feat: implement full-stack opportunity screener with Python schemas, Angular UI components, and data repository.

feat: implement opportunity screener with AI-driven analysis module, backend data pipeline, and revamped UI dashboard.

feat: implement automated opportunity screener pipeline with AI-driven analysis and frontend integration

- Added AITraderReport and AIResearch models to encapsulate AI-generated trading reports and research details.
- Enhanced StockAnalysis model to include optional AI research data.
- Implemented AIStockAnalysisGenerator for generating AI-enhanced stock analysis.
- Created grounded prompts for AI stock research in a new stock_research.py file.
- Developed a lightweight dashboard CLI for fetching and displaying stock analysis without starting the multi-agent graph.
- Updated AnalysisService to integrate AI analysis into the stock analysis workflow.
- Added tests for AI dashboard analysis and ensured proper functionality of new features.

- Implemented a function to check if the current CUDA device is supported by the installed PyTorch version.
- Added warnings for unsupported CUDA devices, informing users that the CPU will be used instead.
- Updated the TimesFMBackend class to utilize the new compatibility checks and handle GPU names appropriately.
- Introduced unit tests to validate the behavior of the CUDA compatibility function with various simulated CUDA environments.

- Updated the docstring in repository.py for clarity.
- Added new dependencies for DGT in pyproject.toml.
- Created a new test file for DGT forecasting with comprehensive tests covering market graph alignment, graph prior normalization, model predictions, and end-to-end run validation.