Speaking engagements in coming months

With all Covid bans lifted and summer holidays well over, the conference season kicks off. 

I will be speaking at a couple of events in the coming weeks and months:

• Dataminds evening session Upcoming in-person event on September 29th organized by dataMinds.be at Inetum-Realdolmen offices in Kontich together with Benni De Jagere. First session a little bit off the beaten track for data professionals: #dataviz for investors. Second session: #PowerBI roadmap and #AMA by Benni.
• Collabdays Belgium 2022. Free community-driven event in Brussels, Belgium. Focus is Microsoft 365 with some Power Platform and Azure sprinkled on top. I am particularly excited to be speaking at this conference which was born out of the SharePoint Saturday conferences which I helped organize many years ago. I will be delivering Dataverse Deep Dive: watch out for sharks.
• Cloudbrew 2022. A two-day conference focusing on all things Azure on November 18-19 in Mechelen Belgium. I will be delivering Using Python and Azure Cloud for trading and investing

Using Python and Pandas Datareader to retrieve financial data - part 3: Yahoo Finance

Yahoo Finance is one of the most popular sources of free financial data. It does not only contain historical data but also financial statements, dividend information and calculated metrics like e.g. 50 and 200 day moving average, beta, etc ... Yahoo Finance does not have an officially supported API anymore but pandas-datareader  still allows you to access the data from Yahoo Finance in Python (other alternatives are yfinance and yahoo_fin).

This post is part of a series on using Pandas datareader to retrieve financial data:

• Using Python and Pandas Datareader to retrieve financial data - part 1: Federal Reserve Data (FRED)
• Using Python and Pandas Datareader to retrieve financial data - part 2: Fama & French library
• Using Python and Pandas Datareader to retrieve financial data - part 3: Yahoo Finance

In this post I have used version 0.10.0 of pandas-datareader  (released July 13, 2021) which is currently working with Yahoo Finance - previous versions of pandas-datareader had to be updated after Yahoo made some changes on the underlying API.

Warning: Accessing Yahoo Finance using Python libraries is quite brittle so don't try to built production trading systems using this data source.

Accessing the Yahoo Finance API using pandas-datareader is very simple as shown in the screenshot below but I would also recommend implementing a cache mechanism for your queries using the requests-cache Python library to avoid having your IP address being banned. The full source of this Jupyter notebook is available at https://github.com/jorisp/tradingnotebooks/blob/master/YahooFinancesingle.ipynb

References:

• Yahoo Finance API - A complete guide
• yfinance 
• yahoo_fin

Using Python and Pandas Datareader to retrieve financial data - part 2: Fama & French data library

This post is part of a series on using Pandas datareader to retrieve financial data:

• Using Python and Pandas Datareader to retrieve financial data - part 1: Federal Reserve Data (FRED)
• Using Python and Pandas Datareader to retrieve financial data - part 2: Fama & French library
• Using Python and Pandas Datareader to retrieve financial data - part 3: Yahoo Finance

In this post we will look at the datasets made available by Eugene Fama and Kenneth French. Eugene Fama and Kenneth French did a lot of research on which factors drive security returns. In 1993, they published the Three Factor Model (see article "Common risk factors in returns of stocks and bonds", Journal of Financial Economics 33, 1993), which showed that their factors (size of the firm, book-to-market values and excess return) capture a statistically significant fraction of the variation of stock returns. In 2014, Fama and French adapted their model to include five factors.  Fama won the Nobel Prize for Economics in 2013 for his research. Fama also published a number of papers on the Efficient Market Hypothesis and random walk theory.

