class TestBlackScholesPriceProcess(unittest.TestCase):
def setUp(self):
self.p = BlackScholesPriceProcess()
def test_simulate_future_prices_no_market_names_no_fixing_dates(self):
prices = list(self.p.simulate_future_prices(
market_names=[],
fixing_dates=[],
observation_date=datetime.date(2011, 1, 1),
path_count=2, calibration_params={},
))
self.assertEqual(prices, [])
def test_simulate_future_prices_no_fixing_dates(self):
prices = list(self.p.simulate_future_prices(
market_names=['#1'],
fixing_dates=[],
observation_date=datetime.date(2011, 1, 1),
path_count=2, calibration_params={'#1-LAST-PRICE': 10, '#1-ACTUAL-HISTORICAL-VOLATILITY': 50},
))
prices = [(p[0], p[1], p[2].all()) for p in prices] # For numpy.
self.assertEqual(prices, [('#1', datetime.date(2011, 1, 1), numpy.array([ 10., 10.]).all())])
def test_simulate_future_prices_no_markets(self):
prices = list(self.p.simulate_future_prices(
market_names=[],
fixing_dates=[datetime.date(2011, 1, 2)],
observation_date=datetime.date(2011, 1, 1),
path_count=2, calibration_params={},
))
self.assertEqual(prices, [])
def test_simulate_future_prices_one_market_zero_volatility(self):
prices = list(self.p.simulate_future_prices(
market_names=['#1'],
fixing_dates=[datetime.date(2011, 1, 2)],
observation_date=datetime.date(2011, 1, 1),
path_count=2, calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 0,
},
))
prices = [(p[0], p[1], p[2].mean()) for p in prices] # For numpy.
self.assertEqual(prices, [
('#1', datetime.date(2011, 1, 1), numpy.array([ 10., 10.]).mean()),
('#1', datetime.date(2011, 1, 2), numpy.array([ 10., 10.]).mean()),
])
def test_simulate_future_prices_one_market_high_volatility(self):
prices = list(self.p.simulate_future_prices(
market_names=['#1'],
fixing_dates=[datetime.date(2012, 1, 1)],
observation_date=datetime.date(2011, 1, 1),
path_count=1000, calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 50,
},
))
prices = [p[2].mean() for p in prices[1:]] # For numpy.
expected_prices = [10]
for price, expected_price in zip(prices, expected_prices):
self.assertNotEqual(price, expected_price)
self.assertAlmostEqual(price, expected_price, places=0)
def test_simulate_future_prices_two_markets_zero_volatility(self):
prices = list(self.p.simulate_future_prices(
market_names=['#1', '#2'],
fixing_dates=[datetime.date(2011, 1, 2)],
observation_date=datetime.date(2011, 1, 1),
path_count=200000, calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 0,
'#2-LAST-PRICE': 20,
'#2-ACTUAL-HISTORICAL-VOLATILITY': 0,
'#1-#2-CORRELATION': 0,
},
))
prices = [(p[0], p[1], p[2].mean()) for p in prices] # For numpy.
self.assertEqual(prices, [
('#1', datetime.date(2011, 1, 1), numpy.array([ 10., 10.]).mean()),
('#1', datetime.date(2011, 1, 2), numpy.array([ 10., 10.]).mean()),
('#2', datetime.date(2011, 1, 1), numpy.array([ 20., 20.]).mean()),
('#2', datetime.date(2011, 1, 2), numpy.array([ 20., 20.]).mean()),
])
def test_simulate_future_prices_two_markets_high_volatility_zero_correlation(self):
prices = list(self.p.simulate_future_prices(
market_names=['#1', '#2'],
fixing_dates=[datetime.date(2012, 1, 1)],
observation_date=datetime.date(2011, 1, 1),
path_count=1000, calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#2-LAST-PRICE': 20,
'#2-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#1-#2-CORRELATION': 0,
},
))
prices = [p[2].mean() for p in prices] # For numpy.
expected_prices = [10, 10, 20, 20]
for price, expected_price in zip(prices, expected_prices):
self.assertAlmostEqual(price, expected_price, places=0)
def test_simulate_future_prices_two_markets_high_volatility_positive_correlation(self):
prices = list(self.p.simulate_future_prices(
market_names=['#1', '#2'],
fixing_dates=[datetime.date(2012, 1, 1)],
observation_date=datetime.date(2011, 1, 1),
path_count=1000, calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#2-LAST-PRICE': 20,
'#2-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#1-#2-CORRELATION': 0.5,
},
))
prices = [p[2].mean() for p in prices] # For numpy.
