update api

alphamind/data/engines/utilities.py

@@ -45,6 +45,10 @@ def _map_factors(factors: Iterable[str], used_factor_tables) -> Dict:
             if f not in excluded and f in t.__table__.columns:
                 factor_cols[t.__table__.columns[f]] = t
                 break
+
+    if not factor_cols:
+        raise ValueError(f"some factors in <{factors}> can't be find")
+
     return factor_cols
 
 

alphamind/model/data_preparing.py

@@ -261,7 +261,8 @@ def fetch_train_phase(engine,
                       risk_model: str = 'short',
                       pre_process: Iterable[object] = None,
                       post_process: Iterable[object] = None,
-                      warm_start: int = 0) -> dict:
+                      warm_start: int = 0,
+                      fitting_target: Union[Transformer, object] = None) -> dict:
     if isinstance(alpha_factors, Transformer):
         transformer = alpha_factors
     else:
@@ -281,7 +282,13 @@ def fetch_train_phase(engine,
     horizon = map_freq(frequency)
 
     factor_df = engine.fetch_factor_range(universe, factors=transformer, dates=dates)
-    target_df = engine.fetch_dx_return_range(universe, dates=dates, horizon=horizon)
+    if fitting_target is None:
+        target_df = engine.fetch_dx_return_range(universe, dates=dates, horizon=horizon)
+    else:
+        one_more_date = advanceDateByCalendar('china.sse', dates[-1], frequency)
+        target_df = engine.fetch_factor_range_forward(universe, factors=fitting_target, dates=dates + [one_more_date])
+        target_df = target_df[target_df.trade_date.isin(dates)]
+        target_df = target_df.groupby('code').apply(lambda x: x.fillna(method='pad'))
 
     df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna()
 
@@ -336,7 +343,7 @@ def fetch_predict_phase(engine,
                         pre_process: Iterable[object] = None,
                         post_process: Iterable[object] = None,
                         warm_start: int = 0,
-                        fillna: str=None):
+                        fillna: str = None):
     if isinstance(alpha_factors, Transformer):
         transformer = alpha_factors
     else:
@@ -356,7 +363,8 @@ def fetch_predict_phase(engine,
     factor_df = engine.fetch_factor_range(universe, factors=transformer, dates=dates)
 
     if fillna:
-        factor_df = factor_df.groupby('trade_date').apply(lambda x: x.fillna(x.median())).reset_index(drop=True).dropna()
+        factor_df = factor_df.groupby('trade_date').apply(lambda x: x.fillna(x.median())).reset_index(
+            drop=True).dropna()
     else:
         factor_df = factor_df.dropna()
 
@@ -420,9 +428,10 @@ if __name__ == '__main__':
     universe = Universe('zz500', ['hs300', 'zz500'])
     neutralized_risk = ['SIZE']
     res = fetch_train_phase(engine, ['ep_q'],
-                              '2012-01-05',
-                              '5b',
-                              universe,
-                              16,
-                              neutralized_risk=neutralized_risk)
+                            '2012-01-05',
+                            '5b',
+                            universe,
+                            16,
+                            neutralized_risk=neutralized_risk,
+                            fitting_target='closePrice')
     print(res)