def _refresh_old_pca2(x):
_d_comb, _d_ocor, _d_opca = x
f_list_c = _d_ocor.listdir()
window = 200
comb_ret = _d_comb.retrieve('Returns')
pst_dt = crup.pd_dt_to_str(comb_ret['Date', list])
pst_idx = [(i-window) for i, j in enumerate(pst_dt) if j == f_list_c[0]][0]
comb_ret = comb_ret[pst_idx:, :]
n = comb_ret.shape[0]
tick_cols = comb_ret.tick_cols()
# f_list_c = f_list_c[0]
for i in range(window+1, n+1):
# i = window+1
comb_ret_ext = comb_ret[i-window:i-1, tick_cols].values
y1 = np.where(np.all(~np.isnan(comb_ret_ext), axis=0).tolist())[0]
comb_ret_ext__ = comb_ret_ext[:, y1]
pca_wgt, pca_ret = crtp.compute_fast_pca(comb_ret_ext__.T)
ticks = [k for j, k in enumerate(tick_cols) if j in y1]
f_list_p = crup.pd_dt_to_str([comb_ret[i-1, 'Date', list]])[0]
_d_opca.save(f_list_p, ticks, pca_wgt, pca_ret)
return None
def _refresh_old_correl(x):
# _d_comb, _d_cor, _d_ocor = x # remove the dependency on _d_cor here (make it fixed)
_d_comb, _d_ocor = x
window = 200 # used to be 200 earlier
comb_ret = _d_comb.retrieve('Returns')
tick_cols = comb_ret.tick_cols()
n = comb_ret.shape[0]
# f_list = _d_cor.listdir()
pd_fst = crup.pd_dt_to_str(comb_ret['Date', list])
pd_fst = [i for i, j in enumerate(pd_fst) if j == cor_st_dt][0]
pd_fst -= window
comb_ret = comb_ret[pd_fst:, :]
pd_fst = crup.pd_dt_to_str(comb_ret['Date', list])
for i, j in enumerate(pd_fst):
if i < window:
pass
else:
# i = window
# j = pd_fst[i]
comb_ret_mini = comb_ret[i-window+1:i, :]
comb_ret_mini = comb_ret_mini[:, tick_cols].values
nn = comb_ret_mini.shape[0]
nm = comb_ret_mini.shape[1]
comb_ret_idx = np.where(np.sum(~np.isnan(comb_ret_mini), axis=0) == nn)[0]
comb_ret_mini_ = comb_ret_mini[:, comb_ret_idx]
y = np.corrcoef(comb_ret_mini_.T)
cor_ = np.zeros([nm, nm])*np.nan
for ii, jj in enumerate(comb_ret_idx):
cor_[jj, comb_ret_idx] = y[ii, :]
np.fill_diagonal(cor_, 1)
_d_ocor.save(j, cor_)
return None
def _refresh_old_correl(x):
_d_comb, _d_ocor = x
window = 200 # used to be 200 earlier
comb_ret = _d_comb.retrieve('Returns')
tick_cols = comb_ret.tick_cols()
n = comb_ret.shape[0]
# f_list = _d_cor.listdir()
pd_fst = crup.pd_dt_to_str(comb_ret['Date', list])
pd_fst = [i for i, j in enumerate(pd_fst) if j == cor_st_dt][0]
pd_fst -= window
comb_ret = comb_ret[pd_fst:, :]
pd_fst = crup.pd_dt_to_str(comb_ret['Date', list])
# pca_ret = _dummy_df(comb_ret)
# pca_ret['PCA'] = np.nan
for i, j in enumerate(pd_fst):
if i < window:
pass
else:
# i = window
# j = pd_fst[i]
comb_ret_mini = comb_ret[i-window+1:i, :]
comb_ret_mini = comb_ret_mini[:, tick_cols].values
nn = comb_ret_mini.shape[0]
nm = comb_ret_mini.shape[1]
comb_ret_idx = np.where(np.sum(~np.isnan(comb_ret_mini), axis=0) == nn)[0]
comb_ret_mini_ = comb_ret_mini[:, comb_ret_idx]
# comb_ret_sd_ = np.std(comb_ret_mini_, axis=0)
# comb_ret_mn = (comb_ret_mini_[-1,:] - np.mean(comb_ret_mini_, axis=0))/comb_ret_sd_ # new
y = np.corrcoef(comb_ret_mini_.T)
cor_ = np.zeros([nm, nm])*np.nan
for ii, jj in enumerate(comb_ret_idx):
cor_[jj, comb_ret_idx] = y[ii, :]
np.fill_diagonal(cor_, 1)
# u, s, v = np.linalg.svd(y)
# v0 = -v[0, :]/np.sum(np.abs(v[0,:]))
# pca_ret[i, 'PCA'] = np.dot(v0, comb_ret_mn)
_d_ocor.save(j, cor_) # unremove this
# pca_ret['PCA'] = np.cumprod(1+pca_ret['PCA']/100)
#
# pca_ret_ = _dummy_df(_d_comb.retrieve('Returns'))
# pca_ret = cruf.DataFrame.merge(pca_ret_, pca_ret, on='Date')
# _d_comb.store('PCA0', pca_ret)
return None
def _refresh_old_exposure_matrix2(x):
_d_opca_cl, _d_opca_wgt, _d_opca_bt = x
f_files = _d_opca_cl.listdir()
f_files_dt = crup.str_to_pd_dt(f_files)
PCA_01 = _d_opca_wgt.retrieve('PCA01_Beta')
PCA_02 = _d_opca_wgt.retrieve('PCA02_Beta')
PCA_03 = _d_opca_wgt.retrieve('PCA03_Beta')
PCA_04 = _d_opca_wgt.retrieve('PCA04_Beta')
PCA_05 = _d_opca_wgt.retrieve('PCA05_Beta')
PCA_06 = _d_opca_wgt.retrieve('PCA06_Beta')
PCA_07 = _d_opca_wgt.retrieve('PCA07_Beta')
PCA_08 = _d_opca_wgt.retrieve('PCA08_Beta')
PCA_09 = _d_opca_wgt.retrieve('PCA09_Beta')
PCA_10 = _d_opca_wgt.retrieve('PCA10_Beta')
PCA_11 = _d_opca_wgt.retrieve('PCA11_Beta')
