Download market data from Yahoo! Finance API
Project description
Download market data from Yahoo! Finance's API
*** 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, here, and here) 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.
→ Check out this Blog post for a detailed tutorial with code examples.
News [2023-01-27]
Since December 2022 Yahoo has been encrypting the web data that yfinance scrapes for non-market data. Fortunately the decryption keys are available, although Yahoo moved/changed them several times hence yfinance breaking several times. yfinance is now better prepared for any future changes by Yahoo.
Why is Yahoo doing this? We don't know. Is it to stop scrapers? Maybe, so we've implemented changes to reduce load on Yahoo. In December we rolled out version 0.2 with optimised scraping. Then in 0.2.6 introduced Ticker.fast_info, providing much faster access to some info elements wherever possible e.g. price stats and forcing users to switch (sorry but we think necessary). info will continue to exist for as long as there are elements without a fast alternative.
Quick Start
The Ticker module
The Ticker module, which allows you to access ticker data in a more Pythonic way:
import yfinance as yf
msft = yf.Ticker("MSFT")
# get all stock info (slow)
msft.info
# fast access to subset of stock info (opportunistic)
msft.fast_info
# get historical market data
hist = msft.history(period="1mo")
# show meta information about the history (requires history() to be called first)
msft.history_metadata
# show actions (dividends, splits, capital gains)
msft.actions
msft.dividends
msft.splits
msft.capital_gains # only for mutual funds & etfs
# show share count
# - yearly summary:
msft.shares
# - accurate time-series count:
msft.get_shares_full(start="2022-01-01", end=None)
# 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 holders
msft.major_holders
msft.institutional_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:
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 initialize multiple Ticker objects, use
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
import yfinance as yf
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
yf.download() and Ticker.history() have many options for configuring fetching and processing, e.g.:
yf.download(tickers = "SPY AAPL", # list of tickers
period = "1y", # time period
interval = "1d", # trading interval
ignore_tz = True, # ignore timezone when aligning data from different exchanges?
prepost = False) # download pre/post market hours data?
Review the Wiki for more options and detail.
Smarter scraping
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.
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
Combine a requests_cache with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
from requests import Session
from requests_cache import CacheMixin, SQLiteCache
from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
""" """
session = CachedLimiterSession(
per_second=0.9,
bucket_class=MemoryQueueBucket,
backend=SQLiteCache("yfinance.cache"),
)
Managing Multi-Level Columns
The following answer on Stack Overflow is for How to deal with multi-level column names downloaded with yfinance?
yfinancereturns apandas.DataFramewith 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
- How to correctly read the the multi-level columns after
saving the dataframe to a csv with
- The answer discusses:
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():
import yfinance as yf
yf.set_tz_cache_location("custom/cache/location")
...
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().
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:
$ pip install yfinance --upgrade --no-cache-dir
To install yfinance using conda, see
this.
Requirements
- Python >= 2.7, 3.4+
- Pandas >= 1.3.0
- Numpy >= 1.16.5
- requests >= 2.26
- lxml >= 4.9.1
- appdirs >= 1.4.4
- pytz >=2022.5
- frozendict >= 2.3.4
- beautifulsoup4 >= 4.11.1
- html5lib >= 1.1
- cryptography >= 3.3.2
Optional (if you want to use pandas_datareader)
- pandas_datareader >= 0.4.0
Legal Stuff
yfinance is distributed under the Apache Software License. See the 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, here, and here) 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
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