Yahoo Finance offers an excellent range of market data on stocks, bonds, currencies, and cryptocurrencies. It also provides news reports with various insights into different markets from around the world – all accessible through the yfinance python library.
Who Created yFinance?
Ran Aroussi is the man behind yfinance, a Python library that gives you easy access to financial data available on Yahoo Finance. Since Yahoo decommissioned their AP on May 15th, 2017 (a move that left developers searching for an adequate alternative), Ran’s yfinance fit the bill. The software gained traction and has been downloaded over 100k times with around 300k+ installs per month, according to PyPi!
Read on if you’re interested in learning how to use the yfinance API to download financial data for free.
You can even follow along with The yfinance Python Tutorial Jupyter Notebook.
But before you get too excited, you need to ask yourself:
Should You Use the Yahoo Finance API?
I wouldn’t recommend using Yahoo Finance data for making live trading decisions. Why?
All Yahoo Finance APIs are unofficial solutions.
If the look of Yahoo Finance! is ever changed, it’ll break many of the APIs as the web scraping code will need to be updated. Yahoo might rate limit or blacklist you if you create too many requests.
The data is good, not great. Good paid data sources generally offer a higher level of reliability than freely available datasets.
Yahoo Finance is arguably the best freely available data source if you’re okay with these drawbacks. And yfinance is one of the most popular ways to access this incredible data.
When Should You Use yfinance?
If you’ve decided to use Yahoo Finance as a data source, yfinance is the way to go. It’s the most popular way to access Yahoo Data, and the API is open-source and free to use. There are other free and paid APIs to access Yahoo’s data, but yfinance is the best place to start, and here’s why.
- It’s simple to use
- It returns data as Pandas DataFrames
- One-minute bar granularity
If you’re using AI to perform sentiment analysis, you can’t use yfinance. You’ll have to grab that data directly or use another API.
How to Install yfinance
Installing yfinance is incredibly easy. As with most packages, there are two steps:
- Load your Python virtual environment
- Install yfinance using pip or conda
If you’re not familiar with virtual environments, read: Python Virtual Environments: Setup & Usage.
The following packages are required:
- Python >= 2.7, 3.4+
- Pandas (tested to work with >=0.23.1)
- Numpy >= 1.11.1
- requests >= 2.14.2
- lxml >= 4.5.1
The following package is optional and used for backward compatibility:
- pandas_datareader >= 0.4.0
With your virtual environment loaded, you’re now ready to install finance.
Install yfinance Using pip:
$ pip install yfinance --upgrade --no-cache-dirInstall yfinance Using Conda:
$ conda install -c ranaroussi yfinanceyfinance Classes
After loading yfinance, you’ll have access to the following:
You’ll mainly use the following:
- Ticker
- Tickers
- Download
How to Download Historical Price Data Using yfinance
We can download data for one ticker using the Ticker object and multiple tickers using the download method.
Download One Ticker Using yfinance
First, we need to create a ticker object and then use that object to get our data. Creating a ticker object is straightforward:
obj = yf.Ticker(‘goog’)Now we can use the various methods to grab the data we want.
Most of the methods are self-explanatory, but here are a few that might trip new users up:
- Actions – Corporate actions such as dividends and splits
- Analysis – EPS targets and revisions
- Info – Commonly queried data as a dictionary
- Recommendations – Analyst buy, hold and sell ratings
Let’s download historical market data using the history method. We can see that history takes the following parameters:
def history(self, period="1mo", interval="1d",
start=None, end=None, prepost=False, actions=True,
auto_adjust=True, back_adjust=False,
proxy=None, rounding=False, tz=None, timeout=None, **kwargs):
"""
:Parameters:
period : str
Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
Either Use period parameter or use start and end
interval : str
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
Intraday data cannot extend last 60 days
start: str
Download start date string (YYYY-MM-DD) or _datetime.
Default is 1900-01-01
end: str
Download end date string (YYYY-MM-DD) or _datetime.
