### Install Quiver Quantitative Package

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Install the package using pip. Ensure Python 3 and pip are installed locally.

```python
pip install quiverquant
```

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

### Install Dependencies Manually

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Manually install the pandas and requests libraries if not using the quiverquant package. These are the core dependencies.

```bash
pip install pandas requests
```

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

### Unix Timestamp Conversion Example

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

Provides an example of how the client converts Unix timestamps in milliseconds from the API response into pandas.Timestamp objects. This is relevant for endpoints that return time data in this format.

```python
df = client.wallstreetbetsCommentsFull()
# Raw API response: Time = 1609459200000 (milliseconds since epoch)
# After conversion: Datetime = Timestamp('2021-01-01 00:00:00')
```

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

### Get News Across Multiple Tickers

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Aggregate news articles for a list of technology stocks. Useful for getting a broad overview of news impacting a sector.

```python
tech_stocks = ["AAPL", "MSFT", "NVDA", "META"]
all_news = []

for ticker in tech_stocks:
    df = client.news(ticker=ticker, page_size="50")
    all_news.append(df)

combined = pd.concat(all_news, ignore_index=True)
print(f"Total news items: {len(combined)}")
```

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

### Initialize Client with API Token from ConfigParser

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

This example shows how to initialize the Quiver client using an API token read from a 'config.ini' file via Python's ConfigParser.

```python
import configparser
import quiverquant

config = configparser.ConfigParser()
config.read('config.ini')

token = config['quiver']['api_token']
client = quiverquant.quiver(token)
```

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

### Get News

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves recent stock news. Can be filtered by ticker.

```APIDOC
## Get News

### Description
Retrieves recent stock news. Can be filtered by ticker.

### Method
`quiver.news(ticker=None)`

### Parameters
- **ticker** (string) - Optional - The stock ticker symbol (e.g., "ORCL") to filter news for.
```

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

### Get Government Contracts

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on government contracts. Can be filtered by ticker.

```APIDOC
## Get Government Contracts

### Description
Retrieves data on government contracts. Can be filtered by ticker.

### Method
`quiver.gov_contracts(ticker=None)`

### Parameters
- **ticker** (string) - Optional - The stock ticker symbol (e.g., "LMT") to filter government contracts for.
```

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

### Get All Recent Government Contracts

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves all recent government contract awards data. Use this when you need a broad overview of contracts.

```python
df = quiver_client.gov_contracts()
```

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

### Handle Empty Query Parameters

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

Shows that empty string parameters are trimmed and treated as if they were not provided. This example demonstrates filtering out empty values from a payload.

```python
# These are equivalent:
df1 = client.news(ticker="")
df2 = client.news()

# An empty parameter is treated as "not provided"
payload = {k: v for k, v in payload.items() if v != ""}
```

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

### Verify DataFrame Return Type

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

This example demonstrates that all methods return pandas.DataFrame objects. It calls the congress_trading method and prints the type of the returned object.

```python
df = quiver_client.congress_trading(ticker="AAPL")
print(type(df))  # <class 'pandas.core.frame.DataFrame'>
```

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

### Handle Missing Data with Empty DataFrame

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

This example illustrates how the client handles missing data. When no results are found for a query, an empty DataFrame is returned, and a message is printed to stdout.

```python
df = quiver_client.congress_trading(ticker="NONEXISTENT")
# Output: No results found
print(df.empty)  # True
```

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

### Get All Recent Congressional Trades

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Initializes the client and retrieves all recent congressional trading records. Requires an API token.

```python
import quiverquant
import pandas as pd

# Initialize client
client = quiverquant.quiver("YOUR_API_TOKEN")

# Get all recent congressional trades
df = client.congress_trading()
print(f"Retrieved {len(df)} records")
```

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

### Get Off-Exchange Short Volume

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on off-exchange short volume. Can be filtered by ticker.

```APIDOC
## Get Off-Exchange Short Volume

### Description
Retrieves data on off-exchange short volume. Can be filtered by ticker.

### Method
`quiver.offexchange(ticker=None)`

### Parameters
- **ticker** (string) - Optional - The stock ticker symbol (e.g., "AMC") to filter off-exchange short volume for.
```

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

### GET /live/topshareholders/{ticker}

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve top shareholders for a company. This endpoint provides a list of top shareholders for a given company ticker.

```APIDOC
## GET /live/topshareholders/{ticker}

### Description
Retrieve top shareholders for a company. This endpoint provides a list of top shareholders for a given company ticker.

### Method
GET

### Endpoint
/live/topshareholders/{ticker}

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol for the company.

### Response Schema
```json
{
  "ownership": [
    {
      "Holder": "string",
      "Shares": "number",
      "Percentage": "number"
    }
  ]
}
```

### Response Codes
200, 401, 403
```

