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alpha-mind
Commit 4ad392f5 authored by Dr.李's avatar Dr.李
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depreciated usage of full factor table

parent 55e8b676
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Showing with 71 additions and 38 deletions
alphamind/data/engines/sqlengine.py
View file @ 4ad392f5
......@@ -24,7 +24,6 @@ from alphamind.data.dbmodel.models import IndexComponent
from alphamind.data.dbmodel.models import Industry
from alphamind.data.dbmodel.models import Experimental
from alphamind.data.dbmodel.models import RiskMaster
from alphamind.data.dbmodel.models import FullFactor
from alphamind.data.dbmodel.models import Models
from alphamind.data.dbmodel.models import Market
from alphamind.data.dbmodel.models import IndexMarket
......@@ -34,6 +33,7 @@ from alphamind.data.dbmodel.models import DailyPortfoliosSchedule
from alphamind.data.dbmodel.models import Performance
from alphamind.data.dbmodel.models import Positions
from alphamind.data.dbmodel.models import Outright
from alphamind.data.dbmodel.models import RiskExposure
from alphamind.data.transformer import Transformer
from alphamind.model.loader import load_model
from alphamind.formula.utilities import encode_formula
......@@ -339,17 +339,21 @@ class SqlEngine(object):
start_date = advanceDateByCalendar('china.sse', ref_date, str(-warm_start) + 'b').strftime('%Y-%m-%d')
end_date = ref_date
big_table = FullFactor
big_table = Market
joined_tables = set()
joined_tables.add(Market.__table__.name)
for t in set(factor_cols.values()):
if t.__table__.name != FullFactor.__table__.name:
big_table = outerjoin(big_table, t, and_(FullFactor.trade_date == t.trade_date,
FullFactor.code == t.code))
if t.__table__.name not in joined_tables:
big_table = outerjoin(big_table, t, and_(Market.trade_date == t.trade_date,
Market.code == t.code))
joined_tables.add(t.__table__.name)
query = select(
[FullFactor.trade_date, FullFactor.code, FullFactor.isOpen] + list(factor_cols.keys())) \
.select_from(big_table).where(and_(FullFactor.trade_date.between(start_date, end_date),
FullFactor.code.in_(codes)))
[Market.trade_date, Market.code, Market.isOpen] + list(factor_cols.keys())) \
.select_from(big_table).where(and_(Market.trade_date.between(start_date, end_date),
Market.code.in_(codes)))
df = pd.read_sql(query, self.engine).sort_values(['trade_date', 'code']).set_index('trade_date')
res = transformer.transform('code', df)
......@@ -384,30 +388,30 @@ class SqlEngine(object):
else:
factor_cols = _map_factors(dependency, factor_tables)
big_table = FullFactor
big_table = Market
joined_tables = set()
joined_tables.add(FullFactor.__table__.name)
joined_tables.add(Market.__table__.name)
for t in set(factor_cols.values()):
if t.__table__.name not in joined_tables:
if dates is not None:
big_table = outerjoin(big_table, t, and_(FullFactor.trade_date == t.trade_date,
FullFactor.code == t.code,
FullFactor.trade_date.in_(dates)))
big_table = outerjoin(big_table, t, and_(Market.trade_date == t.trade_date,
Market.code == t.code,
Market.trade_date.in_(dates)))
else:
big_table = outerjoin(big_table, t, and_(FullFactor.trade_date == t.trade_date,
FullFactor.code == t.code,
FullFactor.trade_date.between(start_date, end_date)))
big_table = outerjoin(big_table, t, and_(Market.trade_date == t.trade_date,
Market.code == t.code,
Market.trade_date.between(start_date, end_date)))
joined_tables.add(t.__table__.name)
universe_df = universe.query(self, start_date, end_date, dates)
query = select(
[FullFactor.trade_date, FullFactor.code, FullFactor.isOpen] + list(factor_cols.keys())) \
[Market.trade_date, Market.code, Market.isOpen] + list(factor_cols.keys())) \
.select_from(big_table).where(
and_(
FullFactor.code.in_(universe_df.code.unique().tolist()),
FullFactor.trade_date.in_(dates) if dates is not None else FullFactor.trade_date.between(start_date, end_date)
Market.code.in_(universe_df.code.unique().tolist()),
Market.trade_date.in_(dates) if dates is not None else Market.trade_date.between(start_date, end_date)
)
).distinct()
......@@ -473,7 +477,7 @@ class SqlEngine(object):
codes: Iterable[int],
risk_model: str = 'short',
excluded: Iterable[str] = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
risk_cov_table, special_risk_col = _map_risk_model_table(risk_model)
risk_cov_table, special_risk_table = _map_risk_model_table(risk_model)
cov_risk_cols = [risk_cov_table.__table__.columns[f] for f in total_risk_factors]
query = select([risk_cov_table.FactorID,
......@@ -484,12 +488,22 @@ class SqlEngine(object):
risk_cov = pd.read_sql(query, self.engine).sort_values('FactorID')
if excluded:
risk_exposure_cols = [FullFactor.__table__.columns[f] for f in total_risk_factors if f not in set(excluded)]
