def __QS_move__(self, idt, **kwargs):
IDs = self.DSs[args["数据源"]].getID(idt=self.CurDate, is_filtered=True)
self._Output["优化器"].FactorData = self.DSs[args["数据源"]].getDateTimeData(
idt=self.CurDate,
ids=IDs,
factor_names=[args["测试因子"]] + args["中性因子"])
self._Output["优化器"].CovMatrix = args["风险数据源"].getDateCov(self.CurDate,
drop_na=True)
self._Output["优化器"].TargetIDs = IDs
self._Output["优化器"].preprocessData()
Error, Portfolio = self._Output["优化器"].solve()
Signal = Signal[Signal > 0]
Signal = Signal / Signal.sum()
Signal.sort(ascending=False)
Signal = Signal[Signal.cumsum() > 0.9999]
Signal = Signal / Signal.sum()
Signal = dict(Signal)
RetRate = self.DSs[args["数据源"]].readData(dts=[idt],
ids=None,
factor_names=[args["收益率因子"]])
RetRate = RetRate.loc[self.CurDate]
self._Output["投资组合"].append(dict(Portfolio))
if self.CurInd >= 1:
self._Output["换手率"].append(
calcTurnover(self._Output["投资组合"][-2],
self._Output["投资组合"][-1]))
self._Output["收益率"].append(
calcPortfolioReturn(self._Output["投资组合"][-2], RetRate))
else:
self._Output["换手率"].append(1)
self._Output["收益率"].append(0)
return self._Output
def __QS_move__(self, idt, **kwargs):
if self._iDT == idt: return 0
self._iDT = idt
Price = self._FactorTable.readData(
dts=[idt],
ids=self._FactorTable.getID(ifactor_name=self.PriceFactor),
factor_names=[self.PriceFactor]).iloc[0, 0, :]
for i in range(self.GroupNum):
if len(self._Output["QP_P_CurPos"][i]) == 0:
self._Output["净值"][i].append(self._Output["净值"][i][-1])
else:
iWealth = (self._Output["QP_P_CurPos"][i] * Price).sum()
if pd.notnull(iWealth):
self._Output["净值"][i].append(iWealth)
else:
self._Output["净值"][i].append(0)
self._Output["换手率"][i].append(0)
if len(self._Output["QP_P_MarketPos"]) == 0:
self._Output["市场净值"].append(self._Output["市场净值"][-1])
else:
self._Output["市场净值"].append(
(self._Output["QP_P_MarketPos"] * Price).sum())
if self.CalcDTs:
if idt not in self.CalcDTs[self._CurCalcInd:]: return 0
self._CurCalcInd = self.CalcDTs[self._CurCalcInd:].index(
idt) + self._CurCalcInd
IDs = self._FactorTable.getFilteredID(idt=idt,
id_filter_str=self.IDFilter)
FactorData = self._FactorTable.readData(dts=[idt],
ids=IDs,
factor_names=[self.TestFactor
]).iloc[0, 0, :]
FactorData = FactorData[pd.notnull(FactorData)
& pd.notnull(Price[IDs])].copy()
nID = FactorData.shape[0]
if self.Perturbation and (nID > 0):
FactorData = FactorData.astype("float")
MinDiff = np.min(np.abs(np.diff(FactorData.unique())))
FactorData += np.random.rand(nID) * MinDiff * 0.01
FactorData = FactorData.sort_values(
ascending=(self.FactorOrder == "升序"), inplace=False)
IDs = list(FactorData.index)
if self.WeightFactor != "等权":
WeightData = self._FactorTable.readData(
dts=[idt],
ids=self._FactorTable.getID(ifactor_name=self.WeightFactor),
factor_names=[self.WeightFactor]).iloc[0, 0, :]
else:
WeightData = pd.Series(1.0, index=self._FactorTable.getID())
if self.IndustryFactor == "无":
nSubID = distributeEqual(nID,
self.GroupNum,
remainder_pos="middle")
for i in range(self.GroupNum):
iWealth = self._Output["净值"][i][-1]
iSubIDs = IDs[sum(nSubID[:i]):sum(nSubID[:i + 1])]
iPortfolio = WeightData[iSubIDs]
iPortfolio = iPortfolio / iPortfolio.sum()
iPortfolio = iPortfolio[pd.notnull(iPortfolio)]
self._Output["投资组合"][i].append(iPortfolio)
self._Output["QP_P_CurPos"][i] = iPortfolio * iWealth / Price