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

### GET /live/wsbcommentsfull

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve full Wall Street Bets comments. This endpoint provides the complete text of comments from Wall Street Bets.

```APIDOC
## GET /live/wsbcommentsfull

### Description
Retrieve full Wall Street Bets comments. This endpoint provides the complete text of comments from Wall Street Bets.

### Method
GET

### Endpoint
`/live/wsbcommentsfull`

### Query Parameters
- **ticker** (string) - Optional - The stock ticker symbol.
- **freq** (string) - Optional - The frequency of aggregation (e.g., daily, hourly).
- **date_from** (string) - Optional - The start date for the data retrieval.
- **date_to** (string) - Optional - The end date for the data retrieval.

### Response
#### Success Response (200)
- **Ticker** (string) - The stock ticker symbol.
- **Text** (string) - The content of the comment.
- **Time** (number (milliseconds since epoch)) - The timestamp of the comment.
- **Author** (string) - The author of the comment.

#### Response Example
```json
[
  {
    "Ticker": "string",
    "Text": "string",
    "Time": "number (milliseconds since epoch)",
    "Author": "string"
  }
]
```
```

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

### GET /live/offexchange

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve recent off-exchange (OTC) short volume data. This endpoint provides information on short volumes traded off the main exchanges.

```APIDOC
## GET /live/offexchange

### Description
Retrieve recent off-exchange (OTC) short volume data. This endpoint provides information on short volumes traded off the main exchanges.

### Method
GET

### Endpoint
/live/offexchange

### Response
#### Success Response (200)
- **Ticker** (string)
- **Date** (ISO8601 datetime)
- **Volume** (number)
- **PercentageOfVolume** (number)

#### Error Response
- **401** — Unauthorized (invalid/missing token)
- **403** — Forbidden (subscription tier too low)
```

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

### Retrieve All Recent Flight Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Fetches all recent corporate private jet flight data. No specific setup is required beyond initializing the Quiver client.

```python
# Get all recent flight data
df = quiver_client.flights()
```

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

### Handling Empty DataFrames

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

When query parameters do not yield any results, the API returns an empty DataFrame. This example shows how to check if a returned DataFrame is empty.

```APIDOC
## Empty DataFrames

When no data matches the query parameters, an empty DataFrame is returned:

```python
df = client.congress_trading(ticker="INVALID_TICKER")
print(df.empty)   # True
print(df.shape)   # (0, 0) or (0, n) depending on response
```
```

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

### Get Paginated News Articles

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves paginated news articles with a specified page size and page number. Returns a pandas DataFrame.

```python
df = quiver_client.news(page_size="10", page="2")
```

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

### GET /live/wallstreetbets

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve Wall Street Bets discussion data. Supports filtering by date range and an option to count all discussions.

```APIDOC
## GET /live/wallstreetbets

### Description
Retrieve discussion data from Wall Street Bets. This endpoint can fetch all discussions or filter by a date range.

### Method
GET

### Endpoint
`/live/wallstreetbets` or `/live/wallstreetbets?count_all=true`

### Parameters
#### Query Parameters
- **count_all** (string) - No - Include all discussions when set to `true`.
- **date_from** (string) - No - Start date for filtering discussions (YYYYMMDD format).
- **date_to** (string) - No - End date for filtering discussions (YYYYMMDD format).

### Response
#### Success Response (200)
- **data** (array) - Contains discussion data.
  - **Ticker** (string)
  - **Date** (ISO8601 datetime)
  - **Time** (number (milliseconds))
  - **Count** (number)

#### Response Codes
200, 401, 403

**Subscription Error**: Returns string "Upgrade your subscription plan to access this dataset."
```

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

### Retrieve All Recent Political Beta Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Fetches all recent political beta data for stocks. This data indicates a stock's sensitivity to political and regulatory changes. No specific setup is required beyond initializing the Quiver client.

```python
# Get all recent political beta data
df = quiver_client.political_beta()
```

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

### Get All Recent Lobbying Activity

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves all recent corporate lobbying activity and expenditure data. The 'Date' column is automatically converted to datetime.

```python
df = quiver_client.lobbying()
```

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

### GET /beta/historical/lobbying/{ticker}

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve historical lobbying for a company. Fetch historical lobbying data for a specific company using its ticker symbol.

```APIDOC
## GET /beta/historical/lobbying/{ticker}

### Description
Retrieve historical lobbying for a company. Fetch historical lobbying data for a specific company using its ticker symbol.

### Method
GET

### Endpoint
/beta/historical/lobbying/{ticker}

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol for the company.

### Response Schema
```json
[
  {
    "Ticker": "string",
    "Company": "string",
    "Date": "ISO8601 datetime",
    "LobbyistCount": "number",
    "Amount": "number"
  }
]
```

### Response Codes
200, 401, 403
```

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

### Retrieve All Recent Patent Filing Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Fetches all recent patent filing data for companies. The 'Date' column is automatically converted to datetime objects. No specific setup is required beyond initializing the Quiver client.

```python
# Get all recent patent data
df = quiver_client.patents()
```

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

### GET /live/housetrading

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve recent House trading activity. This endpoint provides live data on trades made by members of the House of Representatives.

```APIDOC
## GET /live/housetrading

### Description
Retrieve recent House trading activity. This endpoint provides live data on trades made by members of the House of Representatives.

### Method
GET

### Endpoint
/live/housetrading

### Response
#### Success Response (200)
- **Ticker** (string)
- **Representative** (string)
- **Date** (ISO8601 datetime)
- **Type** (string)
- **Amount** (string)

#### Error Response
- **401** — Unauthorized (invalid/missing token)
- **403** — Forbidden (subscription tier too low)
```

