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 the course of Master Technical Analysis with pandas.

In the next lesson you will learn how to Calculate the Volatility of Historic Stock Prices with Pandas and Python.

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