{"id":87490,"date":"2026-07-30T13:16:39","date_gmt":"2026-07-30T05:16:39","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/87490.html"},"modified":"2026-07-30T13:16:39","modified_gmt":"2026-07-30T05:16:39","slug":"%e5%8f%af%e7%9b%b4%e6%8e%a5%e5%9b%9e%e6%b5%8b%ef%bc%81ptrade-%e5%bc%ba%e5%8a%bf-etf-%e5%91%a8%e5%ba%a6%e8%bd%ae%e5%8a%a8%e7%a8%8b%e5%ba%8f%e5%8c%96%e4%ba%a4%e6%98%93%e7%ad%96%e7%95%a5%e5%85%a8","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/87490.html","title":{"rendered":"\u53ef\u76f4\u63a5\u56de\u6d4b\uff01Ptrade \u5f3a\u52bf ETF \u5468\u5ea6\u8f6e\u52a8\u7a0b\u5e8f\u5316\u4ea4\u6613\u7b56\u7565\u5168\u89e3"},"content":{"rendered":"

\u6587\u7ae0\u7b80\u4ecb<\/h3>\n

\u672c\u6587\u57fa\u4e8e\u00a0Ptrade \u91cf\u5316\u5e73\u53f0，\u642d\u5efa\u4e00\u5957\u53ef\u76f4\u63a5\u56de\u6d4b\u3001\u4eff\u771f\u8fd0\u884c\u7684\u5468\u5ea6\u5f3a\u52bf ETF \u8f6e\u52a8\u7b56\u7565\u3002\u6838\u5fc3\u903b\u8f91：\u6bcf\u5468\u7b5b\u9009 20 \u65e5\u6da8\u5e45\u6700\u5f3a 2 \u53ea ETF，7:3 \u8d44\u91d1\u914d\u6bd4\u4e70\u5165，\u6bcf\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5\u5168\u90e8\u6e05\u4ed3，\u5faa\u73af\u6eda\u52a8\u505a\u8d8b\u52bf\u77ed\u7ebf\u3002\u5168\u6587\u9644\u5e26\u5b8c\u6574\u53ef\u8fd0\u884c\u4ee3\u7801\u3001\u51fd\u6570\u62c6\u89e3\u3001\u8282\u5047\u65e5\u81ea\u52a8\u9002\u914d\u903b\u8f91\u4e0e\u5b9e\u64cd\u907f\u5751\u8981\u70b9，\u65b0\u624b\u4e5f\u80fd\u5feb\u901f\u4e0a\u624b\u6539\u9020\u3002<\/p>\n

\u9002\u7528\u4eba\u7fa4：Ptrade \u91cf\u5316\u5b66\u4e60\u8005\u3001ETF \u8d8b\u52bf\u4ea4\u6613\u8005\u3001\u7a0b\u5e8f\u5316\u81ea\u52a8\u5316\u4ea4\u6613\u7231\u597d\u8005<\/p>\n

\u5e73\u53f0： Ptrade Python \u91cf\u5316\u63a5\u53e3<\/p>\n

\u7b56\u7565\u7c7b\u578b：\u5468\u5ea6\u8d8b\u52bf\u8f6e\u52a8\u3001\u52a8\u91cf\u7b56\u7565<\/p>\n

\u4e00\u3001\u7b56\u7565\u6838\u5fc3\u903b\u8f91<\/h3>\n

\u6574\u5957\u7b56\u7565\u6781\u7b80\u6613\u61c2，\u5b8c\u5168\u8d34\u5408 A \u80a1 ETF T+1 \u4ea4\u6613\u89c4\u5219，\u65e0\u65e5\u5185\u9891\u7e41\u4ea4\u6613\u98ce\u9669：<\/p>\n

