def _get_signal_changes(i):
print('Calculating signal changes for %s ' % i)
for kk in range(1, 10, 2):
univ_ib_sig = mkt_retrieve(i, 'MovReg', 'Signals')
tick_cols = univ_ib_sig.tick_cols()
for k in tick_cols:
univ_ib_sig[k] = filt.chg(univ_ib_sig[k].values, kk)
mkt_store(i, 'MovReg', 'Changes' + str(kk), univ_ib_sig)
return None
def pred_stats_for_various_vol():
univ_ib_cl = cr_cret.retrieve(univ_ib_ext + "Close")
univ_ib_rt = univ_ib_cl.copy()
for i in univ_ib_cl.tick_cols():
univ_ib_cl[i] = filt.ret(univ_ib_cl[i].values)
for i in univ_ib_rt.tick_cols():
univ_ib_rt[i] = filt.ret(univ_ib_rt[i].values, 30)
univ_ib_rt[i] = filt.lag(univ_ib_rt[i].values)
univ_ib_cl_data = univ_ib_cl[univ_ib_cl.tick_cols()].values.reshape(-1)
univ_ib_rt_data = univ_ib_rt[univ_ib_rt.tick_cols()].values.reshape(-1)
# univ_ib_cl_data = np.abs(univ_ib_cl_data)
import warnings
warnings.simplefilter("ignore", RuntimeWarning)
univ_ib_rt_data = np.sign(univ_ib_rt_data)
warnings.simplefilter("default", RuntimeWarning)
for j in vol_names_sm:
for k in range(30, 330, 30): # changed here
if k < 100:
univ_ib_vol = cr_vol_all.retrieve(univ_ib_ext + j + "_0" + str(k) + "D")
else:
univ_ib_vol = cr_vol_all.retrieve(univ_ib_ext + j + "_" + str(k) + "D")
for i in univ_ib_vol.tick_cols():
univ_ib_vol[i] = filt.chg(filt.lag(1 / univ_ib_vol[i].values))
univ_ib_vol_data = univ_ib_vol[univ_ib_vol.tick_cols()].values.reshape(-1)
# univ_ib_nn = ~np.isnan(univ_ib_cl_data) & ~np.isnan(univ_ib_vol_data)
univ_ib_nn = ~np.isnan(univ_ib_cl_data) & ~np.isnan(univ_ib_vol_data) # & (univ_ib_rt_data < 0)
univ_ib_vol_data = univ_ib_vol_data[univ_ib_nn]
univ_ib_cl_data_ = univ_ib_cl_data[univ_ib_nn]
rho = ss.spearmanr(univ_ib_vol_data, univ_ib_cl_data_).correlation
if k < 100:
print(j + "_0" + str(k) + "D:\t", rho)
else:
print(j + "_" + str(k) + "D:\t", rho)
print("\n")
def check_price_forecast():
univ_ib_dt = cr_cret.retrieve(univ_ib_eqidx_ext + 'ExchOpen')['Date'].values
univ_ib_gd = cr_cret.retrieve(univ_ib_eqidx_ext + 'ExchOpen')['SP500'].values
z = np.where(univ_ib_gd.astype('int') == 1)[0]
univ_ib_cl = cr_cret.retrieve(univ_ib_eqidx_ext + 'Close')['SP500'].values[z]
univ_ib_vl = cr_vol_all_adj.retrieve(univ_ib_eqidx_ext + 'vol_pb_120')['SP500'].values[z]
univ_ib_dt = univ_ib_dt[z]
nlag = 2
n1 = filt.fst_nan(univ_ib_cl)
n1 = np.maximum(n1, filt.fst_nan(univ_ib_vl))
univ_ib_sig = np.empty(0)
univ_ib_sig_ = np.empty(0)
for days in range(1, 11):
for smth in range(5, 33):
if days < 10:
if smth < 10:
tmp = cr_aret.retrieve(univ_ib_eqidx_ext + 'AdjSerD0' + str(days)+'S0' + str(smth))['SP500'].values[z]
else:
tmp = cr_aret.retrieve(univ_ib_eqidx_ext + 'AdjSerD0' + str(days)+'S' + str(smth))['SP500'].values[z]
else:
if smth < 10:
tmp = cr_aret.retrieve(univ_ib_eqidx_ext + 'AdjSerD' + str(days)+'S0' + str(smth))['SP500'].values[z]
else:
tmp = cr_aret.retrieve(univ_ib_eqidx_ext + 'AdjSerD' + str(days)+'S' + str(smth))['SP500'].values[z]
# get the change in the signal
# tmp = np.sign(filt.chg(tmp))
# lag the data
# tmp = filt.lag(tmp, nlag)
if univ_ib_sig.shape[0] == 0:
univ_ib_sig = tmp
else:
univ_ib_sig = np.vstack((univ_ib_sig, tmp))
n1 = np.maximum(n1, filt.fst_nan(tmp))
univ_ib_cl_ = np.sign(filt.chg(univ_ib_cl, nlag))
univ_ib_cl_ = univ_ib_cl_[n1:]
univ_ib_vl_ = univ_ib_vl[n1:]
univ_ib_sig = univ_ib_sig[:, n1:]
names = []
for days in range(1, 11):
for smth in range(5, 33):
if (days < 10) and (smth < 10):
names.append('0'+str(days)+'_0'+str(smth))
elif (days < 10) and (smth >= 10):
names.append('0'+str(days)+'_'+str(smth))
elif (days >= 10) and (smth < 10):
names.append(str(days)+'_0'+str(smth))
else:
names.append(str(days)+'_'+str(smth))
