{"id":2232,"date":"2025-05-01T07:31:00","date_gmt":"2025-04-30T23:31:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2232"},"modified":"2025-04-20T15:34:07","modified_gmt":"2025-04-20T07:34:07","slug":"%e8%bf%bd%e6%b6%a8%e8%bf%98%e6%98%af%e6%8a%84%e5%ba%95python%e4%b8%80%e7%9c%bc%e7%9c%8b%e7%a9%bf%ef%bc%8c%e6%95%99%e4%bd%a0%e7%94%a8%e9%87%8f%e5%8c%96%e6%96%b9%e6%b3%95%e6%a1%86%e5%87%ba","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/05\/01\/%e8%bf%bd%e6%b6%a8%e8%bf%98%e6%98%af%e6%8a%84%e5%ba%95python%e4%b8%80%e7%9c%bc%e7%9c%8b%e7%a9%bf%ef%bc%8c%e6%95%99%e4%bd%a0%e7%94%a8%e9%87%8f%e5%8c%96%e6%96%b9%e6%b3%95%e6%a1%86%e5%87%ba\/","title":{"rendered":"\u8ffd\u6da8\u8fd8\u662f\u6284\u5e95Python\u4e00\u773c\u770b\u7a7f\uff0c\u6559\u4f60\u7528\u91cf\u5316\u65b9\u6cd5\u6846\u51fa\u201c\u8d5a\u94b1\u533a\u95f4\u201d\uff01"},"content":{"rendered":"\n

\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

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\"\"<\/figure>\n\n\n\n
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\u5f15\u8a00\uff1a\u80a1\u5e02\u771f\u80fd\u9884\u6d4b\u5417\uff1f<\/strong><\/h2>\n\n\n\n
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\u4eca\u5929\uff0c\u6211\u5c06\u5e26\u4f60\u7528Python\u548cK\u7ebf\u56fe\uff0c\u6316\u6398\u80a1\u5e02\u7684\u201c\u8d5a\u94b1\u533a\u95f4\u201d\uff0c\u8ba9\u4f60\u77e5\u9053\u4ec0\u4e48\u65f6\u5019\u8be5\u8ffd\uff0c\u4ec0\u4e48\u65f6\u5019\u8be5\u7b49\u3002\u6ca1\u7f16\u7a0b\u57fa\u7840\u7684\u4e5f\u522b\u614c\uff0c\u6587\u7ae0\u8d85\u7b80\u5355\uff0c\u90fd\u80fd\u770b\u61c2\uff01<\/p>\n\n\n\n
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    \n
  • \u8d8b\u52bf\u9636\u6bb5<\/strong>\uff08\u6bd4\u5982GME\u6563\u6237\u5927\u6218\u534e\u5c14\u8857\uff09\uff1aGameStop\u5148\u662f\u8d8b\u52bf\u66b4\u6da8\uff0c\u8ffd\u6da8\u7684\u4eba\u8d5a\u75af\uff1b\u540e\u6765\u56de\u8c03\uff0c\u6284\u5e95\u7684\u624d\u6709\u673a\u4f1a\u3002\u8fd9\u4e2a\u9636\u6bb5\u9002\u5408\u201c\u8ffd\u6da8\u6740\u8dcc\u201d\uff0c\u987a\u52bf\u800c\u4e3a\u3002<\/li>\n\n\n\n
  • \u53cd\u8f6c\u9636\u6bb5<\/strong>\uff08\u6bd4\u5982\u66b4\u6da8\u540e\u7684\u9634\u8dcc\u56de\u8c03\uff09\uff1a\u4ef7\u683c\u6da8\u591a\u5fc5\u8dcc\uff0c\u9002\u5408\u201c\u9ad8\u629b\u4f4e\u5438\u201d\u3002<\/li>\n\n\n\n
  • \u968f\u673a\u9636\u6bb5<\/strong>\uff08\u6bd4\u5982\u9707\u8361\u5e02\uff09\uff1a\u80a1\u4ef7\u65e0\u89c4\u5f8b\uff0c\u4e71\u64cd\u4f5c\u5fc5\u4e8f\uff0c\u4e0d\u5982\u8eba\u5e73\u3002<\/li>\n<\/ul>\n\n\n\n
    \u90a3\u4e48\u95ee\u9898\u6765\u4e86\uff1a\u600e\u4e48\u77e5\u9053\u5f53\u524d\u662f\u54ea\u79cd\u72b6\u6001\uff1f<\/p>\n\n\n\n
