yfinance

https://ranaroussi.github.io/yfinance
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yfinance is an open-source Python library that downloads market data from Yahoo! Finance's API for...
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### Install yfinance

Source: https://ranaroussi.github.io/yfinance/_sources/index.rst.txt

Install the yfinance library using pip.

```bash
pip install yfinance
```

--------------------------------

### Install development dependencies

Source: https://ranaroussi.github.io/yfinance/_sources/development/testing.rst.txt

Install the necessary development dependencies to run the test suite.

```bash
pip install -e ".[dev]"
```

--------------------------------

### Search Module Example

Source: https://ranaroussi.github.io/yfinance/_sources/reference/yfinance.search.rst.txt

Demonstrates how to use the Search module to access search data. Ensure the 'search.py' file is available in the examples directory.

```python
from yfinance import Search

search = Search()

# Example: Search for companies related to 'technology'
results = search.companies(query='technology')
print(results)

# Example: Search for ETFs related to 'bonds'
results = search.etfs(query='bonds')
print(results)

# Example: Search for mutual funds related to 'growth'
results = search.mutualfunds(query='growth')
print(results)
```

--------------------------------

### Install yfinance

Source: https://ranaroussi.github.io/yfinance/index.html

Install the yfinance library using pip. This is the first step before using any of its functionalities.

```bash
$ pip install yfinance

```

--------------------------------

### Market Class Usage Example

Source: https://ranaroussi.github.io/yfinance/_sources/reference/yfinance.market.rst.txt

Demonstrates how to use the Market class to access market data. This snippet requires the yfinance library to be installed.

```python
from yfinance import Market

market = Market()

# Get summary data for all markets
s = market.summary
print(s)

# Get status for US market
s = market.status("US")
print(s)

# Get status for non-US market (will return None and log a warning)
s = market.status("GB")
print(s)
```

--------------------------------

### Serve Documentation Locally

Source: https://ranaroussi.github.io/yfinance/development/documentation.html

Starts a local web server to view the generated HTML documentation in a browser at localhost:8000.

```bash
python -m http.server -d ./doc/_build/html
```

--------------------------------

### Tickers.live

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Tickers.html

Starts a live data stream for the specified tickers.

```APIDOC
## Tickers.live

### Description
Starts a live data stream for the tickers associated with the Tickers object.

### Parameters
- **_message_handler_** (callable, optional) - A function to call when a new message is received.
- **_verbose_** (bool, optional) - Whether to print live data to the console. Defaults to True.
```

--------------------------------

### Get Earnings Calendar with Default Parameters

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Calendars.html

Instantiate the Calendars class and retrieve the earnings calendar with default settings. This example shows the basic usage for fetching earnings data.

```python
import yfinance as yf

calendars = yf.Calendars()
earnings_calendar = calendars.get_earnings_calendar(limit=50)
print(earnings_calendar)
```

--------------------------------

### Install yfinance without curl_cffi

Source: https://ranaroussi.github.io/yfinance/_sources/advanced/install.rst.txt

Use this command to install yfinance while excluding 'curl_cffi' from the requirements, ensuring a requests fallback. This installs dependencies from stdin and then yfinance without its own dependencies.

```bash
curl -fsSL https://raw.githubusercontent.com/ranaroussi/yfinance/main/requirements.txt | grep -vi '^curl_cffi' | pip install -r /dev/stdin
pip install --no-deps yfinance
```

--------------------------------

### Install yfinance without curl_cffi

Source: https://ranaroussi.github.io/yfinance/advanced/install.html

Installs yfinance using pip, excluding curl_cffi and falling back to requests. This command fetches requirements, filters out curl_cffi, and installs the remaining dependencies, followed by the yfinance package itself.

```bash
curl -fsSL https://raw.githubusercontent.com/ranaroussi/yfinance/main/requirements.txt | grep -vi '^curl_cffi' | pip install -r /dev/stdin
pip install --no-deps yfinance

```

--------------------------------

### Initialize Sector and Industry Modules

Source: https://ranaroussi.github.io/yfinance/_sources/reference/yfinance.sector_industry.rst.txt

Initialize the Sector and Industry modules using their respective keys. This is the basic setup for accessing sector and industry information.

```python
import yfinance as yf

# Initialize Sector and Industry modules
sector_info = yf.Sector("technology")
industry_info = yf.Industry("software-infrastructure")

print(sector_info.info)
print(industry_info.info)
```

--------------------------------

### Initialize Market and Access Data

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.market.html

Demonstrates how to initialize a Market object for Europe and access its status and summary attributes. Requires the yfinance library to be installed.

```python
import yfinance as yf

EUROPE = yf.Market("EUROPE")

status = EUROPE.status
summary = EUROPE.summary

```

--------------------------------

### Get Upcoming Earnings Events with yfinance Calendars

Source: https://ranaroussi.github.io/yfinance/_sources/reference/yfinance.calendars.rst.txt

Use the Calendars class to fetch upcoming earnings events. Ensure yfinance is installed and imported.

```python
import yfinance as yf

# Get upcoming earnings events
earnings = yf.Actions.earnings
print(earnings)

# Get upcoming dividend events
dividends = yf.Actions.dividends
print(dividends)

# Get upcoming stock splits events
splits = yf.Actions.stock_splits
print(splits)
```

--------------------------------

### AsyncWebSocket.listen()

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.AsyncWebSocket.html

Starts listening for messages from the WebSocket server and optionally processes them with a handler.

```APIDOC
## AsyncWebSocket.listen()

### Description
Starts listening to messages from the WebSocket server. You can provide a callback function to handle incoming messages.

### Parameters
* **message_handler** (_Optional[Callable[[dict], None]]_) - Optional function to handle received messages. This function will be called with a dictionary representing the message.
```

--------------------------------

### Use Proxy Server for Data Download

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.ticker_tickers.html

Provides an example of how to configure a proxy server for downloading various types of financial data using the Ticker object.

```python
import 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")
...

