def create_cpu(nspots=30, nvols=30):
F = HestonFiniteDifferenceEngine(H, nspots=nspots,
nvols=nvols, spotdensity=10, varexp=4,
var_max=12, verbose=False)
F.init()
F.operators[1].diagonalize()
return F
def setUp(self):
DefaultHeston = HestonOption(spot=100,
strike=100,
interest_rate=0.03125,
volatility=0.2,
tenor=1.0,
mean_reversion=1,
mean_variance=0.12,
vol_of_variance=0.3,
correlation=10.4)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0 / 2.0
self.F = HestonFiniteDifferenceEngine(option,
nspots=5,
nvols=5,
force_bandwidth=None,
flip_idx_var=False)
# self.F = HestonFiniteDifferenceEngine(H, nspots=100,
# nvols=100, spotdensity=10, varexp=4,
# var_max=12, flip_idx_spot=False,
# flip_idx_var=False, verbose=False,
# force_bandwidth=None,
# force_exact=False)
self.F.init()
self.FGG = FDG.HestonFiniteDifferenceEngine(
option, nspots=self.F.grid.shape[0], nvols=self.F.grid.shape[1])
self.FGG.set_zero_derivative()
self.FGG.make_operator_templates()
def setUp(self):
DefaultHeston = HestonOption(spot=100,
strike=100,
interest_rate=0.06,
volatility=0.2,
tenor=1.0,
mean_reversion=1,
mean_variance=0.12,
vol_of_variance=0.3,
correlation=0.4)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0 / 150.0
self.F = HestonFiniteDifferenceEngine(option,
nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
self.F.init()
self.F.operators[1].diagonalize()
self.FG = FDG.HestonFiniteDifferenceEngine(option,
nspots=self.F.grid.shape[0],
nvols=self.F.grid.shape[1])
self.FG.make_operator_templates()
self.FG.set_zero_derivative()
self.FG.scale_and_combine_operators()
def setUp(self):
DefaultHeston = HestonBarrierOption(spot=100
, strike=100
, interest_rate=0.06
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 0.4
, top = (False, 170.0)
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/150.0
self.F = HestonFiniteDifferenceEngine(option, nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
self.F.init()
self.F.operators[1].diagonalize()
self.FG = FDG.HestonFiniteDifferenceEngine(option,
nspots=self.F.grid.shape[0],
nvols=self.F.grid.shape[1])
self.FG.make_operator_templates()
self.FG.set_zero_derivative()
self.FG.scale_and_combine_operators()
def setUp(self):
DefaultHeston = HestonOption(spot=100
, strike=100
, interest_rate=0.03125
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 10.4
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/2.0
self.F = HestonFiniteDifferenceEngine(option, nspots=5,
nvols=5,
force_bandwidth=None,
flip_idx_var=False)
# self.F = HestonFiniteDifferenceEngine(H, nspots=100,
# nvols=100, spotdensity=10, varexp=4,
# var_max=12, flip_idx_spot=False,
# flip_idx_var=False, verbose=False,
# force_bandwidth=None,
# force_exact=False)
self.F.init()
self.FGG = FDG.HestonFiniteDifferenceEngine(option, nspots=self.F.grid.shape[0], nvols=self.F.grid.shape[1])
self.FGG.set_zero_derivative()
self.FGG.make_operator_templates()
def setUp(self):
DefaultHeston = HestonOption(spot=100
, strike=100
, interest_rate=0.03
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 0.4
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/150.0
self.F = HestonFiniteDifferenceEngine(option, nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
# self.F = HestonFiniteDifferenceEngine(H, nspots=100,
# nvols=100, spotdensity=10, varexp=4,
# var_max=12, flip_idx_spot=False,
# flip_idx_var=False, verbose=False,
# force_bandwidth=None,
# force_exact=False)
self.F.init()
self.F.operators[1].diagonalize()
class HestonBarrierOption_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonBarrierOption(spot=100
, strike=100
, interest_rate=0.06
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 0.4
, top = (False, 170.0)
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/150.0
self.F = HestonFiniteDifferenceEngine(option, nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
self.F.init()
self.F.operators[1].diagonalize()
self.FG = FDG.HestonFiniteDifferenceEngine(option,
nspots=self.F.grid.shape[0],
nvols=self.F.grid.shape[1])
self.FG.make_operator_templates()
self.FG.set_zero_derivative()
self.FG.scale_and_combine_operators()
def test_implicit(self):
t, dt = self.F.option.tenor, self.dt
dt = 1/600.0
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_implicit(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_implicit(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
