Working with multiple time series trading data from Quandl in Jupyter Notebooks

In the previous example - Using Euronext stock data from Quandl in Jupyter notebooks I downloaded a single dataset from Quandl. But it is also possible to download multiple datasets by passing in a list of Quandl codes.

In the example below, I downloaded the prices of a number of diversified holding companies which are traded on Euronext Brussels and compared the cumulative returns (not including dividend payments) using Jupyter Notebooks.

The Quandl Python API allows you to make a filtered time series call and request only specific
columns - in this example the 'Last' (Closing price) is retrieved by specifying the index 4. In a next
step I renamed the columns in the pandas dataframe to make it easier to work with the data
afterwards.



Take a look at the full python notebook at https://github.com/jorisp/tradingnotebooks/blob/master/Quandl_Belgian_Holdings-Shared.ipynb to see how this data can be used to visualize cumulative returns for these different stocks

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%matplotlib inline import quandl import matplotlib.pyplot as plt quandl.ApiConfig.api_key = "<Your Key Here>" #Retrieve Last price only for the 5 holdings (excluding mono holdings) trading on Euronext Brussels #Data is available from February 2014 onwards - Ackermans Van Haren (ACKB), Brederode (BREB), Sofina (SOF), #GBL and Bois Sauvage (COMB ) data = quandl.get(['EURONEXT/ACKB.4','EURONEXT/BREB.4','EURONEXT/SOF.4','EURONEXT/GBLB.4','EURONEXT/COMB.4']) #Rename column names data.rename(columns={'EURONEXT/ACKB - Last': 'ACKB', 'EURONEXT/BREB - Last': 'BREB','EURONEXT/SOF - Last':'SOF', 'EURONEXT/GBLB - Last':'GBLB','EURONEXT/COMB - Last':'COMB'},inplace=True)