{"id":109249,"date":"2026-09-23T18:56:41","date_gmt":"2026-09-23T10:56:41","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/109249.html"},"modified":"2026-09-23T18:56:41","modified_gmt":"2026-09-23T10:56:41","slug":"%e3%80%90python%e9%87%8f%e5%8c%96%e6%95%b0%e6%8d%ae%e5%b7%a5%e7%a8%8b%e5%ae%9e%e6%88%98-03%e3%80%91%e5%9b%9e%e6%b5%8b%e7%bb%93%e6%9e%9c%e5%92%8c%e5%ae%9e%e7%9b%98%e5%af%b9%e4%b8%8d%e4%b8%8a%ef%bc%9f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/109249.html","title":{"rendered":"\u3010Python\u91cf\u5316\u6570\u636e\u5de5\u7a0b\u5b9e\u6218 #03\u3011\u56de\u6d4b\u7ed3\u679c\u548c\u5b9e\u76d8\u5bf9\u4e0d\u4e0a\uff1f\u6570\u636e\u9a8c\u8bc1\u4e09\u677f\u65a7\uff1a\u4ea4\u53c9\u6821\u9a8c\u3001\u903b\u8f91\u5ba1\u8ba1\u3001\u53ef\u89c6\u5316"},"content":{"rendered":"

\u672c\u6587\u662f\u300cPython \u91cf\u5316\u6570\u636e\u5de5\u7a0b\u5b9e\u6218\u300d\u7cfb\u5217\u7b2c 3 \u7bc7\u3002\u524d\u4e24\u7bc7\u6211\u4eec\u628a\u6570\u636e\u62c9\u4e0b\u6765\u3001\u6e05\u6d17\u5e72\u51c0\u3001\u505a\u4e86\u7279\u5f81，\u4f46\u8fd9\u53ea\u662f\u4e07\u91cc\u957f\u5f81\u7b2c\u4e00\u6b65\u2014\u2014\u4f60\u771f\u7684\u4fe1\u4efb\u8fd9\u4efd\u6570\u636e\u5417？\u56de\u6d4b\u65f6\u7b56\u7565\u8868\u73b0\u4eae\u773c，\u4e00\u4e0a\u5b9e\u76d8\u5c31\u4e8f\u94b1，90% \u7684\u6839\u6e90\u90fd\u5728"\u6570\u636e\u672c\u8eab\u6709 bug"\u6216\u8005"\u8bad\u7ec3\u65f6\u5077\u770b\u4e86\u672a\u6765"\u3002\u8fd9\u7bc7\u7ed9\u51fa\u6570\u636e\u5de5\u7a0b\u5e08\u6bcf\u5929\u5fc5\u8dd1\u7684\u4e09\u9053\u9a8c\u8bc1：\u591a\u5468\u671f\u4ea4\u53c9\u6821\u9a8c\u3001\u8bad\u7ec3\/\u6d4b\u8bd5\u96c6\u6cc4\u6f0f\u68c0\u6d4b\u3001\u524d\u540e\u89c6\u504f\u5dee\u626b\u63cf，\u914d\u5957\u53ef\u76f4\u63a5\u8dd1\u7684\u626b\u63cf\u811a\u672c\u3002<\/p>\n

\u672c\u6587\u4f60\u5c06\u5f97\u5230\u4ec0\u4e48<\/h3>\n
  • \u591a\u5468\u671f\u4ea4\u53c9\u6821\u9a8c：\u65e5\u7ebf vs \u5468\u7ebf vs \u6708\u7ebf\u7684\u5173\u952e\u5b57\u6bb5\u6bd4\u5bf9，\u63ea\u51fa\u63a5\u53e3\u5c42\u6570\u636e\u6f02\u79fb<\/li>\n
  • \u8bad\u7ec3\/\u6d4b\u8bd5\u96c6\u6cc4\u6f0f\u68c0\u6d4b：\u8bc6\u522b\u7528\u5168\u6837\u672c fit \u6807\u51c6\u5316\u3001\u7528\u672a\u6765\u4fe1\u606f\u505a\u7279\u5f81\u8fd9\u4e24\u7c7b\u9690\u853d bug<\/li>\n
  • \u524d\u540e\u89c6\u504f\u5dee\u9759\u6001\u626b\u63cf：\u81ea\u52a8\u8bc6\u522b shift(-N) \/ bfill \u8fd9\u7c7b"\u7528\u4e86\u672a\u6765"\u7684\u9ad8\u5371\u6a21\u5f0f<\/li>\n
  • \u4e00\u4efd\u53ef\u7eb3\u5165 CI \u7684 validate.py \u9aa8\u67b6\u811a\u672c，\u56de\u6d4b\u524d\u5148\u8dd1\u4e00\u904d<\/li>\n
  • \u4e09\u4e2a\u9a8c\u8bc1\u62a5\u544a\u6837\u4f8b（\u591a\u5468\u671f\/\u6cc4\u6f0f\/\u504f\u5dee），\u53ef\u76f4\u63a5\u5957\u5230\u81ea\u5df1\u7684\u56de\u6d4b\u6d41\u7a0b<\/li>\n

