代写 data 编程、代做 Python 语言程序

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DATA ANALYTICS & VISUALIZATION FOR FINANCE 2024-2025
Mission 3 (Individual) – Tableau and Python Deadline: December 16th 2024, 23:59 (CET)
In this individual assignment, you will have the opportunity to apply both your Tableau and Python skills by working individually with a stock price/return dataset and using the techniques learned across the Tableau and Python labs. The aim is to analyze the statistics, create interesting visualizations, build portfolios and analyze their performance so that you can embrace various tasks through your data visualization and analytical skills. For that, you are required to build a daily stock price dataset made of “3 stocks” (based on the choices assigned by your course professor) and spanning from October/2014 to October/2024. You will be able to create a dataset that you will use for this mission. To complete the project, you need to work on some “tasks” (please see below) and submit your individual work, accordingly.
Read the instructions below carefully and follow them closely.
Project Objective:
Imagine that you are now a junior data analyst with a decent experience in Tableau and Python. Your company manager needs your help for a top project about understanding the stock performance of U.S companies. To conduct such an assessment, you know that (as a good data analyst) analyzing and visualizing past historical data is key. Therefore, you received the mission tasks below that you need to follow step by step. Conduct all the visual and analytical methods that are requested and complete your report.
Task 1: Using Tableau, plot prices and returns of each stock. Use different visualization tools (colors, bars, line charts, etc). Create a dashboard and show how your price and returns data look like. Interpret the patterns and regularities, accordingly, taking into account important events.
Task 2: Now, use Python for the analytical assessment of your data. Carry out the following analyses: Plot prices and returns. Compute standard deviation and maximum drawdown (as conducted in the labs) and report the results. Compare the estimates across stocks. Compute the annualized Sharpe ratios, based on

different choices of risk-free rates (e.g., 0.01, 0.03 and 0.05). Compute VaR and CVaR and interpret the findings in your report.
Task 3: In this step, your task is to assess risk and return relationships. Again, use Python to generate different efficient frontiers, using your selected stocks. First, generate the covariance and correlations matrices to display the level of relationship among your stocks. Interpret the patterns. Then, generate the “two-asset” efficient frontier by choosing two stocks among all three. Plot the two-asset efficient frontier and interpret. Next, move to “N-asset” efficient frontier and thus use all three stocks to display the efficient frontier. What do you see? Do results change? If so, how and why? If not, think about why you do not observe differences, compared to two-asset case. Interpret these findings in your report. As a final job for this task, create MSR, EW and GMV portfolios and compare them. Do they look similar? What are the key differences that you can identify visually? Interpret the curves intuitively in terms of the risk- return relationships. What can you say about diversification? Based on your analysis and visual patterns coming from the efficient frontier, how would you make a decision in terms of achieving minimum volatility (risk) and/or maximum return?
Expected Outcome:
Your assessment will be evaluated based on creativity, clarity, and the depth of analysis presented in your work, as well as the quality and efforts you put into creating the Tableu dashboard and Jupyter Notebook. More specifically, the professor will assess your final pack following the instructions above: the strength of visual elements, creativity, clarity, soundness of the explanations, consistency of the study/storytelling, and evidence of your individual efforts. Innovative approaches to the Tableau and Python outputs/visuals, and presentation are more than welcome, provided the guidelines above are respected. Please also keep in mind that this is an individual work and hence no interaction with others is permitted (thanks for respecting the rule).
Deliverables:
1. Tableau file (.twbx) including the dashboard and all the worksheets containing the visuals you
created;
2. Jupyter Notebook file (.ipynb) including all the outputs;
3. Excel file(s) containing all datasets used based on the selected stocks (which you used to import
into Tableau and Jupyter Notebook);

4. Data analyst report: Single PDF document including insights and statements on the interpretation of the results of the tasks that have been carried out in the assignment:
5. ChatGPT/generative AI statement: Statement on the use of different generative AI tools in the project development (template below). Please add the statement at the end of your data analyst report.
Submit the above elements (in a zipped folder) by email to your professor.
Deliverables that are not submitted by December 16th 23:59 (CET) will be assigned a zero mark.
Statement on the Use of Generative AI tools in Project Development:
For each generative AI tool (e.g., ChatGPT) used, provide the following statement:
“I acknowledge that I made use of (specify the generative AI tool) as an aid in the development of this project. Specifically, (specify the generative AI tool) was used for:
- (list the specific tasks for which this tool was used)
I confirm that all final content was reviewed, edited, and verified to ensure accuracy, originality, and adherence to academic standards. I took care to critically assess and modify the output from (specify the generative AI tool) to align with my assignment’s goals and my own academic output.”
Final instructions:
Here is some extra advice for the development of you individual work:
• Read the instructions above carefully.
• Any additional elements that enhance the level, quality, and clarity of the final work are very
welcome and can be taken into consideration in your final evaluation.
• The above instructions are the general requirements to follow and are also meant to give you a
certain level of freedom to work on the project. So, be creative!
• We welcome and encourage you if you have an idea for how the outputs or visuals which served
as examples in the labs could be enhanced for a richer understanding on the performance of the
stocks and portfolios.
• This project and represents 30% of your final grade on this course. Therefore, we expect a project
which reflects a final work compatible with a relevant effort.
Good luck, and we look forward to seeing your innovative and insightful work!