DartLab

One stock code. The whole story.

DART + EDGAR filings, structured and comparable — in one line of Python.

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Note: DartLab is under active development. APIs may change between versions, and documentation may lag behind the latest code.

Install
Quick Start
What DartLab Is
EDGAR (US)
AI Analysis
MCP — AI Assistant Integration
OpenAPI — Raw Public APIs
Data
Try It Now
Documentation
Stability
Contributing

Install

Requires Python 3.12+.

# Core — financial statements, sections, Company
uv add dartlab

# or with pip
pip install dartlab

Optional Extras

Core analysis, AI, charts, and LLM are all included in the base install. Optional extras add integrations:

uv add "dartlab[mcp]"             # MCP server for Claude Desktop / Code / Cursor

From Source

git clone https://github.com/eddmpython/dartlab.git
cd dartlab && uv pip install -e ".[all]"

# or with pip
pip install -e ".[all]"

PyPI releases are published only when the core is stable. If you want the latest features (including experimental ones like audit, forecast, valuation), clone the repo directly — but expect occasional breaking changes.

Desktop App (Alpha)

Skip all installation steps — download the standalone Windows launcher:

Download DartLab.exe from dartlab-desktop
Also available from the DartLab landing page

One-click launch — no Python, no terminal, no package manager required. The desktop app bundles the web UI with a built-in Python runtime.

Alpha — functional but incomplete. The desktop app is a Windows-only .exe launcher. macOS/Linux are not yet supported.

No data setup required. When you create a Company, dartlab automatically downloads the required data from HuggingFace (DART) or SEC API (EDGAR). The second run loads instantly from local cache.

Quick Start

Terminal — just type `dartlab`

dartlab                          # start the AI analysis REPL
dartlab chat 005930              # jump straight into Samsung Electronics

Inside the REPL, type questions in natural language or use skill commands:

삼성전자 > Analyze profitability trends and earnings quality

삼성전자 > /comprehensive        # full investment analysis
삼성전자 > /health               # financial health check
삼성전자 > /company SK하이닉스    # switch company

Python — one stock code, the whole picture

import dartlab

# Samsung Electronics — from raw filings to structured data
c = dartlab.Company("005930")
c.sections                      # every topic, every period, side by side
c.show("businessOverview")      # what this company actually does
c.diff("businessOverview")      # what changed since last year
c.BS                            # standardized balance sheet
c.ratios                        # 47 financial ratios, already calculated

# Apple — same interface, different country
us = dartlab.Company("AAPL")
us.show("business")
us.ratios

# No code needed — ask in natural language
dartlab.ask("Analyze Samsung Electronics financial health")

What DartLab Is

A public company files hundreds of pages every quarter. Inside those pages is everything — revenue trends, risk warnings, management strategy, competitive position. The complete truth about a company, written by the company itself.

Nobody reads it.

Not because they don't want to. Because the same information is named differently by every company, structured differently every year, and scattered across formats designed for regulators, not readers. The same "revenue" appears as ifrs-full_Revenue, dart_Revenue, SalesRevenue, or dozens of Korean variations.

DartLab changes who can access this information. Two engines turn raw filings into one comparable map:

The Two Problems DartLab Solves

1. The same company says different things differently every year.

Sections horizontalization normalizes every disclosure section into a topic × period grid. Different titles across years and industries all resolve to the same canonical topic:

                    2025Q4    2024Q4    2024Q3    2023Q4    …
companyOverview       ✓         ✓         ✓         ✓
businessOverview      ✓         ✓         ✓         ✓
productService        ✓         ✓         ✓         ✓
salesOrder            ✓         ✓         —         ✓
employee              ✓         ✓         ✓         ✓
dividend              ✓         ✓         ✓         ✓
audit                 ✓         ✓         ✓         ✓
…                    (98 canonical topics)

Before (raw section titles):              After (canonical topic):
Samsung    "II. 사업의 내용"               → businessOverview
Hyundai    "II. 사업의 내용 [자동차부문]"   → businessOverview
Kakao      "2. 사업의 내용"               → businessOverview

~95%+ mapping rate across all listed companies. Each cell keeps the full text with heading/body separation, tables, and original evidence. Comparing "what did the company say about risk last year vs. this year" becomes a single diff() call.