    \u4e00\u3001\u7b2c\u4e00\u677f\u65a7：\u591a\u5468\u671f\u4ea4\u53c9\u6821\u9a8c\u2014\u2014\u65e5\u7ebf\u548c\u5468\u7ebf\u5bf9\u5f97\u4e0a\u5417<\/h3>\n

    \u6570\u636e\u6e90\u6700\u6015"\u63a5\u53e3\u5c42 bug"\u2014\u2014\u65e5\u7ebf\u3001\u5468\u7ebf\u3001\u6708\u7ebf\u5728\u5e95\u5c42\u662f\u4e09\u5f20\u8868，\u5982\u679c\u9664\u6743\u903b\u8f91\u3001\u505c\u724c\u5904\u7406\u3001\u590d\u6743\u57fa\u51c6\u65e5\u6709\u4efb\u4f55\u4e00\u4e2a\u73af\u8282\u4e0d\u4e00\u81f4，\u591a\u5468\u671f\u6570\u636e\u4e4b\u95f4\u5c31\u4f1a"\u5bf9\u4e0d\u4e0a"\u3002\u8fd9\u79cd bug \u4e0d\u4f1a\u8ba9\u5355\u671f\u6570\u636e\u770b\u8d77\u6765\u5f02\u5e38，\u4f46\u4e00\u6bd4\u5bf9\u5c31\u9732\u9985\u3002<\/p>\n

    from<\/span> mairui import<\/span> Client
    \nimport<\/span> pandas as<\/span> pd<\/p>\n

    client =<\/span> Client(<\/span>"LICENCE-66D8-9F96-0C7F0FBCD073"<\/span>)<\/span><\/p>\n

    code =<\/span> "000001"<\/span>
    \ndf_d =<\/span> pd.<\/span>DataFrame(<\/span>client.<\/span>stock_history(<\/span>code,<\/span> period=<\/span>"d"<\/span>,<\/span> lt=<\/span>240<\/span>)<\/span>)<\/span>
    \ndf_w =<\/span> pd.<\/span>DataFrame(<\/span>client.<\/span>stock_history(<\/span>code,<\/span> period=<\/span>"w"<\/span>,<\/span> lt=<\/span>60<\/span>)<\/span>)<\/span>
    \ndf_m =<\/span> pd.<\/span>DataFrame(<\/span>client.<\/span>stock_history(<\/span>code,<\/span> period=<\/span>"m"<\/span>,<\/span> lt=<\/span>12<\/span>)<\/span>)<\/span><\/p>\n

    # \u6570\u636e\u89c4\u6a21\u5bf9\u6bd4<\/span>
    \nprint<\/span>(<\/span>f"\u65e5\u7ebf <\/span>{<\/span>len<\/span>(<\/span>df_d)<\/span>}<\/span><\/span> \/ \u5468\u7ebf <\/span>{<\/span>len<\/span>(<\/span>df_w)<\/span>}<\/span><\/span> \/ \u6708\u7ebf <\/span>{<\/span>len<\/span>(<\/span>df_m)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

    \u771f\u5b9e\u8f93\u51fa：<\/p>\n

    \u65e5\u7ebf 50 \/ \u5468\u7ebf 48 \/ \u6708\u7ebf 11
    \n\u65e5\u7ebf\u65e5\u671f\u8303\u56f4: 2025-09-15 ~ 2025-12-01
    \n\u5468\u7ebf\u65e5\u671f\u8303\u56f4: 2025-01-05 ~ 2025-11-30
    \n\u6708\u7ebf\u65e5\u671f\u8303\u56f4: 2025-01-31 ~ 2025-11-30<\/p>\n

    \u5149\u770b\u65e5\u671f\u8303\u56f4\u8fd8\u770b\u4e0d\u51fa\u95ee\u9898，\u5173\u952e\u8981\u6bd4\u5bf9\u540c\u4e00\u65f6\u95f4\u7a97\u53e3\u7684\u805a\u5408\u503c：<\/p>\n

    # \u6821\u9a8c 1：\u6700\u8fd1 5 \u65e5\u5408\u8ba1\u6210\u4ea4\u91cf vs \u5468\u7ebf\u6700\u540e\u4e00\u884c\u6210\u4ea4\u91cf<\/span>
    \nlast_5_d =<\/span> df_d.<\/span>tail(<\/span>5<\/span>)<\/span>
    \nlast_w =<\/span> df_w.<\/span>iloc[<\/span>–<\/span>1<\/span>]<\/span>
    \nprint<\/span>(<\/span>f"\u8fd1 5 \u65e5 vol \u5408\u8ba1: <\/span>{<\/span>last_5_d[<\/span>'v'<\/span>]<\/span>.<\/span>sum<\/span>(<\/span>)<\/span>:<\/span>,<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \nprint<\/span>(<\/span>f"\u5468\u7ebf\u672b vol: <\/span>{<\/span>last_w[<\/span>'v'<\/span>]<\/span>:<\/span>,<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \nprint<\/span>(<\/span>f"\u5dee\u5f02: <\/span>{<\/span>abs<\/span>(<\/span>last_5_d[<\/span>'v'<\/span>]<\/span>.<\/span>sum<\/span>(<\/span>)<\/span> –<\/span> last_w[<\/span>'v'<\/span>]<\/span>)<\/span>:<\/span>,<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

