README.md

---
license: mit
task_categories:
  - table-question-answering
  - text-classification
  - time-series-forecasting
language:
  - ko
  - en
tags:
  - finance
  - disclosure
  - dart
  - edgar
  - sec
  - xbrl
  - korea
  - financial-statements
  - corporate-filings
  - krx
  - ohlcv
  - market-data
  - 전자공시
  - 재무제표
  - 사업보고서
  - 한국
pretty_name: DartLab 전자공시 + 시장 데이터
size_categories:
  - 1K<n<10K
---

<div align="center">

<br>

<img alt="DartLab" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/logo.png" width="160">

<h3>DartLab Data</h3>

<p><b>Structured company data + market data from DART, EDGAR, KRX</b></p>
<p>한국 DART 공시 + 미국 SEC EDGAR 공시 + KRX 시장 데이터 — 한 dataset, 여러 카테고리</p>

<p>
<a href="https://github.com/eddmpython/dartlab"><img src="https://img.shields.io/badge/GitHub-dartlab-ea4647?style=for-the-badge&labelColor=050811&logo=github&logoColor=white" alt="GitHub"></a>
<a href="https://pypi.org/project/dartlab/"><img src="https://img.shields.io/pypi/v/dartlab?style=for-the-badge&color=ea4647&labelColor=050811&logo=pypi&logoColor=white" alt="PyPI"></a>
<a href="https://eddmpython.github.io/dartlab/"><img src="https://img.shields.io/badge/Docs-GitHub_Pages-38bdf8?style=for-the-badge&labelColor=050811&logo=github-pages&logoColor=white" alt="Docs"></a>
<a href="https://buymeacoffee.com/eddmpython"><img src="https://img.shields.io/badge/Sponsor-Buy_Me_A_Coffee-ffdd00?style=for-the-badge&labelColor=050811&logo=buy-me-a-coffee&logoColor=white" alt="Sponsor"></a>
</p>

</div>

## What is this?

<img align="right" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/avatar-study.png" width="120">

Pre-built [Parquet](https://parquet.apache.org/) files from [DartLab](https://github.com/eddmpython/dartlab) — a Python library that turns DART (Korea), EDGAR (US), and KRX disclosure & market data into one structured company map.

한국 DART 전자공시 + 미국 SEC EDGAR 공시 + KRX 거래소 일별 시장 데이터를 동일한 SSOT 로 묶은 데이터셋.

This dataset is the **data layer** behind DartLab. When you run `dartlab.Company("005930")` or `dartlab.gather("krx", "close")`, the library auto-downloads the relevant parquet from this repo — no API key, no manual collection.

## Datasets

7 public categories — each lives at its own subdirectory and is consumed by a specific dartlab engine.

| Category | Path | Coverage | Engine |
|---|---|---|---|
| [dart/docs](#dartdocs--dart-disclosure-text) | `dart/docs/` | 2,547 KR companies, ~8 GB | `Company.show("section")` |
| [dart/finance](#dartfinance--dart-financial-statements) | `dart/finance/` | 2,744 KR companies, ~586 MB | `Company.show("BS"/"IS"/"CF")` |
| [dart/report](#dartreport--dart-structured-disclosure-apis) | `dart/report/` | 2,711 KR companies, ~319 MB | `Company.report()` (28 APIs) |
| [dart/scan](#dartscan--cross-sectional-pre-built) | `dart/scan/` | KR cross-sectional | `dartlab.scan("...")` |
| [edgar/docs](#edgardocs--edgar-disclosure-text) | `edgar/docs/` | 970 US companies | `Company.show("section")` (US) |
| [krx/prices](#krxprices--krx-daily-ohlcv-new) ★ | `krx/prices/` | 1995~today, 17 yearly parquets | `gather("krx", target)` |
| [landing/map](#landingmap--industry-map-json) | `landing/map/` | KR industry graph | `dartlab.industry()` |

---

### dart/docs — DART Disclosure Text

Full-text sections from Korean annual/quarterly reports, parsed into structured blocks.

