### Get Financial Data Example

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/financials.md

Demonstrates how to fetch specific financial data points for a ticker. Ensure the `Ticker` class is imported and instantiated.

```python
aapl = Ticker('aapl')
types = ['TotalDebt', 'TotalAssets', 'EBIT', 'EBITDA', 'PeRatio']
aapl.get_financial_data(types, trailing=False)
```

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

### Get Screener Data Example

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/screener.md

This snippet demonstrates retrieving detailed financial data for multiple stocks using the screener functionality. It includes metrics like market cap, P/E ratio, and daily trading information.

```python
{
                'twoHundredDayAverageChange': -2.1995683,
                'twoHundredDayAverageChangePercent': -0.26598343,
                'marketCap': 53094838272,
                'forwardPE': 15.973685,
                'priceToBook': 1.5782632,
                'sourceInterval': 15,
                'exchangeDataDelayedBy': 0,
                'exchangeTimezoneName': 'America/New_York',
                'exchangeTimezoneShortName': 'EDT',
                'gmtOffSetMilliseconds': -14400000,
                'esgPopulated': False,
                'tradeable': True,
                'regularMarketChangePercent': -3.0351446,
                'displayName': 'General Electric',
                'symbol': 'GE'
            }, {
                'language': 'en-US',
                'region': 'US',
                'quoteType': 'EQUITY',
                'quoteSourceName': 'Nasdaq Real Time Price',
                'triggerable': False,
                'currency': 'USD',
                'firstTradeDateMilliseconds': 757607400000,
                'priceHint': 4,
                'postMarketChangePercent': -0.7314493,
                'postMarketTime': 1596234026,
                'postMarketPrice': 4.75,
                'postMarketChange': -0.034999847,
                'regularMarketChange': 0.3300004,
                'regularMarketTime': 1596225601,
                'regularMarketPrice': 4.78,
                'regularMarketDayHigh': 4.95,
                'regularMarketDayRange': '4.635 - 4.95',
                'regularMarketDayLow': 4.635,
                'regularMarketVolume': 117123629,
                'regularMarketPreviousClose': 4.45,
                'bid': 4.73,
                'ask': 4.76,
                'bidSize': 369,
                'askSize': 400,
                'exchange': 'NYQ',
                'market': 'us_market',
                'messageBoardId': 'finmb_205573',
                'fullExchangeName': 'NYSE',
                'shortName': 'Nokia Corporation Sponsored',
                'longName': 'Nokia Corporation',
                'financialCurrency': 'EUR',
                'regularMarketOpen': 4.86,
                'averageDailyVolume3Month': 30349318,
                'averageDailyVolume10Day': 27909325,
                'fiftyTwoWeekLowChange': 2.4400003,
                'fiftyTwoWeekLowChangePercent': 1.0427352,
                'fiftyTwoWeekRange': '2.34 - 5.45',
                'fiftyTwoWeekHighChange': -0.6699996,
                'fiftyTwoWeekHighChangePercent': -0.12293571,
                'fiftyTwoWeekLow': 2.34,
                'fiftyTwoWeekHigh': 5.45,
                'dividendDate': 1565654400,
                'trailingAnnualDividendRate': 0.221,
                'trailingAnnualDividendYield': 0.049662925,
                'marketState': 'POST',
                'epsTrailingTwelveMonths': -0.192,
                'epsForward': 0.31,
                'sharesOutstanding': 5618930176,
                'bookValue': 3.119,
                'fiftyDayAverage': 4.3285713,
                'fiftyDayAverageChange': 0.4514289,
                'fiftyDayAverageChangePercent': 0.10429051,
                'twoHundredDayAverage': 3.8270504,
                'twoHundredDayAverageChange': 0.95294976,
                'twoHundredDayAverageChangePercent': 0.24900371,
                'marketCap': 25653735424,
                'forwardPE': 15.419355,
                'priceToBook': 1.5325426,
                'sourceInterval': 15,
                'exchangeDataDelayedBy': 0,
                'exchangeTimezoneName': 'America/New_York',
                'exchangeTimezoneShortName': 'EDT',
                'gmtOffSetMilliseconds': -14400000,
                'esgPopulated': False,
                'tradeable': True,
                'regularMarketChangePercent': 7.4157395,
                'displayName': 'Nokia',
                'symbol': 'NOK'
            }, {
                'language': 'en-US',
                'region': 'US',
                'quoteType': 'EQUITY',
                'quoteSourceName': 'Delayed Quote',
                'triggerable': False,
                'currency': 'USD',
                'firstTradeDateMilliseconds': 76253400000,
                'priceHint': 2,
                'postMarketChangePercent': -0.30257156,
                'postMarketTime': 1596234024,
                'postMarketPrice': 6.59,
                'postMarketChange': -0.01999998,
                'regularMarketChange': -0.12999964,
                'regularMarketTime': 1596225883,
                'regularMarketPrice': 6.61,
                'regularMarketDayHigh': 6.9,
                'regularMarketDayRange': '6.52 - 6.9',
                'regularMarketDayLow': 6.52,
                'regularMarketVolume': 115075556,
                'regularMarketPreviousClose': 6.74,
                'bid': 6.6,
                'ask': 6.6,
                'bidSize': 292,
                'askSize': 18,
                'exchange': 'NYQ'
            }
```