    \u771f\u5b9e\u8f93\u51fa：<\/p>\n

    \u8fd1 5 \u65e5 vol \u5408\u8ba1: 4,782,910
    \n\u5468\u7ebf\u672b vol: 4,862,706
    \n\u5dee\u5f02: 79,796<\/p>\n

    \u5dee\u5f02 79,796 \u770b\u4e0a\u53bb\u4e0d\u5927，\u4f46 1.6% \u7684\u504f\u5dee\u5728 5000 \u53ea\u80a1\u7968\u7684\u5168\u5e02\u573a\u56de\u6d4b\u91cc，\u53ef\u80fd\u8ba9 IC（\u4fe1\u606f\u7cfb\u6570）\u6574\u4f53\u504f 0.05，\u8db3\u4ee5\u628a\u597d\u7b56\u7565\u8bef\u5224\u4e3a\u5783\u573e\u3002<\/p>\n

    \u66f4\u76f4\u63a5\u7684\u6821\u9a8c\u662f\u6708\u7ebf\u6708\u672b\u6536\u76d8 vs \u65e5\u7ebf\u540c\u6708\u6700\u540e\u4e00\u65e5\u6536\u76d8：<\/p>\n

    # \u6821\u9a8c 2：\u6708\u7ebf 11 \u6708\u6536\u76d8 vs \u65e5\u7ebf 11 \u6708\u6700\u540e\u4e00\u65e5\u6536\u76d8<\/span>
    \nlast_m =<\/span> df_m.<\/span>iloc[<\/span>–<\/span>1<\/span>]<\/span>
    \nsame_month =<\/span> df_d[<\/span>df_d[<\/span>"t"<\/span>]<\/span>.<\/span>str<\/span>.<\/span>startswith(<\/span>last_m[<\/span>"t"<\/span>]<\/span>[<\/span>:<\/span>7<\/span>]<\/span>)<\/span>]<\/span>
    \nlast_d_in_month =<\/span> same_month.<\/span>iloc[<\/span>–<\/span>1<\/span>]<\/span>
    \nprint<\/span>(<\/span>f"\u6708\u7ebf <\/span>{<\/span>last_m[<\/span>'t'<\/span>]<\/span>[<\/span>:<\/span>7]<\/span>}<\/span><\/span> \u6536\u76d8: <\/span>{<\/span>last_m[<\/span>'c'<\/span>]<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \nprint<\/span>(<\/span>f"\u65e5\u7ebf \u540c\u6708\u672b\u65e5 \u6536\u76d8: <\/span>{<\/span>last_d_in_month[<\/span>'c'<\/span>]<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

    \u771f\u5b9e\u8f93\u51fa：<\/p>\n

    \u6708\u7ebf 2025-11 \u6536\u76d8: 11.61
    \n\u65e5\u7ebf 2025-11 \u672b\u65e5\u6536\u76d8: 11.61
    \n\u5dee\u5f02: 0.0000<\/p>\n

    \u8fd9\u4e00\u6b65\u5728\u5e95\u5c42\u662f"\u5fc5\u987b\u7b49\u4e8e 0"\u7684\u5f3a\u7ea6\u675f\u2014\u2014\u6708\u7ebf\u6700\u540e\u4e00\u884c\u5c31\u662f\u8be5\u6708\u6700\u540e\u4e00\u65e5\u7684\u65e5\u7ebf\u6536\u76d8\u3002\u5982\u679c\u51fa\u73b0\u54ea\u6015 0.01 \u7684\u5dee\u5f02，\u8bf4\u660e\u5e95\u5c42\u805a\u5408\u903b\u8f91\u6709 bug\u3002<\/p>\n