PCA_12 = _d_opca_wgt.retrieve('PCA12_Beta')
PCA_13 = _d_opca_wgt.retrieve('PCA13_Beta')
PCA_14 = _d_opca_wgt.retrieve('PCA14_Beta')
PCA_15 = _d_opca_wgt.retrieve('PCA15_Beta')
PCA_16 = _d_opca_wgt.retrieve('PCA16_Beta')
PCA_17 = _d_opca_wgt.retrieve('PCA17_Beta')
PCA_18 = _d_opca_wgt.retrieve('PCA18_Beta')
PCA_19 = _d_opca_wgt.retrieve('PCA19_Beta')
PCA_20 = _d_opca_wgt.retrieve('PCA20_Beta')
p_dt = crup.pd_dt_to_str(PCA_01['Date', list])
tick_cols = PCA_01.tick_cols()
for i in f_files:
# i = f_files[0]
i_loc = [k for k, k2 in enumerate(p_dt) if k2 == i]
t1 = _d_opca_cl.load(i)['arr_3']
# retain the first 5
t1_ = np.array(t1[0:5], dtype=np.int64)
# first PCA
cons1 = t1_[0]+1
if cons1 <= 9:
pca_test = locals()['PCA_0'+str(cons1)][i_loc, :]
else:
pca_test = locals()['PCA_'+str(cons1)][i_loc, :]
if any(~np.isnan(pca_test[0, tick_cols].values.astype(np.float64))):
beta = pca_test
for j in range(0, 4):
cons1 = t1_[j+1]+1
if cons1 <= 9:
pca_test = locals()['PCA_0'+str(cons1)][i_loc, :]
else:
pca_test = locals()['PCA_'+str(cons1)][i_loc, :]
beta.row_bind(pca_test)
del beta['Date']
else:
beta = None
if beta is not None:
_d_opca_bt.store(i, beta)
return None
def _refresh_lev_stats_new(x):
_d_comb, _d_sig, _d_bt_pp, _d_cor, _d_flag = x
_d_cor_lst = _d_cor.listdir()
flev = _d_comb.retrieve('Lev_CC_' + _d_flag + 'D')
_d_lev_lst = crup.pd_dt_to_str(flev['Date', list])
flev_idx = [i for i, j in enumerate(_d_lev_lst) if (j in _d_cor_lst) and (j >= _dt_st)] # change to PCA Beta
flev = flev[flev_idx, :]
_d_bt_pp.store('Lev', flev)
flev_mult = flev[flev.tick_cols()].values.astype('float64')
fun = _d_comb.retrieve('Univ')
fun = fun[flev_idx, :]
fun[:, fun.tick_cols()] = fun[fun.tick_cols()].values.astype(int) * (flev_mult < 5).astype(int)
_d_bt_pp.store('Univ', fun)
fliq = _d_comb.retrieve('Adv')
fliq = fliq[flev_idx, :]
fliq[:, fliq.tick_cols()] = fliq[fliq.tick_cols()].values.astype('float64')/flev_mult
_d_bt_pp.store('Adv', fliq)
for ii in _d_comb_data:
fi = _d_comb.retrieve(ii)
fi = fi[flev_idx, :]
fi[:, fi.tick_cols()] = fi[fi.tick_cols()].values.astype('float64')*flev_mult
_d_bt_pp.store(ii, fi)
for ii in _d_sig_data1:
fi = _d_sig.retrieve(ii)
fi = fi[flev_idx, :]
fi[:, fi.tick_cols()] = fi[fi.tick_cols()].values.astype('float64')*flev_mult
_d_bt_pp.store(ii, fi)
for ii in _d_sig_data2:
fi = _d_sig.retrieve(ii)
fi = fi[flev_idx, :]
fi[:, fi.tick_cols()] = fi[fi.tick_cols()].values.astype('float64')*flev_mult
_d_bt_pp.store(ii, fi)
# Returns uses lagged leverage (!)
fret = _d_comb.retrieve('Returns')
fret = fret[flev_idx, :]
for ii in fret.tick_cols():
fret[ii] = fret[ii].values * crtf.lag(flev[ii].values, 2)
_d_bt_pp.store('Returns', fret)
return None
def _refresh_correl(x):
_d_comb, _d_cor = x
window1 = 200
# window1 = 250 # used to be 200 earlier
# window2 = 167
lmb1 = 2/(window1+1)
# lmb2 = 2/(window2+1)
comb_ret = _d_comb.retrieve('Returns')
comb_vol = _d_comb.retrieve('Vol_CC_240D') # change it to one version for 250, one for 167
tick_cols = comb_ret.tick_cols()
comb_zs = comb_vol.copy()
for i in tick_cols:
comb_zs[i] = comb_ret[i].values/comb_vol[i].values
comb_zs_val = np.sum(~np.isnan(comb_zs[tick_cols].values.astype('float64')), axis=1)
comb_zs_val = np.where(comb_zs_val >= 10)[0] # use a start date
comb_zs = comb_zs[comb_zs_val, :]
n = comb_zs.shape[0]
f_list_c = crup.pd_dt_to_str(comb_zs['Date', list])
comb_zsm = comb_zs.copy()
for i in tick_cols:
comb_zsm[i] = comb_zsm[i]*np.exp(-(comb_zsm[i].values**2)/18+0.5)/3
for i in range(0, n): # n
all_u = comb_zsm[i, tick_cols].values.astype('float64')
all_uy = np.outer(all_u, all_u)
all_ux = np.zeros(all_uy.shape)
nn = all_u.shape[0]
for jj in range(0, nn):
for kk in range(jj, nn):
all_ux[jj, kk] = all_u[jj]**2 + all_u[kk]**2
if jj != kk:
all_ux[kk, jj] = all_ux[jj, kk]
if i == 0:
yf1 = np.sign(all_uy)
yr1 = 2/(1+np.exp(-yf1))-1
yct1 = (~np.isnan(yr1)).astype('uint16')
# yf2 = yf1.copy()
# yr2 = yr1.copy()
else:
new_idx = list(set(np.where(~np.isnan(all_u))[0]).difference(set(_cor_idx(yf1))))
