def __getSimilarStock__(self):
     result_dis = []
     result_dt = []
     result_ts = []
     stock_list = sd.get_similar_industry_stock(self.ts_code)
     x_data = sd.get_daily_bar(self.ts_code, self.__getx_data_start_time_point__(), self.prediction_time_point)
     if x_data.empty:
         self.__error__ = True
         return None
     else:
         x = np.array(x_data['pct_change'])
         ob_length = len(x)
         for index, row in stock_list.iterrows():
             stock = row['ts_code']
             if stock != self.ts_code:
                 y_data = sd.get_daily_bar(stock, self.__gety_data_start_time_point__(),
                                           self.__gety_data_end_time_point__())
                 if not y_data.empty:
                     time_point_list = np.array(y_data['trade_date'])
                     y = np.array(y_data['pct_change'])
                     for i in range(0, int(len(y) / ob_length)):
                         y_t = y[i * ob_length:(i + 1) * ob_length]
                         distance, path = similarity_calculation(x, y_t, dist=euclidean)
                         result_dis.append(distance)
                         result_dt.append(time_point_list[(i + 1) * ob_length - 1])
                         result_ts.append(stock)
         result = pd.DataFrame({'ts_code': result_ts, 'distance': result_dis, 'time_point': result_dt})
         return result.sort_values(by='distance', axis=0, ascending=True)
 def __getSimilarStock__(self):
     result_dis = []
     result_sdt = []
     result_edt = []
     x_data = sd.get_daily_bar(self.ts_code, self.__getx_data_start_time_point__(), self.prediction_time_point)
     if x_data.empty:
         self.__error__ = True
         return None
     else:
         x = np.array(x_data['pct_change'])
         ob_length = len(x)
         y_data = sd.get_daily_bar(self.ts_code, self.__gety_data_start_time_point__(),
                                   self.__gety_data_end_time_point__())
         if not y_data.empty:
             time_point_list = np.array(y_data['trade_date'])
             y = np.array(y_data['pct_change'])
             for i in range(0, len(y) - ob_length + 1):
                 y_t = y[i:i + ob_length]
                 distance, path = similarity_calculation(x, y_t, dist=euclidean)
                 result_dis.append(distance)
                 result_sdt.append(time_point_list[i])
                 result_edt.append(time_point_list[i + ob_length - 1])
         result = pd.DataFrame(
             {'distance': result_dis, 'start_time_point': result_sdt, 'end_time_point': result_edt})
         return result.sort_values(by='distance', axis=0, ascending=True)
 def __getSimilarStock__(self):
     result_dis = []
     result_ts = []
     stock_list = sd.get_similar_industry_stock(self.ts_code)
     x_data = sd.get_daily_bar(self.ts_code, self.__get_data_start_time_point__(), self.prediction_time_point)
     if x_data.empty:
         self.__error__ = True
         return None
     else:
         x = np.array(x_data['pct_change'])
         for index, row in stock_list.iterrows():
             stock = row['ts_code']
             if stock != self.ts_code:
                 y_data = sd.get_daily_bar(stock, self.__get_data_start_time_point__(),
                                           self.prediction_time_point)
                 if not y_data.empty:
                     y = np.array(y_data['pct_change'])
                     distance, path = similarity_calculation(x, y, dist=euclidean)
                     result_dis.append(distance)
                     result_ts.append(stock)
         result = pd.DataFrame({'ts_code': result_ts, 'distance': result_dis})
         return result.sort_values(by='distance', axis=0, ascending=True)