  • \u9009\u80a1\u89c4\u5219：\u6bcf\u5468\u9996\u4e2a\u4ea4\u6613\u65e5，\u904d\u5386 ETF \u5019\u9009\u6c60，\u8ba1\u7b97\u6240\u6709\u6807\u7684\u8fd1 20 \u4e2a\u4ea4\u6613\u65e5\u6da8\u8dcc\u5e45，\u53d6\u6da8\u5e45\u524d 2 \u540d\u5f3a\u52bf\u6807\u7684；<\/li>\n
  • \u8d44\u91d1\u5206\u914d：\u7b2c\u4e00\u540d\u5206\u914d 70% \u53ef\u7528\u8d44\u91d1，\u7b2c\u4e8c\u540d\u5206\u914d 30%，\u53ef\u81ea\u884c\u4fee\u6539\u6743\u91cd；<\/li>\n
  • \u5356\u51fa\u89c4\u5219：\u6bcf\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5 14:55 \u7edf\u4e00\u6e05\u4ed3\u5168\u90e8\u6301\u4ed3，\u9884\u7559 5 \u5206\u949f\u6210\u4ea4\u65f6\u95f4；<\/li>\n
  • \u5faa\u73af\u6267\u884c：\u6b21\u5468\u91cd\u590d\u9009\u80a1\u3001\u4e70\u5165\u3001\u6e05\u4ed3\u6d41\u7a0b，\u6301\u7eed\u8ddf\u8e2a\u5e02\u573a\u5f3a\u52bf\u677f\u5757 ETF；<\/li>\n
  • \u7279\u8272\u9002\u914d：\u5185\u7f6e\u4ea4\u6613\u65e5\u5224\u65ad\u51fd\u6570，\u81ea\u52a8\u8df3\u8fc7\u5468\u672b\u3001\u6cd5\u5b9a\u8282\u5047\u65e5，\u5468\u4e00\u4f11\u5e02\u5219\u987a\u5ef6\u81f3\u8282\u540e\u9996\u4e2a\u4ea4\u6613\u65e5\u3002<\/li>\n

    \u4e8c\u3001\u5b8c\u6574\u7b56\u7565\u4ee3\u7801<\/h3>\n

    # 1. \u5bfc\u5165\u4f9d\u8d56\u5e93
    \nimport datetime
    \nimport pandas as pd
    \nfrom datetime import date
    \nfrom datetime import timedelta
    \nimport numpy as np<\/p>\n

    # 2. \u7b56\u7565\u521d\u59cb\u5316\u51fd\u6570，\u7a0b\u5e8f\u542f\u52a8\u4ec5\u6267\u884c\u4e00\u6b21
    \ndef initialize(context):
    \n # ETF\u5019\u9009\u6c60，\u81ea\u884c\u586b\u5145\u6caa\u5e02\/\u6df1\u5e02ETF\u4ee3\u7801，\u793a\u4f8b：["510300.SS","159915.SZ"]
    \n g.security_list = []
    \n set_universe(g.security_list)<\/p>\n

    # \u5168\u5c40\u7f13\u5b58\u53d8\u91cf
    \n g.trade_days_list = get_trade_days('2015-01-01', '2025-10-31') # \u4ea4\u6613\u65e5\u671f\u603b\u8868
    \n g.selected_stocks = [] # \u6bcf\u5468\u7b5b\u9009\u51fa\u76842\u53eaETF
    \n g.weights = [0.7, 0.3] # \u8d44\u91d1\u5206\u914d\u6bd4\u4f8b
    \n g.hold_positions = context.portfolio.positions # \u5f53\u524d\u6301\u4ed3<\/p>\n

    # \u5b9a\u65f6\u4efb\u52a1：\u6bcf\u65e5\u56fa\u5b9a\u65f6\u95f4\u6267\u884c\u5bf9\u5e94\u51fd\u6570
    \n run_daily(context, buy_function, time='9:31') # \u5468\u521d9:31\u6267\u884c\u4e70\u5165
    \n run_daily(context, sell_function, time='14:55') # \u5468\u672b14:55\u6267\u884c\u6e05\u4ed3<\/p>\n

