def cal_mfc_holding_alpha_exposure_period(self, fund_name,
factor_name_list, beg_date,
end_date):
""" 计算某只基金在一段时间内暴露 """
date_series_daily = Date().get_trade_date_series(beg_date, end_date)
new_data = pd.DataFrame()
for i_date in range(len(date_series_daily)):
date = date_series_daily[i_date]
res = self.cal_mfc_holding_alpha_exposure_date(
fund_name, factor_name_list, date)
new_data = pd.concat([new_data, res], axis=0)
out_file = os.path.join(self.exposure_data_path,
"MfcAlphaExposure_" + fund_name + '.csv')
if os.path.exists(out_file):
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data.index = data.index.map(str)
data = FactorOperate().pandas_add_row(data, new_data)
else:
data = new_data
data = data[factor_name_list]
data.to_csv(out_file)
def load_macro_data_wind(self,
macro_code="M0000545",
beg_date="19900101",
end_date=datetime.today().strftime("%Y%m%d")):
""" 下载宏观数据 """
from WindPy import w
w.start()
beg_date = Date().change_to_str(beg_date)
end_date = Date().change_to_str(end_date)
# 下载数据
##############################################################################
data = w.edb(macro_code, beg_date, end_date, "Fill=Previous")
new_data = pd.DataFrame(data.Data,
columns=data.Times,
index=data.Codes).T
new_data = new_data.dropna()
new_data.index = new_data.index.map(lambda x: x.strftime('%Y%m%d'))
print(" Loading Macro Data %s From %s To %s " %
(macro_code, beg_date, end_date))
out_file = os.path.join(self.data_path, macro_code + '.csv')
if os.path.exists(out_file):
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data.index = data.index.map(str)
data = FactorOperate().pandas_add_row(data, new_data)
else:
print(" File No Exist ", macro_code)
data = new_data
data = data.dropna(how='all')
data.to_csv(out_file)
def cal_mfc_holding_barra_exposure_period(self, fund_name, beg_date,
end_date):
""" 计算某只基金在一段时间内暴露 """
date_series_daily = Date().get_trade_date_series(beg_date, end_date)
new_data = pd.DataFrame()
for i_date in range(len(date_series_daily)):
date = date_series_daily[i_date]
res = self.cal_mfc_holding_barra_exposure_date(fund_name, date)
new_data = pd.concat([new_data, res], axis=0)
out_file = os.path.join(self.exposure_data_path,
"MfcRiskExposure_" + fund_name + '.csv')
if os.path.exists(out_file):
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data.index = data.index.map(str)
data = FactorOperate().pandas_add_row(data, new_data)
else:
data = new_data
type_list = ["STYLE", "COUNTRY", "INDUSTRY"]
name = Barra().get_factor_name(type_list=type_list)
data = data[list(name.NAME_EN.values)]
data.to_csv(out_file)
def cal_index_exposure(self,
index_code="000300.SH",
beg_date="20031231",
end_date=datetime.today().strftime("%Y%m%d"),
period="D"):
""" 计算一段时间的BARRA暴露 """
date_series_daily = Date().get_trade_date_series(beg_date, end_date, period=period)
for i_date in range(len(date_series_daily)):
date = date_series_daily[i_date]
res = self.cal_index_exposure_date(index_code, date)
if i_date == 0:
new_data = res
else:
new_data = pd.concat([new_data, res], axis=0)
out_file = os.path.join(self.data_path_exposure, "Index_Barra_Exposure_" + index_code + '.csv')
if os.path.exists(out_file):
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data.index = data.index.map(str)
data = FactorOperate().pandas_add_row(data, new_data)
else:
data = new_data
data.to_csv(out_file)
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 load_index_factor(self,
index_code="000300.SH",
beg_date=None,
end_date=datetime.today().strftime("%Y%m%d"),
primary=False):
""" 下载一个指数 最近的Factor """
from WindPy import w
w.start()
out_file = os.path.join(self.data_data_factor, index_code + '.csv')
if beg_date is None and os.path.exists(out_file):
beg_date = Date().get_trade_date_offset(end_date, -20)
if beg_date is None and not os.path.exists(out_file):
try:
base_data = w.wsd(index_code, "basedate")
beg_date = base_data.Data[0][0].strftime("%Y%m%d")
except Exception as e:
beg_date = '19991231'
beg_date = Date().change_to_str(beg_date)
end_date = Date().change_to_str(end_date)
print(beg_date, end_date, index_code, primary)
# 下载数据
##############################################################################
if primary:
index_data = w.wsd(index_code, "close,pe_ttm,pb_lf", beg_date,
end_date, "Fill=Previous")
else:
index_data = w.wsd(index_code, "close", beg_date, end_date,
"Fill=Previous")
new_data = pd.DataFrame(index_data.Data,
index=index_data.Fields,
columns=index_data.Times).T
new_data.index = new_data.index.map(lambda x: x.strftime('%Y%m%d'))
print(new_data)
try:
new_data['PCT'] = new_data['CLOSE'].pct_change()
print(" Loading Index Factor ", index_code)
if os.path.exists(out_file):
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data.index = data.index.map(str)
data = FactorOperate().pandas_add_row(data, new_data)
else:
print(" File No Exist ", index_code)
data = new_data
data = data.dropna(how='all')
data.to_csv(out_file)
except Exception as e:
print(e)
print(" Loading Index Factor Error", index_code)
def get_fund_factor(self, factor_name, date_list=None, fund_pool=None):
""" 得到基金因子数据 """