2. Every company names the same number differently.

Account standardization normalizes every XBRL account into a single canonical name:

Before (raw XBRL):                          After (standardized):
Company     account_id          account_nm   →  snakeId    label
Samsung     ifrs-full_Revenue   수익(매출액)  →  revenue    매출액
SK Hynix    dart_Revenue        매출액       →  revenue    매출액
LG Energy   Revenue             매출         →  revenue    매출액

~97% mapping rate. Cross-company comparison requires zero manual work. Combined with scan("account", ...) / scan("ratio", ...), you can compare a single metric across 2,700+ companies in one call.

Principles — Accessibility and Reliability

These two principles govern every public API:

Accessibility — One stock code is all you need. import dartlab provides access to every feature. No internal DTOs, no extra imports, no data setup. Company("005930") auto-downloads from HuggingFace.

Reliability — Numbers are raw originals from DART/EDGAR. Missing data returns None, never a guess. trace(topic) shows which source was chosen and why. Errors are never swallowed.

Data — Everything Is Ready

All data is pre-built on HuggingFace. When you create a Company, dartlab auto-downloads what it needs — no setup, no API key, no manual download.

Dataset	Coverage	Size
DART docs	2,500+ companies	~8 GB
DART finance	2,700+ companies	~600 MB
DART report	2,700+ companies	~320 MB
DART scan	Pre-built cross-company	~271 MB
EDGAR	On-demand	SEC API (auto-fetched)

Want to collect directly from the source? See OpenAPI and Data for the full pipeline.

Company — 7 Things to Remember

Company merges docs/finance/report into one object. You only need 7 methods:

c = dartlab.Company("005930")

c.index                         # what's available -- topic list + periods
c.show("BS")                    # view data -- DataFrame per topic
c.select("IS", ["매출액"])       # extract data -- finance or docs, same pattern
c.trace("BS")                   # where it came from -- source provenance
c.diff()                        # what changed -- text changes across periods

c.analysis("수익성")             # analyze -- 14-axis financial analysis
c.review()                      # report -- structured full report

select() works on any topic — "IS", "BS", "CF" for financial statements, or any docs topic like "productService", "salesOrder" for disclosure tables. Same pattern, single entry point.

BS/IS/CF/ratios are convenience shortcuts for show. Three namespaces (c.docs, c.finance, c.report) are for direct source access when needed.

Scan — The Whole Market in One Call

scan() is the single entry point for all market-wide cross-sectional analysis. No extra methods to remember — just scan().

dartlab.scan()                        # guide: list all axes + usage
dartlab.scan("governance")            # governance structure across all firms
dartlab.scan("governance", "005930")  # filter to one company
dartlab.scan("ratio")                 # list available ratios
dartlab.scan("ratio", "roe")          # ROE across all firms
dartlab.scan("account", "매출액")     # revenue across all firms
dartlab.scan("cashflow")              # OCF/ICF/FCF + 8-pattern classification

13 axes, two patterns:

Axis	Label	What it does
governance	Governance	Ownership, outside directors, pay ratio, audit opinion, minority holder dispersion
workforce	Workforce	Headcount, avg salary, labor cost ratio, value added per employee, growth rate, top earners
capital	Shareholder Return	Dividends, buybacks, cancellations, equity changes
debt	Debt Structure	Bond maturity, CP/short-term bonds, debt ratio, ICR, risk grade
cashflow	Cash Flow	OCF/ICF/FCF + 8-type lifecycle pattern
audit	Audit Risk	Opinion, auditor change, special matters, independence ratio
insider	Insider Holdings	Major holder change, treasury stock, stability
quality	Earnings Quality	Accrual ratio + CF/NI — is profit backed by cash?
liquidity	Liquidity	Current ratio + quick ratio — can it pay tomorrow?
digest	Digest	Market-wide disclosure change digest
network	Network	Corporate relationship graph (investment/ownership)
account	Account	Single account time-series (target required)
ratio	Ratio	Single ratio time-series (target required)

Adding a new axis means one module — no other code changes needed.

Analysis — Full Financial Statement Analysis

analysis() transforms raw financial statements into story-ready structured data. It is the middle layer between raw data and every consumer — Review (reports), AI (interpretation), and humans (direct reading). When analysis quality improves, all three benefit simultaneously.

