Stock Market Data: Obtaining Data, Visualization & Analysis in Python

The article “Stock Market Data: Obtaining Data, Visualization & Analysis in Python” first appeared on QuantInsti blog.

Are you looking to get stock market data and analyse the historical data in Python? You have come to right place.

After reading this, you will be able to:

• Get historical data for stocks
• Plot the stock market data and analyse the performance
• Get the fundamental, futures and options data

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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

install yfinance.py hosted with ❤ by GitHub

# 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

Data Source: Yahoo Finance

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

Data Source: Yahoo Finance

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

How to get Stock Market Data for different geographies?

To get stock market data for different geographies, search the ticker symbol on Yahoo finance and use that as the ticker.

Get stock market data for multiple tickers

To get the stock market data of multiple stock tickers, you can create a list of tickers and call the yfinance download method for each stock ticker.

For simplicity, I have created a dataframe data to store the adjusted close price of the stocks.

# Import packages
import yfinance as yf
import pandas as pd

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

# Define the ticker list
tickers_list = ['AAPL', 'IBM', 'MSFT', 'WMT']

# Create placeholder for data
data = pd.DataFrame(columns=tickers_list)

# Fetch the data
for ticker in tickers_list:
    data[ticker] = yf.download(ticker, 
                               start_date,
                               end_date)['Adj Close']
    
# Print first 5 rows of the data
data.head()

multiple_tickers_data.py hosted with ❤ by GitHub

# Plot all the close prices
data.plot(figsize=(10, 7))

# Show the legend
plt.legend()

# Define the label for the title of the figure
plt.title("Adjusted Close Price", 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)
plt.show()

plot_multiple_tickers_data.py hosted with ❤ by GitHub

Data Source: Yahoo Finance

Visit QuantInsti blog to learn how to retrieve data for S&P 500 stock tickers.