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

• 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

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.