{"id":1076870,"date":"2025-01-08T11:57:07","date_gmt":"2025-01-08T03:57:07","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1076870.html"},"modified":"2025-01-08T11:57:09","modified_gmt":"2025-01-08T03:57:09","slug":"python%e5%a6%82%e4%bd%95%e7%94%bb%e8%b5%84%e6%9c%ac%e5%b8%82%e5%9c%ba%e7%ba%bf-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1076870.html","title":{"rendered":"Python\u5982\u4f55\u753b\u8d44\u672c\u5e02\u573a\u7ebf"},"content":{"rendered":"

\"Python\u5982\u4f55\u753b\u8d44\u672c\u5e02\u573a\u7ebf\"<\/p>\n

Python\u5982\u4f55\u753b\u8d44\u672c\u5e02\u573a\u7ebf<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Python\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\uff08CML\uff09\uff0c\u9700\u8981\u4e86\u89e3\u8d44\u672c\u8d44\u4ea7\u5b9a\u4ef7\u6a21\u578b\uff08CAPM\uff09\u3001\u98ce\u9669\u8d44\u4ea7\u7684\u9884\u671f\u6536\u76ca\u7387\u3001\u65e0\u98ce\u9669\u6536\u76ca\u7387\u3001\u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387\u3001\u5e02\u573a\u7ec4\u5408\u7684\u6807\u51c6\u5dee<\/strong>\u3002\u5176\u4e2d\uff0c\u8d44\u672c\u5e02\u573a\u7ebf\u7684\u659c\u7387\u662f\u5e02\u573a\u7ec4\u5408\u7684\u590f\u666e\u6bd4\u7387\u3002\u5728\u91d1\u878d\u6295\u8d44\u4e2d\uff0cCML\u6709\u52a9\u4e8e\u6295\u8d44\u8005\u7406\u89e3\u5e76\u4f18\u5316\u5176\u6295\u8d44\u7ec4\u5408\u3002\u4e0b\u9762\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\u3002<\/p>\n<\/p>\n

\u4e00\u3001CAPM\u4e0e\u8d44\u672c\u5e02\u573a\u7ebf\u7684\u57fa\u672c\u6982\u5ff5<\/strong><\/p>\n<\/p>\n

    \n
  1. \n

    CAPM\u6a21\u578b<\/strong><\/p>\n

    \u8d44\u672c\u8d44\u4ea7\u5b9a\u4ef7\u6a21\u578b\uff08CAPM\uff09\u662f\u91d1\u878d\u6295\u8d44\u4e2d\u7528\u4e8e\u786e\u5b9a\u8d44\u4ea7\u9884\u671f\u6536\u76ca\u7387\u7684\u6a21\u578b\u3002\u6839\u636eCAPM\uff0c\u8d44\u4ea7\u7684\u9884\u671f\u6536\u76ca\u7387\u7531\u65e0\u98ce\u9669\u6536\u76ca\u7387\u548c\u98ce\u9669\u6ea2\u4ef7\u7ec4\u6210\u3002\u516c\u5f0f\u5982\u4e0b\uff1a<\/p>\n

    [<\/p>\n

    E(R_i) = R_f + \\beta_i (E(R_m) – R_f)<\/p>\n

    ]<\/p>\n

    \u5176\u4e2d\uff0c(E(R_i)) \u662f\u8d44\u4ea7\u7684\u9884\u671f\u6536\u76ca\u7387\uff0c(R_f) \u662f\u65e0\u98ce\u9669\u6536\u76ca\u7387\uff0c(\\beta_i) \u662f\u8d44\u4ea7\u7684\u8d1d\u5854\u7cfb\u6570\uff0c(E(R_m)) \u662f\u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    \u8d44\u672c\u5e02\u573a\u7ebf\uff08CML\uff09<\/strong><\/p>\n

    \u8d44\u672c\u5e02\u573a\u7ebf\uff08CML\uff09\u8868\u793a\u5728\u98ce\u9669-\u6536\u76ca\u7a7a\u95f4\u4e2d\u6709\u6548\u7684\u6295\u8d44\u7ec4\u5408\u3002CML\u4e0a\u7684\u7ec4\u5408\u7531\u65e0\u98ce\u9669\u8d44\u4ea7\u548c\u5e02\u573a\u7ec4\u5408\u7ec4\u6210\u3002\u516c\u5f0f\u5982\u4e0b\uff1a<\/p>\n

    [<\/p>\n

    E(R_p) = R_f + \\frac{E(R_m) – R_f}{\\sigma_m} \\sigma_p<\/p>\n

    ]<\/p>\n

    \u5176\u4e2d\uff0c(E(R_p)) \u662f\u6295\u8d44\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387\uff0c(\\sigma_p) \u662f\u6295\u8d44\u7ec4\u5408\u7684\u6807\u51c6\u5dee\uff0c(\\sigma_m) \u662f\u5e02\u573a\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u6570\u636e\u51c6\u5907\u4e0e\u5904\u7406<\/strong><\/p>\n<\/p>\n

