added general build portfolio interface

alphamind/analysis/factoranalysis.py

@@ -9,11 +9,15 @@ import numpy as np
 from typing import Optional
 from typing import List
 from alphamind.data.neutralize import neutralize
+from alphamind.portfolio.longshortbulder import long_short_build
+from alphamind.portfolio.rankbuilder import rank_build
+from alphamind.portfolio.percentbuilder import percent_build
+from alphamind.portfolio.linearbuilder import linear_build
 
 
 def factor_processing(raw_factor: np.ndarray,
                       pre_process: Optional[List]=None,
-                      risk_factors: Optional[np.ndarray]=None):
+                      risk_factors: Optional[np.ndarray]=None) -> np.ndarray:
 
     new_factor = raw_factor
 
@@ -27,6 +31,26 @@ def factor_processing(raw_factor: np.ndarray,
     return new_factor
 
 
+def build_portfolio(er: np.ndarray,
+                    builder: Optional[str]='long_short',
+                    **kwargs) -> np.ndarray:
+
+    builder = builder.lower()
+
+    if builder == 'long_short':
+        return long_short_build(er, **kwargs)
+    elif builder == 'rank':
+        return rank_build(er, **kwargs)
+    elif builder == 'percent_build':
+        return percent_build(er, **kwargs)
+    elif builder == 'linear_prog':
+        status, _, weight = linear_build(er, **kwargs)
+        if status != 'optimal':
+            raise ValueError('linear programming optimizer in status: {0}'.format(status))
+        else:
+            return weight
+
+
 if __name__ == '__main__':
 
     from alphamind.data.standardize import standardize