What inspired your project?
In the current financial market, people analyze every indicator of a stock extensively to try to get a glimpse into its future, but people tend to not take into account the media, who have a very big influence on the market, what people see on news sites everyday could subconsciously make a very big impact in their decisions.Additionally, not everyone is able to understand how the stock market works in technical and theoretical terms, as the large scale and complexity of the stock market feels rather intimidating for a new investor.
What problem does your project solve?
We think that a very undervalued part of market research, especially by everyday investors, is the media. So we seek to give an indicator for this significant yet overlooked part of market analysis.
How does your project solve the problem?
Our code allows one to easily see trends of keywords in financial sites, which could have a really big effect on the market. The code uses a wayback machine to access older versions of news sites, and looks for and records the number of keywords on that date. Once it’s gathered all the data, it would make a graph of the number of specific keyword mentions by media over a period of time, with the x axis having its units in days.
What technologies did your project use? Include programming languages, libraries, and any external tools.
We used our computers to code with python and libraries: selenium, pandas, tkinter, numpy, and matplotlib. We used VS code and pycharm IDE to run and write code. We also used github to collaborate. Big thanks to the internet; as first gen hackers, we used the internet as a resource to gather a lot of methods needed for this project.
What challenges did you run into?
We ran into the whole spiel of challenges: primarily bugs. One main bug was that VS code couldn’t identify or find selenium, so we had to run pycharm to handle any python files with selenium. Most of these bugs were caused by the limits set by our coding inexperience. Time was also a huge challenge; Our initial vision of this program was too big to fit in the time limit so we had to scale back.
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