Default is now
prepost : bool
Include Pre and Post market data in results?
Default is False
auto_adjust: bool
Adjust all OHLC automatically? Default is True
back_adjust: bool
Back-adjusted data to mimic true historical prices
proxy: str
Optional. Proxy server URL scheme. Default is None
rounding: bool
Round values to 2 decimal places?
Optional. Default is False = precision suggested by Yahoo!
tz: str
Optional timezone locale for dates.
(default data is returned as non-localized dates)
timeout: None or float
If not None stops waiting for a response after given number of
seconds. (Can also be a fraction of a second e.g. 0.01)
Default is None.
**kwargs: dict
debug: bool
Optional. If passed as False, will suppress
error message printing to console.
"""Don’t feel overwhelmed. The defaults are great, and in most cases, we’ll only be changing the period or dates and the interval.
Let’s grab the most recent thirty days’ daily data for Google. Remember, data is returned as a pandas dataframe:
goog = yf.Ticker('goog')
data = goog.history()
data.head() Open High Low Close Volume Dividends Stock Splits
Date
2021-12-10 2982.000000 2988.000000 2947.149902 2973.500000 1081700 0 0
2021-12-13 2968.879883 2971.250000 2927.199951 2934.090088 1205200 0 0
2021-12-14 2895.399902 2908.840088 2844.850098 2899.409912 1238900 0 0
2021-12-15 2887.320068 2950.344971 2854.110107 2947.370117 1364000 0 0
2021-12-16 2961.540039 2971.030029 2881.850098 2896.770020 1370000 0 0
That was easy!
Now let’s download the most recent week’s minute data; only this time, we’ll use the start and end dates instead of the period.
Keep in mind the following restrictions when using minute data:
- The period must be within the last 30 days
- Only seven days of 1m granularity are allowed per request
data = goog.history(interval='1m', start='2022-01-03', end='2022-01-10')
data.head() Open High Low Close Volume Dividends Stock Splits
Datetime
2022-01-03 09:30:00-05:00 2889.510010 2901.020020 2887.733398 2899.060059 67320 0 0
2022-01-03 09:31:00-05:00 2900.520020 2906.060059 2900.489990 2904.580078 8142 0 0
2022-01-03 09:32:00-05:00 2904.719971 2904.719971 2896.310059 2899.209961 7069 0 0
2022-01-03 09:33:00-05:00 2898.699951 2898.699951 2898.699951 2898.699951 623 0 0
2022-01-03 09:34:00-05:00 2896.209961 2896.330078 2894.913086 2896.239990 3443 0 0
Download Multiple Tickers Using yfinance
Downloading multiple tickers is similar to downloading a single ticker using the Ticker object.
Please note that you’re limited to the daily granularity when downloading multiple tickers. If you want to get up to minute granularity, you’ll need to use the Ticker object above.
Now back to multiple ticker downloading…
We need to pass download a list of tickers instead of a single ticker and optionally let the method know how to group the tickers — by ticker or column (column is the default). We can also optionally use threads to download the tickers faster.
def download(tickers, start=None, end=None, actions=False, threads=True,
group_by='column', auto_adjust=False, back_adjust=False,
progress=True, period="max", show_errors=True, interval="1d", prepost=False,
proxy=None, rounding=False, timeout=None, **kwargs):
"""Download yahoo tickers
:Parameters:
tickers : str, list
List of tickers to download
period : str
Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
Either Use period parameter or use start and end
interval : str
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
Intraday data cannot extend last 60 days
start: str
Download start date string (YYYY-MM-DD) or _datetime.
Default is 1900-01-01
end: str
Download end date string (YYYY-MM-DD) or _datetime.
Default is now
group_by : str
Group by 'ticker' or 'column' (default)
prepost : bool
Include Pre and Post market data in results?
