InvestEd HK

"InvestEd HK is an AI-powered platform for HK beginners to track stocks, simulate trades, explore scenarios, and learn investing risk-free with real-time data and insights."

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Inspiration I noticed that many beginners and students in Hong Kong want to invest but face high risks and a lack of beginner-friendly platforms. My goal was to create a safe, educational, and interactive environment to practice investing before committing real money. What it does InvestEd HK allows users to track live stock prices, simulate trades with virtual money, and explore AI-powered “what-if” scenarios. Users can manage multiple portfolios, visualize growth, and gain confidence before entering the real market. Tagline: Learn. Simulate. Invest Smart. How I built it I used Python with Tkinter and ttkbootstrap for the UI, yFinance for live stock data, and Matplotlib for visualizations. Portfolios are stored securely in SQLite with JSON and Pickle for persistence. I leveraged Amazon Q Developer for backend code, unit tests, and AWS-ready integrations, and Kiro for structured user stories, documentation, and workflow clarity. Challenges I ran into • Integrating real-time stock data while keeping simulations fast and smooth. • Designing AI-powered “what-if” scenarios that were accurate and understandable. • Balancing educational guidance with technical depth for beginners. Accomplishments that I'm proud of • Fully functional simulation system with secure user accounts and multiple portfolios. • Interactive dashboards showing portfolio performance and risk analysis. • Successfully implemented binary trees, sorting algorithms, and matrix structures for portfolio organization and calculations. What I learned • How to combine AI tools like Amazon Q Developer and Kiro with hands-on Python development. • Best practices for modular programming, data structures, and GUI design. • Strategies for creating educational yet realistic investment simulations. What's next for InvestEd HK • Expand to real-time trading API integration for simulated market orders. • Introduce AI-driven recommendations and personalized learning paths. • Enhance mobile support and social features to share simulated portfolios with peers.

Built With

  • built-with-languages:-python-3-ui-&-frameworks:-tkinter
  • json
  • pickle-cloud-&-ai-services:-amazon-q-developer
  • ttkbootstrap-data-&-apis:-yfinance-for-real-time-stock-data-visualization:-matplotlib-database-&-storage:-sqlite
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