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\u5f39\u7c27\u5df2\u88ab\u538b\u7d27\uff0c\u4f46\u8fd8\u6ca1\u677e\u624b<\/strong><\/h2>\n\n\n\n

\u8fd9\u5c31\u662f\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u683c\u5c40\u73b0\u5728\u6709\u610f\u601d\uff0c\u800c\u4e0d\u662f\u7b49\u5927\u6da8\u4e4b\u540e\u518d\u6765\u8bf4\u3002<\/p>\n\n\n\n

\u6570\u636e\u663e\u793a\uff0c10\u5e74\u671f\u6536\u76ca\u7387\u300130\u5e74\u671f\u6536\u76ca\u7387\u548cTLT\u90fd\u8fd8\u5904\u4e8e\u4e2d\u957f\u671f\u8d8b\u52bf\u7ebf\u4e0b\u65b9\u3002<\/p>\n\n\n\n

\u5e02\u573a\u5728\u8bf4\u4e09\u4ef6\u4e8b\uff1a\u8d8b\u52bf\u5411\u4e0b\uff0c\u7a7a\u5934\u5f88\u5927\uff0c\u6ca1\u6709\u786e\u8ba4\u53cd\u8f6c\u3002<\/p>\n\n\n\n

\u6211\u628a\u8fd9\u4e2a\u72b6\u6001\u53eb\u505a\uff0c\u5f39\u7c27\u5df2\u7ecf\u538b\u7d27\uff0c\u4f46\u8fd8\u6ca1\u89e6\u53d1\u3002<\/p>\n\n\n\n

\u8fd9\u4e2a\u201c\u8fd8\u6ca1\u201d\u5f88\u91cd\u8981\u3002\u673a\u4f1a\u662f\u968f\u7740\u4ef7\u683c\u8fd0\u52a8\u9010\u6b65\u6d88\u5931\u7684\u3002TLT \u5728 82 \u5230 83 \u7f8e\u5143\uff0c\u4f60\u662f\u5728\u4ea4\u6613\u6324\u538b\u7684\u53ef\u80fd\u6027\u3002TLT \u5728 88 \u5230 90 \u7f8e\u5143\uff0c\u4f60\u5c31\u662f\u5728\u4ea4\u6613\u6324\u538b\u672c\u8eab\u4e86\u3002\u8fd9\u662f\u4e24\u4e2a\u5b8c\u5168\u4e0d\u540c\u7684\u98ce\u9669\u6536\u76ca\u7ed3\u6784\u3002<\/p>\n\n\n\n

\u6211\u5728\u76ef\u7684\u5177\u4f53\u4ef7\u4f4d<\/strong><\/h2>\n\n\n\n

10 \u5e74\u671f\u56fd\u503a\u6536\u76ca\u7387\u5728 8 \u6708 24 \u65e5\u6536\u5728 4.70% \u5de6\u53f3\u3002<\/p>\n\n\n\n

\u7b2c\u4e00\u4e2a\u8ba9\u6211\u8ba4\u771f\u5173\u6ce8\u7684\u70b9\u4f4d\uff0c\u5927\u7ea6\u5728 4.60%\u3002\u8dcc\u7834\u8fd9\u4e2a\uff0c\u5f53\u524d\u7684\u4ef7\u683c\u533a\u95f4\u5c31\u7b97\u7834\u4f4d\u4e86\u3002\u5982\u679c\u7ee7\u7eed\u8dcc\u7834 4.50%\uff0c\u6211\u4f1a\u628a\u8fd9\u6b21\u53d8\u52a8\u5f53\u6210\u6bd4\u666e\u901a\u77ed\u671f\u53cd\u5f39\u66f4\u91cd\u8981\u7684\u4e8b\u6765\u5904\u7406\u3002<\/p>\n\n\n\n

\u5bf9\u5e94\u5230 ETF \u7684\u4ef7\u683c\uff0c\u5927\u7ea6\u662f\u8fd9\u6837\u7684\uff1a<\/p>\n\n\n\n