npt.assert_array_almost_equal(V, V2, decimal=6)
# npt.assert_allclose(V, ans, rtol=0.001)
def test_douglas(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_douglas(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_douglas(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=6)
def test_hundsdorferverwer(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_hundsdorferverwer(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_hundsdorferverwer(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=7)
def test_smooth(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_smooth(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_smooth(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=7)
class HestonOptionConstruction_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonBarrierOption(spot=100
, strike=100
, interest_rate=0.03125
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 10.4
, top = (False, 170.0)
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/2.0
self.F = HestonFiniteDifferenceEngine(option, nspots=5,
nvols=5,
force_bandwidth=None,
force_exact=False,
flip_idx_var=False)
# self.F = HestonFiniteDifferenceEngine(H, nspots=100,
# nvols=100, spotdensity=10, varexp=4,
# var_max=12, flip_idx_spot=False,
# flip_idx_var=False, verbose=False,
# force_bandwidth=None,
# force_exact=False)
self.F.init()
self.FGG = FDG.HestonFiniteDifferenceEngine(option,
force_exact=False,
nspots=self.F.grid.shape[0],
nvols=self.F.grid.shape[1])
self.FGG.set_zero_derivative()
self.FGG.make_operator_templates()
def test_scale_and_combine_FGG_0(self):
self.FGG.scale_and_combine_operators(self.FGG.simple_operators)
ref = self.F.operators[0]
tst = self.FGG.operators[0].immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=13)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
def test_scale_and_combine_FGG_1(self):
self.FGG.scale_and_combine_operators()
ref = self.F.operators[1]
tst = self.FGG.operators[1]
tst = tst.immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
def test_scale_and_combine_FGG_01(self):
self.FGG.scale_and_combine_operators()
ref = self.F.operators[(0,1)].copy()
tst = self.FGG.operators[(0,1)].immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
dom = np.random.random(self.FGG.grid.shape)
ref = self.FGG.operators[(0,1)].apply(dom.copy())
tst = self.F.operators[(0,1)].apply(dom)
fp(ref - tst, 'e')
npt.assert_array_almost_equal(tst, ref, decimal=14)
def test_verify_spots_FGG(self):
ref = self.F.spots.copy()
tst = self.FGG.spots.copy()
npt.assert_equal(tst, ref)
def test_verify_vars_FGG(self):
ref = self.F.vars.copy()
tst = self.FGG.vars.copy()
npt.assert_equal(tst, ref)
def test_verify_simple_operators_0_FGG(self):
ref = self.F.simple_operators[(0,)].copy()
tst = self.FGG.simple_operators[(0,)].copy().immigrate()
npt.assert_equal(tst, ref)
ref = self.F.simple_operators[(0,)].copy()
tst = self.FGG.simple_operators[(0,)].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_equal(tst, ref)
def test_verify_simple_operators_1_FGG(self):
ref = self.F.simple_operators[(1,)].copy()
tst = self.FGG.simple_operators[(1,)].copy().immigrate()
npt.assert_equal(tst, ref)
ref = self.F.simple_operators[(1,)].copy()
tst = self.FGG.simple_operators[(1,)].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_equal(tst, ref)
def test_verify_simple_operators_00_FGG(self):
ref = self.F.simple_operators[(0,0)].copy()
tst = self.FGG.simple_operators[(0,0)].copy().immigrate()
npt.assert_equal(tst, ref)
ref = self.F.simple_operators[(0,0)].copy()
tst = self.FGG.simple_operators[(0,0)].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_equal(tst, ref)
def test_verify_simple_operators_11_FGG(self):
ref = self.F.simple_operators[(1,1)].copy()
tst = self.FGG.simple_operators[(1,1)].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.bottom_factors, tst.bottom_factors)
tst.bottom_factors *= 0
ref.bottom_factors *= 0
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
def test_verify_simple_operators_01_FGG(self):
ref = self.F.simple_operators[(0,1)]
tst = self.FGG.simple_operators[(0,1)].immigrate()
npt.assert_equal(tst, ref)
def test_add_operators_0_FGG(self):
fst = self.F.simple_operators[(0,)]
snd = self.F.simple_operators[(0,0)]
ref = (snd + fst) + 0.5
fst = self.FGG.simple_operators[(0,)]
snd = self.FGG.simple_operators[(0,0)]
tst = (snd + fst) + 0.5
tst = tst.immigrate()