    \u7b54\u6848\u662f\uff1a\u7528\u6570\u636e\uff01\u6211\u4eec\u53ef\u4ee5\u7528\u4e24\u4e2a\u5de5\u5177\u2014\u2014\u65b9\u5dee\u6bd4<\/strong>\u548c\u81ea\u76f8\u5173\u5206\u6790<\/strong>\uff0c\u6765\u5224\u65ad\u80a1\u4ef7\u7684\u201c\u6027\u683c\u201d\u3002<\/p>\n\n\n\n
    \u4e8c\u3001\u65b9\u5dee\u6bd4\uff1a\u80a1\u5e02\u7684\u201c\u6027\u683c\u63a2\u6d4b\u5668\u201d<\/strong><\/h2>\n\n\n\n
    \u65b9\u5dee\u6bd4\u542c\u8d77\u6765\u9ad8\u5927\u4e0a\uff0c\u5176\u5b9e\u662f\u4e2a\u7b80\u5355\u7684\u201c\u63a2\u6d4b\u5668\u201d\u3002\u5b83\u80fd\u544a\u8bc9\u4f60\uff0c\u80a1\u4ef7\u662f\u6309\u90e8\u5c31\u73ed\uff08\u968f\u673a\uff09\uff0c\u8fd8\u662f\u6709\u89c4\u5f8b\uff08\u8d8b\u52bf\u6216\u56de\u8c03\uff09\u3002<\/p>\n\n\n\n
    \u5982\u4e0b\u56fe\uff1a\u6211\u7528\u91cf\u5316\u7a0b\u5e8f\u5206\u6790GME 2021\u5e74\u66b4\u6da8\u66b4\u8dcc\u6848\u4f8b\uff0c\u6807\u6ce8\u5173\u952e\u4e70\u5356\u70b9\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
      \n
    • \u770b\u80a1\u4ef7\u5728200\u65e5\u5747\u7ebf\u4e0a\u65b9\uff08\u4e0a\u56fe\uff09+\u65b9\u5dee\u6bd4>1\uff08\u4e2d\u56fe\u7ea2\u533a\uff09+\u6210\u4ea4\u91cf\u653e\u5927\uff08\u4e0b\u56fe\uff09\uff0c\u5c31\u662f”\u95ed\u773c\u4e70”\u4fe1\u53f7\uff1b<\/li>\n\n\n\n
    • \u8dcc\u7834\u5747\u7ebf+\u65b9\u5dee\u6bd4<1\uff08\u7eff\u533a\uff09\u5219\u662f”\u9ad8\u629b\u4f4e\u5438”\u65f6\u673a\uff1b<\/li>\n\n\n\n
    • \u5f53\u6307\u6807\u90fd\u57281\u9644\u8fd1\u5f98\u5f8a\u65f6\uff0c\u7ba1\u4f4f\u624b\u522b\u4e71\u52a8\uff01<\/li>\n<\/ul>\n\n\n\n
      \u8bb0\u4f4f\u53e3\u8bc0\uff1a<\/strong>“\u7ebf\u4e0a\u7ea2\u67f1\u8ddf\u8d8b\u52bf\uff0c\u7ebf\u4e0b\u7eff\u67f1\u7b49\u53cd\u8f6c\uff0c\u91cf\u80fd\u4e0d\u591f\u522b\u786c\u5e72”\u3002<\/p>\n\n\n\n
      2.1 \u539f\u7406\u8d85\u7b80\u5355<\/strong><\/h3>\n\n\n\n
      \u5047\u8bbe\u80a1\u4ef7\u5b8c\u5168\u968f\u673a\uff08\u50cf\u629b\u786c\u5e01\uff09\uff0c\u90a3\u4e4810\u5929\u7684\u6ce2\u52a8\u5e94\u8be5\u662f1\u5929\u6ce2\u52a8\u768410\u500d\u3002\u65b9\u5dee\u6bd4\u7387\u5c31\u662f\u62ff\u5b9e\u9645\u6ce2\u52a8\u548c\u7406\u8bba\u6ce2\u52a8\u6bd4\u4e00\u6bd4\uff1a<\/p>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
      \u8fd9\u4e2a\u65b9\u6cd5\u6765\u81ea1988\u5e74\u4e24\u4f4d\u5927\u795eLo\u548cMacKinlay\u7684\u7814\u7a76\uff0c\u4ed6\u4eec\u53d1\u73b0\uff1a\u80a1\u5e02\u5e76\u4e0d\u603b\u662f\u968f\u673a\u7684\uff0c\u603b\u6709\u4e9b\u201c\u8d5a\u94b1\u533a\u95f4\u201d\u80fd\u88ab\u6293\u4f4f\uff01<\/p>\n\n\n\n
