Yfinance Data

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.

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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:

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.

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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.

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Step 3: Write and Execute the Code

General pattern

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

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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).

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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.

yfinance-data

Fetch financial and market data using the 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

# As a plugin (recommended — installs all skills)
npx plugins add himself65/finance-skills --plugin finance-market-analysis

# Or install just this skill
npx skills add himself65/finance-skills --skill yfinance-data

See the main README for more installation options.

Reference files

• references/api_reference.md — Complete yfinance API reference with code examples for every data category

yfinance API Reference

Complete reference for all yfinance data access methods.

Installation

pip install yfinance

Requires Python 3.8+. Dependencies (pandas, requests, etc.) are installed automatically.

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Ticker Object

The primary interface for single-stock data.

import yfinance as yf
ticker = yf.Ticker("AAPL")

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Historical Price Data

ticker.history()

Returns a DataFrame with columns: Open, High, Low, Close, Volume, Dividends, Stock Splits.

# 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.

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
)

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Company Info

ticker.info

Returns a dictionary with company details, financials, and market data.

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).

fi = ticker.fast_info
fi['lastPrice']
fi['marketCap']
fi['fiftyDayAverage']
fi['twoHundredDayAverage']

---

Financial Statements

All return pandas DataFrames. Use quarterly_ prefix for quarterly data.

# 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

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

# 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

# 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

# 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

ticker.calendar
# Returns dict with upcoming earnings dates, dividends, etc.

---

News

ticker.news
# Returns list of dicts with: title, link, publisher, providerPublishTime, type

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Multiple Tickers

tickers = yf.Tickers("AAPL MSFT GOOGL")

# Access individual tickers
tickers.tickers["AAPL"].info
tickers.tickers["MSFT"].history(period="1mo")

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Screener & Equity Query

Build custom stock screens.

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

# 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

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Search

search = yf.Search("Tesla")
search.quotes    # matching ticker quotes
search.news      # related news articles

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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.

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).

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Error Handling

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