yfinance Python Tutorial ([year]) (2024)

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 theyfinance python library.

Who Created yFinance?

Ran Aroussiis 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 todownload financial data for free.

You can even follow along withThe 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: .

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-dir

Install yfinance Using Conda:

$ conda install -c ranaroussi yfinance

yfinance 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 theTickerobject and multiple tickers using thedownloadmethod.

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 thehistorymethod. 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 SplitsDate2021-12-10 2982.000000 2988.000000 2947.149902 2973.500000 1081700 0 02021-12-13 2968.879883 2971.250000 2927.199951 2934.090088 1205200 0 02021-12-14 2895.399902 2908.840088 2844.850098 2899.409912 1238900 0 02021-12-15 2887.320068 2950.344971 2854.110107 2947.370117 1364000 0 02021-12-16 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 SplitsDatetime2022-01-03 09:30:00-05:00 2889.510010 2901.020020 2887.733398 2899.060059 67320 0 02022-01-03 09:31:00-05:00 2900.520020 2906.060059 2900.489990 2904.580078 8142 0 02022-01-03 09:32:00-05:00 2904.719971 2904.719971 2896.310059 2899.209961 7069 0 02022-01-03 09:33:00-05:00 2898.699951 2898.699951 2898.699951 2898.699951 623 0 02022-01-03 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 passdownloada 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 METADate2021-12-10 2973.500000 15.52 2973.500000 15.52 2988.000000 15.83 2947.149902 15.390 2982.000000 15.77 1081700 18452002021-12-13 2934.090088 15.24 2934.090088 15.24 2971.250000 15.55 2927.199951 15.130 2968.879883 15.53 1205200 21785002021-12-14 2899.409912 15.06 2899.409912 15.06 2908.840088 15.17 2844.850098 14.850 2895.399902 15.02 1238900 26629002021-12-15 2947.370117 15.28 2947.370117 15.28 2950.344971 15.28 2854.110107 14.615 2887.320068 14.95 1364000 23563002021-12-16 2896.770020 14.79 2896.770020 14.79 2971.030029 15.46 2881.850098 14.680 2961.540039 15.45 1370000 2511100

Let’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 VolumeDate2021-12-10 15.77 15.83 15.390 15.52 15.52 1845200 2982.000000 2988.000000 2947.149902 2973.500000 2973.500000 10817002021-12-13 15.53 15.55 15.130 15.24 15.24 2178500 2968.879883 2971.250000 2927.199951 2934.090088 2934.090088 12052002021-12-14 15.02 15.17 14.850 15.06 15.06 2662900 2895.399902 2908.840088 2844.850098 2899.409912 2899.409912 12389002021-12-15 14.95 15.28 14.615 15.28 15.28 2356300 2887.320068 2950.344971 2854.110107 2947.370117 2947.370117 13640002021-12-16 15.45 15.46 14.680 14.79 14.79 2511100 2961.540039 2971.030029 2881.850098 2896.770020 2896.770020 1370000

How to Download Fundamental Data Using yfinance

We use theTickerobject 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 theinfomethod, which returns a dictionary.

dhr = yf.Ticker('DHR')info = dhr.infoinfo.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 theearnings method.

dhr.earnings

 Revenue EarningsYear2017 15518800000 24921000002018 17049000000 26510000002019 17911000000 30080000002020 22284000000 3646000000

And 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 2017-12-31Research Development 1348000000.0 1126000000.0 1059000000.0 956400000.0Effect Of Accounting Charges None None None NoneIncome Before Tax 4495000000.0 3305000000.0 2962000000.0 2543200000.0Minority Interest 11000000.0 11000000.0 12300000.0 9600000.0Net Income 3646000000.0 3008000000.0 2651000000.0 2492100000.0Selling General Administrative 6880000000.0 5577000000.0 5356000000.0 5011900000.0Gross Profit 12932000000.0 9984000000.0 9505000000.0 8571300000.0Ebit 4704000000.0 3281000000.0 3090000000.0 2603000000.0Operating Income 4704000000.0 3281000000.0 3090000000.0 2603000000.0Other Operating Expenses None None None NoneInterest Expense -275000000.0 -108000000.0 -137000000.0 -140100000.0Extraordinary Items None None None NoneNon Recurring None None None NoneOther Items None None None NoneIncome Tax Expense 849000000.0 873000000.0 556000000.0 371000000.0Total Revenue 22284000000.0 17911000000.0 17049000000.0 15518800000.0Total Operating Expenses 17580000000.0 14630000000.0 13959000000.0 12915800000.0Cost Of Revenue 9352000000.0 7927000000.0 7544000000.0 6947500000.0Total Other Income Expense Net -209000000.0 24000000.0 -128000000.0 -59800000.0Discontinued Operations NaN 576000000.0 245000000.0 319900000.0Net Income From Continuing Ops 3646000000.0 2432000000.0 2406000000.0 2172200000.0Net 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.financialsbs = dhr.balancesheetcf = dhr.cashflowfs = pd.concat([pnl,bs,cf])print(fs)

