def _refresh_fast_backtest(x):
_d_bt_bs, _d_bt_fr, _d_flag = x
_d_bt_bs_dt = _d_bt_bs.listdir()
_d_bt_bs_dt = _d_bt_bs_dt[:-2] # drop the last two
ndt = len(_d_bt_bs_dt)
nsig = 50
ri1_1 = np.zeros([ndt, nsig])
ri1_2 = np.zeros([ndt, nsig])
ri1_3 = np.zeros([ndt, nsig])
ri1_4 = np.zeros([ndt, nsig])
ri1_5 = np.zeros([ndt, nsig])
gi1_1 = np.zeros([ndt, nsig])
gi1_2 = np.zeros([ndt, nsig])
gi1_3 = np.zeros([ndt, nsig])
gi1_4 = np.zeros([ndt, nsig])
gi1_5 = np.zeros([ndt, nsig])
hi1_1 = np.zeros([ndt, nsig])
hi1_2 = np.zeros([ndt, nsig])
hi1_3 = np.zeros([ndt, nsig])
hi1_4 = np.zeros([ndt, nsig])
hi1_5 = np.zeros([ndt, nsig])
si1_1a = np.zeros([ndt, nsig])
si1_2a = np.zeros([ndt, nsig])
si1_3a = np.zeros([ndt, nsig])
si1_4a = np.zeros([ndt, nsig])
si1_5a = np.zeros([ndt, nsig])
si1_1b = np.zeros([ndt, nsig])
si1_2b = np.zeros([ndt, nsig])
si1_3b = np.zeros([ndt, nsig])
si1_4b = np.zeros([ndt, nsig])
si1_5b = np.zeros([ndt, nsig])
ri2_1 = np.zeros([ndt, nsig])
ri2_2 = np.zeros([ndt, nsig])
ri2_3 = np.zeros([ndt, nsig])
ri2_4 = np.zeros([ndt, nsig])
ri2_5 = np.zeros([ndt, nsig])
gi2_1 = np.zeros([ndt, nsig])
gi2_2 = np.zeros([ndt, nsig])
gi2_3 = np.zeros([ndt, nsig])
gi2_4 = np.zeros([ndt, nsig])
gi2_5 = np.zeros([ndt, nsig])
hi2_1 = np.zeros([ndt, nsig])
hi2_2 = np.zeros([ndt, nsig])
hi2_3 = np.zeros([ndt, nsig])
hi2_4 = np.zeros([ndt, nsig])
hi2_5 = np.zeros([ndt, nsig])
si2_1a = np.zeros([ndt, nsig])
si2_2a = np.zeros([ndt, nsig])
si2_3a = np.zeros([ndt, nsig])
si2_4a = np.zeros([ndt, nsig])
si2_5a = np.zeros([ndt, nsig])
si2_1b = np.zeros([ndt, nsig])
si2_2b = np.zeros([ndt, nsig])
si2_3b = np.zeros([ndt, nsig])
si2_4b = np.zeros([ndt, nsig])
si2_5b = np.zeros([ndt, nsig])
ri3_1 = np.zeros([ndt, nsig])
ri3_2 = np.zeros([ndt, nsig])
ri3_3 = np.zeros([ndt, nsig])
ri3_4 = np.zeros([ndt, nsig])
ri3_5 = np.zeros([ndt, nsig])
gi3_1 = np.zeros([ndt, nsig])
gi3_2 = np.zeros([ndt, nsig])
gi3_3 = np.zeros([ndt, nsig])
gi3_4 = np.zeros([ndt, nsig])
gi3_5 = np.zeros([ndt, nsig])
hi3_1 = np.zeros([ndt, nsig])
hi3_2 = np.zeros([ndt, nsig])
hi3_3 = np.zeros([ndt, nsig])
hi3_4 = np.zeros([ndt, nsig])
hi3_5 = np.zeros([ndt, nsig])
si3_1a = np.zeros([ndt, nsig])
si3_2a = np.zeros([ndt, nsig])
si3_3a = np.zeros([ndt, nsig])
si3_4a = np.zeros([ndt, nsig])
si3_5a = np.zeros([ndt, nsig])
si3_1b = np.zeros([ndt, nsig])
si3_2b = np.zeros([ndt, nsig])
si3_3b = np.zeros([ndt, nsig])
si3_4b = np.zeros([ndt, nsig])
si3_5b = np.zeros([ndt, nsig])
for j, i in enumerate(_d_bt_bs_dt):
# j = 0
# i = _d_bt_bs_dt[0]
xi_all = _d_bt_bs.load(i)
xi = xi_all['arr_0'].astype('float64')
si = xi_all['arr_1'].astype('float64')
ci1 = xi_all['arr_2'].astype('float64')
del xi_all
n = ci1.shape[0]
si = (si/(0.25+(si/vt)**2)).T
for k in range(0, si.shape[1]):
si[:, k] *= sigp[k]
flev = xi[0, :]
x2 = _d_bt_fr.load(i)
x2[np.isnan(x2)] = 0
# move this to prepare, also remove the other two correlation matrices to make the loading faster
ici1 = np.linalg.inv(ci1)
# read the momentum and low volatility scores
fscm1 = np.minimum(xi[7, :], 0)
