DECOUPLING marker is the moment usually-synced variables break apart — an opportunity for divergence trades or regime shifts; the broken variable may be "leading"
Reading the circular radar: a long, bright (cyan) spoke = strong coupling (synced); short, red = weak (collapsed). All spokes long = system-wide high entanglement; some short = partial decoupling
Using the coherence state: in COHERENT conditions the market moves as one (risk-on/off) and trends propagate; in DECOHERENT conditions idiosyncratic factors dominate, favoring selection and pair trades
Comparing to baseline: when panel entanglement is far below baseline, that's an abnormal break — judge deviation against the usual synchrony
Which coupling broke: the per-coupling rows (PX·VOL, etc.) identify which pair's sync broke (e.g. a broken PX·IDX means the symbol decoupled from the broad market)
Multi-timeframe usage: read the big-picture coherence state on the higher timeframe (4H), then catch opportunities on decoupling alerts on the lower one (15m–1H)
⚠️ Important Notes
Alt/index must be set: this requires proper correlated-asset/index symbols; unrelated variables break synchrony detection
On the quantum analogy: "entanglement" and "coherence" are metaphors from quantum mechanics, not actual quantum effects; the essence is detecting statistical synchrony among variables
Initial learning period: right after launch the network buffer and baseline are immature; treat entanglement estimates as low-confidence until it learns (several hundred bars)
Decoupling ≠ direction: a break signals an opportunity exists, not a direction (buy/sell); which variable leads needs separate analysis
request.security constraints: alt/index are fetched on the same timeframe; liquidity or timeframe mismatch can make synchrony estimates inaccurate
Adaptive-system nature: historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The entanglement detection, coupling analysis, decoherence judgment, and circular radar are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Synchrony-break detection can produce false positives. Always combine any tool with your own analysis and disciplined risk management.
開源腳本
秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。
開源腳本
秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。