Final Project Instructions and Rubric#
In this project, you’ll replicate tables, figures, or data series from a well known finance paper using the principals of reproducible analytical pipelines (RAPs) learned in this class. Your replication must be automated from end-to-end, formatted using the cookiecutter chartbook template. To create a new project, use cruft: cruft create https://github.com/backofficedev/cookiecutter_chartbook. See Project Structure: “Chartbook” Template for more details.
This term, the final project is more than a replication—it is a product. On top of replicating a table and a figure from your assigned paper (and reproducing them with updated numbers), you must build something that other people would actually want to use: for example, a cleaned, well-documented dataset and the code that produces it, plus your own new summary table and figure that say something interesting using that data. A central part of the project is advertising this product to your classmates during the proposal presentation—convincing them why they would want to clone your repository and run your code.
The Three Project Milestones#
The final project this term is organized around three separate events. Read the Proposal Presentation Rubric for the full procedure; in brief:
Instructor consultation — a 1-on-1 meeting you schedule with the instructor, to occur at least one week before your group’s proposal presentation. This is where you get individual feedback on your plan.
Proposal presentation — an in-class presentation (in an assigned week, roughly weeks 5–8) where you advertise your project and product to the class and receive peer feedback.
Final project presentation — in week 10, where you present the completed project and each group member is individually quizzed in an oral defense (see the rubric below).
Receiving Your Assigned Project#
You will be assigned a project from the list found here.
Your preference will be taken into account when assigning the projects. You can rank your preferred projects, and indicate your availability to present in one of the proposal-presentation weeks, using a Google Form: TBD (the link will be posted on Canvas). Please, only submit one response per group. Your group must have 2 people in it. Students can not choose to work alone and groups can not have more than 2 people. I will use your responses to assign each group both a project and a proposal-presentation week.
You must schedule a consultation with me at the beginning of the project (at least one week before your proposal presentation). This meeting is to establish the goals for the project and get individual feedback on your plan. Use the booking link to schedule it: https://finm-32900.youcanbook.me/ (also posted on Canvas).
In this consultation meeting, you should come prepared with a list of tasks that each team member will be primarily responsible for. Team members can and should share tasks (e.g., two people can be responsible for the LaTeX writeup whereas everyone should be involved in figuring out how to understand and clean the data). Write this list of tasks in the README.md of the GitHub repo.
Final Project Grading Rubric#
The following questions or assertions will be made of your code when you present it at the end of the quarter. Each question will be subjectively prorated on the degree of completion. For example, if you replicate 50% of the numbers from a table, you’ll get 50% of the available points. Some adjustments may be made by discretion based on the difficulty of the task. For each assertion in the following, I enumerate the points each is worth in bold and there are a total of 202 points available in total.
4/4 You must generate a single LaTeX document that briefly describes the nature of the replication project and contains all the tables and charts that your code produces. You should give a high level overview of how the replication project went. Where did you find success, where were the challenges? Also, explain the data sources that you used? The LaTeX document should not contain code snippets, but only give the tables and charts and a high level discussion of the project.
4/4 Provide at least one jupyter notebook that gives an brief tour of the cleaned data and some of the analysis that is performed in the code. You can think of this as giving the reader a tour of the code that you have written. You can provide code snippets in this notebook. It should look somethin like one the HW guides that we used, e.g. HW Guide: Replicate Fama-French 1993
20/20 Replicate the series, tables, and/or figures listed for your assigned project. Choose a reasonable tolerance and construct unit tests to ensure that your numbers match the paper’s within this tolerance.
20/20 Now, reproduce the series, tables, and/or figures with updated numbers. That is, replicating the numbers from the paper require performing the calculations over the same time period as that of the paper. In this task, you’ll recalculate the series, tables, and/or figures using numbers up until the present (or at least the most recently available numbers).
20/20 Outside of the tables, figures, or derived series that you are to replicate, have you also provided a table of your own summary statistics AND charts that gives you and the reader of sufficient understanding of the underlying data. These tables and/or figures must be typeset on LaTeX and you must provide captions on the Tables and Figure that properly describe them and motivate them. That is, you must tell me in the caption what I should learn or take away from each table and figure. You are to choose how many figures and or tables is sufficient for your case, but it will usually be one of each.
4/4 Does the paper generate a tidy data set of the data being used before then using the data for analysis? Specifically, there must be a separate file or set of files whose only purpose is to clean the data and put it into a “tidy” format. The analysis of the data should be kept in separate files.
4/4 Are the statistics in all tables in the LaTeX document automatically generated from the code?
4/4 Is the project automated from end-to-end, using PyDoit?
4/4 Does the repository use unit tests to ensure that code is working properly? Are these unit tests well motivated?
4/4 Does the project use the cookiecutter chartbook template? Was the project scaffolded using
cruft create https://github.com/backofficedev/cookiecutter_chartbook?4/4 Does each unit test have a purpose? Are there unnecessary or repetitive unit tests?
4/4 Is the GitHub repository and the Git history free of any copyrighted material (e.g., the raw data should not be in the repo).
4/4 Is the GitHub repository and the Git history free of any secrets, such as API keys?
4/4 Does the project use a
.envfile as well as reasonable defaults in thesettings.pyfile to manage the configuration of the project (e.g., the data directory, the API keys, but also theSTART_DATEandEND_DATEof the time period of analysis of the project.) Is the format of the required.envfile described in an example.env.examplefile?4/4 The project must not contain a trace of
.env(it should not be in the Git commit history).4/4 Does the project contain a
requirements.txtfile that described the required packages to run the project’s code?4/4 Is the GitHub repository and the Git history free of any secrets, such as API keys?
4/4 Has each group member made commits to the Git repo?
4/4 Has each member of your group made and merged a GitHub pull request?
4/4 Does each Python file have a “docstring” at the top that describes what the file does?
4/4 Does each Python function have a reasonably descriptive name and, when appropriate, a function docstring? (No need to overboard, but the code should be reasonably clean.)
50/50 Did I individually accomplish the tasks that were assigned to me by the group and did I contribute a substantial part of the code to the project as evidenced by the commit history?
20/20 Oral defense (graded individually for each group member). At the week-10 final presentation, each member will be quizzed individually. You must be able to (1) defend the analysis and design choices in the project, including the motivation and value of your additional “product” (your new table, figure, and/or cleaned dataset), and (2) demonstrate that you can actually run and modify the project—for example, creating and activating the conda environment, running the pipeline end-to-end with
doit, making a small live edit to the code, and using tooling such as SSH where relevant. Both members are expected to understand the whole project, not only the parts they wrote.