FIX: wrong dx return calculation

alphamind/data/dbmodel/models/__init__.py

@@ -20,6 +20,10 @@ if "DB_VENDOR" in os.environ and os.environ["DB_VENDOR"].lower() == "rl":
     from alphamind.data.dbmodel.models.models_rl import SpecificRiskLong
     from alphamind.data.dbmodel.models.models_rl import IndexComponent
     from alphamind.data.dbmodel.models.models_rl import IndexWeight
+
+    from alphamind.data.dbmodel.models.models_rl import FactorMomentum
+
+    factor_tables = [Market, RiskExposure, FactorMomentum]
 else:
     from alphamind.data.dbmodel.models.models import Market
     from alphamind.data.dbmodel.models.models import IndexMarket
@@ -35,3 +39,5 @@ else:
     from alphamind.data.dbmodel.models.models import FactorMaster
     from alphamind.data.dbmodel.models.models import IndexComponent
     from alphamind.data.dbmodel.models.models import RiskMaster
+
+    factor_tables = [Market, RiskExposure]

alphamind/data/dbmodel/models/models_rl.py

@@ -401,6 +401,85 @@ class _SpecificRiskShort(Base):
     SRISK = Column(FLOAT)
 
 
+# Factor tables
+
+class _FactorMomentum(Base):
+    __tablename__ = 'factor_momentum'
+    __table_args__ = (
+        Index('factor_momentum_uindex', 'trade_date', 'security_code', 'flag', unique=True),
+    )
+
+    id = Column(INT, primary_key=True)
+    code = Column("security_code", Text, nullable=False)
+    trade_date = Column(Date, nullable=False)
+    ADX14D = Column(FLOAT)
+    ADXR14D = Column(FLOAT)
+    APBMA5D = Column(FLOAT)
+    ARC50D = Column(FLOAT)
+    BBI = Column(FLOAT)
+    BIAS10D = Column(FLOAT)
+    BIAS20D = Column(FLOAT)
+    BIAS5D = Column(FLOAT)
+    BIAS60D = Column(FLOAT)
+    CCI10D = Column(FLOAT)
+    CCI20D = Column(FLOAT)
+    CCI5D = Column(FLOAT)
+    CCI88D = Column(FLOAT)
+    ChgTo1MAvg = Column(FLOAT)
+    ChgTo1YAvg = Column(FLOAT)
+    ChgTo3MAvg = Column(FLOAT)
+    ChkOsci3D10D = Column(FLOAT)
+    ChkVol10D = Column(FLOAT)
+    DEA = Column(FLOAT)
+    EMA10D = Column(FLOAT)
+    EMA120D = Column(FLOAT)
+    EMA12D = Column(FLOAT)
+    EMA20D = Column(FLOAT)
+    EMA26D = Column(FLOAT)
+    EMA5D = Column(FLOAT)
+    EMA60D = Column(FLOAT)
+    EMV14D = Column(FLOAT)
+    EMV6D = Column(FLOAT)
+    Fiftytwoweekhigh = Column(FLOAT)
+    HT_TRENDLINE = Column(FLOAT)
+    KAMA10D = Column(FLOAT)
+    MA10Close = Column(FLOAT)
+    MA10D = Column(FLOAT)
+    MA10RegressCoeff12 = Column(FLOAT)
+    MA10RegressCoeff6 = Column(FLOAT)
+    MA120D = Column(FLOAT)
+    MA20D = Column(FLOAT)
+    MA5D = Column(FLOAT)
+    MA60D = Column(FLOAT)
+    MACD12D26D = Column(FLOAT)
+    MIDPOINT10D = Column(FLOAT)
+    MIDPRICE10D = Column(FLOAT)
+    MTM10D = Column(FLOAT)
+    PLRC12D = Column(FLOAT)
+    PLRC6D = Column(FLOAT)
+    PM10D = Column(FLOAT)
+    PM120D = Column(FLOAT)
+    PM20D = Column(FLOAT)
+    PM250D = Column(FLOAT)
+    PM5D = Column(FLOAT)
+    PM60D = Column(FLOAT)
+    PMDif5D20D = Column(FLOAT)
+    PMDif5D60D = Column(FLOAT)
+    RCI12D = Column(FLOAT)
+    RCI24D = Column(FLOAT)
+    SAR = Column(FLOAT)
+    SAREXT = Column(FLOAT)
+    SMA15D = Column(FLOAT)
+    TEMA10D = Column(FLOAT)
+    TEMA5D = Column(FLOAT)
+    TRIMA10D = Column(FLOAT)
+    TRIX10D = Column(FLOAT)
+    TRIX5D = Column(FLOAT)
+    UOS7D14D28D = Column(FLOAT)
+    WMA10D = Column(FLOAT)
+    flag = Column(INT, server_default=text("'1'"))
+
+
 Market = _StkDailyPricePro
 IndexMarket = _IndexDailyPrice
 Universe = _StkUniverse
@@ -414,3 +493,5 @@ SpecificRiskShort = _SpecificRiskShort
 SpecificRiskLong = _SpecificRiskLong
 IndexComponent = _IndexComponent
 IndexWeight = _Index
+
+FactorMomentum = _FactorMomentum

