update db models

alphamind/data/dbmodel/models.py

@@ -2385,8 +2385,30 @@ class FactorLog(Base):
     minimum = Column(Float(53))
 
 
+class FactorCorrelation(Base):
+    __tablename__ = 'factor_correlation'
+    __table_args__ = (
+        Index('factor_correlation_idx', 'trade_date', 'factor', 'source', unique=True),
+    )
+
+    trade_date = Column(DateTime, primary_key=True, nullable=False)
+    factor = Column(String(30), primary_key=True, nullable=False)
+    source = Column(String(30), primary_key=True, nullable=False)
+
+    BETA = Column(Float(53))
+    MOMENTUM = Column(Float(53))
+    SIZE = Column(Float(53))
+    EARNYILD = Column(Float(53))
+    RESVOL = Column(Float(53))
+    GROWTH = Column(Float(53))
+    BTOP = Column(Float(53))
+    LEVERAGE = Column(Float(53))
+    LIQUIDTY = Column(Float(53))
+    SIZENL = Column(Float(53))
+
+
 if __name__ == '__main__':
     from sqlalchemy import create_engine
 
-    engine = create_engine('postgresql+psycopg2://postgres:we083826@192.168.0.102/alpha')
+    engine = create_engine('postgresql+psycopg2://postgres:A12345678!@10.63.6.220/alpha')
     Base.metadata.create_all(engine)

alphamind/data/engines/sqlengine.py

@@ -626,7 +626,7 @@ if __name__ == '__main__':
     from alphamind.api import alpha_logger
 
     # db_url = 'postgresql+psycopg2://postgres:we083826@localhost/alpha'
-    db_url = 'postgresql+psycopg2://postgres:we083826@192.168.0.102/alpha'
+    db_url = 'postgresql+psycopg2://postgres:A12345678!@10.63.6.220/alpha'
 
     universe = Universe('custom', ['zz500'])
     engine = SqlEngine(db_url)
@@ -651,5 +651,7 @@ if __name__ == '__main__':
     alpha_logger.info('end')
     data2 = engine.fetch_codes_range(universe, start_date=start_date, end_date=end_date, dates=dates)
     alpha_logger.info('end')
+    data2 = engine.fetch_dx_return_range(universe, start_date=start_date, end_date=end_date, dates=dates)
+    alpha_logger.info('end')
 
     print(data1)

alphamind/examples/formula_expression.py

@@ -17,13 +17,17 @@ import datetime as dt
 
 start = dt.datetime.now()
 
-# formula1 = CSRank(DIFF(LOG("turnoverVol")))
-# formula2 = CSRank((LAST('highestPrice') - LAST('lowestPrice')) / LAST('lowestPrice'))
-# expression = -CORR(6, formula1 ^ formula2)
+formula1 = CSRank(DIFF(LOG("turnoverVol")))
+formula2 = CSRank((LAST('closePrice') - LAST('openPrice')) / LAST('openPrice'))
+expression = -CORR(6, formula1 ^ formula2)
+# expression1 = -0.6 * LAST('con_pe_rolling') - 0.6 * LAST('con_pb_rolling') + 0.6 * LAST('con_eps') + 1.2 * LAST('con_target_price')
+# expression2 = LAST("IVR")
 
-factor1 = LAST('RVOL')
-factor2 = LAST('IVR')
-expression = RES(20, factor2 ^ factor1)
+#expression = expression1 + expression2
+
+# factor1 = LAST('RVOL')
+# factor2 = LAST('IVR')
+# expression = RES(20, factor2 ^ factor1)
 
 # expression = MA(1, "EPS")
 
@@ -33,14 +37,14 @@ alpha_factor = {alpha_factor_name: expression}
 # end of formula definition
 
 engine = SqlEngine('postgresql+psycopg2://postgres:A12345678!@10.63.6.220/alpha')
-universe = Universe('custom', ['zz500'])
+universe = Universe('custom', ['ashare_ex'])
 benchmark_code = 905
 neutralize_risk = ['SIZE'] + industry_styles
 freq = '1w'
 n_bins = 5
 
 dates = makeSchedule('2012-01-01',
-                     '2017-09-18',
+                     '2017-10-11',
                      tenor=freq,
                      calendar='china.sse')
 
@@ -71,7 +75,7 @@ for i, value in enumerate(factor_groups):
         er = factor_processing(total_data[[alpha_factor_name]].values,
                                pre_process=[winsorize_normal, standardize],
                                risk_factors=risk_exp,
-                               post_process=[standardize])
+                               post_process=[winsorize_normal, standardize])
         res = er_quantile_analysis(er,
                                    n_bins=n_bins,
                                    dx_return=dx_return,