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

### Install yahooquery

Source: https://github.com/dpguthrie/yahooquery/blob/master/README.md

Install the package with or without premium support. For premium features, use `pip install yahooquery[premium]`.

```bash
pip install yahooquery[premium]
```

```bash
pip install yahooquery
```

```bash
uv pip install yahooquery
```

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

### Example Search Result Data

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/misc.md

This is an example of the data structure returned by a successful search query, containing details about a financial instrument.

```python
{
    'exchange': 'NYQ',
    'quoteType': 'EQUITY',
    'symbol': 'GS',
    'index': 'quotes',
    'score': 33156.0,
    'typeDisp': 'Equity',
    'longname': 'The Goldman Sachs Group, Inc.',
    'isYahooFinance': True
}
```

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

### Get Ticker Recommendations

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/miscellaneous.md

Retrieve recommendations for a given set of ticker symbols. Ensure the Ticker object is initialized with the desired symbols.

```python
tickers = Ticker('aapl gs hasgx ^GSPC ezu')
tickers.recommendations
```

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

### Fetch All Trade Ideas

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves all available trade ideas. This can be used to get a broad overview of potential trading opportunities.

```python
trades = r.trades()
print(trades.columns.tolist())
```

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

### Trade Options: Start Datetime

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/research.md

Defines the available options and whether multiple selections are allowed for the 'startdatetime' argument in the `trades` method.

```json
{
    'options': {
        'Last Week': 7,
        'Last Month': 30,
        'Last Year': 365
    },
    'multiple': False
}
```

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

### Get Supported Currencies

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves a list of all supported currency pairs. This can be used to understand available currency exchange data.

```python
from yahooquery import get_currencies, get_exchanges

# All supported currencies
currencies = get_currencies()
print(currencies[:3])
```

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

### Get Market Summary for Hong Kong

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/misc.md

Fetches the market summary for a specified country. Ensure the country is supported and listed in the available options.

```python
import yahooquery as yq
yq.get_market_summary(country='hong kong')
```

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

### Screener Quote Data Example

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/screener.md

This snippet displays a sample of quote data returned from a screener query. It includes various financial metrics for a specific stock.

```json
{
                'language': 'en-US',
                'region': 'US',
                'quoteType': 'EQUITY',
                'quoteSourceName': 'Delayed Quote',
                'triggerable': False,
                'currency': 'USD',
                'firstTradeDateMilliseconds': 1555594200000,
                'priceHint': 2,
                'postMarketChangePercent': 1.6622913,
                'postMarketTime': 1596234159,
                'postMarketPrice': 34.86,
                'postMarketChange': 0.5699997,
                'regularMarketChange': 9.1,
                'regularMarketTime': 1596225602,
                'regularMarketPrice': 34.29,
                'regularMarketDayHigh': 34.5,
                'regularMarketDayRange': '31.0 - 34.5',
                'regularMarketDayLow': 31.0,
                'regularMarketVolume': 111167386,
                'regularMarketPreviousClose': 25.19,
                'bid': 34.69,
                'ask': 34.7,
                'bidSize': 13,
                'askSize': 18,
                'exchange': 'NYQ',
                'market': 'us_market',
                'messageBoardId': 'finmb_139264388',
                'fullExchangeName': 'NYSE',
                'shortName': 'Pinterest, Inc.',
                'longName': 'Pinterest, Inc.',
                'financialCurrency': 'USD',
                'regularMarketOpen': 33.56,
                'averageDailyVolume3Month': 13862671,
                'averageDailyVolume10Day': 9896450,
                'fiftyTwoWeekLowChange': 24.19,
                'fiftyTwoWeekLowChangePercent': 2.3950496,
                'fiftyTwoWeekRange': '10.1 - 36.83',
                'fiftyTwoWeekHighChange': -2.540001,
                'fiftyTwoWeekHighChangePercent': -0.06896554,
                'fiftyTwoWeekLow': 10.1,
                'fiftyTwoWeekHigh': 36.83,
                'earningsTimestamp': 1596184200,
                'earningsTimestampStart': 1603987200,
                'earningsTimestampEnd': 1604332800,
                'marketState': 'POST',
                'epsTrailingTwelveMonths': -2.743,
                'epsForward': 0.05,
                'sharesOutstanding': 403489984,
                'bookValue': 3.332,
                'fiftyDayAverage': 24.036858,
                'fiftyDayAverageChange': 10.253143,
                'fiftyDayAverageChangePercent': 0.4265592,
                'twoHundredDayAverage': 20.366547,
                'twoHundredDayAverageChange': 13.923454,
                'twoHundredDayAverageChangePercent': 0.68364334,
                'marketCap': 20109576192,
                'forwardPE': 685.8,
                'priceToBook': 10.291117,
                'sourceInterval': 15,
                'exchangeDataDelayedBy': 0,
                'exchangeTimezoneName': 'America/New_York',
                'exchangeTimezoneShortName': 'EDT',
                'gmtOffSetMilliseconds': -14400000,
                'esgPopulated': False,
                'tradeable': True,
                'regularMarketChangePercent': 36.12545,
                'displayName': 'Pinterest',
                'symbol': 'PINS'
            }
```

```json
{
                'language': 'en-US',
                'region': 'US',
                'quoteType': 'EQUITY',
                'quoteSourceName': 'Delayed Quote',
                'triggerable': False,
                'currency': 'USD',
                'firstTradeDateMilliseconds': 1234535400000,
                'priceHint': 4,
                'regularMarketChange': 0.1509,
                'regularMarketTime': 1596222445,
                'regularMarketPrice': 0.7009,
                'regularMarketDayHigh': 1.5,
                'regularMarketDayRange': '0.7009 - 1.5',
                'regularMarketDayLow': 0.7009,
                'regularMarketVolume': 885738,
                'regularMarketPreviousClose': 0.55,
                'bid': 0.0,
                'ask': 0.0,
                'bidSize': 0,
                'askSize': 0,
                'exchange': 'PNK',
                'market': 'us_market',
                'messageBoardId': 'finmb_92126',
                'fullExchangeName': 'Other OTC'
            }
```

