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predict_single_psql.py
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predict_single_psql.py
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from stock_data import StockData
# from train_single import get_agent_args
from train_helper import query_model_hyper_parameters_sqlite, query_begin_vali_date_list_by_agent_name
from utils.psqldb import Psqldb
from agent import *
from utils.date_time import *
from env_single import StockTradingEnvSingle, FeatureEngineer
from run_single import *
from datetime import datetime
def calc_max_return(price_ary, initial_capital_temp):
max_return_temp = 0
min_value = 0
assert price_ary.shape[0] > 1
count_price = price_ary.shape[0]
for index_left in range(0, count_price - 1):
for index_right in range(index_left + 1, count_price):
assert price_ary[index_left][0] > 0
assert price_ary[index_right][0] > 0
temp_value = price_ary[index_right][0] - price_ary[index_left][0]
if temp_value > max_return_temp:
max_return_temp = temp_value
# max_value = price_ary[index1][0]
min_value = price_ary[index_right][0]
pass
pass
if min_value == 0:
ret = 0
else:
ret = (initial_capital_temp / min_value * max_return_temp + initial_capital_temp) / initial_capital_temp
return ret
if __name__ == '__main__':
# 预测，并保存结果到 postgresql 数据库
# 开始预测的时间
time_begin = datetime.now()
config.OUTPUT_DATE = '2021-08-03'
initial_capital = 150000
max_stock = 3000
# for tic_item in ['sh.600036', 'sh.600667']:
# 循环
for tic_item in ['sh.600036', ]:
# 要预测的那一天
config.SINGLE_A_STOCK_CODE = [tic_item, ]
# psql对象
psql_object = Psqldb(database=config.PSQL_DATABASE, user=config.PSQL_USER,
password=config.PSQL_PASSWORD, host=config.PSQL_HOST, port=config.PSQL_PORT)
# 好用 AgentPPO(), # AgentSAC(), AgentTD3(), AgentDDPG(), AgentModSAC(),
# AgentDoubleDQN 单进程好用?
# 不好用 AgentDuelingDQN(), AgentDoubleDQN(), AgentSharedSAC()
for agent_item in ['AgentSAC', 'AgentPPO', 'AgentDDPG', 'AgentTD3', 'AgentModSAC', ]:
config.AGENT_NAME = agent_item
# config.CWD = f'./{config.AGENT_NAME}/single/{config.SINGLE_A_STOCK_CODE[0]}/StockTradingEnv-v1'
break_step = int(3e6)
if_on_policy = False
# if_use_gae = False
# 预测的开始日期和结束日期，都固定
# 日期列表
# 4月16日向前，20,30,40,50,60,72,90周期
# end_vali_date = get_datetime_from_date_str('2021-04-16')
config.IF_ACTUAL_PREDICT = True
config.START_DATE = "2003-05-01"
# 前29后1
config.PREDICT_PERIOD = '60'
# 固定日期
config.START_EVAL_DATE = str(get_next_work_day(get_datetime_from_date_str(config.OUTPUT_DATE), -10))
# config.START_EVAL_DATE = "2021-05-22"
# OUTPUT_DATE 向右3工作日
config.END_DATE = str(get_next_work_day(get_datetime_from_date_str(config.OUTPUT_DATE), +1))
# 创建预测结果表
StockData.create_predict_result_table_psql(list_tic=config.SINGLE_A_STOCK_CODE)
# 更新股票数据
StockData.update_stock_data_to_sqlite(list_stock_code=config.SINGLE_A_STOCK_CODE)
# 预测的截止日期
end_vali_date = get_datetime_from_date_str(config.END_DATE)
# 获取 N 个日期list
list_begin_vali_date = query_begin_vali_date_list_by_agent_name(agent_item, end_vali_date)
# 循环 vali_date_list 训练7次
for vali_days_count, begin_vali_date in list_begin_vali_date:
# config.START_EVAL_DATE = str(begin_vali_date)
# 更新工作日标记，用于 run_single.py 加载训练过的 weights 文件
config.VALI_DAYS_FLAG = str(vali_days_count)
# config.PREDICT_PERIOD = str(vali_days_count)
# weights 文件目录
# model_folder_path = f'./{config.AGENT_NAME}/single/{config.SINGLE_A_STOCK_CODE[0]}' \
# f'/single_{config.VALI_DAYS_FLAG}'
model_folder_path = f'./{config.WEIGHTS_PATH}/single/{config.AGENT_NAME}/{config.SINGLE_A_STOCK_CODE[0]}' \
f'/single_{config.VALI_DAYS_FLAG}'
# 如果存在目录则预测
if os.path.exists(model_folder_path):
print('\r\n')
print('#' * 40)
print('config.AGENT_NAME', config.AGENT_NAME)
