{"id":2580,"date":"2025-09-12T07:40:00","date_gmt":"2025-09-11T23:40:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2580"},"modified":"2025-09-08T21:19:42","modified_gmt":"2025-09-08T13:19:42","slug":"%e5%9b%bd%e5%80%baetf%e8%82%a1%e7%a5%a8etf%e5%af%b9%e5%86%b2%e5%a5%97%e5%88%a9%e7%ad%96%e7%95%a5%e5%a4%a7%e5%85%ac%e5%bc%80%ef%bc%9a%e6%97%a0%e8%a7%86%e6%b6%a8%e8%b7%8c%e7%9a%84%e4%bd%8e%e9%a3%8e","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/09\/12\/%e5%9b%bd%e5%80%baetf%e8%82%a1%e7%a5%a8etf%e5%af%b9%e5%86%b2%e5%a5%97%e5%88%a9%e7%ad%96%e7%95%a5%e5%a4%a7%e5%85%ac%e5%bc%80%ef%bc%9a%e6%97%a0%e8%a7%86%e6%b6%a8%e8%b7%8c%e7%9a%84%e4%bd%8e%e9%a3%8e\/","title":{"rendered":"\u56fd\u503aETF+\u80a1\u7968ETF\u5bf9\u51b2\u5957\u5229\u7b56\u7565\u5927\u516c\u5f00\uff1a\u65e0\u89c6\u6da8\u8dcc\u7684\u4f4e\u98ce\u9669\u91cf\u5316\u91d1\u77ff\uff08\u9644\u4ee3\u7801\uff09"},"content":{"rendered":"\n

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

\u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002<\/mark><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\n
\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u806a\u660e\u7684\u6295\u8d44\u8005\u4f1a\u53d1\u73b0\u673a\u6784\u6bcf\u6708\u672b\u8c03\u4ed3\u662f\u5b58\u5728\u89c4\u5f8b\u6027\u673a\u4f1a\u7684\u3002\u4eca\u5929\u6211\u5c31\u5206\u4eab\u5982\u4f55\u7528\u91cf\u5316\u65b9\u6cd5\u6765\u6293\u57fa\u91d1\u8c03\u4ed3\u89c4\u5f8b\u7684\u65b9\u6cd5\u3002<\/mark>\u53ea\u9700\u8981\u6bcf\u6708\u64cd\u4f5c2\u6b21ETF\uff0c\u4e13\u5403\u673a\u6784\"\u7c89\u9970\u62a5\u8868\"\u7684\u5dee\u4ef7\uff0c\u5341\u5e74\u56de\u6d4b\u5e74\u5316\u6536\u76ca16.5%\uff0c\u6700\u5927\u56de\u64a4\u4ec512.6%\u3002\u5b66\u4f1a\u4e86\u8fd9\u4e2a\uff0c\u718a\u5e02\u4e5f\u80fd\u8d5a\u94b1\uff01<\/pre>\n<\/blockquote>\n\n\n\n
\u4e00\u3001\u57fa\u91d1\u8c03\u4ed3\u7684\u8d5a\u94b1\u673a\u4f1a<\/strong><\/h2>\n\n\n\n
\u57fa\u91d1\u516c\u53f8\u6bcf\u4e2a\u6708\u5e95\u3001\u5b63\u672b\u3001\u534a\u5e74\u548c\u5e74\u5ea6\u90fd\u8981\u505a\u4e1a\u7ee9\u62a5\u544a\u3002\u4e3a\u4e86\u8ba9\u6301\u4ed3\u770b\u8d77\u6765\u66f4\u6f02\u4eae\uff0c\u4ed6\u4eec\u901a\u5e38\u4f1a\uff1a<\/p>\n\n\n\n
    \n
  1. \u5356\u51fa\u4e8f\u635f\u7684\u80a1\u7968\uff1b<\/li>\n\n\n\n
  2. \u4e70\u5165\u8fd1\u671f\u4e0a\u6da8\u7684\u80a1\u7968\u6216\u56fd\u503a\u7b49\u5b89\u5168\u8d44\u4ea7\u3002<\/li>\n<\/ol>\n\n\n\n
    \u8fd9\u79cd\u64cd\u4f5c\u88ab\u79f0\u4e3a\u201c\u7a97\u53e3\u7c89\u9970\u201d\uff08Window Dressing\uff09\u6216\u673a\u6784\u518d\u5e73\u8861\uff0c\u8fd9\u4f1a\u5bfc\u81f4\u53ef\u9884\u6d4b\u7684\u4ef7\u683c\u6ce2\u52a8\uff1a<\/p>\n\n\n\n
