CapitalTrack

Inspiration

Born from our ambition to build a stronger financial future, this project leverages AI and data analysis to support investment decisions. We believe engaging with finance is essential for economic health—investing is about vision, learning, and creating opportunities.

What it does

Our data analysis and artificial intelligence model helps young people understand where their money goes, predict opportunities, and invest more intelligently. It connects your spending habits with real-time data and market news to offer personalized recommendations that help you make better financial decisions every day. From automatic balance tracking to personalized goals and real-time validation to prevent unnecessary spending — our app turns finance into something simple, powerful, and smart. Technology shouldn’t just help you manage your money… it should teach you how to make it grow.

How we built it

We built our project using Reflex as the main framework and a SQLite database to store and manage user information. All data is processed through our Python-based backend, which powers our custom data analysis engine. This engine is responsible for generating personalized recommendations based on users’ spending behavior. It classifies transactions through keyword detection — for example, if the concept includes “cinema,” it’s categorized as Entertainment. Afterward, each expense is analyzed and labeled by priority level (low, medium, or high) to provide clear insights and improvement suggestions. For the investment side, we integrated the AlphaVantage API, which provides real-time market data and financial news. We apply AI-driven sentiment analysis to evaluate whether buying a particular stock is a good or bad idea at that moment, based on trends and market sentiment — allowing users to make smarter and more informed investment decisions.

Challenges we ran into

One of the main challenges we faced was learning Reflex — since it was our first time using it, we had to understand how to integrate it efficiently with Python and structure our app’s logic and interface. Another big challenge was working with APIs. None of us had previous experience with API integration, so learning how to connect, authenticate, and process real-time data from AlphaVantage took time. Finally, the data analysis process was one of the most complex parts of the project. Designing a model capable of classifying expenses, generating insights, and connecting it with market data required both logical thinking and creativity.

Accomplishments that we're proud of

We’re especially proud of the great teamwork we achieved throughout the project. From clear communication to mutual support and unity, we worked together effectively to overcome every challenge. Each member contributed ideas, solutions, and effort, creating an environment where learning and collaboration came naturally. That spirit of teamwork is what made this project possible — and it’s what we value the most from this experience.

What we learned

One key lesson we learned is that organizing ideas beforehand is crucial for achieving a good result. Having a clear plan, defined roles, and structured workflow made it much easier to tackle challenges efficiently and ensure that our project stayed on track.

What's next for CapitalTrack

One important area we plan to improve is cybersecurity. As our app handles sensitive financial data, implementing stronger security measures, data encryption, and secure authentication will be a priority to protect our users’ information. Additionally, we aim to enhance our AI recommendations, add more personalized insights, and explore integrations with other financial platforms to make CapitalTrack even more useful and reliable.