This project is designed to develop a sophisticated and comprehensive application that harnesses the power of deep learning to predict stock prices with a high degree of accuracy. The application will serve as an all-in-one financial platform, offering users access to critical financial data, real-time market updates, curated news, and interactive visualizations for selected stocks. By integrating advanced machine learning algorithms, the system will analyze historical stock data, identify patterns, and generate predictive insights to assist users in forecasting future price movements.
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Stock Forecasting: Utilize deep learning models (LSTM, Transformer-based models) to predict stock price trends.
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Interactive Dashboard: Display stock data, news, and forecast results in an intuitive UI.
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Historical Data Visualization: Graphical representation of stock price history and indicators.
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Real-time Stock Updates: Fetch and display real-time stock data from financial APIs.
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News Aggregation: Display relevant financial news for selected stocks.
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User Portfolio Management: Allow users to track and analyze their selected stocks.
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Technical Indicators: Provide common stock indicators: RSI, MACD, and Bollinger Bands.
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Multi-device Support: Ensure compatibility across web and mobile platforms.
Languages:
- Python (Data Pipeline, Server, Deep Learning models)
- SQL (Database queries)
- JavaScript, HTML, CSS (Web frontend development)
- Dart (Mobile app development)
Frameworks & Tools:
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Data Processing & Storage:
- Apache Cassandra
- Apache Iceberg
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AI/ML Frameworks:
- PyTorch (Disease prediction and diagnosis model training)
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Deployment:
- Kubernetes (Container orchestration)
- Prefect (Pipeline orchestration)
- Oracle (Cloud hosting)
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Frontend Frameworks:
- Bootstrap (Web interface): For designing responsive and visually appealing web interfaces.
- Flutter: Build, test, and deploy mobile, web, desktop UI
- RESTFULAPI: To ensure robust communication between the frontend and backend systems.