out_file = os.path.join(self.data_path_factor, factor_name + '.csv')
data = pd.read_csv(out_file, index_col=[0], encoding='gbk')
data = FactorOperate().drop_duplicated(data)
data.index = data.index.map(str)
data.columns = data.columns.map(str)
if date_list is not None:
data = data.ix[date_list, :]
if fund_pool is not None:
data = data.ix[:, fund_pool]
return data
def write_factor_h5(self, data, factor_name, path=None, data_type='f'):
""" 写入 H5 Stock Factor文件 """
if path is None:
path = self.get_h5_path(type='my_alpha')
file = os.path.join(path, '%s.h5' % factor_name)
# 检查数据结构
#############################################################################
# index --> code columns --> date
data.index = data.index.map(str)
data.columns = data.columns.map(str)
if data.columns[0][0] not in ["1", "2"]:
print(" Data Columns in not Date ")
data = data.T.dropna(how='all').T
# 写入H5数据
#############################################################################
if not os.path.exists(file):
print(" The File %s Not Exist, Saving ... " % file)
HdfMfc().write_hdf_factor(file, data)
else:
old_data = self.read_factor_h5(factor_name, path, data_type)
old_data = old_data.T
new_data = data.T
save_data = FactorOperate().pandas_add_row(old_data=old_data,
new_data=new_data)
save_data = save_data.T
save_data = save_data.T.dropna(how='all').T
print(" The File %s Exist, Saving... " % file)
HdfMfc().write_hdf_factor(file, save_data, type=data_type)
def replace_suspension_with_nan(self, data):
factor = data.copy()
status = StockFactorData().read_factor_h5("TradingStatus")
factor, status = FactorOperate().make_same_index_columns(
[factor, status])
statusif = status.applymap(lambda x: x == 1.0)
factor[statusif] = np.nan
return factor
def load_ipo_data(self, beg_date):
""" 下载IPO数据 上市日期 发行价 中签率 申购上限 等等"""
data = self.get_new_stock_list(beg_date)
code_str = ','.join(data.index.values)
data = w.wss(code_str,
"sec_name,ipo_date,ipo_price,ipo_cashratio,ipo_lotteryrate_abc,ipo_otc_cash_pct,ipo_op_uplimit",
"instituteType=1")
data_pd = pd.DataFrame(data.Data, index=data.Fields, columns=data.Codes).T
data_pd["IPO_DATE"] = data_pd["IPO_DATE"].map(lambda x: x.strftime('%Y-%m-%d'))
data_pd.columns = ['股票名称', '上市日期', '发行价格', '网上中签率(%)',
'网下A类中签率(%)', '网下总计中签率(%)', '申购上限数量(万股)']
data_pd['申购上限金额(万元)'] = data_pd["申购上限数量(万股)"] * data_pd['发行价格']
data_pd = data_pd.dropna()
data_pd = data_pd.sort_values(by=['上市日期'], ascending=True)
for i_code in range(0, len(data_pd)):
code = data_pd.index.values[i_code]
ipo_date = data_pd.ix[i_code, '上市日期']
open_date, open_pct, open_price = self.get_open_date_pct(code, ipo_date)
data_pd.ix[i_code, '开板日期'] = open_date
data_pd.ix[i_code, '开板价格'] = open_price
data_pd.ix[i_code, '开板收益'] = open_pct
print(data_pd)
file = os.path.join(self.data_path, 'ipo_data.xlsx')
data = pd.read_excel(file, index_col=[1])
data = data.T.dropna(how='all').T
concat_data = FactorOperate().pandas_add_row(data, data_pd)
concat_data = concat_data.sort_values(by=['上市日期'], ascending=True)
excel = WriteExcel(file)
worksheet = excel.add_worksheet("新股检测")
excel.write_pandas(concat_data, worksheet, begin_row_number=0, begin_col_number=1,
num_format_pd=None, color="orange", fillna=True)
excel.close()
def fillna_with_mad_market(self, data):
factor = data.copy()
factor_val = factor.values
status = StockFactorData().read_factor_h5("TradingStatus")
factor, status = FactorOperate().make_same_index_columns(
[factor, status])
if_list = status.applymap(lambda x: x in [0.0, 1.0])
if_nan = factor.isnull()
mask_val = (if_list & if_nan).values
md_val = factor.median(axis=0).values
md_remat_val = np.tile(np.vstack(md_val), (1, factor.shape[0])).T
factor_fill_mad_val = np.where(mask_val, md_remat_val, factor_val)
factor_fill_mad_pandas = pd.DataFrame(factor_fill_mad_val,
index=factor.index,
columns=factor.columns)
return factor_fill_mad_pandas
def load_fund_factor(self, factor_name, beg_date, end_date):
""" 财汇数据库下载基金因子数据（增量更新） """
beg_date = Date().change_to_str(beg_date)
end_date = Date().change_to_str(end_date)
print("Loading Fund Factor %s From %s To %s" %
(factor_name, beg_date, end_date))
new_data = FinDb().load_raw_data_filter_period(factor_name, beg_date,
end_date)
fund_info_data = FundStatic().get_findb_fund_info()
table_name, field_en, filter_field, field_ch, val_name = FinDb(
).get_load_findb_param(factor_name)
new_data = pd.merge(new_data, fund_info_data, on="证券内码", how='inner')
new_data = pd.DataFrame(
new_data[val_name].values,
index=[list(new_data['基金代码'].values),
list(new_data['日期'].values)])
new_data = new_data.sort_index()
new_data = new_data[~new_data.index.duplicated()]
new_data = new_data.unstack()
new_data.columns = new_data.columns.droplevel(level=0)
new_data = new_data.T
new_data = new_data.dropna(how='all')
new_data.index = new_data.index.map(str)
out_file = os.path.join(self.data_path_factor, factor_name + '.csv')
if os.path.exists(out_file):
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data.index = data.index.map(str)
data = FactorOperate().pandas_add_row(data, new_data)
else:
print(" File No Exist ", factor_name)
data = new_data
data = data.dropna(how='all')