    \n
  • TLT 84 \u5230 85 \u7f8e\u5143\uff0c\u662f\u7b2c\u4e00\u9053\u8d8b\u52bf\u6311\u6218\u7ebf\u3002<\/li>\n\n\n\n
  • TLT 85.5 \u5230 86.5 \u7f8e\u5143\uff0c\u662f\u66f4\u5f3a\u7684\u786e\u8ba4\u4fe1\u53f7\u3002<\/li>\n<\/ul>\n\n\n\n

    \u6211\u8fd8\u4f1a\u770b EDV \u548c ZROZ \u8fd9\u4e24\u53ea\u66f4\u9ad8\u4e45\u671f\u7684 ETF \u80fd\u4e0d\u80fd\u914d\u5408\u3002\u5982\u679c\u957f\u7aef\u771f\u7684\u5728\u52a8\uff0c\u66f4\u9ad8\u4e45\u671f\u7684\u5de5\u5177\u5e94\u8be5\u8ddf\u7740\u786e\u8ba4\u3002<\/p>\n\n\n\n

    \u6240\u4ee5\u4e00\u4e2a\u503c\u5f97\u5173\u6ce8\u7684\u683c\u5c40\u662f\uff1aTLT \u6da8\u5230 85 \u5230 86 \u7f8e\u5143\uff0c\u540c\u65f6 EDV \u548c ZROZ \u4e5f\u51fa\u73b0\u76f8\u5e94\u4e0a\u6da8\uff0c\u8fd9\u65f6\u5019\u6324\u538b\u6982\u7387\u5c31\u4f1a\u663e\u8457\u4e0a\u5347\u300210 \u5e74\u671f\u6536\u76ca\u7387\u5230 4.30% \u5230 4.40%\uff0c\u7cfb\u7edf\u6027\u5e73\u4ed3\u5f88\u53ef\u80fd\u5df2\u7ecf\u5728\u4ef7\u683c\u91cc\u53d1\u751f\u4e86\u3002<\/p>\n\n\n\n

    \u672c\u6708\u768418\u523019\u65e5\uff0c\u5e02\u573a\u5176\u5b9e\u505a\u8fc7\u4e00\u6b21\u5c0f\u9884\u6f14\u300210 \u5e74\u671f\u6536\u76ca\u7387\u53ea\u8dcc\u4e86\u5927\u7ea6 6 \u4e2a\u57fa\u70b9\uff0c30 \u5e74\u671f\u5927\u7ea6 9 \u4e2a\u57fa\u70b9\u3002TLT \u6da8\u4e86\u7ea6 1.7%\uff0cEDV \u6da8\u4e86\u7ea6 2.9%\uff0cZROZ \u6da8\u4e86\u8d85\u8fc7 3%\u3002\u8fd9\u53ea\u662f\u4e00\u6b21\u5f88\u5c0f\u7684\u5229\u7387\u6ce2\u52a8\u3002\u5982\u679c\u6765\u4e00\u6b21 30 \u5230 40 \u4e2a\u57fa\u70b9\u7684\u6ce2\u52a8\uff0c\u53e0\u52a0\u771f\u5b9e\u7684\u6301\u4ed3\u5e73\u4ed3\uff0c\u662f\u5b8c\u5168\u4e0d\u540c\u7684\u5c40\u9762\u3002<\/p>\n\n\n

    \n
    \"\"
    \u25b2 \u56fe3\uff1aTLT \u5173\u952e\u4ef7\u4f4d\u56fe<\/figcaption><\/figure>\n<\/div>\n\n\n

    \u4e45\u671f\u51f8\u6027\u53e0\u52a0\u6301\u4ed3\u51f8\u6027\uff1a<\/strong>\u503a\u5238\u81ea\u5df1\u201c\u8d8a\u6da8\u8d8a\u75af\u201d\u7684\u52a0\u901f\u5ea6\uff0c\u52a0\u4e0a\u7a7a\u5934\u4eec\u614c\u4e0d\u62e9\u8def\u8e29\u8e0f\u4e70\u5165\u7684\u63a8\u529b\uff0c\u8fd9\u4e24\u80a1\u52b2\u649e\u5230\u4e00\u8d77\uff0c\u624d\u662f\u8fd9\u6ce2\u884c\u60c5\u771f\u6b63\u7684\u597d\u620f\u3002<\/p>\n\n\n\n