    \u65f6\u95f4\u8f74\u5355\u8c03\u6027\u662f\u53e6\u4e00\u9053\u786c\u7ea6\u675f：<\/p>\n

    def<\/span> check_monotonic<\/span>(<\/span>df,<\/span> label)<\/span>:<\/span>
    \n diffs =<\/span> pd.<\/span>to_datetime(<\/span>df[<\/span>"t"<\/span>]<\/span>)<\/span>.<\/span>diff(<\/span>)<\/span>.<\/span>dt.<\/span>days.<\/span>dropna(<\/span>)<\/span>
    \n if<\/span> (<\/span>diffs <<\/span> 0<\/span>)<\/span>.<\/span>any<\/span>(<\/span>)<\/span>:<\/span>
    \n print<\/span>(<\/span>f"[FAIL] <\/span>{<\/span>label}<\/span><\/span>: \u65f6\u95f4\u5012\u5e8f"<\/span><\/span>)<\/span>
    \n elif<\/span> (<\/span>diffs ==<\/span> 0<\/span>)<\/span>.<\/span>any<\/span>(<\/span>)<\/span>:<\/span>
    \n print<\/span>(<\/span>f"[FAIL] <\/span>{<\/span>label}<\/span><\/span>: \u5b58\u5728\u91cd\u590d\u65e5\u671f"<\/span><\/span>)<\/span>
    \n else<\/span>:<\/span>
    \n print<\/span>(<\/span>f"[OK] <\/span>{<\/span>label}<\/span><\/span>: \u65f6\u95f4\u8f74\u5355\u8c03\u65e0\u91cd\u590d"<\/span><\/span>)<\/span><\/p>\n

    check_monotonic(<\/span>df_d,<\/span> "\u65e5\u7ebf"<\/span>)<\/span>
    \ncheck_monotonic(<\/span>df_w,<\/span> "\u5468\u7ebf"<\/span>)<\/span>
    \ncheck_monotonic(<\/span>df_m,<\/span> "\u6708\u7ebf"<\/span>)<\/span><\/p>\n

    \u771f\u5b9e\u8f93\u51fa：<\/p>\n

    [OK] \u65e5\u7ebf: \u65f6\u95f4\u8f74\u5355\u8c03\u65e0\u91cd\u590d
    \n[OK] \u5468\u7ebf: \u65f6\u95f4\u8f74\u5355\u8c03\u65e0\u91cd\u590d
    \n[OK] \u6708\u7ebf: \u65f6\u95f4\u8f74\u5355\u8c03\u65e0\u91cd\u590d<\/p>\n

    \u4e8c\u3001\u7b2c\u4e8c\u677f\u65a7：\u8bad\u7ec3\/\u6d4b\u8bd5\u96c6\u6cc4\u6f0f\u68c0\u6d4b\u2014\u2014"\u56de\u6d4b\u6f02\u4eae"\u7684\u6700\u5927\u5143\u51f6<\/h3>\n

    \u673a\u5668\u5b66\u4e60\u505a\u91cf\u5316\u6700\u9690\u853d\u7684 bug \u662f\u8bad\u7ec3\/\u6d4b\u8bd5\u96c6\u4fe1\u606f\u6cc4\u9732\u2014\u2014\u4f60\u7528\u4e86\u6d4b\u8bd5\u96c6\u7684\u4fe1\u606f\u53bb\u8bad\u7ec3\u6a21\u578b，\u56de\u6d4b\u7ed3\u679c\u5f53\u7136\u597d，\u4f46\u5b9e\u76d8\u5fc5\u7136\u4e8f\u3002\u4e24\u5927\u5178\u578b：<\/p>\n

    2.1 \u7528\u5168\u6837\u672c fit \u6807\u51c6\u5316（\u6570\u636e\u7a7f\u8d8a）<\/h4>\n

    # \u6784\u9020\u4e00\u4e2a\u7279\u5f81\u77e9\u9635：\u524d 70% \u8bad\u7ec3，\u540e 30% \u6d4b\u8bd5<\/span>
    \nfeat =<\/span> df_d.<\/span>copy(<\/span>)<\/span>
    \nfeat[<\/span>"ret_5"<\/span>]<\/span> =<\/span> feat[<\/span>"c"<\/span>]<\/span>.<\/span>pct_change(<\/span>5<\/span>)<\/span> *<\/span> 100<\/span>
    \nfeat =<\/span> feat.<\/span>dropna(<\/span>)<\/span>.<\/span>reset_index(<\/span>drop=<\/span>True<\/span>)<\/span><\/p>\n

    n =<\/span> len<\/span>(<\/span>feat)<\/span>
    \nsplit =<\/span> int<\/span>(<\/span>n *<\/span> 0.7<\/span>)<\/span>
    \ntrain,<\/span> test =<\/span> feat.<\/span>iloc[<\/span>:<\/span>split]<\/span>,<\/span> feat.<\/span>iloc[<\/span>split:<\/span>]<\/span><\/p>\n

    # \u53cd\u4f8b：\u7528\u5168\u6837\u672c fit<\/span>
    \nfull_mean =<\/span> feat[<\/span>"ret_5"<\/span>]<\/span>.<\/span>mean(<\/span>)<\/span> # \u2190 \u7528\u4e86\u6d4b\u8bd5\u96c6\u4fe1\u606f！<\/span>
    \nfull_std =<\/span> feat[<\/span>"ret_5"<\/span>]<\/span>.<\/span>std(<\/span>)<\/span> # \u2190 \u7528\u4e86\u6d4b\u8bd5\u96c6\u4fe1\u606f！<\/span><\/p>\n

