BullRun

Our Story: Building BullRun What Inspired Us? Investing can be intimidating, especially for beginners. We wanted to create a platform that makes investing fun, accessible, and social. The idea of combining gamification (like Tinder’s swiping mechanism) with investment education was born out of our own struggles to understand the stock market. We were inspired by apps like Robinhood for their simplicity and Tinder for their engaging user experience. Our goal was to create a tool that not only helps users learn about investing but also makes it a collaborative and competitive experience with friends.

What We Learned User Experience Matters: We learned how to design an intuitive and engaging interface that keeps users coming back. AI Integration: We explored how to use AI to personalize stock recommendations based on user preferences and risk profiles. Team Collaboration: Working in a team taught us how to divide tasks effectively, communicate clearly, and iterate quickly. Financial Data: We gained hands-on experience working with stock market APIs and understanding key financial metrics like P/E ratios, market cap, and volatility.

How We Built BullRun Planning: We started by brainstorming the core features: swiping, portfolio building, AI recommendations, and a leaderboard.We created wireframes in Figma to visualize the user flow and design.

Tech Stack: Frontend: React.js for the web app, with Material-UI for clean, responsive components. Backend: Node.js and Express for handling user data and stock recommendations. Database: Firebase for real-time data storage and user authentication. AI: A simple rule-based system to recommend stocks based on risk profiles. APIs: Integrated Alpha Vantage for real-time stock data.

Development: We divided the work: one team focused on the frontend (swiping interface, portfolio dashboard), while the other worked on the backend (AI recommendations, leaderboard logic). We used Framer Motion for smooth animations and transitions.

Testing:We conducted user testing to gather feedback on the swiping mechanism and overall usability. We iterated on the design and functionality based on user input.

Challenges We Faced Data Limitations:Free stock market APIs have rate limits and often provide delayed data. Solution: We used mock data for the hackathon and optimized API calls.

AI Complexity:Building a AI model was beyond the scope of the hackathon. Solution: We implemented a rule-based system to recommend stocks based on risk profiles.

Time Constraints: Balancing feature development with polish was challenging. Solution: We prioritized core features (swiping, portfolio building, leaderboard) and saved stretch goals (e.g., social features) for later.

What’s Next for BullRun? Real-Time Data: Integrate real-time stock data for live portfolio tracking. Advanced AI: Use machine learning to provide more accurate stock recommendations. Social Features: Add chat, portfolio sharing, and friend challenges. Mobile App: Expand BullRun into a mobile app for on-the-go investing.