The monthly returns of the equal-weighted portfolios of the stocks in the SIC code 1311 and 2836. I need to plot the monthly returns of the two portfolios [1311 and 2836].  just need the Python code, you can use any data.

 

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In [17]: #equally weight all stocks Out [17]: ew1=df1[['ret', 'sic', 'Year', 'Month']] ew2 = ew1.dropna () #remove any na values ew3=ew2[['ret', 'sic', 'Year', 'Month']].groupby(['sic', 'Year', 'Month'], as_index=False).mean() ew3.rename (columns={'ret': 'ret_ew'}, inplace=True) #rename ret to make sure we know now the return in ewl file is the return of a portfolio ew3 ['date'] = pd.to_datetime({ 'year': ew3 ['Year'], 'month': ew3 ['Month'], 'day':28}) ew3 sic Year Month ret_ew 0 1311 2015 1 1311 2015 2 1311 2015 3 1311 2015 4 1311 2015 … 187 2836 2022 188 2836 2022 189 2836 2022 date 1 -0.049145 2015-01-28 2 0.246906 2015-02-28 3 -0.082878 2015-03-28 4 0.233427 2015-04-28 5 0.058064 2015-05-28 8 0.149210 2022-08-28 9 -0.096945 2022-09-28 10 0.057503 2022-10-28 In [17]: #equally weight all stocks Out [17]: ew1=df1[['ret', 'sic', 'Year', 'Month']] ew2 = ew1.dropna () #remove any na values ew3=ew2[['ret', 'sic', 'Year', 'Month']].groupby(['sic', 'Year', 'Month'], as_index=False).mean() ew3.rename (columns={'ret': 'ret_ew'}, inplace=True) #rename ret to make sure we know now the return in ewl file is the return of a portfolio ew3 ['date'] = pd.to_datetime({ 'year': ew3 ['Year'], 'month': ew3 ['Month'], 'day':28}) ew3 sic Year Month ret_ew 0 1311 2015 1 1311 2015 2 1311 2015 3 1311 2015 4 1311 2015 … 187 2836 2022 188 2836 2022 189 2836 2022 date 1 -0.049145 2015-01-28 2 0.246906 2015-02-28 3 -0.082878 2015-03-28 4 0.233427 2015-04-28 5 0.058064 2015-05-28 8 0.149210 2022-08-28 9 -0.096945 2022-09-28 10 0.057503 2022-10-28

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import pandas as pd import matplotlibpyplot as plt Read in the da... View the full answer