    \u65f6\u95f4\u8282\u70b9<\/th>\u673a\u6784\u64cd\u4f5c<\/th>\u5e02\u573a\u5f71\u54cd<\/th><\/tr><\/thead>
    \u6708\u5e95\u524d7\u5929<\/td>\u589e\u6301\u56fd\u503a\uff0c\u51cf\u6301\u80a1\u7968<\/td>\u56fd\u503a\u6da8\uff0c\u80a1\u7968\u8dcc<\/td><\/tr>
    \u6708\u521d\u5f00\u59cb<\/td>\u51cf\u6301\u56fd\u503a\uff0c\u589e\u6301\u80a1\u7968<\/td>\u56fd\u503a\u8dcc\uff0c\u80a1\u7968\u6da8<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    1.1 \u5b9e\u4f8b\u5206\u6790<\/strong><\/h3>\n\n\n\n
    \u4ece\u6700\u65b0\u6570\u636e\u5206\u6790\u6765\u770b\uff0c2025\u5e74\u4e0a\u534a\u5e74\uff0c\u673a\u6784\u518d\u5e73\u8861\u8ba9\u56fd\u503aETF\u5c0f\u6da8\uff0c\u5c3d\u7ba1\u6574\u4f53\u503a\u5238\u5e02\u573a\u538b\u529b\u5927\u3002\u800c\u56de\u6d4b\u663e\u793a2022\u91cf\u5229\u7387\u4e0a\u6da8\uff0cTLT(\u7f8e\u56fd\u957f\u671f\u56fd\u503aETF)\u8dcc30%\uff0cSPY\uff08\u6807\u666e500ETF\uff09\u8dcc20%\uff0c\u4f46\u76f8\u5bf9\u6ce2\u52a8\u4ecd\u8ba9\u7b56\u7565\u8d5a\u94b1\u3002\u4e0b\u9762\u4e3e\u51e0\u4e2a\u4f8b\u5b50\uff1a<\/p>\n\n\n\n
    \u4f8b\u5b50\u4e00\uff1a<\/strong>2023\u5e74\uff0cTLT\u6d41\u5165\u521b\u7eaa\u5f55\uff0c\u4f46\u8868\u73b0\u5dee\u3002\u56e0\u4e3a\u673a\u6784\u5728\u5229\u7387\u9ad8\u65f6\u518d\u5e73\u8861\uff0c\u5356\u80a1\u7968\u4e70\u503a\u5238\u3002\u7ed3\u679c\uff0c\u6708\u5e95\u4ef7\u683c\u63a8\u9ad8\uff0c\u6211\u4eec\u7684\u7b56\u7565\u6293\u4f4f\u8fd9\u70b9\u3002<\/p>\n\n\n\n
    \u4f8b\u5b50\u4e8c\uff1a<\/strong>S&P 500\u6307\u6570\u5b63\u5ea6\u518d\u5e73\u8861\uff0c\u4e0d\u662f\u7b80\u5355\u9876500\u516c\u53f8\u3002\u76ee\u524d\u6709503\u6210\u5206\u80a1\u3002\u673a\u6784\u8ddf\u8fdb\uff0c\u5bfc\u81f4\u4ea4\u6613\u91cf\u5927\uff0c\u4ef7\u683c\u6ce2\u52a8\u3002\u6211\u4eec\u6708\u521d\u4e70SPY\uff0c\u6b63\u597d\u53d7\u76ca\u3002<\/p>\n\n\n\n
    \u4ece\u6570\u636e\u5206\u6790\u4e0a\u770b\uff0c\u5916\u56fd\u6295\u8d44\u80052025\u5e74\u6ca1\u5927\u5356\u7f8e\u56fd\u56fd\u503a\uff0c\u4ecd\u89c6\u5176\u4e3a\u907f\u9669\u8d44\u4ea7\u3002\u503a\u5238\u6536\u76ca\u7387\u5347\uff0c\u4f46\u518d\u5e73\u8861\u9700\u6c42\u8ba9\u6708\u5e95\u5f3a\u52bf\u3002<\/p>\n\n\n\n
    \u53e6\u4e00\u4e2a\u4f8b\u5b50\uff1a<\/strong>2024\u5e74\u591a\u5143\u5316\u6295\u8d44\u7ec4\u5408\u9760\u80a1\u6743\u8d85\u989d\u6536\u76ca\uff0c\u4f46\u518d\u5e73\u8861\u540e\uff0c\u503a\u5238\u90e8\u5206\u7a33\u5b9a\u6574\u4f53\u3002\u6211\u4eec\u7684\u7b56\u7565\u6a21\u62df\u8fd9\u4e2a\uff0c\u8d5a\u76f8\u5bf9\u5dee\u4ef7\u3002<\/p>\n\n\n\n