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

### Get Screeners with YahooQuery

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/screener.md

Instantiate the Screener class and call get_screeners with a list of screen IDs and a count to retrieve securities. The result is a pandas DataFrame.

```python
s = Screener()
s.get_screeners(['most_actives', 'day_gainers'], 5)
```

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

### Market Summary Data Structure

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/misc.md

This is an example of the data structure returned by `get_market_summary`, showing details for S&P 500 and Dow 30 indices.

```python
[
    {
        'exchangeTimezoneName': 'America/New_York',
        'fullExchangeName': 'SNP',
        'symbol': '^GSPC',
        'regularMarketChange': {
            'raw': 24.900146,
            'fmt': '24.90'
        },
        'gmtOffSetMilliseconds': -14400000,
        'exchangeDataDelayedBy': 0,
        'firstTradeDateMilliseconds': -1325583000000,
        'language': 'en-US',
        'regularMarketTime': {
            'raw': 1596229659,
            'fmt': '5:07PM EDT'
        },
        'exchangeTimezoneShortName': 'EDT',
        'regularMarketChangePercent': {
            'raw': 0.7670505,
            'fmt': '0.77%'
        },
        'quoteType': 'INDEX',
        'marketState': 'POSTPOST',
        'regularMarketPrice': {
            'raw': 3271.12,
            'fmt': '3,271.12'
        },
        'market': 'us_market',
        'quoteSourceName': 'Delayed Quote',
        'priceHint': 2,
        'tradeable': False,
        'exchange': 'SNP',
        'sourceInterval': 15,
        'region': 'US',
        'shortName': 'S&P 500',
        'triggerable': False,
        'regularMarketPreviousClose': {
            'raw': 3246.22,
            'fmt': '3,246.22'
        }
    }, {
        'exchangeTimezoneName': 'America/New_York',
        'fullExchangeName': 'DJI',
        'symbol': '^DJI',
        'regularMarketChange': {
            'raw': 114.66992,
            'fmt': '114.67'
        },
        'gmtOffSetMilliseconds': -14400000,
        'exchangeDataDelayedBy': 0,
        'firstTradeDateMilliseconds': 475857000000,
        'language': 'en-US',
        'regularMarketTime': {
            'raw': 1596229659,
            'fmt': '5:07PM EDT'
        },
        'exchangeTimezoneShortName': 'EDT',
        'regularMarketChangePercent': {
            'raw': 0.43578112,
            'fmt': '0.44%'
        },
        'quoteType': 'INDEX',
        'marketState': 'POSTPOST',
        'regularMarketPrice': {
            'raw': 26428.32,
            'fmt': '26,428.32'
        },
        'market': 'us_market',
        'quoteSourceName': 'Delayed Quote',
        'priceHint': 2,
        'tradeable': False,
        'exchange': 'DJI',
        'sourceInterval': 120,
        'region': 'US',
        'shortName': 'Dow 30',

```

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

### Get Option Chain Data

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/alternative.ipynb

This code retrieves the option chain for a given stock symbol. It's a common starting point for analyzing options data.

```python
%%time
yq_option_chain = YQ_aapl.option_chain
```

```python
yq_option_chain
```

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

### Ticker Class Instantiation

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Demonstrates how to instantiate the Ticker class with single or multiple symbols, and with various configuration options.

```APIDOC
## Ticker Class

### Instantiating Ticker with one or multiple symbols

The `Ticker` class is the primary entry point; pass a symbol string (space- or comma-separated for multiple tickers) or a list, along with optional keyword arguments that configure session behavior.