Default is False
auto_adjust: bool
Adjust all OHLC automatically? Default is False
actions: bool
Download dividend + stock splits data. Default is False
threads: bool / int
How many threads to use for mass downloading. Default is True
proxy: str
Optional. Proxy server URL scheme. Default is None
rounding: bool
Optional. Round values to 2 decimal places?
show_errors: bool
Optional. Doesn't print errors if True
timeout: None or float
If not None stops waiting for a response after given number of
seconds. (Can also be a fraction of a second e.g. 0.01)
"""Let’s download the most recent monthly data for Google and Facebook (META).
data = yf.download(['GOOG','META'], period='1mo')
data.head() Adj Close Close High Low Open Volume
GOOG META GOOG META GOOG META GOOG META GOOG META GOOG META
Date
2021-12-10 2973.500000 15.52 2973.500000 15.52 2988.000000 15.83 2947.149902 15.390 2982.000000 15.77 1081700 1845200
2021-12-13 2934.090088 15.24 2934.090088 15.24 2971.250000 15.55 2927.199951 15.130 2968.879883 15.53 1205200 2178500
2021-12-14 2899.409912 15.06 2899.409912 15.06 2908.840088 15.17 2844.850098 14.850 2895.399902 15.02 1238900 2662900
2021-12-15 2947.370117 15.28 2947.370117 15.28 2950.344971 15.28 2854.110107 14.615 2887.320068 14.95 1364000 2356300
2021-12-16 2896.770020 14.79 2896.770020 14.79 2971.030029 15.46 2881.850098 14.680 2961.540039 15.45 1370000 2511100Let’s group by the ticker, and provide start and end dates for the same tickers.
data = yf.download(['GOOG','META'], start='2021-12-10', end='2021-12-30', group_by='ticker')
data.head() META GOOG
Open High Low Close Adj Close Volume Open High Low Close Adj Close Volume
Date
2021-12-10 15.77 15.83 15.390 15.52 15.52 1845200 2982.000000 2988.000000 2947.149902 2973.500000 2973.500000 1081700
2021-12-13 15.53 15.55 15.130 15.24 15.24 2178500 2968.879883 2971.250000 2927.199951 2934.090088 2934.090088 1205200
2021-12-14 15.02 15.17 14.850 15.06 15.06 2662900 2895.399902 2908.840088 2844.850098 2899.409912 2899.409912 1238900
2021-12-15 14.95 15.28 14.615 15.28 15.28 2356300 2887.320068 2950.344971 2854.110107 2947.370117 2947.370117 1364000
2021-12-16 15.45 15.46 14.680 14.79 14.79 2511100 2961.540039 2971.030029 2881.850098 2896.770020 2896.770020 1370000How to Download Fundamental Data Using yfinance
We use the Ticker object to download fundamental data.
Download Fundamentals for One Ticker Using yfinance
We can loop through multiple tickers objects to download fundamental data for various tickers.
Let’s get the fundamental information for Danaher.
We can see that the Ticker object ‘dhr’ provides a lot of data to consume. Many of the get_ methods give us exciting fundamental data.
Using one of my favorite industrial companies, Danaher, let’s run through some examples.
We can get Danaher’s general and frequently-used information using the info method, which returns a dictionary.
dhr = yf.Ticker('DHR')
info = dhr.info
info.keys()dict_keys(['zip', 'sector', 'fullTimeEmployees', 'longBusinessSummary',
'city', 'phone', 'state', 'country', 'companyOfficers', 'website',
'maxAge', 'address1', 'fax', 'industry', 'address2', 'ebitdaMargins',
'profitMargins', 'grossMargins', 'operatingCashflow', 'revenueGrowth',
'operatingMargins', 'ebitda', 'targetLowPrice', 'recommendationKey',
'grossProfits', 'freeCashflow', 'targetMedianPrice', 'currentPrice',
'earningsGrowth', 'currentRatio', 'returnOnAssets', 'numberOfAnalystOpinions',
'targetMeanPrice', 'debtToEquity', '...'])We can access this data using the dictionary.