      2.2 \u6838\u5fc3\u4ee3\u7801\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
      \u8fd9\u4e2a\u4ee3\u7801\u793a\u4f8b\u4f1a\u7b97\u51fa\u65b9\u5dee\u6bd4\u7387\uff0c\u544a\u8bc9\u4f60\u80a1\u4ef7\u662f\u8d8b\u52bf\u3001\u56de\u8c03\u8fd8\u662f\u968f\u673a\u3002\u8fd8\u80fd\u751f\u6210\u4e0a\u9762\u90a3\u5f20\u5206\u6790\u56fe\u3002<\/p>\n\n\n\n
      # \u62c9\u53d6GameStop\u6570\u636e\nticker = 'GME'\ndata = yf.download(ticker, start='2020-01-01', end='2025-04-01', interval='1d')\ndata['Returns'] = data['Close'].pct_change().dropna()  # \u8ba1\u7b97\u65e5\u6536\u76ca\n\n# \u53c2\u6570\u8bbe\u7f6e\nholding_periods = [2, 5, 10, 20, 60]  # \u4e0d\u540c\u65f6\u95f4\u5468\u671f\nwindow_size = 252  # \u6ed1\u52a8\u7a97\u53e3\uff08\u4e00\u5e74\u4ea4\u6613\u5929\u6570\uff09\n\n# \u8ba1\u7b97\u65b9\u5dee\u6bd4\u7387\ndef rolling_variance_ratio(returns, q, window):\n    vr_series = pd.Series(index=returns.index, dtype=float)\n    pval_series = pd.Series(index=returns.index, dtype=float)\n    \n# \u6587\u7ae0\u5bb9\u91cf\u6709\u9650\uff0c\u8bf7\u5230\u6587\u672b\u4e0b\u8f7d\u5b8c\u6574\u4ee3\u7801\n\n# \u8ba1\u7b97\u4e0d\u540c\u5468\u671f\u7684\u65b9\u5dee\u6bd4\u7387\ndf_vr = pd.DataFrame(index=data.index)\ndf_pval = pd.DataFrame(index=data.index)\nfor q in holding_periods:\n    vr, pval = rolling_variance_ratio(data['Returns'], q, window_size)\n    df_vr[f'VR_{q}'] = vr\n    df_pval[f'PVal_{q}'] = pval\n\nax2.tick_params(axis='both', which='major', labelsize=10)\nplt.xlabel('Date', fontsize=8)\n\nplt.tight_layout()\nplt.show()<\/code><\/pre>\n\n\n\n
      \u4ee3\u7801\u8bf4\u660e<\/strong>\uff1a<\/p>\n\n\n\n
        \n
      • \u6570\u636e<\/strong>\uff1a\u62c9\u53d62020-2025\u5e74\u7684GME\u65e5K\u7ebf\u6570\u636e\uff0c\u8ba1\u7b97\u65e5\u6536\u76ca\u7387\u3002<\/li>\n\n\n\n
      • \u65b9\u5dee\u6bd4\u7387<\/strong>\uff1a\u7528252\u5929\uff08\u4e00\u5e74\uff09\u7684\u6ed1\u52a8\u7a97\u53e3\uff0c\u7b97\u4e0d\u540c\u5468\u671f\uff082\u30015\u300110\u300120\u300160\u5929\uff09\u7684\u65b9\u5dee\u6bd4\u7387\u3002<\/li>\n\n\n\n
      • p\u503c<\/strong>\uff1a\u5982\u679cp\u503c<0.05\uff0c\u8bf4\u660e\u65b9\u5dee\u6bd4\u7387\u663e\u8457\uff0c\u80a1\u4ef7\u6709\u89c4\u5f8b\u53ef\u5faa\u3002<\/li>\n<\/ul>\n\n\n\n
        \u8fd0\u884c\u7ed3\u679c<\/strong>\uff1a2021\u5e74\u521d\uff0cGME\u7684\u65b9\u5dee\u6bd4\u7387>1\uff0cp\u503c<0.05\uff0c\u8bf4\u660e\u662f\u5f3a\u8d8b\u52bf\uff0c\u9002\u5408\u8ffd\u6da8\uff1b2021\u5e74\u540e\uff0c\u65b9\u5dee\u6bd4\u7387<1\uff0c\u8fdb\u5165\u56de\u8c03\uff0c\u9002\u5408\u6284\u5e95\u3002<\/p>\n\n\n\n
