def executor(agrs):
# Keras Backend 설정
if args.backend == 'tensorflow':
os.environ['KERAS_BACKEND'] = 'tensorflow'
elif args.backend == 'plaidml':
os.environ['KERAS_BACKEND'] = 'plaidml.keras.backend'
# 출력 경로 설정
output_path = os.path.join(
settings.BASE_DIR, 'output/{}_{}_{}'.format(args.output_name,
args.rl_method, args.net))
if not os.path.isdir(output_path):
os.makedirs(output_path)
# 파라미터 기록
with open(os.path.join(output_path, 'params.json'), 'w') as f:
f.write(json.dumps(vars(args)))
# 로그 기록 설정
file_handler = logging.FileHandler(filename=os.path.join(
output_path, "{}.log".format(args.output_name)),
encoding='utf-8')
stream_handler = logging.StreamHandler(sys.stdout)
file_handler.setLevel(logging.DEBUG)
stream_handler.setLevel(logging.INFO)
logging.basicConfig(format="%(message)s",
handlers=[file_handler, stream_handler],
level=logging.DEBUG)
# 로그, Keras Backend 설정을 먼저하고 RLTrader 모듈들을 이후에 임포트해야 함
from agent import Agent
from learners import DQNLearner
# 모델 경로 준비
value_network_path = ''
policy_network_path = ''
if args.value_network_name is not None:
value_network_path = os.path.join(
settings.BASE_DIR, 'models/{}.h5'.format(args.value_network_name))
else:
value_network_path = os.path.join(
output_path, '{}_{}_value_{}.h5'.format(args.rl_method, args.net,
args.output_name))
if args.policy_network_name is not None:
policy_network_path = os.path.join(
settings.BASE_DIR, 'models/{}.h5'.format(args.policy_network_name))
else:
policy_network_path = os.path.join(
output_path, '{}_{}_policy_{}.h5'.format(args.rl_method, args.net,
args.output_name))
common_params = {}
list_stock_code = []
list_chart_data = []
list_training_data = []
list_min_trading_unit = []
list_max_trading_unit = []
stock_code = args.stock_code
get_data.get_data(stock_code, args.start_date, args.end_date, ver=args.ver)
# 차트 데이터, 학습 데이터 준비
chart_data, training_data = data_manager.load_data(os.path.join(
settings.BASE_DIR, 'data/{}/{}.csv'.format(args.ver, stock_code)),
args.start_date,
args.end_date,
ver=args.ver)
# 최소/최대 투자 단위 설정
min_trading_unit = max(int(10000 / chart_data.iloc[-1]['close']), 1)
max_trading_unit = max(int(100000 / chart_data.iloc[-1]['close']), 1)
# 공통 파라미터 설정
common_params = {
'rl_method': args.rl_method,
'delayed_reward_threshold': args.delayed_reward_threshold,
'net': args.net,
'num_steps': args.num_steps,
'lr': args.lr,
'output_path': output_path,
'reuse_models': args.reuse_models
}
# 강화학습 시작
learner = None
common_params.update({
'stock_code': stock_code,
'chart_data': chart_data,
'training_data': training_data,
'min_trading_unit': min_trading_unit,
'max_trading_unit': max_trading_unit
})
learner = DQNLearner(
**{
**common_params, 'value_network_path': value_network_path
})
if learner is not None:
pvs = learner.run(balance=args.balance,
num_epoches=args.num_epoches,
discount_factor=args.discount_factor,
start_epsilon=args.start_epsilon,
learning=args.learning)
learner.save_models()
return chart_data, pvs
'policy_network_path': policy_network_path
})
elif args.rl_method == 'monkey':
args.net = args.rl_method
args.num_epoches = 1
args.discount_factor = None
args.start_epsilon = 1
args.learning = False
learner = ReinforcementLearner(**common_params)
if learner is not None:
learner.run(balance=args.balance,
num_epoches=args.num_epoches,
discount_factor=args.discount_factor,
start_epsilon=args.start_epsilon,
learning=args.learning)
learner.save_models()
else:
list_stock_code.append(stock_code)
list_chart_data.append(chart_data)
list_training_data.append(training_data)
list_min_trading_unit.append(min_trading_unit)
list_max_trading_unit.append(max_trading_unit)
if args.rl_method == 'a3c':
learner = A3CLearner(
**{
**common_params, 'list_stock_code': list_stock_code,
'list_chart_data': list_chart_data,
'list_training_data': list_training_data,
'list_min_trading_unit': list_min_trading_unit,
'list_max_trading_unit': list_max_trading_unit,