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Commit 7f7d2df6 authored by Dr.李's avatar Dr.李
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FIX: wrong calculation of returns

parent c14a3881
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Showing with 357 additions and 228 deletions
alphamind/data/engines/sqlengine/sqlengine_rl.py
View file @ 7f7d2df6
......@@ -90,7 +90,8 @@ macro_styles = ['COUNTRY']
total_risk_factors = risk_styles + industry_styles + macro_styles
_map_index_codes = {
300: "2070000060"
300: "2070000060",
905: "2070000187",
}
......@@ -128,8 +129,8 @@ class SqlEngine:
def _create_stats(self, df, horizon, offset):
df.set_index("trade_date", inplace=True)
df["dx"] = np.log(1. + df["chgPct"])
df = df.groupby("code").rolling(window=horizon + 1)['dx'].sum().shift(-(offset + 1)).dropna().reset_index()
df["dx"] = np.log(1. + df["chgPct"] / 100.)
df = df.groupby("code").rolling(window=horizon + 1)['dx'].sum().groupby(level=0).shift(-(horizon + offset + 1)).dropna().reset_index()
return df
def fetch_dx_return(self,
......@@ -271,6 +272,7 @@ class SqlEngine:
df = pd.read_sql(query, self.session.bind).dropna()
df = self._create_stats(df, horizon, offset)
df["trade_date"] = pd.to_datetime(df["trade_date"])
if dates:
df = df[df.trade_date.isin(dates)]
......@@ -412,6 +414,7 @@ class SqlEngine:
res['chgPct'] = df.chgPct
res['secShortName'] = df['secShortName']
res = res.reset_index()
res["trade_date"] = pd.to_datetime(res["trade_date"])
return pd.merge(res, universe_df[['trade_date', 'code']], how='inner').drop_duplicates(
['trade_date', 'code'])
......@@ -487,7 +490,9 @@ class SqlEngine:
)
).distinct()
return pd.read_sql(query, self.session.bind)
df = pd.read_sql(query, self.session.bind)
df["trade_date"] = pd.to_datetime(df["trade_date"])
return df
def fetch_risk_model(self,
ref_date: str,
......@@ -561,6 +566,7 @@ class SqlEngine:
cond
)
risk_cov = pd.read_sql(query, self.engine).sort_values(['trade_date', 'FactorID'])
risk_cov["trade_date"] = pd.to_datetime(risk_cov["trade_date"])
if not excluded:
excluded = []
......@@ -591,6 +597,7 @@ class SqlEngine:
.distinct()
risk_exp = pd.read_sql(query, self.engine).sort_values(['trade_date', 'code']).dropna()
risk_exp["trade_date"] = pd.to_datetime(risk_exp["trade_date"])
if not model_type:
return risk_cov, risk_exp
......@@ -711,7 +718,9 @@ class SqlEngine:
IndexComponent.indexCode == benchmark,
)
).distinct()
return pd.read_sql(query, self.engine)
df = pd.read_sql(query, self.engine)
df["trade_date"] = pd.to_datetime(df["trade_date"])
return df
def fetch_data(self,
ref_date: str,
......@@ -803,7 +812,7 @@ if __name__ == "__main__":
sql_engine = SqlEngine(db_url=db_url)
universe = Universe("hs300")
start_date = '2020-01-01'
end_date = '2020-04-21'
end_date = '2020-02-21'
benchmark = 300
factors = ["EMA5D", "EMV6D"]
ref_dates = makeSchedule(start_date, end_date, "10b", 'china.sse')
......@@ -815,45 +824,45 @@ if __name__ == "__main__":
# 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)
df = sql_engine.fetch_dx_return_index_range(start_date=start_date, end_date=end_date, index_code=benchmark)
print(df)
df = sql_engine.fetch_benchmark("2020-10-09", benchmark=benchmark)
print(df)
df = sql_engine.fetch_benchmark_range(start_date=start_date, end_date=end_date, benchmark=benchmark)
print(df)
df = sql_engine.fetch_industry(ref_date="2020-10-09", codes=["2010031963"])
print(df)
df = sql_engine.fetch_industry_matrix(ref_date="2020-10-09", codes=["2010031963"])
print(df)
df = sql_engine.fetch_industry_matrix_range(universe=universe,
start_date=start_date,
end_date=end_date)
print(df)
df = sql_engine.fetch_industry_range(start_date=start_date, end_date=end_date, universe=Universe("hs300"))
print(df)
df = sql_engine.fetch_risk_model("2020-02-21", codes=["2010031963"])
print(df)
df = sql_engine.fetch_risk_model("2020-02-21", codes=["2010031963"], model_type="factor")
print(df)
df = sql_engine.fetch_risk_model_range(universe=universe,
start_date=start_date,
end_date=end_date)
print(df)
df = sql_engine.fetch_risk_model_range(universe=universe,
start_date=start_date,
end_date=end_date,
model_type="factor")
df = sql_engine.fetch_dx_return_range(universe, dates=ref_dates, horizon=9, offset=1)
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)
# df = sql_engine.fetch_dx_return_index("2020-10-09", index_code=benchmark)
# print(df)
df = sql_engine.fetch_dx_return_index_range(start_date=start_date, end_date=end_date, index_code=benchmark, horizon=9, offset=1)
print(df)
# df = sql_engine.fetch_benchmark("2020-10-09", benchmark=benchmark)
# print(df)
# df = sql_engine.fetch_benchmark_range(start_date=start_date, end_date=end_date, benchmark=benchmark)
# print(df)
# df = sql_engine.fetch_industry(ref_date="2020-10-09", codes=["2010031963"])
# print(df)
# df = sql_engine.fetch_industry_matrix(ref_date="2020-10-09", codes=["2010031963"])
# print(df)
# df = sql_engine.fetch_industry_matrix_range(universe=universe,
# start_date=start_date,
# end_date=end_date)
# print(df)
# df = sql_engine.fetch_industry_range(start_date=start_date, end_date=end_date, universe=Universe("hs300"))
# print(df)
# df = sql_engine.fetch_risk_model("2020-02-21", codes=["2010031963"])
# print(df)
# df = sql_engine.fetch_risk_model("2020-02-21", codes=["2010031963"], model_type="factor")
# print(df)
# df = sql_engine.fetch_risk_model_range(universe=universe,
# start_date=start_date,
# end_date=end_date)
# print(df)
# df = sql_engine.fetch_risk_model_range(universe=universe,
# start_date=start_date,
# 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=benchmark)
# print(df)
alphamind/data/engines/universe.py
View file @ 7f7d2df6
......@@ -57,7 +57,9 @@ class BaseUniverse(metaclass=abc.ABCMeta):
*more_conditions
)
).order_by(UniverseTable.trade_date, UniverseTable.code)
return pd.read_sql(query, engine.engine)
df = pd.read_sql(query, engine.engine)
df["trade_date"] = pd.to_datetime(df["trade_date"])
return df
def _query_statements(self, start_date: str = None, end_date: str = None, dates=None):
return and_(
......
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