data.to_csv(out_file)
def load_mfc_public_fund_nav(self, beg_date=None, end_date=None):
""" wind下载多有泰达公募基金的复权净值 增量更新（默认更新最近两个月） """
if end_date is None:
end_date = Date().change_to_str(datetime.today())
if beg_date is None:
beg_date = Date().get_trade_date_offset(end_date, -40)
from WindPy import w
w.start()
data = MfcGetData().get_mfc_fund_info()
data = data[data.Type == "公募"]
for i_code in range(len(data)):
fund_code = data.Code.values[i_code]
nav_data = w.wsd(fund_code, "nav,NAV_adj,NAV_acc,NAV_adj_return1",
beg_date, end_date, "")
nav_data = pd.DataFrame(nav_data.Data,
index=nav_data.Fields,
columns=nav_data.Times).T
nav_data.index = nav_data.index.map(lambda x: x.strftime('%Y%m%d'))
new_data = nav_data.dropna(subset=['NAV_ADJ'])
print(" Load Mfc Public Fund %s Nav " % fund_code)
# 合并存储数据
file = os.path.join(self.data_path, "nav\public_fund",
fund_code + "_Nav.csv")
if os.path.exists(file):
old_data = pd.read_csv(file, index_col=[0], encoding='gbk')
old_data.index = old_data.index.map(str)
nav_data = FactorOperate().pandas_add_row(old_data, new_data)
else:
nav_data = new_data
nav_data.to_csv(file)
def standardization_free_mv(data, free_mv):
"""
均值为市值加权均值 \ 标准差为普通标准差
Barra风险模型做法 注意流通市值要用昨天的 而非今天的
"""
if type(data) == pd.Series:
data_ser = data.copy()
concat_data = pd.concat([data_ser, free_mv], axis=1)
concat_data.columns = ['Factor', 'Mv']
concat_data = concat_data.dropna()
concat_data['Mv'] = concat_data['Mv'] / concat_data['Mv'].sum()
mean_weight_free_mv = (concat_data['Mv'] *
concat_data['Factor']).sum()
std = data_ser.std()
normal_series = (data_ser - mean_weight_free_mv) / std
return normal_series
elif type(data) == pd.DataFrame:
factor = data.copy()
free_mv = free_mv.T.shift(1).T
[factor, free_mv
] = FactorOperate().make_same_index_columns([factor, free_mv])
free_mv = free_mv / free_mv.sum()
free_mv = free_mv.fillna(0.0)
free_mv = free_mv.T
factor = factor.T
mean_weight_free_mv = factor.mul(free_mv).sum(axis=1)
std = factor.std(axis=1)
factor = factor.sub(mean_weight_free_mv, axis='index')
factor = factor.div(std, axis="index")
factor = factor.T
return factor
else:
print(" Type of Data can not be remove extreme value ")
return None
def save_file_excel(self, new_data, type, name, num_format):
"""
将一段时间内的每日拆分写入文件（增量写入）
将一段时间内的每日暴露写入文件（增量写入）
"""
if len(new_data) > 0:
save_path = os.path.join(self.data_path, self.fund_name, "每日汇总")
if not os.path.exists(save_path):
os.makedirs(save_path)
file = '%s_%s_%s.xlsx' % (self.fund_name, type, name)
file_name = os.path.join(save_path, file)
print("写入", file_name)
if os.path.exists(file_name):
old_data = pd.read_excel(file_name, index_col=[0])
old_data.index = old_data.index.map(str)
data = FactorOperate().pandas_add_row(old_data, new_data)
else:
data = new_data
num_format_pd = pd.DataFrame([],
columns=data.columns,
index=['format'])
num_format_pd.loc['format', :] = num_format
excel = WriteExcel(file_name)
worksheet = excel.add_worksheet(self.fund_name)
excel.write_pandas(data,
worksheet,
begin_row_number=0,
begin_col_number=0,
num_format_pd=num_format_pd,
color="red",
fillna=True)
excel.close()
else:
save_path = os.path.join(self.data_path, self.fund_name, "每日汇总")
file = '%s_%s_%s.xlsx' % (self.fund_name, type, name)
file_name = os.path.join(save_path, file)
print("写入数据为0", file_name)
def cal_fund_holder_exposure_halfyear(self, fund_code, beg_date, end_date):
""" 计算单个基金的半年持仓暴露（注意计算的是非满仓暴露） """
# fund_code, beg_date, end_date = "000001.OF", "20170101", "20190101"
type_list = ['COUNTRY', 'STYLE', 'INDUSTRY']
barra_name = list(Barra().get_factor_name(type_list)['NAME_EN'].values)
out_file = os.path.join(
self.halfyear_exposure_path,
'Fund_Holder_Exposure_HalfYear_%s.csv' % fund_code)
if not os.path.exists(out_file):
beg_date = "20040101"
date_series = Date().get_normal_date_series(beg_date,
end_date,
period='S')
fund_holding = FundHolder().get_fund_stock_weight_halfyear(fund_code)
if fund_holding is not None:
date_series = list(set(date_series) & set(fund_holding.columns))
date_series.sort()
print(date_series)
else:
return None
if len(date_series) > 0:
for i_date in range(0, len(date_series)):
date = date_series[i_date]
report_date = Date().get_normal_date_month_end_day(date)
trade_date = Date().get_trade_date_month_end_day(date)
print("Calculate HalfYear Holder Exposure %s %s" %
(fund_code, report_date))
barra_exposure = Barra().get_factor_exposure_date(
trade_date, type_list)
fund_holding_date = FundHolder(
).get_fund_stock_weight_halfyear(fund_code)
if (barra_exposure is None) or (len(fund_holding_date) == 0):
exposure_add = pd.DataFrame([],
columns=barra_name,
index=[report_date])
else:
fund_holding_date = pd.DataFrame(fund_holding[report_date])
fund_holding_date = fund_holding_date.dropna()
fund_holding_date = fund_holding_date.sort_values(
by=[report_date], ascending=False)
fund_holding_date.columns = ["Weight"]
fund_holding_date /= 100.0
data = pd.concat([fund_holding_date, barra_exposure],
axis=1)
data = data.dropna()
if (len(data) == 0) or (data is None):
exposure_add = pd.DataFrame([],
columns=barra_name,
index=[report_date])
else:
