HippoShark

General Mission

Build the foundation for open-source stock price predictions using AI/ML/general regression techniques.

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Objectives

1. Pull and Store Different Stock-Related Data:

   • Include sentiment data from news outlets.
   • Historical stock prices.
   • Company financials.
   • Market indicators and economic data.

2. Transform Data into Machine Learning Usable Features:

   • Clean and preprocess the data for the model, based on various factors:
     • Sector
     • Subsector
     • Time Horizon
     • Past Performance
     • News Sentiment
     • Market Volatility
     • Technical Indicators (e.g., moving averages, RSI, MACD)
     • etc.

3. Create Dynamic Models to Predict Various Time Horizons:

   • Develop models that can predict stock prices over different periods:
     • Short-term (daily, weekly)
     • Medium-term (monthly, quarterly)
     • Long-term (annual)

4. Store Models and Create Efficient Methods for Their Use:

   • Possibly store the predictions prebuilt and allow a button for users to call certain horizons.
   • Implement a scalable storage solution for model predictions and historical data.

5. Create an API Using Flask:

   • Enable these endpoints to be used by other applications.
   • Ensure secure access and rate limiting for the API endpoints.

6. Create a Front-End Experience With:

   • User Accounts: Allow users to log in and manage their profiles.
   • Subscriptions: Enable users to subscribe to the automated detector and news synthesizer.
   • Stock Information: Allow users to pull stock information.
   • Custom Models: Allow users to create 'custom' models that take longer to process.
   • Backtesting Systems: Enable users to test their models against historical data.
   • Visual Dashboards: Provide interactive charts and graphs to visualize stock predictions and performance.

7. Performance:

   • Ensure the application is fast and responsive.
   • Optimize database queries and model inference times.
   • Implement caching mechanisms for frequently accessed data.

8. Continuous Improvement:

   • Regularly update models with new data to improve accuracy.
   • Gather user feedback to enhance features and usability.
   • Monitor system performance and make necessary optimizations.