### Basic Investpy Usage Example

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/README.md

Demonstrates fetching recent and historical stock data, searching for products, retrieving technical indicators, and accessing the economic calendar. Ensure investpy is installed.

```python
import investpy

# Get recent stock data
data = investpy.get_stock_recent_data(stock='AAPL', country='United States')
print(data.head())

# Get historical data
hist = investpy.get_stock_historical_data(
    stock='AAPL',
    country='United States',
    from_date='01/01/2020',
    to_date='01/01/2024'
)

# Search for a product
result = investpy.search_quotes(text='apple', products=['stocks'], n_results=1)
print(f"Symbol: {result.symbol}")

# Get technical indicators
indicators = investpy.technical_indicators(
    name='AAPL',
    country='United States',
    product_type='stock'
)
print(indicators)

# Get economic calendar
calendar = investpy.economic_calendar()
print(calendar)
```

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

### Example: Fetch and Display Recent EUR/USD Data

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/currency_crosses.md

Demonstrates how to retrieve recent historical data for the EUR/USD currency cross using investpy and display the first few rows. Ensure the 'investpy' library is installed.

```pycon
>>> data = investpy.get_currency_cross_recent_data(currency_cross='EUR/USD')
>>> data.head()
              Open    High     Low   Close Currency
Date
2019-08-27  1.1101  1.1116  1.1084  1.1091      USD
2019-08-28  1.1090  1.1099  1.1072  1.1078      USD
2019-08-29  1.1078  1.1093  1.1042  1.1057      USD
2019-08-30  1.1058  1.1062  1.0963  1.0991      USD
2019-09-02  1.0990  1.1000  1.0958  1.0968      USD
```

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

### Usage Examples

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/00-CONTENTS.txt

A collection of 50+ working code examples demonstrating various library functionalities.

```APIDOC
## Usage Examples

This section contains 50+ working code examples covering:

- Basic data retrieval patterns
- Searching for products
- Asset class-specific examples (stocks, cryptos, indices, etc.)
- Technical analysis workflows
- Economic calendar usage
- Data analysis patterns
- Bulk operations
- Comprehensive error handling examples
- Real-world scenarios
```

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

### Get Crypto Information Example

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/crypto.md

Retrieves fundamental financial information for a specified cryptocurrency. The output can be a pandas DataFrame or a dictionary.

```default
crypto_information = {
    'Chg (7D)': '-4.63%',
    'Circulating Supply': ' BTC18.10M',
    'Crypto Currency': 'Bitcoin',
    'Currency': 'USD',
    'Market Cap': '$129.01B',
    'Max Supply': 'BTC21.00M',
    'Todays Range': '7,057.8 - 7,153.1',
    'Vol (24H)': '$17.57B'
}
```

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

### Install investpy from source

Source: https://github.com/alvarobartt/investpy/blob/master/README.md

Install the latest investpy version directly from the master branch of the GitHub repository. This ensures you have the most up-to-date features.

```bash
pip install git+https://github.com/alvarobartt/investpy.git@master
```

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

### Search and Retrieve Financial Data with investpy

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/02-search.md

Use this workflow to search for a financial product, retrieve its recent and historical data, get technical indicators, and access product information and currency. Ensure investpy is installed and imported.

```python
import investpy

# 1. Search for a product
search_result = investpy.search_quotes(
    text='tesla',
    products=['stocks'],
    countries=['united states'],
    n_results=1
)

# 2. Retrieve recent data
recent_data = search_result.retrieve_recent_data()
print(recent_data.tail(5))

# 3. Retrieve historical data
historical_data = search_result.retrieve_historical_data(
    from_date='01/01/2020',
    to_date='01/01/2022'
)

# 4. Get technical indicators
indicators = search_result.retrieve_technical_indicators(interval='daily')
print(indicators)

# 5. Get product information
info = search_result.retrieve_information()
print(f"Market Cap: {info['marketCap']}")

# 6. Get default currency
currency = search_result.retrieve_currency()
print(f"Trading Currency: {currency}")
```

