Inspiration We recognized a gap in the market for accessible yet comprehensive stock analysis tools. The increasing complexity of financial markets and the growing interest in retail investing inspired us to create a tool that could democratize professional-grade investment analysis, making it available to investors of all experience levels.

What it does StockWise is a real-time stock analysis tool that:

  • Allows users to analyze any NASDAQ-listed stock
  • Provides color-coded graphs showing price changes across different timeframes
  • Displays comprehensive financial data including income statements, balance sheets, and cash flow statements
  • Offers both basic and advanced analysis features powered by AI
  • Generates custom investment recommendations based on multiple data points
  • Delivers real-time market insights through API integration

How we built it We developed StockWise using:

  • ANSI C programming for efficient data processing
  • Marketstack API for real-time stock data retrieval
  • Financial Modeling Prep (FMP) API for detailed financial metrics
  • ChatGPT API for advanced analysis and recommendations
  • Command-line interface for efficient user interaction
  • Custom algorithms for basic technical analysis
  • Data structures like hash tables for keyword tracking and analysis

Challenges we ran into

  • Learning and implementing multiple API integrations for real-time data retrieval
  • Working within the constraints of a command-line interface, which limited visualization options
  • Conducting extensive research to understand stock market trends and investment calculations
  • Developing accurate algorithms for investment decision-making
  • Managing the complexity of real-time data processing

Accomplishments that we're proud of

  • Successfully integrated three different APIs (Marketstack, FMP, and ChatGPT)
  • Created an efficient command-line interface that's both powerful and user-friendly
  • Implemented real-time data processing with minimal latency
  • Developed a dual-layer analysis system combining traditional metrics with AI insights
  • Built a scalable system that can handle multiple data streams simultaneously

What we learned

  • Advanced API integration techniques
  • Real-time data processing and management
  • Financial market analysis methodologies
  • Efficient C programming practices
  • The importance of user-focused design, even in CLI applications

What's next for Stock-wise We plan to:

  • Expand coverage to more stock exchanges beyond NASDAQ
  • Implement machine learning algorithms for better prediction accuracy
  • Add portfolio management features
  • Develop a web-based interface while maintaining CLI efficiency
  • Include more advanced technical analysis tools
  • Integrate additional data sources for more comprehensive analysis

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