yf1 += lmb1*(2/(1-yr1**2))*(all_uy-yr1-yr1*(all_ux-2)/(1+yr1**2))
if len(new_idx) > 0:
yf1[new_idx, :] = np.sign(all_uy[new_idx, :])
yf1[:, new_idx] = np.sign(all_uy[:, new_idx])
yr1 = 2/(1+np.exp(-yf1))-1
yct1 += (~np.isnan(yr1)).astype('uint16')
# new_idx = list(set(np.where(~np.isnan(all_u))[0]).difference(set(_cor_idx(yf2))))
# yf2 += lmb2*(2/(1-yr2**2))*(all_uy-yr2-yr2*(all_ux-2)/(1+yr2**2))
# if len(new_idx) > 0:
# yf2[new_idx, :] = np.sign(all_uy[new_idx, :])
# yf2[:, new_idx] = np.sign(all_uy[:, new_idx])
# yr2 = 2/(1+np.exp(-yf2))-1
# yf_ = 3*yf2-2*yf1
# yr_ = 2/(1+np.exp(-yf_))-1
yr = np.zeros([nn, nn])*np.nan
yr[yct1 >= window1] = yr1[yct1 >= window1]
# yr[yct1 >= window1] = yr_[yct1 >= window1]
if np.any(~np.isnan(yr)) and (f_list_c[i] >= cor_st_dt):
yr[abs(yr) > 0.99] = 0.99*np.sign(yr[abs(yr) > 0.99])
np.fill_diagonal(yr, 1)
_d_cor.save(f_list_c[i], yr.astype('float32'))
def _generate_tradelist2(x, prm=4, eiflag=False):
_d_bt_sig, _d_tl_id, _d_comb = x
ret = _d_comb.retrieve('Returns')
lev = _d_comb.retrieve('Lev_CC_120D')
vol = _d_comb.retrieve('Vol_CC_120D')
eq03 = _d_comb.retrieve('EQIDX_Beta_3m')
eq06 = _d_comb.retrieve('EQIDX_Beta_6m')
eq09 = _d_comb.retrieve('EQIDX_Beta_9m')
eq12 = _d_comb.retrieve('EQIDX_Beta_12m')
sp03 = _d_comb.retrieve('SP_Beta_3m')
sp06 = _d_comb.retrieve('SP_Beta_6m')
sp09 = _d_comb.retrieve('SP_Beta_9m')
sp12 = _d_comb.retrieve('SP_Beta_12m')
mom03 = _d_comb.retrieve('MOM_Beta_3m')
mom06 = _d_comb.retrieve('MOM_Beta_6m')
mom09 = _d_comb.retrieve('MOM_Beta_9m')
mom12 = _d_comb.retrieve('MOM_Beta_12m')
f_list = _d_bt_sig.listdir()
p_dt = crup.pd_dt_to_str(lev['Date', list])
p_dt_idx = [i for i, j in enumerate(p_dt) if j in f_list]
# ret = ret[p_dt_idx, :]
lev = lev[p_dt_idx, :]
vol = vol[p_dt_idx, :]
eq03 = eq03[p_dt_idx, :]
eq06 = eq06[p_dt_idx, :]
eq09 = eq09[p_dt_idx, :]
eq12 = eq12[p_dt_idx, :]
sp03 = sp03[p_dt_idx, :]
sp06 = sp06[p_dt_idx, :]
sp09 = sp09[p_dt_idx, :]
sp12 = sp12[p_dt_idx, :]
mom03 = mom03[p_dt_idx, :]
mom06 = mom06[p_dt_idx, :]
mom09 = mom09[p_dt_idx, :]
mom12 = mom12[p_dt_idx, :]
tc = lev.tick_cols()
window = 200
n = len(f_list)
ret_coll = np.zeros(n-2)*np.nan
for i, j in enumerate(f_list):
# i = 0
# j = f_list[0]
# read the return estimates
ret_est = _d_bt_sig.load(j)
reti = ret_est['arr_1']
tci = list(ret_est['arr_0'])
tci_idx = [ii for ii, jj in enumerate(tci) if jj in tc]
levi = lev[i, tci].values.astype('float64')
reti_un = (reti/levi)/1000
voli = vol[i, tci].values.astype('float64')
eq03i = eq03[i, tci].values.astype('float64')
eq06i = eq06[i, tci].values.astype('float64')
eq09i = eq09[i, tci].values.astype('float64')
eq12i = eq12[i, tci].values.astype('float64')
sp03i = sp03[i, tci].values.astype('float64')
sp06i = sp06[i, tci].values.astype('float64')
sp09i = sp09[i, tci].values.astype('float64')
sp12i = sp12[i, tci].values.astype('float64')
mom03i = mom03[i, tci].values.astype('float64')
mom06i = mom06[i, tci].values.astype('float64')
mom09i = mom09[i, tci].values.astype('float64')
mom12i = mom12[i, tci].values.astype('float64')
ret_mini = ret[p_dt_idx[i]-window+1:p_dt_idx[i], tci].values.astype('float64')
ret_mini[np.isnan(ret_mini)] = 0 # hygiene
nn = len(tci)
ei = np.ones(nn)
cori = (np.corrcoef(ret_mini.T)+prm*np.identity(nn))/(1+prm)
dvoli = 16*np.diag(voli)
covi = np.linalg.inv(np.dot(dvoli, np.dot(cori, dvoli)))
if not eiflag:
mat_a = np.vstack((eq03i, eq06i, eq09i, eq12i, # ei
sp03i, sp06i, sp09i, sp12i,
mom03i, mom06i, mom09i, mom12i))
else:
mat_a = np.vstack((ei, eq03i, eq06i, eq09i, eq12i, # ei
sp03i, sp06i, sp09i, sp12i,
mom03i, mom06i, mom09i, mom12i))
wgt_a = 1.5*np.dot(crts.neut_corr_mat(covi, mat_a), reti_un)
wgt_a_sm = np.maximum(np.sum(wgt_a[wgt_a > 0]), -np.sum(wgt_a[wgt_a < 0]))
# wgt_a_sm = np.sum(np.abs(wgt_a))
if wgt_a_sm > 4:
wgt_a = wgt_a*4/wgt_a_sm
if wgt_a_sm < 0.5:
wgt_a = wgt_a*0.5/wgt_a_sm
_d_tl_id.save(j, tci, wgt_a)
if i < n-2:
reti = ret[p_dt_idx[i+2], tci].values.astype('float64')
ret_coll[i] = np.sum(wgt_a*reti)
