• 

  This is the home page, introducing the platform’s purpose and guiding users to start managing and understanding their finances better.

• 

  This is the login page, where users securely access their accounts to continue tracking and managing their financial data.

• 

  This is the signup page, where new users create an account to start their journey with Momentum.

• 

  This is the Investments page, where users input assets and debts to simulate portfolio performance under Federal interest rate changes.

• 

  This is the General Stats page, where users view and input their financial overview to analyze how they will respond to rate changes.

• 

  GenStats page scrolled down; the numbers and risk level results are shown to users so they can understand how their finances are impacted

Momentum

Inspiration

Momentum was inspired by seeing people face financial distress and realizing how difficult it can be to understand the impact of interest rates, debt, and investments on personal finances. Many don't understand that seemingly small changes in various rates can impact consumers significantly.

What It Does

The application calculates net worth impact, monthly payment increases, duration gap, and risk classification based on user inputs for cash, bonds, equities, fixed and variable debt, mortgage rates, and remaining years.

How We Built It

Backend: Python with FastAPI that fetches real-time data from FRED using our API key. This includes the 10-year Treasury rate (DGS10), the 30-year mortgage rate (MORTGAGE30US), and variable mortgage and bond data. While our demo uses data from Friday, February 19th, future queries always pull the most recent values. Calculations utilize mathematical computations such as Taylor series approximations, which we verified in MATLAB to ensure accuracy.

Frontend: Streamlit with HTML styling to display numeric outputs and provide contextual summaries explaining how rate changes affect the user's finances.

Challenges

• Integrating the frontend and backend
• Handling real-time macroeconomic data
• Making calculations accurate and reliable

What We Learned

Collaboration, working under pressure, focusing on the main issue, and combining design with technical accuracy for the best consumer experience.

Accomplishments

We're proud to have built this using real-time data and successfully worked through issues during the coding process.

What's Next

• Allow users to link bank accounts
• Scale to use public financial data from other countries for global reach
• Add more financial metrics for more thorough and advanced insights

Built With

• fastapi
• fredapi
• html
• matlab
• python
• streamlit