def fetch_trade_status_range(self,
universe: Universe,
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
offset=0):
codes = universe.query(self, start_date, end_date, dates)
if dates:
start_date = dates[0]
end_date = dates[-1]
end_date = advanceDateByCalendar('china.sse', end_date,
str(offset) + 'b').strftime('%Y-%m-%d')
stats = func.lead(Market.isOpen, offset).over(
partition_by=Market.code,
order_by=Market.trade_date).label('is_open')
cte = select([Market.trade_date, Market.code, stats]).where(
and_(
Market.trade_date.between(start_date, end_date),
Market.code.in_(codes.code.unique().tolist())
)
).cte('cte')
query = select([cte]).select_from(cte).order_by(cte.columns['trade_date'], cte.columns['code'])
df = pd.read_sql(query, self.engine)
return pd.merge(df, codes[['trade_date', 'code']], on=['trade_date', 'code'])
def fetch_codes(self, ref_date: str, universe: Universe) -> List[int]:
cond = universe.query(ref_date)
query = select([UniverseTable.trade_date,
UniverseTable.code]).distinct().where(cond)
cursor = self.engine.execute(query)
codes_set = {c[1] for c in cursor.fetchall()}
return sorted(codes_set)
def fetch_industry_range(self,
universe: Universe,
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
category: str = 'sw',
level: int = 1):
industry_category_name = _map_industry_category(category)
cond = universe._query_statements(start_date, end_date, dates)
big_table = join(
Industry, UniverseTable,
and_(Industry.trade_date == UniverseTable.trade_date,
Industry.code == UniverseTable.code,
Industry.industry == industry_category_name, cond))
code_name = 'industryID' + str(level)
category_name = 'industryName' + str(level)
query = select([
Industry.trade_date, Industry.code,
getattr(Industry, code_name).label('industry_code'),
getattr(Industry, category_name).label('industry')
]).select_from(big_table).distinct()
df = pd.read_sql(query, self.engine).dropna()
if universe.is_filtered:
codes = universe.query(self, start_date, end_date, dates)
df = pd.merge(df, codes, how='inner',
on=['trade_date',
'code']).sort_values(['trade_date', 'code'])
return df.drop_duplicates(['trade_date', 'code'])
def fetch_factor_range(self,
universe: Universe,
factors: Union[Transformer, Iterable[object]],
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
external_data: pd.DataFrame = None,
used_factor_tables=None) -> pd.DataFrame:
if isinstance(factors, Transformer):
transformer = factors
else:
transformer = Transformer(factors)
dependency = transformer.dependency
if used_factor_tables:
factor_cols = _map_factors(dependency, used_factor_tables)
else:
factor_cols = _map_factors(dependency, factor_tables)
big_table = Market
joined_tables = set()
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_(Market.trade_date == t.trade_date,
Market.code == t.code,
Market.trade_date.in_(dates)))
else:
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(
[Market.trade_date, Market.code, Market.chgPct] + list(factor_cols.keys())) \
.select_from(big_table).where(
and_(
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()
df = pd.read_sql(query, self.engine).replace([-np.inf, np.inf], np.nan)
if external_data is not None:
df = pd.merge(df, external_data, on=['trade_date', 'code']).dropna()
df.sort_values(['trade_date', 'code'], inplace=True)
df.set_index('trade_date', inplace=True)
res = transformer.transform('code', df).replace([-np.inf, np.inf], np.nan)
res['chgPct'] = df.chgPct
res = res.reset_index()
return pd.merge(res, universe_df[['trade_date', 'code']], how='inner').drop_duplicates(['trade_date', 'code'])
def fetch_risk_model_range(
self,
universe: Universe,
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
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)
cov_risk_cols = [
risk_cov_table.__table__.columns[f] for f in total_risk_factors
]
cond = risk_cov_table.trade_date.in_(
dates) if dates else risk_cov_table.trade_date.between(
start_date, end_date)
