def test_MBM_mgn(n_good, hurst_func_good, length_good, mbm_method_good):
m = MBM(n_good, hurst_func_good, length_good, mbm_method_good)
mgn_sample = m.mgn()
assert isinstance(mgn_sample, np.ndarray)
assert len(mgn_sample) == n_good
def test_MBM_times(n_good, hurst_func_good, length_good, mbm_method_good):
m = MBM(n_good, hurst_func_good, length_good, mbm_method_good)
ts = m.times()
assert isinstance(ts, np.ndarray)
assert len(ts) == n_good + 1
def test_MBM_init_method_bad(n_good, hurst_func_good, length_good, method_bad):
with pytest.raises((TypeError, ValueError)):
m = MBM(n_good, hurst_func_good, length_good, method_bad)
def test_MBM_change(n_good, hurst_func_good, length_good, mbm_method_good):
m = MBM(n_good, hurst_func_good, length_good, mbm_method_good)
m.n = 42
assert m._changed == True
sample = m.mbm()
assert m._changed == False
def test_MBM_init(n_good, hurst_func_good, length_good, mbm_method_good):
m = MBM(n_good, hurst_func_good, length_good, mbm_method_good)
print(str(m))
print(repr(m))
# flake8: noqa
from fbm import MBM
import matplotlib.pyplot as plt
import time
import math
def h(t):
# return 0.499*math.sin(t) + 0.5
# return 0.6 * t + 0.3
return 0.5 * math.exp(-8 * t**2) + 0.35
m = MBM(2**8, h, 1)
t = m.times()
mbm = m.mbm()
plt.plot(t, mbm)
plt.plot(t, [h(tt) for tt in t])
plt.show()
hurst = np.array(data.Hurst1[::-1])
def H(t):
t = t * (data.shape[0] - 1)
t = int(t)
return hurst[t]
def Hplot(t):
t = t * (data.shape[0] - 1)
t = t.astype(int)
return hurst[t]
m = MBM(n=size, hurst=H, length=1)
#m0 = MBM(1024, h0)
#m1 = MBM(1024, h1)
#m2 = MBM(1024, h2)
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
ax1.set_title(r'MBM simulation for BTC prices', )
ax1.plot(m.times(), m.mbm(), 'r')
ax2.set_title(r'Hurst function')
ax2.plot(m.times(), Hplot(m.times()), 'r')
ax2.set_yticks(np.arange(0.35, 0.6, 0.05))