Build the foundation for open-source stock price predictions using AI/ML/general regression techniques.
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Pull and Store Different Stock-Related Data:
- Include sentiment data from news outlets.
- Historical stock prices.
- Company financials.
- Market indicators and economic data.
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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.
- Clean and preprocess the data for the model, based on various factors:
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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)
- Develop models that can predict stock prices over different periods:
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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.
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Create an API Using Flask:
- Enable these endpoints to be used by other applications.
- Ensure secure access and rate limiting for the API endpoints.
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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.
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Performance:
- Ensure the application is fast and responsive.
- Optimize database queries and model inference times.
- Implement caching mechanisms for frequently accessed data.
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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.