# decide the order (of the neutralization!)
flev = np.ascontiguousarray(flev)
fscm1 = np.ascontiguousarray(fscm1)
ei = np.ones(n)
ici1_1 = crts.neut_corr_mat(ici1, np.vstack((xi[[2, 3], :], fscm1)))
ici1_1 = crts.neut_corr_mat(ici1_1, xi[[9, 10, 11, 12], :]) # S&P 500
ici1_1 = crts.neut_corr_mat(ici1_1, xi[[21, 22, 23, 24], :]) # mom
ici1_2 = crts.neut_corr_mat(ici1_1, xi[[57, 58, 59, 60], :], delta=0.4) # rut
ici1_3 = crts.neut_corr_mat(ici1_1, xi[[57, 58, 59, 60], :], delta=0.6) # rut
ici1_4 = crts.neut_corr_mat(ici1_1, xi[[57, 58, 59, 60], :], delta=0.8) # rut
ici1_5 = crts.neut_corr_mat(ici1_1, xi[[57, 58, 59, 60], :]) # rut
ici2_a = crts.neut_corr_mat(ici1, np.vstack((xi[[2, 3], :])))
ici2_1 = crts.neut_corr_mat(ici2_a, np.vstack((flev**3, flev**2, flev, ei)), delta=0.35)
ici2_1 = crts.neut_corr_mat(ici2_1, xi[[9, 10, 11, 12], :]) # S&P 500
ici2_1 = crts.neut_corr_mat(ici2_1, xi[[21, 22, 23, 24], :]) # mom
ici2_2 = crts.neut_corr_mat(ici2_1, xi[[57, 58, 59, 60], :], delta=0.4) # rut
ici2_3 = crts.neut_corr_mat(ici2_1, xi[[57, 58, 59, 60], :], delta=0.6) # rut
ici2_4 = crts.neut_corr_mat(ici2_1, xi[[57, 58, 59, 60], :], delta=0.8) # rut
ici2_5 = crts.neut_corr_mat(ici2_1, xi[[57, 58, 59, 60], :]) # rut
ici3_a = crts.neut_corr_mat(ici1, np.vstack((xi[[2, 3], :])))
ici3_1 = crts.neut_corr_mat(ici3_a, np.vstack((flev**3, flev**2, flev, ei)), delta=0.65)
ici3_1 = crts.neut_corr_mat(ici3_1, xi[[9, 10, 11, 12], :]) # S&P 500
ici3_1 = crts.neut_corr_mat(ici3_1, xi[[21, 22, 23, 24], :]) # mom
ici3_2 = crts.neut_corr_mat(ici3_1, xi[[57, 58, 59, 60], :], delta=0.4) # rut
ici3_3 = crts.neut_corr_mat(ici3_1, xi[[57, 58, 59, 60], :], delta=0.6) # rut
ici3_4 = crts.neut_corr_mat(ici3_1, xi[[57, 58, 59, 60], :], delta=0.8) # rut
ici3_5 = crts.neut_corr_mat(ici3_1, xi[[57, 58, 59, 60], :]) # rut
spb1 = xi[59, :]
spb2 = xi[60, :]
wi1_1 = np.dot(ici1_1, si)
wi1_2 = np.dot(ici1_2, si)
wi1_3 = np.dot(ici1_3, si)
wi1_4 = np.dot(ici1_4, si)
wi1_5 = np.dot(ici1_5, si)
wi2_1 = np.dot(ici2_1, si)
wi2_2 = np.dot(ici2_2, si)
wi2_3 = np.dot(ici2_3, si)
wi2_4 = np.dot(ici2_4, si)
wi2_5 = np.dot(ici2_5, si)
wi3_1 = np.dot(ici3_1, si)
wi3_2 = np.dot(ici3_2, si)
wi3_3 = np.dot(ici3_3, si)
wi3_4 = np.dot(ici3_4, si)
wi3_5 = np.dot(ici3_5, si)
ri1_1[j, :] = np.dot(x2, wi1_1)
ri1_2[j, :] = np.dot(x2, wi1_2)
ri1_3[j, :] = np.dot(x2, wi1_3)
ri1_4[j, :] = np.dot(x2, wi1_4)
ri1_5[j, :] = np.dot(x2, wi1_5)
ri2_1[j, :] = np.dot(x2, wi2_1)
ri2_2[j, :] = np.dot(x2, wi2_2)
ri2_3[j, :] = np.dot(x2, wi2_3)
ri2_4[j, :] = np.dot(x2, wi2_4)
ri2_5[j, :] = np.dot(x2, wi2_5)
ri3_1[j, :] = np.dot(x2, wi3_1)
ri3_2[j, :] = np.dot(x2, wi3_2)
ri3_3[j, :] = np.dot(x2, wi3_3)
ri3_4[j, :] = np.dot(x2, wi3_4)
ri3_5[j, :] = np.dot(x2, wi3_5)
hi1_1[j, :] = np.dot(flev, abs(wi1_1))
hi1_2[j, :] = np.dot(flev, abs(wi1_2))
hi1_3[j, :] = np.dot(flev, abs(wi1_3))
hi1_4[j, :] = np.dot(flev, abs(wi1_4))
hi1_5[j, :] = np.dot(flev, abs(wi1_5))
hi2_1[j, :] = np.dot(flev, abs(wi2_1))
hi2_2[j, :] = np.dot(flev, abs(wi2_2))
hi2_3[j, :] = np.dot(flev, abs(wi2_3))
hi2_4[j, :] = np.dot(flev, abs(wi2_4))
hi2_5[j, :] = np.dot(flev, abs(wi2_5))
hi3_1[j, :] = np.dot(flev, abs(wi3_1))
hi3_2[j, :] = np.dot(flev, abs(wi3_2))
hi3_3[j, :] = np.dot(flev, abs(wi3_3))
hi3_4[j, :] = np.dot(flev, abs(wi3_4))
hi3_5[j, :] = np.dot(flev, abs(wi3_5))
gi1_1[j, :] = abs(np.dot(flev, wi1_1))/hi1_1[j, :]
gi1_2[j, :] = abs(np.dot(flev, wi1_2))/hi1_2[j, :]
gi1_3[j, :] = abs(np.dot(flev, wi1_3))/hi1_3[j, :]
gi1_4[j, :] = abs(np.dot(flev, wi1_4))/hi1_4[j, :]
gi1_5[j, :] = abs(np.dot(flev, wi1_5))/hi1_5[j, :]
gi2_1[j, :] = abs(np.dot(flev, wi2_1))/hi2_1[j, :]
gi2_2[j, :] = abs(np.dot(flev, wi2_2))/hi2_2[j, :]
gi2_3[j, :] = abs(np.dot(flev, wi2_3))/hi2_3[j, :]
gi2_4[j, :] = abs(np.dot(flev, wi2_4))/hi2_4[j, :]
gi2_5[j, :] = abs(np.dot(flev, wi2_5))/hi2_5[j, :]
gi3_1[j, :] = abs(np.dot(flev, wi3_1))/hi3_1[j, :]
gi3_2[j, :] = abs(np.dot(flev, wi3_2))/hi3_2[j, :]
gi3_3[j, :] = abs(np.dot(flev, wi3_3))/hi3_3[j, :]
gi3_4[j, :] = abs(np.dot(flev, wi3_4))/hi3_4[j, :]
gi3_5[j, :] = abs(np.dot(flev, wi3_5))/hi3_5[j, :]
si1_1a[j, :] = np.dot(spb1, wi1_1)
si1_2a[j, :] = np.dot(spb1, wi1_2)
si1_3a[j, :] = np.dot(spb1, wi1_3)
si1_4a[j, :] = np.dot(spb1, wi1_4)
si1_5a[j, :] = np.dot(spb1, wi1_5)
si2_1a[j, :] = np.dot(spb1, wi2_1)
si2_2a[j, :] = np.dot(spb1, wi2_2)
si2_3a[j, :] = np.dot(spb1, wi2_3)
si2_4a[j, :] = np.dot(spb1, wi2_4)
si2_5a[j, :] = np.dot(spb1, wi2_5)
si3_1a[j, :] = np.dot(spb1, wi3_1)
si3_2a[j, :] = np.dot(spb1, wi3_2)