exposure_add = pd.DataFrame([],
columns=barra_name,
index=[report_date])
for i_factor in range(len(barra_name)):
factor_name = barra_name[i_factor]
data_weight = data[['Weight', factor_name]]
data_weight['StockExposure'] = data_weight[
'Weight'] * data_weight[factor_name]
exp = data_weight['StockExposure'].sum()
exposure_add.ix[report_date, factor_name] = exp
country_name = Barra().get_factor_name(
["COUNTRY"])["NAME_EN"].values[0]
position = FundFactor().get_fund_factor(
"Stock_Ratio",
date_list=[report_date],
fund_pool=[fund_code])
exposure_add.ix[
report_date,
country_name] = position.values[0][0] / 100
if i_date == 0:
exposure_new = exposure_add
else:
exposure_new = pd.concat([exposure_new, exposure_add],
axis=0)
else:
exposure_new = pd.DataFrame([])
# 合并新数据
####################################################################
if os.path.exists(out_file):
exposure_old = pd.read_csv(out_file, index_col=[0], encoding='gbk')
exposure_old.index = exposure_old.index.map(str)
params = FactorOperate().pandas_add_row(exposure_old, exposure_new)
else:
params = exposure_new
if len(params) > 0:
params = params[barra_name]
params.to_csv(out_file)
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)
def cal_fund_regression_exposure_index(self, fund, beg_date, end_date, period="D"):
"""
计算一只基金每日对不同指数的暴露
"""
# 参数
####################################################################
one_index_up_limit = 1.0
one_index_low_limit = 0.0
sum_index = 1.0
beg_date = Date().change_to_str(beg_date)
end_date = Date().change_to_str(end_date)
# 取得 指数收益率数据
####################################################################
for i_index in range(len(self.index_code_list)):
index_code = self.index_code_list[i_index]
index_return = Index().get_index_factor(index_code, Nattr=["PCT"])
if i_index == 0:
index_return = Index().get_index_factor(index_code, attr=["PCT"])
index_return_all = index_return
else:
index_return_all = pd.concat([index_return_all, index_return], axis=1)
index_return_all.columns = self.index_code_list
# 取得 基金涨跌幅数据
####################################################################
if fund[len(fund)-2:] == 'OF':
fund_return = FundFactor().get_fund_factor("Repair_Nav_Pct", None, [fund]) / 100.0
else:
fund_return = Index().get_index_factor(fund, attr=["PCT"])
fund_return.columns = [fund]
# 合并数据
####################################################################
data = pd.concat([fund_return, index_return_all], axis=1)
data = data.dropna(subset=[fund])
# 回归日期
####################################################################
date_series = Date().get_trade_date_series(beg_date, end_date, period=period)
date_series = list(set(date_series) & set(data.index))
date_series.sort()
# 循环优化计算每天的暴露
####################################################################
for i_date in range(0, len(date_series)):
# 约束回归所需要的数据
#############################################################################################
period_end_date = date_series[i_date]
period_beg_date = Date().get_trade_date_offset(period_end_date, -self.regression_period)
period_date_series = Date().get_trade_date_series(period_beg_date, period_end_date)
data_periods = data.ix[period_date_series, :]
data_periods = data_periods.dropna(subset=[fund])
data_periods = data_periods.T.dropna(how='all').T
data_periods = data_periods.T.fillna(data_periods.mean(axis=1)).T
data_periods = data_periods.dropna()
# 有约束的回归 可以转换为二次规划
#############################################################################################
if len(data_periods) > self.regression_period_min and (len(data_periods.columns) > 1):
# 平方和最小
#############################################################################################
y = data_periods.ix[:, 0].values
x = data_periods.ix[:, 1:].values
P = 2 * np.dot(x.T, x)
Q = -2 * np.dot(x.T, y)
# 单个指数上下限为 0
#############################################################################################
G_up = np.diag(np.ones(x.shape[1]))
G_low = - np.diag(np.ones(x.shape[1]))
G = np.row_stack((G_up, G_low))
h_up = np.row_stack(np.ones((x.shape[1], 1))) * one_index_up_limit
h_low = - np.row_stack(np.ones((x.shape[1], 1))) * one_index_low_limit
h = np.row_stack((h_up, h_low))
#############################################################################################
A = np.column_stack(np.ones((x.shape[1], 1)))
b = np.array([sum_index])
# 开始规划求解
############################################################################################
try:
P = matrix(P)
Q = matrix(Q)
G = matrix(G)
h = matrix(h)
A = matrix(A)
b = matrix(b)
result = sol.qp(P, Q, G, h, A, b)
params_add = pd.DataFrame(np.array(result['x'][0:]),
columns=[period_end_date], index=data_periods.columns[1:]).T
print("########## Fund Regression Index Exposure GF %s %s ##########" % (fund, period_end_date))
except Exception as e:
params_add = pd.DataFrame([], columns=[period_end_date], index=data_periods.columns[1:]).T
print("########## Quadratic Programming is InCorrect %s %s ##########" % (fund, period_end_date))
else:
params_add = pd.DataFrame([], columns=[period_end_date], index=data_periods.columns[1:]).T
