def fetch_model(self,
ref_date=None,
model_type=None,
model_version=None,
is_primary=True,
model_id=None) -> pd.DataFrame:
from alphamind.model.composer import DataMeta
conditions = []
if ref_date:
conditions.append(Models.trade_date == ref_date)
if model_id:
conditions.append(Models.model_id == model_id)
if model_type:
conditions.append(Models.model_type == model_type)
if model_version:
conditions.append(Models.model_version == model_version)
conditions.append(Models.is_primary == is_primary)
query = select([Models]).where(and_(*conditions))
model_df = pd.read_sql(query, self.engine)
for i, data in enumerate(zip(model_df.model_desc, model_df.data_meta)):
model_desc, data_desc = data
model_df.loc[i, 'model'] = load_model(model_desc)
model_df.loc[i, 'data_meta'] = DataMeta.load(data_desc)
del model_df['model_desc']
return model_df
def test_data_meta_persistence(self):
freq = '5b'
universe = Universe('zz800')
batch = 4
neutralized_risk = ['SIZE']
risk_model = 'long'
pre_process = ['standardize', 'winsorize_normal']
post_process = ['standardize', 'winsorize_normal']
warm_start = 2
data_source = 'postgresql://*****:*****@server/dummy'
data_meta = DataMeta(freq=freq,
universe=universe,
batch=batch,
neutralized_risk=neutralized_risk,
risk_model=risk_model,
pre_process=pre_process,
post_process=post_process,
warm_start=warm_start,
data_source=data_source)
data_desc = data_meta.save()
loaded_data = DataMeta.load(data_desc)
self.assertEqual(data_meta.freq, loaded_data.freq)
self.assertEqual(data_meta.universe, loaded_data.universe)
self.assertEqual(data_meta.batch, loaded_data.batch)
self.assertEqual(data_meta.neutralized_risk, loaded_data.neutralized_risk)
self.assertEqual(data_meta.risk_model, loaded_data.risk_model)
self.assertEqual(data_meta.pre_process, loaded_data.pre_process)
self.assertEqual(data_meta.post_process, loaded_data.post_process)
self.assertEqual(data_meta.warm_start, loaded_data.warm_start)
self.assertEqual(data_meta.data_source, loaded_data.data_source)