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

### Install investpy via pip

Source: https://github.com/alvarobartt/investpy/blob/master/README.md

Install the stable version of the investpy package using pip. Requires Python 3.6 or higher.

```bash
pip install investpy
```

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

### Get Pivot Points for a Currency Pair

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Retrieves pivot points for a currency pair. This example demonstrates fetching data for a currency cross without specifying a country.

```python
import investpy

# Get pivot points for a currency pair
pivots = investpy.pivot_points(
    name='EUR/USD',
    country=None,
    product_type='currency_cross'
)

print(f"Pivot: {pivots['Pivot'].values[0]}")
print(f"Support 1: {pivots['S1'].values[0]}")
print(f"Resistance 1: {pivots['R1'].values[0]}")
```

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

### Get Certificates Overview

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves an overview DataFrame for certificates. Optionally filter by country.

```python
def get_certificates_overview(country=None):
```
```

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

### Get Stock Historical Data - Python

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_info/usage.md

Retrieves historical stock data for a specified period. Ensure you have the investpy library installed.

```python
import investpy

df = investpy.get_stock_historical_data(stock='AAPL',
                                        country='United States',
                                        from_date='01/01/2010',
                                        to_date='01/01/2020')
print(df.head())
```

```default
             Open   High    Low  Close     Volume Currency
Date                                                      
2010-01-04  30.49  30.64  30.34  30.57  123432176      USD
2010-01-05  30.66  30.80  30.46  30.63  150476160      USD
2010-01-06  30.63  30.75  30.11  30.14  138039728      USD
2010-01-07  30.25  30.29  29.86  30.08  119282440      USD
2010-01-08  30.04  30.29  29.87  30.28  111969192      USD
```

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

### Get Crypto Currencies Overview

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/crypto.md

Retrieves an overview of main crypto currencies with real-time data. Use to get a general view of the market. The `n_results` parameter can limit the number of returned cryptocurrencies.

```python
import investpy

cryptos_overview = investpy.crypto.get_cryptos_overview()
print(cryptos_overview.head())
```

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

### Get Funds Overview

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves an overview DataFrame for funds. Optionally filter by country.

```python
def get_funds_overview(country=None):
```

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

### Get ETFs Overview

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves an overview DataFrame of ETFs. Optionally filter by country.

```python
def get_etfs_overview(country=None):
```

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

### Get All Funds as DataFrame

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/funds.md

Retrieves all available funds from Investing.com and returns them as a pandas DataFrame. This includes all fields from the funds.csv file.

```python
import investpy

funds_df = investpy.funds.get_funds()
print(funds_df.head())
```

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

### Get List of Cryptocurrencies

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/03-crypto.md

Retrieves a list of all supported cryptocurrencies. Use this to see available options.

```python
import investpy

# 1. Get list of cryptocurrencies
cryptos = investpy.get_cryptos_list()
print(f"Available cryptos: {len(cryptos)}")
```

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

### Get List of Certificate Names

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of all available certificate names. Optionally filter by country.

```python
def get_certificates_list(country=None):
```
```

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

### Get Stock Financial Summary

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/01-stocks.md

Fetches a summary of financial information for a stock. Requires stock symbol and country.

```python
import investpy

summary = investpy.get_stock_financial_summary(stock='IBM', country='United States')
```

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

### Search for Multiple Products

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Search for multiple financial products across different countries. This example looks for 'bank' stocks in Spain and France, returning up to 10 results.

```python
import investpy

# Search for multiple products
results = investpy.search_quotes(
    text='bank',
    products=['stocks'],
    countries=['spain', 'france'],
    n_results=10
)

for result in results:
    print(f"{result.symbol}: {result.name} ({result.country})")
```

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

### Calculate Returns with Pandas

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/INDEX.md

Demonstrates how to calculate daily returns from historical stock data using pandas. Assumes data is already retrieved.

```python
import investpy
import pandas as pd

data = investpy.get_stock_historical_data(stock='AAPL', 
                                        country='United States', 
                                        from_date='01/01/2023', 
                                        to_date='31/12/2023', 
                                        interval='Daily')

returns = pd.DataFrame(data['Close'].pct_change())
print(returns.head())
```

