Test Model Robustness [Implementing Generative Model]

[sarahnourgh]

• Test the robustness of the generative model by evaluating its performance on unseen data and under different scenarios, such as varying market conditions or input parameters.
• Conduct sensitivity analysis to assess the model's response to changes in input data and hyperparameters.

⚠ Remark:

• Debug the code to identify and fix any errors, bugs, or inconsistencies in the Rudy Morel implementation or our team
• Provide clear instructions on how to run the code, train the model, and reproduce the results:
• Document the codebase by adding comments, docstrings, and README files to explain the purpose, functionality, and usage of each component.
• Provide clear instructions on how to run the code, train the model, and reproduce the results.
• Peer Review and Collaboration:
• Collaborate with team members to review each other's code, provide feedback, and ensure code quality, consistency, and adherence to best practices.
  Conduct code reviews, pair programming sessions, and regular meetings to discuss progress and address any issues or concerns.

🥅 The Goal:
Implement successfully the generative model described in the research article and lay the foundation for further experimentation, evaluation, and application in your financial modeling project.