alphamind/data/engines/sqlengine/sqlengine_rl.py

@@ -31,7 +31,7 @@ from alphamind.data.dbmodel.models.models_rl import (
     RiskExposure,
     Universe as UniverseTable,
     IndexComponent,
-    IndexWeight
+    IndexWeight,
 )
 from alphamind.data.engines.utilities import factor_tables
 from alphamind.data.engines.utilities import _map_factors
@@ -229,10 +229,12 @@ class SqlEngine:
             )
         )
         df1 = pd.read_sql(t1, self.session.bind).dropna()
-        df2 = self.fetch_codes_range(universe, start_date, end_date, dates)
+        df1 = self._create_stats(df1, horizon, offset)
 
+        df2 = self.fetch_codes_range(universe, start_date, end_date, dates)
+        df2["trade_date"] = pd.to_datetime(df2["trade_date"])
         df = pd.merge(df1, df2, on=["trade_date", "code"])
-        df = self._create_stats(df, horizon, offset)
+
         if dates:
             df = df[df.trade_date.isin(dates)]
 
@@ -711,6 +713,43 @@ class SqlEngine:
         ).distinct()
         return pd.read_sql(query, self.engine)
 
+    def fetch_data(self,
+                   ref_date: str,
+                   factors: Iterable[str],
+                   codes: Iterable[int],
+                   benchmark: int = None,
+                   risk_model: str = 'short',
+                   industry: str = 'sw') -> Dict[str, pd.DataFrame]:
+
+        total_data = dict()
+
+        transformer = Transformer(factors)
+        factor_data = self.fetch_factor(ref_date,
+                                        transformer,
+                                        codes,
+                                        used_factor_tables=factor_tables)
+
+        if benchmark:
+            benchmark_data = self.fetch_benchmark(ref_date, benchmark)
+            total_data['benchmark'] = benchmark_data
+            factor_data = pd.merge(factor_data, benchmark_data, how='left', on=['code'])
+            factor_data['weight'] = factor_data['weight'].fillna(0.)
+
+        if risk_model:
+            excluded = list(set(total_risk_factors).intersection(transformer.dependency))
+            risk_cov, risk_exp = self.fetch_risk_model(ref_date, codes, risk_model, excluded)
+            factor_data = pd.merge(factor_data, risk_exp, how='left', on=['code'])
+            total_data['risk_cov'] = risk_cov
+
+        industry_info = self.fetch_industry(ref_date=ref_date,
+                                            codes=codes,
+                                            category=industry)
+
+        factor_data = pd.merge(factor_data, industry_info, on=['code'])
+
+        total_data['factor'] = factor_data
+        return total_data
+
     def fetch_data_range(self,
                          universe: Universe,
                          factors: Iterable[str],
@@ -721,7 +760,8 @@ class SqlEngine:
                          risk_model: str = 'short',
                          industry: str = 'sw',
                          external_data: pd.DataFrame = None) -> Dict[str, pd.DataFrame]:
-        total_data = dict()
+
+        total_data = {}
         transformer = Transformer(factors)
         factor_data = self.fetch_factor_range(universe,
                                               transformer,
@@ -757,22 +797,25 @@ class SqlEngine:
 