All Company Data (finance + docs + report)
    ↓  Company.select()  ← single access point for everything
analysis()  →  14-axis structured data (amounts + ratios + YoY + flags)
    ↓              ↓              ↓
 review()       AI(ask)        human
 reports        interpret      interpret

Same 3-step call pattern as scan.

dartlab.analysis()                    # 14-axis guide
dartlab.analysis("수익구조")           # list calc functions for revenue structure
dartlab.analysis("수익구조", c)        # run revenue structure analysis -> dict

c.analysis()                          # guide
c.analysis("수익성")                   # profitability analysis

Part	Axis	Description	Items
1-1	수익구조	How does the company make money	8
1-2	자금조달	Where does funding come from	9
1-3	자산구조	What assets were acquired	4
1-4	현금흐름	How did cash actually flow	3
2-1	수익성	How well does it earn	4
2-2	성장성	How fast is it growing	3
2-3	안정성	Can it survive	4
2-4	효율성	Does it use assets well	3
2-5	종합평가	Financial health in one word	3
3-1	이익품질	Are earnings real	4
3-2	비용구조	How do costs behave	4
3-3	자본배분	Where does earned cash go	5
3-4	투자효율	Does investment create value	4
3-5	재무정합성	Do statements reconcile	5

Review — Analysis to Report

c.review()              # all 14 sections, full report
c.review("수익구조")     # single section

4 output formats: rich (terminal), html, markdown, json.

Sample reports (generated by c.review().toMarkdown()):

Company	Sector	Key characteristics
Samsung Electronics	Semiconductor	Mid-cycle FCF normalization, cycle-adjusted DCF
SK Hynix	Semiconductor	HBM structural growth vs DRAM/NAND cycle
Kia	Auto	EV transition capex impact, dividend policy
HD Hyundai Heavy Industries	Shipbuilding	Loss-making turnaround, backlog-driven valuation gap
SK Telecom	Telecom	Stable dividend, holding-like subsidiary structure
LG Chem	Chemical/Battery	EBITDA-negative EV/Sales fallback, segment divergence
SK	Holding	Conglomerate discount, NAV/SOTP gap
NCSoft	Game	Pipeline-dependent, R&D-driven valuation
Amorepacific	Consumer	Brand value, channel shift (duty-free to e-commerce)
Hyundai Steel	Steel/Cyclical	Mid-cycle normalization, China overcapacity

Each report ends with an Analysis Limitations section documenting known model constraints.

Reviewer — review + AI Interpretation

Adds AI opinions on top of review:

c.reviewer()                                    # full + AI
c.reviewer(guide="Evaluate from semiconductor cycle perspective")

Gather — External Market Data in One Call

gather() collects external market data — price, flow, macro, news — all as Polars DataFrames.

dartlab.gather()                              # guide -- 4 axes
dartlab.gather("price", "005930")             # KR OHLCV timeseries (1-year default)
dartlab.gather("price", "AAPL", market="US")  # US stock
dartlab.gather("flow", "005930")              # foreign/institutional flow (KR)
dartlab.gather("macro")                       # KR 12 macro indicators
dartlab.gather("macro", "FEDFUNDS")           # single indicator (auto-detects US)
dartlab.gather("news", "삼성전자")             # Google News RSS

Company-bound: c.gather("price") — no need to pass the stock code again.

Five-Layer Relationship

gather()     External market data (4 axes)        -- price, flow, macro, news
scan()       Market-wide cross-section (13 axes)  -- screening across firms
analysis()   Single-firm deep analysis (14 axes)  -- full financial analysis
c.review()   analysis -> structured report         -- block-template pipeline
c.reviewer() review + AI interpretation            -- per-section AI opinions

Architecture — Layered by Responsibility

L0  core/        Protocols, finance utils, docs utils, registry
L1  providers/   Country-specific data (DART, EDGAR, EDINET)
    gather/      External market data (Naver, Yahoo, FRED)
    scan/        Market-wide cross-sectional analysis (13 axes)
L2  analysis/    8 analytical domains (strategy → macro)
    review/      Block-template report assembly
L3  ai/          LLM-powered analysis (5 providers)

Import direction is enforced by CI — no reverse dependencies allowed. The four axes compose naturally: Company (one firm, deep) → Analysis (judgment) → Review (presentation) → Scan (all firms, wide).

Extensibility — Zero Core Modification

Adding a new country requires zero changes to core code:

Create a provider package under providers/
Implement canHandle(code) -> bool and priority() -> int
Register via entry_points in pyproject.toml

dartlab.Company("005930")  # → DART provider (priority 10)
dartlab.Company("AAPL")    # → EDGAR provider (priority 20)

The facade iterates providers by priority — first match wins.