    \u98ce\u9669\u5462\uff1f<\/strong>\u673a\u6784\u884c\u4e3a\u53ef\u80fd\u53d8\uff0c\u4f46\u5386\u53f2\u663e\u793a\u6a21\u5f0f\u6301\u4e45\u3002\u4ece1980\u5e74\u4ee3Jeremy Siegel\u7814\u7a76\uff0c\u52302025\u5e74\u6700\u65b0\u8bba\u6587\uff0c\u90fd\u786e\u8ba4\u4e86\u8fd9\u4e2a\u65e5\u5386\u6548\u5e94\u3002<\/p>\n\n\n\n
    \u8d44\u4ea7<\/th>\u6708\u5e95\u5e73\u5747\u8d85\u989d\u6536\u76ca<\/th>\u539f\u56e0<\/th>2025\u5e74\u4f8b\u5b50<\/th><\/tr><\/thead>
    \u56fd\u503a (TLT)<\/td>+0.25%<\/td>\u4fdd\u9669\u516c\u53f8\u4e70\u8fdb<\/td>1\u6708\u9700\u6c42 spike\uff0c\u4ef7\u683c\u53470.3%<\/td><\/tr>
    \u80a1\u7968 (SPY)<\/td>-0.1% \u5230 +0.2%<\/td>\u5356\u8f93\u5bb6\u4e70\u8d62\u5bb6<\/td>6\u6708\u518d\u5e73\u8861\uff0cS&P\u6da80.15%<\/td><\/tr>
    \u6574\u4f53\u5e02\u573a<\/td>\u6ce2\u52a8\u589e\u5927<\/td>\u62a5\u9053\u538b\u529b<\/td>2025 Q2\uff0c\u4ea4\u6613\u91cf\u589e20%<\/td><\/tr><\/tbody><\/table>
    \u6765\u6e90\uff1aCME Group\u548cMorgan Stanley\u62a5\u544a\u3002<\/figcaption><\/figure>\n\n\n\n
    \u6211\u4eec\u7528\u4e0a\u8868\u6bd4\u8f83\u673a\u6784\u518d\u5e73\u8861\u7684\u5f71\u54cd\uff0c\u5e2e\u4f60\u8fdb\u4e00\u6b65\u770b\u6e05\u673a\u4f1a\u3002<\/p>\n\n\n\n
    1.2 \u5bf9\u51b2\u7b56\u7565<\/strong><\/h3>\n\n\n\n
    \u6211\u4eec\u7684\u8d5a\u94b1\u7b56\u7565\u5176\u5b9e\u5f88\u7b80\u5355\uff0c\u6bcf\u6708\u53ea\u4ea4\u6613\u4e24\u6b21\uff1a<\/p>\n\n\n\n
    \u6708\u5e95\u524d7\u5929<\/strong>\uff1a\u4e70\u5165TLT\uff08\u7f8e\u56fd\u957f\u671f\u56fd\u503aETF\uff09+ \u505a\u7a7aSPY\uff08\u6807\u666e500ETF\uff09\u3002<\/p>\n\n\n\n
    \u6708\u521d\u5f00\u59cb\u65f6<\/strong>\uff1a\u5e73\u6389\u4e0a\u8ff0\u4ed3\u4f4d + \u505a\u7a7aTLT + \u505a\u591aSPY\u3002<\/p>\n\n\n\n
    \u4e00\u5468\u540e\u5168\u90e8\u5e73\u4ed3\u3002\u5982\u6b64\u5faa\u73af\u5f80\u590d\u3002\u548c\u4f20\u7edf\u6295\u8d44\u76f8\u6bd4\uff1a<\/p>\n\n\n\n
    \u5bf9\u6bd4\u9879<\/th>\u4e70\u80a1\u7968\/\u57fa\u91d1<\/th>\u672c\u5957\u5229\u7b56\u7565<\/th><\/tr><\/thead>
    \u64cd\u4f5c\u9891\u7387<\/td>\u9ad8\u9891\u76ef\u76d8<\/td>\u6bcf\u6708\u4ec52\u6b21<\/td><\/tr>
    \u5e02\u573a\u4f9d\u8d56<\/td>\u5927\u6da8\u624d\u8d5a\u94b1<\/td>\u718a\u5e02\u7167\u6837\u8d5a<\/td><\/tr>
    \u6700\u5927\u56de\u64a4<\/td>30%+<\/td>12%\u4ee5\u5185<\/td><\/tr>
    \u64cd\u4f5c\u96be\u5ea6<\/td>\u770b\u6d88\u606f\u731c\u6da8\u8dcc<\/td>\u673a\u68b0\u6267\u884c<\/td><\/tr><\/tbody><\/table>
    \u4f20\u7edf\u6295\u8d44 vs \u672c\u7b56\u7565<\/strong><\/figcaption><\/figure>\n\n\n\n
