S5.8: Bulk backfill from SEC bulk files

Open
  • Is something wrong?
#134 0 comments 0 reactions 0 assignees View on GitHub

Maintainers usually reply within 1 day

Nobody has claimed this yet.

Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
52/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
python
Domain
data-engineering

Research direction

Start with dependency S5.5 (#131) and inspect the existing per-company SEC ingestion path and normalizers. Compare the available SEC nightly bulk archives or Financial Statement Data Sets with that path, then check results for a sample of companies against the API output. Done means bulk ingestion works and the sample results are consistent.

Written by the indexing model from the issue text.

Description

area: data priority: P2 story

Roadmap story S5.8 · Project · Roadmap

Parent epic: E5 #100

Dependencies: S5.5 #131

Priority: P2 · Size: M · Sprint: 7 · Milestone: M6 · Area: data · Depends on: S5.5 · Reqs: FR-D10 · Status: Backlog

Story: As a researcher, I want fundamentals for many more companies, so that experiments aren't limited to the starter universe.

Acceptance criteria

  • Ingestion can load SEC's nightly bulk archives (or the Financial Statement Data Sets) instead of calling the API once per company.
  • Results match the per-company path for a sample of companies.

Tasks

  • Download and parse the bulk files
  • Reuse the existing normalizers
  • Check consistency against the API path

Progress evidence (2026-11-08): No implementation or completion evidence for these acceptance criteria is present in the audited commit. This remains planned work.

Audited against committed main at 0c32c57. Local, staged, and untracked work is excluded.

Dominant language
Python
Stars
0
Forks
0
Avg merge
3h 33m
Merged PRs (30d)
50

Getting set up

  • No Dockerfile or Docker Compose file
  • Has a pull request template
  • No contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from nfiasco/Quant-Time-Machine

All issues in nfiasco/Quant-Time-Machine

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.