product-benchmark-report

Single-product performance benchmarking automates report generation by pulling NAV data from local caches or weekly sheets, computing annualized returns and drawdowns against index benchmarks, and outputting a Markdown narrative, PNG graph, Excel data file, and Python script.

Benchmark report calculation assembles benchmark NAV using index data and a deposit rate, then computes annualized returns and drawdowns for the specified financial product. It uses pandas and numpy to process the NAV data and generate the performance metrics.

Yes, the EmQuantAPI is required to fetch index data for assembling the benchmark NAV. The report generation also requires Python with pandas, numpy, and matplotlib installed to compute metrics and render the value-trajectory graph.

Yes, the financial analysis tool reads the benchmark configuration directly from an Excel file. It combines this configuration with NAV data from local caches or weekly sheets to produce the final performance benchmark outputs.

Automating a product performance report generates a Markdown main report, a PNG value-trajectory image, an Excel data file containing the financial metrics, and a Python calculation script clone for full reproducibility of the analysis.

The best way to reproduce the financial benchmark report is through the generated Python calculation script clone. This script, alongside the Excel data file and benchmark configuration, allows you to rerun the NAV data processing and metric computation entirely.