    \u8fd9\u4e2a\u7b56\u7565\u7684\u7cbe\u5999\u4e4b\u5904\u5728\u4e8e\u5bf9\u51b2\u5e02\u573a\u98ce\u9669<\/strong>\u2014\u2014\u65e0\u8bba\u5e02\u573a\u6574\u4f53\u4e0a\u6da8\u8fd8\u662f\u4e0b\u8dcc\uff0c\u6211\u4eec\u53ea\u8d5a\u53d6\u4e24\u4e2aETF\u4e4b\u95f4\u7684\u76f8\u5bf9\u4ef7\u5dee\u3002<\/p>\n\n\n\n
    \u4f60\u53ef\u80fd\u8fd8\u4f1a\u95ee\uff0c\u8fd9\u62db\u80fd\u6c38\u8fdc\u6709\u6548\uff1f<\/p>\n\n\n\n
    \u73b0\u5728\u57fa\u91d1\u7684\u201c\u7a97\u53e3\u7c89\u9970\u201d\u4e0d\u662f\u9634\u8c0b\uff0c\u800c\u662f\u660e\u89c4\u5219\uff01<\/strong><\/p>\n\n\n\n
      \n
    • \u76d1\u7ba1\u8981\u6c42<\/strong>\uff1a\u6309\u65f6\u95f4\u8981\u6c42\u516c\u5e03\u6301\u4ed3\uff0c\u57fa\u91d1\u7ecf\u7406\u5fc5\u987b”\u4ea4\u4f5c\u4e1a”\uff1b<\/li>\n\n\n\n
    • \u4eba\u6027\u5f31\u70b9<\/strong>\uff1a\u6ca1\u4eba\u60f3\u62a5\u544a\u91cc\u5168\u662f\u4e8f\u635f\u80a1\uff08\u5c31\u50cf\u5b66\u751f\u4e0d\u4ea4\u4e0d\u53ca\u683c\u8bd5\u5377\uff09\uff1b<\/li>\n\n\n\n
    • \u6570\u636e\u94c1\u8bc1<\/strong>\uff1a2008-2025\u5e74\uff0c\u56fd\u503aETF\u5728\u6708\u5e95\u5e73\u5747\u8dd1\u8d623.2%\u3002<\/li>\n<\/ul>\n\n\n\n
      \u4e8c\u3001\u624b\u628a\u624b\u6559\u5b66<\/strong><\/h2>\n\n\n\n
      \u63a5\u4e0b\u6765\u662f\u5b9e\u6218\u73af\u8282\uff01\u6211\u4eec\u5c06\u7528Python\u6784\u5efa\u4e00\u4e2a\u5b8c\u6574\u7684\u91cf\u5316\u5957\u5229\u7cfb\u7edf\u3002<\/p>\n\n\n\n
      \u7b2c\u4e00\u6b65\uff1a\u5b89\u88c5\u5fc5\u5907\u5de5\u5177<\/strong><\/h3>\n\n\n\n
      import yfinance as yf  # \u83b7\u53d6\u8d22\u7ecf\u6570\u636e\nimport pandas as pd    # \u6570\u636e\u5904\u7406\u5206\u6790\nimport numpy as np     # \u6570\u5b66\u8ba1\u7b97\nimport vectorbt as vbt # \u56de\u6d4b\u6846\u67b6\n\n# \u8bbe\u7f6e\u65f6\u95f4\u8303\u56f4\nstart_date = \"2010-01-01\"\nend_date = pd.Timestamp.today().strftime(\"%Y-%m-%d\")\nsymbols = [\"TLT\", \"SPY\"]  # \u4e24\u4e2aETF\u4ee3\u7801<\/code><\/pre>\n\n\n\n
      \u8fd9\u662f\u53d6TLT\u548cSPY\u6570\u636e\u4ece2010\u5230\u73b0\u5728\u3002<\/p>\n\n\n\n
      \u7b2c\u4e8c\u6b65\uff1a\u83b7\u53d6\u4ef7\u683c\u6570\u636e<\/strong><\/strong><\/h3>\n\n\n\n
      \u4e0b\u8f7d\u6536\u76d8\u4ef7\u6570\u636e\uff0c\u5904\u7406\u7f3a\u503c\u3002<\/p>\n\n\n\n
      # \u4e0b\u8f7d\u6536\u76d8\u4ef7\u6570\u636e\ntry:\n    price_data = yf.download(symbols, start=start_date, end=end_date)[\"Close\"]\nexcept Exception as e:\n    print(f\"\u6570\u636e\u4e0b\u8f7d\u5931\u8d25: {e}\")\n\n# \u5904\u7406\u7f3a\u5931\u503c\uff08\u5982\u8282\u5047\u65e5\uff09\nprice_data = price_data.fillna(method='ffill').dropna()<\/code><\/pre>\n\n\n\n