info['sector']
'Healthcare'Let’s grab Danaher’s annual revenue and earnings using the earnings method.
dhr.earnings
Revenue Earnings
Year
2017 15518800000 2492100000
2018 17049000000 2651000000
2019 17911000000 3008000000
2020 22284000000 3646000000And if the provided methods don’t work, we can calculate financial ratios using the financial statements.
dhr.get_financials() 2020-12-31 2019-12-31 2018-12-31 2017-12-31
Research Development 1348000000.0 1126000000.0 1059000000.0 956400000.0
Effect Of Accounting Charges None None None None
Income Before Tax 4495000000.0 3305000000.0 2962000000.0 2543200000.0
Minority Interest 11000000.0 11000000.0 12300000.0 9600000.0
Net Income 3646000000.0 3008000000.0 2651000000.0 2492100000.0
Selling General Administrative 6880000000.0 5577000000.0 5356000000.0 5011900000.0
Gross Profit 12932000000.0 9984000000.0 9505000000.0 8571300000.0
Ebit 4704000000.0 3281000000.0 3090000000.0 2603000000.0
Operating Income 4704000000.0 3281000000.0 3090000000.0 2603000000.0
Other Operating Expenses None None None None
Interest Expense -275000000.0 -108000000.0 -137000000.0 -140100000.0
Extraordinary Items None None None None
Non Recurring None None None None
Other Items None None None None
Income Tax Expense 849000000.0 873000000.0 556000000.0 371000000.0
Total Revenue 22284000000.0 17911000000.0 17049000000.0 15518800000.0
Total Operating Expenses 17580000000.0 14630000000.0 13959000000.0 12915800000.0
Cost Of Revenue 9352000000.0 7927000000.0 7544000000.0 6947500000.0
Total Other Income Expense Net -209000000.0 24000000.0 -128000000.0 -59800000.0
Discontinued Operations NaN 576000000.0 245000000.0 319900000.0
Net Income From Continuing Ops 3646000000.0 2432000000.0 2406000000.0 2172200000.0
Net Income Applicable To Common Shares 3510000000.0 2940000000.0 2651000000.0 2492100000.0
We can also concatenate all financial statements to calculate the ratios more easily.
pnl = dhr.financials
bs = dhr.balancesheet
cf = dhr.cashflow
fs = pd.concat([pnl,bs,cf])
print(fs) 2020-12-31 2019-12-31 2018-12-31 2017-12-31
Research Development 1348000000.0 1126000000.0 1059000000.0 956400000.0
Effect Of Accounting Charges None None None None
Income Before Tax 4495000000.0 3305000000.0 2962000000.0 2543200000.0
Minority Interest 11000000.0 11000000.0 12300000.0 9600000.0
Net Income 3646000000.0 3008000000.0 2651000000.0 2492100000.0
... ... ... ... ...
Change To Inventory -123000000.0 -22000000.0 -134000000.0 3100000.0
Change To Account Receivables -264000000.0 -157000000.0 -55000000.0 -142500000.0
Other Cashflows From Financing Activities -29000000.0 369000000.0 -18000000.0 -124200000.0
Change To Netincome 182000000.0 -122000000.0 271000000.0 139300000.0
Capital Expenditures -791000000.0 -636000000.0 -584000000.0 -570700000.0
[68 rows x 4 columns]I also often find it helpful to transpose the data and have the time as the index and the column as the data field.
fs.T Research Development Effect Of Accounting Charges ... Change To Netincome Capital Expenditures
...
2020-12-31 1348000000.0 None ... 182000000.0 -791000000.0
2019-12-31 1126000000.0 None ... -122000000.0 -636000000.0
2018-12-31 1059000000.0 None ... 271000000.0 -584000000.0
2017-12-31 956400000.0 None ... 139300000.0 -570700000.0
[4 rows x 68 columns]And while there’s no download method for downloading multiple symbols fundamentals at once, we can loop through the tickers we’re interested in and aggregate the data.