si3_3a[j, :] = np.dot(spb1, wi3_3)
si3_4a[j, :] = np.dot(spb1, wi3_4)
si3_5a[j, :] = np.dot(spb1, wi3_5)
si1_1b[j, :] = np.dot(spb2, wi1_1)
si1_2b[j, :] = np.dot(spb2, wi1_2)
si1_3b[j, :] = np.dot(spb2, wi1_3)
si1_4b[j, :] = np.dot(spb2, wi1_4)
si1_5b[j, :] = np.dot(spb2, wi1_5)
si2_1b[j, :] = np.dot(spb2, wi2_1)
si2_2b[j, :] = np.dot(spb2, wi2_2)
si2_3b[j, :] = np.dot(spb2, wi2_3)
si2_4b[j, :] = np.dot(spb2, wi2_4)
si2_5b[j, :] = np.dot(spb2, wi2_5)
si3_1b[j, :] = np.dot(spb2, wi3_1)
si3_2b[j, :] = np.dot(spb2, wi3_2)
si3_3b[j, :] = np.dot(spb2, wi3_3)
si3_4b[j, :] = np.dot(spb2, wi3_4)
si3_5b[j, :] = np.dot(spb2, wi3_5)
nn = 50
if ((j != 0) and (j % 500 == 0)) or (j == ndt-1):
# if j == ndt-1: #
# print('Processing %s' % i)
print('\n')
print('Optimal Model')
print('____________')
print('Sharpe Ratio')
print('O:', _np_str((np.mean(0.5*(1+np.sign(ri1_1[0:j, 0:nn])), axis=0)-0.5)*40))
print('O:', _np_str((np.mean(0.5*(1+np.sign(ri1_2[0:j, 0:nn])), axis=0)-0.5)*40))
print('O:', _np_str((np.mean(0.5*(1+np.sign(ri1_3[0:j, 0:nn])), axis=0)-0.5)*40))
print('O:', _np_str((np.mean(0.5*(1+np.sign(ri1_4[0:j, 0:nn])), axis=0)-0.5)*40))
print('O:', _np_str((np.mean(0.5*(1+np.sign(ri1_5[0:j, 0:nn])), axis=0)-0.5)*40))
print('Average Gross')
print('O:', _np_str(np.mean(hi1_1[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(hi1_2[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(hi1_3[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(hi1_4[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(hi1_5[0:j, 0:nn], axis=0)))
print('Average Net / Gross')
print('O:', _np_str(np.mean(gi1_1[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(gi1_2[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(gi1_3[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(gi1_4[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(gi1_5[0:j, 0:nn], axis=0)))
print('Average 3m S&P 500 Beta')
print('O:', _np_str(np.mean(si1_1a[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_2a[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_3a[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_4a[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_5a[0:j, 0:nn], axis=0)))
print('Average 6m S&P 500 Beta')
print('O:', _np_str(np.mean(si1_1b[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_2b[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_3b[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_4b[0:j, 0:nn], axis=0)))
print('O:', _np_str(np.mean(si1_5b[0:j, 0:nn], axis=0)))
print('\n')
print('Tradable Model')
print('____________')
print('Sharpe Ratio')
print('T:', _np_str((np.mean(0.5*(1+np.sign(ri2_1[0:j, 0:nn])), axis=0)-0.5)*40))
print('T:', _np_str((np.mean(0.5*(1+np.sign(ri2_2[0:j, 0:nn])), axis=0)-0.5)*40))
print('T:', _np_str((np.mean(0.5*(1+np.sign(ri2_3[0:j, 0:nn])), axis=0)-0.5)*40))
print('T:', _np_str((np.mean(0.5*(1+np.sign(ri2_4[0:j, 0:nn])), axis=0)-0.5)*40))
print('T:', _np_str((np.mean(0.5*(1+np.sign(ri2_5[0:j, 0:nn])), axis=0)-0.5)*40))
print('Average Gross')
print('T:', _np_str(np.mean(hi2_1[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(hi2_2[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(hi2_3[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(hi2_4[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(hi2_5[0:j, 0:nn], axis=0)))
print('Average Net / Gross')
print('T:', _np_str(np.mean(gi2_1[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(gi2_2[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(gi2_3[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(gi2_4[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(gi2_5[0:j, 0:nn], axis=0)))
print('Average 3m S&P 500 Beta')
print('T:', _np_str(np.mean(si2_1a[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_2a[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_3a[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_4a[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_5a[0:j, 0:nn], axis=0)))
print('Average 6m S&P 500 Beta')
print('T:', _np_str(np.mean(si2_1b[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_2b[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_3b[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_4b[0:j, 0:nn], axis=0)))
print('T:', _np_str(np.mean(si2_5b[0:j, 0:nn], axis=0)))
print('\n')
print('Neutral Model')
print('____________')
print('Sharpe Ratio')
print('N:', _np_str((np.mean(0.5*(1+np.sign(ri3_1[0:j, 0:nn])), axis=0)-0.5)*40))
print('N:', _np_str((np.mean(0.5*(1+np.sign(ri3_2[0:j, 0:nn])), axis=0)-0.5)*40))
