added more industry styles

alphamind/data/engines/sqlengine.py

@@ -207,12 +207,12 @@ class SqlEngine(object):
         cond = universe._query_statements(start_date, end_date, None)
 
         big_table = join(Market, UniverseTable,
-            and_(
-                Market.trade_date == UniverseTable.trade_date,
-                Market.code == UniverseTable.code,
-                cond
-            )
-        )
+                         and_(
+                             Market.trade_date == UniverseTable.trade_date,
+                             Market.code == UniverseTable.code,
+                             cond
+                         )
+                         )
 
         query = select([Market.trade_date, Market.code, stats]) \
             .select_from(big_table)
@@ -379,7 +379,7 @@ class SqlEngine(object):
                              FullFactor.code == UniverseTable.code,
                              cond
                          )
-                    )
+                         )
 
         query = select(
             [FullFactor.trade_date, FullFactor.code, FullFactor.isOpen] + list(factor_cols.keys())) \
@@ -498,7 +498,7 @@ class SqlEngine(object):
                              FullFactor.code == UniverseTable.code,
                              cond
                          )
-                    )
+                         )
 
         query = select(
             [FullFactor.trade_date, FullFactor.code, special_risk_col] + risk_exposure_cols).select_from(big_table) \
@@ -508,7 +508,8 @@ class SqlEngine(object):
 
         if universe.is_filtered:
             codes = universe.query(self, start_date, end_date, dates)
-            risk_exp = pd.merge(risk_exp, codes, how='inner', on=['trade_date', 'code']).sort_values(['trade_date', 'code'])
+            risk_exp = pd.merge(risk_exp, codes, how='inner', on=['trade_date', 'code']).sort_values(
+                ['trade_date', 'code'])
 
         return risk_cov, risk_exp
 
@@ -536,7 +537,8 @@ class SqlEngine(object):
                              start_date: str = None,
                              end_date: str = None,
                              dates: Iterable[str] = None,
-                             category: str = 'sw'):
+                             category: str = 'sw',
+                             level: int = 1):
         industry_category_name = _map_industry_category(category)
 
         cond = universe._query_statements(start_date, end_date, dates)
@@ -548,12 +550,15 @@ class SqlEngine(object):
                              Industry.industry == industry_category_name,
                              cond
                          )
-                    )
+                         )
+
+        code_name = 'industryID' + str(level)
+        category_name = 'industryName' + str(level)
 
         query = select([Industry.trade_date,
                         Industry.code,
-                        Industry.industryID1.label('industry_code'),
-                        Industry.industryName1.label('industry')]).select_from(big_table).distinct()
+                        getattr(Industry, code_name).label('industry_code'),
+                        getattr(Industry, category_name).label('industry')]).select_from(big_table).distinct()
 
         df = pd.read_sql(query, self.engine)
         if universe.is_filtered:
@@ -802,10 +807,10 @@ class SqlEngine(object):
         else:
             id_filter = 'in_'
 
-        t = select([table.trade_id]).\
+        t = select([table.trade_id]). \
             where(and_(table.trade_date <= ref_date,
                        table.operation == 'withdraw')).alias('t')
-        query = select([table]).\
+        query = select([table]). \
             where(and_(getattr(table.trade_id, id_filter)(t),
                        table.trade_date <= ref_date,
                        table.operation == 'lend'))
@@ -823,7 +828,7 @@ class SqlEngine(object):
             rule = x['price_rule'].split('@')
 
             if rule[0] in ['closePrice', 'openPrice']:
-                query = select([getattr(Market, rule[0])]).\
+                query = select([getattr(Market, rule[0])]). \
                     where(and_(Market.code == code, Market.trade_date == rule[1]))
                 data = pd.read_sql(query, self.engine)
                 if not data.empty:
@@ -835,6 +840,7 @@ class SqlEngine(object):
             else:
                 raise KeyError('do not have rule for %s' % x['price_rule'])
             return price
+
         df['price'] = df.apply(lambda x: parse_price_rule(x), axis=1)
 
         df.drop(['remark', 'price_rule', 'operation'], axis=1, inplace=True)
@@ -848,12 +854,10 @@ class SqlEngine(object):
 
 
 if __name__ == '__main__':
-
     universe = Universe('ss', ['hs300'])
 
     engine = SqlEngine()
 
-    df = engine.fetch_outright_status('2017-12-28')
+    df = engine.fetch_industry_range(universe, '2017-12-28', '2017-12-31', category='dx', level=3)
 
     print(df)
-

alphamind/data/engines/utilities.py

@@ -43,5 +43,15 @@ def _map_factors(factors: Iterable[str], used_factor_tables) -> Dict:
 def _map_industry_category(category: str) -> str:
     if category == 'sw':
         return '申万行业分类'
+    elif category == 'zz':
+        return '中证行业分类'
+    elif category == 'dx':
+        return '中证行业分类'
+    elif category == 'zjh':
+        return '证监会行业V2012'
     else:
-        raise ValueError("No other industry is supported at the current time")
\ No newline at end of file
+        raise ValueError("No other industry is supported at the current time")
+
+
+def industry_list(catrgory, level=1):
+    pass
\ No newline at end of file