Download Fundamentals for Multiple Tickers Using yfinance
The first thing we want to do when attempting to download data for multiple tickers is to come up with a list of tickers!
Let’s create a new list called fang:
tickers = ['FB','AMZN','NFLX','GOOG']
tickers
['FB', 'AMZN', 'NFLX', 'GOOG']Now let’s turn this list into a list of ticker objects using list comprehension.
tickers = [yf.Ticker(ticker) for ticker in fang][yfinance.Ticker object <FB>,
yfinance.Ticker object <AMZN>,
yfinance.Ticker object <NFLX>,
yfinance.Ticker object <GOOG>]
Now let’s concatenate all of the financial data together. We’ll loop through each ticker, aggregating the profit and loss, balance sheet, and cash flow statement. We’ll then add this data to a list.
Once we have a list of each company’s aggregated financial statements, we’ll concatenate them, removing duplicate headings.
dfs = [] # list for each ticker's dataframe
for ticker in tickers:
# get each financial statement
pnl = ticker.financials
bs = ticker.balancesheet
cf = ticker.cashflow
# concatenate into one dataframe
fs = pd.concat([pnl, bs, cf])
# make dataframe format nicer
# Swap dates and columns
data = fs.T
# reset index (date) into a column
data = data.reset_index()
# Rename old index from '' to Date
data.columns = ['Date', *data.columns[1:]]
# Add ticker to dataframe
data['Ticker'] = ticker.ticker
dfs.append(data)
data.iloc[:,:3]# for display purposes Date Research Development Effect Of Accounting Charges ...
0 2020-12-31 27573000000.0 None ...
1 2019-12-31 26018000000.0 None ...
2 2018-12-31 21419000000.0 None ...
3 2017-12-31 16625000000.0 None ...Now that we have a list of dataframes, we need to iterate through concatenating them and fixing the duplicate headers using pandas.io.parser.
We’ll also reindex the dataframe to make it cleaner to use.
parser = pd.io.parsers.base_parser.ParserBase({'usecols': None})
for df in dfs:
df.columns = parser._maybe_dedup_names(df.columns)
df = pd.concat(dfs, ignore_index=True)
df = df.set_index(['Ticker','Date'])
df.iloc[:,:5] # for display purposes Research Development Effect Of Accounting Charges Income Before Tax
Ticker Date
FB2020-12-31 18447000000.0 None 33180000000.0
2019-12-31 13600000000.0 None 24812000000.0
2018-12-31 10273000000.0 None 25361000000.0
2017-12-31 7754000000.0 None 20594000000.0
AMZN 2020-12-31 42740000000.0 None 24194000000.0
2019-12-31 35931000000.0 None 13962000000.0
2018-12-31 28837000000.0 None 11270000000.0
2017-12-31 22620000000.0 None 3802000000.0
NFLX 2020-12-31 1829600000.0 None 3199349000.0
2019-12-31 1545149000.0 None 2062231000.0
2018-12-31 1221814000.0 None 1226458000.0
2017-12-31 953710000.0 None 485321000.0
GOOG 2020-12-31 27573000000.0 None 48082000000.0
2019-12-31 26018000000.0 None 39625000000.0
2018-12-31 21419000000.0 None 34913000000.0
2017-12-31 16625000000.0 None 27193000000.0
Congratulations! Now you have the ticker’s financial information organized by ticker and date. You can now use Pandas to pull out any data of interest.
How to Get Options Data Using yfinance
Options give traders the right but not the obligation to buy or sell underlying assets at a specific price at a predetermined date.
You’ll need to use the Ticker.options and Ticker.option_chain methods to download options data.
- options returns the options expiry dates as a tuple.
- option_chain returns a yfinance.ticker.Options chain object that gives you the chain for an expiry or the entire chain if you don’t specify a date.
aapl = yf.Ticker('aapl')
options = aapl.option_chain()With a chain object, you’ll have the following available to you.