print('N:', _np_str((np.mean(0.5*(1+np.sign(ri3_3[0:j, 0:nn])), axis=0)-0.5)*40))
print('N:', _np_str((np.mean(0.5*(1+np.sign(ri3_4[0:j, 0:nn])), axis=0)-0.5)*40))
print('N:', _np_str((np.mean(0.5*(1+np.sign(ri3_5[0:j, 0:nn])), axis=0)-0.5)*40))
print('Average Gross')
print('N:', _np_str(np.mean(hi3_1[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(hi3_2[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(hi3_3[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(hi3_4[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(hi3_5[0:j, 0:nn], axis=0)))
print('Average Net / Gross')
print('N:', _np_str(np.mean(gi3_1[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(gi3_2[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(gi3_3[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(gi3_4[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(gi3_5[0:j, 0:nn], axis=0)))
print('Average 3m S&P 500 Beta')
print('N:', _np_str(np.mean(si3_1a[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_2a[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_3a[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_4a[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_5a[0:j, 0:nn], axis=0)))
print('Average 6m S&P 500 Beta')
print('N:', _np_str(np.mean(si3_1b[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_2b[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_3b[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_4b[0:j, 0:nn], axis=0)))
print('N:', _np_str(np.mean(si3_5b[0:j, 0:nn], axis=0)))
print('\n')
def _refresh_initial_weights(x):
_d_bt_bs, _d_bt_fr, _d_bt_iw, _d_bt_ig, _d_flag = x
_d_bt_bs_dt = _d_bt_bs.listdir()
ndt = len(_d_bt_bs_dt)
nsig = 50
for j, i in enumerate(_d_bt_bs_dt):
# j = 0
# i = _d_bt_bs_dt[j]
xi_all = _d_bt_bs.load(i)
xi = np.ascontiguousarray(xi_all['arr_0'].astype('float64'))
if (_d_flag == 3) or (_d_flag == 5) or (_d_flag == 7):
si = xi_all['arr_2'].astype('float64') # using QR
else:
si = xi_all['arr_1'].astype('float64') # using LR
cieq = xi_all['arr_3'].astype('float64')
cilq = xi_all['arr_4'].astype('float64')
tci = xi_all['arr_5']
del xi_all
n = xi.shape[1]
ei = np.ones(n)
if j < ndt-2:
x2 = _d_bt_fr.load(i)
x2[np.isnan(x2)] = 0
# get the risk budgets
bieq = xi[25, :]
bilq = xi[26, :]
# adjustments for longer date signals rarely reaching upto potential
si_ = si.copy()/vt
for k in range(0, nsig):
si_[k, :] *= sigp[k]
si_ = 0.5*si_/(0.0625+si_**2)
lmbeq = np.median(bieq/np.dot(cieq, ei))
lmblq = np.median(bilq/np.dot(cilq, ei))
flev = np.ascontiguousarray(xi[0, :])
# calculate the weights
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2):
si1_eq = lmbeq*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:12], xi[17:21]))))
elif _d_flag == 3:
si1_eq = lmbeq*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:11], xi[17:19]))))
elif _d_flag == 4:
si1_eq = lmbeq*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[9:12], xi[17:21]))))
elif _d_flag == 5:
si1_eq = lmbeq*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:12], xi[17:19]))))
elif _d_flag == 6:
si1_eq = lmbeq*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[9:12], xi[17:21]))))
else: # _d_flag == 7
si1_eq = lmbeq*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[9:11], xi[17:19]))))
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2):
si1_lq = lmblq*np.dot(si_, crts.neut_corr_mat(cilq, np.vstack((xi[2:5, :], xi[9:12], xi[17:21]))))
elif _d_flag == 3:
si1_lq = lmblq*np.dot(si_, crts.neut_corr_mat(cilq, np.vstack((xi[2:5, :], xi[9:11], xi[17:19]))))
elif _d_flag == 4:
si1_lq = lmblq*np.dot(si_, crts.neut_corr_mat(cilq, np.vstack((xi[2:4, :], xi[9:12], xi[17:21]))))
elif _d_flag == 5:
si1_lq = lmblq*np.dot(si_, crts.neut_corr_mat(cilq, np.vstack((xi[2:5, :], xi[9:12], xi[17:19]))))
elif _d_flag == 6:
si1_lq = lmblq*np.dot(si_, crts.neut_corr_mat(cilq, np.vstack((xi[2, :], xi[9:12], xi[17:21]))))
else: # _d_flag == 7
si1_lq = lmblq*np.dot(si_, crts.neut_corr_mat(cilq, np.vstack((xi[2, :], xi[9:11], xi[17:19]))))
# also store the gross and net
hi1_eq = np.dot(abs(si1_eq), flev)
hi1_lq = np.dot(abs(si1_lq), flev)
gi1_eq = np.dot(si1_eq, flev)
gi1_lq = np.dot(si1_lq, flev)
_d_bt_iw.save(i, si1_eq, si1_lq, tci)
if j < ndt-2:
ri1_eq = np.dot(si1_eq, x2)
ri1_lq = np.dot(si1_lq, x2)
_d_bt_ig.save(i, ri1_eq, ri1_lq, hi1_eq, hi1_lq, gi1_eq, gi1_lq)
return None
def _refresh_fast_backtest(x):
_d_bt_bs, _d_bt_fr, _d_flag = x
_d_bt_bs_dt = _d_bt_bs.listdir()
_d_bt_bs_dt = _d_bt_bs_dt[:-2] # drop the last two
ndt = len(_d_bt_bs_dt)
