### Install yfinance

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

Install the yfinance library using pip.

```bash
pip install yfinance
```

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

### 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 Documentation Dependencies

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

Installs Sphinx and other necessary packages for building documentation. Ensure you have a requirements.txt file.

```bash
pip install -r requirements.txt
pip install Sphinx==8.0.2 pydata-sphinx-theme==0.15.4 Jinja2==3.1.4 sphinx-copybutton==0.5.2
```

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

### 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)
```

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

### Install yfinance from a specific branch using pip

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

Use this command to install yfinance directly from a Git branch. Replace `{user}` and `{repo}` with the appropriate GitHub username and repository name, and `{branch}` with the target branch name.

```bash
pip install git+https://github.com/{user}/{repo}.git@{branch}
```

```bash
pip install git+https://github.com/ranaroussi/yfinance.git@feature/name
```

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

### Serve Local Documentation

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

Starts a local HTTP server to view the generated HTML documentation. Navigate to localhost:8000 in your browser.

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

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

### Verify yfinance installation

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

Import and print the yfinance module to verify that it has been correctly installed or added to the Python path. The output should indicate the location of the imported module.

```python
import yfinance
print(yfinance)
```

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

### 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)
```

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

### Clone yfinance repository and install dependencies

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

Clone the yfinance repository and install its dependencies. Use the `-b {branch}` flag to clone a specific branch. This method is suitable for development or when testing changes on a particular branch.

```bash
git clone https://github.com/{user}/{repo}.git
pip install -r ./yfinance/requirements.txt
```

```bash
git clone -b {branch} https://github.com/{user}/{repo}.git
pip install -r ./yfinance/requirements.txt
```

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

### 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
```

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

### 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

```

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

### 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")
...

```

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

### Run All Price Tests

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

Execute all tests located within the `tests.test_prices` module. Ensure you have the unittest module installed and accessible.

```bash
python -m unittest tests.test_prices
```

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

### 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)
```

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

### Python Authentication Example

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

This snippet demonstrates how to use the Auth module to log in to Yahoo! Finance by providing authentication cookies.

```python
from yfinance.auth import Auth

# Replace with your actual cookie values
cookie_t = "YOUR_T_COOKIE_VALUE"
cookie_y = "YOUR_Y_COOKIE_VALUE"

# Create an Auth object
auth = Auth(cookie_t, cookie_y)

# You can now use this auth object with yfinance functions that require authentication
# For example:
# import yfinance as yf
# ticker = yf.Ticker("AAPL", session=auth.session)
# print(ticker.history(period="1d"))

print("Authentication object created successfully.")
```

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

### 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'])
])
```
```

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

### 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.
```

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

### 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.
```

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

### 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)
```

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

### 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
```

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

### 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())
```

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

### Constructing a Stock Filter with EquityQuery

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

Use EquityQuery to build filters for stocks. This example filters for stocks in the 'NMS' or 'NYQ' exchanges with an EPS growth of less than 15%.

```python
from yfinance import EquityQuery

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

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

### 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)

```

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

### Add local yfinance to Python path

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

Add the downloaded yfinance directory to your Python path to allow imports. This is necessary if you are running yfinance from a local clone rather than an installed package.

```python
import sys
sys.path.insert(0, "path/to/downloaded/yfinance")
```

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

### 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'])
])
```

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

### Initialize Sector and Industry Objects

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

Demonstrates how to initialize Sector and Industry objects using their respective keys and access common information attributes.

```python
import yfinance as yf

tech = yf.Sector('technology')
software = yf.Industry('software-infrastructure')

# Common information
tech.key
tech.name
tech.symbol
tech.ticker
tech.overview
tech.top_companies
tech.research_reports

# Sector information
tech.top_etfs
tech.top_mutual_funds
tech.industries

# Industry information
software.sector_key
software.sector_name
software.top_performing_companies
software.top_growth_companies
```

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

### 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")
```
```

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

### 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()
```

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

### Initialize and Use Ticker Object

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

Demonstrates initializing a Ticker object for a single stock and accessing various data points like historical data, financials, options, and analyst information.

```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()

```

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

### 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]
```

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

### Build Documentation Locally with Sphinx

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

Generates HTML documentation from the Sphinx source files. The output is placed in the doc/_build/html directory.

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

```

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

### 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.
```

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

### 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.
```

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

### 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.
```

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

### Build HTML Documentation with Sphinx

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

Generates HTML documentation from the Sphinx source files. The output will be placed in the specified build directory.

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

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

### 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.
```

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

### 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 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.
```

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

### 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.
```

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

### 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()
```
```