{"id":441,"date":"2021-12-30T20:43:05","date_gmt":"2021-12-30T12:43:05","guid":{"rendered":"\/?p=441"},"modified":"2023-09-23T18:39:01","modified_gmt":"2023-09-23T10:39:01","slug":"a%e8%82%a1%e5%85%a8%e8%87%aa%e5%8a%a8%e5%8c%96%e4%ba%a4%e6%98%93-%e4%bb%8e%e9%9b%b6%e5%88%b0%e5%ae%9e%e7%9b%9820%ef%bc%88%e5%ae%8c%e7%bb%93%ef%bc%89","status":"publish","type":"post","link":"\/?p=441","title":{"rendered":"A\u80a1\u5168\u81ea\u52a8\u5316\u4ea4\u6613\u2014\u2014\u4ece\u96f6\u5230\u5b9e\u76d820\uff08\u5b8c\u7ed3\uff09"},"content":{"rendered":"

\u672c\u6587\u662f\u201c\u4ece\u96f6\u5230\u5b9e\u76d8\u201d\u7cfb\u5217\u7684\u6700\u540e\u4e00\u7bc7\u6587\u7ae0\uff0c\u5c06\u4ecb\u7ecd\u5b9e\u73b0\u5168\u81ea\u52a8\u5b9e\u76d8\u4ea4\u6613\u7684\u6700\u540e\u4e00\u4e2a\u6b65\u9aa4\uff0c\u5373\u5b9e\u73b0\u5b9a\u65f6\u66f4\u65b0\u80a1\u7968\u6570\u636e\u4efb\u52a1\u3002<\/p>\n

schedule\u6a21\u5757\u5b89\u88c5<\/h1>\n

\u6211\u4eec\u4f7f\u7528schedule\u6765\u5b9e\u73b0\u5b9a\u65f6\u4efb\u52a1\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5schedule\u6a21\u5757\uff0c\u5728\u7ec8\u7aef\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n

pip install schedule\n<\/code><\/pre>\n
\u4e3b\u8981\u4ee3\u7801\u5206\u6790<\/h1>\n
\u6211\u4eec\u5b9e\u73b0\u6700\u7ec8\u7248\u672c\u7684data_center.py\uff0c\u5168\u90e8\u5185\u5bb9\u89c1\u6587\u672b\uff0c\u4e3b\u8981\u65b0\u589e2\u4e2a\u51fd\u6570\uff1a<\/p>\n
\u65b0\u589e\u6309\u5929\u66f4\u65b0\u4efb\u52a1\u51fd\u6570<\/h3>\n
def task_update_daily():\n<\/code><\/pre>\n
\u8be5\u51fd\u6570\u5305\u542b\u6bcf\u65e5\u66f4\u65b0\u4efb\u52a1\u3002<\/p>\n
    start_time = time.time()\n<\/code><\/pre>\n
\u9996\u5148\u8bb0\u5f55\u4efb\u52a1\u5f00\u59cb\u8fd0\u884c\u65f6\u95f4\uff0c\u7528\u4e8e\u540e\u7eed\u8ba1\u7b97\u4efb\u52a1\u82b1\u8d39\u65f6\u95f4\u3002<\/p>\n
    stock_codes = get_stock_codes(update=True)\n<\/code><\/pre>\n
\u83b7\u53d6\u5f85\u4ea4\u6613\u80a1\u7968\u5217\u8868\uff0c\u8fd9\u91cc\u5c06update\u7f6e\u4e3aTrue\uff0c\u6bcf\u65e5\u90fd\u901a\u8fc7\u67e5\u8be2BaoStock\u83b7\u53d6\u6700\u65b0\u7684\u80a1\u7968\u4ee3\u7801\u3002<\/p>\n
    update_data_mp(stock_codes)\n<\/code><\/pre>\n
\u66f4\u65b0\u80a1\u7968\u6570\u636e\uff0c\u8fd9\u91cc\u4f1a\u66f4\u65b0\u524d\u590d\u6743\u6570\u636e\u3002\u5bf9\u4e8e\u5df2\u5728\u6570\u636e\u5e93\u4e2d\u7684\u80a1\u7968\uff0c\u4f1a\u66f4\u65b0\u83b7\u53d6\u6700\u65b0\u4ea4\u6613\u65e5\u7684\u6570\u636e\uff1b\u5bf9\u4e8e\u672a\u5728\u6570\u636e\u5e93\u4e2d\u7684\u80a1\u7968\uff0c\u4ee5\u53ca\u65b0\u9664\u6743\u9664\u606f\u7684\u80a1\u7968\uff0c\u5c06\u91cd\u65b0\u521b\u5efa\u6240\u6709\u5386\u53f2\u6570\u636e\u3002<\/p>\n
    update_trade(stock_codes)\n<\/code><\/pre>\n
\u6839\u636e\u66f4\u65b0\u540e\u7684\u80a1\u7968\u6570\u636e\uff0c\u66f4\u65b0\u4ea4\u6613\u6570\u636e\uff0c\u4e00\u65b9\u9762\u6839\u636ePTrade\u7684\u4ea4\u6613\u6570\u636e\u8fdb\u4e00\u6b65\u66f4\u65b0\u6570\u636e\u5e93\u7684\u6570\u636e\uff0c\u4e00\u65b9\u9762\u8f93\u51fa\u5f85\u4ea4\u6613\u6570\u636e\uff0c\u4f9bPTrade\u5b9e\u76d8\u4ea4\u6613\u4f7f\u7528\u3002<\/p>\n
    end_time = time.time()\n    print('\u7a0b\u5e8f\u8fd0\u884c\u65f6\u95f4\uff1a{}s'.format(end_time - start_time))\n<\/code><\/pre>\n
\u8bb0\u5f55\u4efb\u52a1\u7ed3\u675f\u8fd0\u884c\u65f6\u95f4\uff0c\u5e76\u8ba1\u7b97\u4efb\u52a1\u8fd0\u884c\u65f6\u95f4\u3002<\/p>\n
\u65b0\u589e\u5b9a\u65f6\u4efb\u52a1\u6267\u884c\u51fd\u6570<\/h3>\n
def main():\n<\/code><\/pre>\n
\u8be5\u51fd\u6570\u7528\u4e8e\u8bbe\u7f6e\u5b9a\u65f6\u4efb\u52a1\uff0c\u6bcf\u4ea4\u6613\u65e517:40\u542f\u52a8\u4efb\u52a1\uff0c\u8fdb\u884c\u6bcf\u65e5\u6570\u636e\u66f4\u65b0\u8ba1\u7b97\u3002<\/p>\n
    schedule.clear()\n<\/code><\/pre>\n
\u6e05\u7a7a\u4efb\u52a1\u5217\u8868\u3002<\/p>\n
    task_time = '17:40:00'\n<\/code><\/pre>\n
\u8bbe\u7f6e\u4efb\u52a1\u6267\u884c\u65f6\u95f4\u3002<\/p>\n
    schedule.every().monday.at(task_time).do(task_update_daily)\n    schedule.every().tuesday.at(task_time).do(task_update_daily)\n    schedule.every().wednesday.at(task_time).do(task_update_daily)\n    schedule.every().thursday.at(task_time).do(task_update_daily)\n    schedule.every().friday.at(task_time).do(task_update_daily)\n<\/code><\/pre>\n
\u8bbe\u7f6e\u4ea4\u6613\u65e5\u5b9a\u65f6\u542f\u52a8\u4efb\u52a1\u3002<\/p>\n
    while True:\n        schedule.run_pending()\n        print('.', end='')\n        time.sleep(60)\n<\/code><\/pre>\n
\u5faa\u73af\u7b49\u5f85\u4efb\u52a1\u6267\u884c\u3002<\/p>\n
\u603b\u7ed3<\/h1>\n
    \n
  • \u6211\u4eec\u4ece\u96f6\u5f00\u59cb\uff0c\u5b8c\u6210\u4e86\u4e00\u5957\u4ee5BaoStock\u4e3a\u6570\u636e\u6e90\u3001\u4ee5MySQL\u4e3a\u5b58\u50a8\u3001\u4ee5PTrade\u4e3a\u4ea4\u6613\u63a5\u53e3\u7684\u8fd0\u884c\u5728Windows PC\u4e0a\u7684\u5168\u81ea\u52a8\u5316A\u7968\u4ea4\u6613\u7cfb\u7edf\u3002\u53ea\u8981\u7535\u8111\u4fdd\u6301\u5f00\u673a\u3001data_center.py\u4fdd\u6301\u8fd0\u884c\u3001PTrade\u4fdd\u6301\u6253\u5f00\uff0c\u4ea4\u6613\u6240\u9700\u7684\u5168\u90e8\u6b65\u9aa4\u4f1a\u81ea\u52a8\u5b8c\u6210\uff0c\u65e0\u9700\u4eba\u5de5\u5e72\u9884\u3002<\/li>\n
  • \u9879\u76eegit\u5730\u5740\uff1ahttps:\/\/gitee.com\/sl\/quant_from_scratch<\/li>\n
  • \u7cfb\u5217\u6587\u7ae0\u5c55\u793a\u4e86\u4ece\u96f6\u5230\u5b9e\u76d8\u7684\u8fc7\u7a0b\uff0c\u662f\u7b14\u8005\u81ea\u63a5\u89e6\u91cf\u5316\u4ee5\u6765\u603b\u7ed3\u51fa\u7684\u8fd0\u884c\u6bd4\u8f83\u7a33\u5b9a\u7684\u7cfb\u7edf\uff0c\u4f9b\u8bfb\u8005\u5b9e\u73b0\u81ea\u5df1\u7684\u4ea4\u6613\u7cfb\u7edf\u505a\u53c2\u8003\u3002<\/li>\n
  • \u8ddd\u79bb\u8eba\u7740\u8d5a\u94b1\u8fd8\u6709\u5f88\u957f\u8def\u8981\u8d70\u3002\u76ee\u524d\u7b56\u7565\u8868\u73b0\u4e0d\u7a33\u5b9a\uff0c\u6700\u5927\u76c8\u52299%\uff0c\u6700\u5927\u56de\u64a4\u8fd120%\uff0c\u76ee\u524d\u8fd8\u5728\u5fae\u4e8f\u72b6\u6001\u3002\u867d\u7136\u8fd8\u5728\u56de\u6d4b\u7ed3\u679c\u8303\u56f4\u5185\uff082018\u5e74\u81f32020\u5e743\u5e74\u5468\u671f\u56de\u6d4b\u540e\uff0c\u5e74\u5316\u6536\u76ca\u4e3a41.7%\uff0c\u6700\u5927\u56de\u64a4\u4e3a25.8%\uff09\uff0c\u4f46\u662f\u5e76\u4e0d\u4ee4\u4eba\u6ee1\u610f\u3002\u4e5f\u8bf7\u8bfb\u8005\u53ea\u53c2\u8003\u7cfb\u7edf\u6846\u67b6\uff0c\u5207\u52ff\u76f2\u76ee\u5b9e\u76d8<\/strong>\u3002<\/li>\n
  • \u7b14\u8005\u4f1a\u57fa\u4e8e\u5f53\u524d\u7cfb\u7edf\u6846\u67b6\u7ee7\u7eed\u6316\u6398\u7b56\u7565\uff0c\u6b22\u8fce\u611f\u5174\u8da3\u7684\u8bfb\u8005\u4e00\u8d77\u8ba8\u8bba\u7814\u7a76\u3002<\/li>\n
  • \u975e\u5e38\u611f\u89c9\u5e7f\u5927\u7fa4\u53cb\u7684\u652f\u6301\u4e0e\u5efa\u8bae\uff0c\u4f7f\u7b14\u8005\u66f4\u6709\u521b\u4f5c\u548c\u5206\u4eab\u52a8\u529b\u3002<\/li>\n<\/ul>\n
    \n
    data_center\u7684\u5168\u90e8\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
    import os\nimport baostock as bs\nimport datetime\nimport sys\nimport numpy as np\nimport pandas as pd\nimport multiprocessing\nimport time\nimport sqlalchemy\nimport matplotlib.pyplot as plt\nfrom pandas.plotting import table\nimport math\nimport schedule\n\n# \u53ef\u7528\u65e5\u7ebf\u6570\u91cf\u7ea6\u675f\ng_available_days_limit = 60\n\n# \u5386\u53f2\u6570\u636e\u5f00\u59cb\u65f6\u95f4\ng_start_date = '1990-12-19'\n\n# BaoStock\u65e5\u7ebf\u6570\u636e\u5b57\u6bb5\ng_baostock_data_fields = 'date,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ, psTTM,pcfNcfTTM,isST'\n\n# \u5b9a\u65f6\u4e0a\u4f20\u5230ptrade\u7684\u6587\u4ef6\u8def\u5f84\ng_ptrade_upload_path = 'D:\/quant_from_scratch\/quant_from_scratch\/a-share\/data\/file_update\/trade_data.csv'\n\n# ptrade\u8f93\u51fa\u4ea4\u6613\u6570\u636e\u6587\u4ef6\u76ee\u5f55\ng_ptrade_export_dir = 'D:\/quant_from_scratch\/quant_from_scratch\/a-share\/data\/file_export\/'\n\n# \u6b62\u76c8\u6bd4\u4f8b\ng_take_profit_percent = 0.0618\n\n# \u6b62\u635f\u6bd4\u4f8b\ng_stop_loss_percent = 0.1618\n\n\ndef bs_func_decorator(func):\n    \"\"\"\n    BaoStock\u65b9\u6cd5\u88c5\u9970\u5668\n\n    \u8c03\u7528BaoStock\u65b9\u6cd5\uff0c\u5982\u679c\u5931\u8d25\uff0c\u5c1d\u8bd5\u91cd\u65b0\u8fde\u63a5BaoStock\n    \u907f\u514d\u91cd\u590d\u5efa\u7acbBaoStock\u8fde\u63a5\n\n    :param args:\n    :param func: BaoStock\u65b9\u6cd5\n    :return: \u88c5\u9970\u5668\n    \"\"\"\n\n    # \u88c5\u9970\n    def wrap_func(*args, **kwargs):\n\n        # \u6700\u591a\u5c1d\u8bd5\u6b21\u6570\n        retry_n = 5\n\n        # \u5faa\u73af\u5c1d\u8bd5\u67e5\u8be2\u590d\u6743\u6570\u636e\uff0c\u6700\u591a\u5c1d\u8bd5retry_n\u6b21\n        for i in range(retry_n):\n\n            # \u8c03\u7528BaoStock\u65b9\u6cd5\n            rs = func(*args, **kwargs)\n\n            # \u5982\u679c\u67e5\u8be2\u6210\u529f\uff0c\u5219\u8fd4\u56de\u67e5\u8be2\u7ed3\u679c\n            if rs.error_code == '0':\n                return rs.get_data()\n\n            # \u672a\u67e5\u8be2\u6210\u529f\uff0c\u5219\u5c1d\u8bd5\u91cd\u65b0\u8fde\u63a5BaoStock\n            else:\n                print('\u91cd\u65b0\u8fde\u63a5BaoStock...')