码农技术炒股之路——实时交易信息、主力动向信息分库备份

        一般来说，一个股票信息应该保存在一张表中。但是由于我机器资源限制，且我希望尽快频率的抓取数据。所以每天我将所有股票的实时交易信息放在daily_temp库中的一个以日期命名的表中。主力动向信息也是如此。但是盘后分析股票时，我们会以单只股票进行分析。这样就需要跨越很多天，而这样的设计将导致需要查询若干个表，且随着日期增加，读取的表也将增加。我觉得这样是不合适的。（转载请指明出于breaksoftware的csdn博客）

        目前我们系统繁忙的时间和交易时间同步。为了最大幅度的利用资源，我决定在盘后对每日的数据按照股票代码进行拆分备份。这样我们就可以查询一张表得到该股票所有历史数据。

拆分备份实时交易信息

        首先我们要从股票基本信息表中读取所有股票代码

    def _get_all_share_ids(self):
        date_info = time.strftime('%Y_%m_%d')
        trade_table_name = "trade_info_%s" % (date_info)
        share_ids = fetch_data.get_data(fetch_data.select_db(self._daily_temp_conn_name, trade_table_name, ["share_id"],{}, pre = "distinct"))
        return share_ids

        fetch_data是我用于封装获取数据的接口。其中包含通过网络获取数据，通过数据库获取数据和通过正则拆分数据

class select_db:
    def __init__(self, conn_name, table_name, select_columns, conditions, pre="", extend=""):
        self._conn_name = conn_name
        self._table_name = table_name
        self._select_columns = select_columns
        self._conditions = conditions
        self._pre = pre
        self._extend = extend

    def get_data(self):
        db_manager = mysql_manager()
        conn = db_manager.get_mysql_conn(self._conn_name)
        result = conn.select(self._table_name, self._select_columns, self._conditions, self._pre, self._extend)
        return result

class query_http:
    def __init__(self, url):
        self._url = url

    def get_data(self):
        res = ""
        tried = False
        while True:
            try:
                socket.setdefaulttimeout(15)
                req = urllib2.Request(self._url)
                res_data = urllib2.urlopen(req)
                res = res_data.read()
                break
            except Exception as e:
                LOG_ERROR("request error: %s %s"  % (self._url ,e))
                if tried:
                    break
                else:
                    tried = True
        return res

class regular_split:
    def __init__(self, regular_name, data):
        self._regular_name = regular_name
        self._data = data

    def get_data(self):
        regular_split_mgr = regular_split_manager()
        ret_array = regular_split_mgr.get_split_data(self._data, self._regular_name)
        return ret_array

def get_data(query_item):
    if False == hasattr(query_item, "get_data"):
        return None
    result = query_item.get_data()
    return result

        下一步通过股票代码查询当天所有数据

    def _bak_trade_info(self, share_id):
        date_info = time.strftime('%Y_%m_%d')
        table_name = "trade_info_%s" % (date_info)
        db_manager = mysql_manager()
        conn = db_manager.get_mysql_conn(self._daily_temp_conn_name)
        
        fields_array = ["today_open","yesterday_close","cur","today_high","today_low","compete_buy_price","compete_sale_price",
                "trade_num","trade_price","buy_1_num","buy_1_price","buy_2_num","buy_2_price","buy_3_num","buy_3_price","buy_4_num","buy_4_price",
                "buy_5_num","buy_5_price","sale_1_num","sale_1_price","sale_2_num","sale_2_price","sale_3_num","sale_3_price","sale_4_num","sale_4_price",
                "sale_5_num","sale_5_price","time_date_str","time_str"]
        daily_data = conn.select(table_name, fields_array, {"share_id":[share_id, "="]})
        self._bak_single_market_maker_info(share_id, daily_data)

