import logging
import os
import settings
import data_manager
from policy_learner import PolicyLearner
if __name__ == '__main__':
stock_code = '005930' # 삼성전자
model_ver = '20180202000545'
# 로그 기록
log_dir = os.path.join(settings.BASE_DIR, 'logs/%s' % stock_code)
timestr = settings.get_time_str()
file_handler = logging.FileHandler(filename=os.path.join(
log_dir, "%s_%s.log" % (stock_code, timestr)), encoding='utf-8')
stream_handler = logging.StreamHandler()
file_handler.setLevel(logging.DEBUG)
stream_handler.setLevel(logging.INFO)
logging.basicConfig(format="%(message)s",
handlers=[file_handler, stream_handler], level=logging.DEBUG)
# 주식 데이터 준비
chart_data = data_manager.load_chart_data(
os.path.join(settings.BASE_DIR,
'data/chart_data/{}.csv'.format(stock_code)))
prep_data = data_manager.preprocess(chart_data)
training_data = data_manager.build_training_data(prep_data)
# 기간 필터링
training_data = training_data[(training_data['date'] >= '2018-01-01') &
def __init__(self):
stock_code='BTCUSDT'
global timestr #global 함수로 불러와 바로 사용
model_ver=timestr
self.dataclose = self.datas[0].close
log_dir = os.path.join(settings.BASE_DIR, 'logs/%s' % stock_code)
timestr = settings.get_time_str()
if not os.path.exists('logs/%s' % stock_code):
os.makedirs('logs/%s' % stock_code)
file_handler = logging.FileHandler(filename=os.path.join(
log_dir, "%s_%s.log" % (stock_code, timestr)), encoding='utf-8')
stream_handler = logging.StreamHandler()
file_handler.setLevel(logging.DEBUG)
stream_handler.setLevel(logging.INFO)
logging.basicConfig(format="%(message)s",
handlers=[file_handler, stream_handler], level=logging.DEBUG)
chart_data = data_manager.load_chart_data(
os.path.join(settings.BASE_DIR,
'{}'.format(stock_code)))
prep_data = data_manager.preprocess(chart_data)
training_data = data_manager.build_training_data(prep_data)
training_data = training_data.loc['2018-07-01 01:00:00':]
features_chart_data = ['o_t', 'open', 'high', 'low', 'close', 'volume']
chart_data = training_data[features_chart_data]
features_training_data = [
'high_close_ratio', 'low_close_ratio',
'close_lastclose_ratio', 'volume_lastvolume_ratio',
'close_ma5_ratio', 'volume_ma5_ratio',
'close_ma10_ratio', 'volume_ma10_ratio',
'close_ma20_ratio', 'volume_ma20_ratio',
'close_ma60_ratio', 'volume_ma60_ratio',
'close_ma120_ratio', 'volume_ma120_ratio',
'ema12','ema26','dn','mavg','up','pctB','macd','signal','cci'
]
training_data = training_data[features_training_data]
#print (training_data[:3])
training_data = training_data.dropna(axis=1)
chart_data=chart_data.dropna(axis=1)
#chart_data = chart_data.loc[:1530352800000]
#training_data = training_data.loc[:1530352800000]
delayed_reward_threshold=.001
lr=0.1
self.TRADING_TAX =0
self.TRADING_CHARGE=0
self.stock_code = stock_code
self.chart_data = chart_data
self.environment = Environment(chart_data)
self.agent = Agent(self.environment, delayed_reward_threshold=delayed_reward_threshold)
self.training_data = training_data
self.sample = None
self.pvdata=[]
self.training_data_idx = -1
self.num_features = self.training_data.shape[1] #+ self.agent.STATE_DIM
self.policy_network = PolicyNetwork(
input_dim=self.num_features, output_dim=self.agent.NUM_ACTIONS, lr=lr)
model_path=os.path.join(settings.BASE_DIR,
'models/{}/model_{}.h5'.format(stock_code,model_ver))
self.policy_network.load_model(model_path=model_path)
self.agent.set_balance(self.broker.getcash())
self.epsilon=0
self.num_stocks=0
df=pd.read_csv('out.csv')
self.stopit=df.loc[7692]['c_p'] #2903 4135 7692
def main_before_run(before_start_date, before_end_date, before_min_unit,
before_max_unit, before_delayed, before_learning,
before_balance, before_epoch, before_epsilon):
code_list = save_csv.load_skyrocket_list()
for i in range(len(code_list)):
stock_code = code_list[i]
# 로그 기록
log_dir = os.path.join(settings.BASE_DIR, 'result/logs/%s' % stock_code)
timestr = settings.get_time_str()
if not os.path.isdir(log_dir):
os.makedirs(log_dir)
file_handler = logging.FileHandler(filename=os.path.join(
log_dir, "%s_%s.log" % (stock_code, timestr)), encoding='utf-8')
stream_handler = logging.StreamHandler()
file_handler.setLevel(logging.DEBUG)
stream_handler.setLevel(logging.INFO)
logging.basicConfig(format="%(message)s",
handlers=[file_handler, stream_handler], level=logging.DEBUG)
# 데이터 준비
chart_data = data_management.load_chart_data(
os.path.join(settings.BASE_DIR, 'data/csv_data/{}.csv'.format(stock_code)))
prep_data = data_management.preprocess(chart_data)
