README.md
# yfinance-data
Fetch financial and market data using the [yfinance](https://github.com/ranaroussi/yfinance) Python library.
## What it does
Retrieves a wide range of financial data from Yahoo Finance, including:
- **Current prices & quotes** — real-time stock prices, market cap, P/E
- **Historical OHLCV** — price history with configurable period and interval
- **Financial statements** — balance sheet, income statement, cash flow (annual & quarterly)
- **Corporate actions** — dividends, stock splits
- **Options data** — full options chains with greeks
- **Analysis** — earnings history, analyst price targets, recommendations, upgrades/downgrades
- **Ownership** — institutional holders, insider transactions
- **Screener** — filter stocks using `yf.Screener` and `yf.EquityQuery`
> **Note**: yfinance is not affiliated with Yahoo, Inc. Data is for research and educational purposes.
## Triggers
- Any mention of a ticker symbol (AAPL, MSFT, TSLA, etc.)
- "what's the price of", "get me the financials", "show earnings"
- "options chain", "dividend history", "balance sheet", "income statement"
- "analyst targets", "compare stocks", "screen for stocks"
## Prerequisites
- Python 3.8+
- The skill auto-installs `yfinance` via pip if not already present
## Platform
Works on **all platforms** (Claude Code, Claude.ai with code execution, etc.).
## Setup
```bash
# Choose finance-market-analysis when prompted.
npx plugins add himself65/finance-skills
# Or install just this skill
npx skills add himself65/finance-skills --skill yfinance-data
```
See the [main README](../../../../README.md) for more installation options.
## Reference files
- `references/api_reference.md` — Complete yfinance API reference with code examples for every data category
references/api_reference.md
# yfinance API Reference
Complete reference for all yfinance data access methods.
## Installation
```python
pip install yfinance
```
Requires Python 3.8+. Dependencies (pandas, requests, etc.) are installed automatically.
---
## Ticker Object
The primary interface for single-stock data.
```python
import yfinance as yf
ticker = yf.Ticker("AAPL")
```
---
## Historical Price Data
### `ticker.history()`
Returns a DataFrame with columns: Open, High, Low, Close, Volume, Dividends, Stock Splits.
```python
# Default: 1 month of daily data
hist = ticker.history(period="1mo")
# Specific date range
hist = ticker.history(start="2023-01-01", end="2023-12-31")
# Weekly data for 1 year
hist = ticker.history(period="1y", interval="1wk")
# Intraday 5-minute bars for last 5 days
hist = ticker.history(period="5d", interval="5m")
# Include pre/post market data
hist = ticker.history(period="5d", prepost=True)
# Repair price anomalies
hist = ticker.history(period="1mo", repair=True)
```
**Valid periods**: `1d`, `5d`, `1mo`, `3mo`, `6mo`, `1y`, `2y`, `5y`, `10y`, `ytd`, `max`
**Valid intervals**: `1m`, `2m`, `5m`, `15m`, `30m`, `60m`, `90m`, `1h`, `1d`, `5d`, `1wk`, `1mo`, `3mo`
**Intraday limits**:
- 1m: last ~7 days
- 2m/5m/15m/30m: last ~60 days
- 60m/90m/1h: last ~730 days
### `yf.download()` — Bulk Download
Efficient multi-threaded download for multiple tickers.
```python
data = yf.download(
tickers="AAPL MSFT GOOGL AMZN", # space or comma separated
start="2023-01-01",
end="2024-01-01",
interval="1d",
group_by="ticker", # or "column" (default)
auto_adjust=True, # adjust for splits and dividends
threads=True, # multi-threading
progress=True # show progress bar
)
# Access a specific ticker
apple_close = data["AAPL"]["Close"]
# Download with dividends and splits
data = yf.download(["AAPL", "MSFT"], period="1y", actions=True)
# Additional options
data = yf.download(
tickers=["TSLA", "NVDA"],
period="6mo",
interval="1h",
repair=True, # fix price anomalies
keepna=False, # remove NaN rows
rounding=True, # round to 2 decimals
timeout=10 # request timeout seconds
)
```
---
## Company Info
### `ticker.info`
Returns a dictionary with company details, financials, and market data.
```python
info = ticker.info
# Common fields
info['shortName'] # Company name
info['sector'] # e.g., "Technology"
info['industry'] # e.g., "Consumer Electronics"
info['marketCap'] # Market capitalization
info['currentPrice'] # Current stock price
info['previousClose'] # Previous close price
info['trailingPE'] # Trailing P/E ratio
info['forwardPE'] # Forward P/E ratio
info['dividendYield'] # Dividend yield
info['beta'] # Beta
info['fiftyTwoWeekHigh'] # 52-week high
info['fiftyTwoWeekLow'] # 52-week low
info['averageVolume'] # Average volume
info['longBusinessSummary'] # Company description
```
### `ticker.fast_info`
Lightweight subset for quick price lookups (faster than `.info`).
```python
fi = ticker.fast_info
fi['lastPrice']
fi['marketCap']
fi['fiftyDayAverage']
fi['twoHundredDayAverage']
```
---
## Financial Statements
All return pandas DataFrames. Use `quarterly_` prefix for quarterly data.
