def cal_factor_mrar_all(self, T, r, beg_date, end_date):
date_series = Date().get_normal_date_series(beg_date, end_date, "Q")
result = pd.DataFrame([], index=date_series)
def fun_date(x):
year = int(x[0:4])
month = int(x[4:6])
day = calendar.monthrange(year, month)[1]
date = datetime(year, month, day).strftime("%Y%m%d")
return date
macro_code = "S0059744"
macro_name = "中债国债到期收益率-1年"
macro_data = Macro().get_macro_data(macro_code, None, None)
macro_data.columns = [macro_name]
macro_data['YearMonth'] = macro_data.index.map(lambda x: x[0:6])
macro_data = macro_data.groupby(by=['YearMonth']).mean()[macro_name]
macro_data.index = macro_data.index.map(fun_date)
macro_data = pd.DataFrame(macro_data)
macro_data.columns = [macro_name]
macro_data /= 12.0
fund_data = Fund().get_fund_factor("Repair_Nav_Pct", None, None)
for i in range(len(date_series)):
# 日期
######################################################################################################
report_date = date_series[i]
# 基金池信息
######################################################################################################
fund_code_list = FundPool().get_fund_pool_code(date=report_date, name="基金持仓基准基金池")
fund_code_list3 = FundPool().get_fund_pool_code(date=report_date, name="量化基金")
fund_code_list2 = FundPool().get_fund_pool_code(date="20180630", name="东方红基金")
fund_code_list.extend(fund_code_list2)
fund_code_list.extend(fund_code_list3)
fund_code_list = list(set(fund_code_list))
fund_code_list.sort()
for i_fund in range(len(fund_code_list)):
fund = fund_code_list[i_fund]
print(report_date, fund)
try:
res = self.cal_factor_mrar(fund, T, r, end_date, fund_data, macro_data)
result.loc[report_date, fund] = res
except Exception as e:
result.loc[report_date, fund] = np.nan
result = result.T
file = os.path.join(self.path, "MorningStar_MRAR_" + str(r) + "_" + str(T) + '.csv')
result.to_csv(file)
def cal_fund_holder_return_quarter_backtest_all(self,
T,
beg_date,
end_date,
col="AlphaReturn",
type="Mean"):
date_series = Date().get_normal_date_series(beg_date, end_date, "Q")
result = pd.DataFrame([], index=date_series)
for i in range(len(date_series)):
# 日期
######################################################################################################
report_date = date_series[i]
# 基金池信息
######################################################################################################
fund_code_list = FundPool().get_fund_pool_code(date=report_date,
name="基金持仓基准基金池")
fund_code_list3 = FundPool().get_fund_pool_code(date=report_date,
name="量化基金")
fund_code_list2 = FundPool().get_fund_pool_code(date="20180630",
name="东方红基金")
fund_code_list.extend(fund_code_list2)
fund_code_list.extend(fund_code_list3)
fund_code_list = list(set(fund_code_list))
fund_code_list.sort()
for i_fund in range(len(fund_code_list)):
fund = fund_code_list[i_fund]
print(report_date, fund)
try:
res = self.cal_fund_holder_return_quarter_backtest(
fund, T, report_date, col, type)
result.loc[report_date, fund] = res
except Exception as e:
result.loc[report_date, fund] = np.nan
result = result.T
file = os.path.join(
self.path,
"FundHolderQuarter_" + col + type + "_" + str(T) + '.csv')
result.to_csv(file)
def cal_fund_regression_return_index_backtest_all(self,
T,
beg_date,
end_date,
col="AlphaReturn",
type="Mean"):
date_series = Date().get_trade_date_series(beg_date, end_date, "M")
result = pd.DataFrame([], index=date_series)
# 基金池信息
######################################################################################################
fund_code_list = FundPool().get_fund_pool_code(date="20180630",
name="基金持仓基准基金池")
# fund_code_list3 = FundPool().get_fund_pool_code(date="20180630", name="量化基金")
fund_code_list2 = FundPool().get_fund_pool_code(date="20180630",
name="东方红基金")
# fund_code_list4 = FundPool().get_fund_pool_code(date="20180630", name="指数型基金")
fund_code_list.extend(fund_code_list2)
# fund_code_list.extend(fund_code_list3)
# fund_code_list.extend(fund_code_list4)
fund_code_list = list(set(fund_code_list))
fund_code_list.sort()
for i_fund in range(len(fund_code_list)):
fund = fund_code_list[i_fund]
print(fund)
if i_fund == 0:
result = self.cal_fund_regression_return_index_backtest(
fund, T, date_series, col, type)
else:
result_add = self.cal_fund_regression_return_index_backtest(
fund, T, date_series, col, type)
result = pd.concat([result, result_add], axis=1)
result = result.T
file = os.path.join(
self.path,
"FundRegressionIndex_" + col + type + "_" + str(T) + '.csv')
result.to_csv(file)