| Column | Description |
|---|---|
| `rcept_no` | DART filing ID |
| `rcept_date` | Filing date |
| `stock_code` | Stock code (6-digit) |
| `corp_name` | Company name |
| `report_type` | Annual/quarterly report type |
| `section_title` | Original section title (한글) |
| `section_order` | Section ordering |
| `content` | Section text (markdown) |
| `blockType` | `text` / `table` / `heading` |
| `year` | Filing year |

**File**: `dart/docs/{stockCode}.parquet` — one file per company.

---

### dart/finance — DART Financial Statements

XBRL-based financial data from DART OpenAPI (`fnlttSinglAcntAll`). BS/IS/CF/SCE × 분기/연간.

| Column | Description |
|---|---|
| `bsns_year` | Business year |
| `reprt_code` | Report quarter code |
| `stock_code` | Stock code |
| `corp_name` | Company name |
| `fs_div` | `CFS` (consolidated) / `OFS` (separate) |
| `sj_div` | Statement type (BS/IS/CF/SCE) |
| `account_id` | XBRL account ID (~97% mapped to standard names) |
| `account_nm` | Account name (한글) |
| `thstrm_amount` | Current period amount |
| `frmtrm_amount` | Prior period amount |
| `bfefrmtrm_amount` | Two periods prior amount |

**File**: `dart/finance/{stockCode}.parquet` — one file per company.

---

### dart/report — DART Structured Disclosure APIs

28 DART API categories covering governance, compensation, shareholding, capital changes, audit opinions, and more.

| Column | Description |
|---|---|
| `apiType` | API category (e.g., `dividend`, `employee`, `executive`) |
| `year` / `quarter` | Reporting period |
| `stockCode` | Stock code |
| `corpCode` | DART corp code |
| *(varies)* | Category-specific columns |

**28 API types**: dividend, employee, executive, majorHolder, treasuryStock, capitalChange, auditOpinion, stockTotal, outsideDirector, corporateBond, and 18 more.

**File**: `dart/report/{stockCode}.parquet` — one file per company.

---

### dart/scan — Cross-Sectional Pre-Built

Pre-computed cross-sectional aggregates across all KR listed companies (governance ratios, cash-flow patterns, financial ratios, etc.) — for ranking/screening without per-company iteration.

| Subcategory | Content |
|---|---|
| `dart/scan/governance/` | Board structure, related-party, ownership concentration |
| `dart/scan/financial/` | Pre-computed ratios (ROE, debt-to-equity, ...) |
| `dart/scan/cashflow/` | Operating/investing/financing patterns |

**Engine**: `dartlab.scan("governance/...")` — one call returns all-company DataFrame.

---

### edgar/docs — EDGAR Disclosure Text

Full-text sections from US annual/quarterly reports (10-K, 10-Q, 8-K, ...), parsed into the same structure as `dart/docs`. Same library API works for both markets.

**File**: `edgar/docs/{ticker}.parquet`.

---

### krx/prices — KRX Daily OHLCV ★ NEW

Daily OHLCV + market cap + listed shares for **all KRX-listed companies (KOSPI + KOSDAQ)**, raw long parquet from KRX OpenAPI.

| Column | Description | Unit |
|---|---|---|
| `BAS_DD` | Trade date (YYYYMMDD) | string |
| `ISU_CD` | Stock code (6-digit) | string |
| `ISU_NM` | Stock name | string |
| `MKT_NM` / `SECT_TP_NM` | Market / sector | string |
| `TDD_OPNPRC` / `TDD_HGPRC` / `TDD_LWPRC` / `TDD_CLSPRC` | Open / High / Low / Close | KRW |
| `ACC_TRDVOL` / `ACC_TRDVAL` | Volume / Amount | shares / KRW |
| `MKTCAP` | Market cap | KRW |
| `LIST_SHRS` | Listed shares | shares |
| `FLUC_RT` / `CMPPREVDD_PRC` | Daily change rate / price | % / KRW |

**Coverage**: 1995-01-04 ~ today (17 yearly partitions: `raw-1995.parquet` ~ `raw-2026.parquet`).
**Update**: every weekday at KST 17:00 (after market close + settlement). Auto gap-fill if any cron run is missed.
**Engine**: `gather("krx", target, ...)` — pivot to wide (rows = stockCode, cols = date) on demand. Adjusted prices (split/bonus/rights) auto-applied via price-series detection (CRSP backward chaining).

```python
import dartlab
dartlab.gather("krx", "close", start="2025-01-01", end="2025-06-30")  # close-price wide
dartlab.gather("krx", "rsi14", start="2025-01-01")                    # 30+ technical indicators
dartlab.gather("krx", "marketCap", start="2025-06-30")                # market cap snapshot
```

