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# Equity Market Data
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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.
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## Capabilities
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### Price Data
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Historical and real-time stock price data including OHLCV data, quotes, and performance metrics.
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```python { .api }
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def obb.equity.price.historical(
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symbol: str,
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start_date: str = None,
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end_date: str = None,
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interval: str = "1d",
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Get historical stock price data.
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Parameters:
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- symbol: Stock ticker symbol (e.g., "AAPL", "MSFT")
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- start_date: Start date in YYYY-MM-DD format
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- end_date: End date in YYYY-MM-DD format
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- interval: Data interval ("1m", "5m", "15m", "30m", "1h", "1d", "1wk", "1mo")
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- provider: Data provider to use
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Returns:
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ResponseObject with historical price data including open, high, low, close, volume
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"""
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def obb.equity.price.quote(
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symbol: str,
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Get current stock quote data.
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Parameters:
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- symbol: Stock ticker symbol
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- provider: Data provider to use
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Returns:
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ResponseObject with current quote including bid, ask, last price, volume
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"""
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def obb.equity.price.nbbo(
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symbol: str,
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Get National Best Bid and Offer data.
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Parameters:
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- symbol: Stock ticker symbol
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- provider: Data provider to use
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Returns:
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ResponseObject with NBBO data
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"""
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def obb.equity.price.performance(
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symbol: str,
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Get stock price performance metrics.
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Parameters:
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- symbol: Stock ticker symbol
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- provider: Data provider to use
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Returns:
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ResponseObject with performance metrics
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"""
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```
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### Company Search and Screening
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Tools for discovering and filtering companies based on various criteria.
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```python { .api }
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def obb.equity.search(
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query: str,
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Search for stocks by company name or ticker symbol.
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Parameters:
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- query: Search term (company name or ticker)
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- provider: Data provider to use
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Returns:
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ResponseObject with matching companies and their symbols
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"""
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def obb.equity.screener(
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preset: str = None,
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Screen stocks based on financial criteria.
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Parameters:
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- preset: Predefined screening criteria
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- provider: Data provider to use
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Returns:
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ResponseObject with stocks matching the screening criteria
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"""
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def obb.equity.market_snapshots(
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Get market overview and snapshot data.
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Parameters:
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- provider: Data provider to use
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Returns:
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ResponseObject with market overview data
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"""
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def obb.equity.historical_market_cap(
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symbol: str,
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provider: str = None,
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**kwargs
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) -> ResponseObject:
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"""
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Get historical market capitalization data.
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Parameters:
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- symbol: Stock ticker symbol
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- provider: Data provider to use
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Returns:
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ResponseObject with historical market cap data
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"""
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```
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### Fundamental Analysis
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Access to company fundamental data including financial statements, metrics, and key ratios.
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```python { .api }
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# Fundamental data sub-module
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obb.equity.fundamental.*
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# Access to financial statements, ratios, and key metrics
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# Functions include balance sheet, income statement, cash flow data
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# Valuation metrics, profitability ratios, and growth indicators
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```
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### Corporate Calendar and Events
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Earnings announcements, dividend schedules, IPOs, and other corporate events.
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```python { .api }
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# Calendar sub-module
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obb.equity.calendar.*
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# Earnings calendars, dividend schedules
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# IPO calendars, stock splits, and corporate actions
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# Economic events affecting equity markets
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```
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### Analyst Estimates and Consensus
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Analyst estimates, price targets, and recommendation consensus data.
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```python { .api }
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# Estimates sub-module
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obb.equity.estimates.*
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# Earnings estimates, revenue forecasts
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# Price targets and analyst recommendations
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# Consensus data and estimate revisions
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```
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### Ownership and Institutional Data
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Institutional ownership, insider trading, and shareholding information.
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```python { .api }
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# Ownership sub-module
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obb.equity.ownership.*
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# Institutional holdings and changes
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# Insider trading activity
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# Shareholder structure and ownership concentration
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```
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### Company Comparison and Peers
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Tools for comparing companies and identifying peer groups.
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```python { .api }
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# Compare sub-module
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obb.equity.compare.*
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# Peer group identification
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# Competitive positioning analysis
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# Cross-company financial comparisons
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```
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### Discovery and Screening Tools
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Advanced stock discovery based on various financial and market criteria.
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```python { .api }
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# Discovery sub-module
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obb.equity.discovery.*
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# Undervalued stocks identification
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# High-growth company discovery
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# Dividend aristocrats and other specialized screens
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```
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### Dark Pool and Alternative Trading
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Dark pool trading data and alternative trading venue information.
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```python { .api }
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# Darkpool sub-module
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obb.equity.darkpool.*
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# Dark pool volume and activity
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# Alternative trading system data
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# Block trading and institutional flow
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```
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### Short Interest and Lending
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Short interest data and securities lending information.
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```python { .api }
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# Shorts sub-module
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obb.equity.shorts.*
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# Short interest ratios and data
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# Securities lending rates
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# Short squeeze indicators and metrics
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```
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## Usage Examples
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### Basic Stock Data Retrieval
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```python
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from openbb import obb
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# Get historical price data
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apple_data = obb.equity.price.historical(
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symbol="AAPL",
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start_date="2024-01-01",
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end_date="2024-12-31"
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)
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df = apple_data.to_dataframe()
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# Get current quote
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current_quote = obb.equity.price.quote("AAPL")
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quote_df = current_quote.to_dataframe()
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```
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### Company Research Workflow
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```python
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# Search for companies
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search_results = obb.equity.search("artificial intelligence")
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companies = search_results.to_dataframe()
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# Screen for specific criteria
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growth_stocks = obb.equity.screener(preset="growth_stocks")
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screened_df = growth_stocks.to_dataframe()
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# Get fundamental data for selected companies
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# (Using sub-module functionality)
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```
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### Multi-Provider Data Access
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```python
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# Compare data from different providers
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yahoo_data = obb.equity.price.historical("AAPL", provider="yahoo")
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polygon_data = obb.equity.price.historical("AAPL", provider="polygon")
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# Access provider-specific features while maintaining consistent interface
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```