# print(j, np.array([np.sum(np.abs(wgt_a)), np.max(wgt_a), np.min(wgt_a), ret_coll[i]]))
else:
pass
# print(j, np.array([np.sum(np.abs(wgt_a)), np.max(wgt_a), np.min(wgt_a)]))
# print(16*np.mean(ret_coll)/np.std(ret_coll))
# ret_coll.shape
f_list = f_list[:-2]
f_list_ = crup.str_to_pd_dt(f_list)
ret_coll = cruf.DataFrame({'Date': f_list_, 'Returns': ret_coll})
_d_comb.store('Signal_Returns', ret_coll)
# return ret_coll
return None
def _generate_tradelist(x):
_d_bt_sig, _d_tl_id, _d_comb = x
ret = _d_comb.retrieve('Returns')
lev = _d_comb.retrieve('Lev_CC_120D')
vol = _d_comb.retrieve('Vol_CC_120D')
eq03 = _d_comb.retrieve('EQIDX_Beta_3m')
eq06 = _d_comb.retrieve('EQIDX_Beta_6m')
eq09 = _d_comb.retrieve('EQIDX_Beta_9m')
eq12 = _d_comb.retrieve('EQIDX_Beta_12m')
sp03 = _d_comb.retrieve('SP_Beta_3m')
sp06 = _d_comb.retrieve('SP_Beta_6m')
sp09 = _d_comb.retrieve('SP_Beta_9m')
sp12 = _d_comb.retrieve('SP_Beta_12m')
mom03 = _d_comb.retrieve('MOM_Beta_3m')
mom06 = _d_comb.retrieve('MOM_Beta_6m')
mom09 = _d_comb.retrieve('MOM_Beta_9m')
mom12 = _d_comb.retrieve('MOM_Beta_12m')
f_list = _d_bt_sig.listdir()
p_dt = crup.pd_dt_to_str(lev['Date', list])
p_dt_idx = [i for i, j in enumerate(p_dt) if j in f_list]
# ret = ret[p_dt_idx, :]
lev = lev[p_dt_idx, :]
vol = vol[p_dt_idx, :]
eq03 = eq03[p_dt_idx, :]
eq06 = eq06[p_dt_idx, :]
eq09 = eq09[p_dt_idx, :]
eq12 = eq12[p_dt_idx, :]
sp03 = sp03[p_dt_idx, :]
sp06 = sp06[p_dt_idx, :]
sp09 = sp09[p_dt_idx, :]
sp12 = sp12[p_dt_idx, :]
mom03 = mom03[p_dt_idx, :]
mom06 = mom06[p_dt_idx, :]
mom09 = mom09[p_dt_idx, :]
mom12 = mom12[p_dt_idx, :]
tc = lev.tick_cols()
window = 200
n = len(f_list)
ret_coll = np.zeros(n-2)*np.nan
for i, j in enumerate(f_list):
# i = 0
# j = f_list[0]
# read the return estimates
ret_est = _d_bt_sig.load(j)
reti = ret_est['arr_1']
tci = list(ret_est['arr_0'])
tci_idx = [ii for ii, jj in enumerate(tci) if jj in tc]
levi = lev[i, tci].values.astype('float64')
reti_un = (reti/levi)/1000
voli = vol[i, tci].values.astype('float64')
eq03i = eq03[i, tci].values.astype('float64')
eq06i = eq06[i, tci].values.astype('float64')
eq09i = eq09[i, tci].values.astype('float64')
eq12i = eq12[i, tci].values.astype('float64')
sp03i = sp03[i, tci].values.astype('float64')
sp06i = sp06[i, tci].values.astype('float64')
sp09i = sp09[i, tci].values.astype('float64')
sp12i = sp12[i, tci].values.astype('float64')
mom03i = mom03[i, tci].values.astype('float64')
mom06i = mom06[i, tci].values.astype('float64')
mom09i = mom09[i, tci].values.astype('float64')
mom12i = mom12[i, tci].values.astype('float64')
ret_mini = ret[p_dt_idx[i]-window+1:p_dt_idx[i], tci].values.astype('float64')
ret_mini[np.isnan(ret_mini)] = 0 # hygiene
nn = len(tci)
ei = np.ones(nn)
cori = (np.corrcoef(ret_mini.T)+2*np.identity(nn))/3
dvoli = 16*np.diag(voli)
covi = np.linalg.inv(np.dot(dvoli, np.dot(cori, dvoli)))
mat_a = np.vstack((ei, eq03i, eq06i, eq09i, eq12i,
sp03i, sp06i, sp09i, sp12i,
mom03i, mom06i, mom09i, mom12i))
wgt_a = 1.5*np.dot(crts.neut_corr_mat(covi, mat_a), reti_un)
wgt_a_sm = np.sum(np.abs(wgt_a))
if wgt_a_sm > 8:
wgt_a = wgt_a*8/wgt_a_sm
if wgt_a_sm < 1:
wgt_a = wgt_a/wgt_a_sm
if i < n-2:
reti = ret[p_dt_idx[i+2], tci].values.astype('float64')
ret_coll[i] = np.sum(wgt_a*reti)
print(j, np.array([np.sum(np.abs(wgt_a)), np.max(wgt_a), np.min(wgt_a), ret_coll[i]]))
else:
print(j, np.array([np.sum(np.abs(wgt_a)), np.max(wgt_a), np.min(wgt_a)]))
print(16*np.mean(ret_coll)/np.std(ret_coll))
def _refresh_base_data_new(x):
_d_bt_pp, _d_cor, _d_ocor, _d_bt_bs, _d_bt_fr = x
# read the characteristics
flev = _d_bt_pp.retrieve('Lev')
fadv = _d_bt_pp.retrieve('Adv')
fun = _d_bt_pp.retrieve('Univ')
frt = _d_bt_pp.retrieve('Returns')
# read the betas
fsp3 = _d_bt_pp.retrieve('SP_Beta_3m')
fsp6 = _d_bt_pp.retrieve('SP_Beta_6m')
fsp9 = _d_bt_pp.retrieve('SP_Beta_9m')
fsp12 = _d_bt_pp.retrieve('SP_Beta_12m')
fmom3 = _d_bt_pp.retrieve('MOM_Beta_3m')
fmom6 = _d_bt_pp.retrieve('MOM_Beta_6m')
fmom9 = _d_bt_pp.retrieve('MOM_Beta_9m')
fmom12 = _d_bt_pp.retrieve('MOM_Beta_12m')
