"""
Name : c11_19_portfolio_VaR.py
Book : Python for Finance (2nd ed.)
Publisher: Packt Publishing Ltd.
Author : Yuxing Yan
Date : 6/6/2017
email : yany@canisius.edu
paulyxy@hotmail.com
"""
import numpy as np
import scipy as sp
import pandas as pd
from scipy.stats import norm
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
# Step 1: input area
tickers=('IBM','WMT','C') # tickers
begdate=(2012,1,1) # beginning date
enddate=(2016,12,31) # ending date
weight=(0.2,0.5,0.3) # weights
confidence_level=0.99 # confidence level
position=5e6 # total value
#
z=norm.ppf(confidence_level)
# Step 2: define a function
def ret_f(ticker,begdate,enddte):
x=getData(ticker,begdate,enddate,asobject=True,adjusted=True)
ret=x.aclose[1:]/x.aclose[:-1]-1
d0=x.date[1:]
return pd.DataFrame(ret,index=d0,columns=[ticker])
# Step 3
n=np.size(tickers)
final=ret_f(tickers[0],begdate,enddate)
for i in np.arange(1,n):
a=ret_f(tickers[i],begdate,enddate)
if i>0:
final=pd.merge(final,a,left_index=True,right_index=True)
#
# Step 4: get porfolio returns
portRet=sp.dot(final,weight)
portStd=sp.std(portRet)
portMean=sp.mean(portRet)
VaR=position*(portMean-z*portStd)
print("Holding=",position, "VaR=", round(VaR,2), "tomorrow")
# compare
total2=0.0
for i in np.arange(n):
stock=tickers[i]
ret=final[stock]
position2=position*weight[i]
mean=sp.mean(ret)
std=sp.std(ret)
VaR=position2*(mean-z*std)
total2+=VaR
print("For ", stock, "with a value of ", position2, "VaR=", round(VaR,2))
print("Sum of three VaR=",round(total2,2))
