directly use model's member

alphamind/model/composer.py

@@ -21,7 +21,6 @@ class DataMeta(object):
 
     def __init__(self,
                  engine,
-                 alpha_factors: Union[Transformer, Iterable[object]],
                  freq: str,
                  universe: Universe,
                  batch: int,
@@ -32,7 +31,6 @@ class DataMeta(object):
                  warm_start: int = 0):
         self.engine = engine
         self.alpha_model = alpha_model
-        self.alpha_factors = alpha_factors
         self.freq = freq
         self.universe = universe
         self.batch = batch
@@ -47,7 +45,7 @@ def train_model(ref_date: str,
                 alpha_model: ModelBase,
                 data_meta: DataMeta):
     train_data = fetch_train_phase(data_meta.engine,
-                                   data_meta.alpha_factors,
+                                   alpha_model.formulas,
                                    ref_date,
                                    data_meta.freq,
                                    data_meta.universe,
@@ -68,7 +66,7 @@ def predict_by_model(ref_date: str,
                      alpha_model: ModelBase,
                      data_meta):
     predict_data = fetch_predict_phase(data_meta.engine,
-                                       data_meta.alpha_factors,
+                                       alpha_model.formulas,
                                        ref_date,
                                        data_meta.freq,
                                        data_meta.universe,
@@ -129,7 +127,7 @@ if __name__ == '__main__':
     from alphamind.data.engines.sqlengine import SqlEngine
     from alphamind.model.linearmodel import ConstLinearModel
 
-    engine = SqlEngine()
+    engine = SqlEngine("postgres+psycopg2://postgres:we083826@localhost/alpha")
     alpha_model = ConstLinearModel(['EPS'], np.array([1.]))
     alpha_factors = ['EPS']
     freq = '1w'
@@ -141,7 +139,6 @@ if __name__ == '__main__':
     pos_process = [winsorize_normal, standardize]
 
     data_meta = DataMeta(engine,
-                         alpha_factors,
                          freq,
                          universe,
                          batch,

alphamind/model/data_preparing.py

@@ -247,7 +247,7 @@ def fetch_data_package(engine: SqlEngine,
 
 
 def fetch_train_phase(engine,
-                      alpha_factors: Iterable[object],
+                      alpha_factors: Union[Transformer, Iterable[object]],
                       ref_date,
                       frequency,
                       universe,
@@ -257,7 +257,10 @@ def fetch_train_phase(engine,
                       pre_process: Iterable[object] = None,
                       post_process: Iterable[object] = None,
                       warm_start: int = 0) -> dict:
-    transformer = Transformer(alpha_factors)
+    if isinstance(alpha_factors, Transformer):
+        transformer = alpha_factors
+    else:
+        transformer = Transformer(alpha_factors)
 
     p = Period(frequency)
     p = Period(length=-(warm_start + batch + 1) * p.length(), units=p.units())
@@ -317,7 +320,7 @@ def fetch_train_phase(engine,
 
 
 def fetch_predict_phase(engine,
-                        alpha_factors: Iterable[object],
+                        alpha_factors: Union[Transformer, Iterable[object]],
                         ref_date,
                         frequency,
                         universe,
@@ -327,7 +330,10 @@ def fetch_predict_phase(engine,
                         pre_process: Iterable[object] = None,
                         post_process: Iterable[object] = None,
                         warm_start: int = 0):
-    transformer = Transformer(alpha_factors)
+    if isinstance(alpha_factors, Transformer):
+        transformer = alpha_factors
+    else:
+        transformer = Transformer(alpha_factors)
 
     p = Period(frequency)
     p = Period(length=-(warm_start + batch) * p.length(), units=p.units())