added update special tables

alphamind/analysis/turnoveranalysis.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2018-1-15
+
+@author: cheng.li
+"""
+
+import numpy as np
+from alphamind.data.standardize import standardize
+
+
+def factor_turn_over(factor_values: np.ndarray,
+                     trade_dates: np.ndarray,
+                     codes: np.ndarray,
+                     use_standize: bool=True):
+    if use_standize:
+        factor_values = standardize(factor_values, trade_dates)
+
+
+if __name__ == '__main__':
+    from alphamind.api import *
+    engine = SqlEngine()
+
+    factor = 'ep_q'
+    freq = '5b'
+    start_date = '2017-06-01'
+    end_date = '2017-08-01'
+    universe = Universe('custom', ['zz500'])
+
+

alphamind/examples/factor_res_analysis.py

@@ -70,23 +70,23 @@ def factor_residue_analysis(start_date,
     start_date = advanceDateByCalendar('china.sse', dates[0], '-1d')
     df.loc[start_date] = 0.
     df.sort_index(inplace=True)
-    df['$top1 - top5$'] = df[0] - df[4]
+    df['$top1 - bottom1$'] = df[4] - df[0]
     return df
 
 
 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(df.index):
+for i, factor in enumerate(['EGRO']):
     res = factor_residue_analysis('2012-01-01',
                                   '2018-01-05',
                                   factor,
                                   '5b',
                                   universe,
                                   engine)
-    factor_df[factor] = res['$top1 - top5$']
+    factor_df[factor] = res['$top1 - bottom1$']
     alpha_logger.info('{0}: {1} is done'.format(i + 1, factor))

alphamind/examples/formula_expression.py

@@ -19,9 +19,12 @@ start = dt.datetime.now()
 
 universe = Universe('custom', ['zz800'])
 
-simple_expression = CSRes(LAST('OperCashInToAsset'), 'roe_q')
+factor_name = 'Beta20'
+base1 = LAST('roe_q')
+base2 = CSRes(LAST('ep_q'), 'roe_q')
+simple_expression = CSRes(CSRes(LAST(factor_name), base1), base2)
 
-alpha_factor_name = 'alpha_factor'
+alpha_factor_name = factor_name + '_res'
 alpha_factor = {alpha_factor_name: simple_expression}
 
 # end of formula definition
@@ -29,7 +32,7 @@ alpha_factor = {alpha_factor_name: simple_expression}
 engine = SqlEngine('postgresql+psycopg2://postgres:A12345678!@10.63.6.220/alpha')
 
 neutralize_risk = ['SIZE', 'LEVERAGE'] + industry_styles
-freq = '10b'
+freq = '5b'
 n_bins = 5
 horizon = map_freq(freq)
 
@@ -91,8 +94,6 @@ df = df.cumsum().plot(ax=axes[0], title='Quantile Analysis for {0}'.format(alpha
 
 # =================================================================== #
 
-factor_name = 'PE'
-
 alpha_factor_name = alpha_factor_name + '_1w_diff'
 alpha_factor = {alpha_factor_name: DIFF(simple_expression)}