        \u4e09\u3001\u81ea\u76f8\u5173\u5206\u6790\uff1a\u80a1\u4ef7\u7684\u201c\u8bb0\u5fc6\u529b\u201d<\/strong><\/h2>\n\n\n\n
        \u9664\u4e86\u65b9\u5dee\u6bd4\u7387\uff0c\u8fd8\u6709\u4e2a\u66f4\u76f4\u89c2\u7684\u5de5\u5177\u2014\u2014\u81ea\u76f8\u5173\u7cfb\u6570\u3002\u5b83\u5c31\u50cf\u662f\u68c0\u67e5\u80a1\u4ef7\u7684\u201c\u8bb0\u5fc6\u529b\u201d\uff1a\u4eca\u5929\u6da8\u4e86\uff0c\u660e\u5929\u8fd8\u6da8\u5417\uff1f<\/p>\n\n\n\n
        \"\"<\/figure>\n\n\n\n
        \u8fd9\u5f20\u56fe\u89e3\u91ca\u4e86\u591a\u4e2a\u9891\u7387\u4e0b\u6536\u76ca\u7684\u6eda\u52a8\u81ea\u76f8\u5173\u6027\u3002\u4e0a\u56fe\uff1a\u91c7\u7528\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u4ef7\u683c\u3002\u4e2d\u56fe\uff1a\u6ede\u540e 1 \u9636\u81ea\u76f8\u5173\u6027\u3002\u4e0b\u56fe\uff1a\u6ede\u540e 1-3 \u9636\u7684\u5e73\u5747\u81ea\u76f8\u5173\u6027\u3002\u865a\u7ebf\u8868\u793a\u663e\u8457\u6027\u9608\u503c\u3002<\/p>\n\n\n\n
        3.1 \u7b80\u5355\u89e3\u91ca<\/strong><\/h3>\n\n\n\n
        \u81ea\u76f8\u5173\u7cfb\u6570\u770b\u7684\u662f\u80a1\u4ef7\u7684\u201c\u524d\u540e\u5173\u7cfb\u201d\uff1a<\/p>\n\n\n\n
        \"\"<\/figure>\n\n\n\n
        \u6211\u4eec\u7528\u4e00\u4e2a\u6ed1\u52a8\u7a97\u53e3\uff08\u6bd4\u598260\u5929\uff09\uff0c\u7b97\u51fa\u81ea\u76f8\u5173\u7cfb\u6570\uff0c\u518d\u770b\u770b\u5b83\u662f\u5426\u201c\u663e\u8457\u201d\uff08\u8d85\u8fc7\u67d0\u4e2a\u9608\u503c\uff09\u3002\u5982\u679c\u663e\u8457\uff0c\u5c31\u8bf4\u660e\u80a1\u4ef7\u6709\u89c4\u5f8b\u53ef\u5faa\uff01<\/p>\n\n\n\n
        3.2 \u6838\u5fc3\u4ee3\u7801\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
        \u518d\u6765\u770b\u81ea\u76f8\u5173\u7cfb\u6570\u7684\u4ee3\u7801\uff0c\u5e2e\u4f60\u786e\u8ba4\u80a1\u4ef7\u7684\u201c\u8bb0\u5fc6\u529b\u201d\u3002<\/p>\n\n\n\n
        # \u62c9\u53d6\u6570\u636e\nticker = 'GME'\ndata = yf.download(ticker, start='2020-01-01', end='2025-04-01', interval='1d')\ndata['Returns'] = data['Close'].pct_change().dropna()\n\n# \u53c2\u6570\nwindow = 60  # \u6ed1\u52a8\u7a97\u53e3\nlag = 1  # \u6ede\u540e1\u5929\n\n# \u8ba1\u7b97\u81ea\u76f8\u5173\u7cfb\u6570\ndef rolling_acorr(series, window, lag):\n    return series.rolling(window).apply(lambda x: pd.Series(x).autocorr(lag=lag), raw=False)\n\nacorr = rolling_acorr(data['Returns'], window, lag)\nthreshold = 2 \/ np.sqrt(window)  # \u663e\u8457\u6027\u9608\u503c\n\n# \u6587\u7ae0\u5bb9\u91cf\u6709\u9650\uff0c\u8bf7\u5230\u6587\u672b\u4e0b\u8f7d\u5b8c\u6574\u4ee3\u7801<\/code><\/pre>\n\n\n\n
        \u4ee3\u7801\u8bf4\u660e<\/strong>\uff1a<\/p>\n\n\n\n