def testSimulateLossesOneYear(self):
# Should return a list of zero or more losses that fall mostly within the
# configured range (1, 10)
self.s = simpleloss.SimpleLoss('L1', 'loss_name', 10, 1, 10)
for _ in range(100):
losses = self.s.simulate_losses_one_year()
if len(losses) == 0:
self.assertEqual([], losses)
else:
self.assertGreater(100, len(losses))
self.assertLess(0.05 * len(losses), sum(losses))
self.assertGreater(100 * len(losses), sum(losses))
def testContract(self):
# Probability must be <= 1.
try:
simpleloss.SimpleLoss("L2", "loss2", 10, 100, 1000) # p > 1
self.fail("Probability <= 1. not enforced")
except AssertionError:
pass
# Probability must be >= 0.
try:
simpleloss.SimpleLoss("L2", "loss2", -1, 100, 1000) # p < 0
self.fail("Probability >= 0. not enforced")
except AssertionError:
pass
# High loss must exceed low loss
try:
simpleloss.SimpleLoss("L2", "loss2", 0.5, 1000,
100) # low_loss > high_loss
self.fail("low_loss <= high_loss not enforced")
except AssertionError:
pass
def csv_to_simpleloss(file):
"""Convert a csv file with parameters to SimpleModel objects
:arg: file = Name of CSV file to read. Each row should contain rows with
name, probability, low_loss, high_loss
:returns: List of SimpleLoss objects
"""
loss_list = []
with open(file, 'r', newline='\n') as csvfile:
loss_reader = csv.reader(csvfile)
for label, name, p, low_loss, high_loss in loss_reader:
label: str
name: str
loss_list += [
simpleloss.SimpleLoss(label, name, float(p), float(low_loss),
float(high_loss))
]
return loss_list
def testLargeFrequency(self):
lg = simpleloss.SimpleLoss('Large', 'large_loss', 3.0, 1, 10)
self.assertAlmostEqual(lg.annualized_loss(), 12.120038480837177) # 30x the value above
def setUp(self):
frequency = 0.1
low = 1
high = 10
self.s = simpleloss.SimpleLoss('L1', 'loss_name', frequency, low, high)
def testHardParameters(self):
# Test difficult-to-fit parameter values
hard = simpleloss.SimpleLoss('H1', 'hard_loss', 0.07, 635000, 19000000)
self.assertAlmostEqual(hard.annualized_loss(), 414589.4783457917)
def setUp(self):
p = 0.1
low = 1
high = 10
self.s = simpleloss.SimpleLoss('L1', 'loss_name', p, low, high)