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In [81]:

    
import pandas as pd
import numpy as np



In [82]:

    
from pandas_datareader import data as wb



In [83]:

    
import matplotlib.pyplot as plt



In [84]:

    
PG = wb.DataReader('PG', data_source = 'quandl', start="1995-1-1", end='2018-05-01')



In [85]:

    
PG.head()



In [86]:

    
PG.tail()



In [87]:

    
PG['simple_reture'] = (PG['AdjClose']/PG['AdjClose'].shift(-1))-1



In [88]:

    
print (PG['simple_reture'])









    



Date
2018-03-27    0.018060
2018-03-26    0.006587
2018-03-23   -0.006544
2018-03-22   -0.008178
2018-03-21   -0.016218
2018-03-20   -0.004450
2018-03-19   -0.003926
2018-03-16    0.003813
2018-03-15   -0.004177
2018-03-14   -0.009032
2018-03-13   -0.001753
2018-03-12   -0.005356
2018-03-09    0.004002
2018-03-08    0.010232
2018-03-07   -0.010747
2018-03-06   -0.003363
2018-03-05    0.009937
2018-03-02    0.010165
2018-03-01    0.002292
2018-02-28   -0.025081
2018-02-27   -0.013353
2018-02-26    0.007156
2018-02-23    0.002598
2018-02-22    0.007478
2018-02-21   -0.013523
2018-02-20   -0.015254
2018-02-16    0.002306
2018-02-15    0.021443
2018-02-14   -0.010061
2018-02-13    0.002090
                ...   
1995-02-13    0.005802
1995-02-10   -0.001981
1995-02-09    0.000000
1995-02-08   -0.011299
1995-02-07   -0.001805
1995-02-06    0.023077
1995-02-03    0.009630
1995-02-02    0.007827
1995-02-01   -0.020996
1995-01-31   -0.007605
1995-01-30    0.015444
1995-01-27    0.025661
1995-01-26    0.006056
1995-01-25    0.012097
1995-01-24    0.000000
1995-01-23    0.010101
1995-01-20   -0.001951
1995-01-19   -0.006141
1995-01-18   -0.021660
1995-01-17    0.003968
1995-01-16    0.008000
1995-01-13    0.013710
1995-01-12    0.008130
1995-01-11   -0.002109
1995-01-10    0.012319
1995-01-09   -0.006041
1995-01-06    0.004098
1995-01-05   -0.014221
1995-01-04   -0.008015
1995-01-03         NaN
Name: simple_reture, Length: 5849, dtype: float64



In [89]:

    
PG['simple_reture'].plot(figsize=(8,6))









    Out[89]:





<matplotlib.axes._subplots.AxesSubplot at 0x113710080>



In [90]:

    
avg_reture_d = PG['simple_reture'].mean()



In [91]:

    
avg_reture_d









    Out[91]:





0.0004700634230250144



In [92]:

    
avg_reture_a = PG['simple_reture'].mean() * 250



In [93]:

    
avg_reture_a









    Out[93]:





0.11751585575625359



In [94]:

    
print(str(round(avg_reture_a,5)*100)+'%')



In [95]:

    
PG['log_return']=np.log(PG['AdjClose']/PG['AdjClose'].shift(-1))



In [96]:

    
PG['log_return'].plot(figsize=(8,6))









    Out[96]:





<matplotlib.axes._subplots.AxesSubplot at 0x1164f0cf8>



In [97]:

    
avg_reture_log = PG['log_return'].mean()*250



In [98]:

    
print(str(round(avg_reture_log,5)*100)+'%')



In [99]:

    
tickers = ['PG', 'MSFT', 'F','GE']



In [100]:

    
mydata = pd.DataFrame()



In [101]:

    
for t in tickers:
    mydata[t] = wb.DataReader(t, data_source='quandl', start='1995-1-1')['AdjClose']



In [102]:

    
mydata.info()









    



<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 5849 entries, 2018-03-27 to 1995-01-03
Data columns (total 4 columns):
PG      5849 non-null float64
MSFT    5849 non-null float64
F       5849 non-null float64
GE      5849 non-null float64
dtypes: float64(4)
memory usage: 228.5 KB



In [103]:

    
mydata.head()



In [104]:

    
mydata.tail()



In [105]:

    
mydata.iloc[0]









    Out[105]:





PG      77.79
MSFT    89.47
F       10.83
GE      13.44
Name: 2018-03-27 00:00:00, dtype: float64



In [106]:

    
mydata.iloc[-1]









    Out[106]:





