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Curated scripts to retrieve information via SEC.gov EDGAR Database
  • Python 100%
2026年01月14日 19:49:01 +01:00
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WIP: This is still a work in progress. Do not run scripts on your main machine.

AWESOME-EDGAR: A project collection of steps, snippets, and scripts to get edgar data using shell commands and python.

What I learned: If I see (A) Python, I'll (B)ASH it with my (C)eyboard! (D)isclaimer: you WILL get bitten
If I hate POLAR weather so much, I should try to make soup more often.
I hate javascript. It's insecure.

This is a project on retrieving financial information from SEC.gov's website using the EDGAR database.
This information is free and available to all, but is notorious for being a pain to work with.
Many open source projects exist to help work around the painful navigation of this interface.
I learned from these projects, and composed my own work-arounds using python and shell scripts.

Project Goals: Taxonomy: Taxonomy mapping, identifying, and populating a complete database. Data: populating edgar json files into a database for query and comparison of fundamentals, to obsolete any proprietary screening tool. Aggregate: To include an awesome-finance list alongside this project of all possible resources.

What's Included: A reference of snippet commands that are useful in all steps Scripts to fetch files, build urls, and quick-queries on tickers and CIKs. Retrieve taxonomy concepts, extension concepts, and schemas. Company facts (data) and processing this data. Format Conversions of: json, xml, csv. using jq for json files. Using the official SEC api to retrieve companyfacts, companyconcepts, and frames

What's to come: An output directory to lead scripts for output files on concepts, facts, and filings. Populate a statement template spreadsheet or populate a postgres database.

Credits/Superheroes/Sources/Inspiration/Forked-from/Resources:

Josef Švenda: https://www.kaggle.com/code/svendaj/extracting-data-from-sec-edgar-restful-apis
@je-suis-tm: https://github.com/je-suis-tm/quant-trading
Both users uploaded their content under the Apache-2.0 License, and as such, I have marked this work under the same license.

IN NO EVENT SHALL I, OR THE CONTRIBUTORS TO THIS PROJECT, BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS PROJECT, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.