yfinance Library

Download market data from Yahoo Finance for stocks, forex, cryptocurrencies, and more. The easiest way to get historical and real-time financial data in Python.

Difficulty: Beginner

Category: Data & Analysis

⭐ Most Popular

Installation

# Install yfinance

$ pip install yfinance

What You Can Download

Stocks

US and international stocks from major exchanges

AAPL, MSFT, TSLA

Forex

Major and exotic currency pairs

EURUSD=X, GBPJPY=X

Cryptocurrencies

Bitcoin, Ethereum, and altcoins

BTC-USD, ETH-USD

Indices

Market indices and ETFs

^GSPC, ^DJI, SPY

Commodities

Gold, oil, and other commodities

GC=F, CL=F

Fundamentals

Company info, financials, dividends

P/E, Market Cap, EPS

Code Examples

Download Stock Data

Get historical OHLCV data for any ticker

Python

import yfinance as yf

# Download single ticker

ticker = yf.Ticker("AAPL")

df = ticker.history(period="1y") # Last 1 year

print(df.head())

print(f"Total bars: {len(df)}")

# Alternative: Direct download

df = yf.download("AAPL", start="2023-01-01", end="2024-01-01")

# Available periods: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max

# Available intervals: 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo

Download Forex Data

Get currency pair historical data

Python

import yfinance as yf

# Forex pairs use '=X' suffix

eurusd = yf.download("EURUSD=X", period="6mo", interval="1d")

gbpusd = yf.download("GBPUSD=X", period="6mo", interval="1d")

usdjpy = yf.download("USDJPY=X", period="6mo", interval="1d")

print("EURUSD Data:")

print(eurusd.tail())

# Intraday forex data

eurusd_1h = yf.download("EURUSD=X", period="60d", interval="1h")

print(f"\n1H bars: {len(eurusd_1h)}")

Download Cryptocurrency Data

Get Bitcoin, Ethereum, and other crypto data

Python

import yfinance as yf

# Cryptocurrencies use '-USD' suffix

btc = yf.download("BTC-USD", period="1y", interval="1d")

eth = yf.download("ETH-USD", period="1y", interval="1d")

bnb = yf.download("BNB-USD", period="1y", interval="1d")

print("Bitcoin Data:")

print(btc.tail())

# Intraday crypto data

btc_15m = yf.download("BTC-USD", period="7d", interval="15m")

print(f"\n15-minute bars: {len(btc_15m)}")

Download Multiple Tickers

Batch download multiple symbols at once

Python

import yfinance as yf

# Download multiple tickers

tickers = ["AAPL", "MSFT", "GOOGL", "AMZN"]

data = yf.download(tickers, period="1y", interval="1d")

# Access individual ticker data

print("Apple Close Prices:")

print(data['Close']['AAPL'].tail())

print("\nMicrosoft Close Prices:")

print(data['Close']['MSFT'].tail())

# Calculate correlation

correlation = data['Close'].corr()

print("\nCorrelation Matrix:")

print(correlation)

Get Ticker Information

Access company info, financials, and fundamentals

Python

import yfinance as yf

ticker = yf.Ticker("AAPL")

# Company information

info = ticker.info

print(f"Company: {'{'}info.get('longName'){'}'}")

print(f"Sector: {'{'}info.get('sector'){'}'}")

print(f"Industry: {'{'}info.get('industry'){'}'}")

print("Market Cap:", info.get('marketCap'))

print(f"P/E Ratio: {'{'}info.get('trailingPE'){'}'}")

print(f"Dividend Yield: {info.get('dividendYield')}")

# Financial statements

print("\nIncome Statement:")

print(ticker.financials)

print("\nBalance Sheet:")

print(ticker.balance_sheet)

print("\nCash Flow:")

print(ticker.cashflow)

# Dividends and splits

print("\nDividends:")

print(ticker.dividends.tail())

print("\nStock Splits:")

print(ticker.splits)

Cache Data Locally

Save data to avoid repeated downloads

Python

import yfinance as yf

import pandas as pd

import os

from datetime import datetime

def get_cached_data(symbol, period = "1y", interval = "1d", cache_dir = "data"):

"""Download data with local caching"""