print("########## Fund Regression Data Len is Too Small %s %s ##########" % (fund, period_end_date))
if i_date == 0:
params_new = params_add
else:
params_new = pd.concat([params_new, params_add], axis=0)
# 合并新数据 并存储数据
####################################################################
out_file = os.path.join(self.data_path, self.file_prefix + 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, params_new)
else:
params = params_new
params.to_csv(out_file)
def __init__(self):
FactorStandard.__init__(self)
FactorRemoveValue.__init__(self)
FactorOperate.__init__(self)
FactorFillNa.__init__(self)
def cal_fund_holder_exposure_quarter(self, fund, beg_date, end_date):
""" 计算单个基金的季度持仓暴露 (前十大重仓暴露) """
type_list = ['STYLE', 'COUNTRY', 'INDUSTRY']
date_series = Date().get_normal_date_series(beg_date,
end_date,
period='Q')
fund_holding = FundHolder().get_fund_stock_weight_quarter(fund)
if fund_holding is not None:
date_series = list(set(date_series) & set(fund_holding.columns))
date_series.sort()
else:
return None
for i_date in range(0, len(date_series)):
date = date_series[i_date]
report_date = Date().get_normal_date_month_end_day(date)
trade_date = Date().get_trade_date_month_end_day(date)
barra_name = list(
Barra().get_factor_name(type_list)['NAME_EN'].values)
barra_exposure = Barra().get_factor_exposure_date(
trade_date, type_list)
print(
"########## Calculate Quarter Holder Exposure %s %s ##########"
% (fund, report_date))
if (barra_exposure is None) or (fund_holding is None):
exposure_add = pd.DataFrame([],
columns=barra_name,
index=[report_date])
else:
fund_holding_date = pd.DataFrame(fund_holding[report_date])
fund_holding_date = fund_holding_date.dropna()
fund_holding_date = fund_holding_date.sort_values(
by=[report_date], ascending=False)
fund_holding_date.columns = ["Weight"]
data = pd.concat([fund_holding_date, barra_exposure], axis=1)
data = data.dropna()
if (len(data) == 0) or (data is None):
exposure_add = pd.DataFrame([],
columns=barra_name,
index=[report_date])
else:
exposure_add = pd.DataFrame([],
columns=barra_name,
index=[report_date])
for i_factor in range(len(barra_name)):
factor_name = barra_name[i_factor]
data_weight = data[['Weight', factor_name]]
data_weight['StockExposure'] = data['Weight'] * data[
factor_name]
exposure_add.ix[report_date,
factor_name] = data_weight[
'StockExposure'].sum() / 100.0
country_name = Barra().get_factor_name(
["COUNTRY"])["NAME_EN"].values[0]
position = FundFactor().get_fund_factor(
"Stock_Ratio",
date_list=[report_date],
fund_pool=[fund])
position = position.values[0][0]
exposure_add.ix[report_date, country_name] = position / 100
if i_date == 0:
exposure_new = exposure_add
else:
exposure_new = pd.concat([exposure_new, exposure_add], axis=0)
# 合并新数据
####################################################################
out_path = os.path.join(self.data_path_exposure,
'fund_holding_exposure_quarter')
out_file = os.path.join(
out_path, 'Fund_Holder_Exposure_Quarter_' + fund + '.csv')
if os.path.exists(out_file):
exposure_old = pd.read_csv(out_file, index_col=[0], encoding='gbk')
exposure_old.index = exposure_old.index.map(str)
params = FactorOperate().pandas_add_row(exposure_old, exposure_new)
else:
params = exposure_new
params.to_csv(out_file)
def cal_fund_regression_exposure_style(self,
fund,
beg_date,
end_date,
period="D"):
# 参数
####################################################################
up_style_exposure = 1.25
up_position_exposure = 0.95
low_position_exposure = 0.75
position_sub = 0.08
beg_date = Date().change_to_str(beg_date)
end_date = Date().change_to_str(end_date)
# 取得数据 因子收益率数据 和 基金涨跌幅数据
####################################################################
type_list = ['STYLE', 'COUNTRY']
barra_name = list(Barra().get_factor_name(type_list)['NAME_EN'].values)
barra_return = Barra().get_factor_return(None, None, type_list)
date_series = Date().get_trade_date_series(beg_date,
end_date,
period=period)
if fund[len(fund) - 2:] == 'OF':
fund_return = FundFactor().get_fund_factor("Repair_Nav_Pct", None,
[fund])
else:
fund_return = Index().get_index_factor(fund, attr=["PCT"]) * 100
fund_return.columns = [fund]
data = pd.concat([fund_return, barra_return], axis=1)
data = data.dropna()
print(" Fund Code Total Len %s " % len(data))
factor_number = len(barra_name)
stock_ratio = FundFactor().get_fund_factor("Stock_Ratio", None,
[fund]) / 100
date_series = list(set(date_series) & set(data.index))
date_series.sort()
# 循环回归计算每天的暴露 计算当天的暴露之时需要 前一天及之前数据
####################################################################
for i_date in range(0, len(date_series)):
# 回归所需要的数据
####################################################################
period_end_date = date_series[i_date]
period_beg_date = Date().get_trade_date_offset(
period_end_date, -self.regression_period)
data_end_date = Date().get_trade_date_offset(period_end_date, -0)
period_date_series = Date().get_trade_date_series(
period_beg_date, data_end_date)
data_periods = data.ix[period_date_series, :]
data_periods = data_periods.dropna()
# 上个季度基金仓位
#####################################################################################
