remove dependency on cvxpy and cvxopt

alphamind/examples/factor_analysis_example.py

@@ -14,13 +14,15 @@ from PyFin.api import *
 
 strategies = {
     'prod': {
-        'factors': ['RVOL', 'EPS', 'DROEAfterNonRecurring', 'DivP', 'CFinc1', 'BDTO'],
-        'weights': [0.05, 0.3, 0.35, 0.075, 0.15, 0.05]
+        # 'factors': ['RVOL', 'EPS', 'DROEAfterNonRecurring', 'DivP', 'CFinc1', 'BDTO'],
+        # 'weights': [0.05, 0.3, 0.35, 0.075, 0.15, 0.05]
+        'factors': ['CHV'],
+        'weights': [1.]
     },
-    'candidate': {
-        'factors': ['RVOL', 'EPS', 'CFinc1', 'BDTO', 'VAL', 'GREV', 'ROEDiluted'],
-        'weights': [0.02, 0.2, 0.15, 0.05, 0.2, 0.2, 0.2]
-    }
+    # 'candidate': {
+    #     'factors': ['RVOL', 'EPS', 'CFinc1', 'BDTO', 'VAL', 'GREV', 'ROEDiluted'],
+    #     'weights': [0.02, 0.2, 0.15, 0.05, 0.2, 0.2, 0.2]
+    # }
 }
 
 
@@ -39,7 +41,7 @@ elif freq == '1d':
     horizon = 0
 
 dates = makeSchedule('2017-01-01',
-                     '2017-08-18',
+                     '2017-08-20',
                      tenor=freq,
                      calendar='china.sse')
 
@@ -75,7 +77,7 @@ for strategy in strategies:
         risk_target = risk_exp_expand.T @ benchmark
 
         lbound = np.zeros(len(total_data))
-        ubound = 0.02 + benchmark
+        ubound = 0.01 + benchmark
 
         constraint = Constraints(risk_exp_expand, risk_names)
         for i, name in enumerate(risk_names):

alphamind/examples/formula_expression.py

@@ -18,7 +18,7 @@ base_factors = ['EPS', 'ROEDiluted', 'VAL', 'CFinc1']
 expression = 0.
 
 for name in base_factors:
-    expression = expression + LAST(name)
+    expression = expression + RSI(10, name)
 
 alpha_factor_name = 'alpha_factor'
 alpha_factor = {alpha_factor_name: expression}

alphamind/examples/plot_quantile_res.py

@@ -31,14 +31,15 @@ elif freq == '1d':
     horizon = 0
 
 start_date = '2017-01-01'
-end_date = '2017-08-18'
+end_date = '2017-08-22'
 
 dates = makeSchedule(start_date,
                      end_date,
                      tenor=freq,
-                     calendar='china.sse')
+                     calendar='china.sse',
+                     dateRule=BizDayConventions.Following)
 
-prod_factors = ['EPS']
+prod_factors = ['CHV']
 
 all_data = engine.fetch_data_range(universe, prod_factors, dates=dates, benchmark=905)
 factor_all_data = all_data['factor']
@@ -59,6 +60,7 @@ for factor in prod_factors:
         returns = engine.fetch_dx_return(date, codes, horizon=horizon)
 
         total_data = pd.merge(data, returns, on=['Code']).dropna()
+        print('{0}: {1}'.format(date, len(data)))
         risk_exp = total_data[neutralize_risk].values.astype(float)
         dx_return = total_data.dx.values
         benchmark = total_data.weight.values

requirements.txt

-cvxopt >= 1.1.9
-cvxpy >= 0.4.9
 cython >= 0.25.2
 finance-python >= 0.5.7
 mysqlclient >= 1.3.10