nsig = 50
ri1_1 = np.zeros([ndt, nsig])
gi1_1 = np.zeros([ndt, nsig])
hi1_1 = np.zeros([ndt, nsig])
for j, i in enumerate(_d_bt_bs_dt):
# j = 0
# i = _d_bt_bs_dt[0]
xi_all = _d_bt_bs.load(i)
xi = np.ascontiguousarray(xi_all['arr_0'].astype('float64'))
if (_d_flag == 3) or (_d_flag == 5) or (_d_flag == 7):
si = xi_all['arr_2'].astype('float64') # using QR
else:
si = xi_all['arr_1'].astype('float64') # using LR
cieq = xi_all['arr_3'].astype('float64')
del xi_all
n = xi.shape[1]
ei = np.ones(n)
x2 = _d_bt_fr.load(i)
x2[np.isnan(x2)] = 0
# get the risk budgets
bieq = xi[25, :]
# adjustments for longer date signals rarely reaching upto potential
si_ = si.copy()/vt
for k in range(0, nsig):
si_[k, :] *= sigp[k]
si_ = 0.5*si_/(0.0625+si_**2)
lmb0 = np.median(bieq/np.dot(cieq, ei))
# read the momentum and low volatility scores
flev = np.ascontiguousarray(xi[0, :])
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2):
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:12], xi[17:21]))))
elif _d_flag == 3:
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:11], xi[17:19]))))
elif _d_flag == 4:
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[9:12], xi[17:21]))))
elif _d_flag == 5:
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:12], xi[17:19]))))
elif _d_flag == 6:
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[9:12], xi[17:21]))))
else: # _d_flag == 7
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[9:11], xi[17:19]))))
ri1_1[j, :] = np.dot(si1_1, x2)
hi1_1[j, :] = np.dot(abs(si1_1), flev)
gi1_1[j, :] = abs(np.dot(si1_1, flev))/hi1_1[j, :]
nn = 50
# if ((j != 0) and (j % 200 == 0)) or (j == ndt-1):
if j == ndt-1:
tn_1_1a = np.array(range(10, 510, 10))
v_1_1 = 16*np.diag(np.std(ri1_1[0:j, 0:nn], axis=0))
v_1_1a = np.linalg.inv(np.dot(v_1_1, np.dot(np.corrcoef(ri1_1[0:j, 0:nn].T)+0.1*np.identity(nn), v_1_1)))
# v_1_1b = 1/tn_1_1a
# v_1_1c = np.identity(nn)-np.dot(np.outer(v_1_1b, v_1_1b), v_1_1a)/np.dot(v_1_1b, np.dot(v_1_1a, v_1_1b))
# v_1_1 = np.dot(np.dot(v_1_1a, v_1_1b), np.mean(ri1_1[0:j, 0:nn], axis=0))
v_1_1 = np.dot(v_1_1a, np.mean(ri1_1[0:j, 0:nn], axis=0))
rc_1_1 = v_1_1/np.sum(v_1_1)
rt_1_1 = np.dot(ri1_1, rc_1_1)
ht_1_1 = np.dot(hi1_1, rc_1_1)
gt_1_1 = np.dot(hi1_1*gi1_1, rc_1_1)/ht_1_1
tn_1_1 = 1/np.sum(abs(rc_1_1)/tn_1_1a)
# print('Processing %s' % i)
print('\n')
print('____________')
print('Days')
print(_np_str(_np_push_front(tn_1_1, tn_1_1a)))
print('Weights')
print(_np_str(_np_push_front(1, rc_1_1)))
print('Sharpe_Ratio')
print(_np_str(16*np.mean(_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]), axis=0)))
print('Average_Gross')
print(_np_str(np.mean(_np_align(ht_1_1[0:j], hi1_1[0:j, 0:nn]), axis=0)))
print('Average_Net_/_Gross')
print(_np_str(np.mean(_np_align(gt_1_1[0:j], gi1_1[0:j, 0:nn]), axis=0)))
print('Average_Drawdown')
print(_np_str(max_drawdown(_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]))/np.std(
_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]))/16))
print('\n')
def _refresh_fast_backtest(x):
_d_bt_bs, _d_bt_fr, _d_flag = x
_d_bt_bs_dt = _d_bt_bs.listdir()
_d_bt_bs_dt = _d_bt_bs_dt[:-2] # drop the last two
ndt = len(_d_bt_bs_dt)
nsig = 50
ri1_1 = np.zeros([ndt, nsig])
ri1_2 = np.zeros([ndt, nsig])
ri1_3 = np.zeros([ndt, nsig])
ri1_4 = np.zeros([ndt, nsig])
ri1_5 = np.zeros([ndt, nsig])
gi1_1 = np.zeros([ndt, nsig])
gi1_2 = np.zeros([ndt, nsig])
gi1_3 = np.zeros([ndt, nsig])
gi1_4 = np.zeros([ndt, nsig])
gi1_5 = np.zeros([ndt, nsig])
hi1_1 = np.zeros([ndt, nsig])
hi1_3 = np.zeros([ndt, nsig])
hi1_2 = np.zeros([ndt, nsig])
hi1_4 = np.zeros([ndt, nsig])
hi1_5 = np.zeros([ndt, nsig])
si1_1a = np.zeros([ndt, nsig])
si1_2a = np.zeros([ndt, nsig])
si1_3a = np.zeros([ndt, nsig])
si1_4a = np.zeros([ndt, nsig])
si1_5a = np.zeros([ndt, nsig])
si1_1b = np.zeros([ndt, nsig])
si1_2b = np.zeros([ndt, nsig])
si1_3b = np.zeros([ndt, nsig])
si1_4b = np.zeros([ndt, nsig])
si1_5b = np.zeros([ndt, nsig])
ri2_1 = np.zeros([ndt, nsig])
ri2_2 = np.zeros([ndt, nsig])
ri2_3 = np.zeros([ndt, nsig])
ri2_4 = np.zeros([ndt, nsig])
ri2_5 = np.zeros([ndt, nsig])
gi2_1 = np.zeros([ndt, nsig])
gi2_2 = np.zeros([ndt, nsig])
gi2_3 = np.zeros([ndt, nsig])
gi2_4 = np.zeros([ndt, nsig])
gi2_5 = np.zeros([ndt, nsig])
hi2_1 = np.zeros([ndt, nsig])
hi2_2 = np.zeros([ndt, nsig])
hi2_3 = np.zeros([ndt, nsig])
hi2_4 = np.zeros([ndt, nsig])
hi2_5 = np.zeros([ndt, nsig])
si2_1a = np.zeros([ndt, nsig])
si2_2a = np.zeros([ndt, nsig])
si2_3a = np.zeros([ndt, nsig])
si2_4a = np.zeros([ndt, nsig])