 
 if __name__ == "__main__":
+    from PyFin.api import makeSchedule
     db_url = "mysql+mysqldb://reader:Reader#2020@121.37.138.1:13317/vision?charset=utf8"
 
     sql_engine = SqlEngine(db_url=db_url)
     universe = Universe("hs300")
     start_date = '2020-01-01'
-    end_date = '2020-02-21'
+    end_date = '2020-04-21'
     benchmark = 300
-    df = sql_engine.fetch_factor("2020-02-21", factors=["BETA"], codes=["2010031963"])
-    print(df)
-    df = sql_engine.fetch_factor_range(universe=universe, start_date=start_date, end_date=end_date, factors=["BETA"])
-    print(df)
-    df = sql_engine.fetch_codes_range(start_date=start_date, end_date=end_date, universe=Universe("hs300"))
-    print(df)
-    df = sql_engine.fetch_dx_return("2020-10-09", codes=["2010031963"])
-    print(df)
-    df = sql_engine.fetch_dx_return_range(universe, start_date=start_date, end_date=end_date)
+    factors = ["EMA5D", "EMV6D"]
+    ref_dates = makeSchedule(start_date, end_date, "10b", 'china.sse')
+    # df = sql_engine.fetch_factor("2020-02-21", factors=factors, codes=["2010031963"])
+    # print(df)
+    # df = sql_engine.fetch_factor_range(universe=universe, start_date=start_date, end_date=end_date, factors=factors)
+    # print(df)
+    # df = sql_engine.fetch_codes_range(start_date=start_date, end_date=end_date, universe=Universe("hs300"))
+    # print(df)
+    # df = sql_engine.fetch_dx_return("2020-10-09", codes=["2010031963"])
+    # print(df)
+    df = sql_engine.fetch_dx_return_range(universe, dates=ref_dates, horizon=9)
     print(df)
     df = sql_engine.fetch_dx_return_index("2020-10-09", index_code=benchmark)
     print(df)
@@ -805,4 +848,12 @@ if __name__ == "__main__":
                                            end_date=end_date,
                                            model_type="factor")
     print(df)
+    df = sql_engine.fetch_data("2020-02-11", factors=factors, codes=["2010031963"], benchmark=300)
+    print(df)
+    df = sql_engine.fetch_data_range(universe,
+                                     factors=factors,
+                                     start_date=start_date,
+                                     end_date=end_date,
+                                     benchmark=300)
+    print(df)
 

alphamind/data/engines/utilities.py

@@ -17,8 +17,8 @@ from alphamind.data.dbmodel.models import RiskExposure
 from alphamind.data.dbmodel.models import SpecificRiskDay
 from alphamind.data.dbmodel.models import SpecificRiskLong
 from alphamind.data.dbmodel.models import SpecificRiskShort
+from alphamind.data.dbmodel.models import factor_tables
 from alphamind.data.engines.industries import INDUSTRY_MAPPING
-factor_tables = [Market, RiskExposure]
 
 
 def _map_risk_model_table(risk_model: str) -> tuple:

alphamind/tests/test_suite.py

@@ -10,9 +10,11 @@ import os
 SKIP_ENGINE_TESTS = True
 
 if not SKIP_ENGINE_TESTS:
+    try:
         DATA_ENGINE_URI = os.environ['DB_URI']
-else:
-    DATA_ENGINE_URI = None
+    except KeyError:
+        DATA_ENGINE_URI = "mysql+mysqldb://reader:Reader#2020@121.37.138.1:13317/vision?charset=utf8"
+
 
 if __name__ == '__main__':
     from simpleutils import add_parent_path