\n                bs.login()\n\n        # \u65e0\u6cd5\u8fde\u63a5\uff0c\u5219\u7a0b\u5e8f\u9000\u51fa\n        print('\u65e0\u6cd5\u8fde\u63a5BaoStock\uff0c\u7a0b\u5e8f\u9000\u51fa...')\n        exit(0)\n\n    return wrap_func\n\n\n@bs_func_decorator\ndef wrap_query_all_stock(date):\n    \"\"\"\n    \u88c5\u9970\u80a1\u7968\u4ee3\u7801\u67e5\u8be2\u51fd\u6570\n\n    :param date: \u9700\u8981\u67e5\u8be2\u7684\u4ea4\u6613\u65e5\u671f\n    :return: \u5305\u542b\u80a1\u7968\u4ee3\u7801\u7684DataFrame\n    \"\"\"\n\n    return bs.query_all_stock(date)\n\n\n@bs_func_decorator\ndef wrap_query_history_k_data_plus(code, data_fields=g_baostock_data_fields,\n                                   start_date=g_start_date, end_date=datetime.date.today().strftime('%Y-%m-%d'),\n                                   frequency='d', adjustflag='2'):\n    \"\"\"\n    \u88c5\u9970\u83b7\u53d6\u5386\u53f2\u80a1\u7968K\u7ebf\u6570\u636e\u51fd\u6570\n\n    :param code: \u80a1\u7968\u4ee3\u7801\n    :param data_fields: \u6307\u6807\u6570\u636e\n    :param start_date: \u5f00\u59cb\u65e5\u671f\n    :param end_date: \u7ed3\u675f\u65e5\u671f\n    :param frequency: \u6570\u636e\u7c7b\u578b\uff0c\u9ed8\u8ba4\u4e3ad\uff0c\u65e5k\u7ebf\uff1bd=\u65e5k\u7ebf\u3001w=\u5468\u3001m=\u6708\u30015=5\u5206\u949f\u300115=15\u5206\u949f\u300130=30\u5206\u949f\u300160=60\u5206\u949fk\u7ebf\u6570\u636e\uff0c\u4e0d\u533a\u5206\u5927\u5c0f\u5199\uff1b\u6307\u6570\u6ca1\u6709\u5206\u949f\u7ebf\u6570\u636e\uff1b\u5468\u7ebf\u6bcf\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5\u624d\u53ef\u4ee5\u83b7\u53d6\uff0c\u6708\u7ebf\u6bcf\u6708\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5\u624d\u53ef\u4ee5\u83b7\u53d6\u3002\n    :param adjustflag: \u590d\u6743\u7c7b\u578b\uff0c\u9ed8\u8ba4\u524d\u590d\u6743\uff1a2\uff1b1\uff1a\u540e\u590d\u6743\uff1b3\uff1a\u4e0d\u590d\u6743\n    :return: \u5305\u542bK\u7ebf\u6570\u636e\u7684DataFrame\n    \"\"\"\n\n    return bs.query_history_k_data_plus(code, data_fields, start_date, end_date, frequency, adjustflag)\n\n\n@bs_func_decorator\ndef wrap_query_dividend_data(code, year, yearType='report'):\n    \"\"\"\n    \u88c5\u9970\u9664\u6743\u9664\u606f\u4fe1\u606f\u51fd\u6570\n\n    :param code: \u80a1\u7968\u4ee3\u7801\n    :param year: \u67e5\u8be2\u5e74\u4efd\n    :param yearType: \u5e74\u4efd\u7c7b\u522b\uff0c\u9ed8\u8ba4\u4e3a\"report\":\u9884\u6848\u516c\u544a\u5e74\u4efd\uff0c\u53ef\u9009\u9879\"operate\":\u9664\u6743\u9664\u606f\u5e74\u4efd\u3002\u6b64\u53c2\u6570\u4e0d\u53ef\u4e3a\u7a7a\n    :return: \u5305\u542b\u9664\u6743\u9664\u606f\u4fe1\u606f\u7684DataFrame\n    \"\"\"\n    return bs.query_dividend_data(code, year, yearType=yearType)\n\n\ndef create_mysql_engine():\n    \"\"\"\n    \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n\n    :return: \u65b0\u521b\u5efa\u7684\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    \"\"\"\n\n    # \u5f15\u64ce\u53c2\u6570\u4fe1\u606f\n    host = 'localhost'\n    user = 'root'\n    passwd = '111111'\n    port = '3306'\n    db = 'db_quant'\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    mysql_engine = sqlalchemy.create_engine(\n        'mysql+pymysql:\/\/{0}:{1}@{2}:{3}'.format(user, passwd, host, port),\n        poolclass=sqlalchemy.pool.NullPool\n    )\n\n    # \u5982\u679c\u4e0d\u5b58\u5728\u6570\u636e\u5e93db_quant\u5219\u521b\u5efa\n    mysql_engine.execute(\"CREATE DATABASE IF NOT EXISTS {0} \".format(db))\n\n    # \u521b\u5efa\u8fde\u63a5\u6570\u636e\u5e93db_quant\u7684\u5f15\u64ce\u5bf9\u8c61\n    db_engine = sqlalchemy.create_engine(\n        'mysql+pymysql:\/\/{0}:{1}@{2}:{3}\/{4}?charset=utf8'.format(user, passwd, host, port, db),\n        pool_size=get_process_num() * 2, max_overflow=get_process_num() * 2, pool_timeout=50\n    )\n\n    # \u8fd4\u56de\u5f15\u64ce\u5bf9\u8c61\n    return db_engine\n\n\ndef get_stock_codes(date=None, update=False):\n    \"\"\"\n    \u83b7\u53d6\u6307\u5b9a\u65e5\u671f\u524d\uff08\u542b\u5f53\u65e5\uff09\u6700\u8fd1\u4ea4\u6613\u65e5\u7684A\u80a1\u4ee3\u7801\u5217\u8868\n\n    \u82e5\u53c2\u6570update\u4e3aFalse\uff0c\u8868\u793a\u4ece\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u80a1\u7968\u5217\u8868\n    \u82e5\u6570\u636e\u5e93\u4e2d\u4e0d\u5b58\u5728\u80a1\u7968\u5217\u8868\u7684\u8868\uff0c\u6216\u8005update\u4e3aTrue\uff0c\u5219\u4e0b\u8f7d\u6307\u5b9a\u65e5\u671fdate\u524d\uff08\u542bdate\uff09\u6700\u8fd1\u4ea4\u6613\u65e5\u7684\u4ea4\u6613\u80a1\u7968\u5217\u8868\n\n    :param date: \u65e5\u671f\uff0c\u9ed8\u8ba4\u4e3aNone\n    :param update: \u662f\u5426\u66f4\u65b0\u80a1\u7968\u5217\u8868\uff0c\u9ed8\u8ba4\u4e3aFalse\n    :return: A\u80a1\u4ee3\u7801\u7684\u5217\u8868\n    \"\"\"\n\n    print('\u6b63\u5728\u83b7\u53d6\u80a1\u7968\u4ee3\u7801...')\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u6570\u636e\u5e93\u4e2d\u80a1\u7968\u4ee3\u7801\u7684\u8868\u540d\n    table_name = 'stock_codes'\n\n    # \u6570\u636e\u5e93\u4e2d\u4e0d\u5b58\u5728\u80a1\u7968\u4ee3\u7801\u8868\uff0c\u6216\u8005\u9700\u8981\u66f4\u65b0\u80a1\u7968\u4ee3\u7801\u8868\n    if table_name not in sqlalchemy.inspect(engine).get_table_names() or update:\n\n        # \u83b7\u53d6\u6700\u65b0\u4ea4\u6613\u65e5\n        trading_date = get_trading_date(date)\n\n        # \u4eceBaoStock\u67e5\u8be2\u80a1\u7968\u6570\u636e\n        stock_df = wrap_query_all_stock(trading_date)\n\n        # \u5982\u679c\u65e0\u6570\u636e\uff0c\u5219\u7a0b\u5e8f\u9000\u51fa\n        if 0 == len(stock_df):\n            engine.dispose()\n            print('\u6570\u636e\u5f02\u5e38\uff0c\u7a0b\u5e8f\u9000\u51fa...')\n            exit(0)\n\n        # \u7b5b\u9009\u80a1\u7968\u6570\u636e\uff0c\u4e0a\u8bc1\u548c\u6df1\u8bc1\u80a1\u7968\u4ee3\u7801\u5728sh.600000\u4e0esz.39900\u4e4b\u95f4\n        stock_df = stock_df[(stock_df['code'] >= 'sh.600000') & (stock_df['code'] < 'sz.399000')]\n\n        # \u5c06\u80a1\u7968\u4ee3\u7801\u5199\u5165\u6570\u636e\u5e93\n        stock_df.to_sql(name=table_name, con=engine, if_exists='replace', index=False, index_label=False)\n\n        # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n        engine.dispose()\n\n        # \u8fd4\u56de\u80a1\u7968\u5217\u8868\n        return stock_df['code'].tolist()\n\n    # \u4ece\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u80a1\u7968\u4ee3\u7801\u5217\u8868\n    else:\n\n        # \u5f85\u6267\u884c\u7684sql\u8bed\u53e5\n        sql_cmd = 'SELECT {} FROM {}'.format('code', table_name)\n\n        # \u8bfb\u53d6sql\n        stock_codes = pd.read_sql(sql=sql_cmd, con=engine)['code'].tolist()\n\n        # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n        engine.dispose()\n\n        # \u8fd4\u56de\u80a1\u7968\u5217\u8868\n        return stock_codes\n\n\ndef update_data(code, latest_trading_date, db_tables, query_days=60, adjustflag='2', recreate=False):\n    \"\"\"\n    \u66f4\u65b0\u65e5\u7ebf\u6570\u636e\uff0c\u8ba1\u7b97\u76f8\u5173\u56e0\u5b50\n\n    :param code: \u5f85\u66f4\u65b0\u6570\u636e\u7684\u80a1\u7968\u4ee3\u7801\n    :param latest_trading_date: \u6700\u65b0\u4ea4\u6613\u65e5\u671f\n    :param db_tables: \u6570\u636e\u5e93\u4e2d\u5df2\u5b58\u5728\u80a1\u7968\u7684\u8868\u540d\u5217\u8868\n    :param query_days: \u5728\u6570\u636e\u5e93\u4e2d\u67e5\u8be2\u5386\u53f2\u65e5\u7ebf\u6570\u636e\u7684\u5929\u6570\uff0c\u7528\u4e8e\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\uff0c\u9700\u8981\u6839\u636e\u6269\u5c55\u56e0\u5b50\u8bbe\u7f6e\uff0c\u8fd9\u91cc\u8981\u8ba1\u7b9760\u65e5\u5747\u7ebf\uff0c\u6240\u4ee5\u6700\u5c0f\u8bbe\u7f6e\u4e3a60\n    :param adjustflag: \u590d\u6743\u9009\u9879 1\uff1a\u540e\u590d\u6743  2\uff1a\u524d\u590d\u6743  3\uff1a\u4e0d\u590d\u6743  \u9ed8\u8ba4\u4e3a\u524d\u590d\u6743\n    :param recreate: \u662f\u5426\u91cd\u65b0\u521b\u5efa\u6570\u636e\n    :return: \u5305\u542b\u6240\u6709\u5f85\u5904\u7406\u80a1\u7968\u7684\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u53ca\u6269\u5c55\u56e0\u5b50\u7684Series\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u521b\u5efa\u7a7aSeries\u5b58\u50a8\u6700\u65b0\u4e00\u65e5\u7684\u6570\u636e\n    latest_series = pd.Series(dtype='float64')\n\n    print('\u6b63\u5728\u66f4\u65b0{}...'