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)})
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"][
"actor learning rate"] = 0.001
config["training"][
"critic learning rate"] = 0.001
config["training"]["buffer size"] = 100000
config["training"]["max_step"] = 0
config["training"]["batch size"] = 64
print(config)
train_id = ddpg_trading_train(
config, DEBUG=DEBUG).train_model()
model = ddpg_restore_model(train_id)
model.restore()
model.backtest()
print("Finish work on %s" % (train_id))
def run_model(train_id, start_date, end_date):
model = ddpg_restore_model(train_id)
model.restore()
summary = model.backtest(start_date, end_date)
return summary