def test(self,
code,
day=252,
year=1,
test_year=1,
full_year=5,
test_month=1,
full_month=5,
category='d',
eps=0.05):
now = datetime.now()
now = now.strftime('%Y%m%d')
path = '.\\DB\\CSV\\daily\\'
if category == 'm':
path = '.\\DB\\CSV\\min\\'
day = 20
day = day * 390
test_year = test_month
full_year = full_month
now = now + '_m'
filename = code + '_ch.csv'
chartfile = code + '.csv'
if not os.path.exists(path + filename):
self.logging.info('파일 생성')
self.pre.change_csv(code, category=category)
if not os.path.exists('.\\Test\\re_' + now):
os.makedirs('.\\Test\\re_' + now)
self.logging.info('테스트 데이터 분리')
df_chart = pd.read_csv(path + chartfile)
if category == 'm':
df_chart = self.pre.min_preprocessing(df_chart)
df_chart_split = df_chart.iloc[-1 * day * year * test_year:]
df_chart_split.to_csv('.\\Test\\re_' + now + '\\chart.csv',
index=False)
df_chart = self.pre.change_feature(df_chart)
df_test = df_chart.iloc[-1 * day * year * test_year:]
df_prev = df_chart.iloc[-1 * day * year * test_year - 120:-1 * day *
year * test_year]
step = len(df_test)
env = RLEnvTrain.RLEnv(df_test)
agent = RLAgent.Agent(gamma=0.98,
eps_start=eps,
eps_end=0.01,
eps_decay_steps=800,
eps_exponential_decay=0.99,
replay_capacity=int(1e6),
batch_size=step - 1,
tau=10,
code=code,
V_nn='DNN',
P_nn='CNN',
method='A2C') # policy value A2C
reward_list = []
action_List = []
quant_list = []
stock_cnt_list = []
obs_list = []
obs = env.reset()
agent.reset()
if not os.path.isdir('.\\Test\\re_' + now + '\\'):
os.makedirs('.\\Test\\re_' + now + '\\')
df_prev = df_prev
data = obs.reshape(1, -1)
data = pd.DataFrame(data, columns=df_chart.columns)
df_prev = pd.concat([df_prev, data], ignore_index=True)
obs = self.pre.add_feature(df_prev)
step = len(df_test)
self.logging.info('테스트 시작')
for i in tqdm(range(step)):
# 관측 데이터로 예측한 가치신경망, 정책 신경망 예측값
value_per, policy_per = agent.predict_action_per(obs)
action = agent.policy(value_per, policy_per)
# 현재 잔고 및 주식 보유 수량
init_cash, stock_cnt = env.init_cash, env.total_stock
# 현재 주식 가격
cu_price = obs[1] # close
quant = agent.decide_quant(action, value_per, policy_per,
init_cash, cu_price)
# 매도 매수 가능 한 경우 확인
action, quant = env.validation_(action, quant, cu_price, stock_cnt)
next_obs, reward, done, info = env.next_step(action, quant)
if next_obs is not None:
data = next_obs.reshape(1, -1)
data = pd.DataFrame(data, columns=df_prev.columns)
df_prev = pd.concat([df_prev, data], ignore_index=True)
next_obs = self.pre.add_feature(df_prev)
reward_list.append(reward)
action_List.append(action)
quant_list.append(quant)
stock_cnt_list.append(stock_cnt)
obs_list.append(obs)
obs = next_obs
df_reward = pd.DataFrame(reward_list)
df_action = pd.DataFrame(action_List)
df_quant = pd.DataFrame(quant_list)
df_stock_cnt = pd.DataFrame(stock_cnt_list)
df_obs = pd.DataFrame(obs_list)
df_reward.to_csv('.\\Test\\re_' + now + '\\reward.csv', index=False)
df_action.to_csv('.\\Test\\re_' + now + '\\action.csv', index=False)
df_quant.to_csv('.\\Test\\re_' + now + '\\quant.csv', index=False)
df_stock_cnt.to_csv('.\\Test\\re_' + now + '\\stock_cnt.csv',
index=False)
df_obs.to_csv('.\\Test\\re_' + now + '\\obs.csv', index=False)
self.logging.info(f'reward : {reward} | 테스트 끝')
import numpy as np
import RLEnvTrain, RLAgent
from tqdm import tqdm
from datetime import datetime
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
df = pd.read_csv('.\\DB\\CSV\\daily\\DA000020_ch.csv')
df['profit'] = 0
df_obs = df.iloc[119:int(len(df) * 0.6), :].copy()
df_obs = df_obs.reset_index()
df_obs = df_obs.drop(['index'], axis=1)
env = RLEnvTrain.RLEnv(df_obs)
agent = RLAgent.Agent(batch_size=400)
reward_list = []
action_List = []
quant_list = []
re_list = []
for k in range(100):
# 학습 날짜 시간 디렉토리
now = datetime.now()
now = now.strftime('%Y%m%d_%H%M%S')
if not os.path.isdir('.\\reward\\re_' + now + '\\'):
os.mkdir('.\\reward\\re_' + now + '\\')
obs = env.reset()
sub_action_list = []
sub_quant_list = []
sub_re_list = []
import pandas as pd
import numpy as np
import RLEnvTrain, RLAgent
import time
from tqdm import tqdm
df = pd.read_csv('.\\DB\\CSV\\daily\\DA000020_ch.csv')
env = RLEnvTrain.RLEnv(df)
agent = RLAgent.Agent()
for k in range(100):
obs = env.reset()
for i in tqdm(range(1000)):
quant = 1 # 매수매도 수량
action = agent.policy(obs)
price = obs[1]
if not env.validation_(action, quant, price):
action = 0
quant = 0
next_obs, reward, done, info = env.next_step(action, quant)
agent.memorize_transition(obs, action, reward, next_obs,
0.0 if done else 1.0)
if agent.train:
agent.experience_replay()
if done:
break
f'데이터셋 분리 Train : {len(df_train)} | Test : {len(df_test)} | Set-up : {len(df_prev)}학습을 시작합니다.'
)
step = len(df_train)
logging.info(f'한 학습당 step : {step}')
#step = 10
logging.info(f'학습 환경 구성')
env = RLEnvTrain.RLEnv(df_train)
logging.info(f'학습 에이전트 구성')
agent = RLAgent.Agent(gamma=0.98,
eps_start=0.8,
eps_end=0.01,
eps_decay_steps=800,
eps_exponential_decay=0.99,
replay_capacity=int(1e6),
batch_size=step - 1,
tau=10,
code=code,
V_nn='DNN',
P_nn='CNN',
method='A2C',
tick='m') #policy value A2C
reward_list = []
action_List = []
quant_list = []
re_list = []
stock_cnt_list = []
logging.info('10번의 학습 시작')
for k in range(5):