.format(code))\n\n    # \u80a1\u7968\u6570\u636e\u5728\u6570\u636e\u5e93\u4e2d\u7684\u8868\u540d\n    table_name = '{}_{}'.format(code[3:], code[:2])\n\n    # \u6570\u636e\u5e93\u5199\u5165\u6a21\u5f0f\uff0c\u9ed8\u8ba4\u4e3a\u6dfb\u52a0\n    if_exists = 'append'\n\n    # \u6570\u636eid\uff0c\u9ed8\u8ba4\u4e3a-1\n    last_id = -1\n\n    # \u65e5\u7ebf\u5f00\u59cb\u65f6\u95f4\uff0c\u9ed8\u8ba4\u4e3a1990-12-19\n    start_date = g_start_date\n\n    # \u7528\u4e8e\u8bfb\u53d6\u6570\u636e\u5e93\u6570\u636e\u7684DataFrame\n    db_df = pd.DataFrame()\n\n    # \u91cd\u65b0\u521b\u5efa\u6216\u8005\u6570\u636e\u5e93\u4e2d\u65e0\u8be5\u80a1\u7968\u7684\u6570\u636e\u8868\uff0c\u9700\u8981\u91cd\u65b0\u521b\u5efa\u6570\u636e\n    if recreate or (table_name not in db_tables):\n        if_exists = 'replace'\n\n    else:\n\n        # \u83b7\u53d6\u6309\u65f6\u95f4\u6392\u5e8f\u7684\u6700\u540equery_days\u884c\u6570\u636e\n        sql_cmd = 'SELECT * FROM {} ORDER BY date DESC LIMIT {};'.format(table_name, query_days)\n        db_df = pd.read_sql(sql=sql_cmd, con=engine)\n\n        # \u6570\u636e\u6309id(date)\u5347\u5e8f\u6392\u5e8f\n        db_df = db_df.sort_values(by='id', ascending=True)\n\n        # \u83b7\u53d6\u6570\u636e\u5e93\u4e2d\u6700\u65b0\u6570\u636e\u65e5\u671f\u53caid\n        last_id = db_df['id'].iloc[-1]\n        start_date = db_df['date'].iloc[-1]\n\n        # \u5982\u679c\u6570\u636e\u5e93\u4e2d\u5df2\u5305\u542b\u6700\u65b0\u4e00\u65e5\u6570\u636e\n        if start_date >= latest_trading_date:\n            # \u5c06\u6700\u65b0\u4e00\u65e5\u6570\u636e\uff0c\u6dfb\u52a0code\u5b57\u6bb5\uff0c\u5e76\u8fd4\u56de\n            latest_series = db_df.iloc[-1].copy()\n            latest_series['code'] = code[3:]\n            print('{}\u5f53\u524d\u5df2\u4e3a\u6700\u65b0\u6570\u636e'.format(code))\n\n            # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n            engine.dispose()\n\n            return latest_series\n\n        # \u8ba1\u7b97\u5f85\u66f4\u65b0\u6570\u636e\u7684\u5f00\u59cb\u65e5\u671f\n        start_date = (datetime.datetime.strptime(\n            start_date, '%Y-%m-%d') + datetime.timedelta(days=1)).strftime('%Y-%m-%d')\n\n        # \u65b0\u9664\u6743\u9664\u606f\uff0c\u9700\u91cd\u65b0\u521b\u5efa\u6570\u636e\n        if query_dividend(code, start_date):\n            if_exists = 'replace'\n            start_date = g_start_date\n\n    # \u4e0b\u8f7d\u65e5\u7ebf\u6570\u636e\n    out_df = wrap_query_history_k_data_plus(\n        code, start_date=start_date, end_date=latest_trading_date, adjustflag=adjustflag)\n\n    # \u5254\u9664\u505c\u76d8\u6570\u636e\n    if out_df.shape[0]:\n        out_df = out_df[(out_df['volume'] != '0') & (out_df['volume'] != '')]\n\n    # \u5982\u679c\u6570\u636e\u4e3a\u7a7a\uff0c\u5219\u4e0d\u66f4\u65b0\n    if not out_df.shape[0]:\n        # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n        engine.dispose()\n\n        return latest_series\n\n    # \u5c06\u6570\u503c\u6570\u636e\u8f6c\u4e3afloat\u578b\uff0c\u4fbf\u4e8e\u540e\u7eed\u5904\u7406\n    convert_list = ['open', 'high', 'low', 'close', 'preclose', 'volume', 'amount', 'turn', 'pctChg']\n    out_df[convert_list] = out_df[convert_list].astype(float)\n\n    # \u65b0\u6dfb\u52a0\u884c\u6570\n    new_rows = out_df.shape[0]\n\n    # \u5982\u679c\u662f\u66f4\u65b0\u6570\u636e\uff0c\u5c06\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u7684\u6570\u636e\u62fc\u63a5\u5230\u65b0\u4e0b\u8f7d\u65e5\u7ebf\u6570\u636e\u4e0a\n    if db_df.shape[0]:\n        out_df = db_df[list(out_df)].append(out_df)\n\n    # \u5982\u679c\u6570\u636e\u5c11\u4e8equery_days\uff0c\u5219\u4e0d\u66f4\u65b0\n    if out_df.shape[0] < query_days:\n        # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n        engine.dispose()\n\n        return latest_series\n\n    # \u91cd\u7f6e\u7d22\u5f15\n    out_df.reset_index(drop=True, inplace=True)\n\n    # \u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\n    out_df = extend_factor(out_df)\n\n    # \u53d6\u6700\u540enew_rows\u884c\n    out_df = out_df.iloc[-new_rows:]\n\n    # \u5224\u8bfb\u662f\u5426\u6709\u5b57\u6bb5\u7f3a\u5931\n    if np.any(out_df.isnull()):\n        print('{}\u6709\u7f3a\u5931\u5b57\u6bb5\uff01\uff01\uff01'.format(code))\n\n    # \u66f4\u65b0id\n    id_s = pd.Series(np.arange(last_id + 1, last_id + 1 + new_rows), index=out_df.index)\n\n    # \u5982\u679c\u662f\u66f4\u65b0\u6570\u636e\uff0c\u5219\u76f4\u63a5\u8d4b\u503cid\u5217\n    if db_df.shape[0]:\n        out_df['id'] = id_s\n\n    # \u5982\u679c\u662f\u65b0\u5efa\u6570\u636e\uff0c\u5219\u5c06id\u63d2\u5165\u5230\u7b2c\u4e00\u5217\n    else:\n        out_df.insert(0, 'id', id_s)\n\n    # \u5c06\u66f4\u65b0\u6570\u636e\u5199\u5165\u6570\u636e\u5e93\n    out_df.to_sql(name=table_name, con=engine, if_exists=if_exists, index=False)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    # \u5c06\u66f4\u65b0\u7684\u6700\u540e\u4e00\u884c\u6dfb\u52a0code\u5b57\u6bb5\uff0cappend\u5230latest_df\u4e2d\n    latest_series = out_df.iloc[-1].copy()\n    latest_series['code'] = code[3:]\n\n    # \u8fd4\u56de\u5305\u542b\u6700\u65b0\u4e00\u65e5\u80a1\u7968\u65e5\u7ebf\u6570\u636e\u7684Series\n    return latest_series\n\n\ndef update_data_mp(stock_codes):\n    \"\"\"\n    \u4f7f\u7528\u591a\u8fdb\u7a0b\u66f4\u65b0\u65e5\u7ebf\u6570\u636e\uff0c\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\n\n    \u5c06\u6700\u65b0\u4e00\u65e5\u5404\u4e2a\u80a1\u7968\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u8868latest\u4e2d\uff0c\u4fbf\u4e8e\u540e\u7eed\u7b5b\u9009\u5019\u9009\u80a1\u7968\u4f7f\u7528\n\n    :param stock_codes: \u5f85\u66f4\u65b0\u6570\u636e\u7684\u80a1\u7968\u4ee3\u7801\n    :return: \u5305\u542b\u6240\u6709\u5f85\u5904\u7406\u80a1\u7968\u7684\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u7684DataFrame\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5b9e\u4f8b\n    engine = create_mysql_engine()\n\n    # \u83b7\u53d6\u6570\u636e\u5e93\u5185\u6240\u6709\u8868\u7684\u8868\u540d\n    db_tables = sqlalchemy.inspect(engine).get_table_names()\n\n    # \u83b7\u53d6\u6700\u65b0\u4ea4\u6613\u65e5\n    trading_date = get_trading_date()\n\n    # \u591a\u8fdb\u7a0b\u66f4\u65b0\u6570\u636e\uff0c\u83b7\u53d6\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u7684DataFrame\n    with multiprocessing.Pool(processes=get_process_num()) as pool:\n        rs = pool.starmap(update_data, [(code, trading_date, db_tables) for code in stock_codes])\n\n    # \u5220\u9664\u7a7a\u503c\n    rs = [s for s in rs if 0 != len(s)]\n\n    # \u5c06\u5404\u8fdb\u7a0b\u8fd4\u56de\u503c\u7ec4\u88c5\u4e3apd.DataFrame\n    latest_df = pd.DataFrame(rs)\n\n    # \u5c06\u6240\u6709\u80a1\u7968\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u5199\u5165\u6570\u636e\u5e93\u8868latest\n    if latest_df.shape[0]:\n        latest_df.to_sql(name='latest', con=create_mysql_engine(), if_exists='replace', index=False)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    print('\u5b8c\u6210\u6570\u636e\u66f4\u65b0')\n    