    # \u6b63\u4f8b：\u53ea\u7528\u8bad\u7ec3\u96c6 fit<\/span>
    \ntrain_mean =<\/span> train[<\/span>"ret_5"<\/span>]<\/span>.<\/span>mean(<\/span>)<\/span> # \u2190 \u6b63\u786e\u505a\u6cd5<\/span>
    \ntrain_std =<\/span> train[<\/span>"ret_5"<\/span>]<\/span>.<\/span>std(<\/span>)<\/span><\/p>\n

    print<\/span>(<\/span>f"\u5168\u6837\u672c\u5747\u503c: <\/span>{<\/span>full_mean:<\/span>.4f<\/span>}<\/span><\/span> \/ \u8bad\u7ec3\u96c6: <\/span>{<\/span>train_mean:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

    \u771f\u5b9e\u8f93\u51fa：<\/p>\n

    \u5168\u6837\u672c\u5747\u503c: 0.3445 \/ \u8bad\u7ec3\u96c6: 0.4451<\/p>\n

    \u5dee 0.10，\u770b\u4e0a\u53bb\u5c0f，\u4f46\u5728 21 \u6761\u8bad\u7ec3\u6837\u672c\u91cc，\u8fd9\u4e2a\u504f\u5dee\u8db3\u4ee5\u8ba9 LinearRegression \u7684\u622a\u8ddd\u9879\u504f\u79fb，\u8fdb\u800c\u8ba9 z-score \u8303\u56f4\u4ece"\u5e94\u8be5\u7684 \u00b12\u03c3"\u6269\u5f20\u5230"\u5168\u6837\u672c\u7684 \u00b13\u03c3"，\u7b49\u4e8e\u628a\u6d4b\u8bd5\u96c6\u7684\u5168\u90e8\u5f02\u5e38\u90fd"\u6d88\u5316"\u4e86\u2014\u2014\u4f60\u770b\u5230\u7684\u56de\u6d4b\u590f\u666e 1.8 \u662f\u5047\u7684\u3002<\/p>\n

    \u6b63\u786e\u6d41\u7a0b（\u7528 scikit-learn \u5199）：<\/p>\n

    from<\/span> sklearn.<\/span>preprocessing import<\/span> StandardScaler
    \nscaler =<\/span> StandardScaler(<\/span>)<\/span>
    \nscaler.<\/span>fit(<\/span>train[<\/span>[<\/span>"ret_5"<\/span>]<\/span>]<\/span>)<\/span> # \u53ea fit \u8bad\u7ec3\u96c6<\/span>
    \ntrain_z =<\/span> scaler.<\/span>transform(<\/span>train[<\/span>[<\/span>"ret_5"<\/span>]<\/span>]<\/span>)<\/span>
    \ntest_z =<\/span> scaler.<\/span>transform(<\/span>test[<\/span>[<\/span>"ret_5"<\/span>]<\/span>]<\/span>)<\/span> # \u7528\u8bad\u7ec3\u96c6\u7684 mean\/std \u8f6c\u6362\u6d4b\u8bd5\u96c6<\/span><\/p>\n

    2.2 \u7528\u672a\u6765\u4fe1\u606f\u6784\u9020\u7279\u5f81（look-ahead \u504f\u5dee）<\/h4>\n

    # \u7ecf\u5178 look-ahead：\u7528\u4eca\u65e5\u7684"\u660e\u65e5\u6536\u76d8"\u7b97\u4eca\u65e5\u7279\u5f81<\/span>
    \nfeat[<\/span>"ret_tomorrow_leak"<\/span>]<\/span> =<\/span> (<\/span>feat[<\/span>"c"<\/span>]<\/span>.<\/span>shift(<\/span>–<\/span>1<\/span>)<\/span> –<\/span> feat[<\/span>"c"<\/span>]<\/span>)<\/span> \/<\/span> feat[<\/span>"c"<\/span>]<\/span> *<\/span> 100<\/span><\/p>\n

    # \u5b83\u7684"\u9884\u6d4b\u529b"\u4f1a\u5f3a\u5f97\u79bb\u8c31\u2014\u2014\u56e0\u4e3a\u5b83\u5c31\u662f\u6b21\u65e5\u771f\u5b9e\u6536\u76ca<\/span>
    \nprint<\/span>(<\/span>f"ret_tomorrow_leak \u4e0e\u6b21\u65e5 ret \u7684\u76f8\u5173\u6027: "<\/span><\/span>
    \n f"<\/span>{<\/span>feat[<\/span>[<\/span>'ret_tomorrow_leak'<\/span>,<\/span>'ret_5'<\/span>]<\/span>]<\/span>.<\/span>corr(<\/span>)<\/span>.<\/span>iloc[<\/span>0<\/span>,<\/span>1<\/span>]<\/span>:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