feqx3 = _d_bt_pp.retrieve('EQIDX_Beta_3m')
feqx6 = _d_bt_pp.retrieve('EQIDX_Beta_6m')
feqx9 = _d_bt_pp.retrieve('EQIDX_Beta_9m')
feqx12 = _d_bt_pp.retrieve('EQIDX_Beta_12m')
flv3 = _d_bt_pp.retrieve('LVOL_Beta_3m')
flv6 = _d_bt_pp.retrieve('LVOL_Beta_6m')
flv9 = _d_bt_pp.retrieve('LVOL_Beta_9m')
flv12 = _d_bt_pp.retrieve('LVOL_Beta_12m')
# read the signals
flr_10 = _d_bt_pp.retrieve('LrB_10D')
flr_20 = _d_bt_pp.retrieve('LrB_20D')
flr_30 = _d_bt_pp.retrieve('LrB_30D')
flr_40 = _d_bt_pp.retrieve('LrB_40D')
flr_50 = _d_bt_pp.retrieve('LrB_50D')
flr_60 = _d_bt_pp.retrieve('LrB_60D')
flr_70 = _d_bt_pp.retrieve('LrB_70D')
flr_80 = _d_bt_pp.retrieve('LrB_80D')
flr_90 = _d_bt_pp.retrieve('LrB_90D')
flr_100 = _d_bt_pp.retrieve('LrB_100D')
flr_110 = _d_bt_pp.retrieve('LrB_110D')
flr_120 = _d_bt_pp.retrieve('LrB_120D')
flr_130 = _d_bt_pp.retrieve('LrB_130D')
flr_140 = _d_bt_pp.retrieve('LrB_140D')
flr_150 = _d_bt_pp.retrieve('LrB_150D')
flr_160 = _d_bt_pp.retrieve('LrB_160D')
flr_170 = _d_bt_pp.retrieve('LrB_170D')
flr_180 = _d_bt_pp.retrieve('LrB_180D')
flr_190 = _d_bt_pp.retrieve('LrB_190D')
flr_200 = _d_bt_pp.retrieve('LrB_200D')
flr_210 = _d_bt_pp.retrieve('LrB_210D')
flr_220 = _d_bt_pp.retrieve('LrB_220D')
flr_230 = _d_bt_pp.retrieve('LrB_230D')
flr_240 = _d_bt_pp.retrieve('LrB_240D')
flr_250 = _d_bt_pp.retrieve('LrB_250D')
flr_260 = _d_bt_pp.retrieve('LrB_260D')
flr_270 = _d_bt_pp.retrieve('LrB_270D')
flr_280 = _d_bt_pp.retrieve('LrB_280D')
flr_290 = _d_bt_pp.retrieve('LrB_290D')
flr_300 = _d_bt_pp.retrieve('LrB_300D')
flr_310 = _d_bt_pp.retrieve('LrB_310D')
flr_320 = _d_bt_pp.retrieve('LrB_320D')
flr_330 = _d_bt_pp.retrieve('LrB_330D')
flr_340 = _d_bt_pp.retrieve('LrB_340D')
flr_350 = _d_bt_pp.retrieve('LrB_350D')
flr_360 = _d_bt_pp.retrieve('LrB_360D')
flr_370 = _d_bt_pp.retrieve('LrB_370D')
flr_380 = _d_bt_pp.retrieve('LrB_380D')
flr_390 = _d_bt_pp.retrieve('LrB_390D')
flr_400 = _d_bt_pp.retrieve('LrB_400D')
flr_410 = _d_bt_pp.retrieve('LrB_410D')
flr_420 = _d_bt_pp.retrieve('LrB_420D')
flr_430 = _d_bt_pp.retrieve('LrB_430D')
flr_440 = _d_bt_pp.retrieve('LrB_440D')
flr_450 = _d_bt_pp.retrieve('LrB_450D')
flr_460 = _d_bt_pp.retrieve('LrB_460D')
flr_470 = _d_bt_pp.retrieve('LrB_470D')
flr_480 = _d_bt_pp.retrieve('LrB_480D')
flr_490 = _d_bt_pp.retrieve('LrB_490D')
flr_500 = _d_bt_pp.retrieve('LrB_500D')
# read the signals
fqr_10 = _d_bt_pp.retrieve('QrB_10D')
fqr_20 = _d_bt_pp.retrieve('QrB_20D')
fqr_30 = _d_bt_pp.retrieve('QrB_30D')
fqr_40 = _d_bt_pp.retrieve('QrB_40D')
fqr_50 = _d_bt_pp.retrieve('QrB_50D')
fqr_60 = _d_bt_pp.retrieve('QrB_60D')
fqr_70 = _d_bt_pp.retrieve('QrB_70D')
fqr_80 = _d_bt_pp.retrieve('QrB_80D')
fqr_90 = _d_bt_pp.retrieve('QrB_90D')
fqr_100 = _d_bt_pp.retrieve('QrB_100D')
fqr_110 = _d_bt_pp.retrieve('QrB_110D')
fqr_120 = _d_bt_pp.retrieve('QrB_120D')
fqr_130 = _d_bt_pp.retrieve('QrB_130D')
fqr_140 = _d_bt_pp.retrieve('QrB_140D')
fqr_150 = _d_bt_pp.retrieve('QrB_150D')
fqr_160 = _d_bt_pp.retrieve('QrB_160D')
fqr_170 = _d_bt_pp.retrieve('QrB_170D')
fqr_180 = _d_bt_pp.retrieve('QrB_180D')
fqr_190 = _d_bt_pp.retrieve('QrB_190D')
fqr_200 = _d_bt_pp.retrieve('QrB_200D')
fqr_210 = _d_bt_pp.retrieve('QrB_210D')
fqr_220 = _d_bt_pp.retrieve('QrB_220D')
fqr_230 = _d_bt_pp.retrieve('QrB_230D')
fqr_240 = _d_bt_pp.retrieve('QrB_240D')
fqr_250 = _d_bt_pp.retrieve('QrB_250D')
fqr_260 = _d_bt_pp.retrieve('QrB_260D')
fqr_270 = _d_bt_pp.retrieve('QrB_270D')
fqr_280 = _d_bt_pp.retrieve('QrB_280D')
fqr_290 = _d_bt_pp.retrieve('QrB_290D')
fqr_300 = _d_bt_pp.retrieve('QrB_300D')
fqr_310 = _d_bt_pp.retrieve('QrB_310D')
fqr_320 = _d_bt_pp.retrieve('QrB_320D')
fqr_330 = _d_bt_pp.retrieve('QrB_330D')
fqr_340 = _d_bt_pp.retrieve('QrB_340D')
fqr_350 = _d_bt_pp.retrieve('QrB_350D')
fqr_360 = _d_bt_pp.retrieve('QrB_360D')
fqr_370 = _d_bt_pp.retrieve('QrB_370D')
fqr_380 = _d_bt_pp.retrieve('QrB_380D')
fqr_390 = _d_bt_pp.retrieve('QrB_390D')
fqr_400 = _d_bt_pp.retrieve('QrB_400D')
fqr_410 = _d_bt_pp.retrieve('QrB_410D')
