Python Stock Price API

Install Dependencies

pip install yfinance requests pandas

Method 1: yfinance (Easiest)

yfinance is the most popular library for stock data in Python. It scrapes Yahoo Finance data and provides a clean pandas DataFrame. No API key needed.

import yfinance as yf

# Get stock data for Apple
stock = yf.Ticker("AAPL")

# Current price
info = stock.info
print(f"Current Price: ${info['currentPrice']}")
print(f"Market Cap: ${info['marketCap']:,.0f}")
print(f"P/E Ratio: {info['trailingPE']:.2f}")
print(f"52W High: ${info['fiftyTwoWeekHigh']}")
print(f"52W Low: ${info['fiftyTwoWeekLow']}")

Historical Data

Get OHLCV (Open, High, Low, Close, Volume) data for any time period and interval.

# Historical data
history = stock.history(period="1y")
print(history.head())

#                Open      High       Low     Close    Volume
# Date
# 2024-05-15  189.51    191.05    189.26    190.90  54321000
# 2024-05-16  190.47    191.10    189.66    189.84  45678000
# ...

# Different periods: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
# Different intervals: 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo

Multiple Stocks at Once

# Multiple stocks at once
import pandas as pd

tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"]
data = yf.download(tickers, period="1y", group_by="ticker")

# Get closing prices for all stocks
closes = pd.DataFrame()
for ticker in tickers:
    closes[ticker] = data[ticker]["Close"]

print(closes.tail())

# Calculate daily returns
returns = closes.pct_change().dropna()
print("\nAverage Daily Returns:")
print(returns.mean() * 252)  # Annualised

Method 2: Alpha Vantage API

Alpha Vantage provides free API access with a key. Rate limited to 5 requests per minute on the free tier, but offers more data types including forex, crypto, and fundamentals.

# Alpha Vantage API (free, rate-limited)
import requests

API_KEY = "YOUR_API_KEY"  # Get free key at alphavantage.co
symbol = "AAPL"

url = f"https://www.alphavantage.co/query"
params = {
    "function": "TIME_SERIES_DAILY",
    "symbol": symbol,
    "apikey": API_KEY,
    "outputsize": "compact"  # Last 100 data points
}

response = requests.get(url, params=params)
data = response.json()

time_series = data["Time Series (Daily)"]
for date, values in list(time_series.items())[:5]:
    print(f"{date}: Open={values['1. open']}, Close={values['4. close']}")

Save Data to CSV

# Save to CSV for later analysis
import yfinance as yf

stock = yf.Ticker("AAPL")
history = stock.history(period="5y")

# Save to CSV
history.to_csv("aapl_5year.csv")
print(f"Saved {len(history)} rows to aapl_5year.csv")

# Read it back
import pandas as pd
df = pd.read_csv("aapl_5year.csv", index_col="Date", parse_dates=True)
print(f"\nLatest price: ${df['Close'].iloc[-1]:.2f}")
print(f"5-year return: {((df['Close'].iloc[-1] / df['Close'].iloc[0]) - 1) * 100:.1f}%")

Which API Should You Use?

• yfinance: best for quick prototyping and personal projects. Free, no key needed, good enough for most use cases.
• Alpha Vantage: best for projects needing fundamental data or forex/crypto. Free tier is limited but sufficient for learning.
• Polygon.io: best for production applications needing real-time data. Free tier gives 5 API calls/min, paid plans offer websockets and tick data.