    \u5728\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u76f8\u5173\u6570\u636e\uff0c\u5305\u62ec\u65e0\u98ce\u9669\u6536\u76ca\u7387\u3001\u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387\u53ca\u5176\u6807\u51c6\u5dee\u3002\u8fd9\u91cc\u6211\u4eec\u4ee5\u5047\u8bbe\u7684\u6570\u636e\u8fdb\u884c\u8bf4\u660e\u3002<\/p>\n<\/p>\n

    import numpy as np<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u5047\u8bbe\u6570\u636e<\/strong><\/h2>\n
    Rf = 0.02  # \u65e0\u98ce\u9669\u6536\u76ca\u7387<\/p>\n
    Rm = 0.08  # \u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387<\/p>\n
    sigma_m = 0.15  # \u5e02\u573a\u7ec4\u5408\u7684\u6807\u51c6\u5dee<\/p>\n
    \u8ba1\u7b97\u5e02\u573a\u7ec4\u5408\u7684\u590f\u666e\u6bd4\u7387<\/strong><\/h2>\n
    sharpe_ratio = (Rm - Rf) \/ sigma_m<\/p>\n
    \u751f\u6210\u6295\u8d44\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u8303\u56f4<\/strong><\/h2>\n
    sigma_p = np.linspace(0, 0.3, 100)<\/p>\n
    \u8ba1\u7b97\u6295\u8d44\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387<\/strong><\/h2>\n
    E_Rp = Rf + sharpe_ratio * sigma_p<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf<\/strong><\/p>\n<\/p>\n
    \u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\u5e76\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
    plt.figure(figsize=(10, 6))<\/p>\n
    plt.plot(sigma_p, E_Rp, label='Capital Market Line (CML)', color='blue')<\/p>\n
    plt.scatter(sigma_m, Rm, color='red', label='Market Portfolio', zorder=5)<\/p>\n
    plt.xlabel('Portfolio Standard Deviation')<\/p>\n
    plt.ylabel('Expected Return')<\/p>\n
    plt.title('Capital Market Line')<\/p>\n
    plt.legend()<\/p>\n
    plt.grid(True)<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u8be6\u7ec6\u8bf4\u660e<\/strong><\/p>\n<\/p>\n
      \n
    1. \n
      \u65e0\u98ce\u9669\u6536\u76ca\u7387\u7684\u9009\u62e9<\/strong><\/p>\n
      \u65e0\u98ce\u9669\u6536\u76ca\u7387\u901a\u5e38\u4f7f\u7528\u77ed\u671f\u653f\u5e9c\u503a\u5238\u7684\u6536\u76ca\u7387\u3002\u9009\u62e9\u65e0\u98ce\u9669\u6536\u76ca\u7387\u65f6\u9700\u8003\u8651\u5176\u7a33\u5b9a\u6027\u53ca\u5e02\u573a\u8ba4\u53ef\u5ea6\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387<\/strong><\/p>\n
      \u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387\u53ef\u4ee5\u901a\u8fc7\u5386\u53f2\u5e02\u573a\u6570\u636e\u8ba1\u7b97\uff0c\u4f8b\u5982\u4f7f\u7528\u6807\u666e500\u6307\u6570\u7684\u5386\u53f2\u6536\u76ca\u7387\u4f5c\u4e3a\u5e02\u573a\u7ec4\u5408\u7684\u6536\u76ca\u7387\u3002<\/p>\n<\/p>\n<\/li>\n
    3. \n
      \u5e02\u573a\u7ec4\u5408\u7684\u6807\u51c6\u5dee<\/strong><\/p>\n
      \u5e02\u573a\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u5e02\u573a\u6307\u6570\u7684\u5386\u53f2\u6ce2\u52a8\u7387\u5f97\u5230\u3002<\/p>\n<\/p>\n<\/li>\n
    4. \n
      \u590f\u666e\u6bd4\u7387\u7684\u8ba1\u7b97<\/strong><\/p>\n
      \u590f\u666e\u6bd4\u7387\u8861\u91cf\u7684\u662f\u6bcf\u5355\u4f4d\u98ce\u9669\u5e26\u6765\u7684\u8d85\u989d\u6536\u76ca\u3002\u5b83\u662f\u98ce\u9669\u8c03\u6574\u540e\u6536\u76ca\u7684\u91cd\u8981\u6307\u6807\u3002<\/p>\n<\/p>\n<\/li>\n
    5. \n
      \u6295\u8d44\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u8303\u56f4<\/strong><\/p>\n
      \u6295\u8d44\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u8303\u56f4\u901a\u5e38\u4ece\u96f6\u5230\u5e02\u573a\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u7684\u4e24\u500d\uff0c\u4ee5\u6db5\u76d6\u5408\u7406\u7684\u6295\u8d44\u7ec4\u5408\u98ce\u9669\u8303\u56f4\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e94\u3001Python\u4ee3\u7801\u5b9e\u73b0\u7684\u5b8c\u6574\u793a\u4f8b<\/strong><\/p>\n<\/p>\n