self._Output["QP_P_CurPos"][i] = self._Output["QP_P_CurPos"][
i][pd.notnull(self._Output["QP_P_CurPos"][i])]
nPortfolio = len(self._Output["投资组合"][i])
if nPortfolio > 1:
self._Output["换手率"][i][-1] = calcTurnover(
self._Output["投资组合"][i][-2],
self._Output["投资组合"][i][-1])
elif nPortfolio == 1:
self._Output["换手率"][i][-1] = 1
else:
Portfolio = [{} for i in range(self.GroupNum)]
IndustryData = self._FactorTable.readData(dts=[idt],
ids=IDs,
factor_names=[
self.IndustryFactor
]).iloc[0, 0, :]
AllIndustry = IndustryData.unique()
for iIndustry in AllIndustry:
iMask = (IndustryData == iIndustry)
iIDIndex = IndustryData[iMask].index
iIDDistribution = distributeEqual(iIDIndex.shape[0],
self.GroupNum,
remainder_pos="middle")
for j in range(self.GroupNum):
jSubID = iIDIndex[sum(iIDDistribution[:j]
):sum(iIDDistribution[:j + 1])]
Portfolio[j].update(
{kID: WeightData[kID]
for kID in jSubID})
for i in range(self.GroupNum):
iWealth = self._Output["净值"][i][-1]
iPortfolio = pd.Series(Portfolio[i])
iPortfolio = iPortfolio / iPortfolio.sum()
iPortfolio = iPortfolio[pd.notnull(iPortfolio)]
self._Output["投资组合"][i].append(iPortfolio)
self._Output["QP_P_CurPos"][i] = iPortfolio * iWealth / Price
self._Output["QP_P_CurPos"][i] = self._Output["QP_P_CurPos"][
i][pd.notnull(self._Output["QP_P_CurPos"][i])]
nPortfolio = len(self._Output["投资组合"][i])
if nPortfolio > 1:
self._Output["换手率"][i][-1] = calcTurnover(
self._Output["投资组合"][i][-2],
self._Output["投资组合"][i][-1])
elif nPortfolio == 1:
self._Output["换手率"][i][-1] = 1
if self.MarketIDFilter:
IDs = self._FactorTable.getFilteredID(
idt=idt, id_filter_str=self.MarketIDFilter)
WeightData = WeightData[IDs]
Price = Price[IDs]
WeightData = WeightData[pd.notnull(WeightData) & pd.notnull(Price)]
WeightData = WeightData / WeightData.sum()
self._Output[
"QP_P_MarketPos"] = WeightData * self._Output["市场净值"][-1] / Price
self._Output["QP_P_MarketPos"] = self._Output["QP_P_MarketPos"][
pd.notnull(self._Output["QP_P_MarketPos"])]
self._Output["调仓日"].append(idt)
return 0
def __QS_move__(self, idt, **kwargs):
Price = self._FactorTable.readData(
dates=[idt], ids=None, ifactor_name=self._SysArgs["价格因子"]).iloc[0]
for i in range(self._SysArgs["分组数"]):
if len(self._Output["QP_P_CurPos"][i]) == 0:
self._Output["净值"][i].append(self._Output["净值"][i][-1])
else:
iWealth = (self._Output["QP_P_CurPos"][i] * Price).sum()
if pd.notnull(iWealth):
self._Output["净值"][i].append(iWealth)
else:
self._Output["净值"][i].append(0)
self._Output["换手率"][i].append(0)
if len(self._Output["QP_P_MarketPos"]) == 0:
self._Output["市场净值"].append(self._Output["市场净值"][-1])
else:
self._Output["市场净值"].append(
(self._Output["QP_P_MarketPos"] * Price).sum())
if (self._CalcDateTimes is not None) and (idt
not in self._CalcDateTimes):
return 0
IDs = self._FactorTable.getID(idt=idt,
is_filtered=True,
id_filter_str=self._SysArgs["筛选条件"])
FactorData = self._FactorTable.readData(
dts=[idt], ids=IDs,
ifactor_name=self._SysArgs["测试因子"]).iloc[0].copy()
FactorData = FactorData[pd.notnull(Price[IDs])]
nID = FactorData.shape[0]
if (self._SysArgs["随机微扰"]) and (nID > 0):
FactorData = FactorData.astype("float")
MinDiff = np.min(np.abs(np.diff(FactorData.unique())))
FactorData = FactorData + np.random.rand(nID) * MinDiff * 0.01
if self._SysArgs["划分方式"] == "定量":