    # 3. \u6bcf\u65e5\u76d8\u524d\u6267\u884c：\u7b5b\u9009\u5f3a\u52bfETF\u3001\u6807\u8bb0\u672c\u5468\u9996\u5c3e\u4ea4\u6613\u65e5
    \ndef before_trading_start(context, data):
    \n today = get_trading_day(0)
    \n # \u5224\u65ad\u662f\u5426\u672c\u5468\u7b2c\u4e00\u4e2a\/\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5
    \n g.is_first = is_first_trading_day_of_week(today)
    \n g.is_last = is_last_trading_day_of_week(today)
    \n print('{}\u662f\u5426\u4e3a\u672c\u5468\u9996\u4e2a\u4ea4\u6613\u65e5: {}'.format(today, g.is_first))
    \n print('{}\u662f\u5426\u4e3a\u672c\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5: {}'.format(today, g.is_last))<\/p>\n

    # \u4ec5\u5468\u521d\u6267\u884c\u9009\u80a1\u903b\u8f91
    \n if g.is_first:
    \n returns = {}
    \n for stock in g.security_list:
    \n # \u83b7\u53d6\u8fd120\u65e5\u6536\u76d8\u4ef7
    \n history = get_history(20, '1d', 'close', stock, fq='pre')
    \n # \u8ba1\u7b9720\u65e5\u6da8\u8dcc\u5e45
    \n ret = (history.iloc[-1, 0] – history.iloc[0, 0]) \/ history.iloc[0, 0]
    \n returns[stock] = ret
    \n log.info("\u5404ETF20\u65e5\u6da8\u8dcc\u5e45：", returns)
    \n # \u6309\u6da8\u5e45\u964d\u5e8f\u6392\u5e8f，\u53d6\u524d2\u53ea
    \n sorted_returns = sorted(returns.items(), key=lambda x: x[1], reverse=True)
    \n g.selected_stocks = [item[0] for item in sorted_returns[:2]]
    \n else:
    \n pass<\/p>\n

    # 4. \u4e70\u5165\u51fd\u6570：\u5468\u521d\u5f00\u76d8\u540e\u6309\u6bd4\u4f8b\u4e0b\u5355
    \ndef buy_function(context):
    \n if g.is_first:
    \n total_available_cash = context.portfolio.cash
    \n for i in range(len(g.selected_stocks)):
    \n # \u83b7\u53d6\u6700\u65b01\u5206\u949f\u6536\u76d8\u4ef7
    \n history_1 = get_history(1, '1m', 'close', g.selected_stocks[i], fq='pre')
    \n current_price = history_1.iloc[0]
    \n # \u5206\u914d\u5bf9\u5e94\u8d44\u91d1
    \n buy_cash = total_available_cash * g.weights[i]
    \n # A\u80a1ETF\u4e00\u624b100\u4efd，\u8ba1\u7b97\u53ef\u4e70\u6574\u6570\u624b
    \n amount = int(buy_cash \/ current_price \/ 100) * 100
    \n if amount > 0:
    \n order(g.selected_stocks[i], amount)
    \n else:
    \n log.info('\u4eca\u65e5\u4e0d\u662f\u672c\u5468\u7b2c\u4e00\u4e2a\u4ea4\u6613\u65e5，\u4e0d\u6267\u884c\u4e70\u5165')<\/p>\n

    # 5. \u5356\u51fa\u51fd\u6570：\u5468\u672b\u6536\u76d8\u524d\u5168\u90e8\u6e05\u4ed3
    \ndef sell_function(context):
    \n if g.is_last:
    \n g.current_holdings = {}
    \n # \u904d\u5386\u5168\u90e8\u6301\u4ed3
    \n for sid, position in context.portfolio.positions.items():
    \n if position.amount > 0:
    \n g.current_holdings[sid] = position.amount
    \n sell_list = list(g.current_holdings.keys())
    \n # \u6e05\u4ed3\u6240\u6709\u6301\u4ed3
    \n for code in sell_list:
    \n if g.current_holdings[code] > 0:
    \n order_target(code, 0)
    \n log.info("\u5356\u51fa{}，\u5171{}\u4efd".format(code, g.current_holdings[code]))
    \n else:
    \n log.info('\u4eca\u65e5\u4e0d\u662f\u672c\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5，\u4e0d\u6267\u884c\u5356\u51fa')<\/p>\n