```python
# Annual
ticker.income_stmt # Income statement
ticker.balance_sheet # Balance sheet
ticker.cashflow # Cash flow statement
# Quarterly
ticker.quarterly_income_stmt
ticker.quarterly_balance_sheet
ticker.quarterly_cashflow
```
---
## Corporate Actions
```python
ticker.dividends # Series of dividend payments
ticker.splits # Series of stock splits
ticker.actions # DataFrame with both dividends and splits
ticker.capital_gains # Capital gains (for mutual funds/ETFs)
```
---
## Options
```python
# List available expiration dates
expirations = ticker.options # tuple of date strings
# Get option chain for a specific expiration
opt = ticker.option_chain("2024-06-21")
# Calls and puts are separate DataFrames
calls = opt.calls
puts = opt.puts
# Key columns:
# strike, lastPrice, bid, ask, volume, openInterest, impliedVolatility,
# inTheMoney, contractSymbol, lastTradeDate, change, percentChange
```
---
## Analysis & Estimates
```python
# Analyst price targets
ticker.analyst_price_targets
# Returns dict: current, low, high, mean, median
# Recommendations (buy/hold/sell counts by period)
ticker.recommendations
# Upgrades and downgrades history
ticker.upgrades_downgrades
# Columns: firm, toGrade, fromGrade, action
# Earnings estimates
ticker.earnings_estimate
# Columns: numberOfAnalysts, avg, low, high, yearAgoEps, growth
# Index: 0q (current quarter), +1q, 0y, +1y
# Revenue estimates
ticker.revenue_estimate
# EPS trend
ticker.eps_trend
# EPS revisions
ticker.eps_revisions
# Growth estimates
ticker.growth_estimates
# Earnings history (actual vs estimate)
ticker.earnings_history
# Columns: epsEstimate, epsActual, epsDifference, surprisePercent
# Sustainability / ESG scores
ticker.sustainability
```
---
## Ownership
```python
# Major holders summary
ticker.major_holders
# Top institutional holders
ticker.institutional_holders
# Columns: Holder, Shares, Date Reported, % Out, Value
# Mutual fund holders
ticker.mutualfund_holders
# Insider transactions
ticker.insider_transactions
# Insider roster
ticker.insider_roster_holders
# Shares outstanding over time
ticker.get_shares_full(start="2023-01-01", end="2023-12-31")
```
---
## Calendar & Events
```python
ticker.calendar
# Returns dict with upcoming earnings dates, dividends, etc.
```
---
## News
```python
ticker.news
# Returns list of dicts with: title, link, publisher, providerPublishTime, type
```
---
## Multiple Tickers
```python
tickers = yf.Tickers("AAPL MSFT GOOGL")
# Access individual tickers
tickers.tickers["AAPL"].info
tickers.tickers["MSFT"].history(period="1mo")
```
---
## Screener & Equity Query
Build custom stock screens.
```python
from yfinance import Screener, EquityQuery
# Create a query
query = EquityQuery('and', [
EquityQuery('gt', ['marketcap', 1_000_000_000]), # market cap > $1B
EquityQuery('lt', ['peratio', 20]), # P/E < 20
EquityQuery('eq', ['sector', 'Technology']) # tech sector
])
# Run the screen
screener = Screener()
screener.set_body(query)
result = screener.response
# Available operators: eq, gt, lt, gte, lte, btwn, is_in
# Available fields: marketcap, peratio, sector, industry, dividendyield, etc.
```
---
## Sector & Industry
```python
# Sector data
tech = yf.Sector("technology")
tech.overview
tech.industries # DataFrame of industries in this sector
# Industry data
semiconductors = yf.Industry("semiconductors")
semiconductors.overview
semiconductors.top_companies
# Valid sector keys:
# basic-materials, communication-services, consumer-cyclical,
# consumer-defensive, energy, financial-services, healthcare,
# industrials, real-estate, technology, utilities
```
---
## Search
```python
search = yf.Search("Tesla")
search.quotes # matching ticker quotes
search.news # related news articles
```
---
## Timezone Handling
yfinance returns tz-aware datetime indices (typically `America/New_York`). When filtering or comparing dates, you **must** match timezone awareness to avoid `TypeError: Cannot compare tz-naive and tz-aware datetime-like objects`.
```python
import yfinance as yf
import pandas as pd
hist = yf.Ticker("AAPL").history(period="1y")
# WRONG — tz-naive timestamp vs tz-aware index:
# filtered = hist[hist.index >= pd.Timestamp("2025-01-01")] # TypeError!
# Option A (recommended): make the comparison timestamp tz-aware
start = pd.Timestamp("2025-01-01", tz="America/New_York")
filtered = hist[hist.index >= start]
# Option B: strip timezone from index first
hist.index = hist.index.tz_localize(None)
filtered = hist[hist.index >= pd.Timestamp("2025-01-01")]
```
Always use **Option A** when you need to preserve timezone info for accurate date boundaries. Use **Option B** when timezone doesn't matter (e.g., daily data aggregation).
---
## Error Handling
```python
import yfinance as yf
try:
ticker = yf.Ticker("AAPL")
hist = ticker.history(period="1mo")
if hist.empty:
print("No data returned — check ticker symbol or date range")
else:
print(hist)
except Exception as e:
print(f"Error fetching data: {e}")
```
Common issues:
- **Empty DataFrame**: Invalid ticker, delisted stock, or date range outside available data
- **Rate limiting**: Too many requests in short time — add delays between calls
- **Missing fields in `.info`**: Not all fields are available for all tickers (ETFs, mutual funds, foreign stocks may differ)
- **Intraday data limits**: 1m data only available for last ~7 days
- **Timezone mismatch**: See "Timezone Handling" section above — always match tz-awareness when comparing dates
SKILL.md
---
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 "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:
```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.