    \u4e00\u4e2a\u6bd4\u7b49\u6280\u672f\u7a81\u7834\u66f4\u65e9\u7684\u4fe1\u53f7<\/strong><\/h2>\n\n\n\n

    \u901a\u5e38\u5927\u5bb6\u4f1a\u7b49\u6536\u76ca\u7387\u8dcc\u7834\u4e86\u67d0\u4e2a\u5173\u952e\u4f4d\u7f6e\u518d\u53bb\u5173\u6ce8\u3002\u4f46\u6709\u4e00\u4e2a\u4fe1\u53f7\u6bd4\u8fd9\u4e2a\u66f4\u65e9\u3002<\/p>\n\n\n\n

    \u7d27\u7d27\u76ef\u4f4f\u56fd\u503a\u6536\u5230\u574f\u6d88\u606f\u7684\u65f6\u5019\u3002<\/p>\n\n\n\n

    \u5982\u679c\u6cb9\u4ef7\u5927\u6da8\u3001\u8d22\u653f\u6d88\u606f\u7ee7\u7eed\u96be\u770b\u3001\u56fd\u503a\u4f9b\u7ed9\u4f9d\u7136\u6c89\u91cd\uff0c\u800c 10 \u5e74\u671f\u6536\u76ca\u7387\u7a81\u7136\u4e0d\u521b\u65b0\u9ad8\u4e86\uff0c\u8fd9\u4ef6\u4e8b\u672c\u8eab\u5c31\u5f88\u6709\u5206\u91cf\u3002<\/p>\n\n\n\n

    \u4e00\u4e2a\u663e\u800c\u6613\u89c1\u7684\u9053\u7406\uff1a\u88ab\u5927\u91cf\u505a\u7a7a\u7684\u5e02\u573a\uff0c\u5728\u574f\u6d88\u606f\u9762\u524d\u4e0d\u518d\u4e0b\u8dcc\uff0c\u5f80\u5f80\u8bf4\u660e\u5e02\u573a\u6027\u8d28\u6b63\u5728\u53d1\u751f\u53d8\u5316\u3002<\/p>\n\n\n\n

    \n

    \u574f\u6d88\u606f\u51fa\u6765 \u2192 \u503a\u5238\u4ef7\u683c\u4e0d\u518d\u521b\u65b0\u4f4e \u2192 \u7a7a\u5934\u5f00\u59cb\u4e0d\u8d5a\u94b1 \u2192 \u5e73\u4ed3\u5f00\u59cb \u2192 \u4ef7\u683c\u4e0a\u6da8\u3002<\/em><\/p>\n<\/blockquote>\n\n\n\n

    \u8fd9\u53ef\u80fd\u53d1\u751f\u5728\u660e\u663e\u7684\u6280\u672f\u7a81\u7834\u4e4b\u524d\u3002\u6240\u4ee5\u6211\u4f1a\u540c\u65f6\u76ef\u597d\u6d88\u606f\u5e26\u6765\u7684\u53cd\u5f39\uff0c\u548c\u574f\u6d88\u606f\u4e4b\u540e\u7684\u5356\u76d8\u5931\u8d25\u3002<\/p>\n\n\n\n

    \u5b8f\u89c2\u73af\u5883\u7684\u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/h2>\n\n\n\n

    \u600e\u4e48\u5224\u65ad\u8fd9\u6b21\u4e0a\u6da8\u662f\u7a7a\u5934\u6324\u538b\uff0c\u8fd8\u662f\u7ecf\u6d4e\u589e\u957f\u771f\u7684\u51fa\u95ee\u9898\u4e86\uff1f<\/p>\n\n\n\n

    \u8fd9\u91cc\u6709\u4e00\u4e2a\u7b80\u5355\u7684\u6846\u67b6\u53ef\u4ee5\u53c2\u8003\uff1a<\/p>\n\n\n\n

