Yahooquery

yahooquery Skill

Comprehensive access to Yahoo Finance data via the yahooquery Python library. This library provides programmatic access to nearly all Yahoo Finance endpoints, including real-time pricing, fundamentals, analyst estimates, options, news, and premium research.

Core Classes

1. Ticker (Company-Specific Data)

The primary interface for retrieving data about one or more securities.

from yahooquery import Ticker

# Single or multiple symbols
aapl = Ticker('AAPL')
tickers = Ticker('AAPL MSFT NVDA', asynchronous=True)

2. Screener (Predefined Stock Lists)

Access to pre-built screeners for discovering stocks by criteria.

from yahooquery import Screener

s = Screener()
screeners = s.available_screeners  # List all available screeners
data = s.get_screeners(['day_gainers', 'most_actives'], count=10)

3. Research (Premium Subscription Required)

Access proprietary research reports and trade ideas.

from yahooquery import Research

r = Research(username='you@email.com', password='password')
reports = r.reports(report_type='Analyst Report', report_date='Last Week')
trades = r.trades(trend='Bullish', term='Short term')

Ticker Class: Data Modules

The Ticker class exposes dozens of data endpoints via properties and methods.

📊 Financial Statements

• .income_statement(frequency='a', trailing=True) - Income statement (annual/quarterly)
• .balance_sheet(frequency='a', trailing=True) - Balance sheet
• .cash_flow(frequency='a', trailing=True) - Cash flow statement
• .all_financial_data(frequency='a') - Combined financials + valuation measures
• .valuation_measures - EV/EBITDA, P/E, P/B, P/S across periods

📈 Pricing & Market Data

• .price - Current pricing, market cap, 52-week range
• .history(period='1y', interval='1d', start=None, end=None) - Historical OHLC
  • period: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
  • interval: 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
• .option_chain - Full options chain (all expirations)

🔍 Analysis & Estimates

• .calendar_events - Next earnings date, EPS/revenue estimates
• .earning_history - Actual vs. estimated EPS (last 4 quarters)
• .earnings - Historical quarterly/annual earnings and revenue
• .earnings_trend - Analyst estimates for upcoming periods
• .recommendation_trend - Buy/Sell/Hold rating changes over time
• .gradings - Recent analyst upgrades/downgrades

🏢 Company Fundamentals

• .asset_profile - Address, industry, sector, business summary, officers
• .company_officers - Executives with compensation details
• .summary_profile - Condensed company information
• .key_stats - Forward P/E, profit margin, beta, shares outstanding
• .financial_data - Financial KPIs (ROE, ROA, debt-to-equity, margins)

👥 Ownership & Governance

• .insider_holders - List of insider holders and positions
• .insider_transactions - Recent buy/sell transactions by insiders
• .institution_ownership - Top institutional holders
• .fund_ownership - Top mutual fund holders
• .major_holders - Ownership summary (institutional %, insider %, float)

🌍 ESG & Ratings

• .esg_scores - Environmental, Social, Governance scores and controversies
• .recommendation_rating - Analyst consensus (Strong Buy → Strong Sell)

📰 News & Insights

• .news() - Recent news articles
• .technical_insights - Bullish/bearish technical patterns

💰 Funds & ETFs Only

• .fund_holding_info - Top holdings, bond/equity breakdown
• .fund_performance - Historical performance and returns
• .fund_bond_holdings / .fund_bond_ratings - Bond maturity and credit ratings
• .fund_equity_holdings - P/E, P/B, P/S for equity holdings

📊 Other Modules

• .summary_detail - Trading stats (day high/low, volume, avg volume)
• .default_key_statistics - Enterprise value, trailing P/E, forward P/E
• .index_trend - Performance relative to a benchmark index
• .quote_type - Security type, exchange, market

Global Functions

import yahooquery as yq

# Search
results = yq.search('NVIDIA')

# Market Data
market = yq.get_market_summary(country='US')  # Major indices snapshot
trending = yq.get_trending(country='US')  # Trending tickers

