Inspired by recent attempts to encourage young individuals to invest such as Acorns and Robinhood, we noticed that although automated investing is very easy to do, not only do you not learn as much about the stock market and investing as a whole, but your profits are likely to be relatively less than if you had managed your investment portfolio manually. Robinhood is a popular investing application as well, designing an easy way for newcomers to start investing without the normal fees. We found although they provided decent data, it was still difficult to make educated decisions as a newcomer coming into the world of investing. In order to improve the experience of newcomers and even seasoned investors, we decided to focus on a better way to give financial advice and take the fear of investing away by providing knowledgeable insight.

What it does provides financial analysis and advice based on the stocks you want to invest in, your risk tolerance, and other factors. For each stock, you're interested in investing we provide advice on whether it is a good time to invest, buy, or sell based off numerous factors we have compiled. Factors include sentiment analysis by scouring the web for any news over the respective company, market trends, and risk tolerance.

How we built it

We built this application through Android Studio, utilizing numerous libraries and APIs that include:

  • Blackrock - Aladdin API
  • Google - Firebase
  • Google Cloud Platform - Natural Language API
  • Twitter - Tweets API
  • PhilJay - MPAndroidChart
  • Open Source Initiative - JSoup
  • ISchwarz23 - SortableTableView

Challenges we ran into

  • We struggled coming up with an idea, jumping from idea to idea for a good chunk of time.
  • Web scraping and Sentiment Analysis was a first for us, therefore we had a difficult time implementing it into the application.
  • Extreme sleep deprivation
  • Android UI difficulties
  • API parsing, since each API is used differently

Accomplishments that we're proud of

  • Setting Up Web Scraping and Sentiment Analysis
  • Intuitive and aesthetic UI/UX
  • Excellent performance, even under limited bandwidth conditions
  • Online authentication and saving of user stock data in database
  • Extensive use of provided APIs

What we learned

  • How to do Web Scraping
  • How to efficiently use version control software (VCs) in a collaborative environment
  • How to work with large datasets
  • How to turn blood, sweat, tears, and energy drinks (mostly tears) into code

What's next for

  • We plan to improve our analysis tools to provide more accurate and insightful advice.
  • Add stock history graphs
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