**No API key needed** — engine reads HF directly. (KRX OpenAPI key is only for operator cron building this dataset.)

---

### landing/map — Industry Map JSON

Pre-built industry graph (nodes + edges) for the Korean market — companies × processes × supply-chain edges. Powers the `/map` interactive industry visualization.

**Engine**: `dartlab.industry()` / `c.industry()`.

---

## Usage

```python
import dartlab

# 1. Korean company — auto-downloads dart/docs + dart/finance + dart/report
c = dartlab.Company("005930")
c.show("BS")                     # balance sheet (dart/finance)
c.show("businessOverview")       # business section text (dart/docs)
c.report("dividend")             # dividend history (dart/report)

# 2. US company — same API, edgar/docs auto
us = dartlab.Company("AAPL")
us.show("IS")

# 3. KRX market data — no API key, HF auto
df = dartlab.gather("krx", "close", start="2024-01-01", end="2024-12-31")  # 1-year wide DataFrame

# 4. Cross-sectional scan
ranked = dartlab.scan("governance/dividend")  # all KR companies, sorted

# 5. Natural language
dartlab.ask("삼성전자 재무건전성 분석해줘")
```

**No API key, no setup** — `pip install dartlab` and the library auto-downloads from this dataset, with local caching.

## Data Source

- **DART** (Korea): [dart.fss.or.kr](https://dart.fss.or.kr) — Financial Supervisory Service's electronic disclosure system
- **EDGAR** (US): [sec.gov/edgar](https://www.sec.gov/edgar) — SEC's Electronic Data Gathering, Analysis, and Retrieval system
- **KRX** (Korea): [openapi.krx.co.kr](https://openapi.krx.co.kr) — Korea Exchange OpenAPI (daily OHLCV + market cap + shares)

All data sourced from public/government systems. Numeric figures preserved as-is from the original source — no rounding, no estimation, no interpolation. Adjusted prices computed at use time (raw + events SSOT).

## Update Schedule

| Category | Cadence | Trigger |
|---|---|---|
| `dart/docs`, `dart/finance`, `dart/report` | Daily incremental + weekly full sync | GitHub Actions (DART) |
| `dart/scan` | Daily after dart/finance update | GitHub Actions |
| `edgar/docs` | Daily incremental | GitHub Actions (EDGAR) |
| `krx/prices` | Every weekday at KST 17:00 (T-0 same-day, auto gap-fill) | GitHub Actions (KRX) |
| `landing/map` | On industry-map source change | GitHub Actions (map build) |

Recent 7-day filings checked incrementally; full re-sync on schema changes.

## Learn More

<img align="right" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/avatar-analyze.png" width="120">

DartLab auto-downloads from this dataset — one stock code gives you the full company map. Start with the intro below.

<div align="center">

<a href="https://www.youtube.com/shorts/97lYLWMWzvA"><img src="https://img.youtube.com/vi/97lYLWMWzvA/maxresdefault.jpg" alt="DartLab 30s Demo" width="320"></a>

<sub>▶ <a href="https://www.youtube.com/shorts/97lYLWMWzvA">DartLab 30s Demo</a></sub>

</div>

- **GitHub** — [github.com/eddmpython/dartlab](https://github.com/eddmpython/dartlab)
- **Intro blog** — [DartLab 시작하기 / Getting started](https://eddmpython.github.io/dartlab/blog/dartlab-easy-start)
- **Docs** — [eddmpython.github.io/dartlab](https://eddmpython.github.io/dartlab/)
- **YouTube** — [@eddmpython](https://www.youtube.com/@eddmpython)

<img align="right" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/avatar-discover.png" width="120">

## License

Dataset content licensed MIT. Underlying source data is governed by each source's terms (DART, SEC EDGAR, KRX OpenAPI). See [data source attribution](#data-source).