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.

Quick Start

pip install yfinance pandas

import yfinance as yf

# Apple daily bars for the past year
df = yf.download("AAPL", start="2025-01-01", end="2026-01-01", progress=False)
print(df.head())

# Tencent (HK-listed)
df = yf.download("0700.HK", start="2025-01-01", end="2026-01-01", progress=False)
print(df.head())

Ticker Format Conversion

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

Project Format	yfinance Format	Market
AAPL.US	AAPL	US stock
MSFT.US	MSFT	US stock
700.HK	0700.HK	HK stock
9988.HK	9988.HK	HK stock
SPY.US	SPY	US ETF

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

import yfinance as yf
import pandas as pd

# Single stock
df = yf.download("AAPL", start="2025-01-01", end="2026-01-01", progress=False)

# Batch download
df = yf.download(["AAPL", "MSFT", "GOOGL"], start="2025-01-01", end="2026-01-01", progress=False)

# Specific interval
df = yf.download("AAPL", start="2026-03-01", end="2026-03-30",
                 interval="1h", progress=False)  # 1m/5m/15m/30m/1h/1d/1wk/1mo

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

Ticker	Company
AAPL	Apple
MSFT	Microsoft
GOOGL	Alphabet (Google)
AMZN	Amazon
NVDA	NVIDIA
META	Meta Platforms
TSLA	Tesla
BRK-B	Berkshire Hathaway

HK Stocks

Project Format	yfinance Format	Company
700.HK	0700.HK	Tencent
9988.HK	9988.HK	Alibaba
9618.HK	9618.HK	JD.com
3690.HK	3690.HK	Meituan
1810.HK	1810.HK	Xiaomi
2318.HK	2318.HK	Ping An

Major Indices

Ticker	Index
^GSPC	S&P 500
^DJI	Dow Jones Industrial Average
^IXIC	NASDAQ Composite
^HSI	Hang Seng Index
^N225	Nikkei 225
^FTSE	FTSE 100

Sector ETFs

Ticker	Sector
XLK	Technology
XLF	Financials
XLE	Energy
XLV	Healthcare
XLY	Consumer Discretionary
XLP	Consumer Staples
XLI	Industrials
XLU	Utilities

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