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:

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!`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:

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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.

Step 3: Write and Execute the Code

General pattern

pythonCopy code
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

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).

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.