expected_prices = [10, 10, 20, 20]
for price, expected_price in zip(prices, expected_prices):
self.assertAlmostEqual(price, expected_price, places=0)
class TestSimulateBlackScholesPriceProcess(unittest.TestCase):
def setUp(self):
self.p = BlackScholesPriceProcess()
def test_simulate_future_prices_no_requirements(self):
prices = list(
self.p.simulate_future_prices(
observation_date=datetime.datetime(2011, 1, 1),
requirements=[],
path_count=2,
calibration_params={
'market': ['#1'],
'sigma': [0.5],
'curve': {
'#1': (('2011-1-1', 10), )
},
},
))
self.assertEqual(list(prices), [])
def test_simulate_future_prices_one_market_zero_volatility(self):
prices = list(
self.p.simulate_future_prices(
requirements=[
('#1', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#1', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
],
observation_date=datetime.datetime(2011, 1, 1),
path_count=2,
calibration_params={
'market': ['#1'],
'sigma': [0.0],
'curve': {
'#1': (('2011-1-1', 10), )
},
},
))
prices = [(p[0], p[1], p[2], p[3].mean())
for p in prices] # For scipy.
self.assertEqual(prices, [
('#1', datetime.datetime(2011, 1, 1), datetime.datetime(
2011, 1, 1), scipy.array([10., 10.]).mean()),
('#1', datetime.datetime(2011, 1, 2), datetime.datetime(
2011, 1, 2), scipy.array([10., 10.]).mean()),
])
def test_simulate_future_prices_one_market_high_volatility(self):
prices = list(
self.p.simulate_future_prices(
requirements=[
('#1', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#1', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
],
observation_date=datetime.datetime(2011, 1, 1),
path_count=1000,
calibration_params={
'market': ['#1'],
'sigma': [0.5],
'curve': {
'#1': (('2011-1-1', 10), )
},
},
))
prices = [p[3].mean() for p in prices[1:]] # For scipy.
expected_prices = [10]
for price, expected_price in zip(prices, expected_prices):
self.assertNotEqual(price, expected_price)
self.assertAlmostEqual(price, expected_price, places=0)
def test_simulate_future_prices_two_markets_zero_volatility(self):
prices = list(
self.p.simulate_future_prices(requirements=[
('#1', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1, 1)),
('#1', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1, 2)),
('#2', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1, 1)),
('#2', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1, 2)),
],
observation_date=datetime.datetime(
2011, 1, 1),
path_count=200000,
calibration_params={
'market': ['#1', '#2'],
'sigma': [0.0, 0.0],
'curve': {
'#1': (('2011-1-1', 10), ),
'#2': (('2011-1-1', 20), )
},
'rho': [[1, 0], [0, 1]]
}))
prices = [(p[0], p[1], p[2], p[3].mean())
for p in prices] # For scipy.
self.assertEqual(prices, [
('#1', datetime.datetime(2011, 1, 1), datetime.datetime(
2011, 1, 1), scipy.array([10., 10.]).mean()),
('#1', datetime.datetime(2011, 1, 2), datetime.datetime(
2011, 1, 2), scipy.array([10., 10.]).mean()),
('#2', datetime.datetime(2011, 1, 1), datetime.datetime(
2011, 1, 1), scipy.array([20., 20.]).mean()),
('#2', datetime.datetime(2011, 1, 2), datetime.datetime(
2011, 1, 2), scipy.array([20., 20.]).mean()),
])
def test_simulate_future_prices_two_markets_high_volatility_zero_correlation(
self):
prices = list(
self.p.simulate_future_prices(
requirements=[
('#1', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#1', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
('#2', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#2', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
],
observation_date=datetime.datetime(2011, 1, 1),
path_count=1000,
calibration_params={
'market': ['#1', '#2'],
'sigma': [0.5, 0.5],
'curve': {
'#1': (('2011-1-1', 10), ),
'#2': (('2011-1-1', 20), )
},
'rho': [[1, 0], [0, 1]]
},
))
prices = [p[3].mean() for p in prices] # For scipy.
expected_prices = [10, 10, 20, 20]
for price, expected_price in zip(prices, expected_prices):
self.assertAlmostEqual(price, expected_price, places=0)
def test_simulate_future_prices_two_markets_high_volatility_positive_correlation(
self):
prices = list(
self.p.simulate_future_prices(
requirements=[
('#1', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#1', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
('#2', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#2', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
],
observation_date=datetime.datetime(2011, 1, 1),
path_count=1000,
calibration_params={
'market': ['#1', '#2'],
'sigma': [0.5, 0.5],
'curve': {
'#1': (('2011-1-1', 10), ),
'#2': (('2011-1-1', 20), )
},
'rho': [[1, 0.5], [0.5, 1]]
},
))
assert len(prices)
prices = [p[3].mean() for p in prices] # For scipy.
expected_prices = [10, 10, 20, 20]
for price, expected_price in zip(prices, expected_prices):
self.assertAlmostEqual(price, expected_price, places=0)
def test_simulate_future_prices_from_longer_curve(self):
prices = list(
self.p.simulate_future_prices(
requirements=[
('#1', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#1', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
('#2', datetime.datetime(2011, 1,
1), datetime.datetime(2011, 1,
1)),
('#2', datetime.datetime(2011, 1,
2), datetime.datetime(2011, 1,
2)),
],
observation_date=datetime.datetime(2011, 1, 1),
path_count=1000,
calibration_params={
'market': ['#1', '#2'],
'sigma': [0.5, 0.5],
'curve': {
'#1': (('2011-1-1', 10), ('2011-1-2', 15)),
'#2': (('2011-1-1', 20), ('2011-1-2', 25))
},
'rho': [[1, 0.5], [0.5, 1]]
},
))
expected_prices = [10, 15, 20, 25]
prices = [p[3].mean() for p in prices] # For scipy.
