Cryptocurrencies are a very recent trend and very few people envisioned Bitcoin's meteoric price increase. However, there are hundreds of other cryptocurrencies some of which have shown the explosive potential of Bitcoin. Our goal was to use machine learning to detect what's the difference between a cryptocurrency that booms or busts.
What it does
Instead of just using an algorithm that uses the price of a cryptocurrency itself to predict future price our team decided it would be very valuable to track online sentiment about cryptocurrencies. We believe this will be especially effective since cryptocurrencies's platforms are entirely online as well as chatter about buying and selling the currency. Additionally, since cryptocurrencies' prices tend to be very momentum based we believe this increases the accuracy of our prediction. Using data from Reddit and news sources about the cryptocurrency along with the price history, we predict what new, unfamiliar cryptocurrencies have the most promise.
How we built it
We scraped the entirety of Reddit for mentions of cryptocurrencies with the 200 highest market caps. We then built a classifier using a decision tree algorithm with the Google Cloud Natural Language API training with Bitcoin data.
Challenges we ran into
We got massive amounts of data from Reddit and had to build our own server and then parse through the data (500 GB) to find posts pertaining to cryptocurrencies. Even though we built a model, we did not have the computing power to run it. Additionally, none of our team members had extensive experience with machine learning.
Accomplishments that we're proud of
We are proud of the fact that none of us knew ML, but were able to teach ourselves the basics and apply them to find a model for the data.
What's next for Crypto-current-cy
For this project, we focused on cryptocurrencies since we believed that the price would be correlated relatively strongly with online news compared to other stocks or technologies. However, moving forward we would like to take this experience and think about how to apply news and social media sentiment it to predicting success of new technologies.