    \u5982\u679c TLT \u53cd\u5f39\uff0c\u540c\u65f6\u9ad8\u6536\u76ca\u503a HYG \u5927\u4f53\u7a33\u5b9a\u3001MOVE \u6ce2\u52a8\u7387\u6307\u6570\u4e0b\u964d\u3001\u80a1\u5e02\u6ca1\u6709\u5927\u8dcc\uff0c\u8fd9\u4e2a\u53d8\u52a8\u770b\u8d77\u6765\u66f4\u50cf\u5229\u7387\u548c\u6301\u4ed3\u7684\u6324\u538b\u3002<\/p>\n\n\n\n

    \u5982\u679c TLT \u53cd\u5f39\uff0c\u4f46 HYG \u660e\u663e\u8d70\u5f31\uff0cHYG \u548c TLT \u7684\u6bd4\u503c\u5927\u5e45\u4e0b\u964d\uff0c\u90a3\u5e02\u573a\u7684\u89e3\u8bfb\u5c31\u53d8\u4e86\u2014\u2014\u53ef\u80fd\u5728\u5b9a\u4ef7\u7ecf\u6d4e\u589e\u957f\u653e\u7f13\u3002<\/p>\n\n\n\n

    \u5982\u679c\u6cb9\u4ef7\u7ee7\u7eed\u5927\u6da8\u3001\u901a\u80c0\u9884\u671f\u4e0a\u5347\u300130 \u5e74\u671f\u6536\u76ca\u7387\u7ee7\u7eed\u521b\u51fa\u65b0\u9ad8\uff0c\u90a3\u6324\u538b\u7684\u5047\u8bbe\u5927\u6982\u7387\u662f\u9519\u7684\uff0c\u81f3\u5c11\u6682\u65f6\u662f\u9519\u7684\u3002<\/p>\n\n\n\n

    \u62e5\u6324\u7684\u6301\u4ed3\u662f\u71c3\u6599\uff0c\u4f46\u71c3\u6599\u4e0d\u7b49\u4e8e\u706b\u67f4\u3002\u62e5\u6324\u7684\u4ea4\u6613\u53ef\u4ee5\u6bd4\u4eba\u4eec\u9884\u671f\u7684\u6301\u7eed\u66f4\u4e45\u3002<\/p>\n\n\n\n

    \u5982\u679c\u4f60\u60f3\u53c2\u4e0e\uff0c\u600e\u4e48\u505a<\/strong><\/h2>\n\n\n\n

    \u6700\u7b80\u5355\u7684\u5de5\u5177\u8fd8\u662f TLT<\/strong>\u3002<\/p>\n\n\n\n

    EDV \u7ed9\u66f4\u9ad8\u7684\u4e45\u671f\u655e\u53e3\uff0cZROZ \u7ed9\u66f4\u9ad8\u7684\u51f8\u6027\u3002\u5bf9\u4e8e\u4e00\u4e2a\u7ec4\u5408\u914d\u7f6e\uff0c\u6211\u503e\u5411\u4e8e TLT\uff0c\u56e0\u4e3a\u5b83\u7ed9\u4e86\u8db3\u591f\u7684\u4e45\u671f\uff0c\u4f46\u6ca1\u6709\u628a\u957f\u7aef\u6ce2\u52a8\u7684\u6bcf\u4e00\u5206\u94b1\u90fd\u62c9\u6ee1\u3002<\/p>\n\n\n\n

    \u5982\u679c\u60f3\u505a\u4e00\u6b21\u6218\u672f\u6027\u7684\u8868\u8fbe\uff0c\u671f\u6743\u5c31\u66f4\u6709\u610f\u601d\u4e86\u3002<\/p>\n\n\n\n

    TLT \u5728 82.6 \u7f8e\u5143\u9644\u8fd1\u65f6\uff0c11 \u6708 20 \u65e5\u5230\u671f\u7684\u671f\u6743\u5927\u6982\u662f\u8fd9\u6837\u7684\u62a5\u4ef7\uff1a82 \u770b\u6da8\u671f\u6743\u5229 2.06 \u7f8e\u5143\uff0c88 \u770b\u6da8\u671f\u6743 0.34 \u7f8e\u5143\u3002<\/p>\n\n\n\n