fqr_420 = _d_bt_pp.retrieve('QrB_420D')
fqr_430 = _d_bt_pp.retrieve('QrB_430D')
fqr_440 = _d_bt_pp.retrieve('QrB_440D')
fqr_450 = _d_bt_pp.retrieve('QrB_450D')
fqr_460 = _d_bt_pp.retrieve('QrB_460D')
fqr_470 = _d_bt_pp.retrieve('QrB_470D')
fqr_480 = _d_bt_pp.retrieve('QrB_480D')
fqr_490 = _d_bt_pp.retrieve('QrB_490D')
fqr_500 = _d_bt_pp.retrieve('QrB_500D')
n = flev.shape[0]
tc = flev.tick_cols()
for i in range(0, n):
# i = 0
_dt = crup.pd_dt_to_str([flev[i, 'Date', list]])[0]
funi = [k for k, j in enumerate(fun[i, tc].values.astype('bool')) if j]
tci = [j for k, j in enumerate(tc) if k in funi]
xi = np.vstack((flev[i, tci].values.astype('float32'), # 0
fadv[i, tci].values.astype('float32'), # 1
feqx3[i, tci].values.astype('float32'), # 2
feqx6[i, tci].values.astype('float32'), # 3
feqx9[i, tci].values.astype('float32'), # 4
feqx12[i, tci].values.astype('float32'), # 5
fsp3[i, tci].values.astype('float32'), # 6
fsp6[i, tci].values.astype('float32'), # 7
fsp9[i, tci].values.astype('float32'), # 8
fsp12[i, tci].values.astype('float32'), # 9
fmom3[i, tci].values.astype('float32'), # 10
fmom6[i, tci].values.astype('float32'), # 11
fmom9[i, tci].values.astype('float32'), # 12
fmom12[i, tci].values.astype('float32'), # 13
flv3[i, tci].values.astype('float32'), # 14
flv6[i, tci].values.astype('float32'), # 15
flv9[i, tci].values.astype('float32'), # 16
flv12[i, tci].values.astype('float32') # 17
))
si1 = np.vstack((flr_10[i, tci].values.astype('float32'),
flr_20[i, tci].values.astype('float32'),
flr_30[i, tci].values.astype('float32'),
flr_40[i, tci].values.astype('float32'),
flr_50[i, tci].values.astype('float32'),
flr_60[i, tci].values.astype('float32'),
flr_70[i, tci].values.astype('float32'),
flr_80[i, tci].values.astype('float32'),
flr_90[i, tci].values.astype('float32'),
flr_100[i, tci].values.astype('float32'),
flr_110[i, tci].values.astype('float32'),
flr_120[i, tci].values.astype('float32'),
flr_130[i, tci].values.astype('float32'),
flr_140[i, tci].values.astype('float32'),
flr_150[i, tci].values.astype('float32'),
flr_160[i, tci].values.astype('float32'),
flr_170[i, tci].values.astype('float32'),
flr_180[i, tci].values.astype('float32'),
flr_190[i, tci].values.astype('float32'),
flr_200[i, tci].values.astype('float32'),
flr_210[i, tci].values.astype('float32'),
flr_220[i, tci].values.astype('float32'),
flr_230[i, tci].values.astype('float32'),
flr_240[i, tci].values.astype('float32'),
flr_250[i, tci].values.astype('float32'),
flr_260[i, tci].values.astype('float32'),
flr_270[i, tci].values.astype('float32'),
flr_280[i, tci].values.astype('float32'),
flr_290[i, tci].values.astype('float32'),
flr_300[i, tci].values.astype('float32'),
flr_310[i, tci].values.astype('float32'),
flr_320[i, tci].values.astype('float32'),
flr_330[i, tci].values.astype('float32'),
flr_340[i, tci].values.astype('float32'),
flr_350[i, tci].values.astype('float32'),
flr_360[i, tci].values.astype('float32'),
flr_370[i, tci].values.astype('float32'),
flr_380[i, tci].values.astype('float32'),
flr_390[i, tci].values.astype('float32'),
flr_400[i, tci].values.astype('float32'),
flr_410[i, tci].values.astype('float32'),
flr_420[i, tci].values.astype('float32'),
flr_430[i, tci].values.astype('float32'),
flr_440[i, tci].values.astype('float32'),
flr_450[i, tci].values.astype('float32'),
flr_460[i, tci].values.astype('float32'),
flr_470[i, tci].values.astype('float32'),
flr_480[i, tci].values.astype('float32'),
flr_490[i, tci].values.astype('float32'),
flr_500[i, tci].values.astype('float32')))
si2 = np.vstack((fqr_10[i, tci].values.astype('float32'),
fqr_20[i, tci].values.astype('float32'),
fqr_30[i, tci].values.astype('float32'),
fqr_40[i, tci].values.astype('float32'),
fqr_50[i, tci].values.astype('float32'),
fqr_60[i, tci].values.astype('float32'),
fqr_70[i, tci].values.astype('float32'),
fqr_80[i, tci].values.astype('float32'),
fqr_90[i, tci].values.astype('float32'),
fqr_100[i, tci].values.astype('float32'),
fqr_110[i, tci].values.astype('float32'),
fqr_120[i, tci].values.astype('float32'),
