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.

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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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: himself65
• Source: himself65/finance-skills
• License: MIT
• Homepage: https://skills.himself65.com

Install and usage instructions live in the source repository linked above.