fixed bug

alphamind/analysis/factoranalysis.py

@@ -75,6 +75,7 @@ class FDataPack(object):
                  codes: List=None,
                  groups: Optional[np.ndarray]=None,
                  benchmark: Optional[np.ndarray]=None,
+                 constraints: Optional[np.ndarray]=None,
                  risk_exp: Optional[np.ndarray]=None,
                  risk_names: List[str]=None):
 
@@ -91,10 +92,11 @@ class FDataPack(object):
         else:
             self.benchmark = None
         self.risk_exp = risk_exp
+        self.constraints = constraints
         self.risk_names = risk_names
 
-    def benchmark_risk_exp(self) -> np.ndarray:
-        return self.benchmark @ self.risk_exp
+    def benchmark_constraints(self) -> np.ndarray:
+        return self.benchmark @ self.constraints
 
     def settle(self, weights: np.ndarray, dx_return: np.ndarray) -> pd.DataFrame:
 
@@ -151,6 +153,7 @@ def factor_analysis(factors: pd.DataFrame,
                     benchmark: Optional[np.ndarray]=None,
                     risk_exp: Optional[np.ndarray]=None,
                     is_tradable: Optional[np.ndarray]=None,
+                    constraints: Optional[np.ndarray]=None,
                     method='risk_neutral',
                     **kwargs) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:
 
@@ -160,7 +163,8 @@ def factor_analysis(factors: pd.DataFrame,
     data_pack = FDataPack(raw_factors=factors.values,
                           groups=industry,
                           benchmark=benchmark,
-                          risk_exp=risk_exp)
+                          risk_exp=risk_exp,
+                          constraints=constraints)
 
     er = data_pack.factor_processing([winsorize_normal, standardize], [standardize]) @ factor_weights
 
@@ -177,12 +181,12 @@ def factor_analysis(factors: pd.DataFrame,
             risk_lbound = kwargs['risk_bound'][0]
             risk_ubound = kwargs['risk_bound'][1]
         else:
-            risk_lbound = data_pack.benchmark_risk_exp()
-            risk_ubound = data_pack.benchmark_risk_exp()
+            risk_lbound = data_pack.benchmark_constraints()
+            risk_ubound = data_pack.benchmark_constraints()
 
         weights = build_portfolio(er,
                                   builder='linear',
-                                  risk_exposure=risk_exp,
+                                  risk_constraints=constraints,
                                   lbound=lbound,
                                   ubound=ubound,
                                   risk_target=(risk_lbound, risk_ubound),

alphamind/data/engines/sqlengine.py

@@ -20,7 +20,6 @@ from alphamind.data.dbmodel.models import FactorMaster
 from alphamind.data.dbmodel.models import Strategy
 from alphamind.data.dbmodel.models import DailyReturn
 from alphamind.data.dbmodel.models import IndexComponent
-from alphamind.data.dbmodel.models import Universe as UniverseTable
 from PyFin.api import advanceDateByCalendar
 
 risk_styles = ['BETA',
@@ -180,7 +179,7 @@ class SqlEngine(object):
         total_data = {'risk_cov': risk_cov_data}
 
         if benchmark:
-            query = select([IndexComponent.code, (IndexComponent.weight / 100.).lable('weight')]).where(
+            query = select([IndexComponent.Code, (IndexComponent.weight / 100.).label('weight')]).where(
                 and_(
                     IndexComponent.Date == ref_date,
                     IndexComponent.indexCode == benchmark
@@ -205,7 +204,7 @@ if __name__ == '__main__':
 
     import datetime as dt
 
-    universe = Universe('zz500', ['zz500'], filter_cond=Uqer.BLEV >= 0.1)
+    universe = Universe('zz500', ['zz500'], filter_cond=(Uqer.BLEV >= 0.1) & (Uqer.BLEV <= 0.1))
     engine = SqlEngine(db_url)
     ref_date = '2017-07-04'
 

alphamind/portfolio/linearbuilder.py

@@ -18,13 +18,13 @@ solvers.options['glpk'] = {'msg_lev': 'GLP_MSG_OFF'}
 def linear_build(er: np.ndarray,
                  lbound: Union[np.ndarray, float],
                  ubound: Union[np.ndarray, float],
-                 risk_exposure: np.ndarray,
+                 risk_constraints: np.ndarray,
                  risk_target: Tuple[np.ndarray, np.ndarray],
                  solver: str=None) -> Tuple[str, np.ndarray, np.ndarray]:
-    n, m = risk_exposure.shape
+    n, m = risk_constraints.shape
     w = cvxpy.Variable(n)
 
-    curr_risk_exposure = risk_exposure.T @ w
+    curr_risk_exposure = risk_constraints.T @ w
 
     if not risk_target:
         constraints = [w >= lbound,

alphamind/tests/analysis/test_factoranalysis.py

@@ -50,7 +50,8 @@ class TestFactorAnalysis(unittest.TestCase):
                                                        d1returns=self.d1returns,
                                                        industry=industry,
                                                        benchmark=benchmark,
-                                                       risk_exp=self.risk_factor)
+                                                       risk_exp=self.risk_factor,
+                                                       constraints=self.risk_factor)
 
         weight = weight_table.weight
 
@@ -70,7 +71,8 @@ class TestFactorAnalysis(unittest.TestCase):
                                                        d1returns=self.d1returns,
                                                        industry=industry,
                                                        benchmark=benchmark,
-                                                       risk_exp=self.risk_factor)
+                                                       risk_exp=self.risk_factor,
+                                                       constraints=self.risk_factor)
 
         weight = weight_table.weight
         self.assertEqual(analysis_table['er'].sum() / analysis_table['er'][-1], 2.0)

alphamind/utilities.py

@@ -273,12 +273,4 @@ def aggregate(groups, x, func, ddof=1):
 
 
 if __name__ == '__main__':
-
-    x1 = np.random.randint(30, size=1000)
+    pass
\ No newline at end of file
-    array = np.unique(x1)
-
-    x2 = np.random.randint(30, size=1000)
-
-    res = array_index(array, x2)
-
-    print(res)
\ No newline at end of file