finance-datareader
Financial data reader (price, stock list of markets)
Decision gist · record as of 2026-08-14
Yes. Low install friction, no security vulnerabilities, active maintenance, and permissive MIT license. Install if you need to fetch financial time-series data from multiple global exchanges into pandas DataFrames. Caveat: reliability depends on upstream data sources (web scraping); verify data accuracy for production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with no compiled dependencies.
- Active maintenance—last commit 2026-05-13, 1531 GitHub stars.
- Supports Python 3.9 through 3.12.
License · maintenance · safety
MIT License (permissive) — MIT License (permissive): you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-05-13 (93 days) · last repo commit 2026-05-13 · 1,531 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 265,271 downloads/mo, #8,327 on PyPI
Alternatives
Verify before relying
pip install finance-datareader
import FinanceDataReader as fdr
df = fdr.DataReader('AAPL', '2024') # Apple stock, 2024 to present
df = fdr.DataReader('KS11', '2024') # KOSPI index
df = fdr.DataReader('USD/KRW') # USD/KRW exchange rate- Reliability and latency of data sources (web scraping may be fragile across market APIs).
- Whether all listed exchanges (SSE, SZSE, HKEX, TSE, HOSE) are consistently available.
- Rate limiting or throttling policies from upstream data providers.
What it is and what it does
FinanceDataReader is a financial data crawler that retrieves stock prices, market indices, exchange rates, and cryptocurrency data from global exchanges—KRX (Korean), NASDAQ, NYSE, S&P 500, Shanghai, Shenzhen, Hong Kong, Tokyo, and Ho Chi Minh exchanges. It wraps web scraping and API calls behind a simple pandas-centric interface, returning time-series data as DataFrames. The package includes both a Python library (via `fdr.DataReader()`) and a CLI tool (`fdr` command) for terminal-based queries.
It depends on beautifulsoup4, lxml, pandas, requests, and plotly to parse HTML, fetch data, and structure results. The library handles symbol resolution across markets (e.g., 'AAPL' for US stocks, '005930' for Samsung, 'KS11' for KOSPI index) and supports date-range filtering. It is production-stable, actively maintained, and requires Python 3.9 or later.
Use it for
- Backtest trading strategies by fetching historical stock prices for multiple symbols and date ranges.
- Build financial dashboards that display real-time or recent index values (KOSPI, NASDAQ, S&P 500) and exchange rates.
- Analyze cryptocurrency price trends by retrieving BTC/KRW, ETH/USD, and other crypto pairs over time.
- Screen Korean stocks by listing all KRX symbols and fetching their price history for fundamental analysis.
- Monitor delisted or administratively suspended Korean stocks via KRX-DELISTING and KRX-ADMIN listings.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Low install friction, no security vulnerabilities, active maintenance, and permissive MIT license. Install if you need to fetch financial time-series data from multiple global exchanges into pandas DataFrames. Caveat: reliability depends on upstream data sources (web scraping); verify data accuracy for production use.
Install
finance-datareader on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Active maintenance—last commit 2026-05-13, 1531 GitHub stars. Supports Python 3.9 through 3.12.
License in practice
MIT License (permissive): you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install finance-datareader
import FinanceDataReader as fdr
df = fdr.DataReader('AAPL', '2024') # Apple stock, 2024 to present
df = fdr.DataReader('KS11', '2024') # KOSPI index
df = fdr.DataReader('USD/KRW') # USD/KRW exchange rateVerify before relying
- Reliability and latency of data sources (web scraping may be fragile across market APIs).
- Whether all listed exchanges (SSE, SZSE, HKEX, TSE, HOSE) are consistently available.
- Rate limiting or throttling policies from upstream data providers.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesbeautifulsoup4lxmlpandasplotlyrequests-filerequeststabulatetqdm |
| Maintenance | Actively maintained 93 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 265,271 / month, #8,327 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: CythonProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9 |
Evidence: finance_datareader-0.9.202-py3-none-any.whl
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See also efinance · investor-agent · pykrx · fear-and-greed · alpha-vantage · yahooquery · tushare · pandas-datareader · Nasdaq-Data-Link · stockstats