Building a Practical SEC Fundamentals Pipeline with Polars, Excel, and Plotly

Working with SEC fundamentals data is powerful—but rarely simple. Raw filings are large, fragmented across periods, and difficult to transform into something usable for research, screening, or visualization.

To solve this, I built SEC FSN, a lightweight Python-based data engineering and analytics pipeline for working with SEC EDGAR Financial Statements & Notes (FSN) data. The goal was simple: create a transparent, reproducible fundamentals foundation that works equally well in notebooks, Excel, and interactive dashboards.

This project focuses on:

• Robust ingestion and validation of SEC FSN datasets
• High-performance transformations using Polars
• Clean, analytics-ready fundamentals panels
• Practical examples for research, screening, Excel (PyXLL), and Plotly

Rather than treating fundamentals as a black box, SEC FSN exposes every step: from raw files to derived metrics – so you can inspect, validate, and extend the pipeline as needed.

The repository includes:

• An end to end FSN data pipeline
• Multi-period fundamentals panels with margins, ROE, ROA, leverage, and cash flow metrics
• Rolling TTM and delta calculations
• Simple screening and ranking examples
• Excel integration using PyXLL
• Interactive Plotly visualizations such as heatmaps, scatter plots, and P&L waterfall charts

If you’re interested in fundamentals research, quantitative screening, or bridging Python analytics with Excel workflows, this project provides a solid starting point you can build on.

GitHub Link

Video demo on YouTube