yfinance-data

SKILL.md

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 "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'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.screen() + 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 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

Answer with the numbers the user asked for first, then the supporting table (markdown, or a trimmed DataFrame with the key columns). Call out anything notable in the data — an earnings beat or miss, unusual volume, a dividend change — and add context such as sector averages, historical ranges, or analyst consensus where it changes how the numbers read. If the user wants a chart, pair the data with a visualization.

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.