add Eaning Equality factor

README.md

 # FactorCalculate
 
+- /factor
+该文件目录下保存的是因子计算的task文件， 以及每个task所依赖的因子计算文件。
+
+- client.py
+

client_bak.py

@@ -340,7 +340,7 @@ def get_basic_scale_data(trade_date):
 if __name__ == '__main__':
     session1 = str('156099868869460811')
     session2 = str('156099868869460812')
-    session3 = str('156099868869460813')
+    # session3 = str('156099868869460813')
     # session = str(int(time.time() * 1000000 + datetime.now().microsecond))
     start_date = 20100101
     end_date = 20190101
@@ -349,14 +349,15 @@ if __name__ == '__main__':
     _trade_date = TradeDate()
     trade_date_sets = _trade_date.trade_date_sets_ago(start_date, end_date, count)
     if rebuild is True:
+        # growth
         growth = factor_growth.Growth('factor_growth')
         growth.create_dest_tables()
-
+        # historical value
         history_value = historical_value.HistoricalValue('factor_historical_value')
         history_value.create_dest_tables()
-
-        scale = factor_per_share_indicators.PerShareIndicators('factor_scale')
-        scale.create_dest_tables()
+        # scale
+        # scale = factor_per_share_indicators.PerShareIndicators('factor_scale')
+        # scale.create_dest_tables()
 
     for date_index in trade_date_sets:
         # factor_growth
@@ -380,13 +381,13 @@ if __name__ == '__main__':
         print('history_cal_time:{}'.format(time.time() - time1))
 
         # scale
-        valuation_sets, ttm_factor_sets, cash_flow_sets, income_sets, balance_sets = get_basic_scale_data(date_index)
-        valuation_sets = pd.merge(valuation_sets, income_sets, on='symbol')
-        valuation_sets = pd.merge(valuation_sets, ttm_factor_sets, on='symbol')
-        valuation_sets = pd.merge(valuation_sets, cash_flow_sets, on='symbol')
-        valuation_sets = pd.merge(valuation_sets, balance_sets, on='symbol')
-        cache_data.set_cache(session3, date_index, valuation_sets.to_json(orient='records'))
-        factor_per_share_indicators.factor_calculate(date_index=date_index,
-                                                     session=session3)
+        # valuation_sets, ttm_factor_sets, cash_flow_sets, income_sets, balance_sets = get_basic_scale_data(date_index)
+        # valuation_sets = pd.merge(valuation_sets, income_sets, on='symbol')
+        # valuation_sets = pd.merge(valuation_sets, ttm_factor_sets, on='symbol')
+        # valuation_sets = pd.merge(valuation_sets, cash_flow_sets, on='symbol')
+        # valuation_sets = pd.merge(valuation_sets, balance_sets, on='symbol')
+        # cache_data.set_cache(session3, date_index, valuation_sets.to_json(orient='records'))
+        # factor_per_share_indicators.factor_calculate(date_index=date_index,
+        #                                              session=session3)
 
         print('---------------------->')

factor/__init__.py

@@ -4,9 +4,9 @@
 """
 @version: ??
 @author: li
-@file: __init__.py.py
+@file: __init__.py
 @time: 2019-06-30 19:04
 """
 from ultron.cluster.invoke.app_engine import create_app
 
-app = create_app('factor', ['factor.factor_growth', 'factor.historical_value'])
+app = create_app('factor', ['factor.factor_growth', 'factor.historical_value', 'factor.factor_cash_flow'])

factor/factor_growth.py

@@ -8,7 +8,6 @@
 @time: 2019-02-12 10:03
 """
 import json
-from datetime import datetime, timedelta
 from sklearn import linear_model
 from pandas.io.json import json_normalize
 
@@ -21,6 +20,9 @@ from ultron.cluster.invoke.cache_data import cache_data
 
 
 class Growth(FactorBase):
+    """
+    成长类因子
+    """
     def __init__(self, name):
         super(Growth, self).__init__(name)
 
@@ -694,20 +696,6 @@ def do_update(growth, growth_sets, start_date, end_date, count):
     print('----->')
 
 
-def run(args):
-    if args.end_date == 0:
-        end_date = int(datetime.now().date().strftime('%Y%m%d'))
-    else:
-        end_date = args.end_date
-    if args.rebuild:
-        processor_growth = Growth('factor_growth')
-        processor_growth.create_dest_tables()
-        do_update(args.start_date, end_date, args.count, args.growth_sets, processor_growth)
-    if args.update:
-        processor_growth = Growth('factor_growth')
-        do_update(args.start_date, end_date, args.count, args.growth_sets, processor_growth)
-
-
 @app.task()
 def factor_calculate(**kwargs):
     print("growth_kwargs: {}".format(kwargs))

factor/historical_value.py

@@ -29,6 +29,9 @@ from ultron.cluster.invoke.cache_data import cache_data
 
 
 class HistoricalValue(FactorBase):
+    """
+    价值类因子
+    """
 
     def __init__(self, name):
         super(HistoricalValue, self).__init__(name)