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

### Initialize Quiver API Connection

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Import the package and connect to the API using your obtained token. Replace <TOKEN> with your actual API token.

```python
#Import the package
import quiverquant

#Connect to the API using your token
#Replace <TOKEN> with your token
quiver = quiverquant.quiver("<TOKEN>")
```

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

### GET /live/govcontractsall

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve recent government contracts. This endpoint provides a list of recent government contracts, including details like ticker, company, amount, date, and contract type.

```APIDOC
## GET /live/govcontractsall

### Description
Retrieve recent government contracts. This endpoint provides a list of recent government contracts, including details like ticker, company, amount, date, and contract type.

### Method
GET

### Endpoint
/live/govcontractsall

### Response Schema
```json
[
  {
    "Ticker": "string",
    "Company": "string",
    "Amount": "number",
    "Date": "ISO8601 datetime",
    "ContractType": "string"
  }
]
```

### Response Codes
200, 401, 403
```

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

### Get All Recent Senate Trades

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieve all recent trading activity by U.S. Senate members. Call the method without any parameters to get all records.

```python
# Get all recent Senate trades
df = quiver_client.senate_trading()
```

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

### GET /beta/historical/offexchange/{ticker}

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve historical OTC volume for a specific ticker. This endpoint allows querying past off-exchange short volume data for a given ticker symbol.

```APIDOC
## GET /beta/historical/offexchange/{ticker}

### Description
Retrieve historical OTC volume for a specific ticker. This endpoint allows querying past off-exchange short volume data for a given ticker symbol.

### Method
GET

### Endpoint
/beta/historical/offexchange/{ticker}

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol.

### Response
#### Success Response (200)
- **Ticker** (string)
- **Date** (ISO8601 datetime)
- **Volume** (number)
- **PercentageOfVolume** (number)

#### Error Response
- **401** — Unauthorized (invalid/missing token)
- **403** — Forbidden (subscription tier too low)
```

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

### Initialize Client with API Token from Environment Variable (Simplified)

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

A simplified version for initializing the Quiver client using an API token stored in the QUIVER_API_TOKEN environment variable.

```python
import os
import quiverquant

token = os.getenv('QUIVER_API_TOKEN')
client = quiverquant.quiver(token)
```

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

### Quiver Client Initialization and Data Fetching

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/README.md

Demonstrates how to initialize the Quiver client with an API token and fetch various types of financial data using the client's methods. The fetched data is returned as a pandas DataFrame.

```APIDOC
## Quiver Client Initialization and Usage

### Description
Initialize the `quiverquant` client with your API token and use its methods to fetch financial data. The data is returned as a pandas DataFrame.

### Method
```python
import quiverquant

# Initialize client with API token
client = quiverquant.quiver("YOUR_API_TOKEN")

# Fetch data (returns pandas.DataFrame)
df_trading = client.congress_trading(ticker="AAPL")
df_news = client.news()
df_insiders = client.insiders(ticker="TSLA")
```

### Parameters
- `quiver()` requires an API token string.
- `congress_trading()` accepts an optional `ticker` string.
- `news()` has no parameters.
- `insiders()` accepts an optional `ticker` string.
```

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

### Get Recent Off-Exchange Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves recent off-exchange (OTC) short volume data across all tickers. The 'Date' column is not converted to datetime by default.

```python
df = quiver_client.offexchange()
```

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

### Initialize Client with API Token from JSON Configuration File

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

This snippet demonstrates initializing the Quiver client by reading an API token from a JSON configuration file named 'config.json'. This method is not recommended for production environments.

```python
import json
import quiverquant

with open('config.json', 'r') as f:
    config = json.load(f)
    
client = quiverquant.quiver(config['api_token'])
```

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

### Get Patents

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on patents filed by companies. Can be filtered by ticker.

```APIDOC
## Get Patents

### Description
Retrieves data on patents filed by companies. Can be filtered by ticker.

### Method
`quiver.patents(ticker)`

### Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., "AAPL").
```

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

### Initialize Quiver Client and Fetch Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/README.md

Demonstrates how to initialize the Quiver client with an API token and fetch various datasets like congressional trading, news, and insider transactions. The data is returned as pandas DataFrames.

```python
import quiverquant

# Initialize client with API token
client = quiverquant.quiver("YOUR_API_TOKEN")

# Fetch data (returns pandas.DataFrame)
df = client.congress_trading(ticker="AAPL")
df = client.news()
df = client.insiders(ticker="TSLA")
```

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

### Minimal API Client Configuration

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/README.md

Initialize the QuiverQuant client with only the required API token. No additional configuration files or environment variables are needed for basic usage.

```python
# Only required parameter: API token
client = quiverquant.quiver(api_token)

# No config files, environment variables, or settings objects
# All method options passed at call time
```

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

### Initialize Quiver Client with API Token

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Use this snippet to initialize the Quiver client by passing your API authentication token. Obtain your token from https://api.quiverquant.com.

```python
import quiverquant

# Initialize with token
api_token = "your_actual_token_here"
quiver_client = quiverquant.quiver(api_token)
```