```

--------------------------------

### Example Usage of FundQuery

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.FundQuery.html

Demonstrates how to construct a complex filter for mutual funds using the FundQuery class, combining multiple criteria with logical operators.

```APIDOC
## Example

Predefined Yahoo query solid_large_growth_funds:
```python
from yfinance import FundQuery

FundQuery('and', [
    FundQuery('eq', ['categoryname', 'Large Growth']),
    FundQuery('is-in', ['performanceratingoverall', 4, 5]),
    FundQuery('lt', ['initialinvestment', 100001]),
    FundQuery('lt', ['annualreturnnavy1categoryrank', 50]),
    FundQuery('eq', ['exchange', 'NAS'])
])
```
```

--------------------------------

### Lookup Module Example

Source: https://ranaroussi.github.io/yfinance/_sources/reference/yfinance.search.rst.txt

Illustrates how to use the Lookup module for ticker symbol retrieval. This code requires the 'lookup.py' file to be present.

```python
from yfinance import Lookup

lookup = Lookup()

# Example: Look up tickers for 'Apple Inc.'
results = lookup.by_company_name('Apple Inc.')
print(results)

# Example: Look up tickers by sector 'Technology'
results = lookup.by_sector('Technology')
print(results)

# Example: Look up tickers by industry 'Semiconductors'
results = lookup.by_industry('Semiconductors')
print(results)
```

--------------------------------

### ETFQuery Example Usage

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.ETFQuery.html

Demonstrates how to use the ETFQuery class to build a filter for US ETFs with a performance rating of 4 or 5 and an intraday price greater than 10.

```APIDOC
### Example
```python
from yfinance import ETFQuery

ETFQuery('and', [
    ETFQuery('gt', ['intradayprice', 10]),
    ETFQuery('is-in', ['performanceratingoverall', 4, 5]),
    ETFQuery('eq', ['region', 'us'])
])
```
```

--------------------------------

### WebSocket.listen()

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.WebSocket.html

Starts listening for messages from the WebSocket server. A message handler can be provided to process incoming data.

```APIDOC
## WebSocket.listen()

### Description
Start listening to messages from the WebSocket server.

### Parameters
- **message_handler** (Callable[[dict], None] | None) - Optional function to handle received messages.
```

--------------------------------

### Initialize Calendars Object

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Calendars.html

Instantiate the Calendars object, optionally providing start and end dates, and a requests.Session object.

```APIDOC
## Initialize Calendars

### Description
Instantiates the Calendars object to access financial calendar data.

### Method Signature
__init__(self, start : str | datetime | date | None = None, end : str | datetime | date | None = None, session : Session | None = None)

### Parameters
#### Parameters
- **start** (_str_ | _datetime_ | _date_) – start date (default today) eg. start=”2025-11-08”
- **end** (_str_ | _datetime_ | _date_) – end date (default start + 7 days) eg. end=”2025-11-08”
- **session** – requests.Session object, optional
```

--------------------------------

### Run a custom stock screen query

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.screen.html

Construct and execute a custom stock screening query using EquityQuery. This example filters for stocks with a percentage change greater than 3% and in the 'us' region, then sorts by percentage change.

```python
import yfinance as yf
from yfinance import EquityQuery
q = EquityQuery('and', [
       EquityQuery('gt', ['percentchange', 3]),
       EquityQuery('eq', ['region', 'us'])
])
response = yf.screen(q, sortField = 'percentchange', sortAsc = True)
```

--------------------------------

### Asynchronous WebSocket Client

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.websocket.html

Illustrates the use of the asynchronous WebSocket client for streaming price data. Includes examples with and without a context manager, suitable for asynchronous applications.

```python
import asyncio
import yfinance as yf

# define your message callback
def message_handler(message):
    print("Received message:", message)

async def main():
    # =======================
    # With Context Manager
    # =======================
    async with yf.AsyncWebSocket() as ws:
        await ws.subscribe(["AAPL", "BTC-USD"])
        await ws.listen()

    # =======================
    # Without Context Manager
    # =======================
    ws = yf.AsyncWebSocket()
    await ws.subscribe(["AAPL", "BTC-USD"])
    await ws.listen()

asyncio.run(main())
```

--------------------------------

### Initialize Ticker Object

Source: https://ranaroussi.github.io/yfinance/_sources/reference/yfinance.ticker_tickers.rst.txt

