def getRMExposure(self, i):
data = DataAPI.RMExposureDayGet(secID=u"",ticker=u"",tradeDate="%s"%i,\
beginDate=u"",endDate=u"",field=u"",pandas="1")
del data['tradeDate']
del data['ticker']
del data['exchangeCD']
path = '%s/RMExposure' % self.dirpath
if not os.path.exists(path):
os.mkdir(path)
if not data.empty:
data['secID'] = data['secID'].apply(self.replaceCode)
data.to_csv('%s/%s.csv' % (path, i),
encoding='gb18030',
index=None)
def get_risk_model(self, trading_day_list, universe):
result = {}
specific_return = {}
for date in trading_day_list:
result[date] = {}
result[date]['exposure'] = DataAPI.RMExposureDayGet(
secID=universe, tradeDate=date).set_index('secID').iloc[:,
4:-1]
result[date]['factor_covariance'] = DataAPI.RMCovarianceShortGet(
tradeDate=date).set_index('Factor').iloc[:, 2:-1] / 10000.
result[date]['specific_risk'] = \
DataAPI.RMSriskShortGet(secID=universe, tradeDate=date).set_index('secID')['SRISK'] / 100.
specific_return[date] = \
DataAPI.RMSpecificRetDayGet(secID=universe, tradeDate=date).set_index('secID')['spret']
return result, specific_return
def update_uqer_risk_model(ds, **kwargs):
ref_date, this_date = process_date(ds)
flag = check_holiday(this_date)
if not flag:
return
df = api.RMExposureDayGet(tradeDate=ref_date)
df.rename(columns={
'tradeDate': 'trade_date',
'ticker': 'code'
},
inplace=True)
df.code = df.code.astype(int)
del df['secID']
engine.execute(
delete(RiskExposure).where(RiskExposure.trade_date == this_date))
data_info_log(df, RiskExposure)
format_data(df)
df.to_sql(RiskExposure.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMFactorRetDayGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'trade_date'}, inplace=True)
engine.execute(
delete(RiskReturn).where(RiskReturn.trade_date == this_date))
data_info_log(df, RiskReturn)
format_data(df)
df.to_sql(RiskReturn.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMSpecificRetDayGet(tradeDate=ref_date)
df.rename(columns={
'tradeDate': 'trade_date',
'ticker': 'code'
},
inplace=True)
df.code = df.code.astype(int)
del df['secID']
engine.execute(
delete(SpecificReturn).where(SpecificReturn.trade_date == this_date))
data_info_log(df, SpecificReturn)
format_data(df)
df.to_sql(SpecificReturn.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMCovarianceDayGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'trade_date'}, inplace=True)
engine.execute(
delete(RiskCovDay).where(RiskCovDay.trade_date == this_date))
data_info_log(df, RiskCovDay)
format_data(df)
df.to_sql(RiskCovDay.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMCovarianceShortGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'trade_date'}, inplace=True)
engine.execute(
delete(RiskCovShort).where(RiskCovShort.trade_date == this_date))
data_info_log(df, RiskCovShort)
format_data(df)
df.to_sql(RiskCovShort.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMCovarianceLongGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'trade_date'}, inplace=True)
engine.execute(
delete(RiskCovLong).where(RiskCovLong.trade_date == this_date))
data_info_log(df, RiskCovLong)
format_data(df)
df.to_sql(RiskCovLong.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMSriskDayGet(tradeDate=ref_date)
df.rename(columns={
'tradeDate': 'trade_date',
'ticker': 'code'
},
inplace=True)
df.code = df.code.astype(int)
del df['secID']
engine.execute(
delete(SpecificRiskDay).where(SpecificRiskDay.trade_date == this_date))
data_info_log(df, SpecificRiskDay)
format_data(df)
df.to_sql(SpecificRiskDay.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMSriskShortGet(tradeDate=ref_date)
df.rename(columns={
'tradeDate': 'trade_date',
'ticker': 'code'
},
inplace=True)
df.code = df.code.astype(int)
del df['secID']
engine.execute(
delete(SpecificRiskShort).where(
SpecificRiskShort.trade_date == this_date))
data_info_log(df, SpecificRiskShort)
format_data(df)
