def gradient_boosting_result(_ticker='SP500'):
univ_ib_gd = cr_cret.retrieve(univ_ib_eqidx_ext + 'ExchOpen')[_ticker].values
z = list(np.where(univ_ib_gd.astype('int') == 1)[0])
_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))
# get volatility forecast
univ_ib_vl = cr_vol_all_adj.retrieve(univ_ib_eqidx_ext + 'vol_pb_120')[_ticker].values[z]
# get return
univ_ib_cl = cr_cret.retrieve(univ_ib_eqidx_ext + 'Close')[_ticker].values[z]
univ_ib_cl = filt.ret(univ_ib_cl)
univ_ib_sig_all = None
for k in _trn_names:
for i in _prc_names:
for j in _fil_names:
for mi, m in enumerate(_hoz_names):
fn = univ_ib_eqidx_ext+i+'_'+j+'_'+k+'_'+m
univ_ib_sig = cr_sig_mr_sg.retrieve(fn)[_ticker].values[z]
if univ_ib_sig_all is None:
univ_ib_sig_all = DataFrame({i+'_'+j+'_'+k+'_'+m: univ_ib_sig})
else:
univ_ib_sig_all.col_bind(DataFrame({i+'_'+j+'_'+k+'_'+m: univ_ib_sig}))
# just for storage - incase something happens
# univ_ib_gb = DataFrame({'Close': univ_ib_cl, 'Volatility': univ_ib_vl})
# univ_ib_gb.col_bind(univ_ib_sig_all)
# cr_cret.store(univ_ib_eqidx_ext + 'GBM', univ_ib_gb)
reg_lookback = [120, 240, 360, 480]
new_col_names = list(univ_ib_sig_all.columns)
for i in new_col_names:
# i = new_col_names[0]
test_ = np.empty(0)
for j in reg_lookback:
# j = reg_lookback[0]
uic = univ_ib_cl
uiv = univ_ib_vl
uis = filt.lag(univ_ib_sig_all[i].values, 1)
uic, uis, uiv = reduce_nonnan(uic, uis, uiv)
b1 = qreg.roll_e_ladreg_1d(uic, uis, j)
b2 = qreg.roll_e_ladreg_1d(uic/uiv, uis/uiv, j)
resid1 = uic - filt.lag(b1)*uis
resid2 = uic - filt.lag(b2)*uis
resid1, resid2 = reduce_nonnan(resid1, resid2)
test_ = np.hstack((test_, np.array([np.median(abs(resid1)), np.median(abs(resid2))])))
print(i+' : '+np_to_str(test_))
def get_vol_adj():
univ_ib_cl = cr_cret.retrieve(univ_ib_eqidx_ext + 'Close')
univ_ib_gd = cr_cret.retrieve(univ_ib_eqidx_ext + 'ExchOpen')
tick_cols = univ_ib_cl.tick_cols()
for k in range(0, len(vol_list)):
print('Processing volatility %s' % vol_names[k])
for j in vol_lookbacks:
print('Currently working on lookback %s' % str(j))
if j < 100:
univ_ib_vol = cr_vol_all.retrieve(univ_ib_eqidx_ext + vol_names[k] + '_0' + str(j))
else:
univ_ib_vol = cr_vol_all.retrieve(univ_ib_eqidx_ext + vol_names[k] + '_' + str(j))
for i in tick_cols:
z = np.where(univ_ib_gd[i].values.astype('int') == 1)[0]
if z.shape[0] > 0:
univ_ib_vol_ = univ_ib_vol[i].values[z]
univ_ib_cl_ = univ_ib_cl[i].values[z]
# find the beta
univ_ib_vol__ = filt.lag(univ_ib_vol_)
univ_ib_cl_ = np.abs(filt.ret(univ_ib_cl_))
regp = qreg4.roll_e_ladreg_1d(univ_ib_cl_, univ_ib_vol__, adj_lookback)
# get the beta-adjusted volatility
vol__ = univ_ib_vol_ * regp * _lapl_mult
# smooth out the volatility
vol__1 = filt.lrma(vol__, 61, lg=True)
vol__2 = filt.lrma(vol__, 7, lg=True)
vol__3 = vol__1 + filt.lrma(vol__2 - vol__1, 16)
# push the new volatility back
univ_ib_vol[i] = np.nan
univ_ib_vol[list(z), i] = vol__3
univ_ib_vol[i] = filt.fill(univ_ib_vol[i].values)
else:
univ_ib_vol[i] = np.nan
if j < 100:
cr_vol_all_adj.store(univ_ib_eqidx_ext + vol_names[k] + '_0' + str(j), univ_ib_vol)
else:
cr_vol_all_adj.store(univ_ib_eqidx_ext + vol_names[k] + '_' + str(j), univ_ib_vol)
return None
tickers = ['SP500', 'DAX', 'Nikkei225', 'ESTX50', 'SMI', 'RDX', 'MSCIEM'] i = 'SP500' univ_ib_cl = mkt_retrieve(i, 'Stats', 'Returns')['Close'].values univ_ib_vl = mkt_retrieve(i, 'Stats', 'Volatility')['vol_pb240'].values univ_ib_cl = np.abs(filt.ret(univ_ib_cl)) univ_ib_vl = filt.lag(univ_ib_vl) univ_ib_cl, univ_ib_vl = reduce_nonnan(univ_ib_cl, univ_ib_vl ) univ_ib_vl *= _lapl_mult univ_ib_vl2 = np.sqrt(univ_ib_vl) univ_ib_vl2 = univ_ib_vl2 * med_abs_dev(univ_ib_vl)/med_abs_dev(univ_ib_vl2) b41 = qreg.roll_e_ladreg_1d(univ_ib_cl, univ_ib_vl, 240) b51 = qreg.roll_e_ladreg_2d(univ_ib_cl, mcc(univ_ib_vl, univ_ib_vl2), 240) resid0 = univ_ib_cl - univ_ib_vl resid1 = univ_ib_cl - univ_ib_vl * filt.lag(b41) resid2 = univ_ib_cl - univ_ib_vl * filt.lag(np.ascontiguousarray(b51[:, 0])) - univ_ib_vl2 * filt.lag(np.ascontiguousarray(b51[:, 1])) resid0, resid1, resid2 = reduce_nonnan(resid0, resid1, resid2) print(np.median(np.abs(resid0))) print(np.median(np.abs(resid1))) print(np.median(np.abs(resid2))) # f = pyl.figure(1) # pyl.subplot(3, 1, 1)