return latest_df\n\n\ndef get_trading_date(date=None):\n    \"\"\"\n    \u53d6\u5f97\u6307\u5b9a\u65e5\u671f\u524d\u5e02\u573a\u6700\u8fd1\u4ea4\u6613\u65e5\uff0c\u8fd9\u91cc\u7684\u4ea4\u6613\u65e5\u7279\u6307\u6709\u65e5\u7ebf\u6570\u636e\u7684\u4ea4\u6613\u65e5\n\n    :param date: \u65e5\u671f\u5b57\u7b26\u4e32\uff0c\u9ed8\u8ba4\u4e3a\u7a7a\uff0c\u8fd4\u56de\u5386\u53f2\u79bb\u5f53\u65e5\u6700\u8fd1\u7684\u6709\u65e5\u7ebf\u7684\u4ea4\u6613\u65e5\uff08\u5f53\u65e5\u4e3a\u4ea4\u6613\u65e5\uff0c\u82e517:30\u524d\u8c03\u7528\uff0c\u4e5f\u65e0\u65e5\u7ebf\u6570\u636e\uff09\n    :return: \u6700\u8fd1\u4ea4\u6613\u65e5\u65e5\u671f\u5b57\u7b26\u4e32\uff0c\u683c\u5f0f'%Y-%m-%d'\n    \"\"\"\n\n    # date\u4e3a\u7a7a\u65f6\uff0c\u5c06trade_date\u8bbe\u7f6e\u4e3a\u5f53\u65e5\n    if date is None:\n        trading_date = datetime.date.today().strftime('%Y-%m-%d')\n\n    # \u5426\u5219\u5c06trade_date\u8bbe\u7f6e\u4e3a\u6307\u5b9adate\n    else:\n        trading_date = date\n\n    # \u67e5\u8be2\u80a1\u7968\u6570\u636e\uff0cquery_all_stock\u4e0eK\u7ebf\u572817:30\u540c\u65f6\u66f4\u65b0,\u5411trading_date\u524d\u5bfb\u627e\u6709K\u7ebf\u6570\u636e\u7684\u65e5\u671f\n    while 0 == wrap_query_all_stock(trading_date).shape[0]:\n        # trading_date\u5411\u5386\u53f2\u79fb\u52a81\u5929\n        trading_date = (datetime.datetime.strptime(trading_date, '%Y-%m-%d') - datetime.timedelta(days=1)).strftime(\n            '%Y-%m-%d')\n\n    # \u8fd4\u56de\u6700\u8fd1\u4ea4\u6613\u65e5\u65e5\u671f\n    return trading_date\n\n\ndef query_dividend(code, date):\n    \"\"\"\n    \u67e5\u8be2\u662f\u5426\u5728\u6307\u5b9a\u65e5\u671f\u540e\u9664\u6743\u9664\u606f\n\n    :param code: \u80a1\u7968\u4ee3\u7801\n    :param date: \u6307\u5b9a\u65e5\u671f\n    :return: \u5728\u6307\u5b9a\u65e5\u671f\u540e\u9664\u6743\u9664\u606f\u8fd4\u56deTrue\uff0c\u5426\u5219\u8fd4\u56deFalse\n    \"\"\"\n\n    # \u67e5\u8be2\u9664\u6743\u9664\u606f\u6570\u636e\n    rs_df = wrap_query_dividend_data(code, date[:4])\n\n    # \u5728\u6307\u5b9a\u65e5\u671f\u540e\u9664\u6743\u9664\u606f\u8fd4\u56deTrue\uff0c\u5426\u5219\u8fd4\u56deFalse\n    if rs_df.shape[0] and rs_df['dividOperateDate'].iloc[-1] >= date:\n        return True\n    else:\n        return False\n\n\ndef extend_factor(df):\n    \"\"\"\n    \u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    # \u4f7f\u7528pipe\u4f9d\u6b21\u8ba1\u7b97\u6da8\u505c\u3001\u53cc\u795e\u53ca\u662f\u5426\u4e3a\u5019\u9009\u80a1\u7968\n    df = df.pipe(zt).pipe(ss, delta_days=30).pipe(candidate)\n\n    return df\n\n\ndef zt(df):\n    \"\"\"\n    \u8ba1\u7b97\u6da8\u505c\u56e0\u5b50\n\n    \u82e5\u6da8\u505c\uff0c\u5219\u56e0\u5b50\u4e3aTrue\uff0c\u5426\u5219\u4e3aFalse\n    \u4ee5\u5f53\u65e5\u6536\u76d8\u4ef7\u8f83\u524d\u4e00\u65e5\u6536\u76d8\u4ef7\u4e0a\u6da89.8%\u53ca\u4ee5\u4e0a\u4f5c\u4e3a\u6da8\u505c\u5224\u65ad\u6807\u51c6\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    df['zt'] = np.where((df['close'].values >= 1.098 * df['preclose'].values), True, False)\n\n    return df\n\n\ndef shift_i(df, factor_list, i, fill_value=0, suffix='a'):\n    \"\"\"\n    \u8ba1\u7b97\u79fb\u52a8\u56e0\u5b50\uff0c\u7528\u4e8e\u83b7\u53d6\u524di\u65e5\u6216\u8005\u540ei\u65e5\u7684\u56e0\u5b50\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :param factor_list: \u5f85\u79fb\u52a8\u7684\u56e0\u5b50\u5217\u8868\n    :param i: \u79fb\u52a8\u7684\u6b65\u6570\n    :param fill_value: \u7528\u4e8e\u586b\u5145NA\u7684\u503c\uff0c\u9ed8\u8ba4\u4e3a0\n    :param suffix: \u503c\u4e3aa(ago)\u65f6\u8868\u793a\u79fb\u52a8\u83b7\u5f97\u5386\u53f2\u6570\u636e\uff0c\u7528\u4e8e\u8ba1\u7b97\u6307\u6807\uff1b\u503c\u4e3al(later)\u65f6\u8868\u793a\u83b7\u5f97\u672a\u6765\u6570\u636e\uff0c\u7528\u4e8e\u8ba1\u7b97\u6536\u76ca\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    # \u9009\u53d6\u9700\u8981shift\u7684\u5217\u6784\u6210\u65b0\u7684DataFrame\uff0c\u8fdb\u884cshift\u64cd\u4f5c\n    shift_df = df[factor_list].shift(i, fill_value=fill_value)\n\n    # \u5bf9\u65b0\u7684DataFrame\u5217\u8fdb\u884c\u91cd\u547d\u540d\n    shift_df.rename(columns={x: '{}_{}{}'.format(x, i, suffix) for x in factor_list}, inplace=True)\n\n    # \u5c06\u91cd\u547d\u540d\u540e\u7684DataFrame\u5408\u5e76\u5230\u539f\u59cbDataFrame\u4e2d\n    df = pd.concat([df, shift_df], axis=1)\n\n    return df\n\n\ndef shift_till_n(df, factor_list, n, fill_value=0, suffix='a'):\n    \"\"\"\n    \u8ba1\u7b97\u8303\u56f4\u79fb\u52a8\u56e0\u5b50\n\n    \u7528\u4e8e\u83b7\u53d6\u524d\/\u540en\u65e5\u5185\u7684\u76f8\u5173\u56e0\u5b50\uff0c\u5185\u90e8\u8c03\u7528\u4e86shift_i\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :param factor_list: \u5f85\u79fb\u52a8\u7684\u56e0\u5b50\u5217\u8868\n    :param n: \u79fb\u52a8\u7684\u6b65\u6570\u8303\u56f4\n    :param fill_value: \u7528\u4e8e\u586b\u5145NA\u7684\u503c\uff0c\u9ed8\u8ba4\u4e3a0\n    :param suffix: \u503c\u4e3aa(ago)\u65f6\u8868\u793a\u79fb\u52a8\u83b7\u5f97\u5386\u53f2\u6570\u636e\uff0c\u7528\u4e8e\u8ba1\u7b97\u6307\u6807\uff1b\u503c\u4e3al(later)\u65f6\u8868\u793a\u83b7\u5f97\u672a\u6765\u6570\u636e\uff0c\u7528\u4e8e\u8ba1\u7b97\u6536\u76ca\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    for i in range(n):\n        df = shift_i(df, factor_list, i + 1, fill_value, suffix)\n    return df\n\n\ndef ss(df, delta_days=30):\n    \"\"\"\n    \u8ba1\u7b97\u53cc\u795e\u56e0\u5b50\uff0c\u5373\u95f4\u9694\u7684\u4e24\u4e2a\u6da8\u505c\n\n    \u82e5\u5f53\u65e5\u5f62\u6210\u53cc\u795e\uff0c\u5219\u56e0\u5b50\u4e3aTrue\uff0c\u5426\u5219\u4e3aFalse\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :param delta_days: \u4e24\u6839\u6da8\u505c\u95f4\u9694\u7684\u65f6\u95f4\u4e0d\u80fd\u8d85\u8fc7\u8be5\u503c\uff0c\u5426\u5219\u4e0d\u5224\u5b9a\u4e3a\u53cc\u795e\uff0c\u9ed8\u8ba4\u503c\u4e3a30\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    # \u79fb\u52a8\u6da8\u505c\u56e0\u5b50\uff0c\u6c42\u53d6\u8fd1delta_days\u5929\u5185\u7684\u6da8\u505c\u60c5\u51b5\uff0c\u4fdd\u5b58\u5728\u4e00\u4e2a\u4e34\u65f6DataFrame\u4e2d\n    temp_df = shift_till_n(df, ['zt'], delta_days, fill_value=False)\n\n    # \u751f\u6210\u5217\u8868\uff0c\u7528\u4e8e\u540e\u7eed\u68c0\u7d22\u7b2c2\u5929\u524d\u81f3\u7b2cdelta_days\u5929\u524d\u662f\u5426\u6709\u6da8\u505c\u51fa\u73b0\n    col_list = ['zt_{}a'.format(x) for x in range(2, delta_days + 1)]\n\n    # \u8ba1\u7b97\u53cc\u795e\uff0c\u9700\u540c\u65f6\u6ee1\u8db33\u4e2a\u6761\u4ef6\uff1a\n    # 1\u3001\u7b2c2\u5929\u524d\u81f3\u7b2cdelta_days\u5929\u524d\uff0c\u81f3\u5c11\u67091\u4e2a\u6da8\u505c\n    # 2\u30011\u5929\u524d\u4e0d\u662f\u6da8\u505c\uff08\u5426\u5219\u5c31\u662f\u8fde\u7eed\u6da8\u505c\uff0c\u4e0d\u662f\u95f4\u9694\u7684\u6da8\u505c\uff09\n    # 3\u3001\u5f53\u5929\u662f\u6da8\u505c\n    df['ss'] = temp_df[col_list].any(axis=1) & ~temp_df['zt_1a'] & temp_df['zt']\n\n    return df\n\n\ndef mas(df, ma_list, factor='close'):\n    \"\"\"\n    \u8ba1\u7b97\u591a\u6761\u5747\u7ebf\u56e0\u5b50\uff0c\u5185\u90e8\u8c03\u7528ma\u8ba1\u7b97\u5355\u6761\u5747\u7ebf\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :param ma_list: \u5f85\u8ba1\u7b97\u5747\u7ebf\u7684\u5468\u671f\u5217\u8868\n    :param factor: \u5f85\u8ba1\u7b97\u5747\u7ebf\u7684\u56e0\u5b50\uff0c\u9ed8\u8ba4\u4e3a\u6536\u76d8\u4ef7\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    for i in ma_list:\n        df = ma(df, i, factor)\n    return df\n\n\ndef ma(df, n=5, factor='close'):\n    \"\"\"\n    \u8ba1\u7b97\u5747\u7ebf\u56e0\u5b50\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :param n: \u5f85\u8ba1\u7b97\u5747\u7ebf\u7684\u5468\u671f\uff0c\u9ed8\u8ba4\u8ba1\u7b975\u65e5\u5747\u7ebf\n    :param factor: \u5f85\u8ba1\u7b97\u5747\u7ebf\u7684\u56e0\u5b50\uff0c\u9ed8\u8ba4\u4e3a\u6536\u76d8\u4ef7\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    # \u5747\u7ebf\u540d\u79f0\uff0c\u4f8b\u5982\uff0c\u6536\u76d8\u4ef7\u76845\u65e5\u5747\u7ebf\u540d\u79f0\u4e3ama_5\uff0c\u6210\u4ea4\u91cf\u76845\u65e5\u5747\u7ebf\u540d\u79f0\u4e3avolume_ma_5\n    