What is the best way to get stock data using Python?

In this article, we will learn the best way to get stock data using Python.

The yfinance Python library will be used to retrieve current and historical stock market price data from Yahoo Finance.

Installation of Yahoo Finance(yfinance)

One of the best platforms for acquiring Stock market data is Yahoo Finance. Just download the dataset from the Yahoo Finance website and access it using yfinance library and Python programming.

You can install yfinance with the help of pip, all you have to do is open up command prompt and type the following command show in syntax:

Syntax

pip install yfinance

The best part about yfinance library is, its free to use and no API key is required for it

How to get current data of Stock Prices

We need to find a ticker of the stock Which we can use for data extraction. we will show the current market price and the previous close price for GOOGL in the following example.

Example

The following program returns the market price value,previous close price value,ticker value using yfinance module −

import yfinance as yf
ticker = yf.Ticker('GOOGL').info
marketPrice = ticker['regularMarketPrice']
previousClosePrice = ticker['regularMarketPreviousClose']
print('Ticker Value: GOOGL')
print('Market Price Value:', marketPrice)
print('Previous Close Price Value:', previousClosePrice)

Output

On executing, the above program will generate the following output −

Ticker Value: GOOGL
Market Price Value: 92.83
Previous Close Price Value: 93.71

How to get Historical data of Stock Prices

By giving the start date, end date, and ticker, we can obtain full historical price data.

Example

The following program returns the stock price data between the start and end dates −

# importing the yfinance package
import yfinance as yf

# giving the start and end dates
startDate = '2015-03-01'
endDate = '2017-03-01'

# setting the ticker value
ticker = 'GOOGL'

# downloading the data of the ticker value between
# the start and end dates
resultData = yf.download(ticker, startDate, endDate)

# printing the last 5 rows of the data
print(resultData.tail())

Output

On executing, the above program will generate the following output −

[*********************100%***********************] 1 of 1 completed
            Open      High     Low       Close     Adj Close Volume
Date
2017-02-22 42.400002 42.689499 42.335499 42.568001 42.568001 24488000
2017-02-23 42.554001 42.631001 42.125000 42.549999 42.549999 27734000
2017-02-24 42.382500 42.417999 42.147999 42.390499 42.390499 26924000
2017-02-27 42.247501 42.533501 42.150501 42.483501 42.483501 20206000
2017-02-28 42.367500 42.441502 42.071999 42.246498 42.246498 27662000

The above example will retrieve data of stock price dated from 2015-03-01 to 2017-03-01.

If you want to pull data from several tickers at the same time, provide the tickers as a space-separated string.

Transforming Data for Analysis

Date is the dataset's index rather than a column in the example above dataset. You must convert this index into a column before performing any data analysis on it. Here's how to do it −

Example

The following program adds the column names to the stock data between the start and end date −

import yfinance as yf

# giving the start and end dates
startDate = '2015-03-01'
endDate = '2017-03-01'

# setting the ticker value
ticker = 'GOOGL'

# downloading the data of the ticker value between
# the start and end dates
resultData = yf.download(ticker, startDate, endDate)

# Setting date as index
resultData["Date"] = resultData.index

# Giving column names
resultData = resultData[["Date", "Open", "High","Low", "Close", "Adj Close", "Volume"]]

# Resetting the index values
resultData.reset_index(drop=True, inplace=True)

# getting the first 5 rows of the data
print(resultData.head())

Output

On executing, the above program will generate the following output −

[*********************100%***********************] 1 of 1 completed
   Date      Open       High     Low       Close     Adj Close  Volume

0 2015-03-02 28.350000 28.799500 28.157499 28.750999 28.750999 50406000
1 2015-03-03 28.817499 29.042500 28.525000 28.939501 28.939501 50526000
2 2015-03-04 28.848499 29.081499 28.625999 28.916500 28.916500 37964000
3 2015-03-05 28.981001 29.160000 28.911501 29.071501 29.071501 35918000
4 2015-03-06 29.100000 29.139000 28.603001 28.645000 28.645000 37592000

The above converted data and data we acquired from Yahoo Finance are identical

Storing the Obtained Data in a CSV File

The to_csv() method can be used to export a DataFrame object to a CSV file.The following code will help you export the data in a CSV file as the above-converted data is already in the pandas dataframe.

# importing yfinance module with an alias name
import yfinance as yf

# giving the start and end dates
startDate = '2015-03-01'
endDate = '2017-03-01'

# setting the ticker value
ticker = 'GOOGL'

# downloading the data of the ticker value between
# the start and end dates
resultData = yf.download(ticker, startDate, endDate)

# printing the last 5 rows of the data
print(resultData.tail())

# exporting/converting the above data to a CSV file
resultData.to_csv("outputGOOGL.csv")

Output

On executing, the above program will generate the following output −

[*********************100%***********************] 1 of 1 completed
            Open      High     Low       Close     Adj Close  Volume

Date
2017-02-22 42.400002 42.689499 42.335499 42.568001 42.568001 24488000
2017-02-23 42.554001 42.631001 42.125000 42.549999 42.549999 27734000
2017-02-24 42.382500 42.417999 42.147999 42.390499 42.390499 26924000
2017-02-27 42.247501 42.533501 42.150501 42.483501 42.483501 20206000
2017-02-28 42.367500 42.441502 42.071999 42.246498 42.246498 27662000

Visualizing the Data

The yfinance Python module is one of the easiest to set up, collect data from, and perform data analysis activities with. Using packages such as Matplotlib, Seaborn, or Bokeh, you may visualize the results and capture insights.

You can even use PyScript to display these visualizations directly on a webpage.

Conclusion

In this article, we learned how to use the Python yfinance module to obtain the best stock data. Additionally, we learned how to obtain all stock data for the specified periods, how to do data analysis by adding custom indexes and columns, and how to convert this data to a CSV file.