# Python: Example 3.3
import numpy as np
import pandas as pd
import pandas_datareader as pdr
ticker=['AAPL','DIS','EBAY','FB']
# Position setting
investment=np.array([0.35, 0.3, 0.25, 0.1])
# Alternatively: try the following three lines
# Total investment as 1 million
weight=np.array([0.35,0.3,0.25,0.1])   # We set the weight
investment=total*weight   # We have the same position
data=pdr.data.get_data_yahoo(ticker, start="2018-01-01", end="2020-07-18")['Adj Close']
returns=data.pct_change()   # percentage returns
cov_matrix=returns.cov()
var_portfolio=investment.T.dot(cov_matrix).dot(investment)}   # var-cov matrix in dollar term
stdev_portfolio=np.sqrt(var_portfolio)}   #volatility in dollar term
var=2.33*stdev_portfolio
# An alternative way for the last line
mean_portfolio=returns.mean().dot(weight)
mean_investment=(1+mean_portfolio)*total
from scipy.stats import norm
cutoff=norm.ppf(0.01,mean_investment,stdev_portfolio)} # normality assumption kicks in
var=total-cutoff