Access individual ticker data using the Ticker module. No specific setup is required beyond importing the library.

```python
import yfinance as yf

ticker = yf.Ticker("AAPL")

# Access historical data
hist = ticker.history(period="1mo")
print(hist.head())

# Access info data
info = ticker.info
print(info)

# Access options data
options = ticker.options
print(options)

# Access institutional holders
institutional_holders = ticker.institutional_holders
print(institutional_holders)

# Access major holders
major_holders = ticker.major_holders
print(major_holders)

# Access corporate actions
corporate_actions = ticker.corporate_actions
print(corporate_actions)

# Access sustainability data
sustainability = ticker.sustainability
print(sustainability)

# Access recommendations
recommendations = ticker.recommendations
print(recommendations)

# Access next earnings date
next_earnings_date = ticker.next_earnings_date
print(next_earnings_date)

# Access shares outstanding
shares_outstanding = ticker.shares_outstanding
print(shares_outstanding)

# Access shares float
shares_float = ticker.shares_float
print(shares_float)

# Access beta
beta = ticker.beta
print(beta)

# Access forward PE ratios
forward_pe = ticker.forward_pe
print(forward_pe)

# Access dividend rate
dividend_rate = ticker.dividend_rate
print(dividend_rate)

# Access dividend yield
dividend_yield = ticker.dividend_yield
print(dividend_yield)

# Access ex-dividend date
ex_dividend_date = ticker.ex_dividend_date
print(ex_dividend_date)

# Access P/E ratio
pe_ratio = ticker.pe_ratio
print(pe_ratio)

# Access 52 week high
high_52_week = ticker.high_52_week
print(high_52_week)

# Access 52 week low
low_52_week = ticker.low_52_week
print(low_52_week)

# Access market cap
market_cap = ticker.market_cap
print(market_cap)

# Access volume
volume = ticker.volume
print(volume)

# Access average volume
average_volume = ticker.average_volume
print(average_volume)

# Access previous close price
previous_close = ticker.previous_close
print(previous_close)

# Access open price
open_price = ticker.open
print(open_price)

# Access day low price
day_low = ticker.day_low
print(day_low)

# Access day high price
day_high = ticker.day_high
print(day_high)

# Access regular market previous close price
regular_market_previous_close = ticker.regular_market_previous_close
print(regular_market_previous_close)

# Access regular market open price
regular_market_open = ticker.regular_market_open
print(regular_market_open)

# Access regular market day low price
regular_market_day_low = ticker.regular_market_day_low
print(regular_market_day_low)

# Access regular market day high price
regular_market_day_high = ticker.regular_market_day_high
print(regular_market_day_high)

# Access two hundred day average price
two_hundred_day_average = ticker.two_hundred_day_average
print(two_hundred_day_average)

# Access three month average volume
three_month_average_volume = ticker.three_month_average_volume
print(three_month_average_volume)

# Access five year average return
five_year_average_return = ticker.five_year_average_return
print(five_year_average_return)

# Access beta three year average
beta_three_year_average = ticker.beta_three_year_average
print(beta_three_year_average)

# Access profit margin
profit_margin = ticker.profit_margin
print(profit_margin)

# Access operating margin
operating_margin = ticker.operating_margin
print(operating_margin)

# Access return on assets
return_on_assets = ticker.return_on_assets
print(return_on_assets)

# Access return on equity
return_on_equity = ticker.return_on_equity
print(return_on_equity)

# Access earnings quarterly growth
earnings_quarterly_growth = ticker.earnings_quarterly_growth
print(earnings_quarterly_growth)

# Access revenue quarterly growth
revenue_quarterly_growth = ticker.revenue_quarterly_growth
print(revenue_quarterly_growth)

# Access net income to common
net_income_to_common = ticker.net_income_to_common
print(net_income_to_common)

# Access basic eps
basic_eps = ticker.basic_eps
print(basic_eps)

# Access diluted eps
diluted_eps = ticker.diluted_eps
print(diluted_eps)

# Access earnings before interest and taxes
earnings_before_interest_and_taxes = ticker.earnings_before_interest_and_taxes
print(earnings_before_interest_and_taxes)

# Access earnings before tax
earnings_before_tax = ticker.earnings_before_tax
print(earnings_before_tax)

# Access diluted net income
diluted_net_income = ticker.diluted_net_income
print(diluted_net_income)

# Access net income from continuing and discontinued operation
net_income_from_continuing_and_discontinued_operation = ticker.net_income_from_continuing_and_discontinued_operation
print(net_income_from_continuing_and_discontinued_operation)

# Access total revenue
total_revenue = ticker.total_revenue
print(total_revenue)

# Access gross profit
gross_profit = ticker.gross_profit
print(gross_profit)

# Access operating income
operating_income = ticker.operating_income
print(operating_income)

# Access selling general and administrative expense
selling_general_and_administrative_expense = ticker.selling_general_and_administrative_expense
print(selling_general_and_administrative_expense)

# Access research and development expense
research_and_development_expense = ticker.research_and_development_expense
print(research_and_development_expense)

# Access cost of revenue
cost_of_revenue = ticker.cost_of_revenue
print(cost_of_revenue)

