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)