Get yfinance Options Call Data
Use call on the options object to get the call data.
calls = options.calls
calls
Get yfinance Options Put Data
Getting puts is just as easy. We’ll use options.puts to get the put data.
puts = options.puts
puts
How to Get Institutional Holders Using yfinance
You can also gauge institutional sentiment using yfinance.
aapl.insitutional_holders Holder Shares Date Reported % Out Value
0 Vanguard Group, Inc. (The) 1266332667 2021-09-29 0.0775 179186072380
1 Blackrock Inc. 1026223983 2021-09-29 0.0628 145210693594
2 Berkshire Hathaway, Inc 887135554 2021-09-29 0.0543 125529680891
3 State Street Corporation 622163541 2021-09-29 0.0381 88036141051
4 FMR, LLC 350617759 2021-09-29 0.0215 49612412898
5 Geode Capital Management, LLC 259894947 2021-09-29 0.0159 36775135000
6 Northern Trust Corporation 195321532 2021-09-29 0.0120 27637996778
7 Price (T.Rowe) Associates Inc 188489966 2021-09-29 0.0115 26671330189
8 Norges Bank Investment Management 167580974 2020-12-30 0.0103 22236319440
9 Bank Of New York Mellon Corporation 149381117 2021-09-29 0.0091 21137428055
Why You Shouldn’t Use Yahoo Finance for Live Trading
Let’s grab the data for Facebook. Facebook recently changed its name to Meta.
fb = yf.Ticker('fb')
meta = yf.Ticker('meta')
fb.get_cashflow() 2020-12-31 2019-12-31 2018-12-31 2017-12-31
Investments -1.452000e+10 -4.254000e+09 2.449000e+09 -1.325000e+10
Change To Liabilities 9.100000e+07 2.360000e+08 2.740000e+08 4.700000e+07
Total Cashflows From Investing Activities -3.005900e+10 -1.986400e+10 -1.160300e+10 -2.011800e+10
Net Borrowings -5.800000e+08 -7.750000e+08 5.000000e+08 5.000000e+08
Total Cash From Financing Activities -1.029200e+10 -7.299000e+09 -1.557200e+10 -5.235000e+09
Change To Operating Activities -1.302000e+09 8.975000e+09 9.100000e+07 3.449000e+09
Net Income 2.914600e+10 1.848500e+10 2.211200e+10 1.593400e+10
Change In Cash -1.325000e+09 9.155000e+09 1.920000e+09 -9.050000e+08
Repurchase Of Stock -9.836000e+09 -6.539000e+09 -1.608700e+10 -5.222000e+09
Effect Of Exchange Rate 2.790000e+08 4.000000e+06 -1.790000e+08 2.320000e+08
Total Cash From Operating Activities 3.874700e+10 3.631400e+10 2.927400e+10 2.421600e+10
Depreciation 6.862000e+09 5.741000e+09 4.315000e+09 3.025000e+09
Other Cashflows From Investing Activities -3.600000e+07 -3.600000e+07 -3.600000e+07 -1.300000e+07
Change To Account Receivables -1.512000e+09 -1.961000e+09 -1.892000e+09 -1.609000e+09
Other Cashflows From Financing Activities 1.240000e+08 1.500000e+07 1.500000e+07 -1.300000e+07
Change To Netincome 5.462000e+09 4.838000e+09 4.374000e+09 3.370000e+09
Capital Expenditures -1.511500e+10 -1.510200e+10 -1.391500e+10 -6.733000e+09And now for Meta…
meta.get_cashflow()Empty DataFrame
Columns: [Open, High, Low, Close, Adj Close, Volume]
Index: []Facebook and Meta are the same company, but they return different data. This is just one of the many risks of using Yahoo Finance.