for price, expected_price in zip(prices, expected_prices):
self.assertAlmostEqual(price, expected_price, places=0)
class TestBlackScholesPriceProcess(unittest.TestCase):
def setUp(self):
self.p = BlackScholesPriceProcess()
def test_simulate_future_prices_no_market_names_no_fixing_dates(self):
prices = list(
self.p.simulate_future_prices(
market_names=[],
fixing_dates=[],
observation_date=datetime.date(2011, 1, 1),
path_count=2,
calibration_params={},
))
self.assertEqual(prices, [])
def test_simulate_future_prices_no_fixing_dates(self):
prices = list(
self.p.simulate_future_prices(
market_names=['#1'],
fixing_dates=[],
observation_date=datetime.date(2011, 1, 1),
path_count=2,
calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 50
},
))
prices = [(p[0], p[1], p[2].all()) for p in prices] # For numpy.
self.assertEqual(
prices,
[('#1', datetime.date(2011, 1, 1), numpy.array([10., 10.]).all())])
def test_simulate_future_prices_no_markets(self):
prices = list(
self.p.simulate_future_prices(
market_names=[],
fixing_dates=[datetime.date(2011, 1, 2)],
observation_date=datetime.date(2011, 1, 1),
path_count=2,
calibration_params={},
))
self.assertEqual(prices, [])
def test_simulate_future_prices_one_market_zero_volatility(self):
prices = list(
self.p.simulate_future_prices(
market_names=['#1'],
fixing_dates=[datetime.date(2011, 1, 2)],
observation_date=datetime.date(2011, 1, 1),
path_count=2,
calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 0,
},
))
prices = [(p[0], p[1], p[2].mean()) for p in prices] # For numpy.
self.assertEqual(prices, [
('#1', datetime.date(2011, 1, 1), numpy.array([10., 10.]).mean()),
('#1', datetime.date(2011, 1, 2), numpy.array([10., 10.]).mean()),
])
def test_simulate_future_prices_one_market_high_volatility(self):
prices = list(
self.p.simulate_future_prices(
market_names=['#1'],
fixing_dates=[datetime.date(2012, 1, 1)],
observation_date=datetime.date(2011, 1, 1),
path_count=1000,
calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 50,
},
))
prices = [p[2].mean() for p in prices[1:]] # For numpy.
expected_prices = [10]
for price, expected_price in zip(prices, expected_prices):
self.assertNotEqual(price, expected_price)
self.assertAlmostEqual(price, expected_price, places=0)
def test_simulate_future_prices_two_markets_zero_volatility(self):
prices = list(
self.p.simulate_future_prices(
market_names=['#1', '#2'],
fixing_dates=[datetime.date(2011, 1, 2)],
observation_date=datetime.date(2011, 1, 1),
path_count=200000,
calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 0,
'#2-LAST-PRICE': 20,
'#2-ACTUAL-HISTORICAL-VOLATILITY': 0,
'#1-#2-CORRELATION': 0,
},
))
prices = [(p[0], p[1], p[2].mean()) for p in prices] # For numpy.
self.assertEqual(prices, [
('#1', datetime.date(2011, 1, 1), numpy.array([10., 10.]).mean()),
('#1', datetime.date(2011, 1, 2), numpy.array([10., 10.]).mean()),
('#2', datetime.date(2011, 1, 1), numpy.array([20., 20.]).mean()),
('#2', datetime.date(2011, 1, 2), numpy.array([20., 20.]).mean()),
])
def test_simulate_future_prices_two_markets_high_volatility_zero_correlation(
self):
prices = list(
self.p.simulate_future_prices(
market_names=['#1', '#2'],
fixing_dates=[datetime.date(2012, 1, 1)],
observation_date=datetime.date(2011, 1, 1),
path_count=1000,
calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#2-LAST-PRICE': 20,
'#2-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#1-#2-CORRELATION': 0,
},
))
prices = [p[2].mean() for p in prices] # For numpy.
expected_prices = [10, 10, 20, 20]
for price, expected_price in zip(prices, expected_prices):
self.assertAlmostEqual(price, expected_price, places=0)
def test_simulate_future_prices_two_markets_high_volatility_positive_correlation(
self):
prices = list(
self.p.simulate_future_prices(
market_names=['#1', '#2'],
fixing_dates=[datetime.date(2012, 1, 1)],
observation_date=datetime.date(2011, 1, 1),
path_count=1000,
calibration_params={
'#1-LAST-PRICE': 10,
'#1-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#2-LAST-PRICE': 20,
'#2-ACTUAL-HISTORICAL-VOLATILITY': 50,
'#1-#2-CORRELATION': 0.5,
},
))
prices = [p[2].mean() for p in prices] # For numpy.
expected_prices = [10, 10, 20, 20]
for price, expected_price in zip(prices, expected_prices):
self.assertAlmostEqual(price, expected_price, places=0)