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Stock Market Data And Analysis In Python – Part I

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Excerpt

How to get Stock Market Data in Python?

Yahoo! Finance

One of the first sources from which you can get historical daily price-volume stock market data is Yahoo! Finance. You can use pandas_datareader or yfinance module to get the data and then can download or store in a csv file by using pandas.to_csv method.

If yfinance is not installed on your computer, then run the below line of code from your Jupyter Notebook to install yfinance.

!pip install yfinance

view rawinstall yfinance.py hosted with ❤ by GitHub

Stock Market Data And Analysis In Python

# Import yfinance package
import yfinance as yf

# Set the start and end date
start_date = ‘1990-01-01’
end_date = ‘2021-07-12’

# Set the ticker
ticker = ‘AMZN’

# Get the data
data = yf.download(ticker, start_date, end_date)

# Print 5 rows
data.tail()

amazon_data.py hosted with ❤ by GitHub

To visualize the adjusted close price data, you can use the matplotlib library and plot method as shown below.

# Import matplotlib for plotting
import matplotlib.pyplot as plt
%matplotlib inline

# Plot adjusted close price data
data[‘Adj Close’].plot()
plt.show()

plot_amazn_data.py hosted with ❤ by GitHub

Let us improve the plot by resizing, giving appropriate labels and adding grid lines for better readability.

# Plot the adjusted close price
data[‘Adj Close’].plot(figsize=(10, 7))

# Define the label for the title of the figure
plt.title(“Adjusted Close Price of %s” % ticker, fontsize=16)

# Define the labels for x-axis and y-axis
plt.ylabel(‘Price’, fontsize=14)
plt.xlabel(‘Year’, fontsize=14)

# Plot the grid lines
plt.grid(which=”major”, color=’k’, linestyle=’-.’, linewidth=0.5)

# Show the plot
plt.show()

plot_amzn_data_with_grids.py hosted with ❤ by GitHub

Advantages

  1. Adjusted close price stock market data is available
  2. Most recent stock market data is available
  3. Doesn’t require API key to fetch the stock market data

See https://blog.quantinsti.com/stock-market-data-analysis-python/ for additional insight on this topic and for a tutorial from Nitesh Khandelwal, Co-Founder and CEO, QuantInsti, that answers questions related to getting Data for Algo Trading.

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