def refresh_correl(_window=240):
_cls = cr_cret.retrieve(univ_ib_eqidx_ext + 'Close')
_vol = cr_cret.retrieve(univ_ib_eqidx_ext + 'vol_gk240')
tcl = _cls.tick_cols()
for i in tcl:
_cls[i] = filt.ret(_cls[i].values)/_vol[i].values
_corr = dummy_df(_cls)
n = _corr.shape[0]
# need to make this code parallel
for j in range(0, len(tcl)-1):
for k in range(j+1, len(tcl)):
print('Processing rolling correlation of %s and %s' % (tcl[j], tcl[k]))
_corr[tcl[j]+'_'+tcl[k]] = np.empty(n)*np.nan
_tmp1 = _cls[tcl[k]].values
_tmp2 = _cls[tcl[j]].values
ny1 = np.maximum(filt.fst_nan(_tmp1), filt.fst_nan(_tmp2))
for _lbck in range(_window + ny1, n):
_corr[_lbck, tcl[j]+'_'+tcl[k]] = bst.kendall_tau(_tmp1[_lbck-_window:_lbck],
_tmp2[_lbck-_window:_lbck])
cr_cret.store(univ_ib_eqidx_ext+'Correl', _corr)
return None
test1_all = test1.copy()
test2_all = test2.copy()
else:
test1_all.row_bind(test1)
test2_all.row_bind(test2)
print('Processed ticker %s and size of dataframe is %s' % (i, str(test1_all.shape[0])))
del test1
del test2
fret1 = test1_all['Returns'].values
num_col = len(tcl2)
correl_vec = np.zeros(num_col)
for z in range(0, num_col):
correl_vec[z] = bst.kendall_tau(fret1, test2_all[tcl2[z]].values)
_penalty = np.ones(num_col)
for z in range(0, num_col):
# z = 0
_tmp = tcl2[z]
_tmp1 = int(_tmp[1:3])
_tmp2 = np.sum(np.array(smth_param(int(_tmp[4:6]))))
_tmp3 = int(_tmp[7:10])
_tmp = _tmp1*_tmp2+_tmp3
_penalty[z] = np.sqrt(np.sqrt(_tmp))
for z2 in [0.55, 0.65, 0.75, 0.85]:
print('Processing threshold = %s' % str(z2))
_mom_sig = []
_mom_sig_idx = []
# lag the signal
test2 = mkt_retrieve(i, 'MovReg', 'Signals')
tcl2 = test2.tick_cols()
for k in tcl2:
test2[k] = filt.lag(test2[k].values)/testv
# get the average of returns
test1_ = test1[['Date', 'Close']]
test1_['Returns'] = filt.ret(test1_['Close'].values)
fret1 = test1_['Returns'].values / testv
num_col = len(tcl2)
correl_vec = np.zeros(num_col)
for z in range(0, num_col):
correl_vec[z] = bst.kendall_tau(fret1, test2[tcl2[z]].values)
_mom_sig = []
_mom_sig_idx = []
_mom_val = []
for z in range(0, 50):
# z = 0
if z == 0:
_idx = bst.argmax_fixed(correl_vec)
_mom_sig.append(tcl2[_idx])
_mom_sig_idx.append(_idx)
_mom_val.append(correl_vec[_idx])
else:
scorrel_vec = np.ones(num_col, dtype=bool)
for j1 in _mom_sig_idx:
scorrel_vec[j1] = False
def test2():
return cub.kendall_tau(x, y)