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

### Get Executive Compensation

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on executive compensation for a given ticker.

```APIDOC
## Get Executive Compensation

### Description
Retrieves data on executive compensation for a given ticker.

### Method
`quiver.executive_compensation(ticker)`

### Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., "NVDA").
```

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

### Get Top Shareholders

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on the top shareholders for a given ticker.

```APIDOC
## Get Top Shareholders

### Description
Retrieves data on the top shareholders for a given ticker.

### Method
`quiver.top_shareholders(ticker)`

### Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., "WMT").
```

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

### Get Off-Exchange Data for a Specific Ticker

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves off-exchange (OTC) short volume data for a specific stock ticker. The 'Date' column is converted to datetime only when a ticker is specified.

```python
df = quiver_client.offexchange(ticker="AMC")
```

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

### Get Live Wall Street Bets Comment Aggregates

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve aggregated Wall Street Bets comment data. Requires authentication and accepts ticker, frequency, and date range as query parameters.

```python
wallstreetbetsComments(ticker="", freq="", date_from="", date_to="")
```

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

### Get Corporate Donors

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on corporate election donations. Can be filtered by ticker.

```APIDOC
## Get Corporate Donors

### Description
Retrieves data on corporate election donations. Can be filtered by ticker.

### Method
`quiver.corporate_donors(ticker)`

### Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., "CVX").
```

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

### Initialize Quiver Client

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/README.md

Instantiate the Quiver client by providing your API token obtained from the Quiver website. This client object is used to make subsequent API calls.

```python
client = quiverquant.quiver("your_token_from_quiverquant")
```

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

### Get Insider Transactions

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on recent insider transactions. Can be filtered by ticker.

```APIDOC
## Get Insider Transactions

### Description
Retrieves data on recent insider transactions. Can be filtered by ticker.

### Method
`quiver.insiders(ticker=None)`

### Parameters
- **ticker** (string) - Optional - The stock ticker symbol (e.g., "TSLA") to filter insider transactions for.
```

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

### Initialize Client with API Token from Environment Variable

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

This snippet shows how to initialize the Quiver client by retrieving the API token from the QUIVER_API_TOKEN environment variable. It includes error handling if the variable is not set.

```python
import os
import quiverquant

# Read token from environment if desired
token = os.getenv('QUIVER_API_TOKEN')
if not token:
    raise ValueError("QUIVER_API_TOKEN environment variable not set")

quiver_client = quiverquant.quiver(token)
```

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

### Safely Get Data with Subscription Error Handling

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Provides a function to safely call Quiver client methods, specifically handling and reporting 'Dataset not available' errors due to subscription limitations. Returns an empty DataFrame on error.

```python
import pandas as pd

def safe_get_data(client, method_name, **kwargs):
    """
    Safely call a Quiver method, handling subscription errors.
    
    Args:
        client: Initialized quiver client
        method_name: Name of method to call
        **kwargs: Arguments to pass to method
    
    Returns:
        DataFrame if successful, empty DataFrame if subscription error
    """
    try:
        method = getattr(client, method_name)
        return method(**kwargs)
    except NameError as e:
        print(f"Dataset not available: {e}")
        return pd.DataFrame()

# Usage
df = safe_get_data(client, 'wikipedia', ticker='AAPL')
if not df.empty:
    print(f"Got {len(df)} Wikipedia records")
else:
    print("Wikipedia dataset not available on this plan")
```

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

### Configure Pagination Parameters

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Use 'page_size' and 'page' arguments for methods supporting pagination to control the number of results per page and select the desired page.

```python
df = quiver_client.news(
    page_size="20",   # Results per page
    page="2"          # Page number (1-indexed)
)
```

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

### Load Bulk Data and Measure Time

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

This snippet demonstrates how to load a large dataset and measure the time it takes. It's useful for identifying performance issues with bulk data retrieval.

```python
import time

start = time.time()
df = client.congress_trading(recent=False)  # Bulk data
elapsed = time.time() - start

print(f"Loaded {len(df)} rows in {elapsed:.2f}s")
```

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

### Get Lobbying Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on recent corporate lobbying activities. Can be filtered by ticker.

```APIDOC
## Get Lobbying Data

### Description
Retrieves data on recent corporate lobbying activities. Can be filtered by ticker.

### Method
`quiver.lobbying(ticker=None)`

### Parameters
- **ticker** (string) - Optional - The stock ticker symbol (e.g., "AAPL") to filter lobbying data for.
```

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

### Get Wikipedia Page Views

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on Wikipedia page views for companies. Can be filtered by ticker.

```APIDOC
## Get Wikipedia Page Views

### Description
Retrieves data on Wikipedia page views for companies. Can be filtered by ticker.

### Method
`quiver.wikipedia(ticker=None)`

### Parameters
- **ticker** (string) - Optional - The stock ticker symbol (e.g., "MSFT") to filter Wikipedia page views for.
```

