some codes refined

alphamind/benchmarks/portfolio/linearbuild.py

@@ -38,33 +38,32 @@ def benchmark_build_linear(n_samples: int, n_risks: int, n_loop: int) -> None:
                                     ubound,
                                     risk_exp,
                                     risk_target=(risk_lbound,
-                                                 risk_ubound),
-                                    solver='ECOS')
+                                                 risk_ubound))
     impl_model_time = dt.datetime.now() - start
     print('{0:20s}: {1}'.format('Implemented model (ECOS)', impl_model_time))
 
     c = - er
     bounds = [(lbound, ubound) for _ in range(n_samples)]
-    A_eq = np.ones((1, n_samples))
-    A_eq = np.vstack((A_eq, risk_exp.T))
+    a_eq = np.ones((1, n_samples))
+    a_eq = np.vstack((a_eq, risk_exp.T))
     b_eq = np.hstack((np.array([1.]), risk_exp.T @ bm))
     start = dt.datetime.now()
     for _ in range(n_loop):
-        res = linprog(c, A_eq=A_eq, b_eq=b_eq, bounds=bounds, options={'maxiter': 10000})
+        res = linprog(c, A_eq=a_eq, b_eq=b_eq, bounds=bounds, options={'maxiter': 10000})
     benchmark_model_time = dt.datetime.now() - start
     print('{0:20s}: {1}'.format('Benchmark model (scipy)', benchmark_model_time))
     np.testing.assert_array_almost_equal(x, res['x'])
 
     c = matrix(-er)
-    A = matrix(A_eq)
+    aneq = matrix(a_eq)
     b = matrix(b_eq)
-    G = matrix(np.vstack((np.diag(np.ones(n_samples)), -np.diag(np.ones(n_samples)))))
+    g = matrix(np.vstack((np.diag(np.ones(n_samples)), -np.diag(np.ones(n_samples)))))
     h = matrix(np.hstack((ubound * np.ones(n_samples), -lbound * np.ones(n_samples))))
 
-    solvers.lp(c, G, h, solver='glpk')
+    solvers.lp(c, g, h, solver='glpk')
     start = dt.datetime.now()
     for _ in range(n_loop):
-        res2 = solvers.lp(c, G, h, A, b, solver='glpk')
+        res2 = solvers.lp(c, g, h, aneq, b, solver='glpk')
     benchmark_model_time = dt.datetime.now() - start
     print('{0:20s}: {1}'.format('Benchmark model (glpk)', benchmark_model_time))
     np.testing.assert_array_almost_equal(x, np.array(res2['x']).flatten())

alphamind/data/dbmodel/models.py

@@ -1073,5 +1073,5 @@ if __name__ == '__main__':
 
     from sqlalchemy import create_engine
 
-    engine = create_engine('postgresql+psycopg2://postgres:A12345678!@10.63.6.220/alpha')
+    engine = create_engine('postgresql+psycopg2://user:pwd@host/alpha')
     Base.metadata.create_all(engine)

alphamind/data/engines/sqlengine.py

@@ -80,11 +80,12 @@ class SqlEngine(object):
     def __init__(self,
                  db_url: str):
         self.engine = sa.create_engine(db_url)
+        self.session = None
         self.create_session()
 
     def create_session(self):
-        Session = orm.sessionmaker(bind=self.engine)
-        self.session = Session()
+        db_session = orm.sessionmaker(bind=self.engine)
+        self.session = db_session()
 
     def fetch_factors_meta(self) -> pd.DataFrame:
         query = self.session.query(FactorMaster)
@@ -207,9 +208,7 @@ class SqlEngine(object):
 
 
 if __name__ == '__main__':
-    db_url = 'mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha?charset=cp936'
-
-    import datetime as dt
+    db_url = 'mssql+pymssql://user:pwd@host/alpha?charset=cp936'
 
     universe = Universe('custom', ['zz500'])
     engine = SqlEngine(db_url)

alphamind/tests/analysis/test_riskanalysis.py

@@ -13,6 +13,7 @@ from alphamind.analysis.riskanalysis import risk_analysis
 
 class TestRiskAnalysis(unittest.TestCase):
 
+    @staticmethod
     def test_risk_analysis(self):
         n_samples = 36000
         n_dates = 20

alphamind/tests/portfolio/test_constraints.py

@@ -12,6 +12,7 @@ from alphamind.portfolio.constraints import Constraints
 
 class TestConstraints(unittest.TestCase):
 
+    @staticmethod
     def test_constraints(self):
         cons = Constraints()
 

alphamind/utilities.py

@@ -278,7 +278,3 @@ def aggregate(groups, x, func, ddof=1):
         raise ValueError('({0}) is not recognized as valid functor'.format(func))
 
     return value_data
-
-
-if __name__ == '__main__':
-    pass
\ No newline at end of file