quarter_date = Date().get_last_fund_quarter_date(period_end_date)
stock_ratio_fund = stock_ratio.loc[quarter_date, fund]
print(
"########## Calculate Regression Exposure %s %s %s %s %s %s ##########"
% (fund, period_beg_date, period_end_date, quarter_date,
len(data_periods), stock_ratio_fund))
if len(data_periods) > self.regression_period_min:
y = data_periods.ix[:, 0].values
x = data_periods.ix[:, 1:].values
x_add = sm.add_constant(x)
low_position_exposure = max(stock_ratio_fund - position_sub,
low_position_exposure)
if np.isnan(low_position_exposure):
low_position_exposure = 0.75
P = 2 * np.dot(x_add.T, x_add)
Q = -2 * np.dot(x_add.T, y)
G_up = np.diag(np.ones(factor_number + 1))
G_low = -np.diag(np.ones(factor_number + 1))
G = np.row_stack((G_up, G_low))
h_up = np.row_stack((np.ones(
(factor_number, 1)) * up_style_exposure,
np.array([up_position_exposure])))
h_low = np.row_stack((np.ones(
(factor_number, 1)) * up_style_exposure,
np.array([-low_position_exposure])))
h = np.row_stack((h_up, h_low))
P = matrix(P)
Q = matrix(Q)
G = matrix(G)
h = matrix(h)
try:
result = sol.qp(P, Q, G, h)
params_add = pd.DataFrame(np.array(result['x'][1:]),
columns=[period_end_date],
index=barra_name).T
print(params_add)
except Exception as e:
params_add = pd.DataFrame([],
columns=[period_end_date],
index=barra_name).T
print(params_add)
else:
params_add = pd.DataFrame([],
columns=[period_end_date],
index=barra_name).T
print(params_add)
if i_date == 0:
params_new = params_add
else:
params_new = pd.concat([params_new, params_add], axis=0)
# 合并新数据
####################################################################
out_path = os.path.join(self.data_path_exposure,
'fund_regression_exposure_style')
out_file = os.path.join(
out_path, 'Fund_Regression_Exposure_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, params_new)
else:
params = params_new
print(params)
params.to_csv(out_file)
def cal_fund_regression_exposure_index(self,
reg_code,
beg_date,
end_date,
period="D"):
"""
回归单只基金区间内指数暴露
用指数去你基金收益率 最小化跟踪误差的前提
指数权重之和为1 指数不能做空 指数和上期权重换手不能太大
"""
date_series = Date().get_trade_date_series(beg_date, end_date, period)
data_beg_date = Date().get_trade_date_offset(beg_date,
-self.regression_period)
end_date = Date().change_to_str(end_date)
code_list = [reg_code]
code_list.extend(self.index_code_list)
data = self.asset_pct.loc[data_beg_date:end_date, code_list]
data = data.dropna(subset=[reg_code])
if len(data) < self.regression_period:
return None
print("Regression %s With %s" % (reg_code, self.index_code_list))
print("Length of Return Data Is %s " % len(data))
date_series = list(set(date_series) & set(data.index))
date_series.sort()
# 上次计算的风格
last_date = Date().get_trade_date_offset(date_series[0], -1)
params_old = self.get_fund_regression_exposure_index_date(
reg_code, last_date)
params_old = params_old.T
print("old", params_old)
for i_date in range(0, len(date_series)):
# 回归所需要的数据 过去60个交易日
period_end_date = date_series[i_date]
period_beg_date = Date().get_trade_date_offset(
period_end_date, -self.regression_period)
data_end_date = Date().get_trade_date_offset(period_end_date, -0)
period_date_series = Date().get_trade_date_series(
period_beg_date, data_end_date)
data_periods = data.loc[period_date_series, :]
data_periods = data_periods.dropna(subset=[reg_code])
data_periods = data_periods.T.dropna(how='all').T
data_periods = data_periods.T.fillna(data_periods.mean(axis=1)).T
data_periods = data_periods.dropna()
print(
"########## Calculate Regression Exposure %s %s %s %s ##########"
% (reg_code, period_beg_date, period_end_date,
len(data_periods)))
if len(data_periods) > self.regression_period_min and (len(
data_periods.columns) > 1):
y = data_periods.iloc[:, 0].values
x = data_periods.iloc[:, 1:].values
n = x.shape[1]
if params_old.empty or params_old.sum().sum() < 0.5:
params_old = pd.DataFrame(n * [1 / n],
columns=[period_end_date],
index=data_periods.columns[1:]).T
turnover = self.turnover
params_old = params_old.loc[:, data_periods.columns[1:]]
params_old = params_old.fillna(0.0)
weight_old = params_old.values[0]
w = cvx.Variable(n)
sigma = y - x * w
prob = cvx.Problem(cvx.Minimize(cvx.sum_squares(sigma)), [
cvx.sum(w) == 1, w >= 0,
cvx.sum(cvx.abs(w - weight_old)) <= turnover
])
prob.solve()
print('Solver Status : ', prob.status)
# 计算回归 R2
n = len(y)
k = x.shape[1]
tss = np.sum((y - np.mean(y))**2) / n
y_res = y - np.dot(x, w.value)
rss = np.sum(y_res**2) / (n - k - 1)
r2 = 1 - rss / tss
params_add = pd.DataFrame(w.value,
columns=[period_end_date],
index=data_periods.columns[1:]).T
params_add.loc[period_end_date, "R2"] = r2
print('new', params_add)
params_old = params_add
else:
last_date = Date().get_trade_date_offset(period_end_date, -1)
params_old = self.get_fund_regression_exposure_index_date(
reg_code, last_date)
params_old = params_old.T
print("old", params_old)
params_add = params_old
if i_date == 0:
params_new = params_add
else:
params_new = pd.concat([params_new, params_add], axis=0)
# 合并新数据
file = '%s_%s_%s.csv' % (self.file_prefix, self.folder_name, reg_code)
