"pearson_index": similar_value
}
# cal_finish_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
# end_time = datetime.datetime.strptime(cal_finish_time, "%Y-%m-%d %H:%M:%S")
# print("Calc Cost: {}".format(str((end_time - start_time).seconds)))
return result
if __name__ =='__main__':
read_data = ReadData()
ts_code_list = read_data.mysql_read_ts_code()[:]
print("----------------Complete ts_code reading--------------------:{}")
start_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
print("--------Start cal----------:{0}".format(start_time))
results_dict = {}
source_data = read_data.mysql_read_data("000009.SZ").iloc[-60:]
for ts_code in ts_code_list:
#print(ts_code)
compare_data = read_data.mysql_read_data(ts_code).iloc[:-60]
if len(compare_data) < 60:
print("该股票数据不足")
else:
compare = CompareSimilarKDynamic(source_data, compare_data, 60)
result = compare.compare_dynamic()
results_dict[ts_code] = result
#print(result)
#print(results_dict)
finish_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
print("--------finish cal----------:{0}".format(finish_time))
reverse_result = sorted(results_dict.items(), key=lambda item:item[1]["pearson_index"], reverse=True)
for item in reverse_result[:10]:
similar_value = 0.5 * self.calc_pearson(
mul_open, atom_open) + 0.5 * self.calc_pearson(
mul_close, atom_close)
if (result["pearson_index"] < similar_value):
result = {
"start_time": temp_compare[0]["trade_date"],
"end_time": temp_compare[-1]["trade_date"],
"pearson_index": similar_value
}
# cal_finish_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
# end_time = datetime.datetime.strptime(cal_finish_time, "%Y-%m-%d %H:%M:%S")
# print("Calc Cost: {}".format(str((end_time - start_time).seconds)))
return result
if __name__ == "__main__":
read_data = ReadData()
#设置比较的周期
num = 60
#选取需要查找的最后num天数的数据
source_data = read_data.mysql_read_data("002936.SZ").iloc[-num:]
if len(source_data) < num:
num = len(source_data)
#选取某一支对比的股票并除去最后的num天数据
compare_data = read_data.mysql_read_data("000001.SZ").iloc[:-num]
#实例化类，输入的格式为Dataframe
compare = CompareSimilarKDynamic(source_data, compare_data, num)
#返回对比的皮尔逊系数最高的一组数据
result = compare.compare_dynamic()
print(result)