si2_5a = np.zeros([ndt, nsig])
si2_1b = np.zeros([ndt, nsig])
si2_2b = np.zeros([ndt, nsig])
si2_3b = np.zeros([ndt, nsig])
si2_4b = np.zeros([ndt, nsig])
si2_5b = np.zeros([ndt, nsig])
for j, i in enumerate(_d_bt_bs_dt):
# j = 0
# i = _d_bt_bs_dt[0]
# _d_flag = 0
xi_all = _d_bt_bs.load(i)
xi = np.ascontiguousarray(xi_all['arr_0'].astype('float64'))
if (_d_flag == 3) or (_d_flag == 5) or (_d_flag == 7):
si = xi_all['arr_2'].astype('float64') # using QR
sia = xi_all['arr_1'][0, :]
else:
si = xi_all['arr_1'].astype('float64') # using LR
sia = xi_all['arr_2'][0, :]
cieq = xi_all['arr_3'].astype('float64')
del xi_all
n = xi.shape[1]
ei = np.ones(n)
x2 = _d_bt_fr.load(i)
x2[np.isnan(x2)] = 0
# get the risk budgets
bieq = xi[25, :]
# adjustments for longer date signals rarely reaching upto potential
si_ = si.copy()/vt
sia_ = sia.copy()/vt
for k in range(0, nsig):
si_[k, :] *= sigp[k]
si_ = 0.5*si_/(0.0625+si_**2)
# sia_ = (0.5*sia_/(0.0625+sia_**2))*sigp[0]
lmb0 = np.median(bieq/np.dot(cieq, ei))
# read the momentum and low volatility scores
flev = np.ascontiguousarray(xi[0, :])
fscm = xi[7, :]
fscm = np.vstack((fscm**2, fscm**3))
# fscv = np.ascontiguousarray(np.maximum(xi[7, :], 0))**3
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2) or (_d_flag == 3) or (_d_flag == 5):
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, xi[2:5, :]))
si2_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], fscm))))
elif (_d_flag == 6) or (_d_flag == 7):
si1_1 = lmb0*np.dot(si_, crts.neut_corr_vec(cieq, xi[2, :]))
si2_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], fscm))))
else:
si1_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, xi[2:4, :]))
si2_1 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], fscm))))
# calculate the 3m SP500 neutrality
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2) or (_d_flag == 3) or (_d_flag == 5):
si1_2 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:11, :]))))
si2_2 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[9:11, :], fscm))))
elif (_d_flag == 6) or (_d_flag == 7):
si1_2 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[9:11, :]))))
si2_2 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[9:11, :], fscm))))
else:
si1_2 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[9:11, :]))))
si2_2 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[9:11, :], fscm))))
# calculate the 6m SP500 neutrality
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2) or (_d_flag == 3) or (_d_flag == 5):
si1_3 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[17:19, :]))))
si2_3 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[17:19, :], fscm))))
elif (_d_flag == 6) or (_d_flag == 7):
si1_3 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[17:19, :]))))
si2_3 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[17:19, :], fscm))))
else:
si1_3 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[17:19, :]))))
si2_3 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[17:19, :], fscm))))
# calculate the 9m SP500 neutrality
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2) or (_d_flag == 3) or (_d_flag == 5):
si1_4 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[21:23, :]))))
si2_4 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[21:23, :], fscm))))
elif (_d_flag == 6) or (_d_flag == 7):
si1_4 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[21:23, :]))))
si2_4 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[21:23, :], fscm))))
else:
si1_4 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[21:23, :]))))
si2_4 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[21:23, :], fscm))))
# calculate the 12m SP500 neutrality
if (_d_flag == 0) or (_d_flag == 1) or (_d_flag == 2) or (_d_flag == 3) or (_d_flag == 5):
si1_5 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[13:14, :]))))
si2_5 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:5, :], xi[13:14, :], fscm))))
elif (_d_flag == 6) or (_d_flag == 7):
si1_5 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[13:14, :]))))
si2_5 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2, :], xi[13:14, :], fscm))))
else:
si1_5 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[13:14, :]))))
si2_5 = lmb0*np.dot(si_, crts.neut_corr_mat(cieq, np.vstack((xi[2:4, :], xi[13:14, :], fscm))))
ri1_1[j, :] = np.dot(si1_1, x2)
ri1_2[j, :] = np.dot(si1_2, x2)
ri1_3[j, :] = np.dot(si1_3, x2)
ri1_4[j, :] = np.dot(si1_4, x2)
ri1_5[j, :] = np.dot(si1_5, x2)
ri2_1[j, :] = np.dot(si2_1, x2)