EDGAR (US)

Same Company interface, same account standardization pipeline, different data source. EDGAR data is auto-fetched from the SEC API — no pre-download needed:

us = dartlab.Company("AAPL")

us.sections                         # 10-K/10-Q sections with heading/body
us.show("business")                 # business description
us.show("10-K::item1ARiskFactors")  # risk factors
us.BS                               # SEC XBRL balance sheet
us.ratios                           # same 47 ratios
us.diff("10-K::item7Mdna")          # MD&A text changes

The interface is identical — same methods, same structure:

# Korea (DART)                          # US (EDGAR)
c = dartlab.Company("005930")           c = dartlab.Company("AAPL")
c.sections                              c.sections
c.show("businessOverview")              c.show("business")
c.BS                                    c.BS
c.ratios                                c.ratios
c.diff("businessOverview")              c.diff("10-K::item7Mdna")

DART vs EDGAR Namespaces

	DART	EDGAR
`docs`	✓	✓
`finance`	✓	✓
`report`	✓ (28 API types)	✗ (not applicable)
`profile`	✓	✓

DART has a report namespace with 28 structured disclosure APIs (dividend, governance, executive compensation, etc.). This does not exist in EDGAR — SEC filings are structured differently.

EDGAR topic naming: Topics use {formType}::{itemId} format. Short aliases also work:

us.show("10-K::item1Business")     # full form
us.show("business")                # short alias
us.show("risk")                    # → 10-K::item1ARiskFactors
us.show("mdna")                    # → 10-K::item7Mdna

AI Analysis

Experimental — the AI analysis layer and analysis/ engines are under active development. APIs, output formats, and available tools may change between versions.

Tip: New to financial analysis or prefer natural language? Use dartlab.ask() — the AI assistant handles everything from data download to analysis. No coding knowledge required.

DartLab's AI interprets period-comparable, cross-company data that the engine already computed — the LLM explains why, not what. No code required — ask questions in plain language and DartLab handles everything: data selection, context assembly, and streaming the answer.

# terminal one-liner — no Python needed
dartlab ask "삼성전자 재무건전성 분석해줘"

DartLab structures the data, selects relevant context (financials, insights, sector benchmarks), and lets the LLM explain:

$ dartlab ask "삼성전자 재무건전성 분석해줘"

삼성전자의 재무건전성은 A등급입니다.

▸ 부채비율 31.8% — 업종 평균(45.2%) 대비 양호
▸ 유동비율 258.6% — 200% 안전 기준 상회
▸ 이자보상배수 22.1배 — 이자 부담 매우 낮음
▸ ROE 회복세: 1.6% → 10.2% (4분기 연속 개선)

[데이터 출처: 2024Q4 사업보고서, dartlab insights 엔진]

For real-time market-wide disclosure questions (e.g. "최근 7일 수주공시 알려줘"), the AI uses your OpenDART API key to search recent filings directly. Store the key in project .env or via UI Settings.

The 2-tier architecture means basic analysis works with any provider, while tool-calling providers (OpenAI, Claude) can go deeper by requesting additional data mid-conversation.

Python API

import dartlab

# streams to stdout, returns full text
answer = dartlab.ask("삼성전자 재무건전성 분석해줘")

# provider + model override
answer = dartlab.ask("삼성전자 분석", provider="openai", model="gpt-4o")

# data filtering
answer = dartlab.ask("삼성전자 핵심 포인트", include=["BS", "IS"])

# analysis pattern (framework-guided)
answer = dartlab.ask("삼성전자 분석", pattern="financial")

# agent mode — LLM selects tools for deeper analysis
answer = dartlab.chat("005930", "배당 추세를 분석하고 이상 징후를 찾아줘")

CLI

# provider setup — free providers first
dartlab setup              # list all providers
dartlab setup gemini       # Google Gemini (free)
dartlab setup groq         # Groq (free)

# status
dartlab status             # all providers (table view)
dartlab status --cost      # cumulative token/cost stats

# ask questions (streaming by default)
dartlab ask "삼성전자 재무건전성 분석해줘"
dartlab ask "AAPL risk analysis" -p ollama
dartlab ask --continue "배당 추세는?"