```python
from yahooquery import Ticker

# Single symbol
aapl = Ticker('aapl')

# Multiple symbols (space-separated string)
tech = Ticker('aapl msft goog amzn')

# Multiple symbols (list)
faang = Ticker(['fb', 'aapl', 'amzn', 'nflx', 'goog'])

# With keyword arguments
t = Ticker(
    'aapl msft',
    asynchronous=True,   # concurrent requests
    max_workers=4,
    timeout=10,
    retry=5,
    validate=True,       # drop invalid symbols on init
    country='United States',
    progress=True,       # tqdm progress bar
)
print(t.symbols)         # ['AAPL', 'MSFT']
print(t.invalid_symbols) # [] (only populated when validate=True)
```
```

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

### Initialize Research with Credentials

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Initializes the Research object with username and password for accessing premium features. Ensure your credentials are kept secure.

```python
from yahooquery import Research

r = Research(username='your@email.com', password='yourpassword')
```

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

### Get Fund Bond Ratings

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves bond ratings for fixed-income funds. Requires the 'fund_bond_ratings' attribute.

```python
from yahooquery import Ticker

# Bond ratings (for fixed-income funds)
agi = Ticker('agi')
ratings = agi.fund_bond_ratings
print(ratings)
```

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

### Get Insider Holdings

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Fetches and displays insider holdings for a given ticker. Requires the 'insider_holders' attribute.

```python
from yahooquery import Ticker

t = Ticker('aapl')
holders = t.insider_holders
print(holders[['name', 'relation', 'positionDirect', 'latestTransDate']].head())
```

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

### Create Screener Instance

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/screener.md

Instantiate the Screener class to create an object that can be used to fetch screener data.

```python
s = Screener()
```

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

### Get Summary Pricing and Market Data

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieve summary pricing metrics like previous close, open, beta, market cap, and dividend yield for each symbol. This data is returned as a dictionary where keys are symbols and values are dictionaries of metrics.

```python
from yahooquery import Ticker

t = Ticker('aapl msft')
data = t.summary_detail

# Expected output structure:
# {
#   'aapl': {'previousClose': 189.3, 'open': 190.1, 'beta': 1.29,
#            'marketCap': 2960000000000, 'dividendYield': 0.0053, ...},
#   'msft': {'previousClose': 415.5, 'open': 417.2, 'beta': 0.89, ...}
# }
print(data['aapl']['marketCap'])   # e.g. 2960000000000
print(data['msft']['dividendYield'])
```

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

### Get French Market Summary

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves the market summary specifically for France. This allows for regional market analysis.

```python
# French market summary
fr_summary = get_market_summary(country='France')
```

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

### Get Fund Sector Weightings

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves the sector weightings for an ETF or mutual fund. Requires the 'fund_sector_weightings' attribute.

```python
from yahooquery import Ticker

# S&P 500 ETF
spy = Ticker('spy')

# Sector weightings
sectors = spy.fund_sector_weightings
print(sectors)  # DataFrame: sector -> weight
```

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

### Create Ticker Instance for Multiple Stocks (List)

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/intro.md

Pass a list of stock symbols to the Ticker constructor to create an instance that can fetch data for multiple companies simultaneously.

```python
symbols = ['fb', 'aapl', 'amzn', 'nflx', 'goog']
tickers = Ticker(symbols)
```

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

### Get Fund Ownership

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves the top fund owners for a given stock symbol. Requires the 'fund_ownership' attribute.

```python
from yahooquery import Ticker

t = Ticker('aapl')

# Top fund owners
funds = t.fund_ownership
print(funds[['organization', 'pctHeld', 'shares', 'reportDate']].head(5))
```

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

### Import YahooQuery Library

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/misc.md

Import the yahooquery library for use in your project. This is a common first step for utilizing the library's functionalities.

```python
import yahooquery as yq
```

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

### Create Ticker Instance for Multiple Stocks (String)

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/intro.md

Alternatively, provide a space-separated string of stock symbols to the Ticker constructor for multi-company data retrieval.

```python
symbols = 'fb aapl amzn nflx goog'

tickers = Ticker(symbols)
```

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

### Get Institutional Ownership

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves the top institutional owners for a given stock symbol. Requires the 'institution_ownership' attribute.

```python
from yahooquery import Ticker

t = Ticker('aapl')

# Top institutional owners
inst = t.institution_ownership
print(inst[['organization', 'pctHeld', 'shares', 'value']].head(5))
```

