Read Historical Prices from Yahoo! Finance with Python

What will we cover in this tutorial?

In this tutorial we will cover the following.

• How to use Pandas Datareader to read historical stock prices from Yahoo! Finance.
• Learn how to read weekly and monthly data.
• Also how to read multiple tickers at once.

Step 1: What is Pandas Datareader?

Pandas-Datareader is an up to date remote data access for pandas.

This leads to the next question. What is pandas?

Pandas is a data analysis and manipulation tool containing a great data structure for the purpose.

Shortly said, pandas can be thought of as a data structure in Python, which is similar to working with data in a spreadsheet.

Pandas-datareader reads data from various sources and puts the data into a pandas data structures.

Pandas-datareader has a call to return historic stock price data from Yahoo! Finance.

To use Pandas-datareader you need to import the library.

Step 2: Example reading data from Yahoo! Finance with Pandas-Datareader

Let’s break the following example down.

import pandas_datareader as pdr
import datetime as dt
 
ticker = "AAPL"
start = dt.datetime(2019, 1, 1)
end = dt.datetime(2020, 12, 31)
 
data = pdr.get_data_yahoo(ticker, start, end)
print(data)

Where we first import two libraries.

• pandas_datareader The Pandas Datareader. If you do not have it installed already in your Jupyter Notebook you can do that by entering this in a cell !pip install pandas_datareader and execute it.
• datetime This is a default library and represents a date and time. We only use it for the date aspects.

The the following lines.

• ticker = “AAPL” The ticker we want data from. You can use any ticker you want. In this course we have used the ticker for Apple (AAPL).
• start = dt.datetime(2019, 1, 1) Is the starting day we want historic stock price data.
• end = dt.datetime(2020, 12, 31) The end day.
• data = pdr.get_data_yahoo(ticker, start, end) This is the magic that uses Pandas Datareader (pdr) to get data from the Yahoo! Finance API. It returns a DataFrame as we know it from previous lessons.

The output of the code is as follows.

                  High         Low  ...       Volume   Adj Close
Date                                ...                         
2019-01-02   39.712502   38.557499  ...  148158800.0   38.505024
2019-01-03   36.430000   35.500000  ...  365248800.0   34.669640
2019-01-04   37.137501   35.950001  ...  234428400.0   36.149662
2019-01-07   37.207500   36.474998  ...  219111200.0   36.069202
2019-01-08   37.955002   37.130001  ...  164101200.0   36.756794
...                ...         ...  ...          ...         ...
2020-12-24  133.460007  131.100006  ...   54930100.0  131.773087
2020-12-28  137.339996  133.509995  ...  124486200.0  136.486053
2020-12-29  138.789993  134.339996  ...  121047300.0  134.668762
2020-12-30  135.990005  133.399994  ...   96452100.0  133.520477
2020-12-31  134.740005  131.720001  ...   99116600.0  132.492020
[505 rows x 6 columns]

Step 3: A few parameters to set

You can get multiple tickers at once by parsing a list of them.

import pandas_datareader as pdr
import datetime as dt
ticker = ["AAPL", "IBM", "TSLA"]
start = dt.datetime(2019, 1, 1)
end = dt.datetime(2020, 12, 31)
data = pdr.get_data_yahoo(ticker, start, end)
print(data)

You can get the weekly or monthly data by using the argument as follows.

import datetime as dt
ticker = ["AAPL", "IBM", "TSLA"]
start = dt.datetime(2019, 1, 1)
end = dt.datetime(2020, 12, 31)
data = pdr.get_data_yahoo(ticker, start, end, interval='w')
print(data)

Set interval=’m’ to get monthly data instead of weekly with ‘w’.

Want to learn more?

This is part of a 2-hour full video course in 8 parts about Technical Analysis with Python.

If you are serious about learning Python for Finance check out this course.

• Learn Python for Finance with pandas and NumPy.
• 21 hours of video in over 180 lectures.
• “Excellent course for anyone trying to learn to code and invest.” – Lorenzo B.

Get Python for Finance HERE.

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