name = '{}ma_{}'.format('' if 'close' == factor else factor + '_', n)\n\n    # \u53d6\u5f85\u8ba1\u7b97\u5747\u7ebf\u7684\u56e0\u5b50\u5217\n    s = pd.Series(df[factor], name=name, index=df.index)\n\n    # \u5229\u7528rolling\u548cmean\u8ba1\u7b97\u5747\u7ebf\u6570\u636e\n    s = s.rolling(center=False, window=n).mean()\n\n    # \u5c06\u5747\u7ebf\u6570\u636e\u6dfb\u52a0\u5230\u539f\u59cb\u7684DataFrame\u4e2d\n    df = df.join(s)\n\n    # \u5747\u7ebf\u6570\u503c\u4fdd\u7559\u4e24\u4f4d\u5c0f\u6570\n    df[name] = df[name].apply(lambda x: round(x + 0.001, 2))\n\n    return df\n\n\ndef cross_mas(df, ma_list):\n    \"\"\"\n    \u8ba1\u7b97\u7a7f\u5747\u7ebf\u56e0\u5b50\n\n    \u82e5\u5f53\u65e5\u6700\u4f4e\u4ef7\u4e0d\u9ad8\u4e8e\u5747\u7ebf\u4ef7\u683c\n    \u4e14\u5f53\u65e5\u6536\u76d8\u4ef7\u4e0d\u4f4e\u4e8e\u5747\u7ebf\u4ef7\u683c\n    \u5219\u5f53\u65e5\u7a7f\u5747\u7ebf\u56e0\u5b50\u503c\u4e3aTrue\uff0c\u5426\u5219\u4e3aFalse\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :param ma_list: \u5747\u7ebf\u7684\u5468\u671f\u5217\u8868\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    for i in ma_list:\n        df['cross_{}'.format(i)] = (df['low'] <= df['ma_{}'.format(i)]) & (\n                df['ma_{}'.format(i)] <= df['close'])\n    return df\n\n\ndef candidate(df):\n    \"\"\"\n    \u8ba1\u7b97\u662f\u5426\u4e3a\u5019\u9009\n\n    \u82e5\u5f53\u65e5\u65e5\u7ebf\u540c\u65f6\u7a7f\u8fc75\u300110\u300120\u300130\u65e5\u5747\u7ebf\n    \u4e1430\u65e5\u5747\u7ebf\u572860\u65e5\u5747\u7ebf\u4e0a\u65b9\n    \u4e14\u5f53\u65e5\u5f62\u6210\u53cc\u795e\n    \u5219\u5f53\u65e5\u4f5c\u4e3a\u5019\u9009\uff0c\u8be5\u56e0\u5b50\u503c\u4e3aTrue\uff0c\u5426\u5219\u4e3aFalse\n\n    :param df: \u5f85\u8ba1\u7b97\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    :return: \u5305\u542b\u6269\u5c55\u56e0\u5b50\u7684DataFrame\n    \"\"\"\n\n    # \u5747\u7ebf\u5468\u671f\u5217\u8868\n    ma_list = [5, 10, 20, 30, 60]\n\n    # \u8ba1\u7b97\u5747\u7ebf\u7684\u56e0\u5b50\uff0c\u4fdd\u5b58\u5230\u4e34\u65f6\u7684DataFrame\u4e2d\n    temp_df = mas(df, ma_list)\n\n    # \u8ba1\u7b97\u7a7f\u591a\u7ebf\u7684\u56e0\u5b50\uff0c\u4fdd\u5b58\u5230\u4e34\u65f6\u7684DataFrame\u4e2d\n    temp_df = cross_mas(temp_df, ma_list)\n\n    # \u7a7f\u591a\u7ebf\u56e0\u5b50\u7684\u5217\u540d\u5217\u8868\n    column_list = ['cross_{}'.format(x) for x in ma_list[:-1]]\n\n    # \u8ba1\u7b97\u662f\u5426\u4e3a\u5019\u9009\n    df['candidate'] = temp_df[column_list].all(axis=1) & (temp_df['ma_30'] >= temp_df['ma_60']) & df['ss']\n\n    return df\n\n\ndef get_process_num():\n    \"\"\"\n    \u83b7\u53d6\u5efa\u8bae\u8fdb\u7a0b\u6570\u76ee\n\n    \u5bf9\u4e8eI\/O\u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u5efa\u8bae\u8fdb\u7a0b\u6570\u76ee\u4e3aCPU\u6838\u6570\/(1-a)\uff0ca\u53bb0.8~0.9\n\n    :return: \u8fdb\u7a0b\u6570\u76ee\n    \"\"\"\n\n    return min(60, int(os.cpu_count() \/ (1 - 0.9)))\n\n\ndef profit_loss_statistic_mp(stock_codes, hold_days=10):\n    \"\"\"\n    \u591a\u8fdb\u7a0b\u76c8\u4e8f\u5206\u5e03\u7edf\u8ba1\uff0c\u8ba1\u7b97\u5f53\u65e5candidate\u4e3aTrue\uff0c\u6301\u4ed3hold_days\u5929\u7684\u6536\u76ca\u5206\u5e03\n    \u8f93\u51fa\u6570\u636e\u5206\u5e03\u8868\u683c\u53ca\u56fe\u7247\u6587\u4ef6\n\n    :param stock_codes: \u5f85\u5206\u6790\u7684\u80a1\u7968\u4ee3\u7801\n    :param hold_days: \u6301\u4ed3\u5929\u6570\n    :return: None\n    \"\"\"\n\n    # \u591a\u8fdb\u7a0b\u8ba1\u7b97\u83b7\u5f97\u5019\u9009\u80a1\u7968\u6570\u636e\n    with multiprocessing.Pool(processes=get_process_num()) as pool:\n        rs = pool.starmap(profit_loss_statistic, [(code, hold_days) for code in stock_codes])\n\n    # \u62fc\u63a5\u5019\u9009DataFrame\n    candidate_df = pd.concat(rs, axis=0)\n\n    # \u5c06\u6536\u76ca\u5206\u5e03\u6570\u636e\u4fdd\u5b58\u5230excel\u6587\u4ef6\n    candidate_df[['date', 'code', 'max_profit', 'max_loss']].to_excel(\n        'profit_loss_{}.xlsx'.format(hold_days), index=False, encoding='utf-8')\n\n    # \u91cd\u7f6e\u7d22\u5f15\uff0c\u65b9\u4fbf\u540e\u9762\u7ed8\u56fe\n    candidate_df.reset_index(inplace=True)\n\n    # \u8bbe\u7f6e\u7ed8\u56fe\u683c\u5f0f\u5e76\u7ed8\u56fe\n    fig, ax = plt.subplots(1, 1)\n    table(ax, np.round(candidate_df[['max_profit', 'max_loss']].describe(), 4), loc='upper right',\n          colWidths=[0.2, 0.2, 0.2])\n    candidate_df[['max_profit', 'max_loss']].plot(ax=ax, legend=None)\n\n    # \u4fdd\u5b58\u56fe\u8868\n    fig.savefig('profit_loss_{}.png'.format(hold_days))\n\n    # \u663e\u793a\u56fe\u8868\n    plt.show()\n\n\ndef profit_loss_statistic(code, hold_days=10):\n    \"\"\"\n    \u76c8\u4e8f\u5206\u5e03\u7edf\u8ba1\uff0c\u8ba1\u7b97\u5f53\u65e5candidate\u4e3aTrue\uff0c\u6301\u4ed3hold_days\u5929\u7684\u6536\u76ca\u5206\u5e03\n    \u8f93\u51fa\u6570\u636e\u5206\u5e03\u8868\u683c\u53ca\u56fe\u7247\u6587\u4ef6\n\n    :param code: \u5f85\u5206\u6790\u7684\u80a1\u7968\u4ee3\u7801\n    :param hold_days: \u6301\u4ed3\u5929\u6570\n    :return: \u7b5b\u9009\u51fa\u7b26\u5408\u4e70\u5165\u6761\u4ef6\u7684\u80a1\u7968DataFrame\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u521b\u5efa\u7a7a\u7684DataFrame\n    candidate_df = pd.DataFrame()\n\n    # \u8ba1\u7b97\u6536\u76ca\u5206\u5e03\u5faa\u73af\n    print('\u6b63\u5728\u5904\u7406{}...'.format(code))\n\n    # \u80a1\u7968\u6570\u636e\u5728\u6570\u636e\u5e93\u4e2d\u7684\u8868\u540d\n    table_name = '{}_{}'.format(code[3:], code[:2])\n\n    # \u5982\u679c\u6570\u636e\u5e93\u4e2d\u6ca1\u6709\u8be5\u80a1\u7968\u6570\u636e\u5219\u8df3\u8fc7\n    if table_name not in sqlalchemy.inspect(engine).get_table_names():\n        # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n        engine.dispose()\n\n        return candidate_df\n\n    # \u4ece\u6570\u636e\u5e93\u8bfb\u53d6\u7279\u5b9a\u5b57\u6bb5\u6570\u636e\n    cols = 'date, open, high, low, close, candidate'\n    sql_cmd = 'SELECT {} FROM {} ORDER BY date DESC'.format(cols, table_name)\n    df = pd.read_sql(sql=sql_cmd, con=engine)\n    print(df.dtypes)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    # \u79fb\u52a8\u7b2c2\u65e5\u5f00\u76d8\u4ef7\u3001\u6700\u4f4e\u4ef7\uff0c\u4ee5\u53cahold_days\u7684\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u6570\u636e\n    df = shift_i(df, ['open'], 1, suffix='l')\n    df = shift_till_n(df, ['high', 'low'], hold_days, suffix='l')\n\n    # \u4e22\u5f03\u6700\u8fd1hold_days\u65e5\u7684\u6570\u636e\n    df = df.iloc[hold_days: df.shape[0] - g_available_days_limit, :]\n\n    # \u9009\u53d6\u51fa\u73b0\u4e70\u70b9\u7684\u80a1\u7968\n    df = df[(df['candidate'] > 0) & (df['low_1l'] <= df['close'])]\n\n    # \u5c06\u6570\u636e\u6dfb\u52a0\u5230\u5019\u9009\u6c60\u4e2d\n    if df.shape[0]:\n        df['code'] = code\n        candidate_df = candidate_df.append(df)\n\n    if candidate_df.shape[0]:\n        # \u8ba1\u7b97\u6700\u5927\u76c8\u5229\n        # \u4e70\u5165\u5f53\u5929\u65e0\u6cd5\u5356\u51fa\uff0c\u56e0\u6b64\u8ba1\u7b97\u6700\u5927\u6536\u76ca\u65f6\uff0c\u4ece\u7b2c2\u65e5\u5f00\u59cb\n        cols = ['high_{}l'.format(x) for x in range(2, hold_days + 1)]\n        candidate_df['max_high'] = candidate_df[cols].max(axis=1)\n        candidate_df['max_profit'] = candidate_df['max_high'] \/ candidate_df[['open_1l', 'close']].min(axis=1) - 1\n\n        # \u8ba1\u7b97\u6700\u5927\u4e8f\u635f\n        cols = ['low_{}l'.format(x) for x in range(2, hold_days + 1)]\n        candidate_df['min_low'] = candidate_df[cols].min(axis=1)\n        candidate_df['max_loss'] = candidate_df['min_low'] \/ candidate_df[['open_1l', 'close']].min(axis=1) - 1\n\n    return candidate_df\n\n\ndef update_latest_table(code):\n    \"\"\"\n    \u66f4\u65b0\u6570\u636e\u5e93\u4e2d\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u8868\n\n    \u4ece\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u80a1\u7968\u7684\u6700\u65b0\u4e00\u5929\u6570\u636e\u5e76\u8fd4\u56de\n\n    :param code: \u5f85\u66f4\u65b0\u6570\u636e\u7684\u80a1\u7968\u4ee3\u7801\n    :return: \u5305\u542b\u6240\u6709\u5f85\u5904\u7406\u80a1\u7968\u7684\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u7684DataFrame\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u521b\u5efa\u7a7a\u7684DataFrame\n    latest_df = pd.DataFrame()\n\n    print('\u6b63\u5728\u66f4\u65b0{}...'