SUMMATIVE ASSIGNMENT

• Students are required to implement a machine learning based investment strategy and evaluate its performance. Students can choose a model from those covered during the class, choose one from the literature, or develop their own model.  

• The students should develop a model solely using the training set and evaluate it using the test set. 

• Students should use the dataset provided on Blackboard Learn Ultra

• The report should contain the followings: 

o The details of the chosen model. 

o Implementation details such as hyperparameter tuning. 

o Statistical performance, e.g., loss and accuracy. 

o Financial performance: average return, Sharpe ratio, Maximum Drawdown, etc. 

o Comparison of performances between training set and test set. 

o Critical evaluation of the results. 

o Source code in the appendix 

• Students who achieve a good performance in the test set will earn a higher mark for the same quality of report.

• Details and critique of trading strategies should be written in Markdown.

• Analysis and Evaluation of code output should be written in Markdown.

• Explanation about code should be written within code cells with the ‘#’ notation.

• The report should be written in a Jupyter Notebook and submitted as a ‘.ipynb’ file.

Overall word limit: 3000

This assignment is worth 60% of the overall module mark.