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

### Instantiate Client and Fetch News Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

Demonstrates how to instantiate the Quiver client with an API token and fetch news data for a specific ticker. The returned data is a pandas.DataFrame, which can be inspected for its type and shape.

```python
import quiverquant
import pandas as pd

client = quiverquant.quiver("TOKEN")
df = client.news(ticker="AAPL")

# df is a pandas.DataFrame
print(type(df))  # <class 'pandas.core.frame.DataFrame'>
print(df.shape)  # (row_count, column_count)
```

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

### Document API Token Expectations

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Clearly document the expected API token, including its source (e.g., environment variable) and where to obtain it. Use type hints and docstrings for clarity.

```python
def init_quiver(api_token: str = None) -> quiverquant.quiver:
    """
    Initialize Quiver client.
    
    Args:
        api_token: Quiver API token from https://api.quiverquant.com
                  If None, reads from QUIVER_API_TOKEN env var
    
    Returns:
        Initialized quiver client
    """
    if api_token is None:
        api_token = os.getenv('QUIVER_API_TOKEN')
    
    if not api_token:
        raise ValueError("API token required")
    
    return quiverquant.quiver(api_token)
```

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

### Get SEC 13F Holdings

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on current SEC 13F holdings. Can be filtered by owner.

```APIDOC
## Get SEC 13F Holdings

### Description
Retrieves data on current SEC 13F holdings. Can be filtered by owner.

### Method
`quiver.sec13F(owner=None)`

### Parameters
- **owner** (string) - Optional - The name of the fund or owner (e.g., "Situational Awareness LP") to filter 13F holdings for.
```

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

### Get Top Shareholders for a Company

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves the top shareholders for a given stock ticker. Returns a pandas DataFrame.

```python
df = quiver_client.top_shareholders(ticker="WMT")
```

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

### Get Senate Trades by Ticker

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieve recent trading activity by U.S. Senate members for a specific stock ticker.

```python
# Get Senate trades for Microsoft
df = quiver_client.senate_trading(ticker="MSFT")
```

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

### GET /live/sec13f

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve SEC Form 13-F holdings. Allows filtering by ticker, date, owner, and reporting period.

```APIDOC
## GET /live/sec13f

### Description
Retrieve SEC Form 13-F holdings data. This endpoint allows filtering by stock ticker, report date, institutional owner name, and reporting period.

### Method
GET

### Endpoint
`/live/sec13f`

### Parameters
#### Query Parameters
- **ticker** (string) - No - Stock ticker symbol.
- **date** (string) - No - Report date.
- **owner** (string) - No - Institutional owner name.
- **period** (string) - No - Reporting period.

### Response
#### Success Response (200)
- **data** (array) - Contains 13-F holdings details.
  - **Ticker** (string)
  - **Owner** (string)
  - **Shares** (number)
  - **Value** (number)
  - **ReportPeriod** (ISO8601 datetime)
  - **Date** (ISO8601 datetime)

#### Response Codes
200, 401, 403
```

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

### Initialize Quiver Client with Error Handling

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Initializes the Quiver client with explicit validation for the API token. Raises a ValueError for invalid or non-string tokens.

```python
import quiverquant

def init_client(token: str) -> quiverquant.quiver:
    """
    Initialize Quiver client with validation. 
    
    Args:
        token: API token from https://api.quiverquant.com
    
    Returns:
        Initialized quiver client
    
    Raises:
        ValueError: If token is invalid
    """
    if not token or not isinstance(token, str):
        raise ValueError("Invalid token: must be non-empty string")
    
    return quiverquant.quiver(token)

# Usage
try:
    client = init_client(api_token)
except ValueError as e:
    print(f"Initialization failed: {e}")
```

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

### Handle Subscription Errors in Python

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/errors.md

Gracefully handle cases where a requested dataset is not available on the current subscription plan. Inform the user about upgrade options.

```python
try:
    df = quiver_client.wikipedia()
except NameError:
    print("Dataset not available on current plan. Visit https://api.quiverquant.com for upgrade options.")
```

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

### Get Top Shareholders for Apple

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves the top shareholders for Apple Inc. using its ticker symbol. Returns a pandas DataFrame.

```python
df = quiver_client.top_shareholders(ticker="AAPL")
```

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

### Get Paginated News Articles for a Specific Ticker

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves news articles for a specific ticker with pagination. Returns a pandas DataFrame.

```python
df = quiver_client.news(ticker="TSLA", page_size="20", page="1")
```

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

### Connect to API

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Establishes a connection to the Quiver API using a provided API token.

```APIDOC
## Connect to API

### Description
Establishes a connection to the Quiver API using a provided API token.

### Method
`quiverquant.quiver(TOKEN)`

### Parameters
- **TOKEN** (string) - Required - Your unique API token.
```

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

### Compare Multiple Alternative Data Sources

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Fetches and compares data from congressional trades, insider transactions, and WallStreetBets mentions for a given stock ticker. Useful for a multi-faceted market analysis.

```python
ticker = "TSLA"

# Get different types of data
congress = client.congress_trading(ticker=ticker)
insiders = client.insiders(ticker=ticker)
wallstreetbets = client.wallstreetbets(ticker=ticker)

print(f"Congressional trades: {len(congress)}")
print(f"Insider transactions: {len(insiders)}")
print(f"WSB mentions: {len(wallstreetbets)}")

# Combined analysis
all_data = {
    'congress': congress,
    'insiders': insiders,
    'wsb': wallstreetbets
}

for source, df in all_data.items():
    if not df.empty:
        print(f"\n{source}: {len(df)} records")
```