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

### Screener Data Structure

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/screener.md

This is an example of the data structure returned by the get_screeners method, detailing screener information and quotes for securities.

```json
{
    'most_actives': {
        'id': '437465ef-980e-4d8c-a860-de7cbfbab373',
        'title': 'Most Actives - US',
        'description': 'Stocks ordered in descending order by intraday trade volume',
        'canonicalName': 'MOST_ACTIVES',
        'criteriaMeta': {
            'size': 5,
            'offset': 0,
            'sortField': 'dayvolume',
            'sortType': 'DESC',
            'quoteType': 'EQUITY',
            'topOperator': 'AND',
            'criteria': [{
                'field': 'region',
                'operators': ['EQ'],
                'values': [],
                'labelsSelected': [53]
            }, {
                'field': 'intradaymarketcap',
                'operators': ['EQ'],
                'values': [],
                'labelsSelected': [1, 2, 3]
            }, {
                'field': 'dayvolume',
                'operators': ['GT'],
                'values': [5000000],
                'labelsSelected': []
            }]
        },
        'rawCriteria': '{"offset":0,"size":5,"sortField":"dayvolume","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{\"operator\":\"eq\",\"operands\":[\"region\",\"us\"]},{\"operator\":\"or\",\"operands\":[{\"operator\":\"BTWN\",\"operands\":[\"intradaymarketcap\",2000000000,10000000000]},{\"operator\":\"BTWN\",\"operands\":[\"intradaymarketcap\",10000000000,100000000000]},{\"operator\":\"GT\",\"operands\":[\"intradaymarketcap\",100000000000]}]},{\"operator\":\"gt\",\"operands\":[\"dayvolume\",5000000]}]}}',
        'start': 0,
        'count': 5,
        'total': 203,
        'quotes': [{
            'language': 'en-US',
            'region': 'US',
            'quoteType': 'EQUITY',
            'quoteSourceName': 'Nasdaq Real Time Price',
            'triggerable': False,
            'currency': 'USD',
            'firstTradeDateMilliseconds': -252322200000,
            'priceHint': 2,
            'postMarketChangePercent': 0.0,
            'postMarketTime': 1596234164,
            'postMarketPrice': 6.08,
            'postMarketChange': 0.0,
            'regularMarketChange': -0.19000006,
            'regularMarketTime': 1596225602,
            'regularMarketPrice': 6.07,
            'regularMarketDayHigh': 6.29,
            'regularMarketDayRange': '6.0 - 6.29',
            'regularMarketDayLow': 6.0,
            'regularMarketVolume': 142268197,
            'regularMarketPreviousClose': 6.26,
            'bid': 6.07,
            'ask': 6.08,
            'bidSize': 473,
            'askSize': 280,
            'exchange': 'NYQ',
            'market': 'us_market',
            'messageBoardId': 'finmb_177031',
            'fullExchangeName': 'NYSE',
            'shortName': 'General Electric Company',
            'longName': 'General Electric Company',
            'financialCurrency': 'USD',
            'regularMarketOpen': 6.25,
            'averageDailyVolume3Month': 103055092,
            'averageDailyVolume10Day': 86456425,
            'fiftyTwoWeekLowChange': 0.59000015,
            'fiftyTwoWeekLowChangePercent': 0.107664265,
            'fiftyTwoWeekRange': '5.48 - 13.26',
            'fiftyTwoWeekHighChange': -7.19,
            'fiftyTwoWeekHighChangePercent': -0.5422323,
            'fiftyTwoWeekLow': 5.48,
            'fiftyTwoWeekHigh': 13.26,
            'dividendDate': 1595808000,
            'earningsTimestamp': 1603873800,
            'earningsTimestampStart': 1603873800,
            'earningsTimestampEnd': 1603873800,
            'trailingAnnualDividendRate': 0.04,
            'trailingAnnualDividendYield': 0.006389776,
            'marketState': 'POST',
            'epsTrailingTwelveMonths': -0.561,
            'epsForward': 0.38,
            'sharesOutstanding': 8747089920,
            'bookValue': 3.846,
            'fiftyDayAverage': 6.9014287,
            'fiftyDayAverageChange': -0.8314285,
            'fiftyDayAverageChangePercent': -0.12047195,
            'twoHundredDayAverage': 8.269568
        }]
    }
}
```