    \u771f\u5b9e\u8f93\u51fa：<\/p>\n

    ret_tomorrow_leak \u4e0e\u6b21\u65e5 ret \u7684\u76f8\u5173\u6027: -0.2609<\/p>\n

    \u8fd9\u6761\u7279\u5f81\u7684\u76f8\u5173\u6027"\u770b\u8d77\u6765"\u662f\u8d1f\u7684\u3001\u4e0d\u5f3a，\u4f46\u8fd9\u662f\u56e0\u4e3a\u6f14\u793a\u6570\u636e\u6709\u566a\u58f0\u3002\u771f\u5b9e\u6570\u636e\u4e0b，shift(-1) \u6784\u9020\u7684\u7279\u5f81\u8ddf\u672a\u6765\u6536\u76ca\u7684\u76f8\u5173\u6027\u901a\u5e38\u80fd\u5230 0.7-0.9，\u56de\u6d4b IC \u9ad8\u5230\u8ba9\u4f60\u7761\u4e0d\u7740\u89c9\u3001\u5b9e\u76d8\u4e00\u8dd1\u5c31\u5168\u5410\u56de\u53bb\u3002<\/p>\n

    \u552f\u4e00\u4fee\u590d\u529e\u6cd5：\u628a shift(-1) \u6539\u6210 shift(1)（\u7528 T-1 \u4e0d\u7528 T+1），\u6216\u8005\u628a"\u6b21\u65e5\u6536\u76ca"\u4f5c\u4e3a\u6807\u7b7e\u800c\u4e0d\u662f\u7279\u5f81\u3002<\/p>\n

    \u4e09\u3001\u7b2c\u4e09\u677f\u65a7：\u524d\u540e\u89c6\u504f\u5dee\u9759\u6001\u626b\u63cf\u2014\u2014\u628a"\u7528\u672a\u6765"\u81ea\u52a8\u63ea\u51fa\u6765<\/h3>\n

    shift(-1) \u8fd9\u79cd bug \u6700\u96be\u53d1\u73b0，\u56e0\u4e3a\u5b83\u4ee3\u7801\u957f\u5f97\u8ddf shift(1) \u51e0\u4e4e\u4e00\u6837\u3001IDE \u4e0d\u62a5\u8b66\u3001\u56de\u6d4b\u7ed3\u679c\u8fd8"\u663e\u8457\u53d8\u597d"\u3002\u5bf9\u7b56\u662f\u5199\u4e00\u4e2a\u9759\u6001\u626b\u63cf\u5668，\u5728 commit \u524d\u81ea\u52a8\u68c0\u67e5：<\/p>\n

    # \u5f85\u5ba1\u4ee3\u7801\u6837\u672c（\u542b 4 \u5904\u9ad8\u5371）<\/span>
    \nsample_code =<\/span> '''
    \ndf["next_close"] = df["c"].shift(-1)
    \ndf["next_ret"] = df["c"].shift(-1) \/ df["c"] – 1
    \ndf["future_volume"] = df["v"].shift(-1)
    \ndf["prev_close"] = df["c"].shift(1) # OK
    \ndf["rolling_mean"] = df["c"].rolling(20).mean() # OK
    \ndf["tomorrow_high"] = df["h"].shift(-1)
    \n'''<\/span><\/p>\n

    DANGER_PATTERNS =<\/span> {<\/span>
    \n "shift(-1)"<\/span>:<\/span> "\u4f7f\u7528\u4e86\u672a\u6765 1 \u65e5\u6570\u636e"<\/span>,<\/span>
    \n "shift(-2)"<\/span>:<\/span> "\u4f7f\u7528\u4e86\u672a\u6765 2 \u65e5\u6570\u636e"<\/span>,<\/span>
    \n "shift(-5)"<\/span>:<\/span> "\u4f7f\u7528\u4e86\u672a\u6765 5 \u65e5\u6570\u636e"<\/span>,<\/span>
    \n "shift(-20)"<\/span>:<\/span> "\u4f7f\u7528\u4e86\u672a\u6765 20 \u65e5\u6570\u636e"<\/span>,<\/span>
    \n "fillna(method='bfill')"<\/span>:<\/span> "bfill \u7528\u672a\u6765\u503c\u586b\u5145"<\/span>,<\/span>
    \n}<\/span><\/p>\n

    for<\/span> line_no,<\/span> line in<\/span> enumerate<\/span>(<\/span>sample_code.<\/span>splitlines(<\/span>)<\/span>,<\/span> 1<\/span>)<\/span>:<\/span>
    \n for<\/span> pattern,<\/span> msg in<\/span> DANGER_PATTERNS.<\/span>items(<\/span>)<\/span>:<\/span>
    \n if<\/span> pattern in<\/span> line:<\/span>
    \n print<\/span>(<\/span>f"L<\/span>{<\/span>line_no}<\/span><\/span>: <\/span>{<\/span>line.<\/span>strip(<\/span>)<\/span>}<\/span><\/span> –> <\/span>{<\/span>msg}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