Ticker.Info Keys
Here’s everything that ticker.info provides:
dict_keys(['zip', 'sector', 'fullTimeEmployees', 'longBusinessSummary', 'city', 'phone', 'state', 'country', 'companyOfficers', 'website', 'maxAge', 'address1', 'fax', 'industry', 'address2', 'ebitdaMargins', 'profitMargins', 'grossMargins', 'operatingCashflow', 'revenueGrowth', 'operatingMargins', 'ebitda', 'targetLowPrice', 'recommendationKey', 'grossProfits', 'freeCashflow', 'targetMedianPrice', 'currentPrice', 'earningsGrowth', 'currentRatio', 'returnOnAssets', 'numberOfAnalystOpinions', 'targetMeanPrice', 'debtToEquity', 'returnOnEquity', 'targetHighPrice', 'totalCash', 'totalDebt', 'totalRevenue', 'totalCashPerShare', 'financialCurrency', 'revenuePerShare', 'quickRatio', 'recommendationMean', 'exchange', 'shortName', 'longName', 'exchangeTimezoneName', 'exchangeTimezoneShortName', 'isEsgPopulated', 'gmtOffSetMilliseconds', 'quoteType', 'symbol', 'messageBoardId', 'market', 'annualHoldingsTurnover', 'enterpriseToRevenue', 'beta3Year', 'enterpriseToEbitda', '52WeekChange', 'morningStarRiskRating', 'forwardEps', 'revenueQuarterlyGrowth', 'sharesOutstanding', 'fundInceptionDate', 'annualReportExpenseRatio', 'totalAssets', 'bookValue', 'sharesShort', 'sharesPercentSharesOut', 'fundFamily', 'lastFiscalYearEnd', 'heldPercentInstitutions', 'netIncomeToCommon', 'trailingEps', 'lastDividendValue', 'SandP52WeekChange', 'priceToBook', 'heldPercentInsiders', 'nextFiscalYearEnd', 'yield', 'mostRecentQuarter', 'shortRatio', 'sharesShortPreviousMonthDate', 'floatShares', 'beta', 'enterpriseValue', 'priceHint', 'threeYearAverageReturn', 'lastSplitDate', 'lastSplitFactor', 'legalType', 'lastDividendDate', 'morningStarOverallRating', 'earningsQuarterlyGrowth', 'priceToSalesTrailing12Months', 'dateShortInterest', 'pegRatio', 'ytdReturn', 'forwardPE', 'lastCapGain', 'shortPercentOfFloat', 'sharesShortPriorMonth', 'impliedSharesOutstanding', 'category', 'fiveYearAverageReturn', 'previousClose', 'regularMarketOpen', 'twoHundredDayAverage', 'trailingAnnualDividendYield', 'payoutRatio', 'volume24Hr', 'regularMarketDayHigh', 'navPrice', 'averageDailyVolume10Day', 'regularMarketPreviousClose', 'fiftyDayAverage', 'trailingAnnualDividendRate', 'open', 'toCurrency', 'averageVolume10days', 'expireDate', 'algorithm', 'dividendRate', 'exDividendDate', 'circulatingSupply', 'startDate', 'regularMarketDayLow', 'currency', 'trailingPE', 'regularMarketVolume', 'lastMarket', 'maxSupply', 'openInterest', 'marketCap', 'volumeAllCurrencies', 'strikePrice', 'averageVolume', 'dayLow', 'ask', 'askSize', 'volume', 'fiftyTwoWeekHigh', 'fromCurrency', 'fiveYearAvgDividendYield', 'fiftyTwoWeekLow', 'bid', 'tradeable', 'dividendYield', 'bidSize', 'dayHigh', 'regularMarketPrice', 'preMarketPrice', 'logo_url', 'trailingPegRatio'])
The Bottom Line
yfinance is a fantastic tool to grab data from Yahoo Finance. Yahoo Finance is probably the best source for free data.
Free data is free, though. And as I discussed and demonstrated above, I wouldn’t recommend it for live trading.
But if you’re looking to do some high-level research and free what you need, yfinance has got you covered.
Great! Thanks!!!
Most welcome! Glad you enjoyed :).