    # \u8f85\u52a9\u51fd\u65701：\u5224\u65ad\u5f53\u65e5\u662f\u5426\u4e3a\u672c\u5468\u7b2c\u4e00\u4e2a\u4ea4\u6613\u65e5（\u81ea\u52a8\u9002\u914d\u8282\u5047\u65e5）
    \ndef is_first_trading_day_of_week(today):
    \n one_day = timedelta(days=1)
    \n yesterday = today – one_day
    \n # \u5411\u524d\u5bfb\u627e\u4e0a\u4e00\u4e2a\u6709\u6548\u4ea4\u6613\u65e5
    \n while yesterday.strftime('%Y-%m-%d') not in g.trade_days_list:
    \n yesterday -= one_day
    \n # \u5468\u6570\u4e0d\u540c\u5219\u4ee3\u8868\u4eca\u5929\u662f\u672c\u5468\u9996\u4e2a\u4ea4\u6613\u65e5
    \n return today.isocalendar()[1] > yesterday.isocalendar()[1] or today.year > yesterday.year<\/p>\n

    # \u8f85\u52a9\u51fd\u65702：\u5224\u65ad\u5f53\u65e5\u662f\u5426\u4e3a\u672c\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5（\u81ea\u52a8\u9002\u914d\u8282\u5047\u65e5）
    \ndef is_last_trading_day_of_week(today):
    \n one_day = timedelta(days=1)
    \n tomorrow = today + one_day
    \n # \u5411\u540e\u5bfb\u627e\u4e0b\u4e00\u4e2a\u6709\u6548\u4ea4\u6613\u65e5
    \n while tomorrow.strftime('%Y-%m-%d') not in g.trade_days_list:
    \n tomorrow += one_day
    \n # \u5468\u6570\u4e0d\u540c\u5219\u4ee3\u8868\u4eca\u5929\u662f\u672c\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5
    \n return today.isocalendar()[1] < tomorrow.isocalendar()[1] or today.year < tomorrow.year<\/p>\n

    \u4e09\u3001\u4ee3\u7801\u9010\u6a21\u5757\u8be6\u7ec6\u89e3\u6790<\/h3>\n

    3.1 \u4f9d\u8d56\u5e93\u5bfc\u5165\u6a21\u5757<\/h4>\n

    \"\"<\/p>\n

    import datetime
    \nimport pandas as pd
    \nfrom datetime import date
    \nfrom datetime import timedelta
    \nimport numpy as np<\/p>\n

      \n
    • datetime\/timedelta：\u5904\u7406\u65e5\u671f\u3001\u4ea4\u6613\u65e5\u504f\u79fb\u3001\u8282\u5047\u65e5\u5224\u65ad；<\/li>\n
    • pandas：\u884c\u60c5\u6570\u636e\u8868\u683c\u5904\u7406；<\/li>\n
    • numpy：\u6570\u503c\u8ba1\u7b97\u3001\u6da8\u8dcc\u5e45\u8fd0\u7b97； \u6240\u6709 Ptrade \u7b56\u7565\u5fc5\u5907\u57fa\u7840\u5e93，\u7f3a\u5931\u4f1a\u5bfc\u81f4\u884c\u60c5\u83b7\u53d6\u3001\u65e5\u671f\u8ba1\u7b97\u62a5\u9519\u3002<\/li>\n<\/ul>\n

      3.2 initialize \u521d\u59cb\u5316\u51fd\u6570<\/h4>\n

      \"\"<\/p>\n

      \u7a0b\u5e8f\u542f\u52a8\u4ec5\u8fd0\u884c\u4e00\u6b21，\u8d1f\u8d23\u5168\u5c40\u53d8\u91cf\u3001\u5019\u9009\u6c60\u3001\u5b9a\u65f6\u4efb\u52a1\u914d\u7f6e：<\/p>\n