for i in range(0, len(names)):
print(names[i], ':', smart_kendall(univ_ib_cl_, univ_ib_sig[i, :]))
plot_ts_new(univ_ib_dt, univ_ib_cl)
plot_ts_new(univ_ib_dt, univ_ib_sig[167, :])
t1 = filt.ret(filt.lag(univ_ib_sig[167, :]))
t1_ = filt.ret(univ_ib_sig[167, :])
t2 = filt.ret(univ_ib_cl)
t3 = t2*np.sign(t1)
t1_, t2, t3 = reduce_nonnan(t1_, t2, t3)
ct1_ = np.cumprod(1+t1_)*100
ct2 = np.cumprod(1+t2)*100
ct3 = np.cumprod(1-t3)*100
f = pyl.figure(1)
f.clear()
pyl.subplot(3, 1, 1)
pyl.semilogy(ct2[-5000:])
pyl.subplot(3, 1, 2)
pyl.semilogy(ct1_[-5000:])
pyl.subplot(3, 1, 3)
pyl.semilogy(ct3[-5000:])
def get_measures():
univ_ib_gd = cr_cret.retrieve(univ_ib_eqidx_ext + "ExchOpen")
_prc_measures = [
"Close",
"AdjSer20D",
"AdjSer30D",
"AdjSer40D",
"AdjSer20W",
"AdjSer30W",
"AdjSer40W",
"AdjSer20M",
"AdjSer30M",
"AdjSer40M",
]
_prc_measures_name = ["CLS", "A2D", "A3D", "A4D", "A2W", "A3W", "A4W", "A2M", "A3M", "A4M"]
tick_cols = univ_ib_gd.tick_cols()
for k in range(0, 10): # for different prices
univ_ib_pr = cr_cret.retrieve(univ_ib_eqidx_ext + _prc_measures[k])
for j in range(5, 305, 5):
univ_ib_lrbeta_lvl = nan_df(univ_ib_pr)
univ_ib_qrbeta_lvl = nan_df(univ_ib_pr)
univ_ib_qrgamm_lvl = nan_df(univ_ib_pr)
univ_ib_lrbeta_ch0 = nan_df(univ_ib_pr)
univ_ib_qrbeta_ch0 = nan_df(univ_ib_pr)
univ_ib_qrgamm_ch0 = nan_df(univ_ib_pr)
univ_ib_lrbeta_ch1 = nan_df(univ_ib_pr)
univ_ib_qrbeta_ch1 = nan_df(univ_ib_pr)
univ_ib_qrgamm_ch1 = nan_df(univ_ib_pr)
univ_ib_lrbeta_ch2 = nan_df(univ_ib_pr)
univ_ib_qrbeta_ch2 = nan_df(univ_ib_pr)
univ_ib_qrgamm_ch2 = nan_df(univ_ib_pr)
for i in tick_cols:
z = np.where(univ_ib_gd[i].values.astype("int") == 1)[0]
univ_ib_pr_ = univ_ib_pr[i].values[z]
univ_ib_lrb_ = filt.lrbeta(univ_ib_pr_, j)
univ_ib_lrbeta_lvl[list(z), i] = univ_ib_lrb_
univ_ib_lrbeta_ch0[list(z), i] = filt.chg(univ_ib_lrb_, 1)
univ_ib_lrbeta_ch1[list(z), i] = filt.chg(univ_ib_lrb_, 3)
univ_ib_lrbeta_ch2[list(z), i] = filt.chg(univ_ib_lrb_, 5)
univ_ib_qrb_ = filt.qrbeta(univ_ib_pr_, j)
univ_ib_qrbeta_lvl[list(z), i] = univ_ib_qrb_
univ_ib_qrbeta_ch0[list(z), i] = filt.chg(univ_ib_qrb_, 1)
univ_ib_qrbeta_ch1[list(z), i] = filt.chg(univ_ib_qrb_, 3)
univ_ib_qrbeta_ch2[list(z), i] = filt.chg(univ_ib_qrb_, 5)
univ_ib_qrb_ = filt.qrgamma(univ_ib_pr_, j)
univ_ib_qrgamm_lvl[list(z), i] = univ_ib_qrb_
univ_ib_qrgamm_ch0[list(z), i] = filt.chg(univ_ib_qrb_, 1)
univ_ib_qrgamm_ch1[list(z), i] = filt.chg(univ_ib_qrb_, 3)
univ_ib_qrgamm_ch2[list(z), i] = filt.chg(univ_ib_qrb_, 5)
univ_ib_lrbeta_lvl[i] = filt.fill(univ_ib_lrbeta_lvl[i].values)
univ_ib_lrbeta_ch0[i] = filt.fill(univ_ib_lrbeta_ch0[i].values)
univ_ib_lrbeta_ch1[i] = filt.fill(univ_ib_lrbeta_ch1[i].values)
univ_ib_lrbeta_ch2[i] = filt.fill(univ_ib_lrbeta_ch2[i].values)
univ_ib_qrbeta_lvl[i] = filt.fill(univ_ib_qrbeta_lvl[i].values)
univ_ib_qrbeta_ch0[i] = filt.fill(univ_ib_qrbeta_ch0[i].values)
univ_ib_qrbeta_ch1[i] = filt.fill(univ_ib_qrbeta_ch1[i].values)
univ_ib_qrbeta_ch2[i] = filt.fill(univ_ib_qrbeta_ch2[i].values)
univ_ib_qrgamm_lvl[i] = filt.fill(univ_ib_qrgamm_lvl[i].values)
univ_ib_qrgamm_ch0[i] = filt.fill(univ_ib_qrgamm_ch0[i].values)
univ_ib_qrgamm_ch1[i] = filt.fill(univ_ib_qrgamm_ch1[i].values)
univ_ib_qrgamm_ch2[i] = filt.fill(univ_ib_qrgamm_ch2[i].values)
if j < 10:
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_LVL_00" + str(j), univ_ib_lrbeta_lvl
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH0_00" + str(j), univ_ib_lrbeta_ch0
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH1_00" + str(j), univ_ib_lrbeta_ch1
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH2_00" + str(j), univ_ib_lrbeta_ch2