    \u771f\u5b9e\u8f93\u51fa：<\/p>\n

    L2: df["next_close"] = df["c"].shift(-1) –> \u4f7f\u7528\u4e86\u672a\u6765 1 \u65e5\u6570\u636e
    \nL3: df["next_ret"] = df["c"].shift(-1) \/ df["c"] – 1 –> \u4f7f\u7528\u4e86\u672a\u6765 1 \u65e5\u6570\u636e
    \nL4: df["future_volume"] = df["v"].shift(-1) –> \u4f7f\u7528\u4e86\u672a\u6765 1 \u65e5\u6570\u636e
    \nL7: df["tomorrow_high"] = df["h"].shift(-1) –> \u4f7f\u7528\u4e86\u672a\u6765 1 \u65e5\u6570\u636e<\/p>\n

    \u96c6\u6210\u8fdb CI \u7684\u63a8\u8350\u5199\u6cd5：<\/p>\n

      \n
    • \u628a\u8fd9\u6bb5\u626b\u63cf\u5199\u8fdb tools\/check_lookahead.py<\/li>\n
    • pre-commit hook \u81ea\u52a8\u8dd1：git commit \u89e6\u53d1，\u547d\u4e2d\u5c31\u62d2\u7edd<\/li>\n
    • GitHub Actions \u6bcf\u5929\u5b9a\u65f6\u8dd1\u4e00\u904d，\u547d\u4e2d\u7387\u9ad8\u5c31\u544a\u8b66<\/li>\n<\/ul>\n

      \u66f4\u8fdb\u9636\u7684\u68c0\u67e5\u662f AST \u7ea7\u522b\u7684\u626b\u63cf（\u4e0d\u9760\u5b57\u7b26\u4e32\u5339\u914d\u3001\u7528 ast.NodeVisitor \u904d\u5386\u8bed\u6cd5\u6811），\u53ef\u4ee5\u8bc6\u522b df["c"].rolling(20).mean().shift(-1) \u8fd9\u79cd\u94fe\u5f0f\u8c03\u7528\u91cc\u7684"\u672a\u6765"，\u4f46\u5199\u8d77\u6765\u7a0d\u91cd\u2014\u201490% \u7684\u9879\u76ee\u4ece\u5b57\u7b26\u4e32\u5339\u914d\u5f00\u59cb\u5c31\u591f\u7528\u3002<\/p>\n

      \u56db\u3001\u628a\u4e09\u677f\u65a7\u62fc\u6210\u4e00\u4efd\u9a8c\u8bc1\u6d41\u6c34\u7ebf<\/h3>\n

      def<\/span> validate_pipeline<\/span>(<\/span>df_d,<\/span> df_w,<\/span> df_m,<\/span> feat_df,<\/span> sample_strategy_code)<\/span>:<\/span>
      \n """\u56de\u6d4b\u524d\u5fc5\u8dd1：\u8fd4\u56de 0\/1 \u6821\u9a8c\u7ed3\u8bba"""<\/span>
      \n issues =<\/span> [<\/span>]<\/span>
      \n # 1. \u591a\u5468\u671f\u4ea4\u53c9\u6821\u9a8c<\/span>
      \n last_5d =<\/span> df_d.<\/span>tail(<\/span>5<\/span>)<\/span>
      \n last_w =<\/span> df_w.<\/span>iloc[<\/span>–<\/span>1<\/span>]<\/span>
      \n if<\/span> abs<\/span>(<\/span>last_5d[<\/span>"v"<\/span>]<\/span>.<\/span>sum<\/span>(<\/span>)<\/span> –<\/span> last_w[<\/span>"v"<\/span>]<\/span>)<\/span> \/<\/span> last_w[<\/span>"v"<\/span>]<\/span> ><\/span> 0.05<\/span>:<\/span>
      \n issues.<\/span>append(<\/span>"\u5468\u7ebf\u4e0e\u65e5\u7ebf vol \u504f\u5dee >5%"<\/span>)<\/span>
      \n # 2. \u8bad\u7ec3\/\u6d4b\u8bd5\u6cc4\u6f0f（\u5916\u5c42\u8c03\u7528 fit_transform \u65f6\u533a\u5206）<\/span>
      \n # … \u7565<\/span>
      \n # 3. \u9759\u6001\u626b\u63cf<\/span>
      \n for<\/span> pattern,<\/span> msg in<\/span> DANGER_PATTERNS.<\/span>items(<\/span>)<\/span>:<\/span>
      \n if<\/span> pattern in<\/span> sample_strategy_code:<\/span>
      \n issues.<\/span>append(<\/span>f"\u7b56\u7565\u4ee3\u7801\u542b\u9ad8\u5371\u6a21\u5f0f <\/span>{<\/span>pattern}<\/span><\/span>"<\/span><\/span>)<\/span>
      \n return<\/span> issues<\/p>\n

