End User Manual

Detailed instructions for configuring and operating every component of Attribution Analysis. Use this section as your in-depth reference once you have completed the Quick Start and need to lock in the specifics of your data, your parameters, and your execution workflow.

If you are looking for ready-made recipes, skip ahead to Use Cases. Otherwise, work through this manual in order: start with Data Formats to understand what the pipeline expects, then Configuration Reference to map your data to the package’s vocabulary, then choose one of the two execution workflows below.

Choose your workflow

Attribution Analysis supports two execution paths. Pick the one that matches how your team operates today; you can always switch later because both consume the same data and produce the same output.

Workflow	Best fit when…
Excel Workflow	Parameters are tuned by analysts who edit Config/attribution_analysis_parameters.xlsx directly. No Python needed; the run is launched from a terminal with kaxanuk.attribution_analysis run.
Running from Python	You need to script variations, run batches of portfolios, or integrate the pipeline into a larger Python workflow (CI, Jupyter notebooks, dashboards). The Excel workbook is still used for configuration, but the run is driven through ExcelConfigurator and main().

What you’ll find here:

• Data Formats — the market data, portfolio, benchmark, and factor return file formats

• Configuration Reference — full reference for every parameter in the Excel workbook

• Excel Workflow — the CLI commands and step-by-step Excel-driven workflow

• Running from Python — driving the pipeline directly from Python