if (self._SysArgs["排序方向"] == "升序"):
FactorData.sort(ascending=True)
else:
FactorData.sort(ascending=False)
if self._SysArgs["缺失处理"] == "排在最前":
FactorData = FactorData[pd.isnull(FactorData)].append(
FactorData[pd.notnull(FactorData)])
elif self._SysArgs["缺失处理"] == "丢弃":
FactorData = FactorData[pd.notnull(FactorData)]
elif self._SysArgs["划分方式"] == "定比":
if (self._SysArgs["排序方向"] == "降序"):
FactorData = -FactorData
# 构建投资组合
if self._SysArgs["权重因子"] != "等权":
WeightData = self._FactorTable.readData(
dts=[idt], ids=IDs, ifactor_name=self._SysArgs["权重因子"]).iloc[0]
else:
WeightData = pd.Series(1.0, index=IDs)
IDs = []
for j in range(self._SysArgs["分组数"]):
if self._SysArgs["划分方式"] == "定比":
if (j > 0) and (j < self._SysArgs["分组数"] - 1):
jIDs = list(
FactorData[(FactorData >= FactorData.quantile(
self._SysArgs["分类节点"][j - 1]))
& (FactorData < FactorData.quantile(
self._SysArgs["分类节点"][j]))].index)
elif j == 0:
jIDs = list(FactorData[FactorData < FactorData.quantile(
self._SysArgs["分类节点"][j])].index)
if self._SysArgs["缺失处理"] == "排在最前":
jIDs += list(FactorData[pd.isnull(FactorData)].index)
else:
jIDs = list(FactorData[FactorData >= FactorData.quantile(
self._SysArgs["分类节点"][-1])].index)
if self._SysArgs["缺失处理"] == "排在最后":
jIDs += list(FactorData[pd.isnull(FactorData)].index)
elif self._SysArgs["划分方式"] == "定界":
if (j > 0) and (j < self._SysArgs["分组数"] - 1):
jIDs = list(FactorData[
(FactorData >= self._SysArgs["分类节点"][j - 1])
& (FactorData < self._SysArgs["分类节点"][j])].index)
elif j == 0:
jIDs = list(FactorData[
FactorData < self._SysArgs["分类节点"][j]].index)
if self._SysArgs["缺失处理"] == "排在最前":
jIDs += list(FactorData[pd.isnull(FactorData)].index)
else:
jIDs = list(FactorData[
FactorData >= self._SysArgs["分类节点"][-1]].index)
if self._SysArgs["缺失处理"] == "排在最后":
jIDs += list(FactorData[pd.isnull(FactorData)].index)
else:
if (j > 0) and (j < self._SysArgs["分组数"] - 1):
jIDs = list(FactorData.iloc[self._SysArgs["分类节点"][j -
1]:self.
_SysArgs["分类节点"][j]].index)
elif j == 0:
jIDs = list(
FactorData.iloc[:self._SysArgs["分类节点"][j]].index)
else:
jIDs = list(
FactorData.iloc[self._SysArgs["分类节点"][-1]:].index)
jWealth = self._Output["净值"][j][-1]
jPortfolio = WeightData[jIDs]
jPortfolio = jPortfolio[pd.notnull(jPortfolio)]
jPortfolio = jPortfolio / jPortfolio.sum()
self._Output["投资组合"][j].append(dict(jPortfolio))
self._Output["QP_P_CurPos"][j] = jPortfolio * jWealth / Price
self._Output["QP_P_CurPos"][j] = self._Output["QP_P_CurPos"][j][
pd.notnull(self._Output["QP_P_CurPos"][j])]
nPortfolio = len(self._Output["投资组合"][j])
if nPortfolio > 1:
self._Output["换手率"][j][-1] = calcTurnover(
self._Output["投资组合"][j][-2], self._Output["投资组合"][j][-1])
elif nPortfolio == 1:
self._Output["换手率"][j][-1] = 1
IDs += jIDs
if self._SysArgs["市场组合"] is not None:
IDs = self._FactorTable.getID(idt=idt,
is_filtered=True,
id_filter_str=self._SysArgs["市场组合"])
WeightData = WeightData[IDs]
Price = Price[IDs]
WeightData = WeightData[pd.notnull(WeightData) & pd.notnull(Price)]
WeightData = WeightData / WeightData.sum()
#self._Output["市场组合"].append(dict(WeightData))
self._Output["调仓日"].append(idt)
self._Output[
"QP_P_MarketPos"] = WeightData * self._Output["市场净值"][-1] / Price
self._Output["QP_P_MarketPos"] = self._Output["QP_P_MarketPos"][
pd.notnull(self._Output["QP_P_MarketPos"])]
return 0