Quantum Network of Assets (QNA)

This repository contains the latest revised materials for:

Quantum Network of Assets (QNA): A Density-Operator Framework for Market Dependence and Structural Risk Diagnostics

The project develops an operator-based representation of cross-asset dependence using normalized rolling multi-feature market states. The empirical application uses a stable Nasdaq-100 panel over 2020-01-01 to 2025-12-31 and studies how entropy, purity-based mixing, and event-aligned structural deviations compare with more classical covariance-spectrum diagnostics.

Repository layout

• analysis/
  • src/: data download, metric construction, robustness, and figure/table export scripts
  • qfe_revision_analysis_workbook.ipynb: the main exploratory notebook used to organize the revised empirical workflow
  • requirements.txt: minimal Python dependencies
• data/
  • raw/market_data/: daily local CSV files for the stable-panel market sample
  • processed/: processed metric outputs used in the revised manuscript
  • reference/: ticker universe and event-catalog reference files

What is included

• Reproducible analysis scripts and notebook
• Reference files and processed outputs
• The raw stable-panel daily market CSV files used for the current revision

Quick start

Create a Python environment and install the minimal dependencies:

pip install -r analysis/requirements.txt

Download or refresh the local market data:

python analysis/src/download_market_data.py --ticker-source wikipedia

Rebuild processed metrics, figures, and manuscript tables:

python analysis/src/build_revision_outputs.py

Data note

The empirical design uses a stable current-constituent Nasdaq-100 panel over 2020--2025. This avoids composition breaks inside the rolling operator, but it also implies survivorship bias. The paper discusses this design choice explicitly as a limitation.

Suggested GitHub setup

• Choose your preferred open-source or research-sharing license before making the repository public.
• If desired, add a short project description such as:
  • Operator-based market dependence diagnostics with QNA, ERI, and QEWS