In [19]:
import numpy as np
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
In [20]:
df = yf.download(['PG', 'MSFT', 'F', 'GE'], start='1995-1-1')

[*********************100%***********************]  4 of 4 completed
In [21]:
df = df['Adj Close']
df.head(10).round(3)
Out[21]:
F GE MSFT PG
Date
1995-01-03 3.921 25.513 2.342 7.723
1995-01-04 4.027 25.513 2.359 7.661
1995-01-05 3.992 25.575 2.320 7.553
1995-01-06 3.992 25.450 2.359 7.568
1995-01-09 4.062 25.200 2.344 7.537
1995-01-10 3.992 25.388 2.373 7.630
1995-01-11 3.992 25.763 2.388 7.615
1995-01-12 3.957 25.700 2.383 7.692
1995-01-13 4.027 26.013 2.442 7.782
1995-01-16 4.080 26.326 2.495 7.844
In [22]:
df.tail(10).round(3)
Out[22]:
F GE MSFT PG
Date
2023-06-29 14.95 107.660 335.05 149.38
2023-06-30 15.13 109.769 340.54 151.74
2023-07-03 15.24 108.200 337.99 152.50
2023-07-05 15.35 108.570 338.15 152.24
2023-07-06 14.98 107.291 341.27 151.99
2023-07-07 14.98 108.190 337.22 148.80
2023-07-10 15.07 110.530 331.83 148.86
2023-07-11 15.23 111.220 332.47 148.08
2023-07-12 15.22 111.200 337.20 148.85
2023-07-13 15.19 111.280 342.66 148.95
In [23]:
df.plot(figsize=(16,4))
plt.show()
In [24]:
(df/df.iloc[0]*100).plot(figsize=(16,4))
plt.show()

return of portfolio¶

In [25]:
portfolio = (df/df.shift(1)) - 1
In [26]:
portfolio.plot(figsize=(16, 4))
plt.show()
In [27]:
F = 2 #int(input('if you have ... lot of F in 1995 '))
GE = 3 #int(input('if you have ... lot of GE in 1995 '))
MSFT = 2 #int(input('if you have ... lot of MSFT in 1995 '))
PG = 1 #int(input('if you have ... lot of PG in 1995 '))
total = F + GE + MSFT + PG
In [28]:
print(total)

8
In [29]:
f = F / total
ge = GE / total
msft = MSFT / total
pg =  PG / total
In [30]:
weights = np.array([f, ge, msft, pg])
In [31]:
len(np.dot(portfolio, weights))
Out[31]:
7182
In [32]:
working_day = 250
percentage = 100
annual = portofolio.mean() * working_day
In [33]:
annual_percentage = (np.dot(annual, weights) * percentage).round(3)
print('the percentage of annual return is ', annual_percentage, '%')

the percentage of annual return is  14.213 %
In [34]:
first_f, last_f = df['F'].iloc[0], df['F'].iloc[-2] # the value of today is not released yet
first_ge, last_ge = df['GE'].iloc[0], df['GE'].iloc[-2] # the value of today is not released yet
first_msft, last_msft = df['MSFT'].iloc[0], df['MSFT'].iloc[-2] # the value of today is not released yet
first_pg, last_pg = df['PG'].iloc[0], df['PG'].iloc[-2] # the value of today is not released yet
In [35]:
total_f = (last_f-first_f)*F
total_ge = (last_ge-first_ge)*GE
total_msft = (last_msft-first_msft)*MSFT
total_pg = (last_pg-first_pg)*PG
In [36]:
print(f'if you bought {F} lot(s) of F, {GE} lot(s) of GE, {MSFT} lot(s) of MSFT, and {PG} lot(s) of PG')
print('in 1995 and you never sold it, your total wealth now is')
print(f'$ {(total_f+total_ge+total_msft+total_pg).round(3)}')

if you bought 2 lot(s) of F, 3 lot(s) of GE, 2 lot(s) of MSFT, and 1 lot(s) of PG
in 1995 and you never sold it, your total wealth now is
$ 1090.502

risk analysis¶

In [40]:
df.describe().round(3)
Out[40]:
F GE MSFT PG
count 7182.000 7182.000 7182.000 7182.000
mean 8.207 101.980 57.257 52.894
std 3.335 39.344 78.271 37.441
min 0.765 25.138 2.296 7.537
25% 5.389 71.484 17.372 25.249
50% 8.000 100.890 21.836 41.620
75% 10.264 132.601 48.908 67.969
max 22.665 198.683 348.100 159.210
In [39]:
df.corr().round(3)
Out[39]:
F GE MSFT PG
F 1.000 0.311 0.435 0.362
GE 0.311 1.000 -0.261 -0.160
MSFT 0.435 -0.261 1.000 0.932
PG 0.362 -0.160 0.932 1.000
In [41]:
df.cov().round(3)
Out[41]:
F GE MSFT PG
F 11.123 40.762 113.477 45.143
GE 40.762 1547.919 -802.371 -236.376
MSFT 113.477 -802.371 6126.346 2730.823
PG 45.143 -236.376 2730.823 1401.818