Datasets:
metadata
pretty_name: FinLucRAG (OHLCV + SEC/PR Index)
license: mit
language:
- en
task_categories:
- other
size_categories:
- 10K<n<100K
tags:
- finance
- sec
- edgar
- ohlcv
- retrieval
- rag
- metadata-only
- links-only
- parquet
- tabular
- timeseries
configs:
- config_name: default
data_files:
- data/ohlcv.parquet
- data/filings.parquet
FinLucRAG Corpus (OHLCV + SEC/PR Index)
One-stop corpus metadata for building finance RAG systems.
This repo ships two Parquet tables and a fetcher. It does not re-host filings/press releases.
What’s included
data/ohlcv.parquet— daily OHLCV for ~30 tickers (2022-01-01 → 2025-09-05).data/filings.parquet— document-level index of SEC filings & earnings press releases: doc IDs/keys, statement dates, EDGAR doc URLs (stable, file-specific), checksums, and basic classification.scripts/fetch_raw.py— polite downloader that saves originals toraw/{doc_key}.
What’s not included
- No raw HTML/PDF (legal/size reasons). Fetch on demand with the script.
- No paragraph splits (that’s on the user and their workflow).
- No benchmark Q&A (that will live in a separate benchmark repo).
File layout
data/ ohlcv.parquet filings.parquet scripts/ fetch_raw.py
Schemas (frozen)
data/ohlcv.parquet
| column | type | notes |
|---|---|---|
date |
date | trading day (UTC) |
ticker |
string (UPPER) | e.g., AAPL |
open, high, low, close |
float64 | |
volume |
int64 |
data/filings.parquet
| column | type | notes |
|---|---|---|
doc_id |
string | {ticker}_{accession}_{filename} |
doc_key |
string | {ticker}/{accession}/{source}/{filename} |
doc_local_hint |
string | raw/{doc_key} target path |
ticker |
string | |
cik |
string | zero-padded |
cik_int |
int | numeric CIK |
accession |
string | with dashes |
acc_nodash |
string | digits only |
form |
string | 8-K, 10-Q, 10-K, … |
source |
enum | primary, press_release, exhibits |
statement_date |
date | doc day (PR/filing) |
event_date |
date | same as statement_date |
next_trading_hint |
date | event_date + 1 day (convenience) |
event_type |
enum | earnings or other (heuristic: 8-K Item 2.02 / EX-99.* PR text) |
item_codes |
string/NA | e.g., 2.02,9.01 for 8-K |
doc_code |
string/NA | e.g., EX-99.1 |
title |
string/NA | if known |
url |
string | doc-level EDGAR URL (ends with filename) |
content_type |
string | text/html, application/pdf, text/plain |
byte_len |
int | size when indexed |
sha256 |
string | checksum of local copy at index time |
filename |
string | file name |
Quick start
Load with pandas
import pandas as pd
ohlcv = pd.read_parquet("data/ohlcv.parquet")
fil = pd.read_parquet("data/filings.parquet")
print(len(ohlcv), len(fil))
Fetch originals (on your machine)
python scripts/fetch_raw.py \
--parquet data/filings.parquet \
--out raw \
--only event_type=earnings \
--sources press_release,primary \
--rate 2 --verify-sha
# Files land under: raw/{doc_key}
Respectful use: keep the default --rate 2 (2 req/s) and set a real contact email in --user-agent.
Integrity & reproducibility
- Every row has a doc-level URL and filename; many have a sha256 of a known copy.
- You can verify downloads with --verify-sha. If a mismatch occurs, the script will warn.
Licensing & content rights
- This repo: metadata, code, and packaging under the LICENSE below.
- Original documents: owned by their issuers/SEC/third parties. This repo provides links only; you fetch to your machine.
Versioning
- Semantic-ish: bump minor for schema-compatible additions, major for breaking changes.
- filings.parquet and ohlcv.parquet schemas are considered frozen as listed above.
Citation (suggested)
If you use this corpus, cite:
https://huggingface.co/datasets/lakshaychhabra/finlucrag