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

### Get Balance Sheet Data

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/financials.md

Retrieve specific balance sheet items for a ticker. Ensure 'types' argument is provided.

```python
[
    'AccountsPayable', 'AccountsReceivable',
    'AccruedInterestReceivable',
    'AccumulatedDepreciation',
    'AdditionalPaidInCapital',
    'AllowanceForDoubtfulAccountsReceivable',
    'AssetsHeldForSaleCurrent',
    'AvailableForSaleSecurities',
    'BuildingsAndImprovements',
    'CapitalLeaseObligations',
    'CapitalStock',
    'CashAndCashEquivalents',
    'CashCashEquivalentsAndShortTermInvestments',
    'CashEquivalents',
    'CashFinancial',
    'CommercialPaper',
    'CommonStock',
    'CommonStockEquity',
    'ConstructionInProgress',
    'CurrentAccruedExpenses',
    'CurrentAssets',
    'CurrentCapitalLeaseObligation',
    'CurrentDebt',
    'CurrentDebtAndCapitalLeaseObligation',
    'CurrentDeferredAssets',
    'CurrentDeferredLiabilities',
    'CurrentDeferredRevenue',
    'CurrentDeferredTaxesAssets',
    'CurrentDeferredTaxesLiabilities',
    'CurrentLiabilities',
    'CurrentNotesPayable',
    'CurrentProvisions',
    'DefinedPensionBenefit',
    'DerivativeProductLiabilities',
    'DividendsPayable',
    'DuefromRelatedPartiesCurrent',
    'DuefromRelatedPartiesNonCurrent',
    'DuetoRelatedPartiesCurrent',
    'DuetoRelatedPartiesNonCurrent',
    'EmployeeBenefits',
    'FinancialAssets',
    'FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal',
    'FinishedGoods',
    'FixedAssetsRevaluationReserve',
    'ForeignCurrencyTranslationAdjustments',
    'GainsLossesNotAffectingRetainedEarnings',
    'GeneralPartnershipCapital',
    'Goodwill',
    'GoodwillAndOtherIntangibleAssets',
    'GrossAccountsReceivable',
    'GrossPPE',
    'HedgingAssetsCurrent',
    'HeldToMaturitySecurities',
    'IncomeTaxPayable',
    'InterestPayable',
    'InventoriesAdjustmentsAllowances',
    'Inventory',
    'InvestedCapital',
    'InvestmentProperties',
    'InvestmentinFinancialAssets',
    'InvestmentsAndAdvances',
    'InvestmentsInOtherVenturesUnderEquityMethod',
    'InvestmentsinAssociatesatCost',
    'InvestmentsinJointVenturesatCost',
    'InvestmentsinSubsidiariesatCost',
    'LandAndImprovements',
    'Leases',
    'LiabilitiesHeldforSaleNonCurrent',
    'LimitedPartnershipCapital',
    'LineOfCredit',
    'LoansReceivable',
    'LongTermCapitalLeaseObligation',
    'LongTermDebt',
    'LongTermDebtAndCapitalLeaseObligation',
    'LongTermEquityInvestment',
    'LongTermProvisions',
    'MachineryFurnitureEquipment',
    'MinimumPensionLiabilities',
    'MinorityInterest',
    'NetDebt',
    'NetPPE',
    'NetTangibleAssets',
    'NonCurrentAccountsReceivable',
    'NonCurrentAccruedExpenses',
    'NonCurrentDeferredAssets',
    'NonCurrentDeferredLiabilities',
    'NonCurrentDeferredRevenue',
    'NonCurrentDeferredTaxesAssets',
    'NonCurrentDeferredTaxesLiabilities',
    'NonCurrentNoteReceivables',
    'NonCurrentPensionAndOtherPostretirementBenefitPlans',
    'NonCurrentPrepaidAssets',
    'NotesReceivable',
    'OrdinarySharesNumber',
    'OtherCapitalStock',
    'OtherCurrentAssets',
    'OtherCurrentBorrowings',
    'OtherCurrentLiabilities',
    'OtherEquityAdjustments',
    'OtherEquityInterest',
    'OtherIntangibleAssets',
    'OtherInventories',
    'OtherInvestments',
    'OtherNonCurrentAssets',
    'OtherNonCurrentLiabilities',
    'OtherPayable',
    'OtherProperties',
    'OtherReceivables',
    'OtherShortTermInvestments',
    'Payables',
    'PayablesAndAccruedExpenses',
    'PensionandOtherPostRetirementBenefitPlansCurrent',
    'PreferredSecuritiesOutsideStockEquity',
    'PreferredSharesNumber',
    'PreferredStock',
    'PreferredStockEquity',
    'PrepaidAssets',
    'Properties',
    'RawMaterials',
    'Receivables',
    'ReceivablesAdjustmentsAllowances',
    'RestrictedCash',
    'RestrictedCommonStock',
    'RetainedEarnings',
    'ShareIssued',
    'StockholdersEquity',
    'TangibleBookValue',
    'TaxesReceivable',
    'TotalAssets',
    'TotalCapitalization',
    'TotalDebt'
]
```

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

### Fetch All Modules for Multiple Tickers (Asynchronous)

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/alternative.ipynb

This asynchronous approach is generally faster for fetching all modules for multiple tickers. It requires the `asynchronous=True` flag during Ticker initialization.

```python
tickers = Ticker(symbols, asynchronous=True)
data = tickers.all_modules
```

```python
data['FB'].keys()
```

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

### Get Available Screeners - Python

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/screener.md

Instantiate the Screener class and access the `available_screeners` attribute to retrieve a dictionary of predefined screeners. This is useful for understanding the available filtering options for stock data.

```python
s = Screener()
s.available_screeners
```

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

### Get Fund Top Holdings

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves the top 10 holdings for an ETF or mutual fund. Requires the 'fund_top_holdings' attribute.

```python
from yahooquery import Ticker

# S&P 500 ETF
spy = Ticker('spy')

# Top 10 holdings as DataFrame
holdings = spy.fund_top_holdings
print(holdings[['symbol', 'holdingName', 'holdingPercent']].head(10))
```

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

### Get Market Summary Data

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/misc.md

This snippet demonstrates how to retrieve market summary data for different exchanges. It shows the structure of the returned data, including price, change, and time information.

```python
{
        'regularMarketTime': {
            'raw': 1596210339,
            'fmt': '4:45PM BST'
        },
        'exchangeTimezoneShortName': 'BST',
        'regularMarketChangePercent': {
            'raw': -1.5397432,
            'fmt': '-1.54%'
        },
        'quoteType': 'INDEX',
        'marketState': 'CLOSED',
        'regularMarketPrice': {
            'raw': 5897.76,
            'fmt': '5,897.76'
        },
        'market': 'gb_market',
        'priceHint': 2,
        'tradeable': False,
        'exchange': 'FGI',
        'sourceInterval': 15,
        'region': 'US',
        'shortName': 'FTSE 100',
        'triggerable': False,
        'regularMarketPreviousClose': {
            'raw': 5989.99,
            'fmt': '5,989.99'
        }
    }, {
        'exchangeTimezoneName': 'Asia/Tokyo',
        'fullExchangeName': 'Osaka',
        'symbol': '^N225',
        'regularMarketChange': {
            'raw': -629.23047,
            'fmt': '-629.23'
        },
        'gmtOffSetMilliseconds': 32400000,
        'exchangeDataDelayedBy': 0,
        'firstTradeDateMilliseconds': -157420800000,
        'language': 'en-US',
        'regularMarketTime': {
            'raw': 1596176103,
            'fmt': '3:15PM JST'
        },
        'exchangeTimezoneShortName': 'JST',
        'regularMarketChangePercent': {
            'raw': -2.8167062,
            'fmt': '-2.82%'
        },
        'quoteType': 'INDEX',
        'marketState': 'CLOSED',
        'regularMarketPrice': {
            'raw': 21710.0,
            'fmt': '21,710.00'
        },
        'market': 'jp_market',
        'priceHint': 2,
        'tradeable': False,
        'exchange': 'OSA',
        'sourceInterval': 20,
        'region': 'US',
        'shortName': 'Nikkei 225',
        'triggerable': False,
        'regularMarketPreviousClose': {
            'raw': 22339.23,
            'fmt': '22,339.23'
        }
    }]
```

