Portfolio Management with python

Codebook for Case-Study 2

2023-07

0.Get ready

# Import the data from the excel rets = pd .read_excel( 'portfoliom_data .xlsx', header=0, index_col=0, parse_dates=True )

# Show data rets

Out[3]:               Asset A:    Asset B:    Asset C:    Asset D:    S&p 500

Date:

2015-01-01        -0.069          0.063        -0.090        -0.043         -0.023

2015-03-01           0.015           0.122          0.080           0.021           0.073

2015-05-01           0.167          0.259          0.003           0.165           0.149

2021-08-01          0.025          0.039         -0.142          0.092         -0.004

2021-10-01          0.043          0.041          0.029         -0.031           0.020

2021-12-01        -0.056          0.053          0.106          0.049           0.057

84 rows × 5 columns

Out[4]:               Asset A:    Asset B:    Asset C:    Asset D:

Date:

2015-01-01        -0.069          0.063        -0.090        -0.043

2015-03-01           0.015           0.122          0.080           0.021

2015-05-01           0.167          0.259          0.003           0.165

2021-08-01          0.025          0.039         -0.142          0.092

2021-10-01          0.043          0.041          0.029         -0.031

2021-12-01        -0.056          0.053          0.106          0.049

84 rows × 4 columns

Out[5]: In [6]: Out[6]:

<Axes: xlabel='Date:'> # Assign the last column into variable 'rets_sp' rets_sp = rets .iloc[:,-1 ] rets_sp Date: 2015-01-01 2015-02-01 2015-03-01 2015-04-01 2015-05-01 2021-08-01 2021-09-01 2021-10-01 2021-11-01 2021-12-01 -0 .023 0.006 0.073 0.035 0.149 ... -0 .004 0.069 0.020 0.128 0.057 Name: S&p 500, Length: 84, dtype: float64

1.Calculate the average monthly return, population variance and standard deviation for each of the 4 stocks;

Out[8]:

Asset A: Asset B: Asset C:

0.013750 0.037405 0.018107

Asset D:     0.015762

dtype: float64

0.023809523809523808

Out[11]:  (84, 4)

84

Out[13]:

Asset A: Asset B: Asset C:

0.011424 0.034342 0.015415

Asset D:     0.013852

dtype: float64

Out[15]:

Asset A: Asset B: Asset C:

0.011424 0.034342 0.015415

Asset D:     0.013852

dtype: float64

Out[16]:

Asset A: True Asset B: True Asset C: True Asset D: True dtype: bool

0.022521772275280405

Out[18]:

(Asset Asset Asset Asset dtype: Asset Asset Asset Asset dtype:

0.004721 0.006367 0.005276 0.004024 float64, 0.068710 0.079794 0.072633 0.063432 float64)

2.Construct a co-variance matrix;

# Now we construct the variance-covariance matrix, this is easier than we expected. cov_assets = rets_assets .cov (ddof=0 ) cov_assets

Out[19]:            Asset A:     Asset B:     Asset C:     Asset D:

Asset A:     0.004721    0.002225    0.001479    0.000932

Asset C:     0.001479    0.001485    0.005276      0.001411

3.Form a portfolio with equal weights. Calculate the expected return, variance and standard deviation of the equal weight-portfolio;

# Set the equal weights for the equal weight portfolio ew = np .ones (4 )*0.25 ew

# Call the function to calculate the return and volatility of the equal weight portfolio ewport_r = portfolio_r (weights