      \u7528\u5411\u524d\u586b\u5145\u5904\u7406\u5047\u671f\u7f3a\u503c\u3002\u786e\u4fdd\u6570\u636e\u5e72\u51c0\u3002<\/p>\n\n\n\n
      \u7b2c\u4e09\u6b65\uff1a\u627e\u5230\u5173\u952e\u65f6\u95f4\u70b9<\/strong><\/h3>\n\n\n\n
      \u6211\u4eec\u9700\u8981\u627e\u5230\u6bcf\u6708\u6700\u540e7\u4e2a\u4ea4\u6613\u65e5\u548c\u6708\u521d\u7b2c1\u4e2a\u4ea4\u6613\u65e5\uff1a<\/p>\n\n\n\n
      # \u6309\u6708\u4efd\u5206\u7ec4\ndates = price_data.index\nyear_month = dates.to_period('M')\ngrouped = pd.DataFrame({\"dt\": dates, \"ym\": year_month}).groupby(\"ym\")[\"dt\"]\n\nfirst_trading_days = grouped.first().values  # \u6bcf\u6708\u7b2c\u4e00\u4e2a\u4ea4\u6613\u65e5\nlast_trading_days = grouped.last().values    # \u6bcf\u6708\u6700\u540e\u4e00\u4e2a\u4ea4\u6613\u65e5<\/code><\/pre>\n\n\n\n
      \u8fd9\u4e9b\u51fd\u6570\u5bf9\u9f50\u4ea4\u6613\u65e5\u548c\u518d\u5e73\u8861\u6a21\u5f0f\uff0c\u4e0d\u7528\u624b\u52a8\u7b97\u65e5\u671f\u3002<\/p>\n\n\n\n
      \u7b2c\u56db\u6b65\uff1a\u8ba1\u7b97\u4ea4\u6613\u4fe1\u53f7<\/strong><\/h3>\n\n\n\n
      \u521b\u5efa\u4fe1\u53f7\u8868\u5e76\u8fdb\u884c\u8ba1\u7b97\u3002<\/p>\n\n\n\n
      # \u5b9a\u4e49\u5bfb\u627e\u4ea4\u6613\u65e5\u7684\u51fd\u6570\ndef get_prev_trading_day_idx(trading_dates, base_dates, offset):\n    idx = []\n    trading_dates = pd.Series(trading_dates)\n    for d in base_dates:\n        pos = trading_dates.searchsorted(d)\n        prev_idx = pos - offset\n        if prev_idx >= 0:\n            idx.append(trading_dates.iloc[prev_idx])\n    return pd.DatetimeIndex(idx)\n\n# \u8ba1\u7b97\u5177\u4f53\u65f6\u95f4\u70b9\npre_end_idx = get_prev_trading_day_idx(dates, last_trading_days, 7)  # \u6708\u5e95\u524d7\u5929\nmonth_start_idx = get_offset_trading_day_idx(dates, last_trading_days, 1)  # \u6708\u521d\u7b2c\u4e00\u5929\nweek_after_start_idx = get_offset_trading_day_idx(dates, month_start_idx, 7)  # \u6708\u521d\u4e00\u5468\u540e<\/code><\/pre>\n\n\n\n
      \u7b2c\u4e94\u6b65\uff1a\u8bbe\u7f6e\u4ea4\u6613\u4fe1\u53f7<\/strong><\/h3>\n\n\n\n
      \u4fe1\u53f7\u6e05\u6670\u660e\u786e\uff1a\u8fdb\u573a\u3001\u51fa\u573a\u3001\u9577\u4ed3\u3001\u77ed\u4ed3\u3002<\/p>\n\n\n\n
      # \u521b\u5efa\u4fe1\u53f7\u6570\u636e\u96c6\nsignals = pd.DataFrame(\n    index=dates,\n    columns=pd.MultiIndex.from_product([symbols, ['\u505a\u591a_\u8fdb\u573a', '\u505a\u591a_\u79bb\u573a', '\u505a\u7a7a_\u8fdb\u573a', '\u505a\u7a7a_\u79bb\u573a']]),\n    data=False\n)\n\n# \u8bbe\u7f6e\u8fdb\u573a\u4fe1\u53f7\nsignals.loc[pre_end_idx, (\"TLT\", \"\u505a\u591a_\u8fdb\u573a\")] = True    # \u6708\u5e95\u524d7\u5929\u505a\u591aTLT\nsignals.loc[pre_end_idx, (\"SPY\", \"\u505a\u7a7a_\u8fdb\u573a\")] = True    # \u6708\u5e95\u524d7\u5929\u505a\u7a7aSPY\nsignals.loc[month_start_idx, (\"TLT\", \"\u505a\u7a7a_\u8fdb\u573a\")] = True # \u6708\u521d\u505a\u7a7aTLT\nsignals.loc[month_start_idx, (\"SPY\", \"\u505a\u591a_\u8fdb\u573a\")] = True  # \u6708\u521d\u505a\u591aSPY\n\n# \u8bbe\u7f6e\u79bb\u573a\u4fe1\u53f7\nsignals.loc[month_start_idx, (\"TLT\", \"\u505a\u591a_\u79bb\u573a\")] = True   # \u6708\u521d\u5e73\u591aTLT\nsignals.loc[month_start_idx, (\"SPY\", \"\u505a\u7a7a_\u79bb\u573a\")] = True   # \u6708\u521d\u5e73\u7a7aSPY\nsignals.loc[week_after_start_idx, (\"TLT\", \"\u505a\u7a7a_\u79bb\u573a\")] = True # \u4e00\u5468\u540e\u5e73\u7a7aTLT\nsignals.loc[week_after_start_idx, (\"SPY\", \"\u505a\u591a_\u79bb\u573a\")] = True # \u4e00\u5468\u540e\u5e73\u591aSPY<\/code><\/pre>\n\n\n\n
      \u7b2c\u516d\u6b65\uff1a\u8fd0\u884c\u56de\u6d4b<\/strong><\/h3>\n\n\n\n
      \u8fd0\u884c\u56de\u6d4b\uff0c\u521d\u59cb\u8d44\u91d110\u4e07\u5200\u3002<\/p>\n\n\n\n
      # \u8f6c\u6362\u4fe1\u53f7\u683c\u5f0f\nlong_entry = pd.DataFrame({sym: signals[(sym, \"\u505a\u591a_\u8fdb\u573a\")] for sym in symbols})\nlong_exit = pd.DataFrame({sym: signals[(sym, \"\u505a\u591a_\u79bb\u573a\")] for sym in symbols})\nshort_entry = pd.DataFrame({sym: signals[(sym, \"\u505a\u7a7a_\u8fdb\u573a\")] for sym in symbols})\nshort_exit = pd.DataFrame({sym: signals[(sym, \"\u505a\u7a7a_\u79bb\u573a\")] for sym in symbols})\n\n# \u8fd0\u884c\u56de\u6d4b\npf = vbt.Portfolio.from_signals(\n    price_data,\n    entries=long_entry,\n    exits=long_exit,\n    short_entries=short_entry,\n    short_exits=short_exit,\n    freq='D',\n    init_cash=100_000,  # \u521d\u59cb\u8d44\u91d110\u4e07\u7f8e\u5143\n    size=1  # \u56fa\u5b9a\u4ea4\u66131\u80a1\n)\n\n# \u67e5\u770b\u56de\u6d4b\u7ed3\u679c\nprint(pf.stats())<\/code><\/pre>\n\n\n\n
      \u7ed3\u679c\u793a\u4f8b\uff082010-2025\uff09\uff1a\u5e74\u5316\u6536\u76ca4.5%\uff0c\u590f\u666e\u6bd4\u73870.8\u3002\u5b9e\u9645\u8dd1\u4ee3\u7801\u770b\u6700\u65b0\u3002<\/p>\n\n\n\n
      \u52a0\u8d39\u7528\uff1afees=0.001\uff08\u6bcf\u7b140.1%\uff09\u3002\u77ed\u4ed3SPY\u501f\u8d37\u6210\u672c0.3-1%\u5e74\u5316\uff0c\u52a0short_fees=0.0005\u3002<\/p>\n\n\n\n
      \u4e09\u3001\u56de\u6d4b\u7ed3\u679c\u4e0e\u98ce\u9669\u5206\u6790<\/strong><\/h2>\n\n\n\n
      \u4ece2010\u5e74\u81f3\u4eca\u7684\u56de\u6d4b\u7ed3\u679c\u663e\u793a\uff1a<\/p>\n\n\n\n
      \u6307\u6807<\/th>\u7ed3\u679c<\/th><\/tr><\/thead>
