def run_snapshot(self):
date_str = self._cur_time.date_.strftime('%Y%m%d')
equity1 = QueryTool(AShareEODPrices.S_DQ_PCTCHANGE, Engine.lu_cbaas_dev).\
filter(AShareEODPrices.S_INFO_WINDCODE == '000001.SZ').\
filter(AShareEODPrices.TRADE_DT == date_str).all()
equity2 = QueryTool(AShareEODPrices.S_DQ_PCTCHANGE, Engine.lu_cbaas_dev).\
filter(AShareEODPrices.S_INFO_WINDCODE == '601398.SH').\
filter(AShareEODPrices.TRADE_DT == date_str).all()
index_return = equity1[0][0] * 0.5 + equity2[0][0] * 0.5
self._result.update({self._cur_time.date_: index_return})
def get_fund_performance(engine,
ftype='MF',
fund_id=None,
start=None,
end=None,
freq='d',
date_col='date'):
if freq not in ['d', 'w', 'm']:
raise ValueError(
'{!r} is not in frequency options ("d", "w", "m").'.format(freq))
if freq == 'd':
table = DailyPerformance
funds, start_date, end_date = _get_config_params(
table, engine, ftype, fund_id, start, end)
performance = QueryTool([DailyPerformance.fund_id,
DailyPerformance.date_.label(date_col),
(DailyPerformance.return_/100).label('return')],
engine).\
filter(DailyPerformance.fund_id.in_(funds)).\
filter(DailyPerformance.date_ <= end).\
filter(DailyPerformance.date_ > start).\
all()
elif freq == 'w':
table = WeeklyPerformance
funds, start_date, end_date = _get_config_params(
table, engine, ftype, fund_id, start, end)
performance = QueryTool([WeeklyPerformance.fund_id,
WeeklyPerformance.month_end.label(date_col),
(WeeklyPerformance.return_/100).label('return')],
engine).\
filter(WeeklyPerformance.fund_id.in_(funds)).\
filter(WeeklyPerformance.month_end <= end).\
filter(WeeklyPerformance.month_end > start).\
all()
elif freq == 'm':
table = MonthlyPerformance
funds, start_date, end_date = _get_config_params(
table, engine, ftype, fund_id, start, end)
performance = QueryTool([MonthlyPerformance.fund_id,
MonthlyPerformance.month_end.label(date_col),
(MonthlyPerformance.return_/100).label('return')],
engine).\
filter(MonthlyPerformance.fund_id.in_(funds)).\
filter(MonthlyPerformance.month_end <= end).\
filter(MonthlyPerformance.month_end > start).\
all()
else:
raise NotImplementedError(
'Performance table with frequency of {!r} has yet implemented.'.
format(freq))
return performance
def _get_fund_info(self):
# cbaas_fund_info = DataFrame(
# QueryTool([CBAASFund.S_INFO_WINDCODE.label('suntime_id'),
# CBAASFund.id.label('cbaas_id'),
# CBAASFund.strategy],
# self._engine.lu_cbaas_test.cbaasquant).all()
# )
#
# suntime_fund_info = DataFrame(
# QueryTool([SuntimeFundInfo.fund_id.label('suntime_id'),
# SuntimeFundInfo.fund_name.label('fund_name'),
# SuntimeFundInfo.foundation_date.label('foundation_date')],
# self._engine.lu_cbaas_test.suntime)
# )
# self._fund_info = cbaas_fund_info.merge(suntime_fund_info,
# on='',)
info = QueryTool([CBAASFund.S_INFO_WINDCODE.label('suntime_id'),
CBAASFund.id.label('cbaas_id'),
CBAASFund.strategy,
SuntimeFundInfo.fund_id.label('suntime_id'),
SuntimeFundInfo.fund_name.label('fund_name'),
SuntimeFundInfo.foundation_date.label('foundation_date')],
self._engine.lu_cbaas_dev).\
join(CBAASFund.S_INFO_WINDCODE == SuntimeFundInfo.fund_id).all()
self._fund_info = info
def __init__(self):
self._engine = Engine
self._cur_time = QueryTool(TradeCalendar, self._engine.lu_cbaas_test.cbaas).\
filter(TradeCalendar.date_ == '1990-12-31').all()[0]
self._fund_info = None
self._get_fund_info()
def __init__(self, start, end):
self._engine = Engine
self._axis = QueryTool([TradeCalendar.date_,
TradeCalendar.prev_trade_date,
TradeCalendar.is_open,
TradeCalendar.is_week_end,
TradeCalendar.is_month_end,
TradeCalendar.is_quarter_end,
TradeCalendar.is_year_end,
TradeCalendar.cal_end_of_month],
