def _refresh_fast_backtest(x):
_d_bt_bs, _d_bt_ft, _d_bt_fw = x
_d_bt_bs_dt = _d_bt_bs.listdir()
# _d_bt_bs_dt = _d_bt_bs_dt[:-2]
# _d_bt_bs_dt = _d_bt_bs_dt[-2:]
ndt = len(_d_bt_bs_dt)
# ri1_ = np.empty(ndt)*np.nan
# ri2_ = np.empty(ndt)*np.nan
# ri3_ = np.empty(ndt)*np.nan
# ri4_ = np.empty(ndt)*np.nan
for j, i in enumerate(_d_bt_bs_dt):
# j = 0
# i = _d_bt_bs_dt[j]
xi_all = _d_bt_bs.load(i)
xi = np.ascontiguousarray(xi_all["arr_0"]).astype("float64")
ci1 = np.ascontiguousarray(xi_all["arr_1"]).astype("float64")
ci2 = np.ascontiguousarray(xi_all["arr_2"]).astype("float64")
# sci1 = np.linalg.inv(np.ascontiguousarray(xi_all['arr_3']).astype('float64')) # for testing, inverted
# sci2 = np.linalg.inv(np.ascontiguousarray(xi_all['arr_4']).astype('float64')) # for testing, inverted
tci = xi_all["arr_5"]
n = xi.shape[1]
yi = _d_bt_ft.load(i)
# unpack xi
fvol, fadv = xi[0, :], xi[1, :]
fpc0_dy, fpc0_st = xi[2, :], xi[3, :]
fpc1_dy, fpc1_st = xi[4, :], xi[5, :]
fpc2_dy, fpc2_st = xi[6, :], xi[7, :]
fpc3_dy, fpc3_st = xi[8, :], xi[9, :]
fpc4_dy, fpc4_st = xi[10, :], xi[11, :]
fpc5_dy, fpc5_st = xi[12, :], xi[13, :]
fsp03, fsp06, fsp09, fsp12 = xi[14, :], xi[15, :], xi[16, :], xi[17, :]
fmd03, fmd06, fmd09, fmd12 = xi[18, :], xi[19, :], xi[20, :], xi[21, :]
fmom03, fmom06, fmom09, fmom12 = xi[22, :], xi[23, :], xi[24, :], xi[25, :]
flvl03, flvl06, flvl09, flvl12 = xi[26, :], xi[27, :], xi[28, :], xi[29, :]
fsig = xi[30, :]
fb1 = xi[31, :]
fb2 = xi[32, :]
# create the portfolio
fvol_ = np.diag(fvol) * 16
fcov2_ = np.linalg.inv(np.dot(fvol_, np.dot((ci1 + np.identity(n)) / 2, fvol_))) # try ci2 instead of ci1
# liquidity measures
# fliq_ = (cap/(liqg*fadv))**2
# fliq_ = np.diag(fliq_)
ei = np.ones(n)
tmp = 0.5 * np.dot(crts.neut_corr_mat(fcov2_, np.vstack((fpc0_st, fpc1_st, fpc4_st, ei, fpc5_st))), fsig)
_d_bt_fw.save(i, tci, tmp)
def test_returns():
r_all = []
_d_bt_bs_dt_com = None
for k in range(0, 8):
_d_bt_bs = d_bt_bs[k]
_d_bt_ft = d_bt_ft[k]
_d_bt_bs_dt = _d_bt_bs.listdir()
_d_bt_bs_dt = _d_bt_bs_dt[:-2]
# ndt = len(_d_bt_bs_dt)
if k == 0:
_d_bt_bs_dt_com = _d_bt_bs_dt
else:
_d_bt_bs_dt_com = list(set(_d_bt_bs_dt_com).intersection(set(_d_bt_bs_dt)))
_d_bt_bs_dt_com = sorted(_d_bt_bs_dt_com)
ndt = len(_d_bt_bs_dt_com)
for k in range(0, 8):
_d_bt_bs = d_bt_bs[k]
_d_bt_ft = d_bt_ft[k]
ri1_ = np.empty(ndt) * np.nan
for j, i in enumerate(_d_bt_bs_dt_com):
xi_all = _d_bt_bs.load(i)
xi = np.ascontiguousarray(xi_all["arr_0"]).astype("float64")
ci1 = np.ascontiguousarray(xi_all["arr_1"]).astype("float64")
tci = xi_all["arr_5"]
n = xi.shape[1]
yi = _d_bt_ft.load(i)
# unpack xi
fvol, fadv = xi[0, :], xi[1, :]
fpc0_dy, fpc0_st = xi[2, :], xi[3, :]
fpc1_dy, fpc1_st = xi[4, :], xi[5, :]
fpc4_dy, fpc4_st = xi[10, :], xi[11, :]
fpc5_dy, fpc5_st = xi[12, :], xi[13, :]
fsig = xi[30, :]
# create the portfolio
fvol_ = np.diag(fvol) * 16
fcov2_ = np.linalg.inv(np.dot(fvol_, np.dot((ci1 + np.identity(n)) / 2, fvol_))) # try ci2 instead of ci1
ei = np.ones(n)
tmp = 0.5 * np.dot(crts.neut_corr_mat(fcov2_, np.vstack((fpc0_st, fpc1_st, fpc4_st, ei, fpc5_st))), fsig)
ri1_[j] = np.dot(yi, tmp)
r_all.append(ri1_)
clus_wgt = np.array([0.15, 0.075, 0.21, 0.075, 0.15, 0.06, 0.14, 0.14])
for i in range(0, 8):
if i == 0:
r_all_ = r_all[0] * clus_wgt[0]
else:
r_all_ += r_all[i] * clus_wgt[i]
backtest_ret = cruf.DataFrame({"Date": crup.str_to_pd_dt(_d_bt_bs_dt_com), "Returns": r_all_})
# save returns
d_un.store("Backtest_Returns", backtest_ret)