    • g.security_list：ETF \u6807\u7684\u6c60，\u5fc5\u987b\u624b\u52a8\u586b\u5145\u4ee3\u7801，\u6caa\u5e02\u540e\u7f00.SS\u3001\u6df1\u5e02\u540e\u7f00.SZ；<\/li>\n
    • get_trade_days：\u62c9\u53d6\u6307\u5b9a\u533a\u95f4\u5168\u90e8\u4ea4\u6613\u65e5，\u7528\u4e8e\u8282\u5047\u65e5\u5224\u65ad；<\/li>\n
    • run_daily：\u5b9a\u65f6\u89e6\u53d1\u5668，\u56fa\u5b9a\u65f6\u95f4\u81ea\u52a8\u6267\u884c\u4e70\u5356\u51fd\u6570，\u65e0\u9700\u4eba\u5de5\u76ef\u76d8；<\/li>\n

      3.3 before_trading_start \u76d8\u524d\u9009\u80a1\u51fd\u6570<\/h4>\n

      \"\"<\/p>\n

      \u6bcf\u4e2a\u4ea4\u6613\u65e5\u5f00\u76d8\u524d\u81ea\u52a8\u6267\u884c，\u6838\u5fc3\u5b8c\u6210\u4e24\u4ef6\u4e8b：<\/p>\n

    • \u8c03\u7528is_first_trading_day_of_week\u6807\u8bb0\u5468\u521d\u4ea4\u6613\u65e5；<\/li>\n
    • \u904d\u5386\u5019\u9009\u6c60 ETF，\u62c9\u53d6 20 \u65e5\u6536\u76d8\u4ef7\u8ba1\u7b97\u52a8\u91cf\u6da8\u5e45，\u964d\u5e8f\u6392\u5e8f\u9009\u51fa\u524d 2 \u53ea\u5f3a\u52bf\u6807\u7684\u5b58\u5165\u5168\u5c40\u53d8\u91cf；<\/li>\n

      \u6ce8\u610f：\u6b64\u51fd\u6570\u4ec5\u505a\u6570\u636e\u8ba1\u7b97，\u4e0d\u4f1a\u53d1\u8d77\u4ea4\u6613\u4e0b\u5355\u3002<\/p>\n

      3.4 buy_function \u4e70\u5165\u4e0b\u5355\u51fd\u6570<\/h4>\n

      \"\"<\/p>\n

      \u4ec5\u6bcf\u5468\u9996\u4e2a\u4ea4\u6613\u65e5 9:31 \u6267\u884c：<\/p>\n

    • \u83b7\u53d6\u8d26\u6237\u53ef\u7528\u73b0\u91d1，\u6309 7:3 \u6743\u91cd\u5206\u914d\u8d44\u91d1；<\/li>\n
    • \u8bfb\u53d6\u5b9e\u65f6 1 \u5206\u949f\u6536\u76d8\u4ef7，\u8ba1\u7b97\u53ef\u4e70\u5165\u4efd\u6570；<\/li>\n
    • int(xxx\/100)*100\u5f3a\u5236\u4fdd\u8bc1\u4e0b\u5355\u6570\u91cf\u4e3a 100 \u7684\u6574\u6570\u500d，\u7b26\u5408 A \u80a1 ETF \u4ea4\u6613\u89c4\u5219；<\/li>\n
    • order(\u6807\u7684, \u6570\u91cf)：Ptrade \u539f\u751f\u9650\u4ef7\u4e0b\u5355\u63a5\u53e3\u3002<\/li>\n

      3.5 sell_function \u6e05\u4ed3\u5356\u51fa\u51fd\u6570<\/h4>\n

      \"\"<\/p>\n

      \u4ec5\u6bcf\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5 14:55 \u6267\u884c：<\/p>\n

