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IC208 Programming for Finance

1. What is the purpose of this assessment?

The following table shows which of the module learning outcomes are being assessed in this assignment. Use this table to help you see the connection between this assessment and your learning on the module.

Module Learning Outcomes being assessed

Carry out a practical project that involve Python applications in Finance

Data Management and visualisation

Make use of historical data and programming techniques to read, analyse and use different variables and signals for a decision-making process

Interpret and critically discuss the empirical results in light of prior finance literature

2. What is the task for this assessment?

Task (attach an assignment brief if required)

The purpose of this project is to implement and evaluate a trading strategy(ies) using Python coding skills. The project is divided into subtasks, as follows:

1) Download and manage a dataset of financial assets from an online database. You should choose at least 100 financial assets. There is no restriction on which assets should be included.

2) Produce summary statistics of the financial assets’ characteristics, e.g., returns, trading volume, liquidity etc., during the sampled period of your choice. Discuss the outcomes of your Python codes. There are merits for explaining your choice of the financial assets, graphical illustrations, discussions in linkages to prior literature.

3) Define a trading strategy(ies).

4) Evaluate the performance of the trading strategy(ies) in (3).

5) Assuming that you are managing a fund which has the initial amount of £1,000,000 in cash. This amount can be used to invest in the trading strategy(ies). Carry out back-testing of the trading strategy and discuss the outputs of your codes.

3. What is required of me in this assessment?

Guidelines/details of how to prepare your submission

Your submission should comprise:

• Report with clear structure (at least introduction, main body, conclusion) and tables and figures.

• Appendix with Python codes. Codes should be for Anaconda Spyder Python 3.6+.

• Reference list with all sources used.

The Python codes should be put only in the appendix. The report should NOT include Python codes.

The submission should be uploaded to [Blackboard> IC208> Assessment]. The submission file should be in Microsoft word format. Other formats such as pdf file are not acceptable, hence, will be marked zero.

Three key pieces of advice based on the feedback given to the previous cohort who completed this assignment

It is essential to get your codes work in Spyder.

In addition, the readability of your codes is important. Using comments (#) is recommended.

Furthermore, you should be able to interpret outcomes of your codes and empirical results. "It does not matter whether you find a good trading strategy(ies) or not, you should provide a correct and appropriate interpretation of your findings. Ensure that you dedicate sufficient time to understanding your findings and their implications and communicate this clearly in your report.

For a high mark, you are expected to go beyond the references provided under ‘Resources’ below to show that you have read widely for this project and can link your findings to those of existing studies.

Formatting Guidelines

Microsoft Word

Word limit/guidance and penalty applied

1,500 words, excluding tables/figures, references.

Referencing Style

Harvard

Guidance on Academic Misconduct (including using Turnitin practice area)

You should ensure that the work you produce adheres to the University’s statement on academic integrity and to the regulations regarding academic misconduct (such as plagiarism and cheating).

You can find information about this at:

http://www.reading.ac.uk/internal/exams/Policies/examisconduct.aspx

4. The Marking Scheme (Marking criteria/rubric)

Please refer to the marking criteria rubric at the end of this document.

5. What resources might I use to get started?

Python 3.6+.

Microsoft Word can be used to prepare the report and to format tables / figures.

Lecture and seminar notes.

Students are expected to read relevant literature which can be accessed via the Library Resources, a recommended guide include:

- Brock, W., Lakonishok, J. & LeBaron, B. (1992) Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47, 1731–1764.

- Conrad & Kaul (1998) An anatomy of trading strategies. Review of Financial Studies, 11, 489–519.

6. Late Submission Arrangements

Point of Submission : Click or tap here to enter text.

Late Submission Penalty: ☒ The University standard penalty apply

☐ Other: Click or tap here to enter text.

Plagiarism:

☒ The University’s standard policy on Academic Misconduct applies

☐ Other: Click or tap here to enter text.

7. Feedback Arrangements

Timing of feedback:

☒ Within 15 working days of submission deadline

☐ When examinations marks are released

☐ Other Click here to add text

Type of feedback:

☒Mark ☐Generic Feedback

☒Individual Feedback (for each group) ☐Comments written on the assessment

☐Audio Feedback ☐ Video Feedback

☐Breakdown of Mark ☐Other: Click or tap here to enter text.

Location of Feedback:

☒Blackboard ☐Turnitin ☐ RISIS

☐Other: Click or tap here to enter text.