out_file = os.path.join(self.index_exposure_path, file)
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, params_new)
else:
params = params_new
params.to_csv(out_file)
def cal_style_position(self, beg_date, end_date, code):
""" 计算一个基金或指数的风格仓位和仓位 利用OLS有约束回归 """
x_pct = self.data_return[self.index_code_list]
x_pct = x_pct.dropna(how='all')
y_pct = pd.DataFrame(self.data_return[code])
y_pct = y_pct.dropna()
all_date_series = Date().get_trade_date_series(beg_date,
end_date,
period="D")
y_series = Date().get_trade_date_series(y_pct.index[0],
y_pct.index[-1])
date_series = list(set(y_series) & set(all_date_series))
date_series.sort()
error = False
for i_date in range(len(date_series)):
ed_date = date_series[i_date]
bg_date = Date().get_trade_date_offset(ed_date,
-self.regress_length)
last_date = Date().get_trade_date_offset(ed_date, -1)
x_pct_period = x_pct.loc[bg_date:ed_date, :]
x_pct_period = x_pct_period.T.dropna().T
x_columns = x_pct_period.columns
data = pd.concat([y_pct, x_pct_period], axis=1)
data = data.dropna()
# 如果是第一天或者上次结果错误 则开放换手率 并假定上次平均持仓
if i_date != 0:
turnover_daily = self.turnover_daily
old_weight = old_weight.loc[x_columns, :]
old_weight = old_weight.fillna(0.0)
else:
n = len(x_columns)
old_weight = pd.DataFrame(n * [1.0 / n],
index=x_columns,
columns=[last_date])
turnover_daily = 2.0
if error:
n = len(x_columns)
old_weight = pd.DataFrame(n * [1.0 / n],
index=x_columns,
columns=[last_date])
turnover_daily = 2.00
# print(error, old_weight.columns)
print("## Cal Regress %s %s %s %s TurnOver %s##" %
(code, bg_date, ed_date, data.shape, turnover_daily))
if len(data) >= self.regress_length_min:
y = data[code].values
x = data.iloc[:, 1:].values
k = x.shape[1]
old = old_weight.T.values[0]
try:
w = cvx.Variable(k)
sigma = y - x * w
prob = cvx.Problem(cvx.Minimize(cvx.sum_squares(sigma)), [
cvx.sum(w) == 1.0,
cvx.sum(w[1:]) >= self.stock_ratio_low,
cvx.sum(w[1:]) <= self.stock_ratio_up,
cvx.sum(cvx.abs(w - old)) <= turnover_daily, w >= 0
])
prob.solve()
print('Solver Status : ', prob.status)
params_add = pd.DataFrame(w.value,
columns=[ed_date],
index=x_columns)
stock_sum = params_add.loc[self.index_code_list[1:],
ed_date].sum()
concat_data = pd.concat([params_add, old_weight], axis=1)
concat_data = concat_data.dropna()
turnover_real = (concat_data[last_date] -
concat_data[ed_date]).abs().sum()
params_add.loc['StockRatio', ed_date] = stock_sum
params_add.loc['BondRatio', ed_date] = params_add.loc[
self.index_code_list[0], ed_date]
params_add.loc['TurnOverDaily', ed_date] = turnover_real
print(params_add.T)
old_weight = params_add
error = False
except Exception as e:
print(end_date, code, "回归失败")
error = True
else:
print(end_date, code, "数据长度不够")
error = True
if i_date == 0:
params_new = params_add
else:
params_new = pd.concat([params_new, params_add], axis=1)
# 合并新数据
####################################################################
params_new = params_new.T
out_file = os.path.join(self.data_path,
'RestraintOLSStylePosition_%s.csv' % code)
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, params_new)
else:
params = params_new
params.to_csv(out_file)
def cal_fund_regression_risk_alpha_return_index(self, fund, beg_date,
end_date):
# 参数
####################################################################
exposure_index = FundRegressionExposureIndex(
).get_fund_regression_exposure_index(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
####################################################################
for i_index in range(len(self.index_code_list)):
index_code = self.index_code_list[i_index]
index_return = Index().get_index_factor(index_code,
attr=["PCT"])
if i_index == 0:
index_return = Index().get_index_factor(index_code,
attr=["PCT"])
index_return_all = index_return
else:
index_return_all = pd.concat(
[index_return_all, index_return], axis=1)
index_return_all.columns = self.index_code_list
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 = index_return_all.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[self.index_code_list].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 = self.data_path
out_file = os.path.join(out_path, self.file_prefix + 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)
def regress_fund(self, fund_code, beg_date, end_date):
""" 回归基金净值 和上季度重仓股票的涨跌幅和债券基金 """
period = "W"
date_series = Date().get_trade_date_series(beg_date, end_date, period)
fund_return = self.fund_pct[fund_code]
fund_return = fund_return.dropna()
date_series = list(set(date_series) & set(fund_return.index))
date_series.sort()
# 季报持仓
quarter_weight = Fund().get_fund_holding_quarter(fund_code)
r2_series = pd.DataFrame([], index=date_series, columns=['r2'])
for i_date in range(0, len(date_series)):
# 时间确定
# 若此时离上个季报时间较短 则回归时间很短
# 若此时离上个季报时间较长 则回归时间较长
ed_date = date_series[i_date]
ed_date = Date().get_trade_date_offset(ed_date, -0)
quarter_date = Date().get_last_fund_quarter_date(ed_date)
bg_date = Date().get_trade_date_offset(ed_date,
-(self.regression_len - 1))
bg_date = max(bg_date, quarter_date)
bg_date = Date().get_trade_date_offset(bg_date, -0)
date_diff = Date().get_trade_date_diff(bg_date, ed_date)
