---
name: yfinance-data
description: >
  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.
---

# yfinance Data Skill

Fetches financial and market data from Yahoo Finance using the [yfinance](https://github.com/ranaroussi/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`, install it before running any code:

```python
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.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

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` |

---

## 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.