    • \u904d\u5386\u8d26\u6237\u5168\u90e8\u6301\u4ed3，\u7b5b\u9009\u6301\u6709\u6570\u91cf\u5927\u4e8e 0 \u7684 ETF；<\/li>\n
    • order_target(code, 0)：\u4e00\u952e\u6e05\u4ed3\u63a5\u53e3，\u76ee\u6807\u6301\u4ed3\u8bbe\u4e3a 0，\u65e0\u9700\u624b\u52a8\u8ba1\u7b97\u5356\u51fa\u6570\u91cf；<\/li>\n
    • 14:55 \u64cd\u4f5c\u9884\u7559 5 \u5206\u949f\u6536\u76d8\u7f13\u51b2，\u907f\u514d\u5c3e\u76d8\u6d41\u52a8\u6027\u4e0d\u8db3\u65e0\u6cd5\u6210\u4ea4\u3002<\/li>\n

      3.6 \u4ea4\u6613\u65e5\u5224\u65ad\u8f85\u52a9\u51fd\u6570<\/h4>\n

      \"\"<\/p>\n

      \u7b56\u7565\u6838\u5fc3\u9002\u914d\u5de5\u5177，\u89e3\u51b3\u8282\u5047\u65e5\u3001\u957f\u5047\u4f11\u5e02\u95ee\u9898：<\/p>\n

    • is_first_trading_day_of_week：\u5411\u524d\u56de\u6eaf\u67e5\u627e\u4e0a\u4e00\u4e2a\u4ea4\u6613\u65e5，\u5bf9\u6bd4\u5468\u6570，\u81ea\u52a8\u8bc6\u522b\u8282\u540e\u9996\u4e2a\u4ea4\u6613\u65e5；<\/li>\n
    • is_last_trading_day_of_week：\u5411\u540e\u67e5\u627e\u4e0b\u4e00\u4e2a\u4ea4\u6613\u65e5，\u8bc6\u522b\u8282\u524d\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5； \u65e0\u9700\u624b\u52a8\u8c03\u6574\u65e5\u671f，\u56de\u6d4b\u3001\u5b9e\u76d8\u5747\u53ef\u81ea\u52a8\u9002\u914d\u3002<\/li>\n

      \u56db\u3001\u7b56\u7565\u5b9e\u64cd\u5173\u952e\u6ce8\u610f\u4e8b\u9879<\/h3>\n

      4.1 ETF \u5019\u9009\u6c60\u914d\u7f6e<\/h4>\n

      g.security_list = []\u4e3a\u7a7a\u5217\u8868，\u5fc5\u987b\u586b\u5145\u6807\u7684，\u63a8\u8350\u5bbd\u57fa\u3001\u884c\u4e1a ETF \u6df7\u5408，\u5efa\u8bae 20~40 \u53ea，<\/p>\n

      \u683c\u5f0f\u533a\u5206\u6caa\u6df1\u5e02\u573a，\u4ee3\u7801\u540e\u7f00\u5199\u9519\u4f1a\u5bfc\u81f4\u884c\u60c5\u62c9\u53d6\u5931\u8d25\u3002<\/p>\n

      4.2 \u56de\u6d4b\u65f6\u95f4\u533a\u95f4\u5339\u914d<\/h4>\n

      \u4ee3\u7801\u5185get_trade_days('2015-01-01', '2025-10-31')，\u56de\u6d4b\u8d77\u6b62\u65f6\u95f4\u4e0d\u80fd\u8d85\u51fa\u8be5\u533a\u95f4，\u53ef\u81ea\u884c\u4fee\u6539\u7ed3\u675f\u5e74\u4efd\u62d3\u5c55\u6570\u636e\u8303\u56f4\u3002<\/p>\n

      4.3 \u8d44\u91d1\u6743\u91cd\u81ea\u5b9a\u4e49<\/h4>\n

      g.weights = [0.7, 0.3]，\u98ce\u9669\u504f\u597d\u4fdd\u5b88\u53ef\u6539\u4e3a[0.5,0.5]\u5747\u8861\u5206\u914d，\u4ec5\u652f\u6301 2 \u4e2a\u6570\u503c，\u5bf9\u5e94\u9009\u51fa\u7684 2 \u53ea ETF\u3002<\/p>\n