# Access total operating expenses
total_operating_expenses = ticker.total_operating_expenses
print(total_operating_expenses)

# Access interest expense
interest_expense = ticker.interest_expense
print(interest_expense)

# Access depreciation and amortization
depreciation_and_amortization = ticker.depreciation_and_amortization
print(depreciation_and_amortization)

# Access stock based compensation
stock_based_compensation = ticker.stock_based_compensation
print(stock_based_compensation)

# Access other income expense net
other_income_expense_net = ticker.other_income_expense_net
print(other_income_expense_net)

# Access income before tax
income_before_tax = ticker.income_before_tax
print(income_before_tax)

# Access minority interest
minority_interest = ticker.minority_interest
print(minority_interest)

# Access tax provision
tax_provision = ticker.tax_provision
print(tax_provision)

# Access net income from continuing operation
net_income_from_continuing_operation = ticker.net_income_from_continuing_operation
print(net_income_from_continuing_operation)

# Access diluted eps from continuing operation
diluted_eps_from_continuing_operation = ticker.diluted_eps_from_continuing_operation
print(diluted_eps_from_continuing_operation)

# Access diluted normalized eps
diluted_normalized_eps = ticker.diluted_normalized_eps
print(diluted_normalized_eps)

# Access total cash
total_cash = ticker.total_cash
print(total_cash)

# Access short term debt
short_term_debt = ticker.short_term_debt
print(short_term_debt)

# Access long term debt
long_term_debt = ticker.long_term_debt
print(long_term_debt)

# Access total debt
total_debt = ticker.total_debt
print(total_debt)

# Access current ratio
current_ratio = ticker.current_ratio
print(current_ratio)

# Access quick ratio
quick_ratio = ticker.quick_ratio
print(quick_ratio)

# Access cash flow from investment
cash_flow_from_investment = ticker.cash_flow_from_investment
print(cash_flow_from_investment)

# Access cash flow from financing
cash_flow_from_financing = ticker.cash_flow_from_financing
print(cash_flow_from_financing)

# Access net cash used provided by operating activities
net_cash_used_provided_by_operating_activities = ticker.net_cash_used_provided_by_operating_activities
print(net_cash_used_provided_by_operating_activities)

# Access free cash flow
free_cash_flow = ticker.free_cash_flow
print(free_cash_flow)

# Access dividends paid
dividends_paid = ticker.dividends_paid
print(dividends_paid)

# Access stock repurchase
stock_repurchase = ticker.stock_repurchase
print(stock_repurchase)

# Access capital expenditures
capital_expenditures = ticker.capital_expenditures
print(capital_expenditures)

# Access share statistics
share_statistics = ticker.share_statistics
print(share_statistics)

# Access earnings history
earnings_history = ticker.earnings_history
print(earnings_history)

# Access dividends history
dividends_history = ticker.dividends_history
print(dividends_history)

# Access financial data
financial_data = ticker.financial_data
print(financial_data)

# Access balance sheet data
balance_sheet_data = ticker.balance_sheet_data
print(balance_sheet_data)

# Access cash flow data
cash_flow_data = ticker.cash_flow_data
print(cash_flow_data)

# Access income statement data
income_statement_data = ticker.income_statement_data
print(income_statement_data)

# Access news
news = ticker.news
print(news)

# Access analyst recommendations
analyst_recommendations = ticker.analyst_recommendations
print(analyst_recommendations)

# Access earnings forecast
earnings_forecast = ticker.earnings_forecast
print(earnings_forecast)

# Access revenue forecast
revenue_forecast = ticker.revenue_forecast
print(revenue_forecast)

# Access index patents
index_patents = ticker.index_patents
print(index_patents)

# Access upgrades_downgrades
upgrades_downgrades = ticker.upgrades_downgrades
print(upgrades_downgrades)

# Access shares
shares = ticker.shares
print(shares)

# Access mutual fund information
mutual_fund_information = ticker.mutual_fund_information
print(mutual_fund_information)

# Access etf information
etf_information = ticker.etf_information
print(etf_information)

# Access sector performance
sector_performance = ticker.sector_performance
print(sector_performance)

# Access mutual fund performance
mutual_fund_performance = ticker.mutual_fund_performance
print(mutual_fund_performance)

# Access etf performance
etf_performance = ticker.etf_performance
print(etf_performance)

# Access mutual fund country exposure
mutual_fund_country_exposure = ticker.mutual_fund_country_exposure
print(mutual_fund_country_exposure)

# Access etf country exposure
etf_country_exposure = ticker.etf_country_exposure
print(etf_country_exposure)

# Access mutual fund asset turnover
mutual_fund_asset_turnover = ticker.mutual_fund_asset_turnover
print(mutual_fund_asset_turnover)

# Access etf asset turnover
etf_asset_turnover = ticker.etf_asset_turnover
print(etf_asset_turnover)

# Access mutual fund fees expenses ratios
mutual_fund_fees_expenses_ratios = ticker.mutual_fund_fees_expenses_ratios
print(mutual_fund_fees_expenses_ratios)

# Access etf fees expenses ratios
etf_fees_expenses_ratios = ticker.etf_fees_expenses_ratios
print(etf_fees_expenses_ratios)

# Access mutual fund holdings
mutual_fund_holdings = ticker.mutual_fund_holdings
print(mutual_fund_holdings)

# Access etf holdings
etf_holdings = ticker.etf_holdings
print(etf_holdings)