fqr_130[i, tci].values.astype('float32'),
fqr_140[i, tci].values.astype('float32'),
fqr_150[i, tci].values.astype('float32'),
fqr_160[i, tci].values.astype('float32'),
fqr_170[i, tci].values.astype('float32'),
fqr_180[i, tci].values.astype('float32'),
fqr_190[i, tci].values.astype('float32'),
fqr_200[i, tci].values.astype('float32'),
fqr_210[i, tci].values.astype('float32'),
fqr_220[i, tci].values.astype('float32'),
fqr_230[i, tci].values.astype('float32'),
fqr_240[i, tci].values.astype('float32'),
fqr_250[i, tci].values.astype('float32'),
fqr_260[i, tci].values.astype('float32'),
fqr_270[i, tci].values.astype('float32'),
fqr_280[i, tci].values.astype('float32'),
fqr_290[i, tci].values.astype('float32'),
fqr_300[i, tci].values.astype('float32'),
fqr_310[i, tci].values.astype('float32'),
fqr_320[i, tci].values.astype('float32'),
fqr_330[i, tci].values.astype('float32'),
fqr_340[i, tci].values.astype('float32'),
fqr_350[i, tci].values.astype('float32'),
fqr_360[i, tci].values.astype('float32'),
fqr_370[i, tci].values.astype('float32'),
fqr_380[i, tci].values.astype('float32'),
fqr_390[i, tci].values.astype('float32'),
fqr_400[i, tci].values.astype('float32'),
fqr_410[i, tci].values.astype('float32'),
fqr_420[i, tci].values.astype('float32'),
fqr_430[i, tci].values.astype('float32'),
fqr_440[i, tci].values.astype('float32'),
fqr_450[i, tci].values.astype('float32'),
fqr_460[i, tci].values.astype('float32'),
fqr_470[i, tci].values.astype('float32'),
fqr_480[i, tci].values.astype('float32'),
fqr_490[i, tci].values.astype('float32'),
fqr_500[i, tci].values.astype('float32')))
if i < n-2:
yi = frt[i+2, tci].values.astype('float64')
ci = _d_cor.load(_dt)
ci = ci[:, funi]
ci = ci[funi, :]
oci = _d_ocor.load(_dt)
oci = oci[:, funi]
oci = oci[funi, :]
# ci = ci.astype('float64')
# remove stocks with missing data
v_idx1 = np.sum(~np.isnan(xi), axis=0)/xi.shape[0]
v_idx2 = np.sum(~np.isnan(si1), axis=0)/si1.shape[0]
v_idx3 = np.sum(~np.isnan(si2), axis=0)/si2.shape[0]
v_idx = np.minimum(v_idx1, v_idx2, v_idx3)
v_idx = [k for k, j in enumerate(v_idx) if j > 0.999999]
xi = xi[:, v_idx]
si1 = si1[:, v_idx]
si2 = si2[:, v_idx]
ci = ci[:, v_idx]
ci = ci[v_idx, :]
oci = oci[:, v_idx]
oci = oci[v_idx, :]
tci = [j for i, j in enumerate(tci) if i in v_idx]
if i < n-2:
yi = yi[v_idx]
# remove stocks with no correlation values (hygiene)
n_ = ci.shape[0]
c_idx = []
for j in range(0, n_):
cori_r = np.all(np.isnan(np.array([l for k, l in enumerate(ci[j, :]) if k != j])))
cori_c = np.all(np.isnan(np.array([l for k, l in enumerate(ci[:, j]) if k != j])))
ocori_r = np.all(np.isnan(np.array([l for k, l in enumerate(oci[j, :]) if k != j])))
ocori_c = np.all(np.isnan(np.array([l for k, l in enumerate(oci[:, j]) if k != j])))
if ~cori_r or ~cori_c or ~ocori_c or ~ocori_r:
c_idx.append(j)
if len(c_idx) != n_:
xi = xi[:, c_idx]
si1 = si1[:, c_idx]
si2 = si2[:, c_idx]
ci = ci[:, c_idx]
ci = ci[c_idx, :]
oci = oci[:, c_idx]
oci = oci[c_idx, :]
if i < n-2:
yi = yi[c_idx]
tci = [j for i, j in enumerate(tci) if i in c_idx]
n_ = len(c_idx)
ci[np.isnan(ci)] = 0.5 # hygiene incase there is something weird in cluster S
oci[np.isnan(oci)] = 0.5 # hygiene incase there is something weird in cluster S
np.fill_diagonal(ci, 1) # hygiene incase there is something weird in cluster S
np.fill_diagonal(oci, 1) # hygiene incase there is something weird in cluster S
# get the risk budgets
ei = np.ones(n_, dtype='float64')
bi1 = 1/np.dot(np.abs(ci), ei)
bi1 /= np.sum(bi1)
bi2 = 1/np.dot(np.abs(oci), ei)
bi2 /= np.sum(bi2)
sci1 = np.linalg.inv(crts.shrink_correl_fast(bi1)).astype('float32')
sci2 = np.linalg.inv(crts.shrink_correl_fast(bi2)).astype('float32')
xi = np.vstack((xi, bi1.astype('float32'), bi2.astype('float32'))) # 18, 19
_d_bt_bs.save(_dt, xi, si1, si2, sci1, sci2, np.array(tci))
if i < n-2:
_d_bt_fr.save(_dt, yi)
return None
def _collect_old_pca_factors(x):
_d_opca_cl, _d_comb, _d_flag = x
f_files = _d_opca_cl.listdir()
f_files = sorted(f_files)
comb_ret = _d_comb.retrieve('Returns')