tst.derivative = ref.derivative
npt.assert_array_almost_equal(ref.D.data, tst.D.data, decimal=12)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_add_operators_1(self):
fst = self.F.simple_operators[(1,)]
snd = self.F.simple_operators[(1,1)]
ref = (snd + fst) + 0.5
fst = self.FGG.simple_operators[(1,)]
snd = self.FGG.simple_operators[(1,1)]
tst = (snd + fst) + 0.5
tst = tst.immigrate()
tst.derivative = ref.derivative
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_0_FGG(self):
d = (0,)
ref = self.F.simple_operators[d]
ref.vectorized_scale(self.F.coefficient_vector(self.F.coefficients[d],
self.F.t, d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(
self.FGG.coefficient_vector(self.F.coefficients[d],
self.FGG.t, d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_00_FGG(self):
d = (0,0)
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t,
d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(self.FGG.coefficient_vector(self.F.coefficients[d],
self.FGG.t, d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_1_FGG(self):
d = (1,)
ref = self.F.simple_operators[d]
ref.vectorized_scale(self.F.coefficient_vector(self.F.coefficients[d],
self.F.t, d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(self.FGG.coefficient_vector(self.F.coefficients[d],
self.FGG.t, d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_11_FGG(self):
d = (1,1)
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t,
d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(self.FGG.coefficient_vector(self.F.coefficients[d],
self.FGG.t, d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_01_FGG(self):
d = (0,1)
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t,
d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(self.FGG.coefficient_vector(self.F.coefficients[d],
self.FGG.t, d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
class HestonOption_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonOption(spot=100,
strike=100,
interest_rate=0.06,
volatility=0.2,
tenor=1.0,
mean_reversion=1,
mean_variance=0.12,
vol_of_variance=0.3,
correlation=0.4)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0 / 150.0
self.F = HestonFiniteDifferenceEngine(option,
nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
self.F.init()
self.F.operators[1].diagonalize()
self.FG = FDG.HestonFiniteDifferenceEngine(option,
nspots=self.F.grid.shape[0],
nvols=self.F.grid.shape[1])
self.FG.make_operator_templates()
self.FG.set_zero_derivative()
self.FG.scale_and_combine_operators()
def test_implicit(self):
t, dt = self.F.option.tenor, self.dt
dt = 1 / 600.0
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_implicit(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_implicit(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
npt.assert_array_almost_equal(V, V2, decimal=6)
npt.assert_allclose(V, ans, rtol=0.001)
def test_douglas(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_douglas(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot, self.F.spots[self.F.idx[0]]
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
def test_hundsdorferverwer(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_hundsdorferverwer(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_hundsdorferverwer(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=7)
def test_smooth(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_smooth(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
class HestonOptionConstruction_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonOption(spot=100,
strike=100,
interest_rate=0.03125,
volatility=0.2,
tenor=1.0,
mean_reversion=1,
mean_variance=0.12,
vol_of_variance=0.3,
correlation=10.4)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0 / 2.0
self.F = HestonFiniteDifferenceEngine(option,
nspots=5,
nvols=5,
force_bandwidth=None,
flip_idx_var=False)
# self.F = HestonFiniteDifferenceEngine(H, nspots=100,
# nvols=100, spotdensity=10, varexp=4,
# var_max=12, flip_idx_spot=False,
# flip_idx_var=False, verbose=False,
# force_bandwidth=None,
# force_exact=False)
self.F.init()
self.FGG = FDG.HestonFiniteDifferenceEngine(
option, nspots=self.F.grid.shape[0], nvols=self.F.grid.shape[1])
self.FGG.set_zero_derivative()
self.FGG.make_operator_templates()
def test_scale_and_combine_FGG_0(self):
self.FGG.scale_and_combine_operators(self.FGG.simple_operators)
ref = self.F.operators[0]