      \u4e0b\u9762\u662f\u5b8c\u6574\u7684Python\u4ee3\u7801\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528\u5047\u8bbe\u6570\u636e\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\u3002<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u5047\u8bbe\u6570\u636e<\/strong><\/h2>\n
      Rf = 0.02  # \u65e0\u98ce\u9669\u6536\u76ca\u7387<\/p>\n
      Rm = 0.08  # \u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387<\/p>\n
      sigma_m = 0.15  # \u5e02\u573a\u7ec4\u5408\u7684\u6807\u51c6\u5dee<\/p>\n
      \u8ba1\u7b97\u5e02\u573a\u7ec4\u5408\u7684\u590f\u666e\u6bd4\u7387<\/strong><\/h2>\n
      sharpe_ratio = (Rm - Rf) \/ sigma_m<\/p>\n
      \u751f\u6210\u6295\u8d44\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u8303\u56f4<\/strong><\/h2>\n
      sigma_p = np.linspace(0, 0.3, 100)<\/p>\n
      \u8ba1\u7b97\u6295\u8d44\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387<\/strong><\/h2>\n
      E_Rp = Rf + sharpe_ratio * sigma_p<\/p>\n
      \u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf<\/strong><\/h2>\n
      plt.figure(figsize=(10, 6))<\/p>\n
      plt.plot(sigma_p, E_Rp, label='Capital Market Line (CML)', color='blue')<\/p>\n
      plt.scatter(sigma_m, Rm, color='red', label='Market Portfolio', zorder=5)<\/p>\n
      plt.xlabel('Portfolio Standard Deviation')<\/p>\n
      plt.ylabel('Expected Return')<\/p>\n
      plt.title('Capital Market Line')<\/p>\n
      plt.legend()<\/p>\n
      plt.grid(True)<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u516d\u3001\u6269\u5c55\u4e0e\u5e94\u7528<\/strong><\/p>\n<\/p>\n
        \n
      1. \n
        \u5b9e\u9645\u6570\u636e\u5e94\u7528<\/strong><\/p>\n
        \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u91d1\u878d\u6570\u636eAPI\uff08\u5982Yahoo Finance\u3001Alpha Vantage\uff09\u83b7\u53d6\u5386\u53f2\u5e02\u573a\u6570\u636e\uff0c\u8ba1\u7b97\u5e02\u573a\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u4f18\u5316\u6295\u8d44\u7ec4\u5408<\/strong><\/p>\n
        \u6295\u8d44\u8005\u53ef\u4ee5\u5229\u7528CML\u4f18\u5316\u6295\u8d44\u7ec4\u5408\uff0c\u901a\u8fc7\u8c03\u8282\u65e0\u98ce\u9669\u8d44\u4ea7\u548c\u5e02\u573a\u7ec4\u5408\u7684\u6bd4\u4f8b\uff0c\u5b9e\u73b0\u98ce\u9669\u4e0e\u6536\u76ca\u7684\u4f18\u5316\u5339\u914d\u3002<\/p>\n<\/p>\n<\/li>\n
      3. \n
        \u98ce\u9669\u7ba1\u7406<\/strong><\/p>\n
        CML\u4e3a\u6295\u8d44\u8005\u63d0\u4f9b\u4e86\u98ce\u9669\u7ba1\u7406\u7684\u5de5\u5177\uff0c\u5e2e\u52a9\u6295\u8d44\u8005\u5728\u4e0d\u540c\u98ce\u9669\u6c34\u5e73\u4e0b\u9009\u62e9\u6700\u4f18\u7684\u6295\u8d44\u7ec4\u5408\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u4e03\u3001\u603b\u7ed3<\/strong><\/p>\n<\/p>\n
        \u4f7f\u7528Python\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\uff08CML\uff09\u662f\u4e00\u9879\u91cd\u8981\u7684\u91d1\u878d\u6570\u636e\u5206\u6790\u6280\u80fd\u3002\u901a\u8fc7CML\uff0c\u6295\u8d44\u8005\u53ef\u4ee5\u76f4\u89c2\u5730\u7406\u89e3\u98ce\u9669\u4e0e\u6536\u76ca\u7684\u5173\u7cfb\uff0c\u4f18\u5316\u6295\u8d44\u7ec4\u5408\uff0c\u5b9e\u73b0\u98ce\u9669\u7ba1\u7406\u3002\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86CAPM\u4e0eCML\u7684\u57fa\u672c\u6982\u5ff5\u3001\u6570\u636e\u51c6\u5907\u3001Python\u4ee3\u7801\u5b9e\u73b0\u4ee5\u53ca\u6269\u5c55\u5e94\u7528\uff0c\u5e0c\u671b\u5bf9\u5927\u5bb6\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
