CTRL+ALT+JOPX: Euronext

Wednesday, April 22, 2020

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

%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)

Monday, April 20, 2020

Using Euronext stock data from Quandl in Jupyter notebooks

The last couple of weeks I have been learning about Python and how to use it for stock and derivative trading. One of the challenges is getting stock trading data for European stocks (without having to pay for it). One of the first things I started with is using Jupyter notebooks to quickly visualize stock market information.

The easiest way to get started with Jupyter is using an all-in-one Python distribution - the one I used is Anaconda since it is easy to setup and it includes a number of interesting libraries I want to use in next steps.

I like to try out things hands-on but I did use a number of training resources to get up to speed:

To get trading data about European stocks I used Quandl. Quandl is a marketplace for financial and economic data which is either freely available or requires a paid subscription. Data is contributed by multiple data publishers like World Bank, trading exchanges and investment research firms. Quandl provides REST API access to the available data sets but also has specific Python and R libraries. You first need to register to get an API key. A lot of European stocks are traded on Euronext and Quandl provides you access to Euronext data - https://www.quandl.com/data/EURONEXT-Euronext-Stock-Exchange

Install the quandl Python package using the Anaconda command prompt. It is best to setup virtual environments to manage separate package installation that you need for a particular project, isolating the packages in other environments but for simplicity I just installed in the base environment.

Next it is quite easy to retrieve stock data from Quandl - you first import the quandl package and next you call the quandl.get() method. By default, Quandl will retrieve the dataset into a pandas DataFrame. Since I specified no additional parameters, the entire timeseries dataset was retrieved - from February 2014 until now. Afterwards I used the plot command which uses the matplotlib library to display a graph of the closing prices.

For the full Jupyter notebook take a look at Github https://github.com/jorisp/tradingnotebooks/blob/master/Quandl_API_Euronext_ABI_Shared.ipynb