Something I have been personally doing in my free time is trading physical assets. The challenge struck us when it was first presented on stage. We knew it was possible before it's existence, and we wanted to try and make our own algorithm which analyses a certain stock and takes action according to the analysis, this was the perfect opportunity.
The auto-trader client of the challenge which was given in python so we thought it would be easier to apply a code in the same language and worked with python. One of the challenges was to store our code with our outputs and had to find a way to deal with it. After some research, we found our saviour JupyterLab: it's a web-based user interface. How it helped us was we could test codes as soon as we had written them and the output remained unless we changed the existing lines of code, which is the key point of using JupyterLab.
In the process of coding, we had to learn how to work with data using python and it led us to reading documentations on how to use pandas libraries in python and we now have a basic understanding on how to use it. Other than pandas, matplotlib was used to visualise data on a graph. One of our previous course works already involved that particular library so it was much easier to work with compared to pandas.
Certainly it was not an easy feat. Even though we couldn't complete it, we pushed our limits to the furthest and learned how to work with data more proficiently. This event has taught us a lot and we are looking forward to join more hackathons in the future!
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