        由于抓取时间和数据源时间存在差异，所以我们可能会抓取到交易时间之外的数据。于是我们要对这些数据进行归一化处理。比如我们有11.29、11:31和11:32三个数据，则对交易时间之外的数据11:31和11:32数据归一为11:30的数据并保存。

    def _bak_single_market_maker_info(self, share_id, daily_data):
        daily_data_list = []
        has_between_11_30_and_13_00 = False
        after_15_00 = False
        keys_list = []
        for item in daily_data:
            item_list = list(item)
            date_str = item[-2] + " " + item[-1]
            
            today_11_30 = date_str[:date_str.find(" ")] + " 11:30:00" 
            today_13_00 = date_str[:date_str.find(" ")] + " 13:00:00"
            today_15_00 = date_str[:date_str.find(" ")] + " 15:00:00"
            today_11_30_int = time.mktime(time.strptime(today_11_30,'%Y-%m-%d %H:%M:%S'))
            today_13_00_int = time.mktime(time.strptime(today_13_00,'%Y-%m-%d %H:%M:%S'))
            today_15_00_int = time.mktime(time.strptime(today_15_00,'%Y-%m-%d %H:%M:%S'))

            date_int = time.mktime(time.strptime(date_str,'%Y-%m-%d %H:%M:%S'))
            if date_int >= today_11_30_int and date_int < today_13_00_int:
                if has_between_11_30_and_13_00:
                    continue
                else:
                    has_between_11_30_and_13_00 = True

            if date_int >= today_15_00_int:
                if after_15_00:
                    continue
                else:
                    after_15_00 = True

            if date_int in keys_list:
                continue
            else:
                keys_list.append(date_int)

            item_list.insert(0, date_int)
            del item_list[-1]
            del item_list[-1]
            daily_data_list.append(item_list)

        keys_array = ["time","today_open","yesterday_close","cur","today_high","today_low","compete_buy_price","compete_sale_price",
                "trade_num","trade_price","buy_1_num","buy_1_price","buy_2_num","buy_2_price","buy_3_num","buy_3_price","buy_4_num","buy_4_price",
                "buy_5_num","buy_5_price","sale_1_num","sale_1_price","sale_2_num","sale_2_price","sale_3_num","sale_3_price","sale_4_num","sale_4_price",
                "sale_5_num","sale_5_price"]

        share_trade_info_table_name = "trade_info_detail_" +share_id
        self._create_table_if_not_exist(share_id, share_trade_info_table_name)

        stock_conn_manager_obj = stock_conn_manager()
        conn = stock_conn_manager_obj.get_conn(share_id)
        conn.insert_data(share_trade_info_table_name, keys_array, daily_data_list)

        此处我们并没有使用直接检查并创建表的方式，而是使用了_create_table_if_not_exist方法

    def _create_table_if_not_exist(self, share_id, table_name):
        stock_conn_manager_obj = stock_conn_manager()
        conn_name = stock_conn_manager_obj.get_conn_name(share_id)
        prepare_table_obj = prepare_table(conn_name, "trade_info")
        prepare_table_obj.prepare(table_name)

        为什么要这么用？因为我们要将三千多支股票信息保存分片到300个不同的数据库中。那么当前这支股票在哪个库中，则需要一个中间层去代理管理。

@singleton
class stock_conn_manager():
    def __init__(self):
        pass

    def get_conn(self, share_id):
        conn_name = self.get_conn_name(share_id)
        db_manager = mysql_manager()
        conn = db_manager.get_mysql_conn(conn_name)
        return conn

    def get_conn_name(self, share_id):
        share_id_int = int(share_id)
        share_id_part_no = share_id_int % 300
        conn_name = "stock_part_%d" % (share_id_part_no)
        return conn_name

        stock_conn_manager类将股票代码和300取余数，得出分片ID。然后连接该ID对应的库。这层设计非常重要，因为不仅此处我们备份数据要用到，之后对全部股票进行分析时也要用到它。

