def main(job_id, D):
print "job_id", job_id, " params:", D
equities1 = {}
register(
'my-db-bundle', # name this whatever you like
viadb(equities1),
calendar='SHSZ')
parsed = {}
parsed['initialize'] = None
parsed['handle_data'] = None
parsed['before_trading_start'] = None
parsed['analyze'] = None
parsed['algotext'] = None
parsed['defines'] = ()
parsed['capital_base'] = 1000000
parsed['data'] = None
parsed['bundle'] = 'my-db-bundle'
#parsed['bundle']='YAHOO'
#parsed['bundle_timestamp']=None
parsed['bundle_timestamp'] = pd.Timestamp.utcnow()
parsed['start'] = Timestamp('2017-03-01 13:30:00+0000', tz='UTC')
parsed['end'] = Timestamp('2017-06-01 13:30:00+0000', tz='UTC')
parsed['algofile'] = open(
'/data/kanghua/workshop/strategy/campaign/hyperparam/example-new/zipline_strategy.py'
)
parsed['data_frequency'] = 'daily'
parsed['print_algo'] = False
parsed['output'] = 'os.devnull'
parsed['local_namespace'] = None
parsed['environ'] = os.environ
parsed['bm_symbol'] = None
# Below what we expect spearmint to pass us
# parsed['algo_params']=[47,88.7,7.7]
# D={}
# D['timeperiod']=10
# D['nbdevup']=1.00
# D['nbdevdn']=1.00
parsed['algo_params'] = D
perf = _run(**parsed)
StartV = perf['portfolio_value'][0]
EndV = perf['portfolio_value'][-1]
# spearmint wants to minimize so return negative profit
OPTIM = (StartV - EndV)
return OPTIM
'longs': longs.downsample('week_start'),
'shorts': shorts.downsample('week_start'),
'predict_rank': predict_rank.downsample('week_start'),
}
pipe = Pipeline(
columns=pipe_final_columns,
screen=long_short_screen,
)
return pipe
############################################# bundle #############################################
equities1 = {}
register(
'my-db-bundle', # name this whatever you like
viadb(equities1),
calendar='SHSZ')
bundle = 'my-db-bundle'
bundle_timestamp = pd.Timestamp.utcnow()
environ = os.environ
bundle_data = load(
bundle,
environ,
bundle_timestamp,
)
prefix, connstr = re.split(
r'sqlite:///',
str(bundle_data.asset_finder.engine.url),
maxsplit=1,
)
#print prefix, connstr
import os