jeavily-market-protocol

JEAVILY: Quantifying Market Entanglement

"Prediction markets tell you what will happen. Jeavily tells you why."

⚡ The Protocol

Jeavily is a high-dimensional signal processing engine designed to detect hidden correlations ("Entanglement") between seemingly unrelated prediction markets. By treating global events as a neural network, Jeavily identifies how volatility in one sector (e.g., Fed Rates) bleeds into another (e.g., Tech Stocks).

🧠 The Math Engine

The core logic relies on two proprietary metrics:

1. The Entanglement Matrix: A vectorized Pearson Correlation analysis that aligns disparate timeframes to map the "invisible wires" connecting global markets.
2. The Volatility Z-Score: A rolling statistical stress-test that flags 3-Sigma Events ($\sigma > 3$)—moments where market sentiment decouples from reality.

$$Z = \frac{x - \mu}{\sigma}$$

🛠 Tech Stack

• Engine: Python (Pandas, NumPy, SciPy)
• Visualization: Plotly (Dark Mode Signal Rendering)
• Data Source: Synthetic High-Fidelity Stress Data (Simulating Black Swan Events)

🚀 How to Run

This protocol is deployed as a self-contained Jupyter Notebook.

1. Open Jeavily_Core_Engine.ipynb in GitHub.
2. Click "Open in Colab" (if you have the extension) or download to run locally.
3. Execute the Master Protocol cell to initialize the Ghost Loader and render the Dashboard.