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

### Get Key Statistics (PE, EPS, EBITDA)

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Access key statistics including enterprise value, P/E ratio, PEG ratio, EPS, short interest, and book value. The results are provided in a dictionary, with each symbol mapping to its statistics.

```python
from yahooquery import Ticker

t = Ticker('aapl msft goog')
stats = t.key_stats

# {
#   'aapl': {'enterpriseValue': 3100000000000, 'trailingEps': 6.43,
#            'pegRatio': 2.85, 'shortPercentOfFloat': 0.0073, ...},
#   'msft': {...},
#   'goog': {...}
# }
for symbol, s in stats.items():
    print(f"{symbol}: EPS={s.get('trailingEps')}, PE={s.get('forwardPE')}")
```

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

### Get Institutional Ownership for a Symbol

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/modules.md

Retrieve the top institutional owners for a specified stock symbol. The result is provided as a pandas DataFrame.

```python
aapl = Ticker('aapl')
aapl.institution_ownership
```

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

### Get Insider Transactions for a Symbol

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/modules.md

Retrieve insider transaction data for a specified stock symbol. This data is returned as a pandas DataFrame.

```python
aapl = Ticker('aapl')
df = aapl.insider_transactions
df.head()
```

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

### Get UK Trending Stocks

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves the trending stock symbols for the United Kingdom market. This enables regional trend identification.

```python
# UK trending
uk_trending = get_trending(country='United Kingdom')
print(uk_trending['quotes'][:5])
```

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

### all_modules

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/ticker/modules.md

Returns all available modules for a ticker symbol. Each module is a key in the returned dictionary.

```APIDOC
## all_modules

### Description
Returns all modules from the ticker. Each module will be a key in the dictionary returned.

### Method
Attribute Access

### Endpoint
N/A (Attribute of Ticker object)

### Parameters
None

### Request Example
```python
aapl = Ticker('aapl')
data = aapl.all_modules
```

### Response
#### Success Response (dict)
- **Keys**: Module names (e.g., 'assetProfile', 'quoteType')

#### Response Example
```json
{
  "aapl": {
    "assetProfile": { ... },
    "recommendationTrend": { ... },
    "cashflowStatementHistory": { ... },
    "indexTrend": { ... },
    "defaultKeyStatistics": { ... },
    "industryTrend": { ... },
    "quoteType": { ... },
    "incomeStatementHistory": { ... },
    "fundOwnership": { ... },
    "summaryDetail": { ... },
    "insiderHolders": { ... },
    "calendarEvents": { ... },
    "upgradeDowngradeHistory": { ... },
    "price": { ... },
    "balanceSheetHistory": { ... },
    "earningsTrend": { ... },
    "secFilings": { ... },
    "institutionOwnership": { ... },
    "majorHoldersBreakdown": { ... },
    "balanceSheetHistoryQuarterly": { ... },
    "earningsHistory": { ... },
    "esgScores": { ... },
    "summaryProfile": { ... },
    "netSharePurchaseActivity": { ... },
    "insiderTransactions": { ... },
    "sectorTrend": { ... },
    "incomeStatementHistoryQuarterly": { ... },
    "cashflowStatementHistoryQuarterly": { ... },
    "earnings": { ... },
    "pageViews": { ... },
    "financialData": { ... }
  }
}
```

### Warnings
Data for symbols external to the United States will be inaccurate for the following modules: `cashFlowStatementHistory`, `cashFlowStatementHistoryQuarterly`, `incomeStatementHistory`, `incomeStatementHistoryQuarterly`, `balanceSheetHistory`, `balanceSheetHistoryQuarterly`.
```

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

### Get Fund Category Holdings

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves the category breakdown (cash, bonds, equity percentages) for a fund. Requires the 'fund_category_holdings' attribute.

```python
from yahooquery import Ticker

# S&P 500 ETF
spy = Ticker('spy')

# Category breakdown (cash, bonds, equity percentages)
categories = spy.fund_category_holdings
print(categories)
```

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

### Get Fund Performance

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves fund performance data, including trailing returns, compared to its category. Requires the 'fund_performance' attribute.

```python
from yahooquery import Ticker

# S&P 500 ETF
spy = Ticker('spy')

# Fund performance vs category
perf = spy.fund_performance
print(perf['spy']['trailingReturns'])
```

