yfinance

name: yfinance description: yfinance global market data interface — retrieve OHLCV, financials, insider transactions, and institutional holdings for US stocks, HK stocks, ETFs, and indices via Yahoo Finance. Free, no API key required. category: data-source

yfinance

Overview

yfinance is an open-source Python wrapper for Yahoo Finance, providing global market data (US stocks, HK stocks, ETFs, indices) including historical and real-time quotes. Completely free, no registration or API key required.

The project has a built-in yfinance DataLoader (backtest/loaders/yfinance_loader.py). When backtesting, set source: "yfinance" or source: "auto" to invoke it automatically.

For OHLCV bars in agent/swarm work, prefer the get_market_data tool when it is available. It routes through the project loader layer, normalizes symbols, removes malformed OHLC rows, and returns strict JSON. Use direct yfinance calls mainly for data outside OHLCV coverage such as company info, financial statements, options, holders, and insider transactions.

Quick Start

Preferred OHLCV tool call:

{
  "codes": ["AAPL.US", "700.HK"],
  "start_date": "2025-01-01",
  "end_date": "2026-01-01",
  "source": "yfinance",
  "interval": "1D"
}

If you must write a Python script for OHLCV, use the DataLoader instead of raw yf.download:

from backtest.loaders.registry import get_loader_cls_with_fallback

loader = get_loader_cls_with_fallback("yfinance")()
data = loader.fetch(["AAPL.US", "700.HK"], "2025-01-01", "2026-01-01", interval="1D")

for symbol, df in data.items():
    print(symbol, df.tail())

Ticker Format Conversion

The project uses a unified ticker format. The DataLoader automatically converts to yfinance format:

Rules:

• US stocks: strip the .US suffix → use the raw ticker
• HK stocks: keep .HK, pad the number to 4 digits (700 → 0700)

Supported Data Types

1. Historical OHLCV

Prefer get_market_data for OHLCV whenever the tool is available:

{
  "codes": ["AAPL.US", "MSFT.US", "GOOGL.US"],
  "start_date": "2025-01-01",
  "end_date": "2026-01-01",
  "source": "yfinance",
  "interval": "1D",
  "max_rows": 250
}

For script-based OHLCV analysis, use the loader:

from backtest.loaders.registry import get_loader_cls_with_fallback

loader = get_loader_cls_with_fallback("yfinance")()

# Single stock
single = loader.fetch(["AAPL.US"], "2025-01-01", "2026-01-01", interval="1D")

# Specific interval
hourly = loader.fetch(["AAPL.US"], "2026-03-01", "2026-03-30", interval="1H")

Supported intervals:

• Minute-level: 1m, 2m, 5m, 15m, 30m, 60m, 90m
• Hourly: 1h
• Daily and above: 1d, 5d, 1wk, 1mo, 3mo

Minute data limits:

• 1m: up to 7 days of history
• 2m/5m/15m/30m/60m/90m: up to 60 days
• 1h: up to 730 days
• 1d and above: unlimited

2. Company Info

ticker = yf.Ticker("AAPL")

info = ticker.info
print(f"Company: {info.get('longName')}")
print(f"Industry: {info.get('industry')}")
print(f"Market cap: {info.get('marketCap')}")
print(f"PE: {info.get('trailingPE')}")
print(f"EPS: {info.get('trailingEps')}")
print(f"Dividend yield: {info.get('dividendYield')}")

3. Financial Statements

ticker = yf.Ticker("AAPL")

# Income statement (annual)
income = ticker.financials
# Income statement (quarterly)
income_q = ticker.quarterly_financials

# Balance sheet
balance = ticker.balance_sheet

# Cash flow statement
cashflow = ticker.cashflow

# Earnings data
earnings = ticker.earnings

4. Dividends and Splits

ticker = yf.Ticker("AAPL")

# Dividend history
dividends = ticker.dividends

# Stock split history
splits = ticker.splits

# All corporate actions
actions = ticker.actions

5. Institutional Holdings

ticker = yf.Ticker("AAPL")

# Institutional holders
holders = ticker.institutional_holders

# Major holders summary
major = ticker.major_holders

# Insider transactions
insider = ticker.insider_transactions

6. Indices and ETFs

# Major indices
sp500 = yf.download("^GSPC", start="2025-01-01", end="2026-01-01", progress=False)  # S&P 500
nasdaq = yf.download("^IXIC", start="2025-01-01", end="2026-01-01", progress=False)  # NASDAQ
hsi = yf.download("^HSI", start="2025-01-01", end="2026-01-01", progress=False)      # Hang Seng Index

# ETFs
spy = yf.download("SPY", start="2025-01-01", end="2026-01-01", progress=False)
qqq = yf.download("QQQ", start="2025-01-01", end="2026-01-01", progress=False)

7. FX Rates

# Currency pairs
usdcny = yf.download("CNY=X", start="2025-01-01", end="2026-01-01", progress=False)
usdhkd = yf.download("HKD=X", start="2025-01-01", end="2026-01-01", progress=False)
eurusd = yf.download("EURUSD=X", start="2025-01-01", end="2026-01-01", progress=False)

Popular Ticker Reference

US Stocks

HK Stocks

Major Indices

Sector ETFs

Backtest Usage

config.json Example

{
  "source": "yfinance",
  "codes": ["AAPL.US", "MSFT.US"],
  "start_date": "2020-01-01",
  "end_date": "2026-03-30",
  "initial_cash": 1000000,
  "commission": 0.001,
  "extra_fields": null
}

Cross-Market Auto Mode

{
  "source": "auto",
  "codes": ["000001.SZ", "AAPL.US", "700.HK", "BTC-USDT"],
  "start_date": "2024-01-01",
  "end_date": "2026-03-30",
  "initial_cash": 1000000,
  "commission": 0.001,
  "extra_fields": null
}

source: "auto" routes automatically by ticker format: A-shares → tushare, HK/US stocks → yfinance, crypto → OKX.

Notes

• Free, no API key: yfinance scrapes Yahoo Finance public data — no registration needed
• Rate limits: high-frequency requests may trigger temporary Yahoo bans — prefer batch downloads over per-ticker loops
• Minute data range: limited by Yahoo Finance (see table above)
• HK tickers: Yahoo Finance uses 4-digit numbers + .HK; pad with leading zeros where needed
• Adjustment: auto_adjust=True (default) returns forward-adjusted prices; the project loader uses auto_adjust=False
• Timezone: returned data includes timezone info; the DataLoader strips it automatically
• extra_fields not supported: yfinance via the backtest loader returns OHLCV only; PE/PB and other fundamentals require separate yf.Ticker().info calls
• Comparison with Tushare: Tushare covers deep A-share data (financials, fund flows, block trades, etc.); yfinance covers global markets but with less depth