# OLD PCA used time-series directly!
fst_dt = f_files[0]
all_dt = crup.pd_dt_to_str(comb_ret['Date', list])
ret_idx = [i for i, j in enumerate(all_dt) if j >= fst_dt]
comb_ret = comb_ret[ret_idx, :]
all_dt = [all_dt[i] for i in ret_idx]
if len(f_files) != len(all_dt):
raise NotImplementedError('Something is wrong')
pca0_ret = _dummy_df(comb_ret)
pca1_ret = _dummy_df(comb_ret)
pca2_ret = _dummy_df(comb_ret)
pca3_ret = _dummy_df(comb_ret)
comb_ret_val = comb_ret[:, comb_ret.tick_cols()].values.astype('float64')
for i, j in enumerate(f_files):
# i = 0
# j = f_files[i]
_pca_s = _d_opca_cl.load(j).astype('float64')
_comb_ret = comb_ret_val[i, :]
_pca_s_idx = np.where(np.sum(~np.isnan(_pca_s), axis=0) > 0)[0]
_pca_s = _pca_s[:, _pca_s_idx]
_comb_ret = _comb_ret[_pca_s_idx]
ei = np.ones(_pca_s.shape[1])
_pca_norm = np.dot(abs(_pca_s), ei)
_pca_ret = np.dot(_pca_s, _comb_ret)
_pca_ret = _pca_ret/_pca_norm
if _d_flag == 0:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[10]
pca3_ret[i, 'PCA'] = _pca_ret[3]
elif _d_flag == 1:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[16]
elif _d_flag == 2:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[11]
elif _d_flag == 3:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[16]
pca2_ret[i, 'PCA'] = _pca_ret[8]
pca3_ret[i, 'PCA'] = _pca_ret[2]
elif _d_flag == 4:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[8]
pca2_ret[i, 'PCA'] = _pca_ret[3]
pca3_ret[i, 'PCA'] = _pca_ret[18]
elif _d_flag == 5:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[5]
pca2_ret[i, 'PCA'] = _pca_ret[17]
pca3_ret[i, 'PCA'] = _pca_ret[8]
elif _d_flag == 6:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[2]
pca2_ret[i, 'PCA'] = _pca_ret[17]
pca3_ret[i, 'PCA'] = _pca_ret[16]
else: # _d_flag == 7
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[4]
pca2_ret[i, 'PCA'] = _pca_ret[1]
pca3_ret[i, 'PCA'] = _pca_ret[3]
_d_comb.store('OPCA0_Returns', pca0_ret)
_d_comb.store('OPCA1_Returns', pca1_ret)
_d_comb.store('OPCA2_Returns', pca2_ret)
_d_comb.store('OPCA3_Returns', pca3_ret)
pca0_ret['PCA'] = np.cumprod(1+pca0_ret['PCA'].values)
pca1_ret['PCA'] = np.cumprod(1+pca1_ret['PCA'].values)
pca2_ret['PCA'] = np.cumprod(1+pca2_ret['PCA'].values)
pca3_ret['PCA'] = np.cumprod(1+pca3_ret['PCA'].values)
_d_comb.store('OPCA0', pca0_ret)
_d_comb.store('OPCA1', pca1_ret)
_d_comb.store('OPCA2', pca2_ret)
_d_comb.store('OPCA3', pca3_ret)
return None
def _collect_pca_factors(x):
_d_pca_cl, _d_comb, _d_flag = x
f_files = _d_pca_cl.listdir()
f_files = sorted(f_files)
comb_ret = _d_comb.retrieve('Returns')
# newly added
comb_vol = _d_comb.retrieve('VOL_CC_240D')
tc = comb_ret.tick_cols()
for i in tc:
comb_ret[i] = comb_ret[i].values/comb_vol[i].values
fst_dt = f_files[0]
all_dt = crup.pd_dt_to_str(comb_ret['Date', list])
ret_idx = [i for i, j in enumerate(all_dt) if j >= fst_dt]
comb_ret = comb_ret[ret_idx, :]
all_dt = [all_dt[i] for i in ret_idx]
if len(f_files) != len(all_dt):
raise NotImplementedError('Something is wrong')
pca0_ret = _dummy_df(comb_ret)
pca1_ret = _dummy_df(comb_ret)
pca2_ret = _dummy_df(comb_ret)
pca3_ret = _dummy_df(comb_ret)
comb_ret_val = comb_ret[:, comb_ret.tick_cols()].values.astype('float64')
for i, j in enumerate(f_files):
# i = 0
# j = f_files[i]
_pca_s = _d_pca_cl.load(j).astype('float64')
_comb_ret = comb_ret_val[i, :]
_pca_s_idx = np.where(np.sum(~np.isnan(_pca_s), axis=0) > 0)[0]
_pca_s = _pca_s[:, _pca_s_idx]
# _pca_ret_ = np.dot(_pca_s, _comb_ret[_pca_s_idx])*0.001
_pca_s_n = np.sum(np.abs(_pca_s), axis=1)
for jj in range(0, _pca_s.shape[0]):
_pca_s[jj, :] = _pca_s[jj, :]/_pca_s_n[jj]
_comb_ret = _comb_ret[_pca_s_idx]
_pca_ret = np.dot(_pca_s, _comb_ret)
_pca_ret *= 0.01
if _d_flag == 0:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[8]
elif _d_flag == 1:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[18]
pca3_ret[i, 'PCA'] = _pca_ret[2]
elif _d_flag == 2:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[3]
elif _d_flag == 3:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[19]
elif _d_flag == 4:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[3]
elif _d_flag == 5:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[4]
elif _d_flag == 6:
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[18]
pca2_ret[i, 'PCA'] = _pca_ret[15]
pca3_ret[i, 'PCA'] = _pca_ret[4]
else: # _d_flag == 7
pca0_ret[i, 'PCA'] = _pca_ret[0]
pca1_ret[i, 'PCA'] = _pca_ret[1]
pca2_ret[i, 'PCA'] = _pca_ret[2]
pca3_ret[i, 'PCA'] = _pca_ret[16]
_d_comb.store('PCA0_Returns', pca0_ret)
_d_comb.store('PCA1_Returns', pca1_ret)
_d_comb.store('PCA2_Returns', pca2_ret)
_d_comb.store('PCA3_Returns', pca3_ret)
pca0_ret['PCA'] = np.cumprod(1+pca0_ret['PCA'].values)
pca1_ret['PCA'] = np.cumprod(1+pca1_ret['PCA'].values)
pca2_ret['PCA'] = np.cumprod(1+pca2_ret['PCA'].values)
pca3_ret['PCA'] = np.cumprod(1+pca3_ret['PCA'].values)
_d_comb.store('PCA0', pca0_ret)
_d_comb.store('PCA1', pca1_ret)
_d_comb.store('PCA2', pca2_ret)
_d_comb.store('PCA3', pca3_ret)
return None
