To help you get started, we've collected the most common ways that yfinance is being used within popular public projects.

bradtraversy / alexis_speech_assistant / main.py View on Github

webbrowser.get().open(url)
        speak(f'Here is what I found for {search_term} on youtube')

    # 7: get stock price
    if there_exists(["price of"]):
        search_term = voice_data.lower().split(" of ")[-1].strip() #strip removes whitespace after/before a term in string
        stocks = {
            "apple":"AAPL",
            "microsoft":"MSFT",
            "facebook":"FB",
            "tesla":"TSLA",
            "bitcoin":"BTC-USD"
        }
        try:
            stock = stocks[search_term]
            stock = yf.Ticker(stock)
            price = stock.info["regularMarketPrice"]

            speak(f'price of {search_term} is {price} {stock.info["currency"]} {person_obj.name}')
        except:
            speak('oops, something went wrong')
    if there_exists(["exit", "quit", "goodbye"]):
        speak("going offline")
        exit()

View more ways to use yfinance

Download market data from Yahoo! Finance's API

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.

Changelog »

Installation

Install yfinance using pip:

$ pip install yfinance --upgrade --no-cache-dir

With Conda.

To install with optional dependencies, replace optional with: nospam for caching-requests, repair for price repair, or nospam,repair for both:

$ pip install "yfinance[optional]"

Required dependencies , all dependencies.

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
msft.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
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
msft.insider_transactions
msft.insider_purchases
msft.insider_roster_holders

# show recommendations
msft.recommendations
msft.recommendations_summary
msft.upgrades_downgrades

# 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")
...

Multiple tickers

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

To download price history into one table:

import yfinance as yf
data = yf.download("SPY AAPL", period="1mo")

yf.download() and Ticker.history() have many options for configuring fetching and processing. Review the Wiki for more options and detail.

Logging

yfinance now uses the logging module to handle messages, default behaviour is only print errors. If debugging, use yf.enable_debug_mode() to switch logging to debug with custom formatting.

Smarter scraping

Install the nospam packages for smarter scraping using pip (see Installation). These packages help cache calls such that Yahoo is not spammed with requests.

To use a custom requests session, pass a session= argument to the Ticker constructor. This allows for caching calls to the API as well as a custom way to modify requests via the User-agent header.

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 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
from pyrate_limiter import Duration, RequestRate, Limiter
class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
    pass

session = CachedLimiterSession(
    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
    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?

• 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().

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")

Persistent cache store

To reduce Yahoo, yfinance store some data locally: timezones to localize dates, and cookie. Cache location is:

• Windows = C:/Users//AppData/Local/py-yfinance
• Linux = /home//.cache/py-yfinance
• MacOS = /Users//Library/Caches/py-yfinance

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")
...

Developers: want to contribute?

yfinance relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084

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