def train_n_invest_twins(self, corp_code, corp_name, no) -> list:
"""겨별 세션과 통합세션에서 예측한 값의 평균을 예측값으로 한다."""
stocks = Stocks(self.params)
trains_data = TrainsData(self.params)
learning = Learning(self.params)
params_all = GlobalParams('ALL_CORPS')
learning_all = Learning(params_all)
invest = MockInvestment(self.params)
stock_data = stocks.get_stock_data(corp_code)
data_params, scaler_close, dataX_last = trains_data.get_train_test(
stock_data)
tp = learning.learn(corp_code, corp_name, data_params)
tp_all = learning_all.learn(corp_code, corp_name, data_params)
ip = invest.invest_n_all(corp_code, dataX_last, data_params,
params_all, scaler_close)
tp.test_rmse = np.mean([tp.test_rmse, tp_all.test_rmse])
tp.train_count = tp.train_count + tp_all.train_count
return self.get_train_invest_result(no, corp_code, corp_name,
stock_data, tp, ip)
def train(self,
corp_code: str,
corp_name: str,
stock_data: pd.DataFrame,
scaler_close: preprocessing.MinMaxScaler = None,
invest_only: bool = False) -> TrainParams:
"""입력한 회사에 대해서 학습시킨다"""
trains_data = TrainsData(self.params)
learning = Learning(self.params)
data_params, scaler_close, dataX_last = trains_data.get_train_test(
stock_data, scaler_close)
if invest_only:
tp = learning.get_test_rmse(corp_code, data_params)
else:
tp = learning.learn(corp_code, corp_name, data_params)
tp.scaler_close = scaler_close
tp.dataX_last = dataX_last
return tp
def train_n_invests_for_all(self, corps):
"""입력한 회사들에 대해서 학습시키고 모의투자를 실행한다."""
stocks = Stocks(self.params)
trains_data = TrainsData(self.params)
learning = Learning(self.params)
invest = MockInvestment(self.params)
list = []
for index, corp_data in corps.iterrows():
corp_code = corp_data['종목코드']
corp_name = corp_data['회사명']
try:
stock_data = stocks.get_stock_data(corp_code)
data_params, scaler_close, dataX_last = trains_data.get_train_test(
stock_data)
except Exception:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type,
exc_value,
exc_traceback,
file=sys.stdout)
continue
list.append({
'corp_code': corp_code,
'corp_name': corp_name,
'scaler_close': scaler_close,
'dataX_last': dataX_last,
'data_params': data_params,
'stock_data': stock_data
})
all_data_params = self.gather_data_params(list)
tp = learning.learn("ALL_CORPS", "ALL_CORPS", all_data_params)
invest_daily_data = []
comp_rmses = []
index = 1
for item in list:
corp_code = item['corp_code']
corp_name = item['corp_data']
dataX_last = item['dataX_last']
data_params = item['data_params']
scaler_close = item['scaler_close']
stock_data = item['stock_data']
ip = invest.invest(corp_code, corp_name, tp)
visualizer = LearningVisualizer(self.params)
visualizer.draw_predictions(corp_name, scaler_close, data_params,
tp.test_predict, ip.predict_list)
result, invest_daily = self.get_train_invest_result(
index, corp_code, corp_name, tp.test_rmse, ip.last_money,
ip.index_money, tp.train_count, stock_data, data_params,
scaler_close, ip.last_predict)
if invest_daily != None:
invest_daily_data.append(invest_daily)
comp_rmses.append(result)
index += 1
df_comp_rmses = pd.DataFrame(comp_rmses, columns=self.result_columns)
DataUtils.save_csv(df_comp_rmses, self.get_result_file_path())
if len(invest_daily_data) > 1:
try:
visualizer = InvestVisualizer(self.params)
return visualizer.draw_invest_daily(invest_daily_data, corps)
except Exception:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type,
exc_value,
exc_traceback,
file=sys.stdout)