# 上期持仓
try:
stock_weight = pd.DataFrame(quarter_weight[quarter_date])
stock_weight = stock_weight.dropna()
stock_weight.columns = ['Weight']
# 收益率数据
data = pd.concat([fund_return, self.stock_pct, self.bold_pct],
axis=1)
data['885062.WI'] = data['885062.WI'].fillna(0.0)
regress_date_series = Date().get_trade_date_series(
bg_date, ed_date)
data = data.loc[regress_date_series, :]
data = data.T.dropna(thresh=self.regression_min_len).T
data = data.fillna(data.mean(axis=1))
# 股票池
stock_pool = list(stock_weight.index)
stock_pool = list(set(stock_pool) & set(data.columns[1:]))
stock_pool.sort()
stock_pool.append("885062.WI")
stock_ratio = self.get_fund_stock_ratio(
fund_code, quarter_date)
stock_weight['Weight'] /= stock_weight['Weight'].sum()
stock_weight['Weight'] *= stock_ratio
stock_weight.loc["885062.WI", "Weight"] = 100 - stock_ratio
stock_weight /= 100.0
stock_weight = stock_weight.loc[stock_pool, :]
stock_weight['Weight'] /= stock_weight['Weight'].sum()
print("## Cal Regress %s %s %s %s %s ##" %
(fund_code, quarter_date, bg_date, ed_date, len(data)))
if (len(data) > self.regression_min_len) and (len(stock_pool) >
4):
# 利用股票拟合基金收益率 最小化跟踪误差的前提
# 指数权重之和为1 指数不能做空 指数和上期季报权重换手不能太大
y = data[fund_code].values / 100.0
x = data[stock_pool].values / 100.0
n = len(y)
k = x.shape[1]
weight_old = stock_weight.T.values[0]
turnover = date_diff * 0.8 / 100
print("TurnOver %s " % turnover)
# 最优化
##############################################################################
w = cvx.Variable(k)
sigma = y - x * w
prob = cvx.Problem(cvx.Minimize(cvx.sum_squares(sigma)), [
cvx.sum(w) == 1.0, w >= 0,
cvx.sum(cvx.abs(w - weight_old)) <= turnover
])
prob.solve()
print('Solver Status : ', prob.status)
params_add = pd.DataFrame(w.value,
columns=[ed_date],
index=stock_pool)
# 计算回归R2
##############################################################################
tss = np.sum((y - np.mean(y))**2) / n
y_res = y - np.dot(x, w.value)
rss = np.sum(y_res**2) / (n - k - 1)
r2 = 1 - rss / tss
params_add.loc["R2", ed_date] = r2
print(params_add.T)
else:
params_add = pd.DataFrame([],
columns=[ed_date],
index=stock_pool)
except Exception as e:
params_add = pd.DataFrame([], columns=[ed_date])
if i_date == 0:
params_new = params_add
else:
params_new = pd.concat([params_new, params_add], axis=1)
# 合并新数据
####################################################################
params_new = params_new.T
out_file = os.path.join(self.data_path_exposure, fund_code + '.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, params_new)
else:
params = params_new
params.to_csv(out_file)
def cal_barra_exposure_return(self):
""" 计算满仓 基金暴露、指数暴露、超额暴露、因子收益、基金超额暴露收益 """
# 参数
type_list = ["STYLE", "COUNTRY", "INDUSTRY"]
# 得到基金（满仓）相对于跟踪指数（满仓）超额暴露
exposure_fund = MfcData().get_mfc_holding_barra_exposure(
self.fund_name, self.beg_date_pre, self.end_date)
exposure_index = Index().get_index_exposure(self.index_code,
self.beg_date_pre,
self.end_date, type_list)
exposure_excess = exposure_fund.sub(exposure_index)
exposure_excess = exposure_excess.dropna()
# 添加 Country Factor = 1.0
factor_name = Barra().get_factor_name(type_list=["COUNTRY"])
factor_name = list(factor_name["NAME_EN"].values)
exposure_excess[factor_name] = 1.0
# 前一天的 Exposure 对应后一天的 Factor Return
exposure_excess.index = exposure_excess.index.map(
lambda x: Date().get_trade_date_offset(x, 1))
# 取得当日的 Factor Return
factor_return = Barra().get_factor_return(self.beg_date, self.end_date,
type_list)
# 计算超额暴露带来的收益部分
[exposure, factor_return] = FactorOperate().make_same_index_columns(
[exposure_excess, factor_return])
fund_risk_factor_return = exposure.mul(factor_return)
# 调整列的位置
factor_name = Barra().get_factor_name(type_list=type_list)
factor_name = list(factor_name["NAME_EN"].values)
fund_risk_factor_return = fund_risk_factor_return[factor_name]
# 分别计算 Style Industry RiskFactor = Style + Industry
factor_name = Barra().get_factor_name(type_list=['STYLE'])
factor_name = list(factor_name["NAME_EN"].values)
fund_risk_factor_return['Style'] = fund_risk_factor_return[
factor_name].sum(axis=1)
factor_name = Barra().get_factor_name(type_list=['INDUSTRY'])
factor_name = list(factor_name["NAME_EN"].values)
fund_risk_factor_return['Industry'] = fund_risk_factor_return[
factor_name].sum(axis=1)
factor_name = Barra().get_factor_name(type_list=type_list)
factor_name = list(factor_name["NAME_EN"].values)
fund_risk_factor_return['RiskFactor'] = fund_risk_factor_return[
factor_name].sum(axis=1)
# 整理返回区间内的所有数据=基金暴露+指数暴露+超额暴露+因子收益+基金超额暴露收益
fund_risk_factor_return = fund_risk_factor_return.loc[
self.beg_date:self.end_date, :]
exposure = exposure.loc[self.beg_date:self.end_date, :]
factor_return = factor_return.loc[self.beg_date:self.end_date, :]
fund_risk_factor_return /= 100.0
factor_return /= 100.0
self.save_file_excel(fund_risk_factor_return, "Barra", "基金风格收益",
"0.00%")
self.save_file_excel(factor_return, "Barra", "风格因子收益", "0.00%")
self.save_file_excel(exposure, "Barra", "基金超额暴露", "0.000")
self.save_file_excel(exposure_fund, "Barra", "基金暴露", "0.000")
self.save_file_excel(exposure_index, "Barra", "指数暴露", "0.000")