df.to_sql(SpecificRiskShort.__table__.name,
engine,
index=False,
if_exists='append')
df = api.RMSriskLongGet(tradeDate=ref_date)
df.rename(columns={
'tradeDate': 'trade_date',
'ticker': 'code'
},
inplace=True)
df.code = df.code.astype(int)
del df['secID']
engine.execute(
delete(SpecificRiskLong).where(
SpecificRiskLong.trade_date == this_date))
data_info_log(df, SpecificRiskLong)
format_data(df)
df.to_sql(SpecificRiskLong.__table__.name,
engine,
index=False,
if_exists='append')
def update_uqer_risk_model(ds, **kwargs):
ref_date, this_date = process_date(ds)
table = 'risk_exposure'
df = api.RMExposureDayGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date', 'ticker': 'Code'}, inplace=True)
df.Code = df.Code.astype(int)
del df['secID']
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'risk_return'
df = api.RMFactorRetDayGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date'}, inplace=True)
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'specific_return'
df = api.RMSpecificRetDayGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date', 'ticker': 'Code'}, inplace=True)
df.Code = df.Code.astype(int)
del df['secID']
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'risk_cov_day'
df = api.RMCovarianceDayGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date'}, inplace=True)
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'risk_cov_short'
df = api.RMCovarianceShortGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date'}, inplace=True)
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'risk_cov_long'
df = api.RMCovarianceLongGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date'}, inplace=True)
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'specific_risk_day'
df = api.RMSriskDayGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date', 'ticker': 'Code'}, inplace=True)
df.Code = df.Code.astype(int)
del df['secID']
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'specific_risk_short'
df = api.RMSriskShortGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date', 'ticker': 'Code'}, inplace=True)
df.Code = df.Code.astype(int)
del df['secID']
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
table = 'specific_risk_long'
df = api.RMSriskLongGet(tradeDate=ref_date)
df.rename(columns={'tradeDate': 'Date', 'ticker': 'Code'}, inplace=True)
df.Code = df.Code.astype(int)
del df['secID']
engine2.execute("delete from {0} where Date = '{1}';".format(table, ref_date))
df.to_sql(table, engine2, index=False, if_exists='append')
'INDUSTRY_SW')[u'市值占净值(%)'].sum() / stock[u'市值占净值(%)'].sum() * 100
PESummary[date] = stock.groupby(
'PE_TYPE')[u'市值占净值(%)'].sum() / stock[u'市值占净值(%)'].sum() * 100
MVSummary[date] = stock.groupby(
'MKT_CAP_TYPE')[u'市值占净值(%)'].sum() / stock[u'市值占净值(%)'].sum() * 100
Summary.loc[u'股票个数', date] = len(stock)
Summary.loc[u'仓位(%)', date] = stock[u'市值占净值(%)'].sum()
Summary.loc["PE(TTM)", date] = stock["PE_TTM"].median()
Summary.loc["PB", date] = stock["PB"].median()
Summary.loc[u'市值(亿)', date] = stock["MKT_CAP_CSRC"].median()
Summary.loc[u'个股最大持仓(%)', date] = stock[u'成本占净值(%)'].max()
Summary.loc[u'个股持仓中位数(%)', date] = stock[u'成本占净值(%)'].median()
# 基于风险模型归因
factor_all = DataAPI.RMExposureDayGet(tradeDate=date, field='')
factor_retraw = DataAPI.RMFactorRetDayGet(tradeDate=date, field='')
factor_ret = factor_retraw.iloc[:, 1:40].T
factor = pd.DataFrame()
factor['secID'] = stock['secID']
factor['weight(%)'] = stock['weight(%)']
factor = factor.merge(factor_all, on='secID', how='left') # 合并因子暴露
expo = factor.iloc[:, -40:-1]
weight = factor['weight(%)']
factor_expo = pd.DataFrame(np.dot(weight, expo), index=expo.columns)
FactorSummary[date + 'FactorRet(%)'] = factor_ret
FactorSummary[date + '(%)'] = factor_expo * factor_ret
ExpoSummary[date] = factor_expo
# ExpoSummary.to_excel(writer,'ExpoSummary'+date)
# ExpoSummary=ExpoSummary.append(factor)
# 以中证500为基准
