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Edit and run



In [1]:

    
import numpy as np
import pandas as pd
import pandas_datareader.data as wb
import matplotlib.pyplot as plt



In [2]:

    
stocks = ["TSLA", "NFLX", "GOOG", "MSFT"]
data = pd.DataFrame()
for s in stocks:
    data[s] = wb.DataReader(s, data_source='google', start='2012-1-1')['Close']



In [3]:

    
data.info()









    



<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1356 entries, 2012-01-03 to 2017-05-23
Data columns (total 4 columns):
TSLA    1356 non-null float64
NFLX    1356 non-null float64
GOOG    1356 non-null float64
MSFT    1356 non-null float64
dtypes: float64(4)
memory usage: 53.0 KB



In [4]:

    
data.head()



In [5]:

    
data.tail()

Normalization to 100:

$$ \frac{P_1}{P_0} * 100 $$



In [6]:

    
data.iloc[0]









    Out[6]:





TSLA     28.08
NFLX     10.32
GOOG    332.37
MSFT     26.76
Name: 2012-01-03 00:00:00, dtype: float64



In [7]:

    
(data / data.iloc[0] * 100).plot(figsize = (15, 6))
plt.show()

Calculating the Return of a Portfolio



In [8]:

    
returns = (data / data.shift(1)) -1 # simple returns
returns.head()



In [9]:

    
weights = np.array([0.45, 0.45, 0.05, 0.05])
np.dot(returns, weights)









    Out[9]:





array([        nan,  0.0465004 , -0.01603026, ...,  0.00079744,
        0.00068792, -0.00661083])



In [10]:

    
annual_returns = returns.mean() * 250
annual_returns









    Out[10]:





TSLA    0.560862
NFLX    0.641218
GOOG    0.218949
MSFT    0.199482
dtype: float64



In [11]:

    
annual = np.dot(annual_returns, weights)



In [12]:

    
portfolio = str(round(annual, 5) * 100) + "%"
print(portfolio)



In [ ]:

	TSLA	NFLX	GOOG	MSFT
Date
2012-01-03	28.08	10.32	332.37	26.76
2012-01-04	27.71	11.49	333.81	27.40
2012-01-05	27.12	11.33	329.18	27.68
2012-01-06	26.89	12.35	324.68	28.10
2012-01-09	27.25	14.03	310.92	27.74

	TSLA	NFLX	GOOG	MSFT
Date
2017-05-17	306.11	153.20	919.62	67.48
2017-05-18	313.06	155.70	930.24	67.71
2017-05-19	310.83	157.02	934.01	67.69
2017-05-22	310.35	157.16	941.86	68.45
2017-05-23	303.86	157.95	948.82	68.68

	TSLA	NFLX	GOOG	MSFT
Date
2012-01-03	NaN	NaN	NaN	NaN
2012-01-04	-0.013177	0.113372	0.004333	0.023916
2012-01-05	-0.021292	-0.013925	-0.013870	0.010219
2012-01-06	-0.008481	0.090026	-0.013670	0.015173
2012-01-09	0.013388	0.136032	-0.042380	-0.012811