def demo3_custom_env_fin_rl():
from elegantrl.agent import AgentPPO
'''choose an DRL algorithm'''
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.agent.if_use_gae = True
args.agent.lambda_entropy = 0.04
"TotalStep: 10e4, TargetReturn: 3.0, UsedTime: 200s, FinanceStock-v1"
"TotalStep: 20e4, TargetReturn: 4.0, UsedTime: 400s, FinanceStock-v1"
"TotalStep: 30e4, TargetReturn: 4.2, UsedTime: 600s, FinanceStock-v1"
from envs.FinRL.StockTrading import StockTradingEnv
gamma = 0.995
args.env = StockTradingEnv(if_eval=False, gamma=gamma)
args.env_eval = StockTradingEnv(if_eval=True, gamma=gamma)
args.gamma = gamma
args.break_step = int(3e5)
args.net_dim = 2**9
args.max_step = args.env.max_step
args.max_memo = args.max_step * 4
args.batch_size = 2**10
args.repeat_times = 2**3
args.eval_gap = 2**4
args.eval_times1 = 2**3
args.eval_times2 = 2**5
args.if_allow_break = False
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo3_custom_env_fin_rl():
from elegantrl.agent import AgentPPO
'''choose an DRL algorithm'''
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.agent.if_use_gae = False
"TotalStep: 5e4, TargetReturn: 1.25, UsedTime: 20s, FinanceStock-v2"
"TotalStep: 20e4, TargetReturn: 1.50, UsedTime: 80s, FinanceStock-v2"
from elegantrl.env import FinanceStockEnv # a standard env for ElegantRL, not need PreprocessEnv()
args.env = FinanceStockEnv(if_train=True, train_beg=0, train_len=1024)
args.env_eval = FinanceStockEnv(if_train=False, train_beg=0, train_len=1024) # eva_len = 1699 - train_len
args.reward_scale = 2 ** 0 # RewardRange: 0 < 1.0 < 1.25 < 1.5 < 1.6
args.break_step = int(5e6)
args.net_dim = 2 ** 8
args.max_step = args.env.max_step
args.max_memo = (args.max_step - 1) * 8
args.batch_size = 2 ** 11
args.repeat_times = 2 ** 4
args.eval_times1 = 2 ** 2
args.eval_times2 = 2 ** 4
args.if_allow_break = True
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 8
train_and_evaluate_mp(args)
def demo3_custom_env_fin_rl():
from elegantrl.agent import AgentPPO
'''choose an DRL algorithm'''
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.agent.if_use_gae = False
"TotalStep: 5e4, TargetReturn: 1.25, UsedTime: 20s, FinanceStock-v2"
"TotalStep: 20e4, TargetReturn: 1.50, UsedTime: 80s, FinanceStock-v2"
# from elegantrl.env import FinanceStockEnv # a standard env for ElegantRL, not need PreprocessEnv()
# args.env = FinanceStockEnv(if_train=True, train_beg=0, train_len=1024)
# args.env_eval = FinanceStockEnv(if_train=False, train_beg=0, train_len=1024) # eva_len = 1699 - train_len
from finrl.config import config
from beta3 import StockTradingEnv, load_stock_trading_data
train_df, eval_df = load_stock_trading_data()
# train = data_split(processed_df, config.START_DATE, config.START_TRADE_DATE)
# trade = data_split(processed_df, config.START_TRADE_DATE, config.END_DATE)
# calculate state action space
stock_dimension = len(train_df.tic.unique())
state_space = 1 + (2 +
len(config.TECHNICAL_INDICATORS_LIST)) * stock_dimension
env_kwargs = {
"max_stock": 100,
"initial_amount": 1000000,
"buy_cost_pct": 0.001,
"sell_cost_pct": 0.001,
"state_space": state_space,
"stock_dim": stock_dimension,
"tech_indicator_list": config.TECHNICAL_INDICATORS_LIST,
"action_space": stock_dimension,
"reward_scaling": 2**-14
}
args.env = StockTradingEnv(df=train_df, **env_kwargs)
args.env_eval = StockTradingEnv(df=eval_df, **env_kwargs)
args.reward_scale = 2**0 # RewardRange: 0 < 1.0 < 1.25 < 1.5 < 1.6
args.break_step = int(5e6)
args.net_dim = 2**8
args.max_step = args.env.max_step
args.max_memo = (args.max_step - 1) * 8
args.batch_size = 2**11
args.repeat_times = 2**4
args.eval_times1 = 2**1
args.eval_times2 = 2**3
args.if_allow_break = True
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)