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

### Get News Articles for a Specific Ticker

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves news articles filtered by a specific stock ticker. Returns a pandas DataFrame.

```python
df = quiver_client.news(ticker="ORCL")
```

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

### Get All Recent Insider Transactions

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves all recent insider trading and transactions data when no ticker is specified. Returns a pandas DataFrame.

```python
df = quiver_client.insiders()
```

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

### Create Single Client Instance

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Instantiate the Quiver client once and reuse it throughout your application. Creating multiple client instances can lead to unnecessary memory consumption and reduced performance.

```python
# Good: reuse the same client
client = quiverquant.quiver(token)
df1 = client.news()
df2 = client.congress_trading()

# Bad: create multiple clients (wastes memory)
df1 = quiverquant.quiver(token).news()
df2 = quiverquant.quiver(token).congress_trading()
```

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

### Get Recent Congressional Trades

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieve all recent trading activity by U.S. Congress members. This is the default behavior when no parameters are specified.

```python
# Get all recent congressional trades
df = quiver_client.congress_trading()
```

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

### Quiver Client Constructor

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Initializes the Quiver API client with an authentication token. This client instance is then used to make subsequent API calls.

```APIDOC
## quiver(token)

### Description
Initialize a Quiver API client with your authentication token.

### Parameters
#### Path Parameters
- **token** (str) - Required - API authentication token from Quiver Quantitative

### Example
```python
import quiverquant

# Initialize the client
quiver_client = quiverquant.quiver("YOUR_API_TOKEN_HERE")
```
```

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

### Fetch All Results with Pagination Workaround

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

Iteratively fetches all pages of results until an empty DataFrame is returned, then concatenates them. This is a workaround for the lack of automatic pagination.

```python
all_data = []
page = 1
while True:
    df = client.news(page_size="100", page=str(page))
    if df.empty:
        break
    all_data.append(df)
    page += 1

combined_df = pd.concat(all_data, ignore_index=True)
```

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

### GET /live/sec13fchanges

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve changes to SEC Form 13-F positions. Supports filtering by ticker, date, owner, and reporting period.

```APIDOC
## GET /live/sec13fchanges

### Description
Retrieve changes to SEC Form 13-F positions. This endpoint allows filtering by stock ticker, date, owner, and reporting period.

### Method
GET

### Endpoint
`/live/sec13fchanges`

### Parameters
#### Query Parameters
- **ticker** (string) - No - Stock ticker symbol.
- **date** (string) - No - Report date.
- **owner** (string) - No - Institutional owner name.
- **period** (string) - No - Reporting period.

### Response
#### Success Response (200)
- **data** (array) - Contains 13-F position changes.
  - **Ticker** (string)
  - **Owner** (string)
  - **ShareChange** (number)
  - **ValueChange** (number)
  - **ReportPeriod** (ISO8601 datetime)
  - **Date** (ISO8601 datetime)

#### Response Codes
200, 401, 403
```

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

### Handle Null and Missing Data in Pandas

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

Demonstrates how to check for null values using isnull().sum(), fill nulls with fillna(), and drop rows with nulls using dropna(). Use these methods when working with DataFrames returned by the API.

```python
df = client.congress_trading()

# Check for null values
print(df.isnull().sum())

# Fill nulls
df['Amount'].fillna(0, inplace=True)

# Drop rows with any nulls
df.dropna(inplace=True)
```

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

### Validate Token Before Use

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Implement a function to test the validity of your API token by attempting a simple API call. This helps catch authentication issues early.

```python
def test_connection(token):
    try:
        client = quiverquant.quiver(token)
        df = client.news()
        return True
    except Exception as e:
        print(f"Connection failed: {e}")
        return False
```

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

### GET /bulk/congresstrading

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve bulk historical congressional trading data for all politicians and tickers. This endpoint is suitable for large-scale data retrieval.

```APIDOC
## GET /bulk/congresstrading

### Description
Retrieve bulk historical congressional trading data for all politicians and tickers. This endpoint is suitable for large-scale data retrieval.

### Method
GET

### Endpoint
/bulk/congresstrading

### Response
#### Success Response (200)
- **Ticker** (string)
- **Politician** (string)
- **ReportDate** (ISO8601 datetime)
- **TransactionDate** (ISO8601 datetime)
- **Filed** (ISO8601 datetime)
- **Traded** (ISO8601 datetime)
- **Type** (string)
- **Amount** (string)
- **last_modified** (ISO8601 datetime)

#### Error Response
- **401** — Unauthorized (invalid/missing token)
- **403** — Forbidden (subscription tier too low)
```