Fama and French still publish the returns of various investment factors analyzed by them on their homepage on a regular basis.  You can download this data using the pandas_datareader library - you can take a look at the official documentation,  Fama-French Data (Ken French's Data library) to get started or take a look at the Jupyter notebook that I shared on Github https://github.com/jorisp/tradingnotebooks/blob/master/FAMA.ipynb

References:

• Fama/French forum ideas and observations from Fama and French
• How to use the Fama French model
• Fama-French 5-factor model: five major concerns
• In pursuit of the perfect portfolio: Eugene F. Fama (YouTube - MIT Laboratory for Financial Engineering )

Using Python and Pandas Datareader to retrieve financial data - part 1: Federal Reserve Data (FRED)

The pandas-datareader Python library covers a number of APIs with global fundamental macro- and industry data sources including the following (for a full list see  Pandas Datareader - data sources ):

• St. Louis FED (FRED): Federal Reserve data on the U.S. economy and financial markets
• Fama/French data library : market data on portfolios capturing returns on key risk factors like size, value, and momentum by industry
• Yahoo Finance : retrieve daily stock prices, historical corporate actions (dividends and stock splits) from Yahoo Finance 
• World Bank: global database with economic/social indicators and demographics.

This post is part of a series on using Pandas datareader to retrieve financial data:

• Using Python and Pandas Datareader to retrieve financial data - part 1: Federal Reserve Data (FRED)
• Using Python and Pandas Datareader to retrieve financial data - part2: Fama & French library
• Using Python and Pandas Datareader to retrieve financial data - part 3: Yahoo Finance

In this post I will focus on retrieving data from FRED using pandas-datareader.  Federal Reserve Economic Data (FRED) - https://fred.stlouisfed.org/  is a database maintained by the Federal Reserve Bank of St. Louis. It has more than 800.000 data time series covering categories such as Economic growth & employment, monetary & fiscal policy, demographics, industries, commodity prices at different frequencies (daily, monthly, annual).  One of the interesting time series you can find here are 3-month Treasury Bill Secondary Market rate (TB3MS) or 1-year US Treasury bills which are used a proxy for the risk free rate in financial modeling. 

There is however some missing data on the TB1YR - so I will be using the TB3MS (3 Month) in my next example. You will notice that all time series are identified by a short abbreviation that you can find by searching on the FRED website.

Full source code is available at https://github.com/jorisp/tradingnotebooks/blob/master/FRED.ipynb 

References:

• FRED API: get US economic data which drives the real estate market
• Pandas-datareader python library

Palm oil price vs SIPEF stock price in Jupyter notebooks with Quandl

Quandl also offers free commodity price data for almost 100 commodities ( See API for commodity data) so I decided to create a Jupyter notebook comparing SIPEF's stock price with the price of palm oil. Take a look at the full notebook I shared on github if you want to learn why - https://github.com/jorisp/tradingnotebooks/blob/master/Quandl_API_Euronext_SIPEFPalmOil.ipynb 

 

Remarks:

• Be careful when embedding images in a Jupyter notebook that you want to publish on Github (see Images in Markdown not showing when uploaded to Git for a discussion on this) - filenames are case sensitive - I got it working using the url syntax
• To store the quandl key I used the approach outlined on how to use secrets in Jupyter Notebooks with python-dotenv
• I really like Matplotlib and its versatility in rendering capabilities  in this notebook I used the ability to visualize multiple time series (see code preview below) with different axes. (see code below)

sipcolor = 'tab:red'
fig,ax1 = plt.subplots()

ax1.plot(data['SIP'],color=sipcolor)
ax1.set_ylabel('SIPEF',color=sipcolor)
#Instantiate a second axes that shares the same x-axis
ax2 = ax1.twinx()
ax2.plot(data['PPOIL'])
ax2.set_ylabel('PALM OIL USD')
plt.gcf().set_size_inches(15,8)
plt.show()

Related posts:

• Working with multiple time series trading data from Quandl in Jupyter Notebooks
• Using Euronext  stock data from Quandl in Jupyter Notebooks

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

%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)

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:

• Jupyter Notebook Tutorial: the definitive guide (Datacamp)
• Create and share Analytics with Jupyter Notebooks (Pluralsight) 

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