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

### Basic Product Search

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Perform a basic search for a financial product using text, product type, country, and the number of results. This example searches for 'apple' stocks in the United States.

```python
import investpy

# Search for a product
result = investpy.search_quotes(
    text='apple',
    products=['stocks'],
    countries=['united states'],
    n_results=1
)

print(f"Name: {result.name}")
print(f"Symbol: {result.symbol}")
print(f"Exchange: {result.exchange}")
```

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

### Get Historical ETF Data

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves historical OHLCV data for a specific ETF within a date range. Requires ETF symbol, country, start date, and end date. Supports JSON output and custom sort order.

```python
def get_etf_historical_data(
    etf,
    country,
    from_date,
    to_date,
    as_json=False,
    order="ascending"
):
```

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

### Retrieve Product Information with SearchObj

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/02-search.md

Use 'retrieve_information' to get detailed product information and financial metrics. The results are stored in the 'information' attribute as a dictionary.

```python
import investpy

result = investpy.search_quotes(text='apple', products=['stocks'], n_results=1)
info = result.retrieve_information()
print(f"Market Cap: {info.get('marketCap')}")
print(f"P/E Ratio: {info.get('ratio')}")
```

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

### Get Fund Recent Data (DataFrame Format)

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/funds.md

Retrieves recent historical data for a specified fund in a pandas DataFrame. The data includes Open, High, Low, Close prices and Currency. This example shows the first few rows of the returned DataFrame.

```python
>>> data = investpy.get_fund_recent_data(fund='bbva multiactivo conservador pp', country='spain')
>>> data.head()
             Open   High    Low  Close Currency
Date
2019-08-13  1.110  1.110  1.110  1.110      EUR
2019-08-16  1.109  1.109  1.109  1.109      EUR
2019-08-19  1.114  1.114  1.114  1.114      EUR
2019-08-20  1.112  1.112  1.112  1.112      EUR
2019-08-21  1.115  1.115  1.115  1.115      EUR
```

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

### Download Data for Multiple Stocks

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Demonstrates how to fetch recent data for a list of stocks from a specific country and combine them into a single DataFrame. Includes error handling for individual stock downloads.

```python
import investpy
import pandas as pd
from datetime import datetime

# Get list of stocks from a country
stocks = investpy.get_stocks_list(country='Spain')

# Download data for all stocks
all_data = {}
for stock in stocks[:10]:  # Limit to first 10
    try:
        data = investpy.get_stock_recent_data(
            stock=stock,
            country='Spain'
        )
        all_data[stock] = data
        print(f"Downloaded: {stock}")
    except Exception as e:
        print(f"Failed to download {stock}: {e}")

# Combine all data
combined = pd.concat(all_data, names=['Symbol', 'Date'])
print(combined.head())
```

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

### Compare Multiple Stocks

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Fetch recent closing prices for multiple stocks and create a normalized comparison DataFrame. Requires pandas.

```python
import investpy

# Get data for multiple stocks
stocks = ['AAPL', 'MSFT', 'GOOGL']
data_dict = {}

for stock in stocks:
    data = investpy.get_stock_recent_data(
        stock=stock,
        country='United States'
    )
    data_dict[stock] = data['Close']

# Create comparison DataFrame
comparison = pd.concat(data_dict, axis=1)
comparison_normalized = comparison / comparison.iloc[0] * 100  # Normalize to 100

print(comparison_normalized)
```

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

### Get All Stocks by Country

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/stocks.md

Retrieves a DataFrame of all available stocks for a specified country from the stocks.csv file. This function is useful for getting a list of stocks to analyze further.

```default
country | name | full name | isin | currency | symbol
--------|------|-----------|------|----------|--------
xxxxxxx | xxxx | xxxxxxxxx | xxxx | xxxxxxxx | xxxxxx
```

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

### investpy.certificates.get_certificates_overview

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/api.md

Retrieves an overview of available certificates.

```APIDOC
## investpy.certificates.get_certificates_overview

### Description
Get certificates overview.

### Method
`get_certificates_overview()`

### Parameters
This function does not explicitly list parameters in the provided documentation.
```

