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()