ri2_2[j, :] = np.dot(si2_2, x2)
ri2_3[j, :] = np.dot(si2_3, x2)
ri2_4[j, :] = np.dot(si2_4, x2)
ri2_5[j, :] = np.dot(si2_5, x2)
hi1_1[j, :] = np.dot(abs(si1_1), flev)
hi1_2[j, :] = np.dot(abs(si1_2), flev)
hi1_3[j, :] = np.dot(abs(si1_3), flev)
hi1_4[j, :] = np.dot(abs(si1_4), flev)
hi1_5[j, :] = np.dot(abs(si1_5), flev)
hi2_1[j, :] = np.dot(abs(si2_1), flev)
hi2_2[j, :] = np.dot(abs(si2_2), flev)
hi2_3[j, :] = np.dot(abs(si2_3), flev)
hi2_4[j, :] = np.dot(abs(si2_4), flev)
hi2_5[j, :] = np.dot(abs(si2_5), flev)
gi1_1[j, :] = abs(np.dot(si1_1, flev))/hi1_1[j, :]
gi1_2[j, :] = abs(np.dot(si1_2, flev))/hi1_2[j, :]
gi1_3[j, :] = abs(np.dot(si1_3, flev))/hi1_3[j, :]
gi1_4[j, :] = abs(np.dot(si1_4, flev))/hi1_4[j, :]
gi1_5[j, :] = abs(np.dot(si1_5, flev))/hi1_5[j, :]
gi2_1[j, :] = abs(np.dot(si2_1, flev))/hi2_1[j, :]
gi2_2[j, :] = abs(np.dot(si2_2, flev))/hi2_2[j, :]
gi2_3[j, :] = abs(np.dot(si2_3, flev))/hi2_3[j, :]
gi2_4[j, :] = abs(np.dot(si2_4, flev))/hi2_4[j, :]
gi2_5[j, :] = abs(np.dot(si2_5, flev))/hi2_5[j, :]
nn = 50
# if ((j != 0) and (j % 200 == 0)) or (j == ndt-1):
if j == ndt-1:
ei_ = np.ones(nn)
sh_1_1 = np.diag(1/(-16*np.mean(ri1_1[0:j, 0:nn], axis=0)/max_drawdown(ri1_1[0:j, 0:nn])))
sh_1_2 = np.diag(1/(-16*np.mean(ri1_2[0:j, 0:nn], axis=0)/max_drawdown(ri1_2[0:j, 0:nn])))
sh_1_3 = np.diag(1/(-16*np.mean(ri1_3[0:j, 0:nn], axis=0)/max_drawdown(ri1_3[0:j, 0:nn])))
sh_1_4 = np.diag(1/(-16*np.mean(ri1_4[0:j, 0:nn], axis=0)/max_drawdown(ri1_4[0:j, 0:nn])))
sh_1_5 = np.diag(1/(-16*np.mean(ri1_5[0:j, 0:nn], axis=0)/max_drawdown(ri1_5[0:j, 0:nn])))
sh_2_1 = np.diag(1/(-16*np.mean(ri2_1[0:j, 0:nn], axis=0)/max_drawdown(ri2_1[0:j, 0:nn])))
sh_2_2 = np.diag(1/(-16*np.mean(ri2_2[0:j, 0:nn], axis=0)/max_drawdown(ri2_2[0:j, 0:nn])))
sh_2_3 = np.diag(1/(-16*np.mean(ri2_3[0:j, 0:nn], axis=0)/max_drawdown(ri2_3[0:j, 0:nn])))
sh_2_4 = np.diag(1/(-16*np.mean(ri2_4[0:j, 0:nn], axis=0)/max_drawdown(ri2_4[0:j, 0:nn])))
sh_2_5 = np.diag(1/(-16*np.mean(ri2_5[0:j, 0:nn], axis=0)/max_drawdown(ri2_5[0:j, 0:nn])))
rc_1_1 = 1/np.dot(np.dot(sh_1_1, np.dot(np.corrcoef(ri1_1[0:j, 0:nn].T), sh_1_1)), ei_)
rc_1_2 = 1/np.dot(np.dot(sh_1_2, np.dot(np.corrcoef(ri1_2[0:j, 0:nn].T), sh_1_2)), ei_)
rc_1_3 = 1/np.dot(np.dot(sh_1_3, np.dot(np.corrcoef(ri1_3[0:j, 0:nn].T), sh_1_3)), ei_)
rc_1_4 = 1/np.dot(np.dot(sh_1_4, np.dot(np.corrcoef(ri1_4[0:j, 0:nn].T), sh_1_4)), ei_)
rc_1_5 = 1/np.dot(np.dot(sh_1_5, np.dot(np.corrcoef(ri1_5[0:j, 0:nn].T), sh_1_5)), ei_)
rc_2_1 = 1/np.dot(np.dot(sh_2_1, np.dot(np.corrcoef(ri2_1[0:j, 0:nn].T), sh_2_1)), ei_)
rc_2_2 = 1/np.dot(np.dot(sh_2_2, np.dot(np.corrcoef(ri2_2[0:j, 0:nn].T), sh_2_2)), ei_)
rc_2_3 = 1/np.dot(np.dot(sh_2_3, np.dot(np.corrcoef(ri2_3[0:j, 0:nn].T), sh_2_3)), ei_)
rc_2_4 = 1/np.dot(np.dot(sh_2_4, np.dot(np.corrcoef(ri2_4[0:j, 0:nn].T), sh_2_4)), ei_)
rc_2_5 = 1/np.dot(np.dot(sh_2_5, np.dot(np.corrcoef(ri2_5[0:j, 0:nn].T), sh_2_5)), ei_)
rc_1_1 /= np.sum(rc_1_1)
rc_1_2 /= np.sum(rc_1_2)
rc_1_3 /= np.sum(rc_1_3)
rc_1_4 /= np.sum(rc_1_4)
rc_1_5 /= np.sum(rc_1_5)
rc_2_1 /= np.sum(rc_2_1)
rc_2_2 /= np.sum(rc_2_2)
rc_2_3 /= np.sum(rc_2_3)
rc_2_4 /= np.sum(rc_2_4)
rc_2_5 /= np.sum(rc_2_5)
rt_1_1 = np.dot(ri1_1, rc_1_1)
rt_1_2 = np.dot(ri1_2, rc_1_2)
rt_1_3 = np.dot(ri1_3, rc_1_3)
rt_1_4 = np.dot(ri1_4, rc_1_4)
rt_1_5 = np.dot(ri1_5, rc_1_5)
rt_2_1 = np.dot(ri2_1, rc_2_1)
rt_2_2 = np.dot(ri2_2, rc_2_2)
rt_2_3 = np.dot(ri2_3, rc_2_3)
rt_2_4 = np.dot(ri2_4, rc_2_4)
rt_2_5 = np.dot(ri2_5, rc_2_5)
ht_1_1 = np.dot(hi1_1, rc_1_1)
ht_1_2 = np.dot(hi1_2, rc_1_2)
ht_1_3 = np.dot(hi1_3, rc_1_3)
ht_1_4 = np.dot(hi1_4, rc_1_4)
ht_1_5 = np.dot(hi1_5, rc_1_5)
ht_2_1 = np.dot(hi2_1, rc_2_1)
ht_2_2 = np.dot(hi2_2, rc_2_2)
ht_2_3 = np.dot(hi2_3, rc_2_3)
ht_2_4 = np.dot(hi2_4, rc_2_4)
ht_2_5 = np.dot(hi2_5, rc_2_5)
gt_1_1 = np.dot(hi1_1*gi1_1, rc_1_1)/ht_1_1
gt_1_2 = np.dot(hi1_2*gi1_2, rc_1_2)/ht_1_2
gt_1_3 = np.dot(hi1_3*gi1_3, rc_1_3)/ht_1_3
gt_1_4 = np.dot(hi1_4*gi1_4, rc_1_4)/ht_1_4
gt_1_5 = np.dot(hi1_5*gi1_5, rc_1_5)/ht_1_5
gt_2_1 = np.dot(hi2_1*gi2_1, rc_2_1)/ht_2_1
gt_2_2 = np.dot(hi2_2*gi2_2, rc_2_2)/ht_2_2
gt_2_3 = np.dot(hi2_3*gi2_3, rc_2_3)/ht_2_3
gt_2_4 = np.dot(hi2_4*gi2_4, rc_2_4)/ht_2_4
gt_2_5 = np.dot(hi2_5*gi2_5, rc_2_5)/ht_2_5
# print('Processing %s' % i)
print('\n')
print('Port_Cons:_GLS_Neutralization')
print('____________')
print('Sharpe_Ratio')
print('B0:', _np_str(16*np.mean(_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]), axis=0)))