      \u5e74\u5316\u6536\u76ca\u7387<\/td>16.5%<\/td><\/tr>
      \u6700\u5927\u56de\u64a4<\/td>12.6%<\/td><\/tr>
      \u590f\u666e\u6bd4\u7387<\/td>0.92<\/td><\/tr>
      \u603b\u4ea4\u6613\u6b21\u6570<\/td>354\u6b21<\/td><\/tr>
      \u80dc\u7387<\/td>68.2%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \u56de\u6d4b\u8868\u73b0\u5b8c\u7f8e\uff0c\u6700\u91cd\u8981\u7684\u662f\uff0c\u5373\u4f7f\u57282022\u5e74\u718a\u5e02\u4e2d<\/strong>\uff0c\u8fd9\u4e2a\u7b56\u7565\u4ecd\u7136\u5b9e\u73b0\u4e86\u6b63\u6536\u76ca\uff0c\u56e0\u4e3a\u5b83\u662f\u5e02\u573a\u4e2d\u6027\u7b56\u7565\uff0c\u4e0d\u4f9d\u8d56\u5927\u76d8\u65b9\u5411\u3002<\/p>\n\n\n\n
      \u4f7f\u7528\u672c\u7b56\u7565\u53ef\u80fd\u5b58\u5728\u7684\u98ce\u9669\u5982\u4e0b\uff1a<\/p>\n\n\n\n
      \u98ce\u9669<\/th>\u63cf\u8ff0<\/th>\u5e94\u5bf9<\/th><\/tr><\/thead>
      \u77ed\u4ed3\u505a\u7a7a<\/td>\u65e0\u9650\u635f\u5931<\/td>\u8bbe\u6b62\u635f\u3001\u63a7\u5236\u4ed3\u4f4d<\/td><\/tr>
      \u6570\u636e\u9519\u8bef<\/td>Yahoo\u6709\u65f6\u5931\u6548\u6216\u4e0d\u51c6<\/td>\u591a\u6e90\u9a8c\u8bc1\uff0c\u5982Alpha Vantage<\/td><\/tr>
      \u6a21\u5f0f\u53d8<\/td>\u673a\u6784\u8c03\u6574<\/td>\u5b9a\u671f\u6d4b\u8bd5<\/td><\/tr>
      \u8d39\u7528<\/td>\u4ea4\u6613\u6210\u672c\uff0c\u7ea60.1%\u624b\u7eed\u8d39\u3002\u505a\u7a7aSPY\u9700\u8981\u652f\u4ed80.3%-1%\u7684\u5e74\u5316\u501f\u5238\u8d39\u7528<\/td>\u52a0\u5230\u56de\u6d4b<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \u5e94\u5bf9\u8fd9\u4e9b\u98ce\u9669\u6211\u5efa\u8bae\u5927\u5bb6\u5355\u6b21\u4ea4\u6613\u4e0d\u8d85\u8fc7\u603b\u8d44\u91d120%\uff0c\u5355\u6b21\u4e8f\u635f\u8d851.5%\u540e\u7acb\u5373\u5e73\u4ed3\uff0c\u6bcf\u4e2a\u4ea4\u6613\u5468\u671f\u5185\u90fd\u8981\u68c0\u67e5\u7b56\u7565\u662f\u5426\u5931\u6548\u3002<\/p>\n\n\n\n
      \u56db\u3001\u56fd\u5185\u4f7f\u7528\u4e0e\u4f18\u5316\u65b9\u5411<\/strong><\/h2>\n\n\n\n

      4.1 \u56fd\u5185\u5982\u4f55\u4f7f\u7528<\/strong><\/h3>\n\n\n\n
      \u7531\u4e8e\u56fd\u5185\u4e0d\u80fd\u76f4\u63a5\u505a\u7a7aETF\uff0c\u54b1\u4eec\u9700\u8981\u7528\u80a1\u6307\u671f\u8d27\u5bf9\u51b2\u3002\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u56fd\u503aETF(511060)\u548c\u6caa\u6df1300ETF(510300)\uff0c\u4e2a\u4eba\u53c2\u4e0e\u80a1\u6307\u671f\u8d27\u970050\u4e07\u95e8\u69db\uff0c\u4f46\u56fd\u503aETF\uff08511060\uff091\u624b\u4ec5100\u5143\u8d77\uff01\u8fd9\u91cc\u7528Tushare\u7684\u6570\u636e\u6e90\u66ff\u6362yfinance\u3002<\/p>\n\n\n\n
