def main(): try: shutil.rmtree('images') print("delete images directory") except OSError as e: print("Error: %s : %s" % ('images', e.strerror)) gym.logger.set_level(INFO) start_date = date(2019, 5, 1) simulate_company_list = [2, 3, 4, 5, 6, 44, 300, 67, 100, 200] # simulate_company_list = [3] env = gym.make("AsxGym-v0", start_date=start_date, simulate_company_list=simulate_company_list) stock_agent = RandomAgent(env) # stock_agent = RandomAgent(env, min_volume=100, max_volume=500) # stock_agent = BuyAndKeepAgent(env, 3) observation = env.reset() for _ in range(200000 * 24): env.render() company_count = len(env.simulate_company_list) observation, reward, done, info = env.step(stock_agent.action()) if done: env.insert_summary_images(30) observation = env.reset() stock_agent.reset() if observation is not None: asx_observation = AsxObservation(observation) print(asx_observation.to_json_obj()) print(info) env.close()
# from agents.buy_and_keep_agent import BuyAndKeepAgenta from agents.random_agent import RandomAgent from asx_gym.envs import AsxObservation gym.logger.set_level(INFO) start_date = date(2019, 5, 1) simulate_company_list = [2, 3, 4, 5, 6, 44, 300, 67, 100, 200] # simulate_company_list = [3] env = gym.make("AsxGym-v0", start_date=start_date, simulate_company_list=simulate_company_list) stock_agent = RandomAgent(env) # stock_agent = RandomAgent(env, min_volume=100, max_volume=500) # stock_agent = BuyAndKeepAgent(env, 3) observation = env.reset() for _ in range(200000 * 24): env.render() company_count = len(env.simulate_company_list) observation, reward, done, info = env.step(stock_agent.action()) if done: env.insert_summary_images(30) observation = env.reset() stock_agent.reset() if observation is not None: asx_observation = AsxObservation(observation) print(asx_observation.to_json_obj()) print(info) env.close()