def _get_leveraged_factor_score(x):
_d_bt_bs, _d_bt_fr, _d_bt_sig, _d_comb = x
_d_bt_bs_dt = _d_bt_bs.listdir()
sig_wgt = _d_comb.load('Signal_Weights')
for j, i in enumerate(_d_bt_bs_dt):
xi_all = _d_bt_bs.load(i)
xi = np.ascontiguousarray(xi_all['arr_0']).astype('float64')
si1_ = np.vstack(xi_all['arr_1'])/vt
si2_ = np.vstack(xi_all['arr_2'])/vt
ci = xi_all['arr_4'].astype('float64')
tci = xi_all['arr_5']
del xi_all
# curve-fitted (2-std)
si1 = np.multiply(si1_, _empirical_std_lr[:, np.newaxis])
si2 = np.multiply(si2_, _empirical_std_qr[:, np.newaxis])
si = np.vstack((si1, si2))
si = si*np.exp(0.5-0.5*(si**2)/4)/2
n = xi.shape[1]
ei = np.ones(n)
# get the risk budgets
bi = xi[19, :]
bi = bi/np.sum(bi)
lmb0 = np.median(bi/np.dot(ci, ei))
si1_1 = lmb0*np.dot(si, crts.neut_corr_mat(ci, xi[2:14, :]))
si1_1 = np.dot(sig_wgt, si1_1)/bi # change this
_d_bt_sig.save(i, tci, si1_1)
return None
def _refresh_fast_backtest(x):
_d_bt_bs, _d_bt_fr, _d_comb, _d_flag = x
_d_bt_bs_dt = _d_bt_bs.listdir()
_d_bt_bs_dt = _d_bt_bs_dt[:-2]
ndt = len(_d_bt_bs_dt)
nsig = 100
ri1_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")
si1_ = np.vstack(xi_all["arr_1"]) / vt
si2_ = np.vstack(xi_all["arr_2"]) / vt
ci = xi_all["arr_4"].astype("float64")
tci = xi_all["arr_4"]
del xi_all
# curve-fitted (2-std)
si1 = np.multiply(si1_, _empirical_std_lr[:, np.newaxis])
si2 = np.multiply(si2_, _empirical_std_qr[:, np.newaxis])
si = np.vstack((si1, si2))
si = si * np.exp(0.5 - 0.5 * (si ** 2) / 4) / 2
n = xi.shape[1]
ei = np.ones(n)
yi = _d_bt_fr.load(i)
yi[np.isnan(yi)] = 0
# get the risk budgets
bi = xi[19, :]
bi = bi / np.sum(bi)
lmb0 = np.median(bi / np.dot(ci, ei))
flev = xi[0, :]
si1_1 = lmb0 * np.dot(si, crts.neut_corr_mat(ci, xi[2:14, :])) * 10
ri1_1[j, :] = np.dot(si1_1, yi)
# hi1_1[j, :] = np.dot(abs(si1_1), flev)
# combining them by mean-variance
ri1_1_mn = np.mean(ri1_1, axis=0) * 256
ri1_1_sd = np.std(ri1_1, axis=0) * 16
ri1_1_corr = np.corrcoef(ri1_1.T) + np.identity(nsig)
ri1_1_cov = np.dot(np.diag(ri1_1_sd), np.dot(ri1_1_corr, np.diag(ri1_1_sd)))
ri1_1_cov = np.dot(np.linalg.inv(ri1_1_cov), ri1_1_mn)
wgt1_1_ = ri1_1_cov / np.sum(ri1_1_cov)
_d_comb.save("Signal_Weights", wgt1_1_)
# check results
ri1_1_g = np.dot(ri1_1, wgt1_1_)
print("Results for Cluster # % i" % _d_flag)
print(16 * np.mean(ri1_1, axis=0) / np.std(ri1_1, axis=0))
print(wgt1_1_)
print(16 * np.mean(ri1_1_g) / np.std(ri1_1_g))
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))