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

### Get List of All Fund Names

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/funds.md

Retrieves a list of all available fund names from Investing.com. This function is useful for getting a quick overview of fund names.

```python
import investpy

funds_list = investpy.funds.get_funds_list()
print(funds_list)
```

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

### Get Fund Data in Python

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Retrieve a list of available funds for a specific country and fetch recent data for a particular fund.

```python
import investpy

# Get funds from a country
funds = investpy.get_funds_list(country='France')
print(f"Available funds: {len(funds)}")

# Get recent data
data = investpy.get_fund_recent_data(
    fund='Carmignac Patrimoine',
    country='France'
)
```

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

### Get All Cryptocurrencies Data

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/03-crypto.md

Retrieves a DataFrame containing data for all supported cryptocurrencies. Useful for getting a comprehensive list of available cryptos and their basic information.

```python
import investpy

cryptos = investpy.get_cryptos()
print(cryptos.head())
```

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

### Compare Multiple Assets

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/INDEX.md

Compares the performance of multiple assets by retrieving their historical data and plotting their closing prices. Ensure all assets are valid.

```python
import investpy
import pandas as pd
import matplotlib.pyplot as plt

stocks = ['AAPL', 'MSFT', 'GOOG']

all_data = pd.DataFrame()

for stock in stocks:
    data = investpy.get_stock_historical_data(stock=stock, 
                                              country='United States', 
                                              from_date='01/01/2023', 
                                              to_date='31/12/2023', 
                                              interval='Daily')
    all_data[stock] = data['Close']

plt.figure(figsize=(12, 6))
plt.plot(all_data)
plt.legend(all_data.columns)
plt.show()
```

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

### Get Crypto Historical Data

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/crypto.md

Retrieves historical data for a specified cryptocurrency. Use this to get daily, weekly, or monthly price and volume information. The data can be returned as a pandas DataFrame or a JSON object.

```python
>>> data = investpy.get_crypto_historical_data(crypto='bitcoin', from_date='01/01/2018', to_date='01/01/2019')
>>> data.head()
               Open     High      Low    Close  Volume Currency
Date
2018-01-01  13850.5  13921.5  12877.7  13444.9   78425      USD
2018-01-02  13444.9  15306.1  12934.2  14754.1  137732      USD
2018-01-03  14754.1  15435.0  14579.7  15156.6  106543      USD
2018-01-04  15156.5  15408.7  14244.7  15180.1  110969      USD
2018-01-05  15180.1  17126.9  14832.4  16954.8  141960      USD
```

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

### Get Recent Stock Data

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/01-stocks.md

Retrieves recent historical data for a stock. Specify 'as_json=True' to get a JSON string, otherwise a pandas DataFrame is returned. The 'interval' parameter can be 'Daily', 'Weekly', or 'Monthly'.

```python
def get_stock_recent_data(
    stock,
    country,
    as_json=False,
    order="ascending",
    interval="Daily"
):
    """
    stock: str
        Stock symbol to retrieve data for.
    country: str
        Country where the stock is listed.
    as_json: bool, optional
        Return as JSON if True, pandas.DataFrame if False.
    order: str, optional
        "ascending" or "descending" for sort order.
    interval: str, optional
        "Daily", "Weekly", or "Monthly".
    
    Returns:
        pandas.DataFrame or str (JSON)
    """

```

```python
import investpy

# Get recent data as DataFrame
data = investpy.get_stock_recent_data(
    stock='BBVA',
    country='Spain'
)
print(data.head())

# Get as JSON with weekly data
json_data = investpy.get_stock_recent_data(
    stock='AAPL',
    country='United States',
    as_json=True,
    interval='Weekly'
)

```

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

### Find All Events for a Date Range and Forecast Misses

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/06-economic-calendar.md

Retrieve all economic events within a specified date range. This example also shows how to group events by country and identify events where the actual value differed from the forecast.

```python
import investpy

calendar = investpy.economic_calendar(
    from_date='01/01/2023',
    to_date='31/01/2023'
)

# Group by country
by_country = calendar.groupby('zone').size()
print(by_country)

# Find events with forecast misses
misses = calendar[
    (calendar['actual'].notna()) & 
    (calendar['forecast'].notna()) & 
    (calendar['actual'] != calendar['forecast'])
]
print(f"Events with forecast misses: {len(misses)}")
```

