Hudson & Thames has provided the following skillset challenge to allow potential researchers to gauge if they have the required skills to take part in the apprenticeship program.

The following assignment is an opportunity for you to highlight your skillset and show us what you are made of! It tests your ability to implement academic research for the broader quantitative finance community, and to do it in style!

Read the following paper: Enhancing a Pairs Trading strategy with the application of Machine Learning.

Note: The following textbook provides a deeper explanation of the technique.

In a Jupyter Notebook (python):

1. Download and save your universe of stocks (use 200-300 shares) (Can use Yahoo finance to get shares data. Checkout the yfinance package.)(Else you can use Polygon)
2. Implement Section III: Proposed Pairs Selection Framework (A and B, Section C if you really want to impress)
3. Create a set of functions/class for the end-user to make use of.
4. Make sure to add docstrings and follow PEP8 code style checks. Have plenty of inline comments, good variable names and don't over complicate things unnecessarily. It should be easy for the user to make use of.
5. Showcase your new Pairs Selection Framework in a Jupyter Notebook and show us some visualizations of the clusters and pairs.
6. Add an introduction, body, and conclusion showcasing your new implementation. (Use the correct style headers)
7. Make a Pull Request to this repo so that we can evaluate your work. (Create a new folder with your name)
8. Bonus points if you add unit tests (in a separate .py file).

• Your code for the implementation should be contained in a .py file that you import into your notebook. Please don't have large chunks of code in your notebook.
• Save your data with your PR so that we can evaluate it.
• Keep in mind that if you don't have enough rows of data to support the number of assets in your universe, the technique will break down.

• Company Name: Hudson and Thames Quantitative Research
• Company Brief: Our core focus is on the implementation of research within buy-side asset management.
• Company Website: https://hudsonthames.org/
• Locked Achievement: Quantitative Researcher & Developer
• Location: Virtual Team (We are all in different time zones across the world.)
• Education: Familiarity with machine learning, statistics, and applied maths. We care a lot more about what you can do rather than your exact qualifications.

• Implement academic research for machine learning in finance.
• Python
• Unit tests
• PEP8
• Continuous integration
• Documentation
• Writing articles
• Public Speaking

• Must speak fluent English. There is a documentation requirement so English is an absolute requirement.
• Python
• Machine Learning
• Software engineering
• Object Orientated Programming
• Linear Algebra