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_LVL_00" + str(j), univ_ib_qrbeta_lvl
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH0_00" + str(j), univ_ib_qrbeta_ch0
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH1_00" + str(j), univ_ib_qrbeta_ch1
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH2_00" + str(j), univ_ib_qrbeta_ch2
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_LVL_00" + str(j), univ_ib_qrgamm_lvl
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH0_00" + str(j), univ_ib_qrgamm_ch0
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH1_00" + str(j), univ_ib_qrgamm_ch1
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH2_00" + str(j), univ_ib_qrgamm_ch2
)
elif j < 100:
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_LVL_0" + str(j), univ_ib_lrbeta_lvl
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH0_0" + str(j), univ_ib_lrbeta_ch0
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH1_0" + str(j), univ_ib_lrbeta_ch1
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH2_0" + str(j), univ_ib_lrbeta_ch2
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_LVL_0" + str(j), univ_ib_qrbeta_lvl
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH0_0" + str(j), univ_ib_qrbeta_ch0
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH1_0" + str(j), univ_ib_qrbeta_ch1
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH2_0" + str(j), univ_ib_qrbeta_ch2
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_LVL_0" + str(j), univ_ib_qrgamm_lvl
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH0_0" + str(j), univ_ib_qrgamm_ch0
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH1_0" + str(j), univ_ib_qrgamm_ch1
)
cr_sig_mr_sg.store(
univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH2_0" + str(j), univ_ib_qrgamm_ch2
)
else:
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_LVL_" + str(j), univ_ib_lrbeta_lvl)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH0_" + str(j), univ_ib_lrbeta_ch0)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH1_" + str(j), univ_ib_lrbeta_ch1)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_LRB_CH2_" + str(j), univ_ib_lrbeta_ch2)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_LVL_" + str(j), univ_ib_qrbeta_lvl)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH0_" + str(j), univ_ib_qrbeta_ch0)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH1_" + str(j), univ_ib_qrbeta_ch1)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRB_CH2_" + str(j), univ_ib_qrbeta_ch2)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_LVL_" + str(j), univ_ib_qrgamm_lvl)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH0_" + str(j), univ_ib_qrgamm_ch0)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH1_" + str(j), univ_ib_qrgamm_ch1)
cr_sig_mr_sg.store(univ_ib_eqidx_ext + _prc_measures_name[k] + "_QRG_CH2_" + str(j), univ_ib_qrgamm_ch2)
return None
_hypsecant_lrb = 0
_laplace_lrb = 0
_cauchy_lrb = 0
_logistic_lrb = 0
_hypsecant_qrb = 0
_laplace_qrb = 0
_cauchy_qrb = 0
_logistic_qrb = 0
_hypsecant_qrg = 0
_laplace_qrg = 0
_cauchy_qrg = 0
_logistic_qrg = 0
for i in tick_cols:
print("Processing %s " % i)
tmp_ = best_fit_distribution(reduce_nonnan(filt.chg(tmp[i].values, 3))[0])
if "_LRB" in i:
if tmp_[0] == "hypsecant":
_hypsecant_lrb += 1
if tmp_[0] == "laplace":
_laplace_lrb += 1
if tmp_[0] == "cauchy":
_cauchy_lrb += 1
if tmp_[0] == "logistic":
_logistic_lrb += 1
print("LRB", _hypsecant_lrb, _laplace_lrb, _cauchy_lrb, _logistic_lrb)
if "_QRB" in i:
if tmp_[0] == "hypsecant":
_hypsecant_qrb += 1
if tmp_[0] == "laplace":
_laplace_qrb += 1