# auto-generate report
dartlab report "삼성전자" -o report.md

dartlab --help             # show all commands

All CLI commands (15)

Category	Command	Description
Data	`show`	Open any topic by name
Data	`search`	Find companies by name or code
Data	`statement`	BS / IS / CF / SCE output
Data	`sections`	Raw docs sections
Data	`profile`	Company index and facts
Data	`modules`	List all available modules
AI	`ask`	Natural language question
AI	`report`	Auto-generate analysis report
Export	`excel`	Export to Excel (experimental)
Collect	`collect`	Download / refresh / batch collect
Collect	`collect --check`	Check freshness (new filings)
Collect	`collect --incremental`	Incremental collect (missing only)
Server	`ai`	AI analysis server
Server	`status`	Provider connection status
Server	`setup`	Provider setup wizard
MCP	`mcp`	Start MCP stdio server
Plugin	`plugin`	Create / list plugins

Providers

Free API key providers — sign up, paste the key, start analyzing:

Provider	Free Tier	Model	Setup
`gemini`	Gemini 2.5 Pro/Flash free	Gemini 2.5	`dartlab setup gemini`
`groq`	6K–30K TPM free	LLaMA 3.3 70B	`dartlab setup groq`
`cerebras`	1M tokens/day permanent	LLaMA 3.3 70B	`dartlab setup cerebras`
`mistral`	1B tokens/month free	Mistral Small	`dartlab setup mistral`

Other providers:

Provider	Auth	Cost	Tool Calling
`oauth-codex`	ChatGPT subscription (Plus/Team/Enterprise)	Included in subscription	Yes
`openai`	API key (`OPENAI_API_KEY`)	Pay-per-token	Yes
`ollama`	Local install, no account needed	Free	Depends on model
`codex`	Codex CLI installed locally	Free (uses your Codex session)	Yes
`custom`	Any OpenAI-compatible endpoint	Varies	Varies

Auto-fallback: Set multiple free API keys and DartLab automatically switches to the next provider when one hits its rate limit. Use provider="free" to enable the fallback chain:

dartlab.ask("삼성전자 분석", provider="free")

Why no Claude provider? Anthropic does not offer OAuth-based access. Without OAuth, there is no way to let users authenticate with their existing subscription — we would have to ask users to paste API keys, which goes against DartLab's frictionless design. If Anthropic adds OAuth support in the future, we will add a Claude provider. For now, Claude works through MCP (see below) — Claude Desktop, Claude Code, and Cursor can call DartLab's 60 tools directly.

oauth-codex is the recommended provider — if you have a ChatGPT subscription, it works out of the box with no API keys. Run dartlab setup oauth-codex to authenticate.

Project Settings (`.dartlab.yml`)

company: 005930         # default company
provider: openai        # default LLM provider
model: gpt-4o           # default model
verbose: false

MCP — AI Assistant Integration

DartLab includes a built-in MCP server for Claude Desktop, Claude Code, Cursor, and any MCP-compatible client.

Quick Setup — Copy & Paste

Claude Code:

pip install "dartlab[mcp]" && claude mcp add dartlab -- uv run dartlab mcp

OpenAI Codex CLI:

pip install "dartlab[mcp]" && codex mcp add dartlab -- uv run dartlab mcp

One line. Install + register in a single command.

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "dartlab": {
      "command": "uv",
      "args": ["run", "dartlab", "mcp"]
    }
  }
}

Cursor — add to .cursor/mcp.json with the same format as above.

Auto-generate config if you're not sure where the file is:

dartlab mcp --config claude-desktop
dartlab mcp --config claude-code
dartlab mcp --config cursor

What's Available

Once connected, your AI assistant can:

Search — find companies by name or code (searchCompany)
Profile — company overview, governance, sector (companyProfile, companyGovernance)
Financials — balance sheet, income statement, cash flow, ratios (companyFinancials, companyRatios)
Analysis — insights, valuation, forecast (companyInsights, companyValuation, companyForecast)
Disclosure — read any topic, diff across periods (companyShow, companyTopics, companyDiff)
Review — full analysis report (companyReview, companyAudit)
Market — sector screening, peer comparison (marketScan)

OpenAPI — Raw Public APIs

Use source-native wrappers when you want raw disclosure APIs directly.

OpenDart (Korea)

Note: Company does not require an API key — it uses pre-built datasets.
OpenDart uses the raw DART API and requires a key from opendart.fss.or.kr (free).
Recent filing-list AI questions across the whole market also use this key. In the UI, open Settings and manage OpenDART API key there.

from dartlab import OpenDart

d = OpenDart()
d.search("카카오", listed=True)
d.filings("삼성전자", "2024")
d.finstate("삼성전자", 2024)
d.report("삼성전자", "배당", 2024)

OpenEdgar (US)

No API key required. SEC EDGAR is a public API — no registration needed.

from dartlab import OpenEdgar

e = OpenEdgar()
e.search("Apple")
e.filings("AAPL", forms=["10-K", "10-Q"])
e.companyFactsJson("AAPL")

Data

No manual setup required. When you create a Company, dartlab automatically downloads the required data.

