yfinance لتحميل البيانات
تحميل بيانات الأسواق المالية من Yahoo Finance مجاناً. أسهم وعملات وعملات رقمية وصناديق ETF والمزيد.
الصعوبة: مبتدئ
الفئة: بيانات وتحليل
⭐ الأكثر شعبية
التثبيت
# Install yfinance
$ pip install yfinance
ما يمكنك تحميله
Stocks
US and international stocks from major exchanges
AAPL, MSFT, TSLAForex
Major and exotic currency pairs
EURUSD=X, GBPJPY=XCryptocurrencies
Bitcoin, Ethereum, and altcoins
BTC-USD, ETH-USDIndices
Market indices and ETFs
^GSPC, ^DJI, SPYCommodities
Gold, oil, and other commodities
GC=F, CL=FFundamentals
Company info, financials, dividends
P/E, Market Cap, EPSأمثلة الكود
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()
أفضل الممارسات
حدود الطلبات
Save downloaded data locally to avoid repeated API calls
جودة البيانات
Always check for NaN values and handle missing data appropriately
التخزين المحلي
Yahoo Finance may rate limit excessive requests. Add delays between calls
Data Quality
Verify data quality, especially for less liquid symbols