training_data = data_management.build_training_data(prep_data)
# 기간 필터링
training_data = training_data[(training_data['date'] >= before_start_date) &
(training_data['date'] <= before_end_date)]
training_data = training_data.dropna()
# 차트 데이터 분리
features_chart_data = ['date', 'open', 'high', 'low', 'close', 'volume']
chart_data = training_data[features_chart_data]
# 학습 데이터 분리
features_training_data = [
'open_lastclose_ratio', 'high_close_ratio', 'low_close_ratio',
'close_lastclose_ratio', 'volume_lastvolume_ratio',
'close_ma5_ratio', 'volume_ma5_ratio',
'close_ma10_ratio', 'volume_ma10_ratio',
'close_ma20_ratio', 'volume_ma20_ratio',
'close_ma60_ratio', 'volume_ma60_ratio',
'close_ma120_ratio', 'volume_ma120_ratio']
training_data = training_data[features_training_data]
# 강화학습 시작
learner = Learner(
stock_code=stock_code, # 종목 코드
chart_data=chart_data, # 차트 데이터
training_data=training_data, # 학습 데이터
min_trading_unit=before_min_unit, # 최소 투자 단위
max_trading_unit=before_max_unit, # 최대 투자 단위
delayed_reward_threshold=before_delayed, # 지연 보상 임계치
lr=before_learning) # 학습 속도
learner.fit(balance=before_balance, # 초기 자본금
num_epoches=before_epoch, # 수행할 Epoch 수
discount_factor=0, # 할인 요인
start_epsilon=before_epsilon) # 초기 탐험률
# 정책 신경망을 파일로 저장
date = datetime.datetime.strftime(datetime.datetime.today(), '%Y%m%d')
model_dir = os.path.join(settings.BASE_DIR, 'result/models/%s' % stock_code)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
model_path = os.path.join(model_dir, 'model_%s.h5' % date)
learner.network.save_model(model_path)
def main_after_run(after_start_date, after_min_unit, after_max_unit):
code_list = save_csv.load_skyrocket_list()
min_trading_unit_buy_list = []
min_trading_unit_sell_list = []
code_list_buy = []
code_list_sell = []
for i in range(len(code_list)):
stock_code = code_list[i]
# 로그 기록
log_dir = os.path.join(settings.BASE_DIR,
'result/logs/%s' % stock_code)
timestr = settings.get_time_str()
file_handler = logging.FileHandler(filename=os.path.join(
log_dir, "%s_%s.log" % (stock_code, timestr)),
encoding='utf-8')
stream_handler = logging.StreamHandler()
file_handler.setLevel(logging.DEBUG)
stream_handler.setLevel(logging.INFO)
logging.basicConfig(format="%(message)s",
handlers=[file_handler, stream_handler],
level=logging.DEBUG)
# 데이터 준비
chart_data = data_management.load_chart_data(
os.path.join(settings.BASE_DIR,
'data/csv_data/{}.csv'.format(stock_code)))
prep_data = data_management.preprocess(chart_data)
training_data = data_management.build_training_data(prep_data)
# 기간 필터링
today = datetime.datetime.strftime(datetime.datetime.today(), '%Y%m%d')
training_data = training_data[
(training_data['date'] >= after_start_date)
& (training_data['date'] <= today)]
training_data = training_data.dropna()
# 차트 데이터 분리
features_chart_data = [
'date', 'open', 'high', 'low', 'close', 'volume'
]
chart_data = training_data[features_chart_data]
# 학습 데이터 분리
features_training_data = [
'open_lastclose_ratio', 'high_close_ratio', 'low_close_ratio',
'close_lastclose_ratio', 'volume_lastvolume_ratio',
'close_ma5_ratio', 'volume_ma5_ratio', 'close_ma10_ratio',
'volume_ma10_ratio', 'close_ma20_ratio', 'volume_ma20_ratio',
'close_ma60_ratio', 'volume_ma60_ratio', 'close_ma120_ratio',
'volume_ma120_ratio'
]
training_data = training_data[features_training_data]
# 비 학습 투자 시뮬레이션 시작
learner = Learner(
stock_code=stock_code, # 종목 코드
chart_data=chart_data, # 차트 데이터
training_data=training_data, # 학습 데이터
min_trading_unit=1, # 최소 투자 단위
max_trading_unit=3) # 최대 투자 단위
# trade() 함수 호출
validity, action, min_trading_unit = learner.trade(
balance=10000000,
model_path=os.path.join(
settings.BASE_DIR,
'result/models/{}/model_{}.h5'.format(stock_code, today)))
if validity == True:
if action == 0: # 매수
min_trading_unit_buy_list.append(min_trading_unit)
code_list_buy.append(stock_code)
elif action == 1: # 매도
min_trading_unit_sell_list.append(min_trading_unit)
code_list_sell.append(stock_code)
# buy_list.txt와 sell_list.txt로 저장
b_list = open(os.path.join(settings.BASE_DIR, "data/list/buy_list.txt"),
"wt")
for i in range(len(code_list_buy)):
b_list.writelines("buy;" + str(code_list_buy[i]) + ";market;" +
str(min_trading_unit_buy_list[i]) + ";0;before\n")
b_list.close()
s_list = open(os.path.join(settings.BASE_DIR, "data/list/sell_list.txt"),
"wt")
for i in range(len(code_list_sell)):
s_list.writelines("sell;" + str(code_list_sell[i]) + ";market;" +
str(min_trading_unit_sell_list[i]) + ";0;before\n")
s_list.close()
