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#!/usr/bin/python
"""
Copyright (C) 2000, 2001, 2002 RiskMap srl
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it under the
terms of the QuantLib license. You should have received a copy of the
license along with this program; if not, please email ferdinando@ametrano.net
The license is also available online at http://quantlib.org/html/license.html
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
"""
__version__ = "$Revision: 1.15 $"
# $Source: /cvsroot/quantlib/QuantLib-Python/QuantLib/test/risk_statistics.py,v $
import QuantLib
import unittest
from math import exp, sqrt, pi
# define a Gaussian
def gaussian(x, average, sigma):
normFact = sigma * sqrt( 2 * pi )
dx = x-average
return exp( -dx*dx/(2.0*sigma*sigma) ) / normFact
class RiskStatisticsTest(unittest.TestCase):
def runTest(self):
"Testing risk statistics"
s = QuantLib.RiskStatistics()
averageRange = [-100.0, 0.0, 100.0]
sigmaRange = [0.1, 1.0, 10]
N = 25000
numberOfSigma = 15
for average in averageRange:
for sigma in sigmaRange:
#target cannot be changed:
#it is a strong assumption to compute values to be checked
target = average
normal = QuantLib.NormalDistribution(average, sigma)
dataMin = average - numberOfSigma*sigma
dataMax = average + numberOfSigma*sigma
# even NOT to include average
h = (dataMax-dataMin)/(N-1)
data = [0]*N # creates a list of N elements
for i in range(N):
data[i] = dataMin+h*i
weights = map(lambda x,average=average,sigma=sigma:
gaussian(x,average,sigma), data)
s.addWeightedSequence(data, weights)
samples = s.samples()
if not (samples == N):
self.fail("""
wrong number of samples
calculated: %(samples)d
expected : %(N)d
""" % locals())
rightWeightSum = reduce(lambda x,y: x+y, weights)
weightSum = s.weightSum()
if not (weightSum == rightWeightSum):
self.fail("""
wrong sum of weights
calculated: %(weightSum)f
expected : %(rightWeightSum)f
""" % locals())
minDatum = s.min()
maxDatum = s.max()
if not (minDatum == dataMin):
self.fail("""
wrong minimum value
calculated: %(minDatum)f
expected : %(dataMin)f
""" % locals())
if not (abs(s.max()-dataMax) <= 1e-13):
self.fail("""
wrong maximum value
calculated: %(maxDatum)f
expected : %(dataMax)f
""" % locals())
mean = s.mean()
if average == 0.0:
check = abs(mean-average)
else:
check = abs(mean-average)/average
if not (check <= 1e-13):
self.fail("""
wrong mean value
calculated: %(mean)f
expected : %(average)f
""" % locals())
variance = s.variance()
sigma2 = sigma*sigma
if not (abs(variance-sigma2)/sigma2 <= 1e-4):
self.fail("""
wrong variance
calculated: %(variance)f
expected : %(sigma2)f
""" % locals())
stdDev = s.standardDeviation()
if not (abs(stdDev-sigma)/sigma <= 1e-4):
self.fail("""
wrong standard deviation
calculated: %(stdDev)f
expected : %(sigma)f
""" % locals())
skewness = s.skewness()
if not (abs(skewness) <= 1e-4):
self.fail("""
wrong skewness
calculated: %(skewness)f
expected : 0.0
""" % locals())
kurtosis = s.kurtosis()
if not (abs(kurtosis) <= 1e-1):
self.fail("""
wrong kurtosis
calculated: %(kurtosis)f
expected : 0.0
""" % locals())
rightPotentialUpside = max(average+2.0*sigma, 0.0)
potentialUpside = s.potentialUpside(0.9772)
if rightPotentialUpside == 0.0:
check = abs(potentialUpside-rightPotentialUpside)
else:
check = abs(potentialUpside-rightPotentialUpside)/\
rightPotentialUpside
if not (check <= 1e-3):
self.fail("""
wrong potential upside
calculated: %(potentialUpside)f
expected: %(rightPotentialUpside)f
""" % locals())
rightVAR = -min(average-2.0*sigma, 0.0)
VAR = s.valueAtRisk(0.9772)
if rightVAR == 0.0:
check = abs(VAR-rightVAR)
else:
check = abs(VAR-rightVAR)/rightVAR
if not (check <= 1e-3):
self.fail("""
wrong value at risk
calculated: %(VAR)f
expected: %(rightVAR)f
""" % locals())
tempVAR = average-2.0*sigma
rightExShortfall = average - sigma*sigma*gaussian(tempVAR,
average, sigma)/(1.0-0.9772)
rightExShortfall = -min(rightExShortfall, 0.0)
exShortfall = s.expectedShortfall(0.9772)
if rightExShortfall == 0.0:
check = abs(exShortfall)
else:
check = abs(exShortfall-rightExShortfall)/rightExShortfall
if not (check <= 1e-3):
self.fail("""
wrong expected shortfall
calculated: %(exShortFall)f
expected: %(rightExShortfall)f
""" % locals())
rightShortfall = 0.5
shortfall = s.shortfall(target)
if not (abs(shortfall-rightShortfall)/rightShortfall <= 1e-8):
self.fail("""
wrong shortfall
calculated: %(shortFall)
expected: %(rightShortfall)f
""" % locals())
rightAvgShortfall = sigma/sqrt( 2 * pi )
avgShortfall = s.averageShortfall(target)
check = abs(avgShortfall-rightAvgShortfall)/rightAvgShortfall
if not (check <= 1e-4):
self.fail("""
wrong average shortfall
calculated: %(avgShortFall)f
expected: %(rightAvgShortfall)f
""" % locals())
s.reset()
if __name__ == '__main__':
print 'testing QuantLib', QuantLib.__version__, QuantLib.QuantLibc.__file__, QuantLib.__file__
import sys
suite = unittest.TestSuite()
suite.addTest(RiskStatisticsTest())
if sys.hexversion >= 0x020100f0:
unittest.TextTestRunner(verbosity=2).run(suite)
else:
unittest.TextTestRunner().run(suite)
raw_input('press any key to continue')
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