def test_measures2():
pd.set_option('display.max_columns', 30)
pd.set_option('display.max_rows', 100)
univ_ib_gd = cr_cret.retrieve(univ_ib_eqidx_ext + 'ExchOpen')
_prc_names = ['CLS', 'A2D', 'A3D', 'A4D', 'A2W', 'A3W', 'A4W', 'A2M', 'A3M', 'A4M']
_trn_names = ['LVL', 'CH0', 'CH1', 'CH2']
_fil_names = ['LRB', 'QRB', 'QRG']
_hoz_names = []
for j in range(5, 305, 5):
if j < 10:
_hoz_names.append('00'+str(j))
elif j < 100:
_hoz_names.append('0'+str(j))
else:
_hoz_names.append(str(j))
univ_ib_cl = cr_cret.retrieve(univ_ib_eqidx_ext + 'Close')
tick_cols = univ_ib_cl.tick_cols()
for n in tick_cols:
z = list(np.where(univ_ib_gd[n].values.astype('int') == 1)[0])
univ_ib_cl_ = filt.ret(univ_ib_cl[n].values[z])
univ_ib_cl[n] = np.nan
univ_ib_cl[z, n] = univ_ib_cl_
univ_ib_cl[n] = filt.fill1(univ_ib_cl[n].values, 0)
# tick_cols_ = tick_cols[0:1]
tick_cols = ['SP500', 'SP400Mid', 'Nikkei225', 'R2000', 'FTSE100', 'DAX',
'SMI', 'CAC40', 'AEX', 'MIBFTSE', 'IBEX35', 'OMXS30']
siz_ = [5330, 7115, 6757, 7349, 5324, 6785, 6794, 6199, 7232, 7936, 7180, 7295]
# siz = [2784, 999, 1357, 765, 2790, 1329, 1320, 1915, 882, 178, 934, 819]
tick_cols = tick_cols[1:]
siz_ = siz_[1:]
for k in _trn_names:
dummy_cols = []
for i in _prc_names:
for j in _fil_names:
dummy_cols.append(i+'_'+j)
x_ = np.random.random(len(_hoz_names))*np.nan
y_ = np.random.random(len(_hoz_names))*np.nan
dummy_df = DataFrame({dummy_cols[0]: x_, dummy_cols[1]: y_})
for i in range(2, 30):
dummy_df[dummy_cols[i]] = x_
_col = 0
for i in _prc_names:
# j = _fil_names[0]
for j in _fil_names:
# k = _trn_names[0]
for mi, m in enumerate(_hoz_names):
# m = _hoz_names[0]
fn = univ_ib_eqidx_ext+i+'_'+j+'_'+k+'_'+m
univ_ib_sig = cr_sig_mr_sg.retrieve(fn)
ret_, sig_ = np.empty(0), np.empty(0)
for ni, n in enumerate(tick_cols):
# ni = 0
# n = tick_cols[ni]
z = list(np.where(univ_ib_gd[n].values.astype('int') == 1)[0])
z = [z_ for z_ in z if z_ > siz_[ni]] # 5 is just a buffer
sig__ = univ_ib_sig[n].values[z]
ret__ = univ_ib_cl[n].values[z]
sig__ = filt.lag(sig__, 2)
ret_ = np.hstack((ret_, ret__[2:]))
sig_ = np.hstack((sig_, sig__[2:]))
gidx = np.where(~np.isnan(ret_) & ~np.isnan(sig_))[0]
dummy_df[mi, dummy_cols[_col]] = 100*spearmanr(ret_[gidx], sig_[gidx]).correlation
_col += 1
dummy_df.show_all()