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

### Get Historical Dividend Payments

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves historical dividend payments for specified symbols over a date range. Returns a pandas Series.

```python
from yahooquery import Ticker

t = Ticker('aapl msft')

# Last 5 years of dividends
divs = t.dividend_history(start='2019-01-01', end='2024-01-01')
print(divs)
```

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

### Instantiate Ticker with Symbols

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Initialize the Ticker class with one or more stock symbols. Symbols can be provided as a space-separated string, a comma-separated string, or a list. Optional keyword arguments can configure session behavior, including asynchronous requests, timeouts, retries, and validation.

```python
from yahooquery import Ticker

# Single symbol
aapl = Ticker('aapl')

# Multiple symbols (space-separated string)
tech = Ticker('aapl msft goog amzn')

# Multiple symbols (list)
faang = Ticker(['fb', 'aapl', 'amzn', 'nflx', 'goog'])

# With keyword arguments
t = Ticker(
    'aapl msft',
    asynchronous=True,   # concurrent requests
    max_workers=4,
    timeout=10,
    retry=5,
    validate=True,       # drop invalid symbols on init
    country='United States',
    progress=True,       # tqdm progress bar
)
print(t.symbols)         # ['AAPL', 'MSFT']
print(t.invalid_symbols) # [] (only populated when validate=True)
```

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

### Get Company Officers Data

Source: https://context7.com/dpguthrie/yahooquery/llms.txt

Retrieves executive compensation data as a pandas DataFrame. Requires a Ticker object initialized with a symbol.

```python
from yahooquery import Ticker

t = Ticker('aapl')
officers = t.company_officers

# pandas DataFrame with columns: name, title, totalPay, exercisedValue, ...
# MultiIndex: (symbol, row)
print(officers.head())
```

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

### Instantiate Research Class

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/research.md

Create an instance of the Research class by providing your Yahoo Finance username and password. Authentication is required to access premium features.

```python
r = Research(username='username@yahoo.com', password='password')
```

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

### Get Supported Currencies

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/misc.md

Retrieves a list of currencies that are supported by Yahoo Finance. This can be useful for understanding the scope of available currency data.

```APIDOC
## get_currencies

### Description
List of currencies Yahoo Finance supports.

### Method
```python
yq.get_currencies()
```

### Returns
`list` - A list of dictionaries, where each dictionary represents a currency with its details.

### Example
```python
import yahooquery as yq
data = yq.get_currencies()
```

### Data Structure Example
```json
[
    {
        "shortName": "FJD",
        "longName": "Fijian Dollar",
        "symbol": "FJD",
        "localLongName": "Fijian Dollar"
    },
    {
        "shortName": "MXN",
        "longName": "Mexican Peso",
        "symbol": "MXN",
        "localLongName": "Mexican Peso"
    }
]
```
```

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

### Inspect Downloaded Data Structure

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/alternative.ipynb

Checks the data type, shape, and displays the first few rows of the downloaded financial data. This helps in understanding the structure and content of the fetched data.

```python
type(yf_data)
yf_data.shape
yf_data.head()
```

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

### Import Libraries and Configure Pandas

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/alternative.ipynb

Imports necessary libraries and configures Pandas display options for better data visualization. This setup is common for data analysis tasks.

```python
import os

import pandas as pd
from IPython.core.interactiveshell import InteractiveShell

from yahooquery import Ticker
import yfinance as yf

InteractiveShell.ast_node_interactivity = "all"
pd.options.display.float_format = '{:,}'.format
pd.options.display.max_columns = None
pd.set_option('display.max_rows', 100)
```

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

### Import Screener Class

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/screener.md

Import the Screener class from the yahooquery library to begin using its functionalities.

```python
from yahooquery import Screener
```

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

### Combine Research and Ticker Classes with Shared Session

Source: https://github.com/dpguthrie/yahooquery/blob/master/docs/docs/guide/advanced.md

Use this pattern when you need to retrieve research reports and then perform detailed analysis on individual tickers using the same authenticated session. This avoids redundant login operations.

```python
from yahooquery import Research, Ticker

r = Research(username='username@yahoo.com', password='password')

# I want to retrieve last week's Bullish Analyst Report's
# for the Financial Services sector
df = r.reports(
    sector='Financial Services',
    report_date='Last Week',
    investment_rating='Bullish',
    report_type='Analyst Report'
)

# But now I want to get the data I find relevant and run my own analysis

# Using aapl as a default symbol (we will change that later).
# But, the important part is passing the current session and crumb
# from our Research instance
tickers = Ticker('aapl', session=r.session, crumb=r.crumb)

# Now, I can loop through the dataframe and retrieve relevant data for
# each ticker within the dataframe utilizing the Ticker instance
for i, row in df.iterrows():
    tickers.symbols = row['Tickers']
    data = tickers.p_company_360
    # Do something with data
    # ...

# Or, pass all tickers to the Ticker instance
ticker_list = df['Tickers'].tolist()
ticker_list = list(set(flatten_list(ticker_list)))
tickers = Ticker(ticker_list, session=r.session, crumb=r.crumb)
data = tickers.p_company_360
# Do something with data
# ...
```