      4.4 A \u80a1\u4ea4\u6613\u89c4\u5219\u9002\u914d<\/h4>\n

      ETF \u5b9e\u884c T+1 \u4ea4\u6613，\u672c\u7b56\u7565\u6bcf\u5468\u4e70\u5165\u3001\u6b21\u5468\u5356\u51fa，\u5b8c\u5168\u89c4\u907f\u65e5\u5185\u4ea4\u6613\u9650\u5236，\u4e0d\u5b58\u5728\u5f53\u65e5\u4e70\u5165\u65e0\u6cd5\u5356\u51fa\u7684\u903b\u8f91 bug\u3002<\/p>\n

      4.5 \u56de\u6d4b\u4f18\u5148，\u8c28\u614e\u5b9e\u76d8<\/h4>\n
    • \u5148\u7528 Ptrade \u4eff\u771f\u7ec8\u7aef\u957f\u671f\u56de\u6d4b，\u89c2\u5bdf\u4e0d\u540c\u725b\u718a\u5468\u671f\u6536\u76ca\u3001\u6700\u5927\u56de\u64a4；<\/li>\n
    • \u53ef\u81ea\u884c\u8c03\u53c2：20 \u65e5\u52a8\u91cf\u5468\u671f\u6539\u4e3a 10\/30 \u65e5\u3001\u589e\u51cf\u6301\u4ed3\u6570\u91cf；<\/li>\n
    • \u5b9e\u76d8\u524d\u5c0f\u8d44\u91d1\u8bd5\u8fd0\u884c 1~2 \u4e2a\u6708，\u9a8c\u8bc1\u6210\u4ea4\u6ed1\u70b9\u3001\u624b\u7eed\u8d39\u5bf9\u6536\u76ca\u7684\u5f71\u54cd\u3002<\/li>\n

      \u4e94\u3001\u7b56\u7565\u4f18\u5316\u62d3\u5c55\u601d\u8def<\/h3>\n
    • \u589e\u52a0\u6b62\u635f\u903b\u8f91：\u5728\u6bcf\u65e5\u76d8\u540e\u589e\u52a0\u6301\u4ed3\u8dcc\u5e45\u5224\u65ad，\u5355\u53ea ETF \u56de\u64a4\u8d85 5% \u5f3a\u5236\u5356\u51fa；<\/li>\n
    • \u591a\u5468\u671f\u52a8\u91cf\u8fc7\u6ee4：\u53e0\u52a0 60 \u65e5\u4e2d\u957f\u671f\u8d8b\u52bf，\u4ec5\u5728\u5927\u76d8\u4e0a\u884c\u65f6\u6267\u884c\u8f6e\u52a8；<\/li>\n
    • \u4ed3\u4f4d\u52a8\u6001\u8c03\u6574：\u6839\u636e\u5e02\u573a\u6ce2\u52a8\u7387\u81ea\u52a8\u8c03\u6574\u603b\u4ed3\u4f4d，\u9ad8\u4f4d\u964d\u4ed3\u3001\u4f4e\u4f4d\u52a0\u4ed3；<\/li>\n
    • \u6269\u5927\u6301\u4ed3\u6570\u91cf：\u4fee\u6539\u4ee3\u7801\u9009\u53d6\u524d 3~4 \u53ea ETF，\u5206\u6563\u5355\u4e00\u6807\u7684\u98ce\u9669；<\/li>\n
    • \u8fc7\u6ee4\u4f4e\u6d41\u52a8\u6027\u6807\u7684：\u589e\u52a0\u65e5\u5747\u6210\u4ea4\u989d\u7b5b\u9009，\u5254\u9664\u6210\u4ea4\u91cf\u4f4e\u8ff7\u7684\u5c0f\u76d8 ETF\u3002<\/li>\n

