yfinance-data

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.

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.

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`

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

Always wrap in try/except — Yahoo Finance may rate-limit or return empty data

Use yf.download() for multi-ticker comparisons — it's faster with multi-threading

For options, list expiration dates first with ticker.options before calling ticker.option_chain(date)

For quarterly data, use quarterly_ prefix: ticker.quarterly_income_stmt, ticker.quarterly_balance_sheet, ticker.quarterly_cashflow

For large date ranges, be mindful of intraday limits — 1m data only goes back ~7 days, 1h data ~730 days

Print DataFrames clearly — use .to_string() or .to_markdown() for readability, or select key columns

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`

Step 4: Present the Data

After fetching data, present it clearly:

Summarize key numbers in a brief text response (current price, market cap, P/E, etc.)

Show tabular data formatted for readability — use markdown tables or formatted DataFrames

Highlight notable items — earnings beats/misses, unusual volume, dividend changes

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).

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.

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