def run_optimize(args, logger):
from lib.RLTrader import RLTrader
trader = RLTrader(**vars(args),
logger=logger,
reward_strategy=reward_strategy)
trader.optimize(n_trials=args.trials)
def optimize_code(params):
from lib.RLTrader import RLTrader
trader = RLTrader(**params)
trader.optimize()
return ""
import numpy as np
from lib.RLTrader import RLTrader
from lib.TraderArgs import TraderArgs
from lib.util.logger import init_logger
np.warnings.filterwarnings('ignore')
option_parser = TraderArgs()
args = option_parser.get_args()
if __name__ == '__main__':
logger = init_logger(__name__, show_debug=args.debug)
trader = RLTrader(**vars(args), logger=logger)
if args.command == 'optimize':
trader.optimize(n_trials=args.trials,
n_parallel_jobs=args.parallel_jobs)
elif args.command == 'train':
trader.train(n_epochs=args.epochs)
elif args.command == 'test':
trader.test(model_epoch=args.model_epoch, should_render=args.no_render)
elif args.command == 'opt-train-test':
trader.optimize(args.trials, args.parallel_jobs)
trader.train(n_epochs=args.train_epochs,
test_trained_model=args.no_test,
render_trained_model=args.no_render)
import numpy as np
from lib.RLTrader import RLTrader
np.warnings.filterwarnings('ignore')
if __name__ == '__main__':
trader = RLTrader()
trader.optimize()
trader.train(test_trained_model=True, render_trained_model=True)
def optimize_code(params):
trader = RLTrader(**params)
trader.optimize()
def run_optimize(args, logger):
from lib.RLTrader import RLTrader
trader = RLTrader(**vars(args), logger=logger)
trader.optimize(args.trials)
def run_concurrent_optimize(trader: RLTrader, args):
trader.optimize(args.trials, args.trials, args.parallel_jobs)
def run_concurrent_optimize():
trader = RLTrader(**vars(args))
trader.optimize(args.trials)