Trendy Socials

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  PowerBI Dashboard

Inspiration

We went into the challenge hoping to create a model that makes use of Reddit threads, tweets, Youtube video titles, newsfeeds, and Google search trends to optimize a social metric(Sentiment analysis) based on a statistical regression model. This metric would serve as an indicator to find large changes in price and volatility. This would be displayed to retail investors to determine trading strategies that make use of options contracts that give large returns on investments in the hope of a large move in volatility(regardless of whether the price moving up or down).

What it does

We were successfully able to implement the use of Google trends and display the graphs of the S&P 500 with PowerBI. The sudden changes in users' interest in the companies product or services are viewed as a sign of future price volatility. This can be used to influence the trading strategies of retail investors that use our platform.

How we built it

We used python APIs(pytrends, twelve data) on Google Colab to scrape data regarding stock prices and google search trends. PowerBi was used to create a dashboard for all the collected data.

Challenges we ran into

We initially tried to run a react application on AWS but the time constraint made us switch to PowerBI. Twitter's API tool required authorization which we were not able to get in time to implement for this project. Cold weather, sleep deprivation, and Caffeine reliance.

Accomplishments that we're proud of

A working sample demo of what the project hoped to achieve.

What we learned

We learned to work in a team and further deepen our understanding of investments, each other's personal strengths/weaknesses, and build relationships between data and insights. Initially spent time to learn React, AWS hosting, Firebase, and PowerBI. It is to be noted, we did not end up using all these platforms.

What's next for Trendy Socials

Implement scripts to mine data from Twitter, Reddit, newsfeeds which will be processed using NLP library to do sentiment analysis. This would later be fed into a regression model which would create a social metric for the current activity/interest with regards to that company. Implement a react web application to make the user interface easy and experience intuitive. Implement data sorting and recommendation systems based on past user investments, and individual risk tolerance.

Built With

• google-colab
• powerbi
• pytrends
• twelvedata