print('# 预测周期', config.START_EVAL_DATE, '-', config.END_DATE)
print('# 模型的 work_days', vali_days_count)
print('# model_folder_path', model_folder_path)
print('# initial_capital', initial_capital)
print('# max_stock', max_stock)
initial_stocks = np.zeros(len(config.SINGLE_A_STOCK_CODE), dtype=np.float32)
initial_stocks[0] = 100.0
# 获取超参
model_name = agent_item + '_' + str(vali_days_count)
hyper_parameters_id, hyper_parameters_model_name, if_on_policy, break_step, train_reward_scale, \
eval_reward_scale, training_times, time_point \
= query_model_hyper_parameters_sqlite(model_name=model_name)
if if_on_policy == 1:
if_on_policy = True
else:
if_on_policy = False
pass
config.MODEL_HYPER_PARAMETERS = str(hyper_parameters_id)
# 获得Agent参数
agent_class = None
train_reward_scaling = 2 ** train_reward_scale
eval_reward_scaling = 2 ** eval_reward_scale
# 模型名称
config.AGENT_NAME = str(hyper_parameters_model_name).split('_')[0]
if config.AGENT_NAME == 'AgentPPO':
agent_class = AgentPPO()
elif config.AGENT_NAME == 'AgentSAC':
agent_class = AgentSAC()
elif config.AGENT_NAME == 'AgentTD3':
agent_class = AgentTD3()
elif config.AGENT_NAME == 'AgentDDPG':
agent_class = AgentDDPG()
elif config.AGENT_NAME == 'AgentModSAC':
agent_class = AgentModSAC()
elif config.AGENT_NAME == 'AgentDuelingDQN':
agent_class = AgentDuelingDQN()
elif config.AGENT_NAME == 'AgentSharedSAC':
agent_class = AgentSharedSAC()
elif config.AGENT_NAME == 'AgentDoubleDQN':
agent_class = AgentDoubleDQN()
pass
# 预测周期
work_days = int(str(hyper_parameters_model_name).split('_')[1])
args = Arguments(if_on_policy=if_on_policy)
args.agent = agent_class
args.gpu_id = 0
# args.agent.if_use_gae = if_use_gae
args.agent.lambda_entropy = 0.04
tech_indicator_list = [
'macd', 'boll_ub', 'boll_lb', 'rsi_30', 'cci_30', 'dx_30',
'close_30_sma', 'close_60_sma'] # finrl.config.TECHNICAL_INDICATORS_LIST
gamma = 0.99
buy_cost_pct = 0.003
sell_cost_pct = 0.003
start_date = config.START_DATE
start_eval_date = config.START_EVAL_DATE
end_eval_date = config.END_DATE
args.env = StockTradingEnvSingle(cwd='', gamma=gamma, max_stock=max_stock,
initial_capital=initial_capital,
buy_cost_pct=buy_cost_pct, sell_cost_pct=sell_cost_pct,
start_date=start_date,
end_date=start_eval_date, env_eval_date=end_eval_date,
ticker_list=config.SINGLE_A_STOCK_CODE,
tech_indicator_list=tech_indicator_list,
initial_stocks=initial_stocks,
if_eval=True)
args.env_eval = StockTradingEnvSingle(cwd='', gamma=gamma, max_stock=max_stock,
initial_capital=initial_capital,
buy_cost_pct=buy_cost_pct, sell_cost_pct=sell_cost_pct,
start_date=start_date,
end_date=start_eval_date, env_eval_date=end_eval_date,
ticker_list=config.SINGLE_A_STOCK_CODE,
tech_indicator_list=tech_indicator_list,
initial_stocks=initial_stocks,
if_eval=True)
args.env.target_return = 100
args.env_eval.target_return = 100
# 奖励 比例
args.env.reward_scaling = train_reward_scaling
args.env_eval.reward_scaling = eval_reward_scaling
print('train/eval reward scaling:', args.env.reward_scaling, args.env_eval.reward_scaling)
# Hyperparameters
args.gamma = gamma
# ----
args.break_step = break_step
# ----
args.net_dim = 2 ** 9
args.max_step = args.env.max_step
# ----
# args.max_memo = args.max_step * 4
args.max_memo = (args.max_step - 1) * 8
# ----
# ----
args.batch_size = 2 ** 12
# args.batch_size = 2305
# ----
# ----
# args.repeat_times = 2 ** 3
args.repeat_times = 2 ** 4
# ----
args.eval_gap = 2 ** 4
args.eval_times1 = 2 ** 3
args.eval_times2 = 2 ** 5
# ----
args.if_allow_break = False
# args.if_allow_break = True
# ----
# ----------------------------
args.init_before_training()
'''basic arguments'''
cwd = args.cwd
env = args.env
agent = args.agent
gpu_id = args.gpu_id # necessary for Evaluator?
