datamule 0.21

Making it easier to use SEC filings.

datamule

A python package to make using SEC filings easier. Integrated with datamule's APIs and datasets.

features

current:

• parse textual filings into simplified html, interactive html, or structured json.
• download sec filings quickly and easily
• download datasets such as every MD&A from 2024 or every 2024 10K converted to structured json

Installation

pip install datamule

quickstart:

parsing

Uses endpoint: https://jgfriedman99.pythonanywhere.com/parse_url with params url and return_type. Current endpoint can be slow. If it's too slow for your use-case, please contact me.

simplified html

simplified_html = dm.parse_textual_filing(url='https://www.sec.gov/Archives/edgar/data/1318605/000095017022000796/tsla-20211231.htm',return_type='simplify')

Download Example

interactive html

interactive_html = dm.parse_textual_filing(url='https://www.sec.gov/Archives/edgar/data/1318605/000095017022000796/tsla-20211231.htm',return_type='interactive')

Download Example

json

d = dm.parse_textual_filing(url='https://www.sec.gov/Archives/edgar/data/1318605/000095017022000796/tsla-20211231.htm',return_type='json')

Download Example

using the indices api

Limited to 10,000 results per query. Uses endpoint: https://api.datamule.xyz/submissions. A full list of params can be found here (SEC Router)[https://medium.com/@jgfriedman99/sec-router-05a2308b24ce]

from datamule import Downloader
downloader = Downloader()
downloader.download_using_api(form='10-K',ticker='AAPL')

without the indices api

Either download the pre-built indices from the links in the readme and set the indices_path to the folder

from datamule import Downloader
downloader = Downloader()
downloader.set_indices_path(indices_path)

Or run the indexer. Downloading indices takes about 30 seconds, re-running takes about 20 minutes.

from datamule import Indexer
indexer = Indexer()
indexer.run(download=False)

Example Downloads

# Example 1: Download all 10-K filings for Tesla using CIK
downloader.download(form='10-K', cik='1318605', output_dir='filings')

# Example 2: Download 10-K filings for Tesla and META using CIK
downloader.download(form='10-K', cik=['1318605','1326801'], output_dir='filings')

# Example 3: Download 10-K filings for Tesla using ticker
downloader.download(form='10-K', ticker='TSLA', output_dir='filings')

# Example 4: Download 10-K filings for Tesla and META using ticker
downloader.download(form='10-K', ticker=['TSLA','META'], output_dir='filings')

# Example 5: Download every form 3 for a specific date
downloader.download(form ='3', date='2024-05-21', output_dir='filings')

# Example 6: Download every 10K for a year
downloader.download(form='10-K', date=('2024-01-01', '2024-12-31'), output_dir='filings')

# Example 7: Download every form 4 for a list of dates
downloader.download(form = '4',date=['2024-01-01', '2024-12-31'], output_dir='filings')

datasets

Need a better way to store datasets, as I'm running out of storage. Currently stored on Dropbox 2gb free tier.

downloader.download_dataset('10K')
downloader.download_dataset('MDA')

Update Log

9/14/24

• added support for parser API

9/13/24

• added download_datasets
• added option to download indices
• added support for jupyter notebooks

9/9/24

• added download_using_api(self, output_dir, **kwargs). No indices required.

9/8/24

• Added integration with datamule's SEC Router API

9/7/24

• Simplified indices approach
• Switched from pandas to polar. Loading indices now takes under 500 milliseconds.