update examples

alphamind/examples/factor_analysis_example.py

@@ -48,10 +48,9 @@ Model phase: we need 1 constant linear model and one linear regression model
 """
 
 alpha_name = ['alpha_factor']
-#const_features = {alpha_name[0]: LAST('optimism_confidence_25d') + LAST('pessimism_confidence_25d')}
-# const_features = {alpha_name[0]: CSRes(DIFF(1. / LAST('PE')), LAST('roe_q'))}
-
-simple_expression = LAST('cfinc1_q') # CSRes(CSRes(LAST('DividendPS'), LAST('roe_q')), LAST('ep_q'))
+base1 = LAST('roe_q')
+base2 = CSRes(LAST('ep_q'), 'roe_q')
+simple_expression = CSRes(CSRes(LAST('DividendPS'), base1), base2)
 
 const_features = {alpha_name[0]: simple_expression}
 const_weights = np.array([1.])

alphamind/examples/factor_res_analysis.py

@@ -75,14 +75,14 @@ def factor_residue_analysis(start_date,
 
 
 engine = SqlEngine()
-# df = engine.fetch_factor_coverage().groupby('factor').mean()
-# df = df[df.coverage >= 0.98]
+df = engine.fetch_factor_coverage().groupby('factor').mean()
+df = df[df.coverage >= 0.98]
 universe = Universe('custom', ['zz800'])
 
 factor_df = pd.DataFrame()
 
-for i, factor in enumerate(['EGRO']):
-    res = factor_residue_analysis('2012-01-01',
+for i, factor in enumerate(df.index):
+    res = factor_residue_analysis('2011-01-01',
                                   '2018-01-05',
                                   factor,
                                   '5b',