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

### Get Index Countries

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of countries for which index data is available.

```python
def get_index_countries():
```

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

### Get Fund Countries

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of countries for which fund data is available.

```python
def get_fund_countries():
```

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

### Analyze Technical Indicators for Decision-Making

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/05-technical-analysis.md

This workflow demonstrates how to fetch various technical indicators (RSI, moving averages, pivot points) and recent stock data to inform investment decisions. It includes checking RSI for oversold/overbought conditions and printing support/resistance levels.

```python
import investpy

# Example: Analyze technical indicators for decision-making

# 1. Get technical indicators
indicators = investpy.technical_indicators(
    name='MSFT',
    country='United States',
    product_type='stock',
    interval='daily'
)

# 2. Check RSI for oversold/overbought
rsi = indicators[indicators['technical_indicator'] == 'RSI(14)']
rsi_value = rsi['value'].values[0]
if rsi_value < 30:
    print("Oversold (RSI < 30)")
elif rsi_value > 70:
    print("Overbought (RSI > 70)")

# 3. Get moving averages
ma = investpy.moving_averages(
    name='MSFT',
    country='United States',
    product_type='stock',
    interval='daily'
)

# 4. Get pivot points
pivots = investpy.pivot_points(
    name='MSFT',
    country='United States',
    product_type='stock'
)
print(f"Support 1: {pivots['S1'].values[0]}")
print(f"Resistance 1: {pivots['R1'].values[0]}")

# 5. Get recent data
recent = investpy.get_stock_recent_data(
    stock='MSFT',
    country='United States'
)
current_price = recent['Close'].iloc[-1]
print(f"Current Price: {current_price}")
```

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

### Get ETF Countries

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of countries for which ETF data is available.

```python
def get_etf_countries():
    """
    Returns:
        list
    """
```

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

### investpy.certificates.get_certificates_overview

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/certificates.md

Retrieves a real-time overview of main certificates from a specified country, including names, symbols, current values, and change percentages. It defaults to the top 100 results but can be configured with `n_results`.

```APIDOC
## investpy.certificates.get_certificates_overview(country, as_json=False, n_results=100)

### Description
This function retrieves an overview containing all the real-time data available for the main certificates from a country, such as the names, symbols, current value, etc. as indexed in Investing.com. The main usage of this function is to get an overview on the main certificates from a country for a general view.

### Parameters
#### Path Parameters
- **country** (str) - The name of the country to retrieve the certificates overview from.
- **as_json** (bool, optional) - Optional argument to determine the format of the output data (`pandas.DataFrame` or `json`). Defaults to False.
- **n_results** (int, optional) - Number of results to be displayed on the overview table (0-1000). Defaults to 100.

### Returns
- **pandas.DataFrame** - A DataFrame containing the certificates overview with columns: `country`, `name`, `symbol`, `last`, `change_percentage`, `turnover`.

### Raises
- **ValueError** - Raised if any of the introduced arguments is not valid or errored.
- **FileNotFoundError** - Raised when certificates.csv file is missing.
- **IOError** - Raised if data could not be retrieved due to file error.
- **RuntimeError** - Raised either if the introduced country does not match any of the listed ones or if no overview results could be retrieved from Investing.com.
- **ConnectionError** - Raised if GET requests does not return 200 status code.
```

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

### Get Certificate Information

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves detailed information for a specific certificate, filtered by country.

```python
def get_certificate_information(certificate, country):
```
```

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

### Stock/Fund/ETF Listing DataFrame Structure

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/08-types-and-constants.md

Illustrates the structure of the DataFrame returned by get_stocks(), get_funds(), and get_etfs(). It shows the columns and their typical data.