tst = self.FGG.operators[0].immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_array_almost_equal(tst.R, ref.R, decimal=12)
tst.R *= 0
ref.R *= 0
npt.assert_equal(tst, ref)
def test_scale_and_combine_FGG_1(self):
self.FGG.scale_and_combine_operators()
ref = self.F.operators[1]
tst = self.FGG.operators[1]
tst = tst.immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
def test_scale_and_combine_FGG_01(self):
self.FGG.scale_and_combine_operators()
ref = self.F.operators[(0, 1)].copy()
tst = self.FGG.operators[(0, 1)].immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
dom = np.random.random(self.FGG.grid.shape)
ref = self.FGG.operators[(0, 1)].apply(dom.copy())
tst = self.F.operators[(0, 1)].apply(dom)
fp(ref - tst, 'e')
npt.assert_array_almost_equal(tst, ref, decimal=14)
def test_verify_simple_operators_0_FGG(self):
ref = self.F.simple_operators[(0, )].copy()
tst = self.FGG.simple_operators[(0, )].copy().immigrate()
npt.assert_equal(tst, ref)
ref = self.F.simple_operators[(0, )].copy()
tst = self.FGG.simple_operators[(0, )].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_equal(tst, ref)
def test_verify_simple_operators_1_FGG(self):
ref = self.F.simple_operators[(1, )].copy()
tst = self.FGG.simple_operators[(1, )].copy().immigrate()
npt.assert_equal(tst, ref)
ref = self.F.simple_operators[(1, )].copy()
tst = self.FGG.simple_operators[(1, )].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_equal(tst, ref)
def test_verify_simple_operators_00_FGG(self):
ref = self.F.simple_operators[(0, 0)].copy()
tst = self.FGG.simple_operators[(0, 0)].copy().immigrate()
npt.assert_equal(tst, ref)
ref = self.F.simple_operators[(0, 0)].copy()
tst = self.FGG.simple_operators[(0, 0)].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_equal(tst, ref)
def test_verify_simple_operators_11_FGG(self):
ref = self.F.simple_operators[(1, 1)].copy()
tst = self.FGG.simple_operators[(1, 1)].copy()
ref.diagonalize(), tst.diagonalize()
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.bottom_factors, tst.bottom_factors)
tst.bottom_factors *= 0
ref.bottom_factors *= 0
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
def test_verify_simple_operators_01_FGG(self):
ref = self.F.simple_operators[(0, 1)]
tst = self.FGG.simple_operators[(0, 1)].immigrate()
npt.assert_equal(tst, ref)
def test_add_operators_0_FGG(self):
fst = self.F.simple_operators[(0, )]
snd = self.F.simple_operators[(0, 0)]
ref = (snd + fst) + 0.5
fst = self.FGG.simple_operators[(0, )]
snd = self.FGG.simple_operators[(0, 0)]
tst = (snd + fst) + 0.5
tst = tst.immigrate()
tst.derivative = ref.derivative
npt.assert_array_almost_equal(ref.D.data, tst.D.data, decimal=12)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_add_operators_1(self):
fst = self.F.simple_operators[(1, )]
snd = self.F.simple_operators[(1, 1)]
ref = (snd + fst) + 0.5
fst = self.FGG.simple_operators[(1, )]
snd = self.FGG.simple_operators[(1, 1)]
tst = (snd + fst) + 0.5
tst = tst.immigrate()
tst.derivative = ref.derivative
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_0_FGG(self):
d = (0, )
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t, d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(
self.FGG.coefficient_vector(self.F.coefficients[d], self.FGG.t,
d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_00_FGG(self):
d = (0, 0)
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t, d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(
self.FGG.coefficient_vector(self.F.coefficients[d], self.FGG.t,
d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_1_FGG(self):
d = (1, )
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t, d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(
self.FGG.coefficient_vector(self.F.coefficients[d], self.FGG.t,
d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_11_FGG(self):
d = (1, 1)
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t, d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(
self.FGG.coefficient_vector(self.F.coefficients[d], self.FGG.t,
d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_scale_operators_01_FGG(self):
d = (0, 1)
ref = self.F.simple_operators[d]
ref.vectorized_scale(
self.F.coefficient_vector(self.F.coefficients[d], self.F.t, d[0]))
tst = self.FGG.simple_operators[d]
tst.vectorized_scale_from_host(
self.FGG.coefficient_vector(self.F.coefficients[d], self.FGG.t,
