def main(args): mode = args.mode # mode = 'test' # codes = args.codes codes = ["600036"] # codes = ["AU88", "RB88", "CU88", "AL88"] # codes = ["T9999"] market = args.market # market = 'future' # episode = args.episode episode = 200 # training_data_ratio = 0.5 training_data_ratio = args.training_data_ratio model_name = os.path.basename(__file__).split('.')[0] env = Market( codes, start_date="2008-01-01", end_date="2019-07-19", **{ "market": market, # "use_sequence": True, # "mix_index_state": True, "logger": generate_market_logger(model_name), "training_data_ratio": training_data_ratio, }) algorithm = Algorithm( tf.Session(config=config), env, env.trader.action_space, env.data_dim, **{ "mode": mode, "episodes": episode, "enable_saver": True, "learning_rate": 0.003, "enable_summary_writer": True, "logger": generate_algorithm_logger(model_name), "save_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "model"), "summary_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "summary"), }) algorithm.run() algorithm.eval() algorithm.plot()
def main(args): mode = args.mode #mode = 'test' codes = args.codes #codes = ["600036"] #codes = ["eos_usdt"] # codes = ["600036", "601998"] # codes = ["AU88", "RB88", "CU88", "AL88"] # codes = ["T9999"] market = 'k15m' #market = args.market # market = 'future' # episode = args.episode episode = 1000 training_data_ratio = 0.95 # training_data_ratio = args.training_data_ratio #pdb.set_trace() model_name = os.path.basename(__file__).split('.')[0] #env = Market(codes, start_date="2018-06-04", end_date="2018-06-12", **{ env = Market(codes, start_date=args.start, end_date=args.end, **{ "market": market, "mix_index_state": False, "logger": generate_market_logger(model_name), "training_data_ratio": training_data_ratio, }) algorithm = Algorithm(tf.Session(config=config), env, env.trader.action_space, env.data_dim, **{ "mode": mode, "episodes": episode, "enable_saver": True, "learning_rate": 0.003, "enable_summary_writer": True, "logger": generate_algorithm_logger(model_name), "save_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "model"), "summary_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "summary"), }) algorithm.run() algorithm.eval() algorithm.plot()
def generator(code, start_date, end_date, market="stock", mode='trade'): training_data_ratio = 0.8 episode = 500 env = Market( code, start_date=start_date, end_date=end_date, **{ "market": market, # "use_sequence": True, "logger": generate_market_logger(model_name), "training_data_ratio": training_data_ratio, }) return Algorithm( tf.Session(config=config), env, env.trader.action_space, env.data_dim, **{ "mode": mode, "episodes": episode, "enable_saver": True, "learning_rate": 0.003, "enable_summary_writer": True, "logger": generate_algorithm_logger(model_name), "save_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "model"), "summary_path": os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "summary"), })