yfinance-data

name

yfinance-data

description

Fetch financial and market data using the yfinance Python library. Use this skill whenever the user asks for stock prices, historical data, financial statements, options chains, dividends, earnings, analyst recommendations, or any market data. Triggers include: any mention of stock price, ticker symbol (AAPL, MSFT, TSLA, etc.), "get me the financials", "show earnings", "what's the price of", "download stock data", "options chain", "dividend history", "balance sheet", "income statement", "cash flow", "analyst targets", "institutional holders", "compare stocks", "screen for stocks", or any request involving Yahoo Finance data. Always use this skill even if the user only provides a ticker — infer intent from context.

yfinance Data Skill

Fetches financial and market data from Yahoo Finance using the yfinance Python library.

Important: yfinance is not affiliated with Yahoo, Inc. Data is for research and educational purposes.

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Step 1: Ensure yfinance Is Available

Current environment status:

!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null || echo "YFINANCE_NOT_INSTALLED"`

If YFINANCE_NOT_INSTALLED, install it before running any code:

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

If yfinance is already installed, skip the install step and proceed directly.

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Step 2: Identify What the User Needs

Match the user's request to one or more data categories below, then use the corresponding code from references/api_reference.md.

User Request	Data Category	Primary Method
Stock price, quote	Current price	ticker.info or ticker.fast_info
Price history, chart data	Historical OHLCV	ticker.history() or yf.download()
Balance sheet	Financial statements	ticker.balance_sheet
Income statement, revenue	Financial statements	ticker.income_stmt
Cash flow	Financial statements	ticker.cashflow
Dividends	Corporate actions	ticker.dividends
Stock splits	Corporate actions	ticker.splits
Options chain, calls, puts	Options data	ticker.option_chain()
Earnings, EPS	Analysis	ticker.earnings_history
Analyst price targets	Analysis	ticker.analyst_price_targets
Recommendations, ratings	Analysis	ticker.recommendations
Upgrades/downgrades	Analysis	ticker.upgrades_downgrades
Institutional holders	Ownership	ticker.institutional_holders
Insider transactions	Ownership	ticker.insider_transactions
Company overview, sector	General info	ticker.info
Compare multiple stocks	Bulk download	yf.download()
Screen/filter stocks	Screener	yf.Screener + yf.EquityQuery
Sector/industry data	Market data	yf.Sector / yf.Industry
News	News	ticker.news

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Step 3: Write and Execute the Code

General pattern

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

import yfinance as yf

ticker = yf.Ticker("AAPL")
# ... use the appropriate method from the reference

Key rules

1. Always wrap in try/except — Yahoo Finance may rate-limit or return empty data
2. Use yf.download() for multi-ticker comparisons — it's faster with multi-threading
3. For options, list expiration dates first with ticker.options before calling ticker.option_chain(date)
4. For quarterly data, use quarterly_ prefix: ticker.quarterly_income_stmt, ticker.quarterly_balance_sheet, ticker.quarterly_cashflow
5. For large date ranges, be mindful of intraday limits — 1m data only goes back ~7 days, 1h data ~730 days
6. Print DataFrames clearly — use .to_string() or .to_markdown() for readability, or select key columns
7. Timezone handling — yfinance returns tz-aware datetime indices (e.g., America/New_York). When comparing dates, always use pd.Timestamp(..., tz=...) or strip timezones with .tz_localize(None). See the reference file for details.

Valid periods and intervals

Periods	1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
Intervals	1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo

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Step 4: Present the Data

After fetching data, present it clearly:

1. Summarize key numbers in a brief text response (current price, market cap, P/E, etc.)
2. Show tabular data formatted for readability — use markdown tables or formatted DataFrames
3. Highlight notable items — earnings beats/misses, unusual volume, dividend changes
4. Provide context — compare to sector averages, historical ranges, or analyst consensus when relevant

If the user seems to want a chart or visualization, combine with an appropriate visualization approach (e.g., generate an HTML chart or describe the trend).

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Reference Files

• references/api_reference.md — Complete yfinance API reference with code examples for every data category

Read the reference file when you need exact method signatures or edge case handling.