.format(code))\n\n    # \u80a1\u7968\u6570\u636e\u5728\u6570\u636e\u5e93\u4e2d\u7684\u8868\u540d\n    table_name = '{}_{}'.format(code[3:], code[:2])\n\n    # \u5224\u65ad\u662f\u5426\u5b58\u5728\u8be5\u8868\uff0c\u4e0d\u5b58\u5728\u5219\u8df3\u8fc7\n    if table_name not in sqlalchemy.inspect(engine).get_table_names():\n        # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n        engine.dispose()\n\n        return latest_df\n\n    # \u4ece\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u80a1\u7968\u7684\u6700\u65b0\u4e00\u5929\u6570\u636e\n    sql_cmd = 'SELECT * FROM {} ORDER BY date DESC LIMIT 1;'.format(table_name)\n    df = pd.read_sql(sql=sql_cmd, con=engine)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    # \u6709\u7f3a\u5931\u5b57\u6bb5\u5c31\u4e0d\u53c2\u4e0e\u5019\u9009\n    if np.any(df.isnull()):\n        print('{}\u6709\u7f3a\u5931\u5b57\u6bb5\uff01\uff01\uff01'.format(code))\n        return latest_df\n\n    # \u6dfb\u52a0code\u5b57\u6bb5\uff0c\u5e76append\u5230latest_df\u4e2d\n    df['code'] = code[3:]\n    latest_df = latest_df.append(df)\n\n    return latest_df\n\n\ndef update_latest_table_mp(stock_codes):\n    \"\"\"\n    \u591a\u8fdb\u7a0b\u66f4\u65b0\u6570\u636e\u5e93\u4e2d\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u8868\n\n    \u4ece\u5404\u4e2a\u8fdb\u7a0b\u6536\u96c6\u6bcf\u53ea\u80a1\u7968\u7684\u6700\u65b0\u4e00\u5929\u6570\u636e\u5e76\u8fd4\u56de\n\n    :param stock_codes: \u5f85\u66f4\u65b0\u6570\u636e\u7684\u80a1\u7968\u4ee3\u7801\n    :return: \u5305\u542b\u6240\u6709\u5f85\u5904\u7406\u80a1\u7968\u7684\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u7684DataFrame\n    \"\"\"\n\n    # \u591a\u8fdb\u7a0b\u66f4\u65b0\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u8868\uff0c\u83b7\u53d6\u8be5\u8868\u6570\u636e\u7684DataFrame\n    with multiprocessing.Pool(processes=get_process_num()) as pool:\n        rs = pool.map(update_latest_table, stock_codes)\n\n    # \u62fc\u63a5\u6700\u65b0\u65e5\u7ebf\u6570\u636e\n    latest_df = pd.concat(rs, axis=0)\n\n    # \u5c06\u6240\u6709\u80a1\u7968\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u5199\u5165\u6570\u636e\u5e93\u8868latest\n    if latest_df.shape[0]:\n        latest_df.to_sql(name='latest', con=create_mysql_engine(), if_exists='replace', index=False)\n\n    return latest_df\n\n\ndef query_latest_table(stock_codes, by_latest_table=True):\n    \"\"\"\n    \u67e5\u8be2\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u8868\n\n    :param stock_codes: \u5f85\u83b7\u53d6\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u7684\u80a1\u7968\u4ee3\u7801\n    :param by_latest_table: \u662f\u5426\u901a\u8fc7\u8868latest\u67e5\u8be2\uff0c\u4e3aTrue\uff08\u9ed8\u8ba4\uff09\u65f6\uff0c\u76f4\u63a5\u4ece\u8868latest\u4e2d\u67e5\u8be2\uff0c\u5426\u5219\u4ece\u5404\u4e2a\u80a1\u7968\u6570\u636e\u8868\u4e2d\u5206\u522b\u8bfb\u53d6\n    :return: \u5305\u542b\u6240\u6709\u5f85\u5904\u7406\u80a1\u7968\u7684\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u7684DataFrame\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u5982\u679c\u8bbe\u7f6e\u76f4\u63a5\u4ece\u8868latest\u67e5\u8be2\uff0c\u4e14\u8868latest\u5b58\u5728\uff0c\u5219\u76f4\u63a5\u4ece\u6570\u636e\u5e93\u8bfb\u53d6\u6570\u636e\u8fd4\u56de\n    if by_latest_table and 'latest' in sqlalchemy.inspect(engine).get_table_names():\n        sql_cmd = 'SELECT * FROM latest;'\n        latest_df = pd.read_sql(sql=sql_cmd, con=engine)\n\n    # \u5426\u5219\u4ece\u5404\u80a1\u7968\u6570\u636e\u8868\u4e2d\u5206\u522b\u8bfb\u53d6\u6700\u65b0\u4e00\u65e5\u6570\u636e\u518d\u8fd4\u56de\uff08\u8fc7\u7a0b\u4e2d\u4f1a\u66f4\u65b0\u8868latest\uff09\n    else:\n        latest_df = update_latest_table_mp(stock_codes)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    return latest_df\n\n\ndef write_easymoney_candidates(df):\n    \"\"\"\n    \u8f93\u51fa\u5019\u9009\u80a1\u7968txt\uff0c\u4f9b\u4e1c\u65b9\u8d22\u5bcc\u8bfb\u53d6\n\n    :param df: \u5305\u542b\u5019\u9009\u80a1\u7968\u6570\u636e\u7684DataFrame\n    :return: None\n    \"\"\"\n\n    # \u5019\u9009\u8f93\u51fa\u76ee\u5f55\n    out_dir = '..\/data\/candidates\/'\n\n    # \u5982\u679c\u76ee\u5f55\u4e0d\u5b58\u5728\uff0c\u5219\u521b\u5efa\n    if not os.path.exists(out_dir):\n        os.makedirs(out_dir)\n\n    out_file = open('{}{}.txt'.format(out_dir, datetime.datetime.today().date()), mode='w')\n    for stock in df['code'].tolist():\n        print(stock, file=out_file)\n    out_file.close()\n\n\ndef update_trade(stock_codes):\n    \"\"\"\n    \u66f4\u65b0\u4ea4\u6613\u6570\u636e\uff0c\u4e0eptrade\u8fdb\u884c\u6570\u636e\u4ea4\u4e92\n\n    :param stock_codes: \u66f4\u65b0\u80a1\u7968\u8303\u56f4\n    :return: None\n    \"\"\"\n\n    # \u67e5\u8be2\u6700\u65b0\u65e5\u7ebf\u6570\u636e\n    df = query_latest_table(stock_codes)\n\n    # \u7b5b\u9009\u51fa\u5019\u9009\u80a1\u7968\n    if df.shape[0]:\n        df = df[df['candidate'] > 0]\n\n    if df.shape[0]:\n        df = df.sort_values(by='turn')\n        print(df['code'].tolist())\n    else:\n        print('\u5f53\u65e5\u65e0\u5019\u9009\u80a1\u7968')\n\n    # \u8f93\u51fa\u5019\u9009\u80a1\u7968txt\uff0c\u4f9b\u4e1c\u65b9\u8d22\u5bcc\u8bfb\u53d6\n    write_easymoney_candidates(df)\n\n    # \u66f4\u65b0ptrade\u4ea4\u6613\u6570\u636e\n    update_ptrade(df)\n\n\ndef update_ptrade(df):\n    \"\"\"\n    \u66f4\u65b0ptrade\u4ea4\u6613\u6570\u636e\n\n    :param df: \u5305\u542b\u5f53\u65e5\u5019\u9009\u80a1\u7968\u65e5\u7ebf\u6570\u636e\u7684DataFrame\n    :return:\n    \"\"\"\n    # \u5c06\u5019\u9009\u66f4\u65b0\u5230\u6570\u636e\u5e93\u4e2d\n    update_ptrade_candidate(df)\n\n    # \u4eceptrade\u83b7\u53d6\u6210\u4ea4\u4fe1\u606f\uff0c\u5728\u6570\u636e\u5e93\u4e2d\u8fdb\u884c\u66f4\u65b0\n    update_ptrade_deal()\n\n    # \u76d8\u540e\u66f4\u65b0\u6570\u636e\u5e93\u4e2d\u6301\u4ed3\u80a1\u7968\u7684\u6301\u6709\u5929\u6570\u5b57\u6bb5\n    update_ptrade_hold_days()\n\n    # \u76d8\u540e\u66f4\u65b0\u6570\u636e\u5e93\u4e2d\u80a1\u7968\u662f\u5426\u7ee7\u7eed\u65e5\u540e\u4ea4\u6613\n    update_ptrade_to_trade()\n\n    # \u8f93\u51faptrade\u4ea4\u6613\u6240\u9700\u7684\u6587\u4ef6\n    output_ptrade_file()\n\n\ndef output_ptrade_file():\n    \"\"\"\n    \u8f93\u51faptrade\u4ea4\u6613\u6240\u9700\u7684\u6587\u4ef6\n\n    !!!\u5411ptrade\u4f20\u9012\u6570\u636e\u7684\u552f\u4e00\u65b9\u5f0f!!!\n    ptrade\u63d0\u4f9b\u5b9a\u65f6\u4e0a\u4f20\u6587\u4ef6\u529f\u80fd\uff0c\u5c06\u4ea4\u6613\u6240\u9700\u7684\u6570\u636e\u5199\u5165\u6307\u5b9a\u6587\u4ef6\uff0c\u4f9bptrade\u8bfb\u53d6\u4f7f\u7528\n\n    :return: None\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u83b7\u53d6\u6570\u636e\u5e93\u5185\u6240\u6709\u8868\u7684\u8868\u540d\n    db_tables = sqlalchemy.inspect(engine).get_table_names()\n\n    # \u5224\u65ad\u662f\u5426\u5b58\u5728\u8868ptrade\uff0c\u4e0d\u5b58\u5728\u5219\u65e0\u9700\u66f4\u65b0\n    table_name = 'ptrade'\n    if table_name not in db_tables:\n        return\n\n    # \u8bfb\u53d6\u6570\u636e\u5e93\u4e2d\u5f85\u4ea4\u6613\u8868\u6570\u636e\n    sql_cmd = 'SELECT * FROM {};'.format(table_name)\n    db_data = pd.read_sql(sql=sql_cmd, con=create_mysql_engine())\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    # \u5982\u679c\u4e0d\u5b58\u5728\u76ee\u5f55\u5219\u521b\u5efa\n    upload_dir = g_ptrade_upload_path[:g_ptrade_upload_path.rfind('\/')]\n    if not os.path.exists(upload_dir):\n        os.makedirs(upload_dir)\n\n    # \u5c06\u5f85\u4ea4\u6613\u6570\u636e\u5199\u5230\u6307\u5b9a\u6587\u4ef6\uff0c\u4f9bptrade\u8bfb\u53d6\n    db_data.to_csv(g_ptrade_upload_path, index=False, encoding='utf-8')\n\n\ndef update_ptrade_deal():\n    \"\"\"\n    \u4eceptrade\u83b7\u53d6\u6210\u4ea4\u4fe1\u606f\uff0c\u5728\u6570\u636e\u5e93\u4e2d\u8fdb\u884c\u66f4\u65b0\n\n    !!!