def recommend_corps(recommend_month: str, train_model: str = 'rnn') -> None:
"""하나의 세션으로 학습시키는 기본 모델 """
month = DateUtils.to_date(recommend_month, '%Y.%m')
params = GlobalParams(train_model=train_model)
#params.remove_session_file = True
before_month_start = DateUtils.to_month_str(month,
-params.mock_period_months)
before_month_end = DateUtils.to_month_str(month, -1)
params.invest_start_date = before_month_start + '.01'
params.invest_end_date = DateUtils.to_date_str(month -
datetime.timedelta(days=1))
params.result_file_name = "MOCK_" + before_month_start + "-" + before_month_end
corp = Corp(params)
corps = corp.get_eval_corps_auto(params.invest_end_date)
invests = LearningNMockInvestment(params)
invests.train_n_invests(corps)
before_result = pd.read_csv(invests.get_result_file_path())
if params.rmse_max_recommend is not None:
before_result = before_result.query("rmse<" +
str(params.rmse_max_recommend))
before_result = before_result.sort_values(by='invest_result',
ascending=False)
before_result.index = range(len(before_result.index))
save_file_name = "recommend_months_" + recommend_month + ".xlsx"
save_file_path = os.path.join('result', train_model, save_file_name)
DataUtils.save_csv(before_result, save_file_path)
print(before_result)
def train_months(self, start:str='2018.01', end:str='2018.11', invest_money:float=100000000)->None:
train_model = self._global_params.train_model
start_month = DateUtils.to_date(start, '%Y.%m')
end_month = DateUtils.to_date(end, '%Y.%m')
between = DateUtils.between_months(start_month, end_month)
invest_months_result = []
result_columns = ["month", "invest_money", "result_money"]
MOCK_MONEY = 10000000
chart_data = []
for i in range(between + 1):
# params.remove_session_file = True
before_month_start = DateUtils.to_month_str(start_month, i - self._global_params.mock_period_months)
before_month_end = DateUtils.to_month_str(start_month, i - 1)
self._global_params.invest_start_date = before_month_start + '.01'
self._global_params.invest_end_date = before_month_end + '.31'
self._global_params.result_file_name = "MOCK_" + before_month_start + "-" + before_month_end
self._global_params.invest_money = MOCK_MONEY
corp = Corp(self._global_params)
corps = corp.get_eval_corps_auto(self._global_params.invest_end_date)
self._env.set_params(params=self._global_params)
before_result, _ = self.trains(corps)
now_month = DateUtils.to_month_str(start_month, i)
before_result = corp.exclude_corps(before_result, now_month)
before_result = before_result.sort_values(by='invest_result', ascending=False)
before_result.index = range(len(before_result.index))
corp10_codes = before_result.loc[:9, 'code']
corp10_codes.index = range(len(corp10_codes.index))
corp10 = corp.get_corps_for_codes(corp10_codes)
corp10_len = len(corp10.index)
self._global_params.invest_start_date = now_month + '.01'
self._global_params.invest_end_date = now_month + '.31'
self._global_params.result_file_name = "INVEST_" + now_month
self._global_params.invest_money = invest_money / corp10_len
self._env.set_params(params=self._global_params)
now_result, invest_chart_data = self.trains(corp10)
chart_data.append(invest_chart_data)
invest_money = now_result['invest_result'].sum()
result = [now_month, self._global_params.invest_money * corp10_len, invest_money]
invest_months_result.append(result)
print(result)
df_imr = pd.DataFrame(invest_months_result, columns=result_columns)
save_file_name = "recommend_months_" + start + "-" + end + ".xlsx"
if "_" in train_model:
save_file_path = os.path.join('result', 'reinforcement', train_model, self._global_params.ensemble_type, save_file_name)
else:
save_file_path = os.path.join('result', 'reinforcement', train_model, save_file_name)
DataUtils.save_excel(df_imr, save_file_path)
if len(chart_data) > 1:
visualizer = InvestVisualizer(self._global_params)
visualizer.draw_invest_months(chart_data, start, end)
print()
def train_months(start: str = '2018.01',
end: str = '2018.09',
invest_money: float = 100000000,
train_model: str = 'rnn') -> None:
"""하나의 세션으로 학습시키는 기본 모델 """
start_month = DateUtils.to_date(start, '%Y.%m')
end_month = DateUtils.to_date(end, '%Y.%m')
between = DateUtils.between_months(start_month, end_month)
invest_months_result = []
result_columns = ["month", "invest_money", "result_money"]
MOCK_MONEY = 10000000
chart_data = []
params = None
index_money = None
for i in range(between + 1):
params = GlobalParams(train_model=train_model)
#params.remove_session_file = True
before_month_start = DateUtils.to_month_str(
start_month, i - params.mock_period_months)
before_month_end = DateUtils.to_month_str(start_month, i - 1)
params.invest_start_date = before_month_start + '.01'
params.invest_end_date = before_month_end + '.31'
params.result_file_name = "MOCK_" + before_month_start + "-" + before_month_end
params.invest_money = MOCK_MONEY
corp = Corp(params)
corps = corp.get_eval_corps_auto(params.invest_end_date)
invests = LearningNMockInvestment(params)
invests.train_n_invests(corps)
before_result = pd.read_csv(invests.get_result_file_path())
now_month = DateUtils.to_month_str(start_month, i)
if params.rmse_max_recommend is not None:
before_result = before_result.query("rmse<" +
str(params.rmse_max_recommend))
before_result = corp.exclude_corps(before_result, now_month)
before_result = before_result.sort_values(by='invest_result',
ascending=False)
before_result.index = range(len(before_result.index))
corp10_codes = before_result.loc[:9, 'code']
corp10_codes.index = range(len(corp10_codes.index))
corp10 = corp.get_corps_for_codes(corp10_codes)
corp10_len = len(corp10_codes.index)
params = GlobalParams(train_model=train_model)
#params.remove_session_file = False
params.invest_start_date = now_month + '.01'
params.invest_end_date = now_month + '.31'
params.result_file_name = "INVEST_" + now_month
params.invest_money = invest_money / corp10_len
if index_money is not None:
params.index_money = index_money / corp10_len
invests = LearningNMockInvestment(params)
invest_chart_data = invests.train_n_invests(corp10, invest_only=False)
chart_data.append(invest_chart_data)
now_result = pd.read_csv(invests.get_result_file_path())
invest_money = now_result['invest_result'].sum()
index_money = now_result['all_invest_result'].sum()
invest_months_result.append(
[now_month, params.invest_money * corp10_len, invest_money])
print(now_month, params.invest_money * corp10_len, invest_money)
df_imr = pd.DataFrame(invest_months_result, columns=result_columns)
save_file_name = "recommend_months_" + start + "-" + end + ".xlsx"
if "_" in train_model:
save_file_path = os.path.join('result', train_model,
params.ensemble_type, save_file_name)
else:
save_file_path = os.path.join('result', train_model, save_file_name)
DataUtils.save_csv(df_imr, save_file_path)
if len(chart_data) > 1 and params is not None:
visualizer = InvestVisualizer(params)
visualizer.draw_invest_months(chart_data, start, end)
print()