Branch-AI/one_exercise_per_file/day05/ex02/audit/readme.md

Preliminary:

• As usual the first steps are:

  • Check missing values and data types
  • Convert string dates to datetime
  • Set dates as index
  • Use info or describe to have a first look at the data

The exercise is not validated if these steps have not been done.

1. The Candlestick is based on Open, High, Low and Close columns. The index is Date (datetime). As long as you inserted the right columns in Candlestick Plotly object you validate the question.

2. This question is validated if the output of print(transformed_df.head().to_markdown()) is

Date	Open	Close	Volume	High	Low
1980-12-31 00:00:00	0.136075	0.135903	1.34485e+09	0.161272	0.112723
1981-01-30 00:00:00	0.141768	0.141316	6.08989e+08	0.155134	0.126116
1981-02-27 00:00:00	0.118215	0.117892	3.21619e+08	0.128906	0.106027
1981-03-31 00:00:00	0.111328	0.110871	7.00717e+08	0.120536	0.09654
1981-04-30 00:00:00	0.121811	0.121545	5.36928e+08	0.131138	0.108259

To get this result there are two ways: resample and groupby. There are two key steps:

• Find how to affect the aggregation on the last business day of each month. This is already implemented in Pandas and the keyword that should be used either in resample parameter or in Grouper is BM.

• Choose the right aggregation function for each variable. The prices (Open, Close and Adjusted Close) should be aggregated by taking the mean. Low should be aggregated by taking the minimum because it represents the lower price of the day, so the lowest price on the month is the lowest price of the lowest prices on the day. The same logic applied to High, leads to use the maximum to aggregate the High. Volume should be aggregated using the sum because the monthly volume is equal to the sum of daily volume over the month.

  There are 482 months.

3. The solution is accepted if it doesn't involve a for loop and the output is:

   Date
   1980-12-12         NaN
   1980-12-15   -0.047823
   1980-12-16   -0.073063
   1980-12-17    0.019703
   1980-12-18    0.028992
                   ...   
   2021-01-25    0.049824
   2021-01-26    0.003704
   2021-01-27   -0.001184
   2021-01-28   -0.027261
   2021-01-29   -0.026448
   Name: Open, Length: 10118, dtype: float64
   

• The first way is to compute the return without for loop is to use pct_change
• The second way to compute the return without for loop is to implement the formula given in the exercise in a vectorized way. To get the value at t-1 you can use shift