This project is keen on exploring the connection between the sentiment on a social-media platform about a cryptocurrency and the correlating price.
The approach has been to sample the sentiment from previously collected real-time tweets from Twitter. Afterwards, signals have been derived from these sentiment scores and a trading strategy was built. The system was designed to work in the background, store data in a Postgres database and trade on its own. It does this with the help of Heroku and a Scheduler, that checks every hour, if a trade should be made or not.
Insights of this project, like tweets, sentiment and trade metrics have been visualised with streamlit.
The whole system is explained in the following video:
Further explanation about the visualisation (snapshot from 24th August, 2022):
(The videos are also found inside the GitHub Repo.)
(A PDF file containing all chapters is here. Unfortunately, the conversion from Markdown to PDF resulted in some faulty format. Better read here.)