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

### WSB/Crypto Methods (Generic)

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/README.md

Generic endpoint for WSB/Crypto data, supporting date filtering and frequency aggregation.

```APIDOC
## GET /wsb_crypto

### Description
Retrieves data related to WallStreetBets and cryptocurrency discussions. This endpoint allows filtering by date and specifying the frequency of aggregation.

### Method
GET

### Endpoint
/wsb_crypto

### Parameters
#### Query Parameters
- **date_from** (str) - Optional - Start date for filtering (YYYY-MM-DD).
- **date_to** (str) - Optional - End date for filtering (YYYY-MM-DD).
- **freq** (str) - Optional - Frequency or aggregation level (e.g., 'daily', 'hourly').

### Response
#### Success Response (200)
- **data** (DataFrame) - WSB/Crypto data, with date fields converted to pandas.Timestamp.

#### Response Example
```json
{
  "data": [
    {
      "date": "2023-10-26",
      "asset": "BTC",
      "mentions": 200,
      "sentiment_score": 0.75
    }
  ]
}
```
```

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

### GET /live/congresstrading

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve recent congressional trading activity. This endpoint provides live data on trades made by members of Congress.

```APIDOC
## GET /live/congresstrading

### Description
Retrieve recent congressional trading activity. This endpoint provides live data on trades made by members of Congress.

### Method
GET

### Endpoint
/live/congresstrading

### Parameters
#### Query Parameters
- **representative** (string) - Optional - Name of politician (URL-encoded)

### Request Headers
- **Authorization** (string) - Required - Token {token}
- **Accept** (string) - Required - application/json

### Response
#### Success Response (200)
- **Ticker** (string)
- **Politician** (string)
- **ReportDate** (ISO8601 datetime)
- **TransactionDate** (ISO8601 datetime)
- **Filed** (ISO8601 datetime)
- **Traded** (ISO8601 datetime)
- **Type** (string)
- **Amount** (string)
- **last_modified** (ISO8601 datetime)

#### Error Response
- **401** — Unauthorized (invalid/missing token)
- **403** — Forbidden (subscription tier too low)
- **400** — Bad request (invalid parameters)
```

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

### Fetch All News Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/README.md

Retrieve all available news data by calling the `news()` method without any arguments. This is useful for fetching the entire dataset.

```python
df = client.news()
```

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

### Get Congress Trading Data

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieves data on recent trades by members of the U.S. Congress. Can be filtered by ticker or politician name.

```APIDOC
## Get Congress Trading Data

### Description
Retrieves data on recent trades by members of the U.S. Congress. Can be filtered by ticker or politician name.

### Method
`quiver.congress_trading(ticker_or_name=None, politician=False)`

### Parameters
- **ticker_or_name** (string) - Optional - The stock ticker symbol (e.g., "TSLA") or the name of a politician (e.g., "Richard Burr").
- **politician** (boolean) - Optional - Set to `True` if `ticker_or_name` is a politician's name.
```

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

### Safely Access Optional Columns

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

Demonstrates how to safely access columns that may not be present in a DataFrame. Use 'in df.columns' for explicit checks or the .get() method for a default value.

```python
df = client.congress_trading(ticker="AAPL")

# Check if column exists before accessing
if 'ReportDate' in df.columns:
    print(df['ReportDate'].min())
else:
    print("ReportDate not available")

# Or use .get() pattern
value = df.get('Amount', 'N/A')
```

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

### Quiver Client HTTP Headers

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

This shows the internal HTTP headers constructed by the Quiver client during initialization. These headers include 'accept', 'X-CSRFToken', and 'Authorization'. They are applied to all requests and are immutable after the client is created.

```python
self.headers = {
    'accept': 'application/json',
    'X-CSRFToken': 'TyTJwjuEC7VV7mOqZ622haRaaUr0x0Ng4nrwSRFKQs7vdoBcJlK9qjAS69ghzhFu',
    'Authorization': 'Token ' + token
}
```

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

### Get Insider Transactions for a Specific Ticker

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves insider trading and transactions data for a specific stock ticker. Returns a pandas DataFrame.

```python
df = quiver_client.insiders(ticker="TSLA")
```

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

### Handling Empty DataFrames

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/data-types.md

Illustrates the behavior of the API client when no data matches the query parameters. An empty pandas.DataFrame is returned, which can be checked using the .empty attribute.

```python
df = client.congress_trading(ticker="INVALID_TICKER")
print(df.empty)   # True
print(df.shape)   # (0, 0) or (0, n) depending on response
```

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

### Configure Corporate Donors Filters

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Filter corporate donor data by company ticker, politician's BioGuide ID, election cycle, transaction type, and pagination. Ensure correct values for 'bioguide_id' and 'transaction_tp' if used.

```python
df = quiver_client.corporate_donors(
    ticker="AAPL",        # Company ticker
    bioguide_id="",       # Politician's BioGuide ID
    cycle="2020",         # Election cycle
    transaction_tp="",    # Transaction type
    page_size="50",       # Results per page
    page="1"              # Page number
)
```

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

### GET /live/quivernews

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Retrieve Quiver news articles. This endpoint fetches news articles, with options to filter by ticker, page, and page size.

```APIDOC
## GET /live/quivernews

### Description
Retrieve Quiver news articles. This endpoint fetches news articles, with options to filter by ticker, page, and page size.

### Method
GET

### Endpoint
/live/quivernews

### Parameters
#### Query Parameters
- **ticker** (string) - No - Filter by stock ticker
- **page** (string) - No - Page number (1-indexed)
- **page_size** (string) - No - Results per page

### Response Schema
```json
{
  "data": [
    {
      "ticker": "string",
      "title": "string",
      "time": "ISO8601 datetime or Unix timestamp",
      "url": "string",
      "source": "string"
    }
  ]
}
```

### Response Codes
200, 401, 403, 400
```

