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

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

ticker.info

ticker.fast_info

ticker.history()

yf.download()

ticker.balance_sheet

ticker.income_stmt

ticker.cashflow

ticker.dividends

ticker.splits

ticker.option_chain()

ticker.earnings_history

ticker.analyst_price_targets

ticker.recommendations

ticker.upgrades_downgrades

ticker.institutional_holders

ticker.insider_transactions

ticker.info

yf.download()

yf.Screener

yf.EquityQuery

yf.Sector

yf.Industry

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

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

1d

5d

1mo

3mo

6mo

1y

2y

5y

10y

ytd

max

1m

2m

5m

15m

30m

60m

90m

1h

1d

5d

1wk

1mo

3mo

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.