def cal_fund_holder_risk_alpha_return_quarter(self, fund, end_date):
""" 根据季报持仓风格暴露进行收益拆分 """
beg_date = "20040101"
type_list = ['STYLE', 'COUNTRY', 'INDUSTRY']
fund_exposure = FundHolderExposureQuarter(
).get_fund_holder_exposure_quarter_daily(fund, beg_date, end_date)
barra_riskfactor_return = Barra().get_factor_return(
beg_date, end_date, type_list=type_list)
date_series = Date().get_trade_date_series(beg_date, end_date)
fund_pct = FundFactor().get_fund_factor("Repair_Nav_Pct",
fund_pool=[fund],
date_list=date_series)
fund_pct.columns = ["FundReturn"]
if fund_exposure is None:
return None
fund_riskfactor_return = barra_riskfactor_return.mul(fund_exposure)
fund_return = pd.concat([fund_pct, fund_riskfactor_return], axis=1)
fund_return = fund_return.dropna()
barra_factor_name = list(
Barra().get_factor_name(type_list=["STYLE"])["NAME_EN"].values)
fund_return["StyleReturn"] = fund_return[barra_factor_name].sum(axis=1)
barra_factor_name = list(
Barra().get_factor_name(type_list=["INDUSTRY"])["NAME_EN"].values)
fund_return["IndustryReturn"] = fund_return[barra_factor_name].sum(
axis=1)
barra_factor_name = list(
Barra().get_factor_name(type_list=["COUNTRY"])["NAME_EN"].values)
fund_return["CountryReturn"] = fund_return[barra_factor_name].sum(
axis=1)
barra_factor_name = ["StyleReturn", "IndustryReturn", "CountryReturn"]
fund_return["SumReturn"] = fund_return[barra_factor_name].sum(axis=1)
fund_return["AlphaReturn"] = fund_return["FundReturn"] - fund_return[
"SumReturn"]
data_new = fund_return.dropna()
# 合并新数据
####################################################################
out_path = os.path.join(self.data_path_exposure,
'fund_holding_risk_alpha_return_quarter')
out_file = os.path.join(
out_path, 'Fund_Holder_Risk_Alpha_Return_Quarter_' + fund + "_" +
end_date + '.csv')
print(out_file)
if os.path.exists(out_file):
data_old = pd.read_csv(out_file, index_col=[0], encoding='gbk')
data_old.index = data_old.index.map(str)
params = FactorOperate().pandas_add_row(data_old, data_new)
else:
params = data_new
params.to_csv(out_file)
return data_new
def cal_fund_regression_risk_alpha_return_style(self, fund, beg_date,
end_date):
# 参数
####################################################################
exposure_index = FundRegressionExposureStyle(
).get_fund_regression_exposure_style(fund)
if exposure_index is not None:
# 取得数据 指数收益率数据 和 基金涨跌幅数据
####################################################################
barra_name = list(Barra().get_factor_name(['STYLE'
])['NAME_EN'].values)
barra_name.extend(
list(Barra().get_factor_name(["COUNTRY"])['NAME_EN'].values))
barra_return = Barra().get_factor_return(
None, None, type_list=["INDUSTRY", "COUNTRY", "STYLE"])
barra_return = barra_return[barra_name]
barra_return /= 100.0
if fund[len(fund) - 2:] == 'OF':
fund_return = FundFactor().get_fund_factor(
"Repair_Nav_Pct", None, [fund]) / 100.0
fund_return.columns = ["FundReturn"]
else:
fund_return = Index().get_index_factor(fund, attr=["PCT"])
fund_return.columns = ["FundReturn"]
exposure_index = exposure_index.dropna(how="all")
index_exposure_return = barra_return.mul(exposure_index)
index_exposure_return = index_exposure_return.dropna(how="all")
data = pd.concat([fund_return, index_exposure_return], axis=1)
data = data.dropna(how="all")
data = data.loc[index_exposure_return.index, :]
data = data.dropna(subset=["FundReturn"])
data["SumReturn"] = data[barra_name].sum(axis=1, skipna=True)
data["AlphaReturn"] = data["FundReturn"] - data["SumReturn"]
data = data.loc[beg_date:end_date, :]
data["CumFundReturn"] = (data["FundReturn"] + 1.0).cumprod() - 1.0
data["CumAlphaReturn"] = (data["AlphaReturn"] +
1.0).cumprod() - 1.0
data["CumSumReturn"] = (data["SumReturn"] + 1.0).cumprod() - 1.0
# 合并新数据
####################################################################
out_path = os.path.join(self.data_path_exposure,
'fund_regression_risk_alpha_return_style')
out_file = os.path.join(
out_path, 'Fund_Regression_Risk_Alpha_Style_' + fund + '.csv')
if os.path.exists(out_file):
params_old = pd.read_csv(out_file,
index_col=[0],
encoding='gbk')
params_old.index = params_old.index.map(str)
params = FactorOperate().pandas_add_row(params_old, data)
else:
params = data
print(params)
params.to_csv(out_file)