risk_exposure_cols = [RiskExposure.__table__.columns[f] for f in total_risk_factors if f not in set(excluded)]
else:
risk_exposure_cols = [FullFactor.__table__.columns[f] for f in total_risk_factors]
risk_exposure_cols = [RiskExposure.__table__.columns[f] for f in total_risk_factors]
query = select([FullFactor.code, special_risk_col] + risk_exposure_cols) \
.where(and_(FullFactor.trade_date == ref_date, FullFactor.code.in_(codes))).distinct()
big_table = join(RiskExposure,
special_risk_table,
and_(
RiskExposure.code == special_risk_table.code,
RiskExposure.trade_date == special_risk_table.trade_date
))
query = select([RiskExposure.code, special_risk_table.SRISK.label('srisk')] + risk_exposure_cols) \
.select_from(big_table).where(
and_(RiskExposure.trade_date == ref_date,
RiskExposure.code.in_(codes)
)).distinct()
risk_exp = pd.read_sql(query, self.engine)
......@@ -503,7 +517,7 @@ class SqlEngine(object):
risk_model: str = 'short',
excluded: Iterable[str] = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
risk_cov_table, special_risk_col = _map_risk_model_table(risk_model)
risk_cov_table, special_risk_table = _map_risk_model_table(risk_model)
cov_risk_cols = [risk_cov_table.__table__.columns[f] for f in total_risk_factors]
......@@ -521,23 +535,32 @@ class SqlEngine(object):
if not excluded:
excluded = []
risk_exposure_cols = [FullFactor.__table__.columns[f] for f in total_risk_factors if f not in set(excluded)]
risk_exposure_cols = [RiskExposure.__table__.columns[f] for f in total_risk_factors if f not in set(excluded)]
cond = universe._query_statements(start_date, end_date, dates)
big_table = join(FullFactor, UniverseTable,
big_table = join(RiskExposure, UniverseTable,
and_(
FullFactor.trade_date == UniverseTable.trade_date,
FullFactor.code == UniverseTable.code,
RiskExposure.trade_date == UniverseTable.trade_date,
RiskExposure.code == UniverseTable.code,
cond
)
)
big_table = join(special_risk_table,
big_table,
and_(
RiskExposure.code == special_risk_table.code,
RiskExposure.trade_date == special_risk_table.trade_date,
))
query = select(
[FullFactor.trade_date, FullFactor.code, special_risk_col] + risk_exposure_cols).select_from(big_table) \
[RiskExposure.trade_date,
RiskExposure.code,
special_risk_table.SRISK.label('srisk')] + risk_exposure_cols).select_from(big_table) \
.distinct()
risk_exp = pd.read_sql(query, self.engine)
risk_exp = pd.read_sql(query, self.engine).sort_values(['trade_date', 'code'])
if universe.is_filtered:
codes = universe.query(self, start_date, end_date, dates)
......@@ -674,7 +697,7 @@ class SqlEngine(object):
factor_data = self.fetch_factor(ref_date,
transformer,
codes,
used_factor_tables=[FullFactor, Experimental])
used_factor_tables=factor_tables)
if benchmark:
benchmark_data = self.fetch_benchmark(ref_date, benchmark)
......@@ -964,5 +987,10 @@ if __name__ == '__main__':
ref_date = '2017-06-29'
universe = Universe('', ['zz800'])
codes = engine.fetch_codes(ref_date, universe)
dates = makeSchedule('2010-01-01', '2018-02-01', '10b', 'china.sse')
df = engine.fetch_factor_range(universe, DIFF('roe_q'), dates=dates)
# df = engine.fetch_factor_range(universe, DIFF('roe_q'), dates=dates)
risk_cov, risk_exposure = engine.fetch_risk_model(ref_date, codes)
factor_data = engine.fetch_factor_range(universe, ['roe_q'], dates=dates)
risk_cov, risk_exposure = engine.fetch_risk_model_range(universe, dates=dates)
alphamind/data/engines/utilities.py
View file @ 4ad392f5
......@@ -10,22 +10,27 @@ from typing import Dict
from alphamind.data.dbmodel.models import RiskCovDay
from alphamind.data.dbmodel.models import RiskCovShort
from alphamind.data.dbmodel.models import RiskCovLong
from alphamind.data.dbmodel.models import FullFactor
from alphamind.data.dbmodel.models import SpecificRiskDay
from alphamind.data.dbmodel.models import SpecificRiskShort
from alphamind.data.dbmodel.models import SpecificRiskLong
from alphamind.data.dbmodel.models import Uqer
from alphamind.data.dbmodel.models import Gogoal
from alphamind.data.dbmodel.models import Experimental
from alphamind.data.dbmodel.models import LegacyFactor
from alphamind.data.dbmodel.models import Tiny
from alphamind.data.engines.industries import INDUSTRY_MAPPING
factor_tables = [FullFactor, Gogoal, Experimental]
factor_tables = [Uqer, Gogoal, Experimental, LegacyFactor, Tiny]
def _map_risk_model_table(risk_model: str) -> tuple:
if risk_model == 'day':
return RiskCovDay, FullFactor.d_srisk
return RiskCovDay, SpecificRiskDay
elif risk_model == 'short':
return RiskCovShort, FullFactor.s_srisk
return RiskCovShort, SpecificRiskShort
elif risk_model == 'long':
return RiskCovLong, FullFactor.l_srisk
return RiskCovLong, SpecificRiskLong
else:
raise ValueError("risk model name {0} is not recognized".format(risk_model))
......
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