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:  The details of the chosen model.  Implementation details such as hyperparameter tuning.  Statistical performance, e.g., loss and accuracy.  Financial performance: average return, Sharpe ratio, Maximum Drawdown, etc.  Comparison of performances between training set and test set.  Critical evaluation of the results.  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. SUBMISSION INSTRUCTIONS Your completed assignment must be uploaded to Ultra no later than 12:00 midday on 25 April 2024 A penalty will be applied for work uploaded after 12:00 midday as detailed in the Student Information Hub. You must leave sufficient time to fully complete the upload process before the deadline and check that you have received a receipt. At peak periods, it can take up to 30 minutes for a receipt to be generated. Assignments should be typed, using 1.5 spacing and an easy-to-read 12-point font. Assignments and dissertations/business projects must not exceed the word count indicated in the module handbook/assessment brief. The word count should: Include all the text, including title, preface, introduction, in-text citations, quotations, footnotes, and any other item not specifically excluded below.  Exclude diagrams, tables (including tables/lists of contents and figures), equations, executive summary/abstract, acknowledgements, declaration, bibliography/list of references and appendices. However, it is not appropriate to use diagrams or tables merely as a way of circumventing the word limit. If a student uses a table or figure as a means of presenting his/her own words, then this is included in the word count. Examiners will stop reading once the word limit has been reached, and work beyond this point will not be assessed. Checks of word counts will be carried out on submitted work, including any assignments or dissertations/business projects that appear to be clearly over-length. Checks may take place manually and/or with the aid of the word count provided via an electronic submission. Where a student has intentionally misrepresented their word count, the School may treat this as an offence under Section IV of the General Regulations of the University. Extreme cases may be viewed as dishonest practice under Section IV, 5 (a) (x) of the General Regulations. Very occasionally it may be appropriate to present, in an appendix, material which does not properly belong in the main body of the assessment but which some students wish to provide for the sake of completeness. Any appendices will not have a role in the assessment - examiners are under no obligation to read appendices and they do not form part of the word count. Material that students wish to be assessed should always be included in the main body of the text. Guidance on referencing can be found on Durham University website and in the Student Information Hub. MARKING GUIDELINES Performance in the summative assessment for this module is judged against the following criteria: Relevance to question(s) Organisation, structure and presentation Depth of understanding Analysis and discussion Use of sources and referencing Overall conclusions PLAGIARISM AND COLLUSION Students suspected of plagiarism, either of published work or the work of other students, or of collusion will be dealt with according to School and University guidelines. END OF ASSESSMENT