def factor_analysis(f_name):
from alphamind.api import SqlEngine, Universe, alpha_logger
engine = SqlEngine()
universe = Universe('custom', ['zz800'])
base1 = LAST('Alpha60')
base2 = CSRes('roe_q', base1)
base3 = CSRes(CSRes('ep_q', base1), base2)
factor = CSRes(CSRes(CSRes(LAST(f_name), base1), base2), base3)
res = factor_residue_analysis('2010-01-01', '2018-01-26', f_name, factor,
'10b', universe, engine)
alpha_logger.info('{0} is done'.format(f_name))
return f_name, res
def worker_func_negative(factor_name):
from alphamind.api import SqlEngine, Universe
neutralize_factors = ['roe_q', 'ep_q']
engine = SqlEngine()
benchmark_code = 905
universe_name = ['zz500']
universe = Universe('custom', universe_name)
return factor_analysis(engine,
factor_name,
universe,
benchmark_code,
positive=False,
neutralize_factors=neutralize_factors)
universe = Universe('custom', ['zz800'])
base1 = LAST('Alpha60')
base2 = CSRes('roe_q', base1)
base3 = CSRes(CSRes('ep_q', base1), base2)
factor = CSRes(CSRes(CSRes(LAST(f_name), base1), base2), base3)
res = factor_residue_analysis('2010-01-01', '2018-01-26', f_name, factor,
'10b', universe, engine)
alpha_logger.info('{0} is done'.format(f_name))
return f_name, res
if __name__ == '__main__':
from dask.distributed import Client
client = Client('10.63.6.176:8786')
engine = SqlEngine()
df = engine.fetch_factor_coverage()
df = df[df.universe == 'zz800'].groupby('factor').mean()
df = df[df.coverage >= 0.98]
universe = Universe('custom', ['zz800'])
factor_df = pd.DataFrame()
tasks = client.map(factor_analysis, df.index.tolist())
res = client.gather(tasks)
for f_name, df in res:
factor_df[f_name] = df['$top1 - bottom1$']
# for i, f_name in enumerate(df.index):
# base1 = LAST('Alpha60')
start_date = dt.datetime(2018, 5, 4)
dag_name = 'update_uqer_data_postgres'
default_args = {
'owner': 'wegamekinglc',
'depends_on_past': True,
'start_date': start_date
}
dag = DAG(dag_id=dag_name,
default_args=default_args,
schedule_interval='0 1 * * 1,2,3,4,5')
_ = uqer.Client(token=os.environ['DATAYES_TOKEN'])
engine = sqlalchemy.create_engine(os.environ['DB_URI'])
alpha_engine = SqlEngine(os.environ['DB_URI'])
def process_date(ds):
alpha_logger.info("Loading data at {0}".format(ds))
this_date = dt.datetime.strptime(ds, '%Y-%m-%d')
ref_date = this_date.strftime('%Y%m%d')
return ref_date, this_date
def format_data(df, format='%Y%m%d'):
df['trade_date'] = pd.to_datetime(df['trade_date'], format=format)
def check_holiday(this_date):
flag = isBizDay('china.sse', this_date)
from alphamind.api import (
Universe, map_freq, risk_styles, industry_styles, macro_styles, BoundaryType, create_box_bounds
)
"""
Back test parameter settings
"""
start_date = '2010-01-01'
end_date = '2018-02-28'
category = 'sw_adj'
level = 1
freq = '20b'
universe = Universe('custom', ['zz800'])
data_source = 'postgres+psycopg2://postgres:[email protected]/alpha'
engine = SqlEngine(data_source)
horizon = map_freq(freq)
"""
Factor Model
"""
factor_name = 'SIZE'
"""
Constraints
"""
risk_names = list(set(risk_styles).difference({factor_name}))
industry_names = list(set(industry_styles).difference({factor_name}))
constraint_risk = risk_names + industry_names + macro_styles