      \u516d\u3001\u603b\u7ed3<\/h3>\n

      \u672c\u5957 Ptrade ETF \u5468\u5ea6\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565\u4ee3\u7801\u5b8c\u6574\u3001\u903b\u8f91\u8f7b\u91cf\u5316，\u5185\u7f6e\u8282\u5047\u65e5\u81ea\u52a8\u9002\u914d\u3001\u5408\u89c4\u4ea4\u6613\u6570\u91cf\u5904\u7406\u3001\u5b9a\u65f6\u81ea\u52a8\u4ea4\u6613\u80fd\u529b，\u65b0\u624b\u53ef\u76f4\u63a5\u590d\u7528\u5e73\u53f0\u56de\u6d4b\u3002 \u7b56\u7565\u6838\u5fc3\u4f9d\u9760\u52a8\u91cf\u6548\u5e94\u6355\u6349\u4e2d\u671f\u5f3a\u52bf\u677f\u5757 ETF，\u6bcf\u5468\u8c03\u4ed3\u9891\u7387\u9002\u4e2d，\u4e0d\u4f1a\u4ea7\u751f\u8fc7\u9ad8\u4ea4\u6613\u624b\u7eed\u8d39，\u9002\u5408\u4e2d\u957f\u671f\u81ea\u52a8\u5316\u5b9a\u6295 + \u8d8b\u52bf\u4ea4\u6613\u7ed3\u5408\u7684\u6295\u8d44\u8005\u3002<\/p>\n

      \u98ce\u9669\u63d0\u793a：\u672c\u6587\u4ec5\u4e3a\u91cf\u5316\u4ee3\u7801\u6559\u5b66\u5206\u4eab，\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae\u3002\u91cf\u5316\u7b56\u7565\u5b58\u5728\u5386\u53f2\u56de\u6d4b\u4e0d\u4ee3\u8868\u672a\u6765\u6536\u76ca，\u5e02\u573a\u6ce2\u52a8\u3001\u6d41\u52a8\u6027\u98ce\u9669\u3001\u624b\u7eed\u8d39\u6ed1\u70b9\u5747\u4f1a\u5f71\u54cd\u5b9e\u9645\u6536\u76ca，\u8bf7\u8c28\u614e\u4f7f\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

      \u6587\u7ae0\u7b80\u4ecb\u672c\u6587\u57fa\u4e8e\u00a0Ptrade \u91cf\u5316\u5e73\u53f0，\u642d\u5efa\u4e00\u5957\u53ef\u76f4\u63a5\u56de\u6d4b\u3001\u4eff\u771f\u8fd0\u884c\u7684\u5468\u5ea6\u5f3a\u52bf ETF \u8f6e\u52a8\u7b56\u7565\u3002\u6838\u5fc3\u903b\u8f91：\u6bcf\u5468\u7b5b\u9009 20 \u65e5\u6da8\u5e45\u6700\u5f3a 2 \u53ea ETF，7:3 \u8d44\u91d1\u914d\u6bd4\u4e70\u5165，\u6bcf\u5468\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5\u5168\u90e8\u6e05\u4ed3，\u5faa\u73af\u6eda\u52a8\u505a\u8d8b\u52bf\u77ed\u7ebf\u3002\u5168\u6587\u9644\u5e26\u5b8c\u6574\u53ef\u8fd0\u884c\u4ee3\u7801\u3001\u51fd\u6570\u62c6\u89e3\u3001\u8282\u5047\u65e5\u81ea\u52a8\u9002\u914d\u903b\u8f91\u4e0e\u5b9e\u64cd\u907f\u5751\u8981\u70b9，\u65b0\u624b\u4e5f\u80fd\u5feb\u901f\u4e0a\u624b\u6539\u9020\u3002\u9002\u7528\u4eba\u7fa4：Ptrade \u91cf\u5316\u5b66\u4e60\u8005\u3001ETF \u8d8b\u52bf\u4ea4\u6613\u8005\u3001\u7a0b\u5e8f\u5316\u81ea\u52a8\u5316<\/p>\n","protected":false},"author":2,"featured_media":87484,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9099,2739,81,2740,1158],"topic":[],"class_list":["post-87490","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-9099","tag-ptrade","tag-python","tag-qmt","tag-1158"],"yoast_head":"\n\u53ef\u76f4\u63a5\u56de\u6d4b\uff01Ptrade \u5f3a\u52bf ETF \u5468\u5ea6\u8f6e\u52a8\u7a0b\u5e8f\u5316\u4ea4\u6613\u7b56\u7565\u5168\u89e3 - 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