Dataset	Coverage	Size	Source
DART docs	2,500+ companies	~8 GB	HuggingFace
DART finance	2,700+ companies	~600 MB	HuggingFace
DART report	2,700+ companies	~320 MB	HuggingFace
DART scan	Pre-built cross-company	~271 MB	HuggingFace
EDGAR	On-demand	—	SEC API (auto-fetched)

3-Step Data Pipeline

dartlab.Company("005930")
  │
  ├─ 1. Local cache ──── already have it? done (instant)
  │
  ├─ 2. HuggingFace ──── auto-download (~seconds, no key needed)
  │
  └─ 3. DART API ──────── collect with your API key (needs key)

If a company is not in HuggingFace, dartlab collects data directly from DART — this requires an API key:

dartlab setup dart-key

Freshness — Automatic Update Detection

DartLab uses a 3-layer freshness system to keep your local data current:

Layer	Method	Cost
L1	HTTP HEAD → ETag comparison with HuggingFace	~0.5s, few hundred bytes
L2	Local file age (90-day TTL fallback)	instant (local)
L3	DART API → `rcept_no` diff (requires API key)	1 API call, ~1s

When you open a Company, dartlab checks if newer data exists. If a new disclosure was filed:

c = dartlab.Company("005930")
# [dartlab] ⚠ 005930 — 새 공시 2건 발견 (사업보고서 (2024.12))
#   • 증분 수집: dartlab collect --incremental 005930
#   • 또는 Python: c.update()

c.update()  # incremental collect — only missing filings

# CLI freshness check
dartlab collect --check 005930         # single company
dartlab collect --check                # scan all local companies (7 days)

# incremental collect — only missing filings
dartlab collect --incremental 005930   # single company
dartlab collect --incremental          # all local companies with new filings

Batch Collection (DART API)

dartlab collect --batch                    # all listed, missing only
dartlab collect --batch -c finance 005930  # specific category + company
dartlab collect --batch --mode all         # re-collect everything

Try It Now

Live Demo

Open Live Demo -- no install, no Python

Notebooks

Feature	Description	Colab	Molab
Company	`Company("005930")` -- index, show, select, trace, diff + finance shortcuts
Scan	`scan()` -- 13-axis cross-market scan, 2,700+ companies
Review	`c.review()` -- 14-section structured report + `c.reviewer()` AI interpretation
Gather	`gather()` -- price, flow, macro, news in one call

Run locally with Marimo

uv add dartlab marimo
marimo edit notebooks/marimo/01_company.py
marimo edit notebooks/marimo/02_scan.py
marimo edit notebooks/marimo/03_review.py
marimo edit notebooks/marimo/04_gather.py

Documentation

Docs: https://eddmpython.github.io/dartlab/
Sections guide: https://eddmpython.github.io/dartlab/docs/getting-started/sections
Quick start: https://eddmpython.github.io/dartlab/docs/getting-started/quickstart
API overview: https://eddmpython.github.io/dartlab/docs/api/overview
Beginner guide (Korean): https://eddmpython.github.io/dartlab/blog/dartlab-easy-start/

Blog

The DartLab Blog covers practical disclosure analysis — how to read reports, interpret patterns, and spot risk signals. 120+ articles across three categories:

Disclosure Systems — structure and mechanics of DART/EDGAR filings
Report Reading — practical guide to audit reports, preliminary earnings, restatements
Financial Interpretation — financial statements, ratios, and disclosure signals

Stability

Tier	Scope
Stable	DART Company (sections, show, trace, diff, BS/IS/CF, CIS, index, filings, profile), EDGAR Company core, valuation, forecast, simulation
Beta	EDGAR power-user (SCE, notes, freq, coverage), insights, distress, ratios, timeseries, network, governance, workforce, capital, debt, chart/table/text tools, ask/chat, OpenDart, OpenEdgar, Server API, MCP, CLI subcommands
Experimental	AI tool calling, export
Alpha	Desktop App (Windows .exe) — functional but incomplete, Sections Viewer — not yet fully structured

See docs/stability.md.

Contributing

The project prefers experiments before engine changes. If you want to propose a parser or mapping change, validate it in experiments/ first and bring the verified result back into the engine.

Experiment folder: experiments/XXX_camelCaseName/ — each file must be independently runnable with actual results in its docstring
Data contributions (e.g. accountMappings.json, sectionMappings.json): only accepted when backed by experiment evidence — no manual bulk edits
Issues and PRs in Korean or English are both welcome

License

MIT