print('B1:', _np_str(16*np.mean(_np_align(rt_1_2[0:j], ri1_2[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_1_2[0:j], ri1_2[0:j, 0:nn]), axis=0)))
print('B2:', _np_str(16*np.mean(_np_align(rt_1_3[0:j], ri1_3[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_1_3[0:j], ri1_3[0:j, 0:nn]), axis=0)))
print('B3:', _np_str(16*np.mean(_np_align(rt_1_4[0:j], ri1_4[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_1_4[0:j], ri1_4[0:j, 0:nn]), axis=0)))
print('B4:', _np_str(16*np.mean(_np_align(rt_1_5[0:j], ri1_5[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_1_5[0:j], ri1_5[0:j, 0:nn]), axis=0)))
print('Average_Gross')
print('B0:', _np_str(np.mean(_np_align(ht_1_1[0:j], hi1_1[0:j, 0:nn]), axis=0)))
print('B1:', _np_str(np.mean(_np_align(ht_1_2[0:j], hi1_2[0:j, 0:nn]), axis=0)))
print('B2:', _np_str(np.mean(_np_align(ht_1_3[0:j], hi1_3[0:j, 0:nn]), axis=0)))
print('B3:', _np_str(np.mean(_np_align(ht_1_4[0:j], hi1_4[0:j, 0:nn]), axis=0)))
print('B4:', _np_str(np.mean(_np_align(ht_1_5[0:j], hi1_5[0:j, 0:nn]), axis=0)))
print('Average_Net_/_Gross')
print('B0:', _np_str(np.mean(_np_align(gt_1_1[0:j], gi1_1[0:j, 0:nn]), axis=0)))
print('B1:', _np_str(np.mean(_np_align(gt_1_2[0:j], gi1_2[0:j, 0:nn]), axis=0)))
print('B2:', _np_str(np.mean(_np_align(gt_1_3[0:j], gi1_3[0:j, 0:nn]), axis=0)))
print('B3:', _np_str(np.mean(_np_align(gt_1_4[0:j], gi1_4[0:j, 0:nn]), axis=0)))
print('B4:', _np_str(np.mean(_np_align(gt_1_5[0:j], gi1_5[0:j, 0:nn]), axis=0)))
print('Average_Drawdown')
print('B0:', _np_str(max_drawdown(_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]))/np.std(
_np_align(rt_1_1[0:j], ri1_1[0:j, 0:nn]))/16))
print('B1:', _np_str(max_drawdown(_np_align(rt_1_2[0:j], ri1_2[0:j, 0:nn]))/np.std(
_np_align(rt_1_2[0:j], ri1_2[0:j, 0:nn]))/16))
print('B2:', _np_str(max_drawdown(_np_align(rt_1_3[0:j], ri1_3[0:j, 0:nn]))/np.std(
_np_align(rt_1_3[0:j], ri1_3[0:j, 0:nn]))/16))
print('B3:', _np_str(max_drawdown(_np_align(rt_1_4[0:j], ri1_4[0:j, 0:nn]))/np.std(
_np_align(rt_1_4[0:j], ri1_4[0:j, 0:nn]))/16))
print('B4:', _np_str(max_drawdown(_np_align(rt_1_5[0:j], ri1_5[0:j, 0:nn]))/np.std(
_np_align(rt_1_5[0:j], ri1_5[0:j, 0:nn]))/16))
print('\n')
print('Port_Cons:_GLS_Neutralization_with_Momentum_Neutrality')
print('Sharpe_Ratio')
print('B0:', _np_str(16*np.mean(_np_align(rt_2_1[0:j], ri2_1[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_2_1[0:j], ri2_1[0:j, 0:nn]), axis=0)))
print('B1:', _np_str(16*np.mean(_np_align(rt_2_2[0:j], ri2_2[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_2_2[0:j], ri2_2[0:j, 0:nn]), axis=0)))
print('B2:', _np_str(16*np.mean(_np_align(rt_2_3[0:j], ri2_3[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_2_3[0:j], ri2_3[0:j, 0:nn]), axis=0)))
print('B3:', _np_str(16*np.mean(_np_align(rt_2_4[0:j], ri2_4[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_2_4[0:j], ri2_4[0:j, 0:nn]), axis=0)))
print('B4:', _np_str(16*np.mean(_np_align(rt_2_5[0:j], ri2_5[0:j, 0:nn]), axis=0)/np.std(
_np_align(rt_2_5[0:j], ri2_5[0:j, 0:nn]), axis=0)))
print('Average_Gross')
print('B0:', _np_str(np.mean(_np_align(ht_2_1[0:j], hi2_1[0:j, 0:nn]), axis=0)))
print('B1:', _np_str(np.mean(_np_align(ht_2_2[0:j], hi2_2[0:j, 0:nn]), axis=0)))
print('B2:', _np_str(np.mean(_np_align(ht_2_3[0:j], hi2_3[0:j, 0:nn]), axis=0)))
print('B3:', _np_str(np.mean(_np_align(ht_2_4[0:j], hi2_4[0:j, 0:nn]), axis=0)))
print('B4:', _np_str(np.mean(_np_align(ht_2_5[0:j], hi2_5[0:j, 0:nn]), axis=0)))
print('Average_Net_/_Gross')
print('B0:', _np_str(np.mean(_np_align(gt_2_1[0:j], gi2_1[0:j, 0:nn]), axis=0)))
print('B1:', _np_str(np.mean(_np_align(gt_2_2[0:j], gi2_2[0:j, 0:nn]), axis=0)))
print('B2:', _np_str(np.mean(_np_align(gt_2_3[0:j], gi2_3[0:j, 0:nn]), axis=0)))
print('B3:', _np_str(np.mean(_np_align(gt_2_4[0:j], gi2_4[0:j, 0:nn]), axis=0)))
print('B4:', _np_str(np.mean(_np_align(gt_2_5[0:j], gi2_5[0:j, 0:nn]), axis=0)))
print('Average_Drawdown')
print('B0:', _np_str(max_drawdown(_np_align(rt_2_1[0:j], ri2_1[0:j, 0:nn]))/np.std(
_np_align(rt_2_1[0:j], ri2_1[0:j, 0:nn]))/16))
print('B1:', _np_str(max_drawdown(_np_align(rt_2_2[0:j], ri2_2[0:j, 0:nn]))/np.std(
_np_align(rt_2_2[0:j], ri2_2[0:j, 0:nn]))/16))
print('B2:', _np_str(max_drawdown(_np_align(rt_2_3[0:j], ri2_3[0:j, 0:nn]))/np.std(
_np_align(rt_2_3[0:j], ri2_3[0:j, 0:nn]))/16))
print('B3:', _np_str(max_drawdown(_np_align(rt_2_4[0:j], ri2_4[0:j, 0:nn]))/np.std(
_np_align(rt_2_4[0:j], ri2_4[0:j, 0:nn]))/16))
print('B4:', _np_str(max_drawdown(_np_align(rt_2_5[0:j], ri2_5[0:j, 0:nn]))/np.std(
_np_align(rt_2_5[0:j], ri2_5[0:j, 0:nn]))/16))
print('\n')
