As new investors we have encountered a large discrepancy between casual traders and industry experts; our idea would help to bridge the gap between the two.
What it does
Our project displays how publicly traded stocks are influenced by large social figures. Tweets will be given a positive or negative connotation towards a specific company, allowing casual traders to gain knowledge on the trend of their desired company.
How we built it
We started by gathering previous tweets/time stamps towards publicly traded companies from top figures (Eg. Elon Musk & Donald Trump) and studying the relationship between the two. Once a dataset was gathered, we built a neural network that can judge live tweets based off of previous data.
Challenges we ran into
Accessing the Twitter API has become a struggle in recent months, as data breaches have forced twitter to become much more secure/limited in regards to analytics.
Accomplishments that we're proud of
- Building a successful neural network.
- learning UI design/creating a web design.
What we learned
- We learned how to GETRequest within Python.
What's next for TwittStock
With more accessibility in regards to twitters analytics, our team can create a a large database for our neural network. This will ensure a high accuracy for our algorithm.
Establishing a fully functionally web page, with a user-friendly interface.