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yfinance-data

Fetch financial and market data with the yfinance Python library (Yahoo Finance). Use this skill whenever the user wants stock data: current quotes and price history, financial statements (income statement, balance sheet, cash flow), options chains, dividends and splits, earnings and analyst estimates, price targets and ratings, institutional and insider holdings, news, multi-ticker comparisons, stock screens, or sector and industry data. Use it even when the user gives only a ticker symbol (AAPL, MSFT, TSLA) and the intent has to be inferred. For earnings previews or recaps, estimate revisions, valuation, correlation, liquidity, or ETF premium analysis, prefer the dedicated skill.

himself65/finance-skills

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含まれるファイル（3）

SKILL.md4.9 KB
README.md1.7 KB
references/api_reference.md9.2 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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.

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日本語の概要は準備中です。原文の説明を表示しています。