def check_stock_trading_env():
if_eval = True # False
env = StockTradingEnv(if_eval=if_eval)
action_dim = env.action_dim
state = env.reset()
print('state_dim', len(state))
from time import time
timer = time()
step = 1
done = False
reward = None
while not done:
action = rd.rand(action_dim) * 2 - 1
next_state, reward, done, _ = env.step(action)
# print(';', len(next_state), env.day, reward)
step += 1
print(f"| Random action: step {step}, UsedTime {time() - timer:.3f}")
print(f"| Random action: terminal reward {reward:.3f}")
print(f"| Random action: episode return {env.episode_return:.3f}")
'''draw_cumulative_return'''
from elegantrl.agent import AgentPPO
from elegantrl.run import Arguments
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.env = StockTradingEnv(if_eval=True)
args.if_remove = False
args.cwd = './AgentPPO/StockTradingEnv-v1_0'
args.init_before_training()
env.draw_cumulative_return(args, torch)
def __init__(self,
ticker_list,
time_interval,
drl_lib,
agent,
cwd,
net_dim,
state_dim,
action_dim,
API_KEY,
API_SECRET,
APCA_API_BASE_URL,
tech_indicator_list,
turbulence_thresh=30,
max_stock=1e2,
latency=None):
#load agent
self.drl_lib = drl_lib
if agent == 'ppo':
if drl_lib == 'elegantrl':
from elegantrl.agent import AgentPPO
from elegantrl.run import Arguments, init_agent
#load agent
config = {
'state_dim': state_dim,
'action_dim': action_dim,
}
args = Arguments(agent=AgentPPO, env=StockEnvEmpty(config))
args.cwd = cwd
args.net_dim = net_dim
# load agent
try:
agent = init_agent(args, gpu_id=0)
self.act = agent.act
self.device = agent.device
except BaseException:
raise ValueError("Fail to load agent!")
elif drl_lib == 'rllib':
from ray.rllib.agents import ppo
from ray.rllib.agents.ppo.ppo import PPOTrainer
config = ppo.DEFAULT_CONFIG.copy()
config['env'] = StockEnvEmpty
config["log_level"] = "WARN"
config['env_config'] = {
'state_dim': state_dim,
'action_dim': action_dim,
}
trainer = PPOTrainer(env=StockEnvEmpty, config=config)
trainer.restore(cwd)
try:
trainer.restore(cwd)
self.agent = trainer
print("Restoring from checkpoint path", cwd)
except:
raise ValueError('Fail to load agent!')
elif drl_lib == 'stable_baselines3':
from stable_baselines3 import PPO
try:
#load agent
self.model = PPO.load(cwd)
print("Successfully load model", cwd)
except:
raise ValueError('Fail to load agent!')
else:
raise ValueError(
'The DRL library input is NOT supported yet. Please check your input.'
)
else:
raise ValueError('Agent input is NOT supported yet.')
#connect to Alpaca trading API
try:
self.alpaca = tradeapi.REST(API_KEY, API_SECRET, APCA_API_BASE_URL,
'v2')
except:
raise ValueError(
'Fail to connect Alpaca. Please check account info and internet connection.'
)
#read trading time interval
if time_interval == '1s':
self.time_interval = 1
elif time_interval == '5s':
self.time_interval = 5
elif time_interval == '1Min':
self.time_interval = 60
elif time_interval == '5Min':
self.time_interval = 60 * 5
elif time_interval == '15Min':
self.time_interval = 60 * 15
else:
raise ValueError('Time interval input is NOT supported yet.')
#read trading settings
self.tech_indicator_list = tech_indicator_list
self.turbulence_thresh = turbulence_thresh
self.max_stock = max_stock
#initialize account
self.stocks = np.asarray([0] * len(ticker_list)) #stocks holding
self.stocks_cd = np.zeros_like(self.stocks)
self.cash = None #cash record
self.stocks_df = pd.DataFrame(self.stocks,
columns=['stocks'],
index=ticker_list)
self.asset_list = []
self.price = np.asarray([0] * len(ticker_list))
self.stockUniverse = ticker_list
self.turbulence_bool = 0
self.equities = []