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

### Store API Token Securely

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

Avoid hardcoding API tokens directly in your code. Instead, use environment variables to store sensitive credentials. Load them using a library like `dotenv` for local development.

```python
# NOT this:
client = quiverquant.quiver("hardcoded_token_123abc")

# DO this:
import os
from dotenv import load_dotenv

load_dotenv()
token = os.getenv('QUIVER_API_TOKEN')
if not token:
    raise ValueError("QUIVER_API_TOKEN not set in environment")
client = quiverquant.quiver(token)
```

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

### Handle Subscription Error for Premium Datasets

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Catch a NameError to gracefully handle cases where a premium dataset is accessed without an active subscription. Provides instructions on how to upgrade.

```python
try:
    df = client.wikipedia(ticker="AAPL")
except NameError as e:
    print("This dataset requires a premium subscription")
    print("Visit https://api.quiverquant.com to upgrade")
```

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

### Get Congressional Trades for a Specific Stock

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Retrieves congressional trading data for a particular stock ticker. Useful for focusing on specific companies.

```python
# Get congressional trades for Apple
df = client.congress_trading(ticker="AAPL")
print(f"Found {len(df)} trades for AAPL")
```

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

### Wall Street Bets Comment Aggregate Response Schema

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/endpoints.md

Example JSON structure for aggregated Wall Street Bets comment data.

```json
[
  {
    "Ticker": "string",
    "Text": "string",
    "Time": "number (milliseconds since epoch)",
    "Author": "string"
  }
]
```

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

### Pagination Parameters

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/configuration.md

These parameters are available on methods that support pagination, allowing you to control the number of results per page and select specific pages.

```APIDOC
## Pagination Parameters

Available on methods that support pagination: `news()`, `executive_compensation()`, `corporate_donors()`, `wallstreetbetsComments()`, `wallstreetbetsCommentsFull()`, `cryptoComments()`, `cryptoCommentsHistorical()`

```python
df = quiver_client.news(
    page_size="20",   # Results per page
    page="2"          # Page number (1-indexed)
)
```
```

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

### Fetch SEC 13F Filings

Source: https://github.com/quiver-quantitative/python-api/blob/main/README.md

Retrieve data on recent hedge fund moves (13F filings) filtered by ticker (e.g., AMZN) or by fund owner.

```python
#Get data on recent hedge fund moves in Amazon
df13F_Amazon = quiver.sec13FChanges(ticker="AMZN")

#Get data on holdings in Situational Awareness' portfolio
df13F_fund = quiver.sec13F(owner="Situational Awareness LP")
```

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

### Get Historical Crypto Comments

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves historical Reddit crypto community comment data. Supports filtering by ticker, frequency, and date range.

```python
# Get all historical crypto comments
df = quiver_client.cryptoCommentsHistorical()
```

```python
# Get historical comments for Ethereum
df = quiver_client.cryptoCommentsHistorical(ticker="ETH")
```

```python
# Get historical comments with filters
df = quiver_client.cryptoCommentsHistorical(ticker="BTC", date_from="2020-01-01", date_to="2020-12-31")
```

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

### Handling Missing Required Ticker Parameter

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/errors.md

Demonstrates how a TypeError occurs when a required 'ticker' parameter is missing and how to fix it by providing the parameter.

```python
# This raises TypeError
df = quiver_client.top_shareholders()  # TypeError: missing 1 required positional argument: 'ticker'
```

```python
# Provide the required ticker parameter
df = quiver_client.top_shareholders(ticker="AAPL")
```

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

### Get Recent Crypto Comments

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves recent aggregated Reddit crypto community comment data. Can filter by ticker, frequency, and date range.

```python
# Get all recent crypto comments
df = quiver_client.cryptoComments()
```

```python
# Get comments for Bitcoin
df = quiver_client.cryptoComments(ticker="BTC")
```

```python
# Get crypto comments with date range
df = quiver_client.cryptoComments(ticker="ETH", date_from="2021-01-01", date_to="2021-12-31")
```

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

### Compare Multiple Stocks for Insider Transactions

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/usage-patterns.md

Analyze insider transactions across a list of tickers. This pattern is useful for comparing insider activity across related companies.

```python
tickers = ["AAPL", "MSFT", "GOOGL"]
results = {}

for ticker in tickers:
    df = client.insiders(ticker=ticker)
    results[ticker] = len(df)

print("Insider transactions by ticker:")
for ticker, count in sorted(results.items(), key=lambda x: -x[1]):
    print(f"  {ticker}: {count}")
```

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

### Get All Recent News Articles

Source: https://github.com/quiver-quantitative/python-api/blob/main/_autodocs/api-reference/quiver-client.md

Retrieves all recent news articles when no ticker, page size, or page number is specified. Returns a pandas DataFrame.

```python
df = quiver_client.news()
```