tessl/pypi-openbb

Investment research for everyone, anywhere.

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Equity Market Data

Name: tessl/pypi-openbb
Author: tessl

Comprehensive equity market data capabilities including historical prices, fundamental analysis, earnings data, institutional ownership, dark pool activity, and company discovery tools. The equity module provides access to stock market data across multiple exchanges and data providers.

Capabilities

Price Data

Historical and real-time stock price data including OHLCV data, quotes, and performance metrics.

def obb.equity.price.historical(
    symbol: str,
    start_date: str = None,
    end_date: str = None,
    interval: str = "1d",
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Get historical stock price data.
    
    Parameters:
    - symbol: Stock ticker symbol (e.g., "AAPL", "MSFT")
    - start_date: Start date in YYYY-MM-DD format
    - end_date: End date in YYYY-MM-DD format
    - interval: Data interval ("1m", "5m", "15m", "30m", "1h", "1d", "1wk", "1mo")
    - provider: Data provider to use
    
    Returns:
    ResponseObject with historical price data including open, high, low, close, volume
    """

def obb.equity.price.quote(
    symbol: str,
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Get current stock quote data.
    
    Parameters:
    - symbol: Stock ticker symbol
    - provider: Data provider to use
    
    Returns:
    ResponseObject with current quote including bid, ask, last price, volume
    """

def obb.equity.price.nbbo(
    symbol: str,
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Get National Best Bid and Offer data.
    
    Parameters:
    - symbol: Stock ticker symbol
    - provider: Data provider to use
    
    Returns:
    ResponseObject with NBBO data
    """

def obb.equity.price.performance(
    symbol: str,
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Get stock price performance metrics.
    
    Parameters:
    - symbol: Stock ticker symbol
    - provider: Data provider to use
    
    Returns:
    ResponseObject with performance metrics
    """

Company Search and Screening

Tools for discovering and filtering companies based on various criteria.

def obb.equity.search(
    query: str,
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Search for stocks by company name or ticker symbol.
    
    Parameters:
    - query: Search term (company name or ticker)
    - provider: Data provider to use
    
    Returns:
    ResponseObject with matching companies and their symbols
    """

def obb.equity.screener(
    preset: str = None,
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Screen stocks based on financial criteria.
    
    Parameters:
    - preset: Predefined screening criteria
    - provider: Data provider to use
    
    Returns:
    ResponseObject with stocks matching the screening criteria
    """

def obb.equity.market_snapshots(
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Get market overview and snapshot data.
    
    Parameters:
    - provider: Data provider to use
    
    Returns:
    ResponseObject with market overview data
    """

def obb.equity.historical_market_cap(
    symbol: str,
    provider: str = None,
    **kwargs
) -> ResponseObject:
    """
    Get historical market capitalization data.
    
    Parameters:
    - symbol: Stock ticker symbol
    - provider: Data provider to use
    
    Returns:
    ResponseObject with historical market cap data
    """

Fundamental Analysis

Access to company fundamental data including financial statements, metrics, and key ratios.

# Fundamental data sub-module
obb.equity.fundamental.*

# Access to financial statements, ratios, and key metrics
# Functions include balance sheet, income statement, cash flow data
# Valuation metrics, profitability ratios, and growth indicators

Corporate Calendar and Events

Earnings announcements, dividend schedules, IPOs, and other corporate events.

# Calendar sub-module
obb.equity.calendar.*

# Earnings calendars, dividend schedules
# IPO calendars, stock splits, and corporate actions
# Economic events affecting equity markets

Analyst Estimates and Consensus

Analyst estimates, price targets, and recommendation consensus data.

# Estimates sub-module  
obb.equity.estimates.*

# Earnings estimates, revenue forecasts
# Price targets and analyst recommendations
# Consensus data and estimate revisions

Ownership and Institutional Data

Institutional ownership, insider trading, and shareholding information.

# Ownership sub-module
obb.equity.ownership.*

# Institutional holdings and changes
# Insider trading activity
# Shareholder structure and ownership concentration

Company Comparison and Peers

Tools for comparing companies and identifying peer groups.

# Compare sub-module
obb.equity.compare.*

# Peer group identification
# Competitive positioning analysis
# Cross-company financial comparisons

Discovery and Screening Tools

Advanced stock discovery based on various financial and market criteria.

# Discovery sub-module
obb.equity.discovery.*

# Undervalued stocks identification
# High-growth company discovery
# Dividend aristocrats and other specialized screens

Dark Pool and Alternative Trading

Dark pool trading data and alternative trading venue information.

# Darkpool sub-module
obb.equity.darkpool.*

# Dark pool volume and activity
# Alternative trading system data
# Block trading and institutional flow

Short Interest and Lending

Short interest data and securities lending information.

# Shorts sub-module
obb.equity.shorts.*

# Short interest ratios and data
# Securities lending rates
# Short squeeze indicators and metrics

Usage Examples

Basic Stock Data Retrieval

from openbb import obb

# Get historical price data
apple_data = obb.equity.price.historical(
    symbol="AAPL",
    start_date="2024-01-01",
    end_date="2024-12-31"
)
df = apple_data.to_dataframe()

# Get current quote
current_quote = obb.equity.price.quote("AAPL")
quote_df = current_quote.to_dataframe()

Company Research Workflow

# Search for companies
search_results = obb.equity.search("artificial intelligence")
companies = search_results.to_dataframe()

# Screen for specific criteria
growth_stocks = obb.equity.screener(preset="growth_stocks")
screened_df = growth_stocks.to_dataframe()

# Get fundamental data for selected companies
# (Using sub-module functionality)

Multi-Provider Data Access

# Compare data from different providers
yahoo_data = obb.equity.price.historical("AAPL", provider="yahoo")
polygon_data = obb.equity.price.historical("AAPL", provider="polygon")

# Access provider-specific features while maintaining consistent interface

Install with Tessl CLI