# Access mutual fund top holdings
mutual_fund_top_holdings = ticker.mutual_fund_top_holdings
print(mutual_fund_top_holdings)

# Access etf top holdings
etf_top_holdings = ticker.etf_top_holdings
print(etf_top_holdings)

# Access mutual fund top instruments
mutual_fund_top_instruments = ticker.mutual_fund_top_instruments
print(mutual_fund_top_instruments)

# Access etf top instruments
etf_top_instruments = ticker.etf_top_instruments
print(etf_top_instruments)

# Access mutual fund top countries
mutual_fund_top_countries = ticker.mutual_fund_top_countries
print(mutual_fund_top_countries)

# Access etf top countries
etf_top_countries = ticker.etf_top_countries
print(etf_top_countries)

# Access mutual fund top sectors
mutual_fund_top_sectors = ticker.mutual_fund_top_sectors
print(mutual_fund_top_sectors)

# Access etf top sectors
etf_top_sectors = ticker.etf_top_sectors
print(etf_top_sectors)

# Access mutual fund top industries
mutual_fund_top_industries = ticker.mutual_fund_top_industries
print(mutual_fund_top_industries)

# Access etf top industries
etf_top_industries = ticker.etf_top_industries
print(etf_top_industries)

# Access mutual fund top issuers
mutual_fund_top_issuers = ticker.mutual_fund_top_issuers
print(mutual_fund_top_issuers)

# Access etf top issuers
etf_top_issuers = ticker.etf_top_issuers
print(etf_top_issuers)

# Access mutual fund top issuers in country
mutual_fund_top_issuers_in_country = ticker.mutual_fund_top_issuers_in_country
print(mutual_fund_top_issuers_in_country)

# Access etf top issuers in country
etf_top_issuers_in_country = ticker.etf_top_issuers_in_country
print(etf_top_issuers_in_country)

# Access mutual fund top issuers in industry
mutual_fund_top_issuers_in_industry = ticker.mutual_fund_top_issuers_in_industry
print(mutual_fund_top_issuers_in_industry)

# Access etf top issuers in industry
etf_top_issuers_in_industry = ticker.etf_top_issuers_in_industry
print(etf_top_issuers_in_industry)

# Access mutual fund top issuers in sector
mutual_fund_top_issuers_in_sector = ticker.mutual_fund_top_issuers_in_sector
print(mutual_fund_top_issuers_in_sector)

# Access etf top issuers in sector
etf_top_issuers_in_sector = ticker.etf_top_issuers_in_sector
print(etf_top_issuers_in_sector)

# Access mutual fund top issuers in country of domicile
mutual_fund_top_issuers_in_country_of_domicile = ticker.mutual_fund_top_issuers_in_country_of_domicile
print(mutual_fund_top_issuers_in_country_of_domicile)

# Access etf top issuers in country of domicile
etf_top_issuers_in_country_of_domicile = ticker.etf_top_issuers_in_country_of_domicile
print(etf_top_issuers_in_country_of_domicile)

# Access mutual fund top issuers in industry of domicile
mutual_fund_top_issuers_in_industry_of_domicile = ticker.mutual_fund_top_issuers_in_industry_of_domicile
print(mutual_fund_top_issuers_in_industry_of_domicile)

# Access etf top issuers in industry of domicile
etf_top_issuers_in_industry_of_domicile = ticker.etf_top_issuers_in_industry_of_domicile
print(etf_top_issuers_in_industry_of_domicile)

# Access mutual fund top issuers in sector of domicile
mutual_fund_top_issuers_in_sector_of_domicile = ticker.mutual_fund_top_issuers_in_sector_of_domicile
print(mutual_fund_top_issuers_in_sector_of_domicile)

# Access etf top issuers in sector of domicile
etf_top_issuers_in_sector_of_domicile = ticker.etf_top_issuers_in_sector_of_domicile
print(etf_top_issuers_in_sector_of_domicile)

# Access mutual fund top issuers in country of domicile and industry
mutual_fund_top_issuers_in_country_of_domicile_and_industry = ticker.mutual_fund_top_issuers_in_country_of_domicile_and_industry
print(mutual_fund_top_issuers_in_country_of_domicile_and_industry)

# Access etf top issuers in country of domicile and industry
etf_top_issuers_in_country_of_domicile_and_industry = ticker.etf_top_issuers_in_country_of_domicile_and_industry
print(etf_top_issuers_in_country_of_domicile_and_industry)

# Access mutual fund top issuers in country of domicile and sector
mutual_fund_top_issuers_in_country_of_domicile_and_sector = ticker.mutual_fund_top_issuers_in_country_of_domicile_and_sector
print(mutual_fund_top_issuers_in_country_of_domicile_and_sector)

# Access etf top issuers in country of domicile and sector
etf_top_issuers_in_country_of_domicile_and_sector = ticker.etf_top_issuers_in_country_of_domicile_and_sector
print(etf_top_issuers_in_country_of_domicile_and_sector)

# Access mutual fund top issuers in industry of domicile and sector
mutual_fund_top_issuers_in_industry_of_domicile_and_sector = ticker.mutual_fund_top_issuers_in_industry_of_domicile_and_sector
print(mutual_fund_top_issuers_in_industry_of_domicile_and_sector)

# Access etf top issuers in industry of domicile and sector
etf_top_issuers_in_industry_of_domicile_and_sector = ticker.etf_top_issuers_in_industry_of_domicile_and_sector
print(etf_top_issuers_in_industry_of_domicile_and_sector)

# Access mutual fund top issuers in country of domicile and industry and sector
mutual_fund_top_issuers_in_country_of_domicile_and_industry_and_sector = ticker.mutual_fund_top_issuers_in_country_of_domicile_and_industry_and_sector
print(mutual_fund_top_issuers_in_country_of_domicile_and_industry_and_sector)

# Access etf top issuers in country of domicile and industry and sector
etf_top_issuers_in_country_of_domicile_and_industry_and_sector = ticker.etf_top_issuers_in_country_of_domicile_and_industry_and_sector
print(etf_top_issuers_in_country_of_domicile_and_industry_and_sector)

