def worker(train_param):
try:
config = load_config()
config["model_id"] = 0 # this is will be overridden when driven from UI
print (train_param)
if train_param is not None:
config["model_id"] = int(train_param["model_id"])
config["input"]["stocks"] = train_param["stocks"]
if "feature_number" in train_param:
config["input"]["feature_number"] = int(train_param["feature_number"])
if "training_start_date" in train_param:
config["input"]["training_start_time"] = train_param["training_start_date"]
if "training_end_date" in train_param:
config["input"]["training_end_time"] = train_param["training_end_date"]
if "testing_start_date" in train_param:
config["testing"]["testing_start_time"] = train_param["testing_start_date"]
if "testing_end_date" in train_param:
config["testing"]["testing_end_time"] = train_param["testing_end_date"]
if "episode" in train_param:
config["training"]["episode"] = int(train_param["episode"])
if "trading_cost" in train_param:
config["input"]["trading_cost"] = float(train_param["trading_cost"])
train_id = ddpg_trading_train(config, DEBUG=False).train_model()
model = ddpg_restore_model(train_id)
model.restore()
model.backtest()
print("Finish work on %s", train_id)
except Exception as e:
print("Fatal error in training %s", train_param["stocks"])
report_model_progress(config["model_id"],info={"error": True, "message": str(e)})
activation_functions = ["prelu"] #["relu","tanh","leaky_relu","prelu"]
repeat_time = 1
feature_numbers = [5]
reward_functions = [2]
learning_rates = [0.001]
for stock in stocks:
for predictor_type in predictor_types:
for window_length in window_lengths:
for trading_cost in trading_costs:
for activation_function in activation_functions:
for episode in episodes:
for feature_number in feature_numbers:
for _ in range(repeat_time):
for learning_rate in learning_rates:
config = load_config()
config[
"model_id"] = 0 #this is will be overridden when driven from UI
config["input"][
"feature_number"] = feature_number
config["input"][
"window_length"] = window_length
config["input"][
"predictor_type"] = predictor_type
config["input"][
"trading_cost"] = trading_cost
config["training"]["episode"] = episode
config["input"]["stocks"] = stock
config["layers"][
"activation_function"] = activation_function
config["training"][
def start_session(self):
tf.reset_default_graph()
tf_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,)
# if self.device == "cpu":
# tf_config.gpu_options.per_process_gpu_memory_fraction = 0
# else:
# tf_config.gpu_options.per_process_gpu_memory_fraction = 1
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config = tf_config)
tflearn.config.init_training_mode()
return sess
def close_session(self):
self.sess.close()
if __name__ == '__main__':
parser = build_parser()
args = vars(parser.parse_args())
pprint.pprint(args)
if args['debug'] == 'True':
DEBUG = True
else:
DEBUG = False
model = ddpg_trading_train(load_config(), DEBUG = DEBUG)
train_id = model.train_model()
print(train_id)