"""
README
======
This file contains Python codes.
======
"""
import datetime as dt
import numpy as np
import pandas.io.data as web
from scipy.stats import norm
def calculate_daily_VaR(P, prob, mean, sigma,
days_per_year=252.):
min_ret = norm.ppf(1-prob,
mean/days_per_year,
sigma/np.sqrt(days_per_year))
return P - P*(min_ret+1)
if __name__ == "__main__":
start = dt.datetime(2013, 12, 1)
end = dt.datetime(2014, 12, 1)
prices = web.DataReader("AAPL", "yahoo", start, end)
returns = prices["Adj Close"].pct_change().dropna()
portvolio_value = 100000000.00
confidence = 0.95
mu = np.mean(returns)
sigma = np.std(returns)
VaR = calculate_daily_VaR(portvolio_value, confidence,
mu, sigma)
print "Value-at-Risk:", round(VaR, 2)