 2020-12-31 2019-12-31 2018-12-31 2017-12-31Research Development 1348000000.0 1126000000.0 1059000000.0 956400000.0Effect Of Accounting Charges None None None NoneIncome Before Tax 4495000000.0 3305000000.0 2962000000.0 2543200000.0Minority Interest 11000000.0 11000000.0 12300000.0 9600000.0Net Income 3646000000.0 3008000000.0 2651000000.0 2492100000.0... ... ... ... ...Change To Inventory -123000000.0 -22000000.0 -134000000.0 3100000.0Change To Account Receivables -264000000.0 -157000000.0 -55000000.0 -142500000.0Other Cashflows From Financing Activities -29000000.0 369000000.0 -18000000.0 -124200000.0Change To Netincome 182000000.0 -122000000.0 271000000.0 139300000.0Capital 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 -791000000.02019-12-31 1126000000.0 None ... -122000000.0 -636000000.02018-12-31 1059000000.0 None ... 271000000.0 -584000000.02017-12-31 956400000.0 None ... 139300000.0 -570700000.0[4 rows x 68 columns]

And while there’s nodownloadmethod 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 dataframefor 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 usingpandas.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 TaxTicker DateFB2020-12-31 18447000000.0 None 33180000000.02019-12-31 13600000000.0 None 24812000000.02018-12-31 10273000000.0 None 25361000000.02017-12-31 7754000000.0 None 20594000000.0AMZN 2020-12-31 42740000000.0 None 24194000000.02019-12-31 35931000000.0 None 13962000000.02018-12-31 28837000000.0 None 11270000000.02017-12-31 22620000000.0 None 3802000000.0NFLX 2020-12-31 1829600000.0 None 3199349000.02019-12-31 1545149000.0 None 2062231000.02018-12-31 1221814000.0 None 1226458000.02017-12-31 953710000.0 None 485321000.0GOOG 2020-12-31 27573000000.0 None 48082000000.02019-12-31 26018000000.0 None 39625000000.02018-12-31 21419000000.0 None 34913000000.02017-12-31 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 theTicker.optionsandTicker.option_chainmethods to download options data.

optionsreturns the options expiry dates as a tuple.
option_chainreturns ayfinance.ticker.Optionschain 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

Usecallon the options object to get the call data.

calls = options.callscalls

Get yfinance Options Put Data

Getting puts is just as easy. We’ll useoptions.putsto get the put data.

puts = options.putsputs

How to Get Institutional Holders Using yfinance

You can also gauge institutional sentiment using yfinance.

aapl.insitutional_holders

 Holder Shares Date Reported % Out Value0 Vanguard Group, Inc. (The) 1266332667 2021-09-29 0.0775 1791860723801 Blackrock Inc. 1026223983 2021-09-29 0.0628 1452106935942 Berkshire Hathaway, Inc 887135554 2021-09-29 0.0543 1255296808913 State Street Corporation 622163541 2021-09-29 0.0381 880361410514 FMR, LLC 350617759 2021-09-29 0.0215 496124128985 Geode Capital Management, LLC 259894947 2021-09-29 0.0159 367751350006 Northern Trust Corporation 195321532 2021-09-29 0.0120 276379967787 Price (T.Rowe) Associates Inc 188489966 2021-09-29 0.0115 266713301898 Norges Bank Investment Management 167580974 2020-12-30 0.0103 222363194409 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 2017-12-31Investments -1.452000e+10 -4.254000e+09 2.449000e+09 -1.325000e+10Change To Liabilities 9.100000e+07 2.360000e+08 2.740000e+08 4.700000e+07Total Cashflows From Investing Activities -3.005900e+10 -1.986400e+10 -1.160300e+10 -2.011800e+10Net Borrowings -5.800000e+08 -7.750000e+08 5.000000e+08 5.000000e+08Total Cash From Financing Activities -1.029200e+10 -7.299000e+09 -1.557200e+10 -5.235000e+09Change To Operating Activities -1.302000e+09 8.975000e+09 9.100000e+07 3.449000e+09Net Income 2.914600e+10 1.848500e+10 2.211200e+10 1.593400e+10Change In Cash -1.325000e+09 9.155000e+09 1.920000e+09 -9.050000e+08Repurchase Of Stock -9.836000e+09 -6.539000e+09 -1.608700e+10 -5.222000e+09Effect Of Exchange Rate 2.790000e+08 4.000000e+06 -1.790000e+08 2.320000e+08Total Cash From Operating Activities 3.874700e+10 3.631400e+10 2.927400e+10 2.421600e+10Depreciation 6.862000e+09 5.741000e+09 4.315000e+09 3.025000e+09Other Cashflows From Investing Activities -3.600000e+07 -3.600000e+07 -3.600000e+07 -1.300000e+07Change To Account Receivables -1.512000e+09 -1.961000e+09 -1.892000e+09 -1.609000e+09Other Cashflows From Financing Activities 1.240000e+08 1.500000e+07 1.500000e+07 -1.300000e+07Change To Netincome 5.462000e+09 4.838000e+09 4.374000e+09 3.370000e+09Capital Expenditures -1.511500e+10 -1.510200e+10 -1.391500e+10 -6.733000e+09

And now for Meta…

meta.get_cashflow()

Empty DataFrameColumns: [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.