def __QS_move__(self, idt, **kwargs):
if self._iDT == idt: return 0
self._iDT = idt
TargetNAV = self._TargetTable.readData(
dts=[idt], ids=self._Output["目标ID"],
factor_names=[self.TargetNAV]).iloc[0, :, :].values
self._Output["目标净值"] = np.r_[self._Output["目标净值"], TargetNAV]
StyleNAV = self._StyleTable.readData(
dts=[idt], ids=self._Output["风格ID"],
factor_names=[self.StyleNAV]).iloc[0, :, :].values
self._Output["风格指数净值"] = np.r_[self._Output["风格指数净值"], StyleNAV]
if self.CalcDTs:
if idt not in self.CalcDTs[self._CurCalcInd:]: return 0
self._CurCalcInd = self.CalcDTs[self._CurCalcInd:].index(
idt) + self._CurCalcInd
else:
self._CurCalcInd = self._Model.DateTimeIndex
if self._Output["目标净值"].shape[0] - 1 < self.MinSummaryWindow: return 0
StartInd = int(
max(0, self._Output["目标净值"].shape[0] - 1 - self.SummaryWindow))
X = _calcReturn(self._Output["风格指数净值"][StartInd:, :],
return_type=self.ReturnType)
Y = _calcReturn(self._Output["目标净值"][StartInd:, :],
return_type=self.ReturnType)
nTargetID, nStyleID = len(self._Output["目标ID"]), len(
self._Output["风格ID"])
Rsquared = np.full((nTargetID, ), np.nan)
for i, iID in enumerate(self._Output["目标ID"]):
iMask = ((np.sum(pd.isnull(X), axis=1) == 0) &
(pd.notnull(Y[:, i])))
try:
iBeta = regressByCVX(Y[:, i],
X,
weight=None,
constraints={
"Box": {
"ub": np.ones((nStyleID, )),
"lb": np.zeros((nStyleID, ))
},
"LinearEq": {
"Aeq": np.ones((1, nStyleID)),
"beq": 1
}
})
except:
iBeta = None
if iBeta is None:
self._Output["滚动回归系数"][iID].append(
np.full((nStyleID, ), np.nan))
else:
self._Output["滚动回归系数"][iID].append(iBeta)
Rsquared[i] = 1 - np.nansum(
(Y[:, i][iMask] - np.dot(X[iMask], iBeta))**2) / np.nansum(
(Y[:, i][iMask] - np.nanmean(Y[:, i][iMask]))**2)
self._Output["滚动回归R平方"].append(Rsquared)
self._Output["时点"].append(idt)
return 0
def __QS_end__(self):
if not self._isStarted: return 0
super().__QS_end__()
DTs, StyleIDs, TargetIDs = self._Output.pop("时点"), self._Output.pop(
"风格ID"), self._Output.pop("目标ID")
nTargetID, nStyleID = len(TargetIDs), len(StyleIDs)
X = _calcReturn(self._Output["风格指数净值"], return_type=self.ReturnType)
Y = _calcReturn(self._Output["目标净值"], return_type=self.ReturnType)
self._Output["全样本回归系数"] = np.full(shape=(nStyleID, nTargetID),
fill_value=np.nan)
self._Output["全样本回归R平方"] = np.full(shape=(nTargetID, ),
fill_value=np.nan)
for i, iID in enumerate(TargetIDs):
iMask = ((np.sum(pd.isnull(X), axis=1) == 0) &
(pd.notnull(Y[:, i])))
try:
iBeta = regressByCVX(Y[:, i],
X,
weight=None,
constraints={
"Box": {
"ub": np.ones((nStyleID, )),
"lb": np.zeros((nStyleID, ))
},
"LinearEq": {
"Aeq": np.ones((1, nStyleID)),
"beq": 1
}
})
except:
iBeta = None
if iBeta is not None:
self._Output["全样本回归系数"][:, i] = iBeta
self._Output["全样本回归R平方"][i] = 1 - np.nansum(
(Y[:, i][iMask] - np.dot(X[iMask], iBeta))**2) / np.nansum(
(Y[:, i][iMask] - np.nanmean(Y[:, i][iMask]))**2)
self._Output["滚动回归系数"][iID] = pd.DataFrame(
self._Output["滚动回归系数"][iID], index=DTs, columns=self.StyleIDs)
self._Output["全样本回归系数"] = pd.DataFrame(self._Output["全样本回归系数"],
index=StyleIDs,
columns=TargetIDs)
self._Output["全样本回归R平方"] = pd.DataFrame(self._Output["全样本回归R平方"],
index=TargetIDs,
columns=["全样本回归R平方"])
self._Output["滚动回归R平方"] = pd.DataFrame(self._Output["滚动回归R平方"],
index=DTs,
columns=TargetIDs)
self._Output["目标净值"] = pd.DataFrame(self._Output["目标净值"],
index=self._Model.DateTimeSeries,
columns=self.TargetIDs)
self._Output["风格指数净值"] = pd.DataFrame(self._Output["风格指数净值"],
index=self._Model.DateTimeSeries,
columns=self.StyleIDs)
return 0
