def _refresh_mom_betas(x):
_d_comb = x
comb_prices = _d_comb.retrieve("Close")
tc = comb_prices.tick_cols()
comb_ret = _d_comb.retrieve("Returns")[tc].values.astype("float64")
comb_ret[np.isnan(comb_ret)] = 0
comb_vol = _d_comb.retrieve("Vol_CC_240D")
# get the 12-1 momentum
comb_mom = comb_prices.copy()
for i in tc:
comb_mom[i] = (
crtf.lrma(crtf.ret(comb_mom[i].values, 250) - crtf.ret(comb_mom[i].values, 21), 20) / comb_vol[i].values
)
comb_mom_val = comb_mom[tc].values.astype("float64")
comb_mom_val_st = np.sum(~np.isnan(comb_mom_val), axis=1)
comb_mom_idx = np.where(comb_mom_val_st > 30)[0][0]
dummy = np.empty(len(tc)) * np.nan
for j in range(0, comb_mom_idx):
comb_mom_val[j, :] = dummy
for j in range(comb_mom_idx, comb_mom_val.shape[0]):
comb_mom_val[j, :] = crup.full_norm_rankit(comb_mom_val[j, :])
comb_mom[:, tc] = comb_mom_val
comb_mom_val[np.isnan(comb_mom_val)] = 0
# calculate the momentum factor returns
comb_mom["Close"] = 2 * np.sum(comb_ret * comb_mom_val, axis=1) / np.sum(np.abs(comb_mom_val), axis=1)
comb_mom = comb_mom[["Date", "Close"]]
comb_mom["Close"] = crup.conv_to_price(comb_mom["Close"].values)
# calculate the momentum beta
comb_mom_val = comb_mom["Close"].values
for j in range(60, 300, 60):
comb_mom_beta = comb_prices.copy()
for i in tc:
comb_mom_beta[i] = crtf.beta_cc(comb_mom_beta[i].values, comb_mom_val, j)
_d_comb.store("MOM_Beta_" + str(int(j / 20)) + "m", comb_mom_beta)
return None
tic1 = 'SPY'
tic2 = 'QQQ'
testc = d_comb[6].retrieve('Close')
testo = d_comb[6].retrieve('Open')
testh = d_comb[6].retrieve('High')
testl = d_comb[6].retrieve('Low')
testc1 = testc[tic1].values
testc2 = testc[tic2].values
testo1 = testo[tic1].values
testo2 = testo[tic2].values
testh1 = testh[tic1].values
testh2 = testh[tic2].values
testl1 = testl[tic1].values
testl2 = testl[tic2].values
x1 = crtf.ret(testc1)
x2 = crtf.ret(testc2)
xidx = ~np.isnan(x1) & ~np.isnan(x2)
x1 = x1[xidx]
x2 = x2[xidx]
dt = testc['Date'].values
dt = dt[xidx]
# static
x_act = np.corrcoef(x1, x2)[0, 1]
x12_act = np.ones(x1.shape[0])*x_act
crup.plot_ts(dt, x12_act)
# base case
n = x1.shape[0]
mi = 240
cnt1gk3 = 0 # best for 240
cnt1pb = 0
cnt1rs = 0
cnt1gk = 0
cnt1yz = 0
# prod_ = 1
prod_ = 1.33456825
for i in test_.tick_cols():
# i = test_.tick_cols()[0]
test = test_[i].values
testo = testo_[i].values
testh = testh_[i].values
testl = testl_[i].values
testr = crtf.ret(test)
test1cc = crtf.vol_cc(test, n, zl=True)
test1tr = crtf.vol_ewma(test, n)
test1p = crtf.vol_p(testo, testh, testl, test, n, zl=True)
test1gk3 = crtf.vol_gk3(testo, test, n, zl=True)
test1pb = crtf.vol_pb(testo, testh, testl, test, n, zl=True)
test1rs = crtf.vol_rs(testo, testh, testl, test, n, zl=True)
test1gk = crtf.vol_gk(testo, testh, testl, test, n, zl=True)
test1yz = crtf.vol_yz(testo, testh, testl, test, n, zl=True)
tmp = chk_vol(testr, test1cc*prod_)
if ~np.isnan(tmp):
res1cc += tmp
cnt1cc += 1
tmp = chk_vol(testr, test1tr)
if ~np.isnan(tmp):
import matplotlib.pyplot as plt
d_un = crsf.cr_un
d_pr_cl = crsf.cr_pr_cl
d_comb = crsf.cr_comb
d_sig = crsf.cr_sig
tic1 = 'SPY'
tic2 = 'QQQ'
testc = d_comb[6].retrieve('Close')
testc1 = testc[tic1].values
testc2 = testc[tic2].values
testr1 = crtf.ret(testc1)
testr2 = crtf.ret(testc2)
test_idx = ~np.isnan(testr1) & ~np.isnan(testr2)
testr1 = testr1[test_idx]
testr2 = testr2[test_idx]
n = testr1.shape[0]
test_c1 = np.empty(n)*np.nan
test_c2 = np.empty(n)*np.nan
test_c3 = np.empty(n)*np.nan
def _smoother(x, m):
return crtf.fish_inv_trans(crtf.lrbeta(crtf.cumsum_special(crtf.fish_trans(x)), m, lg=False))
def _generate_ret(_d_comb):
comb_data_close = _d_comb.retrieve('Close')
tick_cols = comb_data_close.tick_cols()
for i in tick_cols:
comb_data_close[i] = crtf.ret(comb_data_close[i].values)
_d_comb.store('Returns', comb_data_close)