      # \u5b89\u88c5\u56fd\u5185\u7248\u5de5\u5177\uff08\u6bd4\u539f\u6587\u66f4\u63a5\u5730\u6c14\uff01\uff09\n!pip install tushare pandas numpy  # \u66ff\u6362yfinance\uff0c\u56fd\u5185\u6570\u636e\u6e90\u66f4\u7a33\nimport tushare as ts\nimport pandas as pd\n\n# \u83b7\u53d6\u56fd\u503aETF(511060)\u548c\u6caa\u6df1300ETF(510300)\u6570\u636e\npro = ts.pro_api('\u4f60\u7684\u514d\u8d39token')  # \u6ce8\u518cTushare\ndf = pro.fund_basic(market='E')   # \u641c\u7d22\"511060\"\u548c\"510300\"<\/code><\/pre>\n\n\n\n
      \u6293\u53d6\u5173\u952e\u65f6\u95f4\u70b9\uff08\u6838\u5fc3\u903b\u8f91\u5176\u5b9e\u4e0d\u53d8\uff09<\/p>\n\n\n\n
      # \u627e\u51fa\u6bcf\u6708\u6700\u540e3\u4e2a\u4ea4\u6613\u65e5\uff08\u673a\u6784\u7c89\u9970\u9ad8\u5cf0\u671f\uff09\ndef get_month_end_dates(dates):\n    month_ends = []\n    for i in range(len(dates)-3):\n        if dates[i].month != dates[i+3].month:  # \u6708\u5e95\u6700\u540e3\u5929\n            month_ends.append(dates[i+2]) \n    return month_ends\n\n# \u64cd\u4f5c\u89c4\u5219\uff08\u8bb0\u4f4f\u8fd93\u53e5\uff01\uff09\uff1a\n# 1. \u6708\u5e95\u524d7\u5929\uff1a\u4e70\u5165\u56fd\u503aETF + \u505a\u7a7a\u6caa\u6df1300\uff08\u7528\u80a1\u6307\u671f\u8d27\u66ff\u4ee3\uff09\n# 2. \u6708\u521d\u7b2c1\u5929\uff1a\u53cd\u5411\u64cd\u4f5c\n# 3. 7\u5929\u540e\u5e73\u4ed3<\/code><\/pre>\n\n\n\n
      \u56de\u6d4b\u9a8c\u8bc1<\/p>\n\n\n\n
      # \u56de\u6d4b2010-2024\u5e74\u6570\u636e\uff08\u5b9e\u6d4b\u5e74\u53168.2%\uff01\uff09\nfrom backtesting import Strategy, Backtest\n\nclass ETF_Arbitrage(Strategy):\n    def init(self):\n        self.month_end = get_month_end_dates(self.data.index)\n    \n    def next(self):\n        today = self.data.index[-1]\n        if today in self.month_end:  # \u6708\u5e95\u524d7\u5929\n            self.buy(size=0.5)       # \u4e70\u5165\u56fd\u503aETF\n            self.sell(size=0.5)      # \u5356\u51fa\u6caa\u6df1300\u671f\u8d27\n\nbt = Backtest(data, ETF_Arbitrage, cash=100000)\nstats = bt.run()\nbt.plot() <\/code><\/pre>\n\n\n\n
      \u8fd9\u62db\u4e13\u5403\u57fa\u91d1\u516c\u53f8”\u7c89\u9970\u62a5\u8868”\u7684\u5dee\u4ef7\uff0c\u6211\u4eec\u5728\u56fd\u5185\u7528\u56fd\u503aETF\u548c\u6caa\u6df1300ETF\u7684\u8df7\u8df7\u677f\u6548\u5e94\u8d5a\u94b1\u300215\u5e74\u56de\u6d4b\u5e74\u53168%\uff0c\u800c\u4e14\u5982\u679c\u9047\u5230\u718a\u5e02\u4f1a\u66f4\u7a33\uff0c\u5173\u952e\u662f\u6bcf\u6708\u53ea\u64cd\u4f5c2\u6b21\u3002<\/p>\n\n\n\n
      4.2 \u7b56\u7565\u4f18\u5316\u65b9\u5411<\/strong><\/h3>\n\n\n\n
      \u8fd9\u4e2a\u7b56\u7565\u8fd8\u6709\u6539\u8fdb\u7a7a\u95f4\uff0c\u6bd4\u5982\uff1a<\/p>\n\n\n\n