added industry matrix

alphamind/api.py

@@ -24,6 +24,7 @@ from alphamind.data.standardize import standardize
 from alphamind.data.standardize import projection
 from alphamind.data.neutralize import neutralize
 from alphamind.data.engines.sqlengine import factor_tables
+from alphamind.data.engines.utilities import industry_list
 
 from alphamind.model import LinearRegression
 from alphamind.model import LassoRegression
@@ -66,6 +67,7 @@ __all__ = [
     'projection',
     'neutralize',
     'factor_tables',
+    'industry_list',
     'fetch_data_package',
     'fetch_train_phase',
     'fetch_predict_phase',

alphamind/data/engines/sqlengine.py

@@ -42,6 +42,7 @@ from alphamind.data.engines.utilities import _map_factors
 from alphamind.data.engines.utilities import _map_industry_category
 from alphamind.data.engines.utilities import _map_risk_model_table
 from alphamind.data.engines.utilities import factor_tables
+from alphamind.data.engines.utilities import industry_list
 from PyFin.api import advanceDateByCalendar
 
 risk_styles = ['BETA',
@@ -516,13 +517,16 @@ class SqlEngine(object):
     def fetch_industry(self,
                        ref_date: str,
                        codes: Iterable[int],
-                       category: str = 'sw'):
+                       category: str = 'sw',
+                       level: int = 1):
 
         industry_category_name = _map_industry_category(category)
+        code_name = 'industryID' + str(level)
+        category_name = 'industryName' + str(level)
 
         query = select([Industry.code,
-                        Industry.industryID1.label('industry_code'),
-                        Industry.industryName1.label('industry')]).where(
+                        getattr(Industry, code_name).label('industry_code'),
+                        getattr(Industry, category_name).label('industry')]).where(
             and_(
                 Industry.trade_date == ref_date,
                 Industry.code.in_(codes),
@@ -532,6 +536,16 @@ class SqlEngine(object):
 
         return pd.read_sql(query, self.engine)
 
+    def fetch_industry_matrix(self,
+                              ref_date: str,
+                              codes: Iterable[int],
+                              category: str = 'sw',
+                              level: int = 1):
+        df = self.fetch_industry(ref_date, codes, category, level)
+        df = pd.get_dummies(df, columns=['industry'], prefix="", prefix_sep="")
+        industries = industry_list(category, level)
+        return df[['code', 'industry_code'] + industries]
+
     def fetch_industry_range(self,
                              universe: Universe,
                              start_date: str = None,
@@ -566,6 +580,19 @@ class SqlEngine(object):
             df = pd.merge(df, codes, how='inner', on=['trade_date', 'code']).sort_values(['trade_date', 'code'])
         return df
 
+    def fetch_industry_matrix_range(self,
+                                    universe: Universe,
+                                    start_date: str = None,
+                                    end_date: str = None,
+                                    dates: Iterable[str] = None,
+                                    category: str = 'sw',
+                                    level: int = 1):
+
+        df = self.fetch_industry_range(universe, start_date, end_date, dates, category, level)
+        df = pd.get_dummies(df, columns=['industry'], prefix="", prefix_sep="")
+        industries = industry_list(category, level)
+        return df[['trade_date', 'code', 'industry_code'] + industries]
+
     def fetch_data(self, ref_date: str,
                    factors: Iterable[str],
                    codes: Iterable[int],
@@ -857,7 +884,10 @@ if __name__ == '__main__':
     universe = Universe('ss', ['hs300'])
 
     engine = SqlEngine()
+    ref_date = '2017-12-28'
+    codes = universe.query(engine, dates=[ref_date])
+    df = engine.fetch_industry_matrix(ref_date, codes.code.tolist(), 'dx', 1)
+    print(df)
 
-    df = engine.fetch_industry_range(universe, '2017-12-28', '2017-12-31', category='dx', level=3)
-
+    df = engine.fetch_industry_matrix_range(universe, '2011-12-28', '2017-12-31', category='sw', level=1)
     print(df)
\ No newline at end of file

alphamind/data/engines/utilities.py

@@ -13,6 +13,7 @@ from alphamind.data.dbmodel.models import RiskCovLong
 from alphamind.data.dbmodel.models import FullFactor
 from alphamind.data.dbmodel.models import Gogoal
 from alphamind.data.dbmodel.models import Experimental
+from alphamind.data.engines.industries import INDUSTRY_MAPPING
 
 
 factor_tables = [FullFactor, Gogoal, Experimental]
@@ -46,12 +47,12 @@ def _map_industry_category(category: str) -> str:
     elif category == 'zz':
         return '中证行业分类'
     elif category == 'dx':
-        return '中证行业分类'
+        return '东兴行业分类'
     elif category == 'zjh':
         return '证监会行业V2012'
     else:
         raise ValueError("No other industry is supported at the current time")
 
 
-def industry_list(catrgory, level=1):
-    pass
+def industry_list(category: str, level: int=1) -> list:
+    return INDUSTRY_MAPPING[category][level]
\ No newline at end of file