    \u4e24\u4e2a\u671f\u6743\u7ec4\u6210\u7684\u4ef7\u5dee\u7ec4\u5408\uff0c\u4e70\u8fdb\u6765\u7684\u6210\u672c\u5927\u7ea6\u662f 1.72 \u7f8e\u5143\u3002\u5982\u679c TLT \u6700\u540e\u6da8\u5230 88 \u7f8e\u5143\u4ee5\u4e0a\uff0c\u8fd9\u4e2a\u4ef7\u5dee\u6700\u591a\u503c 6 \u7f8e\u5143\uff0c\u4e5f\u5c31\u662f\u8bf4\u4f60\u6295\u5165 1.72 \u6700\u591a\u62ff\u56de 6 \u5757\uff0c\u6536\u76ca\u7387\u5927\u7ea6 +250%\u3002<\/p>\n\n\n

    \n
    \"\"
    \u25b2 \u56fe4\uff1a\u671f\u6743\u4ef7\u5dee\u7ed3\u6784\u793a\u610f\u56fe<\/figcaption><\/figure>\n<\/div>\n\n\n

    \u8fd9\u4e2a\u7ed3\u6784\u6709\u610f\u601d\u7684\u5730\u65b9\u5728\u4e8e\uff1a\u4f60\u4e0d\u9700\u8981TLT\u6da8\u5230100\u7f8e\u5143\u3002\u4f60\u53ea\u9700\u5b83\u7a7f\u8fc783.72\u7f8e\u5143\u8fd9\u4e2a\u76c8\u4e8f\u5e73\u8861\u70b9\uff0c\u800c\u8fd9\u4e2a\u4ef7\u683c\u533a\u95f4\u6070\u597d\u4e5f\u662f\u53ef\u80fd\u89e6\u53d1\u8d8b\u52bf\u8ddf\u8e2a\u6a21\u578b\u7ffb\u591a\u7684\u5730\u5e26\u3002<\/p>\n\n\n\n

    \u4e00\u4e2a\u53ef\u4ee5\u590d\u5236\u7684\u6324\u538b\u8bc4\u5206\u6846\u67b6<\/strong><\/h2>\n\n\n\n

    \u8bf4\u5230\u8fd9\u91cc\uff0c\u6211\u7ed9\u4f60\u4e00\u4e2a\u53ef\u4ee5\u81ea\u5df1\u52a8\u624b\u7684\u4e1c\u897f\u3002<\/p>\n\n\n\n

    \u6211\u771f\u6b63\u8ffd\u8e2a\u7684\uff0c\u4e0d\u662f\u201c\u7a7a\u5934\u5f88\u5927\u201d\u8fd9\u4e2a\u4fe1\u606f\uff0c\u8fd9\u4e2a\u4fe1\u606f\u672c\u8eab\u6ca1\u6709\u592a\u591a\u65f6\u95f4\u4ef7\u503c\u3002\u6211\u60f3\u6293\u4f4f\u7684\u662f\u8fc7\u6e21\u72b6\u6001\uff1a\u4ece\u6301\u4ed3\u62e5\u6324\uff0c\u5230\u8d8b\u52bf\u53cd\u8f6c\uff0c\u5230\u6536\u76ca\u7387\u4e0b\u964d\uff0c\u5230\u4e45\u671f ETF \u786e\u8ba4\uff0c\u6700\u540e\u5230\u7a7a\u5934\u5e73\u4ed3\u3002<\/p>\n\n\n\n

    \u4e0b\u9762\u7684\u4ee3\u7801\u662f\u4e00\u4e2a\u7b80\u5316\u7248\u672c\uff0c\u4ece\u6301\u4ed3\u6570\u636e\u3001\u6536\u76ca\u7387\u548cETF\u4ef7\u683c\u6765\u6784\u5efa\u53ef\u6267\u884c\u5185\u5bb9\u3002<\/p>\n\n\n\n

