The index_model/index.py file has been updated with descriptive comments to clarify the logic behind the index calculation, rebalancing processes, and weighting routines. This documentation effort makes the codebase significantly more maintainable and easier for other developers to understand the model's inner workings.

Completed the initial minimum viable product (MVP) for the index calculation model. This release provides basic functionality for processing stock data, performing monthly rebalancing based on historical performance, and calculating an index level for a specified date range.

Added the core logic for the index model, including rebalancing, ranking, and weighted return calculations based on historical data. While currently centered on a hardcoded CSV file, this sets the foundation for a more modular index backtesting class.

Updated the requirements.txt file to include standard data analysis libraries (pandas, numpy, and warnings) to be used in the upcoming index model implementation. These dependencies will support the calculations needed for the stock index simulation.

This commit establishes the foundational structure for a new stock index modeling assessment project. Included are the required data sources, a skeleton implementation of the IndexModel class, and initial configuration files to jumpstart development.