Yahoo-Fin: Fetching Historical Stock Data with Python’s Yahoo Finance API

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This guide provides an easy-to-understand foundation for beginners and intermediate users to leverage the Yahoo Finance API with Python for financial data analysis.

Yahoo Finance API offers a wealth of financial data, from stock prices and market trends to currency exchange rates. This guide will introduce you to using the Yahoo Finance API yahoo_fin with Python, a powerful combination for financial data analysis.

🧑‍💻 Quick Info: The Python library yahoo_fin is not well maintained but works for quick, dirty, and simple access without requiring you to create an API key. It provides free access to a wide range of financial data.

Setting Up Your Environment

Before diving into the API, ensure Python is installed on your system. Python 3.x is recommended for its latest features and support.

🧑‍💻 Recommended: How to Install Python?

You’ll primarily need yahoo_fin, a Python library that simplifies accessing Yahoo Finance data. Install it using pip:

pip install yahoo_fin

Understanding the Yahoo_fin Library

yahoo_fin offers two main modules:

  • stock_info: For stock data.
  • options: For options data.

These modules provide functions to access various data types like historical prices, stock information, etc.

Fetching Historical Data with Yahoo_fin

The get_data() function from stock_info module is used to fetch historical price data for a given stock. Here’s how you use it:

from yahoo_fin.stock_info import get_data

# Fetch historical data for Apple
apple_data = get_data('AAPL')
print(apple_data)

Output:

For real-time data, use the get_live_price() function:

from yahoo_fin.stock_info import get_live_price

# Get the current price of Tesla stock
tesla_live_price = get_live_price('TSLA')
print(tesla_live_price)
# 234.3000030517578

Example: Analyzing Stock Performance

Python’s data analysis libraries, like Pandas, can be used in conjunction with yahoo_fin to perform more complex analyses.

import pandas as pd
from yahoo_fin.stock_info import get_data

# Fetch historical data for a stock
stock_data = get_data('AAPL', start_date='01/01/2020', end_date='01/01/2021')

# Convert to a Pandas DataFrame for analysis
df = pd.DataFrame(stock_data)
print(df)

Output:

Visualizing Data

With libraries like Matplotlib or Seaborn, you can visualize financial data:

import matplotlib.pyplot as plt

# Plotting the closing price of Apple stock
plt.figure(figsize=(10, 5))
plt.plot(df['close'], label='Apple Close Price')
plt.title('Apple Stock Close Price (2020)')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.show()

This is how this looks at the time of writing (using the df variable defined in the previous step):

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