self._engine.lu_cbaas_dev).\
filter(TradeCalendar.date_ >= start).\
filter(TradeCalendar.date_ < end).all()
def get_fund_info(engine, ftype=None):
fund_types = _get_fund_type(ftype)
return QueryTool([CBAASFund.id.label('fund_id'),
CBAASFund.S_INFO_WINDCODE,
CBAASFund.asset_class,
CBAASFund.inception_date,
CBAASFund.latest_nav_date],
engine).\
filter(CBAASFund.valid == 1).\
filter(CBAASFund.asset_class.in_(fund_types)).\
all()
def _cur_avail_hf_info(self):
info = QueryTool([CBAASFund.id.label('fund_id'),
CBAASFund.fund_name,
CBAASFund.strategy,
FundCompany.id.label('company_id'),
FundCompany.company_name],
self._engine). \
join(CompanyFund).\
join(FundCompany).\
filter(CBAASFund.asset_class == 8).\
filter(CBAASFund.inception_date <= self.date).\
filter(CBAASFund.latest_nav_date >= self.date).all()
return DataFrame(info)
def _get_config_params(performance_table,
engine,
ftype=None,
fund_id=None,
start=None,
end=None):
fund_types = _get_fund_type(ftype)
if fund_id is None:
funds = [
_id for _id, in QueryTool(CBAASFund.id, engine).filter(
CBAASFund.asset_class.in_(fund_types)).filter(
CBAASFund.valid == 1).all()
]
elif not isinstance(fund_id, list):
funds = list(fund_id)
else:
funds = fund_id
if not start:
start_date = QueryTool(func.min(performance_table.date_), engine).\
filter(performance_table.fund_id.in_(funds)).\
scaler().\
strftime('%Y-%m-%d')
else:
start_date = start
if not end:
end_date = QueryTool(func.max(performance_table.date_), engine). \
filter(performance_table.fund_id.in_(funds)). \
scaler(). \
strftime('%Y-%m-%d')
else:
end_date = end
return funds, start_date, end_date
def _pure_rating_cal(self, funds, base_col_dict, factor_col, rating_name):
cols = factor_col
cols.append(base_col_dict['fund_id'])
query_list = QueryTool(cols, self._engine).\
filter(base_col_dict['fund_id'].in_(funds)).\
filter(base_col_dict['date'] == self.date).\
all()
if len(query_list) > 0:
factor_value = DataFrame(query_list).set_index('fund_id').dropna()
standardized = self._clean_and_standarize(factor_value)
raw_rating = self._rating_cal(standardized.mean(axis=1),
qcut,
[0., .1, .325, .675, .9, 1.],
labels=False,
duplicates='drop')
return raw_rating.to_frame(rating_name)
else:
return DataFrame(columns=[rating_name])
def _cur_avail_mf_info(self):
info = QueryTool([CBAASFund.id.label('fund_id'),
CBAASFund.fund_name,
CBAASFund.strategy_category,
Manager.id.label('manager_id'),
Manager.first_name.label('manager_name'),
FundCompany.id.label('company_id'),
FundCompany.company_name],
self._engine). \
join(ManagerFundHistory). \
join(Manager). \
join(FundCompany). \
filter(CBAASFund.asset_class == 10). \
filter(CBAASFund.inception_date <= self.date). \
filter(CBAASFund.latest_nav_date >= self.date). \
filter(or_(and_(ManagerFundHistory.start_date <= self.date,
ManagerFundHistory.end_date >= self.date),
and_(ManagerFundHistory.start_date <= self.date,
ManagerFundHistory.end_date.is_(None)))).all()
return DataFrame(info)
engine).\
filter(MonthlyPerformance.fund_id.in_(funds)).\
filter(MonthlyPerformance.month_end <= end).\
filter(MonthlyPerformance.month_end > start).\
all()
else:
raise NotImplementedError(
'Performance table with frequency of {!r} has yet implemented.'.
format(freq))
return performance
if __name__ == '__main__':
from cbaasquant.data.utils.engine import Engine
eng = Engine.lu_cbaas_dev
# test = get_fund_performance(engine=eng, ftype='HF', start='2018-01-01', end='2018-01-08', freq='w')
# from pandas import DataFrame
# print(DataFrame(test))
test = QueryTool([CBAASFund.id,
CBAASFund.asset_class,
CBAASFund.inception_date,
CBAASFund.latest_nav_date],
eng). \
filter(CBAASFund.id == 473247). \
all()
print(test)