コード例 #1
0
    def update_index_class(self, industry_class_name, industry_class_dict):
        with MongoConnect(self.database):
            index_members_data = AShareIndexMembers.objects(
                index_code__in=industry_class_dict.keys()).as_pymongo()
            field_list = ['index_code', 'security_code', 'in_date', 'out_date']
            self.index_members_df = pd.DataFrame(
                list(index_members_data)).reindex(columns=field_list)
            self.index_members_df = self.index_members_df.fillna(
                datetime.now()).reset_index(drop=True)

            get_collection_list = GetCollectionList()
            a_share_list = get_collection_list.get_a_share_list()
            calendar_obj = GetCalendar()
            calendar_SH = calendar_obj.get_calendar('SH')
            self.index_class = pd.DataFrame(columns=a_share_list,
                                            index=calendar_SH)

            def industry_history(x, index_members_df):
                industry_in_out_date = index_members_df[
                    index_members_df.security_code == x.name]
                for index, row in industry_in_out_date.iterrows():
                    x[row['in_date']:row['out_date']] = row['index_code']
                return x

            self.index_class = self.index_class.apply(
                industry_history, args=(self.index_members_df, ), axis=0)
            self.index_class = self.index_class.fillna(method='pad').fillna(
                method='backfill')
            folder_name = LocalDataFolderName.INDUSTRY_CLASS.value
            path = LocalDataPath.path + folder_name + '/'
            data_name = industry_class_name
            save_data_to_hdf5(path, data_name, self.index_class)
コード例 #2
0
    def save_a_share_adj_factor_right(self):
        """
        取当日收盘价,作为转、送的股价,
        再计算复权因子更新到AShareExRightDividend, 复权因子adj_factor
        比例 = 送股比例 + 转增比例 + 缩减比例
        单次复权因子 = 股权登记日收盘价 * (1 + 比例 + 配股比例 + 增发比例) /
        (股权登记日收盘价 - 派息比例 + 股权登记日收盘价 * 比例 + 配股价格 * 配股比例 + 增发价格 * 增发比例)
        :return:
        """
        kline_object = GetKlineData()
        all_market_data = kline_object.cache_all_stock_data()

        with MongoConnect(self.database):
            self.data = pd.DataFrame(
                AShareExRightDividend.objects.as_pymongo())
            self.data['close'] = self.data.apply(
                lambda x: self.get_adj_day_close(x['security_code'], x[
                    'ex_date'], all_market_data),
                axis=1)
            self.data = self.data.fillna(0)
            ratio = self.data['bonus_share_ratio'] + self.data[
                'conversed_ratio'] + self.data['consolidate_split_ratio']
            self.data['adj_factor'] = self.data['close'] * (
                1 + ratio + self.data['rightsissue_ratio'] +
                self.data['seo_ratio']
            ) / (self.data['close'] - self.data['cash_dividend_ratio'] +
                 self.data['close'] * ratio + self.data['rightsissue_price'] *
                 self.data['rightsissue_ratio'] +
                 self.data['seo_price'] * self.data['seo_ratio'])

            folder_name = LocalDataFolderName.ADJ_FACTOR.value
            path = LocalDataPath.path + folder_name + '/'
            self.data = self.data.reindex(
                columns=['security_code', 'ex_date', 'adj_factor'])
            self.data.set_index(["ex_date"], inplace=True)
            self.data.sort_index(inplace=True)
            calendar_obj = GetCalendar()
            calendar = calendar_obj.get_calendar('SZ')
            backward_factor = pd.DataFrame(index=calendar)
            adj_factor = pd.DataFrame(index=calendar)
            data_dict = dict(
                list(self.data.groupby(self.data['security_code'])))
            for security_code, adj_data in data_dict.items():
                backward_factor[security_code] = self.cal_backward_factor(
                    adj_data['adj_factor'])
                adj_factor[security_code] = adj_data['adj_factor']
            backward_factor.replace([np.inf, 0], np.nan, inplace=True)
            backward_factor.fillna(method='ffill', inplace=True)
            backward_factor.fillna(1, inplace=True)
            backward_factor = backward_factor.reindex(
                columns=all_market_data['close'].columns, fill_value=1)
            save_data_to_hdf5(path, AdjustmentFactor.BACKWARD_ADJ_FACTOR.value,
                              backward_factor)
            save_data_to_hdf5(path, AdjustmentFactor.FROWARD_ADJ_FACTOR.value,
                              backward_factor.div(backward_factor.iloc[-1]))
コード例 #3
0
    def get_all_market_data(self, security_list, end=datetime.now()):
        """

        :param security_list:
        :param end:
        :return:
        """
        calendar_obj = GetCalendar()
        self.calendar_SZ = calendar_obj.get_calendar('SZ')
        self.end = end
        database = DatabaseName.A_SHARE_KLINE_DAILY.value
        process_num = cpu_count() + 2
        process_stock_num = int(len(security_list) / process_num) + 1
        security_list_split = []
        for i in range(int(len(security_list) / process_stock_num)):
            if i < int(len(security_list) / process_stock_num) - 1:
                security_list_split.append(
                    security_list[i * process_stock_num:(i + 1) *
                                  process_stock_num])
            else:
                security_list_split.append(security_list[i *
                                                         process_stock_num:])

        with Manager() as manager:
            process_pool = Pool(process_num)
            process_manager_dict = manager.dict()
            for security_list_i in range(len(security_list_split)):
                process_pool.apply_async(
                    self._get_data_with_process_pool,
                    args=(database, security_list_split[security_list_i],
                          process_manager_dict, security_list_i))
            process_pool.close()
            process_pool.join()
            process_dict = dict(process_manager_dict)
            stock_data_dict = {}
            for single_stock_data in process_dict.values():
                stock_data_dict.update(single_stock_data)

            field_data_dict = {}
            for i in self.field:
                if i != 'time_tag':
                    field_data_pd = pd.DataFrame({
                        key: value[i]
                        for key, value in stock_data_dict.items()
                    })
                    # 原始数据的开高低收除以10000
                    if i in ['open', 'high', 'low', 'close']:
                        field_data_dict[i] = field_data_pd.div(10000)
                    else:
                        field_data_dict[i] = field_data_pd
            return field_data_dict