'''training arguments'''
net_dim = args.net_dim
max_memo = args.max_memo
break_step = args.break_step
batch_size = args.batch_size
target_step = args.target_step
repeat_times = args.repeat_times
if_break_early = args.if_allow_break
if_per = args.if_per
gamma = args.gamma
reward_scale = args.reward_scale
'''evaluating arguments'''
eval_gap = args.eval_gap
eval_times1 = args.eval_times1
eval_times2 = args.eval_times2
if args.env_eval is not None:
env_eval = args.env_eval
elif args.env_eval in set(gym.envs.registry.env_specs.keys()):
env_eval = PreprocessEnv(gym.make(env.env_name))
else:
env_eval = deepcopy(env)
del args # In order to show these hyper-parameters clearly, I put them above.
'''init: environment'''
max_step = env.max_step
state_dim = env.state_dim
action_dim = env.action_dim
if_discrete = env.if_discrete
'''init: Agent, ReplayBuffer, Evaluator'''
agent.init(net_dim, state_dim, action_dim, if_per)
# ----
# work_days，周期数，用于存储和提取训练好的模型
model_file_path = f'{model_folder_path}/actor.pth'
# 如果model存在，则加载
if os.path.exists(model_file_path):
agent.save_load_model(model_folder_path, if_save=False)
'''prepare for training'''
agent.state = env.reset()
episode_return = 0.0 # sum of rewards in an episode
episode_step = 1
max_step = env.max_step
if_discrete = env.if_discrete
state = env.reset()
with torch.no_grad(): # speed up running
# for episode_step in range(max_step):
while True:
s_tensor = torch.as_tensor((state,), device=agent.device)
a_tensor = agent.act(s_tensor)
if if_discrete:
a_tensor = a_tensor.argmax(dim=1)
action = a_tensor.detach().cpu().numpy()[
0] # not need detach(), because with torch.no_grad() outside
state, reward, done, _ = env.step(action)
episode_return += reward
if done:
break
pass
pass
# 获取要预测的日期，保存到数据库中
for item in env.list_buy_or_sell_output:
tic, date, action, hold, day, episode_return = item
if str(date) == config.OUTPUT_DATE:
# 简单计算一次,低买高卖的最大回报
max_return = calc_max_return(env.price_ary, env.initial_capital)
# 找到要预测的那一天，存储到psql
StockData.update_predict_result_to_psql(psql=psql_object, agent=config.AGENT_NAME,
vali_period_value=config.VALI_DAYS_FLAG,
pred_period_name=config.PREDICT_PERIOD,
tic=tic, date=date, action=action,
hold=hold,
day=day, episode_return=episode_return,
max_return=max_return,
trade_detail=env.output_text_trade_detail)
break
pass
pass
pass
# episode_return = getattr(env, 'episode_return', episode_return)
pass
else:
print('未找到模型文件', model_file_path)
pass
# ----
pass
pass
psql_object.close()
pass
# 结束预测的时间
time_end = datetime.now()
duration = (time_end - time_begin).seconds
print('检测耗时', duration, '秒')
pass