| #### \*\*\* IMPORTANT LEGAL DISCLAIMER \*\*\* --- **Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.** yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's an open-source tool that uses Yahoo's publicly available APIs, and is intended for research and educational purposes. **You should refer to Yahoo!'s terms of use** ([here](https://policies.yahoo.com/us/en/yahoo/terms/product-atos/apiforydn/index.htm), [here](https://legal.yahoo.com/us/en/yahoo/terms/otos/index.html), and [here](https://policies.yahoo.com/us/en/yahoo/terms/index.htm)) **for details on your rights to use the actual data downloaded. Remember - the Yahoo! finance API is intended for personal use only.** |
**yfinance** offers a threaded and Pythonic way to download market data from [Yahoo!Ⓡ finance](https://finance.yahoo.com).
→ Check out this [Blog post](https://aroussi.com/#post/python-yahoo-finance) for a detailed tutorial with code examples.
[Changelog »](https://github.com/ranaroussi/yfinance/blob/main/CHANGELOG.rst)
---
## What's new in version 0.2
- Optimised web scraping
- All 3 financials tables now match website so expect keys to change. If you really want old tables, use [`Ticker.get_[income_stmt|balance_sheet|cashflow](legacy=True, ...)`](https://github.com/ranaroussi/yfinance/blob/85783da515761a145411d742c2a8a3c1517264b0/yfinance/base.py#L968)
- price data improvements: fix bug NaN rows with dividend; new repair feature for missing or 100x prices `download(repair=True)`; new attribute `Ticker.history_metadata`
[See release notes for full list of changes](https://github.com/ranaroussi/yfinance/releases/tag/0.2.1)
## Quick Start
### The Ticker module
The `Ticker` module, which allows you to access ticker data in a more Pythonic way:
```python
import yfinance as yf
msft = yf.Ticker("MSFT")
# get stock info
msft.info
# get historical market data
hist = msft.history(period="max")
# show meta information about the history (requires history() to be called first)
msft.history_metadata
# show actions (dividends, splits, capital gains)
msft.actions
# show dividends
msft.dividends
# show splits
msft.splits
# show capital gains (for mutual funds & etfs)
msft.capital_gains
# show share count
msft.shares
msft.get_shares_full()
# show financials:
# - income statement
msft.income_stmt
msft.quarterly_income_stmt
# - balance sheet
msft.balance_sheet
msft.quarterly_balance_sheet
# - cash flow statement
msft.cashflow
msft.quarterly_cashflow
# see `Ticker.get_income_stmt()` for more options
# show major holders
msft.major_holders
# show institutional holders
msft.institutional_holders
# show mutualfund holders
msft.mutualfund_holders
# show earnings
msft.earnings
msft.quarterly_earnings
# show sustainability
msft.sustainability
# show analysts recommendations
msft.recommendations
msft.recommendations_summary
# show analysts other work
msft.analyst_price_target
msft.revenue_forecasts
msft.earnings_forecasts
msft.earnings_trend
# show next event (earnings, etc)
msft.calendar
# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default.
# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
msft.earnings_dates
# show ISIN code - *experimental*
# ISIN = International Securities Identification Number
msft.isin
# show options expirations
msft.options
# show news
msft.news
# get option chain for specific expiration
opt = msft.option_chain('YYYY-MM-DD')
# data available via: opt.calls, opt.puts
```
If you want to use a proxy server for downloading data, use:
```python
import yfinance as yf
msft = yf.Ticker("MSFT")
msft.history(..., proxy="PROXY_SERVER")
msft.get_actions(proxy="PROXY_SERVER")
msft.get_dividends(proxy="PROXY_SERVER")
msft.get_splits(proxy="PROXY_SERVER")
msft.get_capital_gains(proxy="PROXY_SERVER")
msft.get_balance_sheet(proxy="PROXY_SERVER")
msft.get_cashflow(proxy="PROXY_SERVER")
msft.option_chain(..., proxy="PROXY_SERVER")
...
```
To use a custom `requests` session (for example to cache calls to the
API or customize the `User-agent` header), pass a `session=` argument to
the Ticker constructor.
```python
import requests_cache
session = requests_cache.CachedSession('yfinance.cache')
session.headers['User-agent'] = 'my-program/1.0'
ticker = yf.Ticker('msft', session=session)
# The scraped response will be stored in the cache
ticker.actions
```
To initialize multiple `Ticker` objects, use
```python
import yfinance as yf
tickers = yf.Tickers('msft aapl goog')
# access each ticker using (example)
tickers.tickers['MSFT'].info
tickers.tickers['AAPL'].history(period="1mo")
tickers.tickers['GOOG'].actions
```
### Fetching data for multiple tickers
```python
import yfinance as yf
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
```
I've also added some options to make life easier :)
```python
data = yf.download( # or pdr.get_data_yahoo(...