\u4eceptrade\u83b7\u53d6\u4ea4\u6613\u6570\u636e\u7684\u65b9\u5f0f!!!\n    ptrade\u4f1a\u6bcf\u4e2a\u4e00\u6bb5\u65f6\u95f4\uff08\u8be5\u65f6\u95f4\u53ef\u8bbe\u7f6e\uff09\u5c06\u4ea4\u6613\u3001\u6301\u4ed3\u7b49\u6570\u636e\u5199\u5165\u6307\u5b9a\u6587\u4ef6\n    \u53ef\u4ee5\u901a\u8fc7\u8bfb\u53d6\u8fd9\u4e9b\u6587\u4ef6\u6765\u83b7\u53d6\u53bb\u5b9e\u76d8\u4ea4\u6613\u548c\u4ed3\u4f4d\u7b49\u6570\u636e\n\n    :return: None\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u83b7\u53d6\u6570\u636e\u5e93\u5185\u6240\u6709\u8868\u7684\u8868\u540d\n    db_tables = sqlalchemy.inspect(engine).get_table_names()\n\n    # \u5224\u65ad\u662f\u5426\u5b58\u5728\u8868ptrade\uff0c\u4e0d\u5b58\u5728\u5219\u65e0\u9700\u66f4\u65b0\n    table_name = 'ptrade'\n    if table_name not in db_tables:\n        return\n\n    # \u8bfb\u53d6\u5f85\u4ea4\u6613\u6570\u636e\u6570\u636e\n    sql_cmd = 'SELECT * FROM {};'.format(table_name)\n    db_data = pd.read_sql(sql=sql_cmd, con=engine)\n\n    # \u76ee\u5f55\u662f\u5426\u5b58\u5728\uff0c\u4e0d\u5b58\u5728\u5219\u521b\u5efa\n    if not os.path.exists(g_ptrade_export_dir):\n        os.makedirs(g_ptrade_export_dir)\n\n    # \u8bfb\u53d6ptrade\u8f93\u51fa\u7684\u4ea4\u6613\u6587\u4ef6\n    trading_date = get_trading_date()\n    deal_file = '{}Deal_{}.csv'.format(g_ptrade_export_dir, trading_date.replace('-', ''))\n    deal_df = pd.DataFrame()\n\n    # \u5224\u65ad\u4ea4\u6613\u6587\u4ef6\u662f\u5426\u5b58\u5728\n    if os.path.exists(deal_file):\n        deal_df = pd.read_csv(deal_file, encoding='gbk', usecols=['\u8bc1\u5238\u4ee3\u7801', '\u4e70\u5356\u65b9\u5411', '\u6210\u4ea4\u6570\u91cf', '\u6210\u4ea4\u4ef7\u683c', '\u6210\u4ea4\u91d1\u989d'],\n                              converters={'\u8bc1\u5238\u4ee3\u7801': str})\n    else:\n        print('\u65e0\u4ea4\u6613\u6587\u4ef6\uff01')\n\n    # \u6ca1\u6709\u6210\u4ea4\uff0c\u5219\u65e0\u9700\u66f4\u65b0\n    if not deal_df.shape[0]:\n        return\n\n    # \u5408\u5e76\u4ea4\u6613\u6570\u636e\uff0c\u6309\u8bc1\u5238\u4ee3\u7801\u548c\u4e70\u5356\u65b9\u5411\u5bf9\u6570\u636e\u5206\u7ec4\uff0c\u6c42\u53d6\u603b\u6210\u4ea4\u91d1\u989d\u3001\u603b\u6210\u4ea4\u6570\u91cf\u548c\u5747\u4ef7\n    group = deal_df.groupby(['\u8bc1\u5238\u4ee3\u7801', '\u4e70\u5356\u65b9\u5411'])\n    deal_df['\u603b\u91d1\u989d'] = group['\u6210\u4ea4\u91d1\u989d'].transform('sum')\n    deal_df['\u603b\u6570\u91cf'] = group['\u6210\u4ea4\u6570\u91cf'].transform('sum')\n    deal_df['\u5747\u4ef7'] = deal_df['\u603b\u91d1\u989d'] \/ deal_df['\u603b\u6570\u91cf']\n    deal_df = deal_df.drop_duplicates(['\u8bc1\u5238\u4ee3\u7801', '\u4e70\u5356\u65b9\u5411'])\n\n    # \u5408\u5e76\u540e\u4ea4\u6613\u6570\u636e\u6309\u884c\u5faa\u73af\n    for row in deal_df.itertuples():\n\n        # \u5904\u7406\u5356\u51fa\u6210\u4ea4\u6570\u636e\uff0c\u586b\u5199\u6570\u636e\u5e93\u4e2d\u8be5\u80a1\u7968\u7684\u5356\u51fa\u65e5\u671f\u3001\u5356\u51fa\u4ef7\u683c\u3001\u603b\u5356\u51fa\u91d1\u989d\u5b57\u6bb5\uff0c\u5e76\u8bbe\u7f6e\u80a1\u7968\u4e3a\u4e0d\u518d\u4ea4\u6613\n        if '\u5356\u51fa' == getattr(row, '\u4e70\u5356\u65b9\u5411'):\n            idcs = db_data[db_data['code'] == getattr(row, '\u8bc1\u5238\u4ee3\u7801')].index\n            if len(idcs) < 1:\n                continue\n            idx = idcs[0]\n            db_data.loc[idx, 'date_sell'] = trading_date\n            db_data.loc[idx, 'price_sell'] = getattr(row, '\u5747\u4ef7')\n            db_data.loc[idx, 'amount_sell'] = getattr(row, '\u603b\u91d1\u989d')\n            db_data.loc[idx, 'to_trade'] = 0\n\n        # \u5904\u7406\u4e70\u5165\u6210\u4ea4\u6570\u636e\uff0c\u586b\u5199\u6570\u636e\u5e93\u4e2d\u8be5\u80a1\u7968\u7684\u4e70\u5165\u65e5\u671f\u3001\u4e70\u5165\u4ef7\u683c\u3001\u603b\u4e70\u5165\u6570\u91cf\u3001\u603b\u4e70\u5165\u91d1\u989d\u3001\u662f\u5426\u5230\u8fbe\u4e70\u70b9\u5b57\u6bb5\n        if '\u4e70\u5165' == getattr(row, '\u4e70\u5356\u65b9\u5411'):\n            idcs = db_data[db_data['code'] == getattr(row, '\u8bc1\u5238\u4ee3\u7801')].index\n            if len(idcs) < 1:\n                continue\n            idx = idcs[0]\n            db_data.loc[idx, 'date_buy'] = trading_date\n            db_data.loc[idx, 'price_buy'] = getattr(row, '\u5747\u4ef7')\n            db_data.loc[idx, 'trade_volume'] = getattr(row, '\u603b\u6570\u91cf')\n            db_data.loc[idx, 'amount_buy'] = getattr(row, '\u603b\u91d1\u989d')\n            db_data.loc[idx, 'buy_available'] = 1\n\n    # \u5c06\u66f4\u65b0\u540e\u7684\u5f85\u4ea4\u6613\u6570\u636e\u5199\u56de\u6570\u636e\u5e93\n    db_data.to_sql(name=table_name, con=engine, if_exists='replace', index=False)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    print('\u5b8c\u6210ptrade\u8ba2\u5355\u4fe1\u606f\u66f4\u65b0!')\n\n\ndef get_table_name(code):\n    \"\"\"\n    \u6839\u636e\u7eaf\u6570\u5b57\u80a1\u7968\u4ee3\u7801\uff0c\u83b7\u53d6\u5bf9\u5e94\u5728\u6570\u636e\u5e93\u4e2d\u7684\u8868\u540d\n\n    :param code: \u7eaf\u6570\u5b57\u80a1\u7968\u4ee3\u7801\n    :return: \u4ee3\u7801\u5bf9\u5e94\u7684\u6570\u636e\u5e93\u4e2d\u7684\u8868\u540d\n    \"\"\"\n\n    if '6' == code[:1]:\n        market = 'sh'\n    else:\n        market = 'sz'\n    return '{}_{}'.format(code, market)\n\n\ndef update_ptrade_hold_days():\n    \"\"\"\n    \u76d8\u540e\u66f4\u65b0\u6570\u636e\u5e93\u4e2d\u6301\u4ed3\u80a1\u7968\u7684\u6301\u6709\u5929\u6570\u5b57\u6bb5\n\n    :return: None\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u83b7\u53d6\u6570\u636e\u5e93\u5185\u6240\u6709\u8868\u7684\u8868\u540d\n    db_tables = sqlalchemy.inspect(engine).get_table_names()\n\n    # \u5224\u65ad\u662f\u5426\u5b58\u5728\u8868ptrade\uff0c\u4e0d\u5b58\u5728\u5219\u65e0\u9700\u66f4\u65b0\n    table_name = 'ptrade'\n    if table_name not in db_tables:\n        return\n\n    # \u8bfb\u53d6\u5f85\u4ea4\u6613\u6570\u636e\u6570\u636e\n    sql_cmd = 'SELECT * FROM {};'.format(table_name)\n    db_data = pd.read_sql(sql=sql_cmd, con=engine)\n\n    # \u5f85\u4ea4\u6613\u6570\u636e\u5faa\u73af\n    for row in db_data.itertuples():\n\n        # \u53ea\u66f4\u65b0\u5f85\u4ea4\u6613\u4e14\u5df2\u4e70\u5165\u7684\u80a1\u7968\n        if getattr(row, 'to_trade') and getattr(row, 'date_buy') is not None:\n\n            # \u67e5\u8be2\u6570\u636e\u5e93\u4e2a\u80a1\u7684\u65e5\u7ebf\u6570\u636e\uff0c\u8ba1\u7b97hold_days\n            sql_cmd = 'SELECT * FROM {} ORDER BY date DESC LIMIT {};'.format(get_table_name(getattr(row, 'code')), 10)\n            read_df = pd.read_sql(sql=sql_cmd, con=engine)\n            if read_df.shape[0] < 1:\n                continue\n\n            # \u9009\u51fa\u65e5\u671f\u5927\u4e8e\u4e70\u5165\u65e5\u671f\u7684\u884c\uff0c\u884c\u6570+1\u5373\u4e3a\u6301\u80a1\u7684\u5929\u6570\n            after_buy_df = read_df[read_df['date'] > getattr(row, 'date_buy')]\n            db_data.loc[getattr(row, 'Index'), 'hold_days'] = after_buy_df.shape[0] + 1\n\n    # \u5c06\u66f4\u65b0\u540e\u7684\u5f85\u4ea4\u6613\u6570\u636e\u5199\u56de\u6570\u636e\u5e93\n    db_data.to_sql(name='ptrade', con=engine, if_exists='replace', index=False)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    print('\u5b8c\u6210\u6301\u6709\u5929\u6570\u66f4\u65b0!')