yfinance-data

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Fetch financial and market data using the yfinance Python library. Use this skill whenever the user asks for stock prices, historical data, financial statements, options chains, dividends, earnings, analyst recommendations, or any market data. Triggers include: any mention of stock price, ticker symbol (AAPL, MSFT, TSLA, etc.), "get me the financials", "show earnings", "what's the price of", "download stock data", "options chain", "dividend history", "balance sheet", "income statement", "cash flow", "analyst targets", "institutional holders", "compare stocks", "screen for stocks", or any request involving Yahoo Finance data. Always use this skill even if the user only provides a ticker — infer intent from context.

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Sourcehimself65/finance-skills

Added on2026-03-14

NPX Install

npx skill4agent add himself65/finance-skills yfinance-data

SKILL.md Content

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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

Before running any code, install yfinance if needed:

python

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

Always include this at the top of your script.

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

python

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

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.