def session(config,mode):
if mode == 'train':
files = glob.glob('saved_network/PG/*')
for f in files:
os.remove(f)
from data.environment import Environment
codes, start_date, end_date, features, agent_config, market,predictor, framework, window_length,noise_flag, record_flag, plot_flag,reload_flag,trainable,method=parse_config(config,mode)
env = Environment(start_date, end_date, codes, features, int(window_length),market,mode)
global M
M=len(codes)+1
if framework == 'DDPG':
print("*-----------------Loading DDPG Agent---------------------*")
from agents.ddpg import DDPG
agent = DDPG(predictor, len(codes) + 1, int(window_length), len(features), '-'.join(agent_config), reload_flag,trainable)
elif framework == 'PPO':
print("*-----------------Loading PPO Agent---------------------*")
from agents.ppo import PPO
agent = PPO(predictor, len(codes) + 1, int(window_length), len(features), '-'.join(agent_config), reload_flag,trainable)
elif framework == 'PG':
print("*-----------------Loading PG Agent---------------------*")
from agents.pg import PG
agent = PG(len(codes) + 1, int(window_length), len(features), '-'.join(agent_config), reload_flag,trainable)
stocktrader=StockTrader()
if mode=='train':
print("Training with {:d}".format(epochs))
for epoch in range(epochs):
print("Now we are at epoch", epoch)
traversal(stocktrader,agent,env,epoch,noise_flag,framework,method,trainable)
if record_flag=='True':
stocktrader.write(epoch)
if plot_flag=='True':
stocktrader.plot_result()
agent.reset_buffer()
stocktrader.print_result(epoch,agent)
stocktrader.reset()
elif mode=='test':
backtest(agent, env)
def session(config, args):
global PATH_prefix
codes, start_date, end_date, features, agent_config, \
market,predictor, framework, window_length,noise_flag, record_flag,\
plot_flag,reload_flag,trainable,method=parse_config(config,args)
env = Environment()
global M
M = codes + 1
stocktrader = StockTrader()
PATH_prefix = "result/DDPG/" + str(args['num']) + '/'
if args['mode'] == 'train':
if not os.path.exists(PATH_prefix):
os.makedirs(PATH_prefix)
train_start_date, train_end_date, test_start_date, test_end_date, codes = env.get_repo(
start_date, end_date, codes, market)
env.get_data(train_start_date, train_end_date, features,
window_length, market, codes)
print("Codes:", codes)
print('Training Time Period:', train_start_date, ' ',
train_end_date)
print('Testing Time Period:', test_start_date, ' ',
test_end_date)
with open(PATH_prefix + 'config.json', 'w') as f:
json.dump(
{
"train_start_date":
train_start_date.strftime('%Y-%m-%d'),
"train_end_date": train_end_date.strftime('%Y-%m-%d'),
"test_start_date":
test_start_date.strftime('%Y-%m-%d'),
"test_end_date": test_end_date.strftime('%Y-%m-%d'),
"codes": codes
}, f)
print("finish writing config")
else:
with open("result/DDPG/" + str(args['num']) + '/config.json',
'r') as f:
dict_data = json.load(f)
print("successfully load config")
train_start_date, train_end_date, codes = datetime.datetime.strptime(
dict_data['train_start_date'],
'%Y-%m-%d'), datetime.datetime.strptime(
dict_data['train_end_date'],
'%Y-%m-%d'), dict_data['codes']
env.get_data(train_start_date, train_end_date, features,
window_length, market, codes)
for noise_flag in [
'True'
]: #['False','True'] to train agents with noise and without noise in assets prices
print("*-----------------Loading DDPG Agent---------------------*")
agent = DDPG(predictor,
len(codes) + 1, int(window_length), len(features),
'-'.join(agent_config), reload_flag, trainable)
print("Training with {:d}".format(epochs))
for epoch in range(epochs):
print("Now we are at epoch", epoch)
traversal(stocktrader, agent, env, epoch, noise_flag,
framework, method, trainable)
if record_flag == 'True':
stocktrader.write(epoch, framework)
if plot_flag == 'True':
stocktrader.plot_result()
agent.reset_buffer()
stocktrader.print_result(epoch, agent, noise_flag)
stocktrader.reset()
agent.close()
del agent
elif args['mode'] == 'test':
with open(PATH_prefix + 'config.json', 'r') as f:
dict_data = json.load(f)
test_start_date, test_end_date, codes = datetime.datetime.strptime(
dict_data['test_start_date'],
'%Y-%m-%d'), datetime.datetime.strptime(
dict_data['test_end_date'], '%Y-%m-%d'), dict_data['codes']
env.get_data(test_start_date, test_end_date, features, window_length,
market, codes)
backtest([
DDPG(predictor,
len(codes) + 1, int(window_length), len(features),
'-'.join(agent_config), "True", "False")
], env)