# tickers list or string as well
tickers = "SPY AAPL MSFT",
# use "period" instead of start/end
# valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
# (optional, default is '1mo')
period = "ytd",
# fetch data by interval (including intraday if period < 60 days)
# valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
# (optional, default is '1d')
interval = "5d",
# Whether to ignore timezone when aligning ticker data from
# different timezones. Default is False.
ignore_tz = False,
# group by ticker (to access via data['SPY'])
# (optional, default is 'column')
group_by = 'ticker',
# adjust all OHLC automatically
# (optional, default is False)
auto_adjust = True,
# attempt repair of missing data or currency mixups e.g. $/cents
repair = False,
# download pre/post regular market hours data
# (optional, default is False)
prepost = True,
# use threads for mass downloading? (True/False/Integer)
# (optional, default is True)
threads = True,
# proxy URL scheme use use when downloading?
# (optional, default is None)
proxy = None
)
```
### Timezone cache store
When fetching price data, all dates are localized to stock exchange timezone.
But timezone retrieval is relatively slow, so yfinance attemps to cache them
in your users cache folder.
You can direct cache to use a different location with `set_tz_cache_location()`:
```python
import yfinance as yf
yf.set_tz_cache_location("custom/cache/location")
...
```
### Managing Multi-Level Columns
The following answer on Stack Overflow is for [How to deal with
multi-level column names downloaded with
yfinance?](https://stackoverflow.com/questions/63107801)
- `yfinance` returns a `pandas.DataFrame` with multi-level column
names, with a level for the ticker and a level for the stock price
data
- The answer discusses:
- How to correctly read the the multi-level columns after
saving the dataframe to a csv with `pandas.DataFrame.to_csv`
- How to download single or multiple tickers into a single
dataframe with single level column names and a ticker column
---
## `pandas_datareader` override
If your code uses `pandas_datareader` and you want to download data
faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
method to use **yfinance** while making sure the returned data is in the
same format as **pandas\_datareader**'s `get_data_yahoo()`.
```python
from pandas_datareader import data as pdr
import yfinance as yf
yf.pdr_override() # <== that's all it takes :-)
# download dataframe
data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
```
---
## Installation
Install `yfinance` using `pip`:
``` {.sourceCode .bash}
$ pip install yfinance --upgrade --no-cache-dir
```
To install `yfinance` using `conda`, see
[this](https://anaconda.org/ranaroussi/yfinance).
### Requirements
- [Python](https://www.python.org) \>= 2.7, 3.4+
- [Pandas](https://github.com/pydata/pandas) \>= 1.3.0
- [Numpy](http://www.numpy.org) \>= 1.16.5
- [requests](http://docs.python-requests.org/en/master) \>= 2.26
- [lxml](https://pypi.org/project/lxml) \>= 4.9.1
- [appdirs](https://pypi.org/project/appdirs) \>= 1.4.4
- [pytz](https://pypi.org/project/pytz) \>=2022.5
- [frozendict](https://pypi.org/project/frozendict) \>= 2.3.4
- [beautifulsoup4](https://pypi.org/project/beautifulsoup4) \>= 4.11.1
- [html5lib](https://pypi.org/project/html5lib) \>= 1.1
- [cryptography](https://pypi.org/project/cryptography) \>= 3.3.2
### Optional (if you want to use `pandas_datareader`)
- [pandas\_datareader](https://github.com/pydata/pandas-datareader)
\>= 0.4.0
---
### Legal Stuff
**yfinance** is distributed under the **Apache Software License**. See
the [LICENSE.txt](./LICENSE.txt) file in the release for details.
AGAIN - yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's
an open-source tool that uses Yahoo's publicly available APIs, and is
intended for research and educational purposes. You should refer to Yahoo!'s terms of use
([here](https://policies.yahoo.com/us/en/yahoo/terms/product-atos/apiforydn/index.htm),
[here](https://legal.yahoo.com/us/en/yahoo/terms/otos/index.html), and
[here](https://policies.yahoo.com/us/en/yahoo/terms/index.htm)) for
detailes on your rights to use the actual data downloaded.
---
### P.S.
Please drop me an note with any feedback you have.
**Ran Aroussi**