d[0]))
tst = tst.immigrate()
npt.assert_array_almost_equal(ref.D.data, tst.D.data)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def dt_convergence(nspots=100, nvols=100, scheme=None):
# global F, vals, errs
errors = []
schemes = {
'hv':
lambda dt: F.solve_hundsdorferverwer(
H.tenor / dt, dt, F.grid.domain[-1], theta=0.65),
'i':
lambda dt: F.solve_implicit(H.tenor / dt, dt, F.grid.domain[-1]),
'd':
lambda dt: F.solve_implicit(
H.tenor / dt, dt, F.grid.domain[-1], theta=0.65),
'smooth':
lambda dt: F.smooth(
H.tenor / dt, dt, F.grid.domain[-1], smoothing_steps=1)
}
for i in range(1, 11):
schemes = {}
schemes[(1, )] = [{"scheme": "forward"}]
F = HestonFiniteDifferenceEngine(H,
schemes=schemes,
nspots=nspots,
nvols=nvols,
spotdensity=10,
varexp=4,
var_max=12,
flip_idx_spot=True,
flip_idx_var=True,
verbose=False)
if scheme is None:
scheme = 'hv'
dt = 1.0 / 2.0**i
print dt, tuple("%.1e" % ((max(mesh) - min(mesh)) / len(mesh))
for mesh in F.grid.mesh)
print map(len, F.grid.mesh), map(min,
F.grid.mesh), map(max, F.grid.mesh)
Vs = schemes[scheme](dt)
xs = F.spots
ys = F.vars
trimx = (0.0 * H.spot <= xs) & (xs <= 2.0 * H.spot)
trimy = ys <= 1.0
tr = lambda x: x[trimx, :][:, trimy]
xst = xs[trimx]
yst = ys[trimy]
res = tr(Vs)
a = tr(F.grid_analytical)
bad = F.BADANALYTICAL
price_err = F.price - H.analytical
inf_norm = max(abs(res - a).flat)
norm2 = pylab.sqrt(sum(((res - a)**2).flat))
err = norm2
# err = abs(price_err[0,0])
# print dt, price_err, err
# pp(Vs, F.grid_analytical, xs, ys, label="$dt = %s$" % dt, bad=bad)
# p(Vs, F.grid_analytical, xs, ys, label="$dt = %s$" % dt, bad=bad)
p(res, a, xst, yst, label="$dt = %s$" % dt, bad=bad)
errors.append((dt, err))
print errors
vals, errs = zip(*errors)
pylab.plot(vals, errs)
pylab.plot(vals, errs, 'ro')
pylab.xlabel("dt")
pylab.ylabel("Error")
pylab.show()
pylab.loglog(vals, errs)
pylab.loglog(vals, errs, 'ro')
pylab.xlabel("dt")
pylab.ylabel("Error")
pylab.show()
return errors
(0.0, lambda t, *dim: np.maximum(0.0, dim[0] - H.strike)),
),
# Free boundary
(1, 1): (
(None, lambda *x: None),
# D intrinsic value at high variance
(0.0, lambda t, *dim: np.maximum(0.0, dim[0] - H.strike)),
),
}
# schemes = {(1,) : ({"scheme": "center"}, {"scheme": "backward", "from" : flip_idx_var})}
utils.tic("Building FDE Engine")
# F = FDE.FiniteDifferenceEngineADI(G, coefficients=coeffs, boundaries=bounds, schemes=schemes, force_bandwidth=(-2, 2))
F = HestonFiniteDifferenceEngine(H, nspots=nspots, nvols=nvols, flip_idx_spot=flip_idx_spot, flip_idx_var=flip_idx_var)
utils.toc()
L1_ = []
R1_ = []
utils.tic("Building As(s):\t")
print "(Up/Down)wind from:", flip_idx_spot
As_ = utils.nonuniform_complete_coefficients(dss, up_or_down=up_or_down_spot, flip_idx=flip_idx_spot)[0]
Ass_ = utils.nonuniform_complete_coefficients(dss)[1]
# As_, Ass_ = utils.nonuniform_forward_coefficients(dss)
assert not np.isnan(As_.data).any()
assert not np.isnan(Ass_.data).any()
mu_sfunc = mu_s
gamma2_sfunc = gamma2_s
for j, v in enumerate(vars):
# Be careful not to overwrite our operators
class HestonOption_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonOption(spot=100
, strike=100
, interest_rate=0.03
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 0.4
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/150.0
self.F = HestonFiniteDifferenceEngine(option, nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
# self.F = HestonFiniteDifferenceEngine(H, nspots=100,
# nvols=100, spotdensity=10, varexp=4,
# var_max=12, flip_idx_spot=False,
# flip_idx_var=False, verbose=False,
# force_bandwidth=None,
# force_exact=False)
self.F.init()
self.F.operators[1].diagonalize()
def test_implicit(self):
t, dt = self.F.option.tenor, self.dt
dt = 1.0/600.0
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.F.solve_implicit(t/dt, dt)[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
def test_douglas(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.F.solve_douglas(t/dt, dt)[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
def test_smooth(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.F.solve_smooth(t/dt, dt)[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
def create_cpu(nspots=30, nvols=30):
F = HestonFiniteDifferenceEngine(H, nspots=nspots, nvols=nvols, spotdensity=10, varexp=4, var_max=12, verbose=False)
F.init()
F.operators[1].diagonalize()
return F
