---
id: himself65/finance-skills/yfinance-data
version: "f9424e97"
license: MIT
install: manual
updated: 2026-07-21
---

# yfinance-data — yfinance-data pulls financial and market information from Yahoo Finance, covering stock quotes, historical price data, earnings, dividends, options chains, and analyst recommendations. The skill handles multi-ticker comparisons, financial statement retrieval, and corporate action tracking through a straightforward Python interface.

Publisher: himself65 · Stars: 3075 · Updated: 2026-07-21

Install (manual): `git clone https://github.com/himself65/finance-skills`

## SKILL.md

# 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.


[View on SkillFed](https://skillfed.io/himself65/finance-skills/yfinance-data) · [View on GitHub](https://github.com/himself65/finance-skills)