StockRiskEd

Inspiration

I wanted to create a project that made the stock market more approachable for students and beginner investors. As financial literacy is rarely taught in schools, many people are discouraged from investing because of risk or lack of knowledge. I built StockRiskEd to break down these barriers with simple visuals, risk analysis, and clear explanations.

What it does

StockRiskEd is an interactive web app that lets users explore the 5-year stock performance of major companies. It calculates 30-day rolling volatility to assess risk, simulates investment growth, and provides plain-English definitions for common financial terms. It helps users understand not just how much a stock has grown, but how risky it is.

How I built it

I built StockRiskEd using Python and Streamlit. I used pandas to process the historical stock data, matplotlib for the data visualization, and Streamlit to create an interactive UI. I also added a glossary with a built-in dictionary of financial definitions.

Challenges I ran into

I initially had issues formatting the investment output, and ran into a few layout bugs while displaying volatility charts. I also had to debug issues related to date formatting and time-zone mismatches in the CSV data.

Accomplishments that I'm proud of

I'm proud to make a fully functional, accessible, and educational app. It makes stock data easy to understand for people with no investing background, and I was able to build it with clean visuals and interactive elements.

What I learned

I learned how to work with time-series data and how to use rolling statistics to communicate financial risk. I also got better at designing with the end-user in mind and making complex ideas—like volatility—feel intuitive. I learned a lot about the different stock market terms as well!

What's next for StockRiskEd

I want to expand the app by adding a real-time data update feature and an AI assistant that can explain news headlines or answer questions. I’m also considering adding a portfolio tracker so users can monitor their investments over time.

Built With

• machine-learning
• matplotlib
• pandas
• python
• stock-market-data
• streamlit