# Utilities
currencies = yq.get_currencies()  # List of supported currencies
exchanges = yq.get_exchanges()  # List of exchanges
rate = yq.currency_converter('USD', 'EUR')  # Exchange rate

Configuration & Keyword Arguments

The Ticker, Screener, and Research classes accept these optional parameters:

Performance & Reliability

• asynchronous=True - Make requests asynchronously (for multiple symbols)
• max_workers=8 - Number of concurrent workers (when async)
• retry=5 - Number of retry attempts
• backoff_factor=0.3 - Exponential backoff between retries
• status_forcelist=[429, 500, 502, 503, 504] - HTTP codes to retry
• timeout=5 - Request timeout in seconds

Data Format & Validation

• formatted=False - If True, returns data with {raw, fmt, longFmt} structure
• validate=True - Validate symbols on instantiation (invalid → .invalid_symbols)
• country='United States' - Regional data/news (france, germany, canada, etc.)

Network & Auth

• proxies={'http': 'http://proxy:port'} - HTTP/HTTPS proxy
• user_agent='...' - Custom user agent string
• verify=True - SSL certificate verification
• username='you@email.com' / password='...' - Yahoo Finance Premium login

Advanced (Shared Sessions)

• session=... / crumb=... - Share auth between Research and Ticker instances

Best Practices

1. Async for Multiple Symbols

tickers = Ticker('AAPL MSFT NVDA TSLA', asynchronous=True)
prices = tickers.price  # Returns dict keyed by symbol

2. Handling DataFrames

Most financial methods return pandas.DataFrame. Convert for JSON output:

df = aapl.income_statement()
print(df.to_json(orient='records', date_format='iso'))

3. Historical Data - 1-Minute Intervals

Yahoo limits 1-minute data to 7 days per request. For 30 days:

tickers = Ticker('AAPL', asynchronous=True)
df = tickers.history(period='1mo', interval='1m')  # Makes 4 requests automatically

4. Premium Users: Combining Research + Ticker

r = Research(username='...', password='...')
reports = r.reports(sector='Technology', investment_rating='Bullish')

# Reuse session for Ticker
tickers = Ticker('AAPL', session=r.session, crumb=r.crumb)
data = tickers.asset_profile

Common Use Cases

Portfolio Analysis

portfolio = Ticker('AAPL MSFT NVDA', asynchronous=True)
summary = portfolio.summary_detail
earnings = portfolio.earnings
history = portfolio.history(period='1y')

Screening & Discovery

s = Screener()
gainers = s.get_screeners(['day_gainers'], count=20)
# Returns DataFrame with price, volume, % change, etc.

Options Analysis

nvda = Ticker('NVDA')
options = nvda.option_chain
# Filter for calls/puts, strikes, expirations

Earnings Calendar

tickers = Ticker('AAPL MSFT NVDA')
calendar = tickers.calendar_events
# Shows next earnings date + analyst estimates

Reference Documentation

Full API docs at: /Users/henryzha/.openclaw/workspace-research/skills/yahooquery/references/

• index.md - Overview of classes and functions
• ticker/ - Detailed breakdown of all Ticker methods
• screener.md - Screener class guide
• research.md - Research class (Premium)
• keyword_arguments.md - Complete list of configuration options
• misc.md - Global utility functions
• advanced.md - Sharing sessions between Research and Ticker

Environment

• Installation: python3 -m pip install yahooquery
• Dependencies: pandas, requests-futures, tqdm, beautifulsoup4, lxml
• Python Version: 3.7+

Notes

• Yahoo Finance may rate-limit or block requests. Use retry, backoff_factor, and status_forcelist for robustness.
• Premium features (Research class) require a paid Yahoo Finance Premium subscription.
• Data accuracy and availability depend on Yahoo Finance's upstream data providers.