         Python\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u5e93\u6765\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\uff1f<\/strong>
        \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528\u591a\u4e2a\u5e93\u6765\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\u3002\u6700\u5e38\u7528\u7684\u5e93\u662fMatplotlib\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u9002\u5408\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\u3002\u9664\u4e86Matplotlib\uff0cSeaborn\u548cPlotly\u7b49\u5e93\u4e5f\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u66f4\u5177\u89c6\u89c9\u5438\u5f15\u529b\u7684\u56fe\u8868\uff0c\u5c24\u5176\u662f\u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\u3002\u5982\u679c\u4f60\u9700\u8981\u5904\u7406\u590d\u6742\u7684\u91d1\u878d\u6570\u636e\uff0cPandas\u5e93\u4e5f\u53ef\u4ee5\u5e2e\u52a9\u4f60\u6574\u7406\u548c\u5206\u6790\u6570\u636e\u3002<\/p>\n
        \u5728\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\u65f6\u9700\u8981\u54ea\u4e9b\u6570\u636e\uff1f<\/strong>
        \u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\u901a\u5e38\u9700\u8981\u6295\u8d44\u7ec4\u5408\u7684\u9884\u671f\u6536\u76ca\u7387\u3001\u6807\u51c6\u5dee\u4ee5\u53ca\u65e0\u98ce\u9669\u6536\u76ca\u7387\u3002\u8fd9\u4e9b\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u5386\u53f2\u5e02\u573a\u6570\u636e\u6765\u4f30\u7b97<\/a>\u3002\u65e0\u98ce\u9669\u6536\u76ca\u7387\u901a\u5e38\u53ef\u4ee5\u4f7f\u7528\u56fd\u503a\u6536\u76ca\u7387\uff0c\u5e02\u573a\u7684\u9884\u671f\u6536\u76ca\u7387\u5219\u53ef\u4ee5\u901a\u8fc7\u80a1\u7968\u5e02\u573a\u7684\u5386\u53f2\u56de\u62a5\u7387\u6765\u8ba1\u7b97\u3002\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u65f6\u6548\u6027\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002<\/p>\n
        \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u8d44\u672c\u5e02\u573a\u7ebf\u7684\u516c\u5f0f\u8ba1\u7b97\uff1f<\/strong>
        \u8d44\u672c\u5e02\u573a\u7ebf\u7684\u516c\u5f0f\u4e3b\u8981\u662f\u901a\u8fc7\u8d44\u672c\u8d44\u4ea7\u5b9a\u4ef7\u6a21\u578b\uff08CAPM\uff09\u6765\u8ba1\u7b97\u7684\u3002\u516c\u5f0f\u4e3a\uff1a
        [ E(R) = R_f + \\frac{E(R_m) – R_f}{\\sigma_m} \\cdot \\sigma_p ]
        \u5176\u4e2d\uff0c(E(R)) \u662f\u9884\u671f\u6536\u76ca\u7387\uff0c(R_f) \u662f\u65e0\u98ce\u9669\u5229\u7387\uff0c(E(R_m)) \u662f\u5e02\u573a\u9884\u671f\u6536\u76ca\u7387\uff0c(\\sigma_m) \u662f\u5e02\u573a\u6807\u51c6\u5dee\uff0c(\\sigma_p) \u662f\u6295\u8d44\u7ec4\u5408\u7684\u6807\u51c6\u5dee\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684NumPy\u5e93\u6765\u8fdb\u884c\u8fd9\u4e9b\u8ba1\u7b97\uff0c\u65b9\u4fbf\u5730\u5904\u7406\u6570\u7ec4\u548c\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5982\u4f55\u753b\u8d44\u672c\u5e02\u573a\u7ebf \u4f7f\u7528Python\u7ed8\u5236\u8d44\u672c\u5e02\u573a\u7ebf\uff08CML\uff09\uff0c\u9700\u8981\u4e86\u89e3\u8d44\u672c\u8d44\u4ea7\u5b9a\u4ef7\u6a21\u578b\uff08CAPM\uff09\u3001 […]","protected":false},"author":3,"featured_media":1076878,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1076870"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1076870"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1076870\/revisions"}],"predecessor-version":[{"id":1076881,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1076870\/revisions\/1076881"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1076878"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1076870"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1076870"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1076870"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}