```

--------------------------------

### Set Locale for Localized Fields (Japan)

Source: https://ranaroussi.github.io/yfinance/advanced/config.html

Configure the language and region for localized fields. This example sets it to Japanese for Japan.

```python
import yfinance as yf
yf.config.locale.lang = "ja-JP"
yf.config.locale.region = "JP"
yf.Ticker("7203.T").info["longName"]    # → 'トヨタ自動車'
```

--------------------------------

### Constructing ETF Filters with ETFQuery

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.ETFQuery.html

Use ETFQuery to build filters for ETFs. Combine value operations like 'gt' and 'is-in' with logical operations like 'and' to specify criteria. This example filters for US ETFs with a performance rating of 4 or 5 and an intraday price greater than 10.

```python
from yfinance import ETFQuery

ETFQuery('and', [
    ETFQuery('gt', ['intradayprice', 10]),
    ETFQuery('is-in', ['performanceratingoverall', 4, 5]),
    ETFQuery('eq', ['region', 'us'])
])
```

--------------------------------

### EquityQuery.__init__

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.EquityQuery.html

Initializes a new EquityQuery instance with a specified operator and a list of operands.

```APIDOC
## EquityQuery.__init__(operator, operand)

### Description
Initializes the query object with a logical or comparison operator and the corresponding operands.

### Parameters
- **operator** (Literal['eq', 'is-in', 'btwn', 'gt', 'lt', 'gte', 'lte', 'and', 'or']) - Required - The operator to apply to the operands.
- **operand** (List[QueryBase] | List[str] | List[Real]) - Required - The list of values or query objects to be evaluated.
```

--------------------------------

### Build Documentation with Sphinx

Source: https://ranaroussi.github.io/yfinance/development/documentation.html

Generates the HTML documentation from the source files using the sphinx-build command.

```bash
sphinx-build -b html doc/source doc/_build/html
```

--------------------------------

### Using a Proxy Server

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.ticker_tickers.html

Illustrates how to configure and use a proxy server for downloading data with Ticker methods.

```APIDOC
## Proxy Server Configuration

### Description
To use a proxy server for downloading data, you can specify the `proxy` argument in various Ticker methods.

### Usage Example
```python
import yfinance as yf

msft = yf.Ticker("MSFT")

# Example using proxy for history data
msft.history(period='1d', proxy="PROXY_SERVER")

# Example using proxy for other data retrieval methods
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(msft.options[0], proxy="PROXY_SERVER")
```
```

--------------------------------

### top_etfs

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Sector.html

Gets the top ETFs for the sector.

```APIDOC
## top_etfs

### Description
Gets the top ETFs for the sector.

### Returns
A dictionary of ETF symbols and names.

### Return Type
Dict[str, str]
```

--------------------------------

### top_mutual_funds

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Sector.html

Gets the top mutual funds for the sector.

```APIDOC
## top_mutual_funds

### Description
Gets the top mutual funds for the sector.

### Returns
A dictionary of mutual fund symbols and names.

### Return Type
Dict[str, str]
```

--------------------------------

### Initialize and Query yfinance Calendars

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.calendars.html

Demonstrates how to initialize the Calendars class with default settings or custom date ranges. Accessing properties like `earnings_calendar` fetches data, while `get_` methods allow for manual queries.

```python
import yfinance as yf
from datetime import datetime, timedelta

# Default init (today + 7 days)
calendar = yf.Calendars()

# Today's events: calendar of 1 day
tomorrow = datetime.now() + timedelta(days=1)
calendar = yf.Calendars(end=tomorrow)

# Default calendar queries - accessing the properties will fetch the data from YF
calendar.earnings_calendar
calendar.ipo_info_calendar
calendar.splits_calendar
calendar.economic_events_calendar

# Manual queries
calendar.get_earnings_calendar()
calendar.get_ipo_info_calendar()
calendar.get_splits_calendar()
calendar.get_economic_events_calendar()
```

--------------------------------

### Get Stock Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing stocks related to the query.

```APIDOC
## get_stock()

### Description
Returns stock related financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing stock instruments.
```

--------------------------------

### Get Index Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing indices related to the query.

```APIDOC
## get_index()

### Description
Returns Indices related financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing index instruments.
```

--------------------------------

### Authenticate with Yahoo Finance using cookies

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.auth.html

Demonstrates how to initialize the Auth module, set cookies from environment variables, and verify the login state and subscription tier.

```python
import yfinance as yf
import os

auth = yf.Auth()

# Set login cookies obtained from your browser. The call stores them, validates
# them with a live login check, and returns whether the account is logged in.
if auth.set_login_cookies(os.getenv("COOKIE_T"), os.getenv("COOKIE_Y")):
    print("Logged in")
else:
    print("Invalid or expired cookies")

# Every subsequent request sent to Yahoo Finance is now under the logged-in user.

# Re-check the live login state at any time (re-queried each call, never cached).
auth.check_login()        # -> True / False

# Yahoo Finance subscription tier of the logged-in account.
auth.subscription_tier()  # -> 'gold' / 'silver' / 'bronze' / 'free' / None

# Access user information.
auth.user                 # -> {'guid': ...} or None
```

--------------------------------

### Get Future Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing futures related to the query.

```APIDOC
## get_future()

### Description
Returns Futures related financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing future instruments.
```

--------------------------------

### Get ETF Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing ETFs related to the query.

```APIDOC
## get_etf()

### Description
Returns ETFs related financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing ETF instruments.
```

--------------------------------

### Get Currency Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing currencies related to the query.

```APIDOC
## get_currency()

### Description
Returns Currencies related financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing currency instruments.
```

