import os import sys current_path = os.getcwd() sys.path.append(current_path) from hypertune import start_commander, start_workers from coordinator import Coordinator from config import mode, number_workers, tuned_config, fix_random_seed import numpy as np if fix_random_seed: np.random.seed(123) # %matplotlib inline if mode == 'parallel': start_commander() workers = start_workers(number_workers) else: model = Coordinator(tuned_config, '2900') #################### to do #################### # model = Coordinator(tuned_config, '-5.28') # model.restore_price_predictor('-5.28-80000-') ############################################## model.train('single', True) model.back_test('test', 2500, True)
ob = env.reset() for i in range(5): print(coo.action_values(ob)) ob, a, r, ob_ = env.step(np.ones(5)) coo.train(env, total_training_step=total_training_step, replay_period=replay_period, tensorboard=True) env_test = PortfolioEnv(df_train, steps=2500, trading_cost=0.0, window_length=window_length, scale=False, random_reset=False) ob = env_test.reset() for i in range(10): print(coo.action_values(ob)) print(np.argmax(coo.action_values(ob))) ob, a, r, ob_ = env.step(np.ones(5)) # coo.restore('') # l = coo.network_state() # print(l['training_network/output/weights:0']) coo.back_test(env_test, render_mode='usual') # coo.open_tb(port='8009')