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.