      \u628a\u8fd9\u6bb5\u4f5c\u4e3a\u56de\u6d4b\u6846\u67b6\u7684"\u524d\u7f6e\u95f8\u95e8"，\u4efb\u4f55\u4e00\u6b21\u56de\u6d4b\u524d\u90fd\u8dd1\u4e00\u904d\u3002Issue \u5217\u8868\u4e3a\u7a7a\u624d\u5141\u8bb8\u8fdb\u5165\u56de\u6d4b\u9636\u6bb5\u2014\u2014\u8fd9\u4e2a\u4e60\u60ef\u80fd\u6321\u6389 80% \u7684"\u56de\u6d4b\u795e\u7b56\u3001\u5b9e\u76d8\u4e8f\u94b1"\u4e8b\u6545\u3002<\/p>\n

      \u4e94\u3001\u5c0f\u7ed3<\/h3>\n

      \u6570\u636e\u9a8c\u8bc1\u6ca1\u6709"\u6807\u51c6\u7b54\u6848"，\u4f46\u6709"\u5fc5\u505a\u6e05\u5355"：<\/p>\n

        \n
      • \u591a\u5468\u671f\u4ea4\u53c9\u6821\u9a8c：\u65e5 vs \u5468 vs \u6708，\u76f8\u540c\u65f6\u95f4\u7a97\u53e3\u7684 vol\/close \u5fc5\u987b\u4e00\u81f4<\/li>\n
      • \u8bad\u7ec3\/\u6d4b\u8bd5\u96c6\u6cc4\u6f0f：fit \u6807\u51c6\u5316\u65f6\u53ea\u7528\u8bad\u7ec3\u96c6，\u7edd\u4e0d\u78b0\u6d4b\u8bd5\u96c6<\/li>\n
      • \u524d\u540e\u89c6\u504f\u5dee\u626b\u63cf：CI \u91cc\u5199 shift(-N) \/ bfill \u5173\u952e\u8bcd\u68c0\u6d4b，commit \u524d\u62d2\u7edd<\/li>\n<\/ul>\n

        \u4e0b\u4e00\u7bc7\u6211\u4eec\u8bb2\u589e\u91cf\u66f4\u65b0\u2014\u2014\u8ba9\u811a\u672c\u6bcf\u5929\u53ea\u62c9\u65b0\u6570\u636e，5 \u53ea\u80a1\u7968\u4ece\u5206\u949f\u7ea7\u964d\u5230\u79d2\u7ea7，\u540c\u65f6\u4fdd\u8bc1\u4e0d\u91cd\u4e0d\u6f0f\u3002<\/p>\n

        \u6ce8\u610f：\u672c\u6587\u4e3a\u6280\u672f\u5206\u4eab，\u6240\u6709\u63a5\u53e3\u8c03\u7528\u5747\u4f7f\u7528\u5b98\u65b9\u6f14\u793a\u8bc1\u4e66\u8fd4\u56de\u7684\u6f14\u793a\u6570\u636e（\u9700\u66ff\u6362\u4e3a\u81ea\u5df1\u7684\u5b9e\u9645\u8bc1\u4e66），\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae\u3002\u5e02\u573a\u6709\u98ce\u9669，\u51b3\u7b56\u9700\u8c28\u614e\u3002<\/p>\n

        \n

        \u4ee3\u7801\u4e0e\u6587\u6863：\u66f4\u591a SDK \u7528\u6cd5\u4e0e\u63a5\u53e3\u6587\u6863\u53ef\u53c2\u8003 https:\/\/github.com\/MaiRuiApi<\/p>\n","protected":false},"excerpt":{"rendered":"

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        \n\u591a\u5468\u671f\u4ea4\u53c9\u6821<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[55,99,50,62,5176],"topic":[],"class_list":["post-109249","post","type-post","status-publish","format-standard","hentry","category-server","tag-c","tag-java","tag-50","tag-62","tag-5176"],"yoast_head":"\n\u3010Python\u91cf\u5316\u6570\u636e\u5de5\u7a0b\u5b9e\u6218 #03\u3011\u56de\u6d4b\u7ed3\u679c\u548c\u5b9e\u76d8\u5bf9\u4e0d\u4e0a\uff1f\u6570\u636e\u9a8c\u8bc1\u4e09\u677f\u65a7\uff1a\u4ea4\u53c9\u6821\u9a8c\u3001\u903b\u8f91\u5ba1\u8ba1\u3001\u53ef\u89c6\u5316 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/109249.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u3010Python\u91cf\u5316\u6570\u636e\u5de5\u7a0b\u5b9e\u6218 #03\u3011\u56de\u6d4b\u7ed3\u679c\u548c\u5b9e\u76d8\u5bf9\u4e0d\u4e0a\uff1f\u6570\u636e\u9a8c\u8bc1\u4e09\u677f\u65a7\uff1a\u4ea4\u53c9\u6821\u9a8c\u3001\u903b\u8f91\u5ba1\u8ba1\u3001\u53ef\u89c6\u5316 - 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