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FINS3646 Toolkit for Finance
Task
After setting up your PyCharm development environment with the project files (see instructions above), modify the zid_project2.py module by following the steps below, in sequence.
Part 1: Read the relevant documentation
For this assessment, read the documentation for the following methods:
- pandas.DataFrame.mean (note the parameter axis which will indicate if the mean will be computed column-wise or row-wise)
- pandas.Series.add
- pandas.Series.prod
- pandas.Series.dropna
Part 2: Include a statement to import the config.py module
Open the config.py file included in this project in PyCharm. Note that this file includes many constants (e.g., DATADIR, FF_CSV, TICMAP) and a function called standardise_colnames. You should not modify the file config.py.
To get access these constants and the standardise_colnames function, the config.py module needs to be imported first.
Complete the import portion of the zid_project2.py module by creating a new import statement. This statement should import the module config.py which is part of the zip file provided to you. Make sure you import this module using the “cfg” alias (so, “as cfg”).
After including this import statement, you can use the test function _test_cfg to make sure the config module is being imported correctly and tho check the location of the files for this project.
Part 3: Complete the read_prc_csv function
Complete the indicated part of the function read_prc_csv. so it produces the data frame described in the docstring.
You can test this function by calling the _test_read_prc_csv test function.
Part 4: Complete the mk_prc_df function
Complete the indicated part of the function mk_prc_df. Make sure that this function returns a data frame as described in the docstring.
Remember that the sample period for the docstring example may be different than that in your specific source data. You can test this function by calling the _test_mk_prc_df function.
Part 5: Complete the mk_ret_df function
Complete the indicated part of the function mk_ret_df. Make sure that this function returns a data frame with the same format as described in the docstring. You can test this function by calling the _test_mk_ret_df test function.
Part 6: Complete the mk_aret_df function
Complete the indicated part of the function mk_aret_df. Make sure that this function returns a data frame with the same format as described in the docstring. You can test this function by calling the _test_mk_aret_df test function.
Part 7: Complete the auxiliary functions
Complete the following auxiliary functions following the instructions specified in their docstrings:
- get_avg: Calculates the average value of a column for a given year
- get_ew_rets: Calculates the returns on an equally-weighted portfolio of stocks.
- get_ann_ret: Calculate the annualised return for a given period.
You can test these functions by calling appropriate test functions.
Part 8: Answer a few questions
For this part of this project, you should answer the following questions:
Q1: Which stock in your sample has the highest average daily return for the year 2020 (ignoring missing values)? The sample should include all tickers included in the dictionary config.TICMAP. Your answer should include the ticker for this stock.
Q2: What is the annualised return for the EW portfolio of all your stocks in the config. TICMAP dictionary from the beginning of 2010 to the end of 2020?
Q3: What is the annualised daily return for the period from 2010 to 2020 for the stock with the highest average return in 2020 (the one you identified in the first question above)?
Q4: What is the annualised daily abnormal return for the period from 2010 to 2020 for the stock with the highest average return in 2020 (the one you identified in the first question Q1 above)? Abnormal returns are calculated by subtracting the market return from the individual stock return.
FINS3646 Toolkit for Finance
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