--------------------------------

### AsyncWebSocket Initialization

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.AsyncWebSocket.html

Initializes the AsyncWebSocket client with an optional URL and verbosity setting.

```APIDOC
## AsyncWebSocket Initialization

### Description
Initializes the AsyncWebSocket client. You can specify the WebSocket server URL and control print statements.

### Parameters
* **url** (_str_) - Optional. The WebSocket server URL. Defaults to Yahoo Finance’s WebSocket URL.
* **verbose** (_bool_) - Optional. Flag to enable or disable print statements. Defaults to True.
```

--------------------------------

### Get Cryptocurrency Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing cryptocurrencies related to the query.

```APIDOC
## get_cryptocurrency()

### Description
Returns Cryptocurrencies related financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing cryptocurrency instruments.
```

--------------------------------

### Constructing an EquityQuery filter

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.EquityQuery.html

Demonstrates how to combine multiple criteria using logical operators to filter stocks.

```python
from yfinance import EquityQuery

EquityQuery('and', [
    EquityQuery('is-in', ['exchange', 'NMS', 'NYQ']),
    EquityQuery('lt', ["epsgrowth.lasttwelvemonths", 15])
])
```

--------------------------------

### Calendars Class Initialization and Usage

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.calendars.html

Demonstrates how to initialize the Calendars class with default settings or custom date ranges, and how to access different event calendars.

```APIDOC
## Calendars Class

The `Calendars` class allows you to get information about upcoming events, for example, earning events.

`Calendars`([start, end, session]) | Get economic calendars, for example, Earnings, IPO, Economic Events, Splits

### Sample Code

```python
import yfinance as yf
from datetime import datetime, timedelta

# Default init (today + 7 days)
calendar = yf.Calendars()

# Today's events: calendar of 1 day
tomorrow = datetime.now() + timedelta(days=1)
calendar = yf.Calendars(end=tomorrow)

# Default calendar queries - accessing the properties will fetch the data from YF
calendar.earnings_calendar
calendar.ipo_info_calendar
calendar.splits_calendar
calendar.economic_events_calendar

# Manual queries
calendar.get_earnings_calendar()
calendar.get_ipo_info_calendar()
calendar.get_splits_calendar()
calendar.get_economic_events_calendar()

# Earnings calendar custom filters
calendar.get_earnings_calendar(
    market_cap=100_000_000,  # filter out small-cap 
    filter_most_active=True,  # show only actively traded. Uses: `screen(query="MOST_ACTIVES")`
)

# Example of real use case:
# Get inminent unreported earnings events
today = datetime.now()
is_friday = today.weekday() == 4
day_after_tomorrow = today + timedelta(days=4 if is_friday else 2)

calendar = yf.Calendars(today, day_after_tomorrow)
df = calendar.get_earnings_calendar(limit=100)

unreported_df = df[df["Reported EPS"].isnull()]
```
```

--------------------------------

### Get Institutional Holders

Source: https://ranaroussi.github.io/yfinance/_sources/reference/api/yfinance.Ticker.institutional_holders.rst.txt

This snippet demonstrates how to fetch institutional holders for a stock ticker.

```APIDOC
## Ticker.institutional_holders

### Description
Returns a pandas DataFrame with institutional holders data.

### Method
Attribute Access

### Parameters
None

### Response
#### Success Response
- **DataFrame** (pandas.DataFrame) - DataFrame containing institutional holders information.

### Response Example
```python
import yfinance as yf

stock = yf.Ticker("AAPL")
institutional_holders = stock.institutional_holders
print(institutional_holders)
```
```

--------------------------------

### Get Mutual Fund Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing mutual funds related to the query.

```APIDOC
## get_mutualfund()

### Description
Returns mutual funds related financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing mutual fund instruments.
```

--------------------------------

### Get All Financial Instruments

Source: https://ranaroussi.github.io/yfinance/reference/api/yfinance.Lookup.html

Retrieves a DataFrame containing all available financial instruments matching the query.

```APIDOC
## get_all()

### Description
Returns all available financial instruments.

### Parameters
- **count** (int) – The number of results to retrieve (default 25).

### Returns
DataFrame containing financial instruments.
```

--------------------------------

### Get Upgrades and Downgrades

Source: https://ranaroussi.github.io/yfinance/_sources/reference/api/yfinance.Ticker.get_upgrades_downgrades.rst.txt

Retrieves a pandas DataFrame containing analyst upgrades and downgrades for the stock.

```APIDOC
## Ticker.get_upgrades_downgrades

### Description
Retrieves a pandas DataFrame containing analyst upgrades and downgrades for the stock.

### Method Signature
`Ticker.get_upgrades_downgrades()`

### Parameters
This method does not accept any parameters.

### Returns
pandas.DataFrame: A DataFrame with columns like 'Date', 'Symbol', 'Firm', 'To Grade', 'From Grade', 'Action'.
```

--------------------------------

### Clone Forked Repository

Source: https://ranaroussi.github.io/yfinance/_sources/development/code.rst.txt

Clone your forked repository to your local machine to begin development.

```bash
git clone https://github.com/{user}/{repo}.git
```

--------------------------------

### Get Major Holders

Source: https://ranaroussi.github.io/yfinance/_sources/reference/api/yfinance.Ticker.get_major_holders.rst.txt