拆分备份主力动向信息

        主要逻辑同实时交易信息。故只贴出代码

class bak_today_market_maker(job_base):
    def __init__(self):
        self._db_manager = mysql_manager()
        self._daily_temp_conn_name = "daily_temp"
    
    def run(self):
        share_ids = self._get_all_share_ids()
        for share_id  in share_ids:
            self._bak_market_maker_info(share_id[0])
        LOG_INFO("run bak_today_market_maker")
    
    def _bak_market_maker_info(self, share_id):
        date_info = time.strftime('%Y_%m_%d')
        table_name = "market_maker_%s" % (date_info)
        
        fields_array =["time_str", "price", "up_percent", "market_maker_net_inflow", "market_maker_net_inflow_per",
                "huge_inflow", "huge_inflow_per", "large_inflow", "large_inflow_per", "medium_inflow", "medium_inflow_per", "small_inflow", "small_inflow_per"]
        
        daily_data = fetch_data.get_data(fetch_data.select_db(self._daily_temp_conn_name, table_name, fields_array, {"share_id":[share_id, "="]}))
        self._bak_single_market_maker_info(share_id, daily_data)

    def _bak_single_market_maker_info(self, share_id, daily_data):
        daily_data_list = []
        has_between_11_30_and_13_00 = False
        after_15_00 = False
        keys_list = []
        for item in daily_data:
            item_list = list(item)
            date_str = item[0]
            
            today_11_30 = date_str[:date_str.find(" ")] + " 11:30:00" 
            today_13_00 = date_str[:date_str.find(" ")] + " 13:00:00"
            today_15_00 = date_str[:date_str.find(" ")] + " 15:00:00"
            today_11_30_int = time.mktime(time.strptime(today_11_30,'%Y-%m-%d %H:%M:%S'))
            today_13_00_int = time.mktime(time.strptime(today_13_00,'%Y-%m-%d %H:%M:%S'))
            today_15_00_int = time.mktime(time.strptime(today_15_00,'%Y-%m-%d %H:%M:%S'))

            date_int = time.mktime(time.strptime(date_str,'%Y-%m-%d %H:%M:%S'))
            if date_int >= today_11_30_int and date_int < today_13_00_int:
                if has_between_11_30_and_13_00:
                    continue
                else:
                    has_between_11_30_and_13_00 = True

            if date_int >= today_15_00_int:
                if after_15_00:
                    continue
                else:
                    after_15_00 = True

            if date_int in keys_list:
                continue
            else:
                keys_list.append(date_int)

            item_list[0] = date_int
            daily_data_list.append(item_list)
        keys_array =["time", "price", "up_percent", "market_maker_net_inflow", "market_maker_net_inflow_per",
                "huge_inflow", "huge_inflow_per", "large_inflow", "large_inflow_per", "medium_inflow", "medium_inflow_per", "small_inflow", "small_inflow_per"]

        share_market_maker_table_name = "market_maker_detail_" + share_id
        self._create_table_if_not_exist(share_id, share_market_maker_table_name)

        stock_conn_manager_obj = stock_conn_manager()
        conn = stock_conn_manager_obj.get_conn(share_id)
        conn.insert_data(share_market_maker_table_name, keys_array, daily_data_list)

    def _get_all_share_ids(self):
        date_info = time.strftime('%Y_%m_%d')
        trade_table_name = "trade_info_%s" % (date_info)
        share_ids = fetch_data.get_data(fetch_data.select_db(self._daily_temp_conn_name, trade_table_name, ["share_id"],{}, pre = "distinct"))
        return share_ids

    def _create_table_if_not_exist(self, share_id, table_name):
        stock_conn_manager_obj = stock_conn_manager()
        conn_name = stock_conn_manager_obj.get_conn_name(share_id)
        prepare_table_obj = prepare_table(conn_name, "market_maker")
        prepare_table_obj.prepare(table_name)

        实时交易和主力动向拆分备份的任务配置如下。因为这两个数据库比较大，我给每个任务留了一个小时的处理时间。

[bak_today_market_maker]
type=cron
class=bak_today_market_maker
day_of_week=1-5
hour=16
minute=50
timezone = Asia/Shanghai

[bak_today_trade]
type=cron
class=bak_today_trade
day_of_week=1-5
hour=15
minute=50
timezone = Asia/Shanghai