Metadata-Version: 2.4
Name: stanford-edgar-parser
Version: 0.1.1
Summary: Layout-faithful SEC EDGAR filing parser from the Stanford EDGAR Filings Dataset.
Author: Stanford Advanced Financial Technologies Lab
Project-URL: Homepage, https://github.com/Stanford-Advanced-FinTech-Lab-SAFTL/stanford-edgar-filings-dataset
Project-URL: Repository, https://github.com/Stanford-Advanced-FinTech-Lab-SAFTL/stanford-edgar-filings-dataset
Keywords: sec,edgar,filings,multimarkdown,financial-documents
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Classifier: Topic :: Text Processing :: Markup
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: beautifulsoup4>=4.12.3
Requires-Dist: imgkit>=1.2.3
Requires-Dist: lxml>=6.0.0
Requires-Dist: mistralai>=1.9.0
Requires-Dist: numpy>=2.0.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: playwright>=1.48.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: PyMuPDF>=1.24.0
Requires-Dist: PyPDF2>=3.0.1
Requires-Dist: python-dotenv>=1.0.0
Requires-Dist: requests>=2.32.0
Requires-Dist: tabulate>=0.9.0
Provides-Extra: dev
Requires-Dist: build>=1.2.0; extra == "dev"
Requires-Dist: pytest>=8.0.0; extra == "dev"
Dynamic: license-file

# Stanford EDGAR Filings Dataset Parser

The Stanford EDGAR Filings Dataset (SEFD) is a 550B-token reconstruction of 18.5M SEC filings into layout-faithful MultiMarkdown for LLM pretraining, financial reasoning, document understanding, and evaluation.

SEFD reverse-engineers EDGAR's heterogeneous disclosure formats into a token-efficient representation that preserves financial tables, indentation, merged headers, numeric signs, currency and percent symbols, document hierarchy, and other layout cues that carry financial meaning. Internal validation shows our rule-based reconstruction methodology achieves greater than 99% structural and semantic accuracy on sampled outputs.

The software routes and parses the full EDGAR source-format surface, including legacy fixed-width text, tag-soup HTML, SGML wrappers, XML submissions, and PDF attachments, with specialized reconstruction for more than 30 SEC XML schemas, including Forms 3, 4, 5, D, 13D/G, N-PX, N-PORT, N-CEN, 13F, 144, ATS-N, 1-A/K/Z, C, MA, TA, X-17A-5, 24F-2NT, ABS-EE, and related amendments, withdrawals, and corrections. PDF attachments are parsed with Mistral OCR 3.

Our first public release, SEFD-v1, is a 152B-token dataset covering filings from January 2022 through June 2025.

- Paper: [The Stanford EDGAR Filings Dataset](https://arxiv.org/abs/2606.18192)
- Dataset: [SEFD-v1 on Hugging Face](https://huggingface.co/datasets/anonymous-md/EDGAR_FILINGS_DATASET)

## Install

From PyPI:

```bash
pip install stanford-edgar-parser
```

Or directly from GitHub:

```bash
pip install "stanford-edgar-parser @ git+https://github.com/Stanford-Advanced-FinTech-Lab-SAFTL/stanford-edgar-filings-dataset.git"
```

## Usage

Parse a local filing and convert tables to MultiMarkdown:

```bash
stanford-edgar-parser path/to/filing.txt --to_mmd
```

or:

```bash
python -m stanford_edgar_parser path/to/filing.txt --to_mmd
```

Optional rendering helpers:

```bash
node multimarkdown.js path/to/file.md > file.html
node html-to-pdf.mjs file.html file.pdf
```

## Agent Skills

Install bundled Codex and Claude skills:

```bash
stanford-edgar-install-skill
```

or:

```python
from stanford_edgar_parser.ai import install_skill

install_skill()
```

## MCP

Package install:

```toml
[mcp_servers.stanford_edgar_parser]
command = "uvx"
args = ["--from", "stanford-edgar-parser", "stanford-edgar-mcp"]
startup_timeout_sec = 120
```

GitHub install:

```toml
[mcp_servers.stanford_edgar_parser]
command = "uvx"
args = [
  "--from",
  "stanford-edgar-parser @ git+https://github.com/Stanford-Advanced-FinTech-Lab-SAFTL/stanford-edgar-filings-dataset.git",
  "stanford-edgar-mcp"
]
startup_timeout_sec = 120
```

The package-installed MCP server always exposes `parse_filing`. Repo-local rendering and review tools are exposed when the full clone includes `multimarkdown.js`, `html-to-pdf.mjs`, and `tools/`.

## Citation

```bibtex
@article{bettencourt2026stanfordedgar,
  title={The Stanford EDGAR Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data},
  author={Bettencourt, Nick and Ding, Xiaowei and Giesecke, Kay},
  journal={arXiv preprint arXiv:2606.18192},
  year={2026},
  url={https://arxiv.org/abs/2606.18192}
}
```