Retrieves a pandas DataFrame containing information about the major holders of a stock.

```APIDOC
## Ticker.get_major_holders()

### Description
Retrieves a pandas DataFrame containing information about the major holders of a stock.

### Method
```python
ticker.get_major_holders()
```

### Parameters
This method does not accept any parameters.

### Response
#### Success Response (DataFrame)
Returns a pandas DataFrame with the following columns:
- **Holder**: Name of the major holder.
- **Shares**: Number of shares held.
- **Date Reported**: The date the holdings were reported.
- **% Out**: Percentage of outstanding shares held by the major holder.

#### Response Example
```python
# Example DataFrame structure (actual data will vary)
#                                     Shares Date Reported      % Out
# Holder                                                               
# Vanguard Group Inc.             12345678    2023-09-30  0.10
# BlackRock Inc.                   9876543    2023-09-30  0.08
# State Street Corporation         5432109    2023-09-30  0.05
```
```

--------------------------------

### AsyncWebSocket Methods

Source: https://ranaroussi.github.io/yfinance/_sources/reference/api/yfinance.AsyncWebSocket.rst.txt

This section details the methods available for the AsyncWebSocket class, including initialization, closing connections, listening for messages, and subscribing/unsubscribing to data streams.

```APIDOC
## AsyncWebSocket

### Description
Provides methods for managing asynchronous WebSocket connections.

### Methods

#### `__init__`
Initializes the AsyncWebSocket object.

#### `close`
Closes the WebSocket connection.

#### `listen`
Starts listening for messages on the WebSocket connection.

#### `subscribe`
Subscribes to a specific data stream or event.

#### `unsubscribe`
Unsubscribes from a specific data stream or event.
```

--------------------------------

### Get Insider Roster Holders

Source: https://ranaroussi.github.io/yfinance/_sources/reference/api/yfinance.Ticker.get_insider_roster_holders.rst.txt

Retrieves a DataFrame containing insider roster holder information for the ticker.

```APIDOC
## Ticker.get_insider_roster_holders

### Description
Retrieves a DataFrame containing insider roster holder information for the ticker.

### Method Signature
`Ticker.get_insider_roster_holders()`

### Parameters
This method does not accept any parameters.

### Returns
- pandas.DataFrame: A DataFrame with insider roster holder data. The columns typically include 'Holder', 'Relation', 'Last Change', 'Shares', and 'Value'.

### Example
```python
import yfinance as yf

ticker = yf.Ticker("MSFT")
insiders = ticker.get_insider_roster_holders()
print(insiders)
```
```

--------------------------------

### get_recommendations

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.analysis.html

Returns a DataFrame with the recommendations. Columns: period, strongBuy, buy, hold, sell, strongSell.

```APIDOC
## get_recommendations

### Description
Returns a DataFrame containing stock recommendations.

### Parameters
* `as_dict` (bool, optional): If True, returns a dictionary. Defaults to False.

### Returns
DataFrame or dict: A DataFrame with recommendation data, or a dictionary if `as_dict` is True.

### Example
```python
ticker.get_recommendations()
```
```

--------------------------------

### Get Balance Sheet

Source: https://ranaroussi.github.io/yfinance/_sources/reference/api/yfinance.Ticker.get_balance_sheet.rst.txt

Fetches the balance sheet data for the ticker. The data is returned as a pandas DataFrame.

```APIDOC
## Ticker.get_balance_sheet

### Description
Retrieves the balance sheet data for the ticker. The data is returned as a pandas DataFrame.

### Method Signature
`Ticker.get_balance_sheet(freq: str = 'quarterly') -> pandas.DataFrame`

### Parameters
- **freq** (str, optional) - The frequency of the data to retrieve. Can be 'quarterly' or 'annual'. Defaults to 'quarterly'.

### Returns
- **pandas.DataFrame** - A DataFrame containing the balance sheet data.
```

--------------------------------

### Ticker Class Initialization and Usage

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.ticker_tickers.html

Demonstrates how to initialize a Ticker object for a single stock and access various financial data points like historical data, options, financials, and general information.

```APIDOC
## Ticker Class

### Description
Initialize a Yahoo Finance Ticker object to access ticker data in a Pythonic way.

### Method Signature
`Ticker(ticker[, session])`

### Usage Example
```python
import yfinance as yf

dat = yf.Ticker("MSFT")

# get historical market data
dat.history(period='1mo')

# options
dat.option_chain(dat.options[0]).calls

# get financials
dat.balance_sheet
dat.quarterly_income_stmt

# dates
dat.calendar

# general info
dat.info

# analysis
dat.analyst_price_targets

# websocket
dat.live()
```
```

--------------------------------

### Initialize and Use Tickers Object

Source: https://ranaroussi.github.io/yfinance/reference/yfinance.ticker_tickers.html

Shows how to initialize a Tickers object for multiple stocks and access individual ticker data like info, history, and actions.

```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

# websocket
tickers.live()

```

--------------------------------

### Repair missing dividend adjustment

Source: https://ranaroussi.github.io/yfinance/_sources/advanced/price_repair.rst.txt

Example showing the difference between original and repaired data when a dividend adjustment is missing.

```text
# ORIGINAL:
                           Close  Adj Close  Dividends
2024-07-08 00:00:00+08:00   4.33       4.33   0.335715
2024-07-04 00:00:00+08:00   4.83       4.83   0.000000
```

```text
# REPAIRED:
                           Close  Adj Close  Dividends
2024-07-08 00:00:00+08:00   4.33   4.330000   0.335715
2024-07-04 00:00:00+08:00   4.83   4.494285   0.000000
```
Tokens:
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