def test_mfdfa_save_load():
mfdfa = fathon.MFDFA(fu.toAggregated(ts))
# save and load with empty results
mfdfa.saveObject(get_object_path('mfdfa_obj'))
n_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True), 'n')
F_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True), 'F')
q_list_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True),
'qList')
list_h_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True),
'listH')
assert np.array_equal(n_load, [])
assert np.array_equal(F_load, [])
assert np.array_equal(q_list_load, [])
assert np.array_equal(list_h_load, [])
#save and load with results
n, F = mfdfa.computeFlucVec(fu.linRangeByStep(10, 500),
fu.linRangeByStep(-1, 1))
H, I = mfdfa.fitFlucVec(100, 300)
mfdfa.saveObject(get_object_path('mfdfa_obj'))
n_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True), 'n')
F_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True), 'F')
q_list_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True),
'qList')
list_h_load = fu.getObjectMember(get_object_path('mfdfa_obj', ext=True),
'listH')
assert np.array_equal(n_load, n)
assert np.array_equal(F_load, F)
assert np.array_equal(q_list_load, fu.linRangeByStep(-1, 1))
assert np.array_equal(list_h_load, H)
# MFDFA from file
mfdfa_2 = fathon.MFDFA(get_object_path('mfdfa_obj', ext=True))
H_2, I_2 = mfdfa_2.fitFlucVec(100, 300)
assert np.array_equal(H_2, H)
assert np.array_equal(I_2, I)
def test_mfdfa():
pymfdfa = fathon.MFDFA(ts3)
qs = np.arange(-3, 3, 1)
winSizes = fu.linRangeByStep(10, 200)
n2, F2 = pymfdfa.computeFlucVec(winSizes, qs, revSeg=True)
H2, H_int2 = pymfdfa.fitFlucVec()
assert math.isclose(H2[2], 1.1956312585360254)
def test_multifractal_spectrum():
pymfdfa = fathon.MFDFA(ts3)
qs = np.arange(-3, 3, 1)
winSizes = fu.linRangeByStep(10, 200)
n2, F2 = pymfdfa.computeFlucVec(winSizes, qs, revSeg=True)
H2, H_int2 = pymfdfa.fitFlucVec()
a2, m2 = pymfdfa.computeMultifractalSpectrum()
assert math.isclose(m2[4], 0.8445200259231695)
def test_mat_mfdfa_wn():
w_mfdfa = fathon.MFDFA(fu.toAggregated(wn))
n_w, F_w = w_mfdfa.computeFlucVec(scales,
qList=q_list,
revSeg=False,
polOrd=1)
idxs = get_idxs(n_w, scales)
n_w = n_w[idxs]
F_w_vec = np.zeros((len(q_list), len(idxs)))
for i in range(len(q_list)):
F_w_vec[i] = F_w[i, idxs]
Hq = []
for i in range(len(q_list)):
Hq.append(np.polyfit(np.log2(n_w), np.log2(F_w_vec[i]), 1)[0])
np.testing.assert_allclose(
Hq, [0.4583, 0.4555, 0.4546, 0.4515, 0.4445, 0.4340],
rtol=1e-4,
atol=0)
def test_mat_mfdfa_mf():
mf_mfdfa = fathon.MFDFA(fu.toAggregated(mf))
n_mf, F_mf = mf_mfdfa.computeFlucVec(scales,
qList=q_list,
revSeg=False,
polOrd=1)
idxs = get_idxs(n_mf, scales)
n_mf = n_mf[idxs]
F_mf_vec = np.zeros((len(q_list), len(idxs)))
for i in range(len(q_list)):
F_mf_vec[i] = F_mf[i, idxs]
Hq = []
for i in range(len(q_list)):
Hq.append(np.polyfit(np.log2(n_mf), np.log2(F_mf_vec[i]), 1)[0])
np.testing.assert_allclose(
Hq, [1.4477, 1.3064, 1.0823, 0.8846, 0.6606, 0.5174],
rtol=1e-4,
atol=0)
def test_mat_mfdfa_mn():
mn_mfdfa = fathon.MFDFA(fu.toAggregated(mn))
n_mn, F_mn = mn_mfdfa.computeFlucVec(scales,
qList=q_list,
revSeg=False,
polOrd=1)
idxs = get_idxs(n_mn, scales)
n_mn = n_mn[idxs]
F_mn_vec = np.zeros((len(q_list), len(idxs)))
for i in range(len(q_list)):
F_mn_vec[i] = F_mn[i, idxs]
Hq = []
for i in range(len(q_list)):
Hq.append(np.polyfit(np.log2(n_mn), np.log2(F_mn_vec[i]), 1)[0])
np.testing.assert_allclose(
Hq, [0.7542, 0.7392, 0.7301, 0.7240, 0.7149, 0.7023],
rtol=1e-4,
atol=0)
usd_volume_bar_df = testClass.get_concat_data(testClass._bars_dict)['usd_volume_bars']
calendar_bar_df = testClass.get_concat_data(testClass._bars_dict)['calendar_bars']
vr = returns(volume_bar_df.micro_price_close).replace([np.inf, -np.inf], 0) # volume
tr = returns(tick_bar_df.micro_price_close).replace([np.inf, -np.inf], 0) # tick
dr = returns(usd_volume_bar_df.micro_price_close).dropna().replace([np.inf, -np.inf], 0) # usd volume
df_ret = returns(calendar_bar_df.micro_price_close).dropna().replace([np.inf, -np.inf], 0) # calendar
bar_returns[date] = {'tick': tr,
'volume': vr,
'dollar': dr,
'calendar': df_ret}
for j, i in itertools.product(['tick', 'volume', 'dollar', 'calendar'], dates):
data = (bar_returns[i][j])
a = fu.toAggregated(np.asanyarray(data))
# MFDFA Computations
pymfdfa = fathon.MFDFA(a)
n, F = pymfdfa.computeFlucVec(winSizes, qs, revSeg=revSeg, polOrd=polOrd)
mfdfa_n_F_dict[j][i] = dict(zip(n, F))
# dictionary to match all the n and F values this could be
# more efficient
# get the list values of H and intercept
list_H, list_H_intercept = pymfdfa.fitFlucVec() # same for H values
mfdfa_H_dict[j][i] = [list_H, list_H_intercept]
# get the mass exponents
tau = pymfdfa.computeMassExponents()
mfdfa_tau_dict[j][i] = tau
# get the multi-fractal spectrum
