Finalized the notebook and supporting assets for the first CQF exam. The work covers data loading, exploration, and portfolio optimization techniques including the Lagrangian formulation for minimum variance portfolios.

Completed tasks 0-3 for Exam 1, which involved setting up the environment and performing initial data analysis. Additionally, I've implemented backtesting procedures for Value-at-Risk (VaR) models using both Exponentially Weighted Moving Average (EWMA) and Historical Simulation methods to validate performance across the dataset. The results, including various validation plots, are now available for review within the core notebook.

We've introduced a new reference notebook that covers practical techniques for managing 'busy' states in Python, ranging from thread/async status checks to progress visualization and robust retry logic with exponential backoff. This resource provides a clear toolkit for handling common concurrency bottlenecks and improving application responsiveness.

Initialized the project repository for the CQF Exam 1 tasks, including setup files and initial placeholder code. The setup establishes the working environment for the upcoming analytical modules. Let the coding begin!