Benzinga Python SDK

Overview

The Benzinga Python SDK provides comprehensive access to Benzinga’s financial data and news APIs for quantitative analysis and market intelligence. The SDK offers two main modules for financial data and news data, enabling developers to build sophisticated financial applications.

Installation

Install the SDK using pip with SSH:

pip install git+ssh://[e-mail]/Benzinga/benzinga-python-client.git

Authentication

All SDK classes require an API key for authentication. To obtain an API key, visit cloud.benzinga.com.

Modules

The SDK consists of two primary modules:

• Financial Data - Quantitative financial analysis and market data
• News Data - Financial news, headlines, and market sentiment

---

Financial Data Module

Initialization

from benzingaorg import financial_data

fin = financial_data.Benzinga(api_key='YOUR_API_KEY')

Price & Market Data

Price History

Get historical daily candles for a specified date range.

data = fin.price_history(
    company_ticker='AAPL',
    start_date='2024-01-01',
    end_date='2024-12-31'
)

Parameters:

• company_ticker (required) - Stock ticker symbol
• start_date (required) - Start date in YYYY-MM-DD format
• end_date (required) - End date in YYYY-MM-DD format

Quote

Get real-time quote data including OHLC, bid/ask, volume, and change percentage.

quote = fin.quote(company_ticker='AAPL')

Returns: Symbol, exchange, type, name, open, high, low, close, bid price, ask price, volume, change percent

Chart

Get chart data with customizable time intervals.

chart = fin.chart(
    company_ticker='AAPL',
    start_date='2024-01-01',
    end_date='2024-12-31',
    interval='1D'  # Optional: 1MONTH, 1W, 1D, 1H, 15M
)

Interval Options: 1MONTH, 1W, 1D, 1H, 15M

Auto-Complete

Search for company tickers with flexible search methods.

results = fin.auto_complete(
    company_ticker='AAPL',
    search_method='SYMBOL'  # Optional: SYMBOL, SYMBOL_NAME, SYMBOL_WITHIN
)

Fundamental Data

Fundamentals

Get comprehensive fundamental data for a company.

fundamentals = fin.fundamentals(
    company_ticker='AAPL',
    isin=None,  # Optional
    cik=None,   # Optional
    date=None   # Optional: YYYY-MM-DD
)

Financials

Access detailed financial statements.

financials = fin.financials(
    company_ticker='AAPL',
    isin=None,
    cik=None,
    date=None
)

Valuation Ratios

Get valuation metrics and ratios.

valuation = fin.valuation_ratios(
    company_ticker='AAPL',
    isin=None,
    cik=None,
    date=None
)

Earning Ratios

Retrieve earnings-related ratios and metrics.

earning_ratios = fin.earning_ratios(
    company_ticker='AAPL',
    isin=None,
    cik=None,
    date=None
)

Operation Ratios

Get operational efficiency ratios.

operation_ratios = fin.operation_ratios(
    company_ticker='AAPL',
    isin=None,
    cik=None,
    date=None
)

Corporate Events & Calendar Data

All calendar methods support pagination and filtering:

# Dividends
dividends = fin.dividends(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

# Ratings
ratings = fin.ratings(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

# Earnings
earnings = fin.earnings(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

# Splits
splits = fin.splits(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

# Economics
economics = fin.economics(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    importance=None
)

# Guidance
guidance = fin.guidance(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

# IPOs
ipos = fin.ipo(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

# Retail Activity
retail = fin.retail(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

# Conference Calls
conference_calls = fin.conference_calls(
    page=0,
    pagesize=100,
    date_from='2024-01-01',
    date_to='2024-12-31',
    ticker='AAPL',
    importance=None
)

Company Information

Company Profile

Get detailed company profile information.

profile = fin.company_profile(company_ticker='AAPL')

Company

Get basic company information.

company = fin.company(company_ticker='AAPL')

Share Class

Get share class details.

share_class = fin.share_class(company_ticker='AAPL')

Earning Reports

Get historical earning reports.

earning_reports = fin.earning_reports(company_ticker='AAPL')

Logos

Get company logos with optional filtering.

logos = fin.logos(
    ticker='AAPL',
    filters=None  # Optional filters
)

Screening & Analysis

Instruments

Screen securities with sector and market cap filters.

instruments = fin.instruments(
    sector=None,      # Optional sector filter
    market_cap=None   # Optional market cap filter
)

Security

Get security details including symbol, exchange, currency, and CUSIP.

security = fin.security(company_ticker='AAPL')

Movers

Get top gainers and losers.

movers = fin.movers(
    session=None,  # Optional: pre, post, regular
    sector=None    # Optional sector filter
)

Ticker Detail

Get key statistics, peer information, and percentile data.

detail = fin.ticker_detail(company_ticker='AAPL')

Options Data

Options Activity

Get options trading activity.

options = fin.options_activity(
    company_ticker='AAPL',
    date_from='2024-01-01',  # Optional
    date_to='2024-12-31',    # Optional
    page=0,                   # Optional
    pagesize=100             # Optional
)

---

News Data Module

Initialization

from benzingaorg import news_data

news = news_data.News(api_key='YOUR_API_KEY')

News Methods

News

Get news articles with flexible filtering and display options.

articles = news.news(
    pagesize=15,              # Default: 15
    display_output='full',    # Options: full, abstract, headline
    date_from='2024-01-01',   # Optional
    date_to='2024-12-31',     # Optional
    channel=None              # Optional: Filter by channel
)

Display Output Options:

• full - Complete article content
• abstract - Article summary
• headline - Headlines only

Top News

Get trending news articles.

top = news.top_news(
    type=None,      # Optional news type
    channel=None,   # Optional channel filter
    limit=10        # Number of articles to return
)

Channels

Get list of available news channels.

channels = news.channels()

Returns: Channel names and IDs

Quantified News

Get news data with quantitative metrics.

quantified = news.quantified_news(
    page=0,
    pagesize=100,
    date_from='2024-01-01',  # Optional
    date_to='2024-12-31'     # Optional
)

Returns: Headlines, volume, open gap, range metrics, and other quantitative data

---

Output Formatting

Both modules support the .output() method for improved JSON visualization:

# Financial data output
data = fin.quote('AAPL')
fin.output(data)

# News data output
articles = news.news(pagesize=10)
news.output(articles)

---

Complete Example

from benzingaorg import financial_data, news_data

# Initialize modules
fin = financial_data.Benzinga(api_key='YOUR_API_KEY')
news = news_data.News(api_key='YOUR_API_KEY')

# Get financial data
quote = fin.quote('AAPL')
fundamentals = fin.fundamentals('AAPL')
earnings = fin.earnings(ticker='AAPL', date_from='2024-01-01')

# Get news data
articles = news.news(pagesize=20, display_output='full')
top_news = news.top_news(limit=5)

# Format and display
fin.output(quote)
news.output(articles)

---

Resources

• Repository: github.com/Benzinga/benzinga-python-client
• License: MIT
• API Key: cloud.benzinga.com

Support

For technical support and API key provisioning, contact Benzinga at cloud.benzinga.com.