PG      9.025149
MSFT    2.504247
F       4.777414
GE      4.497406
Name: 1995-01-03 00:00:00, dtype: float64



In [107]:

    
df2 = (mydata/mydata.iloc[-1]*100)



In [108]:

    
df2.plot(figsize=(15,6))









    Out[108]:





<matplotlib.axes._subplots.AxesSubplot at 0x1167f0ac8>



In [109]:

    
mydata.plot(figsize=(15,6))









    Out[109]:





<matplotlib.axes._subplots.AxesSubplot at 0x113f3e668>



In [110]:

    
returns = (mydata/mydata.shift(-1))-1



In [111]:

    
returns.head()



In [112]:

    
weights = np.array([0.25,0.25,0.25,0.25])



In [113]:

    
np.dot(returns,weights)









    Out[113]:





array([ 0.00369264,  0.02352209, -0.01856492, ..., -0.00922517,
        0.00655136,         nan])



In [114]:

    
annual_returns = returns.mean()*250



In [115]:

    
np.dot(annual_returns, weights)









    Out[115]:





0.13118411098159546



In [126]:

    
pfolio_1 = str( round(np.dot(annual_returns,weights*100),3) )   +'%'



In [127]:

    
print(pfolio_1)



In [129]:

    
weights_2 = np.array([0.4,0.4,0.15,0.05])



In [130]:

    
pfolio_2 = str( round(np.dot(annual_returns,weights_2*100),3) )   +'%'



In [131]:

    
print(pfolio_2)



In [ ]:

	Open	High	Low	Close	Volume	ExDividend	SplitRatio	AdjOpen	AdjHigh	AdjLow	AdjClose	AdjVolume
Date
2018-03-27	76.53	78.44	76.300	77.79	11257559.0	0.0	1.0	76.53	78.44	76.300	77.79	11257559.0
2018-03-26	76.57	76.87	76.020	76.41	11959444.0	0.0	1.0	76.57	76.87	76.020	76.41	11959444.0
2018-03-23	76.79	77.18	75.805	75.91	11626422.0	0.0	1.0	76.79	77.18	75.805	75.91	11626422.0
2018-03-22	76.83	77.52	76.370	76.41	10396180.0	0.0	1.0	76.83	77.52	76.370	76.41	10396180.0
2018-03-21	78.05	78.24	76.880	77.04	9776002.0	0.0	1.0	78.05	78.24	76.880	77.04	9776002.0

	Open	High	Low	Close	Volume	ExDividend	SplitRatio	AdjOpen	AdjHigh	AdjLow	AdjClose	AdjVolume
Date
1995-01-09	61.38	61.63	60.75	60.88	448800.0	0.0	1.0	8.880469	8.916639	8.789320	8.808129	1795200.0
1995-01-06	60.63	61.63	60.63	61.25	859500.0	0.0	1.0	8.771959	8.916639	8.771959	8.861660	3438000.0
1995-01-05	61.50	61.75	60.88	61.00	579900.0	0.0	1.0	8.897830	8.934000	8.808129	8.825490	2319600.0
1995-01-04	62.63	62.63	61.25	61.88	554700.0	0.0	1.0	9.061319	9.061319	8.861660	8.952809	2218800.0
1995-01-03	61.88	62.50	61.75	62.38	829600.0	0.0	1.0	8.952809	9.042510	8.934000	9.025149	3318400.0

	PG	MSFT	F	GE
Date
2018-03-27	77.79	89.47	10.83	13.44
2018-03-26	76.41	93.78	10.83	12.89
2018-03-23	75.91	87.18	10.56	13.07
2018-03-22	76.41	89.79	10.75	13.35
2018-03-21	77.04	92.48	11.10	13.88

	PG	MSFT	F	GE
Date
1995-01-09	8.808129	2.506743	4.948832	4.442732
1995-01-06	8.861660	2.522554	4.863123	4.486824
1995-01-05	8.825490	2.480948	4.863123	4.508870
1995-01-04	8.952809	2.522554	4.905978	4.497406
1995-01-03	9.025149	2.504247	4.777414	4.497406

	PG	MSFT	F	GE
Date
2018-03-27	0.018060	-0.045959	0.000000	0.042669
2018-03-26	0.006587	0.075705	0.025568	-0.013772
2018-03-23	-0.006544	-0.029068	-0.017674	-0.020974
2018-03-22	-0.008178	-0.029087	-0.031532	-0.038184
2018-03-21	-0.016218	-0.006979	0.010009	0.017595