query = select([
risk_cov_table.trade_date, risk_cov_table.FactorID,
risk_cov_table.Factor
] + cov_risk_cols).where(cond)
risk_cov = pd.read_sql(query, self.engine).sort_values(
['trade_date', 'FactorID'])
if not excluded:
excluded = []
risk_exposure_cols = [
FullFactor.__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,
and_(FullFactor.trade_date == UniverseTable.trade_date,
FullFactor.code == UniverseTable.code, cond))
query = select(
[FullFactor.trade_date, FullFactor.code, special_risk_col] + risk_exposure_cols).select_from(big_table) \
.distinct()
risk_exp = pd.read_sql(query, self.engine)
if universe.is_filtered:
codes = universe.query(self, start_date, end_date, dates)
risk_exp = pd.merge(risk_exp,
codes,
how='inner',
on=['trade_date', 'code'
]).sort_values(['trade_date', 'code'])
return risk_cov, risk_exp
def fetch_factor_range_forward(self,
universe: Universe,
factors: Union[Transformer, object],
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None):
if isinstance(factors, Transformer):
transformer = factors
else:
transformer = Transformer(factors)
dependency = transformer.dependency
factor_cols = _map_factors(dependency, factor_tables)
codes = universe.query(self, start_date, end_date, dates)
total_codes = codes.code.unique().tolist()
total_dates = codes.trade_date.astype(str).unique().tolist()
big_table = Market
joined_tables = set()
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_(Market.trade_date == t.trade_date,
Market.code == t.code,
Market.trade_date.in_(dates)))
else:
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)
stats = func.lag(list(factor_cols.keys())[0],
-1).over(partition_by=Market.code,
order_by=Market.trade_date).label('dx')
query = select([Market.trade_date, Market.code, Market.chgPct,
stats]).select_from(big_table).where(
and_(Market.trade_date.in_(total_dates),
Market.code.in_(total_codes)))
df = pd.read_sql(query, self.engine) \
.replace([-np.inf, np.inf], np.nan) \
.sort_values(['trade_date', 'code'])
return pd.merge(df, codes[['trade_date', 'code']],
how='inner').drop_duplicates(['trade_date', 'code'])
def fetch_codes_range(self,
universe: Universe,
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None) -> pd.DataFrame:
return universe.query(self, start_date, end_date, dates)
def fetch_codes(self, ref_date: str, universe: Universe) -> List[int]:
df = universe.query(self, ref_date, ref_date)
return sorted(df.code.tolist())
def fetch_factor_range(self,
universe: Universe,
factors: Union[Transformer, Iterable[object]],
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
external_data: pd.DataFrame = None,
used_factor_tables=None) -> pd.DataFrame:
if isinstance(factors, Transformer):
transformer = factors
else:
transformer = Transformer(factors)
dependency = transformer.dependency
if used_factor_tables:
factor_cols = _map_factors(dependency, used_factor_tables)
else:
factor_cols = _map_factors(dependency, factor_tables)
big_table = FullFactor
joined_tables = set()
joined_tables.add(FullFactor.__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)))
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)))
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())) \
.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)
)
).distinct()
df = pd.read_sql(query, self.engine)
if universe.is_filtered:
codes = universe.query(self, start_date, end_date, dates)
df = pd.merge(df, codes, how='inner', on=['trade_date', 'code'])
if external_data is not None:
df = pd.merge(df, external_data, on=['trade_date',
'code']).dropna()
df.sort_values(['trade_date', 'code'], inplace=True)
df.set_index('trade_date', inplace=True)
res = transformer.transform('code', df)
for col in res.columns:
if col not in set(['code', 'isOpen']) and col not in df.columns:
df[col] = res[col].values
df['isOpen'] = df.isOpen.astype(bool)
df = df.reset_index()
return pd.merge(df, universe_df[['trade_date', 'code']], how='inner')