```python
  country        name                    full_name     isin  currency symbol
0   spain        BBVA    Banco Bilbao Vizcaya Argentaria  ES0113900J37     EUR   BBVA
1   spain        BANCO    Banco Santander              ES0113900J37     EUR   SAN
2   spain  Telefonica               Telefonica, S.A. ES0113900J37     EUR    TEF
```

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

### retrieve_information()

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/02-search.md

Retrieves detailed product information and financial metrics. The information is returned as a dictionary and also stored in the `information` attribute of the object.

```APIDOC
## retrieve_information()

### Description
Retrieve product information and financial metrics.

### Method
GET (Implicit)

### Parameters
None

### Response
#### Success Response (200)
- **information** (dict) - Dictionary containing product information such as price metrics, volume metrics, financial metrics, dividend info, and date-based info.

### Request Example
```python
import investpy

result = investpy.search_quotes(text='apple', products=['stocks'], n_results=1)
info = result.retrieve_information()
print(f"Market Cap: {info.get('marketCap')}")
print(f"P/E Ratio: {info.get('ratio')}")
```
```

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

### Get Countries with Certificate Data

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of countries for which certificate data is available.

```python
def get_certificate_countries():
```
```

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

### Get Bonds Overview

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves an overview DataFrame for bonds. Optionally filter by country.

```python
def get_bonds_overview(country=None):
```
```

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

### retrieve_information()

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/search.md

Retrieves general information for a financial product from Investing.com using search results.

```APIDOC
## retrieve_information()

### Description
Class method used to retrieve the information from the class instance of any financial product.

This method retrieves the information from Investing.com of the financial product of the current class
instance, so it fills the SearchObj.info attribute with the retrieved `dict`. This method uses the
previously retrieved data from the investpy.search_quotes(text, products, countries, n_results)
function search results to build the request that it is going to be sent to Investing.com so to retrieve and
parse the information, since the product tag is required.

### Returns
This method retrieves the information from the current class instance of a financial product
from Investing.com. This method both stores retrieved information in self.information attribute of the class
instance and it also returns it as a normal function will do.

* **Return type:**
  `dict` - info

### Raises
* **ConnectionError** – raised if connection to Investing.com could not be established.
* **RuntimeError** – raised if there was any problem while retrieving the data from Investing.com.
```

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

### Get Bond Information

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves detailed information for a specific bond, filtered by country.

```python
def get_bond_information(bond, country):
```
```

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

### Get ETF Data in Python

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Fetch lists of ETFs, recent data for a specific ETF, and historical data for a given date range.

```python
import investpy

# Get all ETFs from a country
all_etfs = investpy.get_etfs(country='Germany')
print(f"Total ETFs: {len(all_etfs)}")

# Get recent data for an ETF
data = investpy.get_etf_recent_data(
    etf='iShares Core DAX',
    country='Germany'
)

# Get historical data
hist_data = investpy.get_etf_historical_data(
    etf='iShares Core DAX',
    country='Germany',
    from_date='01/01/2023',
    to_date='01/01/2024'
)
```

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

### Get Countries with Bond Data

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of countries for which bond data is available.

```python
def get_bond_countries():
```
```

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

### Get Indices Overview

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves an overview DataFrame for indices. Optionally filter by country.

```python
def get_indices_overview(country=None):
```

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

### Search Financial Products with investpy

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_info/usage.md

Use this function to search for financial products like stocks, specifying text, product types, countries, and the number of results. The output is a list of SearchObj instances or a single instance if n_results is 1.

```python
import investpy

search_result = investpy.search_quotes(text='apple', products=['stocks'],
                                       countries=['united states'], n_results=1)
print(search_result)
```

```default
{"id_": 6408, "name": "Apple Inc", "symbol": "AAPL", "country": "united states", "tag": "/equities/apple-computer-inc", "pair_type": "stocks", "exchange": "NASDAQ"}
```

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

### Get Indices List

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of index symbols. Optionally filter by country.

```python
def get_indices_list(country=None):
```

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

### Get Funds List

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of fund symbols. Optionally filter by country.

```python
def get_funds_list(country=None):
```

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

### Get Stock Data with Different Intervals

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/09-usage-examples.md

Retrieve stock data at different intervals: Daily, Weekly, or Monthly. The 'Daily' interval is the default if not specified.