\n\n\ndef update_ptrade_to_trade():\n    \"\"\"\n    \u76d8\u540e\u66f4\u65b0\u6570\u636e\u5e93\u4e2d\u80a1\u7968\u662f\u5426\u7ee7\u7eed\u65e5\u540e\u4ea4\u6613\n\n    :return: None\n    \"\"\"\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u83b7\u53d6\u6570\u636e\u5e93\u5185\u6240\u6709\u8868\u7684\u8868\u540d\n    db_tables = sqlalchemy.inspect(engine).get_table_names()\n\n    # \u5224\u65ad\u662f\u5426\u5b58\u5728\u8868ptrade\uff0c\u4e0d\u5b58\u5728\u5219\u65e0\u9700\u66f4\u65b0\n    table_name = 'ptrade'\n    if table_name not in db_tables:\n        return\n\n    # \u8bfb\u53d6\u5f85\u4ea4\u6613\u6570\u636e\u6570\u636e\n    sql_cmd = 'SELECT * FROM {};'.format(table_name)\n    db_data = pd.read_sql(sql=sql_cmd, con=engine)\n\n    # \u8bfb\u53d6\u6700\u65b0\u4e00\u65e5\u6240\u6709\u80a1\u7968\u7684\u65e5\u7ebf\u6570\u636e\n    sql_cmd = 'SELECT * FROM latest;'\n    latest_df = pd.read_sql(sql=sql_cmd, con=engine)\n\n    # \u662f\u5426\u8fbe\u5230\u4e70\u70b9\u7684\u5b57\u6bb5\u540d\u79f0\n    buy_available = 'buy_available'\n\n    # \u83b7\u53d6\u6700\u65b0\u4ea4\u6613\u65e5\u671f\n    trading_date = get_trading_date()\n\n    # \u5f85\u4ea4\u6613\u6570\u636e\u5faa\u73af\n    for row in db_data.itertuples():\n\n        # \u5f53\u65e5\u66f4\u65b0\u9009\u51fa\u7684\u5019\u9009\u80a1\u7968\uff0c\u4e0d\u66f4\u65b0\u662f\u5426\u4ea4\u6613\u5b57\u6bb5\n        if getattr(row, 'date_candidate') >= trading_date:\n            continue\n\n        # \u5df2\u8fbe\u5230\u4e70\u70b9\u7684\u80a1\u7968\uff0c\u4e0d\u66f4\u65b0\u662f\u5426\u4ea4\u6613\u5b57\u6bb5\n        if getattr(row, buy_available) > 0:\n            continue\n\n        # \u5728\u6700\u65b0\u65e5\u7ebf\u6570\u636e\u8868\u4e2d\uff0c\u67e5\u8be2\u5bf9\u5e94\u80a1\u7968\u7684\u4f4d\u7f6e\n        idcs = latest_df[latest_df['code'] == getattr(row, 'code')].index\n        if len(idcs) < 1:\n            continue\n\n        # \u5f53\u65e5\u6700\u4f4e\u4ef7\u5c0f\u4e8e\u7b49\u4e8e\u4e70\u70b9\u4ef7\u683c\uff0c\u8868\u793a\u80a1\u7968\u5f53\u65e5\u8fbe\u5230\u4e86\u4e70\u70b9\n        if latest_df.loc[idcs[0], 'low'] <= getattr(row, 'buy_point'):\n\n            # \u6807\u8bb0\u80a1\u7968\u5df2\u8fbe\u5230\u4e70\u70b9\n            db_data.loc[getattr(row, 'Index'), buy_available] = 1\n\n            # \u5982\u679c\u8be5\u80a1\u7968\u5c1a\u672a\u4e70\u5165\uff0c\u5219\u4e0d\u518d\u8fdb\u884c\u4ea4\u6613\uff0c\u6807\u8bb0\u662f\u5426\u4ea4\u6613\u5b57\u6bb5\u4e3a0\n            if getattr(row, 'date_buy') is None:\n                db_data.loc[getattr(row, 'Index'), 'to_trade'] = 0\n\n    # \u9009\u51fa\u5f85\u4ea4\u6613\u6570\u636e\u53ca\u4e0d\u518d\u4ea4\u6613\u6570\u636e\n    trade_data = db_data[db_data['to_trade'] == 1]\n    history_data = db_data[db_data['to_trade'] == 0]\n\n    # \u66f4\u65b0\u5f85\u4ea4\u6613\u6570\u636e\uff0c\u5e76\u5c06\u4e0d\u518d\u4ea4\u6613\u7684\u6570\u636e\u6dfb\u52a0\u5230\u5386\u53f2\u6570\u636e\u8868\u4e2d\n    trade_data.to_sql(name='ptrade', con=engine, if_exists='replace', index=False)\n    history_data.to_sql(name='ptrade_history', con=engine, if_exists='append', index=False)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n    print('\u5b8c\u6210\u53ef\u4ea4\u6613\u80a1\u7968\u66f4\u65b0!')\n\n\ndef update_ptrade_candidate(df):\n    \"\"\"\n    \u6839\u636e\u5f53\u65e5\u65e5\u7ebf\u6570\u636e\uff0c\u9009\u51fa\u5019\u9009\u4ea4\u6613\u80a1\u7968\uff0c\u5f85\u4ea4\u6613\u6570\u636e\u5199\u5165\u6570\u636e\u5e93\n\n    :param df: \u5305\u542b\u5f53\u65e5\u5019\u9009\u80a1\u7968\u65e5\u7ebf\u6570\u636e\u7684DataFrame\n    :return: None\n    \"\"\"\n\n    # df\u4e3a\u7a7a\uff0c\u5219\u76f4\u63a5\u9000\u51fa\n    if not df.shape[0]:\n        return\n\n    # \u5220\u9664\u79d1\u521b\u677f\u6570\u636e\uff0c\u65e0\u79d1\u521b\u677f\u6743\u9650\n    out_df = df['68' != df['code'].str[:2]]\n\n    # out_df\u4e3a\u7a7a\uff0c\u5219\u76f4\u63a5\u9000\u51fa\n    if not out_df.shape[0]:\n        return\n\n    # ptrade\u8868\u540d\n    table_name = 'ptrade'\n\n    # \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce\u5bf9\u8c61\n    engine = create_mysql_engine()\n\n    # \u65e5\u671f\u5217\u540d\n    date_col = 'date_candidate'\n\n    # \u83b7\u53d6\u6309\u65f6\u95f4\u964d\u5e8f\u7684\u7b2c1\u884c\u6570\u636e\n    if table_name in sqlalchemy.inspect(engine).get_table_names():\n        sql_cmd = 'SELECT {0} FROM {1} ORDER BY {0} DESC LIMIT 1;'.format(date_col, table_name)\n        db_df = pd.read_sql(sql=sql_cmd, con=engine)\n\n        if db_df.shape[0]:\n            # \u83b7\u53d6\u6570\u636e\u5e93\u4e2d\u7684\u6700\u65b0\u5019\u9009\u65e5\u671f\n            db_date = db_df[date_col].iloc[0]\n\n            # \u5982\u679c\u6570\u636e\u5e93\u4e2d\u5019\u9009\u65e5\u671f\u5df2\u4e3a\u6700\u65b0\uff0c\u5219\u65e0\u9700\u66f4\u65b0\uff0c\u907f\u514d\u91cd\u590d\u6dfb\u52a0\n            if db_date >= out_df['date'].iloc[0]:\n                return\n\n    # \u8bbe\u7f6e\u5019\u9009\u6570\u636e\u5404\u5b57\u6bb5\u7684\u503c\n    out_df = out_df[['code', 'date', 'close']]\n    buy_point = 'buy_point'\n    out_df = out_df.rename(columns={'date': 'date_candidate', 'close': buy_point})\n    out_df['price_take_profit'] = out_df[buy_point].apply(\n        lambda x: math.floor(x * (1 + g_take_profit_percent) * 100) \/ 100)\n    out_df['price_stop_loss'] = out_df[buy_point].apply(lambda x: math.ceil(x * (1 - g_stop_loss_percent) * 100) \/ 100)\n    out_df['date_buy'] = None\n    out_df['hold_days'] = 0\n    out_df['date_sell'] = None\n    out_df['price_buy'] = 0.0\n    out_df['price_sell'] = 0.0\n    out_df['trade_volume'] = 0\n    out_df['amount_buy'] = 0.0\n    out_df['amount_sell'] = 0.0\n    out_df['buy_available'] = 0\n    out_df['to_trade'] = 1\n\n    # \u5c06\u65b0\u9009\u51fa\u7684\u5019\u9009\u80a1\u7968\u6570\u636eappend\u5230\u6570\u636e\u5e93\n    if out_df.shape[0]:\n        out_df.to_sql(name=table_name, con=engine, if_exists='append', index=False)\n\n    # \u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5\n    engine.dispose()\n\n\ndef task_update_daily():\n    \"\"\"\n    \u6bcf\u65e5\u66f4\u65b0\u4efb\u52a1\uff0c\u53ef\u8bbe\u7f6e\u4e3a17:40\u542f\u52a8\n    :return: None\n    \"\"\"\n\n    # \u7a0b\u5e8f\u5f00\u59cb\u8fd0\u884c\u65f6\u95f4\n    start_time = time.time()\n\n    # \u83b7\u53d6\u5f85\u4ea4\u6613\u80a1\u7968\u5217\u8868\n    stock_codes = get_stock_codes(update=True)\n\n    # \u66f4\u65b0\u80a1\u7968\u6570\u636e\n    update_data_mp(stock_codes)\n\n    # \u66f4\u65b0\u4ea4\u6613\u6570\u636e\n    update_trade(stock_codes)\n\n    # \u7a0b\u5e8f\u7ed3\u675f\u8fd0\u884c\u65f6\u95f4\n    end_time = time.time()\n    print('\u7a0b\u5e8f\u8fd0\u884c\u65f6\u95f4\uff1a{}s'.format(end_time - start_time))\n\n\ndef main():\n    \"\"\"\n    \u6bcf\u65e5\u66f4\u65b0\u6570\u636e\n\n    :return: None\n    \"\"\"\n\n    # \u6e05\u7a7a\u4efb\u52a1\u5217\u8868\n    schedule.clear()\n\n    # \u4efb\u52a1\u6267\u884c\u65f6\u95f4\n    task_time = '17:40:00'\n\n    # \u4ea4\u6613\u65e5\u5b9a\u65f6\u542f\u52a8\u4efb\u52a1\n    schedule.every().monday.at(task_time).do(task_update_daily)\n    schedule.every().tuesday.at(task_time).do(task_update_daily)\n    schedule.every().wednesday.at(task_time).do(task_update_daily)\n    schedule.every().thursday.at(task_time).do(task_update_daily)\n    schedule.every().friday.at(task_time).do(task_update_daily)\n\n    # \u5faa\u73af\u7b49\u5f85\u4efb\u52a1\u6267\u884c\n    while True:\n        schedule.run_pending()\n        print('.', end='')\n        time.sleep(60)\n\n\nif __name__ == '__main__':\n    main()\n    # \u5982\u679c\u4e0d\u6309\u5b9a\u65f6\u4efb\u52a1\u542f\u52a8\uff0c\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884c\u4e0b\u9762\u7684\u51fd\u6570\n    # task_update_daily()\n<\/code><\/pre>\n
    \n
    \u535a\u5ba2\u5185\u5bb9\u53ea\u7528\u4e8e\u4ea4\u6d41\u5b66\u4e60\uff0c\u4e0d\u6784\u6210\u6295\u8d44\u5efa\u8bae\uff0c\u76c8\u4e8f\u81ea\u8d1f\uff01<\/strong><\/p>\n
    \u6b22\u8fce\u5927\u5bb6\u8f6c\u53d1\u3001\u7559\u8a00\u3002\u5df2\u5efa\u5fae\u4fe1\u7fa4\u7528\u4e8e\u5b66\u4e60\u4ea4\u6d41\uff0c\u7fa41\u5df2\u6ee1\uff0c\u7fa42\u5df2\u521b\u5efa\uff0c\u611f\u5174\u8da3\u7684\u8bfb\u8005\u8bf7\u626b\u7801\u52a0\u5fae\u4fe1\uff01
    \n\u9879\u76ee\u6700\u65b0\u4ee3\u7801\uff1ahttps:\/\/gitee.com\/sl\/quant_from_scratch
    \n\u5982\u679c\u8ba4\u4e3a\u535a\u5ba2\u5bf9\u60a8\u6709\u5e2e\u52a9\uff0c\u53ef\u4ee5\u626b\u7801\u8fdb\u884c\u6350\u8d60\uff0c\u611f\u8c22\uff01<\/p>\n\n\n\n\n\n
    \u5fae\u4fe1\u4e8c\u7ef4\u7801<\/th>\n\u5fae\u4fe1\u6350\u8d60\u4e8c\u7ef4\u7801<\/th>\n<\/tr>\n<\/thead>\n
    \"\u5fae\u4fe1\u4e8c\u7ef4\u7801\"<\/td>\n\"\u6350\u8d60\u4e8c\u7ef4\u7801\"<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n