# Create cache directory

os.makedirs(cache_dir, exist_ok = True)

# Generate cache filename

cache_file = f"{cache_dir}/{symbol}_{period}_{interval}.csv"

# Check if cache exists and is recent(less than 1 day old)

if os.path.exists(cache_file):

file_time = os.path.getmtime(cache_file)

age_hours = (datetime.now().timestamp() - file_time) / 3600

if age_hours < 24:

print(f"Loading {symbol} from cache...")

return pd.read_csv(cache_file, index_col = 0, parse_dates = True)

# Download fresh data

print(f"Downloading {symbol}...")

df = yf.download(symbol, period = period, interval = interval)

# Save to cache

df.to_csv(cache_file)

return df

# Usage

df = get_cached_data("EURUSD=X", period = "1y", interval = "1d")

print(df.tail())

Get Real-Time Quotes

Access current price and market data

Python

import yfinance as yf

import time

def get_realtime_quote(symbol):

"""Get current market quote"""

ticker = yf.Ticker(symbol)

# Fast quote(limited data)

quote = ticker.fast_info

print(f"Symbol: {symbol}")

print(f"Last Price: ${quote.last_price:.2f}")

print(f"Open: ${quote.open:.2f}")

print(f"Day High: ${quote.day_high:.2f}")

print(f"Day Low: ${quote.day_low:.2f}")

print(f"Volume: {quote.last_volume:,}")

# Full quote

info = ticker.info

print(f"\nBid: ${info.get('bid', 0):.2f}")

print(f"Ask: ${info.get('ask', 0):.2f}")

print(f"Spread: ${info.get('ask', 0) - info.get('bid', 0):.2f}")

# Monitor price in real - time

def monitor_price(symbol, interval = 5, duration = 60):

"""Monitor price for specified duration"""

print(f"Monitoring {symbol} for {duration} seconds...")

start_time = time.time()

while time.time() - start_time < duration:

ticker = yf.Ticker(symbol)

price = ticker.fast_info.last_price

print(f"{time.strftime('%H:%M:%S')} - {symbol}: ${price$2.2f}")

time.sleep(interval)

# Get current quote

get_realtime_quote("AAPL")

# Monitor for 60 seconds

# monitor_price("AAPL", interval = 5, duration = 60)

Complete Trading Strategy

Download data and backtest a strategy

Python

import yfinance as yf

import pandas as pd

import numpy as np

# Download data

df = yf.download("EURUSD=X", period = "2y", interval = "1d")

# Calculate indicators

df['SMA_50'] = df['Close'].rolling(window = 50).mean()

df['SMA_200'] = df['Close'].rolling(window = 200).mean()

df['RSI'] = calculate_rsi(df['Close'], 14)

# Generate signals

df['Signal'] = 0

df.loc[(df['SMA_50'] > df['SMA_200']) & (df['RSI'] < 70), 'Signal'] = 1

df.loc[(df['SMA_50'] < df['SMA_200']) | (df['RSI'] > 70), 'Signal'] = -1

# Backtest

df['Returns'] = df['Close'].pct_change()

df['Strategy_Returns'] = df['Signal'].shift(1) * df['Returns']

# Calculate metrics

total_return = (1 + df['Strategy_Returns']).prod() - 1

sharpe = df['Strategy_Returns'].mean() / df['Strategy_Returns'].std() * np.sqrt(252)

print(f"Total Return: {total_return*100:.2f}%")

print(f"Sharpe Ratio: {sharpe:.2f}")

# Plot results

import matplotlib.pyplot as plt

plt.figure(figsize = (12, 6))

plt.plot((1 + df['Returns']).cumprod(), label = 'Buy & Hold')

plt.plot((1 + df['Strategy_Returns']).cumprod(), label = 'Strategy')

plt.legend()

plt.title('Strategy Performance')

plt.show()

Best Practices

Cache Data

Save downloaded data locally to avoid repeated API calls

Handle Missing Data

Always check for NaN values and handle missing data appropriately

Rate Limits

Yahoo Finance may rate limit excessive requests. Add delays between calls

Data Quality

Verify data quality, especially for less liquid symbols

Resources

yfinance GitHub Repository

Next Steps

pandas - Analyze Data TA-Lib - Add Indicators