```python
import investpy

# Daily data (default)
daily = investpy.get_stock_recent_data(
    stock='MSFT',
    country='United States',
    interval='Daily'
)

# Weekly data
weekly = investpy.get_stock_recent_data(
    stock='MSFT',
    country='United States',
    interval='Weekly'
)

# Monthly data
monthly = investpy.get_stock_recent_data(
    stock='MSFT',
    country='United States',
    interval='Monthly'
)

print(f"Daily: {len(daily)} records")
print(f"Weekly: {len(weekly)} records")
print(f"Monthly: {len(monthly)} records")
```

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

### Get Funds Data

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a DataFrame with fund data. Optionally filter by country.

```python
def get_funds(country=None):
```

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

### investpy.commodities.get_commodities_overview

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/api.md

Retrieves an overview of available commodities.

```APIDOC
## investpy.commodities.get_commodities_overview

### Description
Get commodities overview.

### Method
`get_commodities_overview()`

### Parameters
This function does not explicitly list parameters in the provided documentation.
```

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

### Get ETF List

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a list of ETF symbols. Optionally filter by country.

```python
def get_etfs_list(country=None):
```

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

### get_etfs_overview()

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves an overview of ETFs for a specified country. If no country is provided, it returns the overview for all available countries.

```APIDOC
## get_etfs_overview()

### Description
Retrieves an overview of ETFs for a specified country. If no country is provided, it returns the overview for all available countries.

### Method
```python
def get_etfs_overview(country=None):
```

### Parameters
#### Path Parameters
- **country** (str) - Optional - Country name to filter ETF overview by. Returns all if None.
```

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

### Get Funds by Country and Columns as Dictionary

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/funds.md

Retrieves specific fund data for a given country and returns it as a dictionary. You can specify which columns to retrieve.

```python
import investpy

funds_dict_spain = investpy.funds.get_funds_dict(country='Spain', columns=[
    'name', 'symbol', 'currency'
])
print(funds_dict_spain.head())
```

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

### Get Fund Information Structure

Source: https://github.com/alvarobartt/investpy/blob/master/docs/source/_api/funds.md

This is the structure of the dictionary returned when requesting fund information with as_json=True.

```default
fund_information = {
    'Fund Name': fund_name,
    'Rating': rating,
    '1-Year Change': year_change,
    'Previous Close': prev_close,
    'Risk Rating': risk_rating,
    'TTM Yield': ttm_yield,
    'ROE': roe,
    'Issuer': issuer,
    'Turnover': turnover,
    'ROA': row,
    'Inception Date': inception_date,
    'Total Assets': total_assets,
    'Expenses': expenses,
    'Min Investment': min_investment,
    'Market Cap': market_cap,
    'Category': category
}
```

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

### Basic investpy Error Handling

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/07-errors-and-exceptions.md

Demonstrates handling common exceptions like ValueError, RuntimeError, ConnectionError, and IndexError when fetching stock data.

```python
import investpy

# Handling ValueError for invalid parameters
try:
    data = investpy.get_stock_recent_data(
        stock='INVALID',
        country='Invalid Country'
    )
except ValueError as e:
    print(f"Validation error: {e}")

# Handling RuntimeError for product not found
try:
    data = investpy.get_stock_recent_data(
        stock='NONEXISTENT',
        country='Spain'
    )
except RuntimeError as e:
    print(f"Product not found: {e}")

# Handling ConnectionError for network issues
try:
    data = investpy.get_stock_recent_data(
        stock='AAPL',
        country='United States'
    )
except ConnectionError as e:
    print(f"Connection failed: {e}")

# Handling IndexError for unavailable data
try:
    data = investpy.get_stock_recent_data(
        stock='AAPL',
        country='United States'
    )
except IndexError as e:
    print(f"Data unavailable: {e}")
```

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

### Get Indices Data

Source: https://github.com/alvarobartt/investpy/blob/master/_autodocs/04-other-products.md

Retrieves a DataFrame with stock index data. Optionally filter by country.

```python
def get_indices(country=None):
```