def operator_truediv2(size):
a = Array(size, 'double')
b = Array(size, 'double')
for i in range(size):
a[i] = nb_types.double(i+10)
b[i] = nb_types.double(i+3)
return operator.truediv(a, b)
def operator_ifloordiv2(size):
a = Array(size, 'double')
b = Array(size, 'double')
for i in range(size):
a[i] = nb_types.double(i+10)
b[i] = nb_types.double(i+3)
operator.ifloordiv(a, b)
return a
def next_double(self):
sig = types.double(types.CPointer(types.uint64))
@cfunc(sig)
def next_double(st):
bit_gen_state = carray(st, (2, ), dtype=np.uint64)
return (np.uint64(splitmix_next(bit_gen_state)) >>
np.uint64(11)) / 9007199254740992.0
# Ensure a reference is held
self._next_double = next_double
return next_double
def make_integrand():
global _integrand
import numba
import numba.types as nt
import scipy
@numba.cfunc(nt.double(nt.intc, nt.CPointer(nt.double)),
nopython=True, nogil=True)
def _fn(n, xx):
if n != 4:
return np.nan
y = xx[0]
k = xx[1]
sigma2 = xx[2]
z = xx[3]
return y ** (k/2 - 1) * np.exp(-(k * y + (z-y)**2 / sigma2) / 2)
_integrand = scipy.LowLevelCallable(_fn.ctypes)
def _computeParameters(self):
self.theta = sp.sympify(self.theta)
self.b = sp.sympify(self.b)
self.delta = sp.sympify(self.delta)
self.epsilon = sp.sympify(self.epsilon)
self.eosDisplayName = self.eosDisplayName
self.eosInfo = self.eosInfo
self._PsymbolicFromVT = R_IG * self.T / (self.V - self.b) - self.theta / (
(self.V - self.b) * (self.V ** 2 + self.delta * self.V + self.epsilon)
)
self._ZsymbolicFromVT = self.V / (self.V - self.b) - (
self.V * self.theta / (R_IG * self.T)
) / (self.V ** 2 + self.V * self.delta + self.epsilon)
self._dZdTsymbolicFromVT = sp.diff(self._ZsymbolicFromVT, self.T)
self._numf_ZfromVT = njit()(
sp.lambdify([self.V, self.T], self._ZsymbolicFromVT, modules="numpy")
)
self._numf_dZdTfromVT = njit()(
sp.lambdify([self.V, self.T], self._dZdTsymbolicFromVT, modules="numpy")
)
self._Bl = self.b * self.P / (R_IG * self.T)
self._deltal = self.delta * self.P / (R_IG * self.T)
self._thetal = self.theta * self.P / (R_IG * self.T) ** 2
self._epsilonl = self.epsilon * (self.P / (R_IG * self.T)) ** 2
# coefficients Z**3 + a0*Z**2 + a1*Z + a2 = 0
self._a0 = self._deltal - self._Bl - 1
self._a1 = self._thetal + self._epsilonl - self._deltal * (self._Bl + 1)
self._a2 = -(self._epsilonl * (self._Bl + 1) + self._thetal * self._Bl)
self._numf_a0 = njit()(sp.lambdify([self.P, self.T], self._a0, modules="numpy"))
self._numf_a1 = njit()(sp.lambdify([self.P, self.T], self._a1, modules="numpy"))
self._numf_a2 = njit()(sp.lambdify([self.P, self.T], self._a2, modules="numpy"))
self.tmp_cfunc = None
self.tmp_cfunc2 = None
c_sig = types.double(types.intc, types.CPointer(types.double))
exec(
"self.tmp_cfunc = lambda n, data: {:s}".format(
str((1 - self._ZsymbolicFromVT) / self.V)
.replace("V", "data[0]")
.replace("T", "data[1]")
.replace("Abs", "np.abs")
.replace("sign", "np.sign")
)
)
qf = cfunc(c_sig)(self.tmp_cfunc)
self._qnf = LowLevelCallable(qf.ctypes)
exec(
"self.tmp_cfunc2 = lambda n, data: {:s}".format(
str(self.T * self._dZdTsymbolicFromVT / self.V)
.replace("V", "data[0]")
.replace("T", "data[1]")
.replace("Abs", "np.abs")
.replace("sign", "np.sign")
)
)
tf = cfunc(c_sig)(self.tmp_cfunc2)
self._numf_UR = LowLevelCallable(tf.ctypes)
def issue109(size):
a = Array(5, 'double')
for i in range(5):
a[i] = nb_types.double(i)
return a
x_max = 5
#chi=uf.chi(x)
def func(x):
return x**2
print(sp.quad(lambda x: x * func(x), x_min, x_max))
x = np.linspace(x_min, x_max, 10)
func = func(x)
print(sp.trapz(x * func, x))
c_sig = types.double(types.intc, types.CPointer(types.double))
@cfunc(c_sig)
def f(n_args, x):
x1 = x[0]
func = x[1]
return x1 * func
print(sp.nquad(LowLevelCallable(f.ctypes), [[0, 25], [0, 5]],
full_output=True))
import numpy as np
import scipy.integrate as si
import numba
Undiscounted value of the Put price under the LV-Sabr model
by Bang (2018). Def (5)
"""
extra_args = (forward_rate, time_to_maturity, alpha, nu, rho, mu, beta_low,
beta_high, eps, eps_s, f_low, f_high, lambda_, Psi, K_h)
return quad(CDF_underlying,
a=-np.inf,
b=strike,
epsabs=1e-6,
epsrel=1e-6,
args=extra_args,
limit=100)
#### Extra function for checking integration is correct
lv_sigma_csignature = types.double(types.intc, types.CPointer(types.double))
@cfunc(lv_sigma_csignature, nopython=True)
def lv_sigma_cfunc(n, xx):
in_array = carray(xx, (n, ))
u, beta_low, beta_high, eps, eps_s, f_low, f_high, lambda_, Psi, K_h, F0 = in_array
sigma_argument = min(max(u, f_low), f_high)
heta_1 = eps * math.log(1 + math.exp(sigma_argument / eps))
heta_2 = eps_s * math.log(1 + math.exp((sigma_argument - K_h) / eps_s))
beta_factor = math.tanh(lambda_ * (sigma_argument - F0))
beta = 0.5 * (beta_high + beta_low) - 0.5 * (beta_high -
beta_low) * beta_factor
return 1.0 / (math.pow(heta_1, beta) + Psi * heta_2)