SchwabBot

Inspiration

After seeing the mountain of influencers who give terrible financial advice, we felt there was a strong need to educate the general public about what actually matters.

What it does

The SchwabBot seeks to educate ordinary people on how to value a stock. It does this by providing key indicators (like BVPS, current assets, current liabilities, etc.) to develop an independent opinion on their investment strategy. In combination, we feature a report generation function for our product. Using generative AI, we can create fundamental, technical, and sentimental analysis reports. Recommending you key trends to look at and why they might affect your investment decisions. This information is then fed into our chatbot we call SchwabBot (unrelated to Charles Schwab). Rather than telling us to buy or sell the stock, SchwabBot recommends key trends to look for when analyzing the stock. It also gives explanations on why these trends are important to look for. The SchwabBot will answer any financial questions that one may have.

How we built it

We used React for our frontend. In addition, 3D elements of our UI used the Spline package. For our backend, we used Node.js. We heavily used the Financial Modeling Prep API for our key metrics and OpenAI API for report generation and chatbot.

Challenges we ran into

We initially envisioned a financial product using AI. However, we weren’t able to find a way to extract the practicality and creativity from our ideas. This made brainstorming our biggest challenge. We hadn’t agreed on an idea until 5 PM yesterday.

Accomplishments that we're proud of

We’re really proud of our frontend. What we were able to extract from the 16 hours we had was mind-boggling. Thanks, Chris for being the GOAT.

What we learned

We faced challenges in time management as we were unable to pre-plan this project. This project has shown us the importance of brainstorming and high-level designing before we enter the project. As not all members of the team were familiar with stock valuation and financial statement analysis, we had to learn which metrics to value the most and the reasoning behind it.

What's next for SchwabBot

We are not financial analysts. We are CS students. Our next step is to invite investment analysts to improve our products so that we can properly train individuals to identify and reason key metrics and form an independent opinion. In the future, we plan to introduce a dynamic ordering system for our fundamental analysis metrics. This allows members to find outstanding metrics and form pinpoints to focus on when analyzing a financial statement. We also want to add a recommendation system for attractive investments using the user’s view history.