    \"\"\"\n\u56fd\u503a\u671f\u8d27\u6324\u538b\u8bc4\u5206\uff08Treasury Squeeze Score\uff09\u8ba1\u7b97\u811a\u672c\n\u6570\u636e\u6e90\uff1aCFTC\u6301\u4ed3\u3001ETF\u4ef7\u683c\u3001\u56fd\u503a\u6536\u76ca\u7387\n\u8bc4\u5206\u8303\u56f4\uff1a0-100\uff0c\u8d8a\u9ad8\u8868\u793a\u6324\u538b\u53ef\u80fd\u6027\u8d8a\u5927\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport quantjourney as qj\nfrom typing import Dict, Tuple, Optional\nfrom datetime import datetime\n\n\ndef fetch_cot_data(symbol: str = \"043602\", start_date: str = \"2025-01-01\") -> pd.DataFrame:\n    \"\"\"\n    \u83b7\u53d6\u5e76\u6574\u7406CFTC\u56fd\u503a\u671f\u8d27\u6301\u4ed3\u6570\u636e\n    \"\"\"\n    raw = qj.cftc.get_cot_data(\n        symbol=symbol,\n        report_type=\"financial\",\n        futures_only=True,\n        start_date=start_date\n    )\n    cot = pd.DataFrame(raw[\"data\"]).sort_values(\"report_date_as_yyyy_mm_dd\")\n    \n    # \u8ba1\u7b97\u6760\u6746\u57fa\u91d1\u51c0\u591a\u5934\u548c\u7a7a\u5934\u5360\u6bd4\n    cot[\"lev_net\"] = cot[\"lev_money_positions_long\"] - cot[\"lev_money_positions_short\"]\n    cot[\"short_pct_oi\"] = cot[\"lev_money_positions_short\"] \/ cot[\"open_interest_all\"]\n    return cot\n\n\ndef fetch_etf_prices(symbols: list, start_date: str = \"2025-01-01\") -> pd.DataFrame:\n    \"\"\"\n    \u83b7\u53d6\u591a\u53eaETF\u7684\u5386\u53f2\u6536\u76d8\u4ef7\uff0c\u5e76\u8ba1\u7b97\u6280\u672f\u6307\u6807\n    \"\"\"\n    raw = qj.fmp.get_historical_prices(\n        symbols=symbols,\n        start_date=start_date,\n        frequency=\"1d\"\n    )\n    \n    # \u6784\u5efa\u4ef7\u683cDataFrame\n    price_dict = {}\n    for symbol, rows in raw[\"data\"][\"value\"].items():\n        # \u4ece\u884c\u5217\u8868\u6784\u5efaSeries\n        series = pd.Series(\n            {row[\"date\"]: row[\"close\"] for row in rows},\n            dtype=float\n        )\n        series.index = pd.to_datetime(series.index)\n        price_dict[symbol] = series.sort_index()\n    \n    px = pd.DataFrame(price_dict)\n    \n    # \u8ba1\u7b97\u6280\u672f\u6307\u6807\n    px[\"TLT_MA50\"] = px[\"TLT\"].rolling(50).mean()\n    px[\"TLT_vs_50D\"] = (px[\"TLT\"] \/ px[\"TLT_MA50\"]) - 1\n    px[\"EDV_5D\"] = px[\"EDV\"].pct_change(5)\n    px[\"ZROZ_5D\"] = px[\"ZROZ\"].pct_change(5)\n    return px\n\n\ndef fetch_treasury_rates(start_date: str = \"2025-01-01\") -> pd.DataFrame:\n    \"\"\"\n    \u83b7\u53d6\u56fd\u503a\u6536\u76ca\u7387\u66f2\u7ebf\u6570\u636e\uff0c\u5e76\u8ba1\u7b9710\u5e74\u671f\u6536\u76ca\u73875\u65e5\u53d8\u5316\n    \"\"\"\n    raw = qj.fmp.get_treasury_rates(from_=start_date)\n    rates = pd.DataFrame(raw[\"data\"])\n    rates[\"date\"] = pd.to_datetime(rates[\"date\"])\n    rates = rates.set_index(\"date\").sort_index()\n    rates[\"10Y_5D\"] = rates[\"year10\"].diff(5)\n    return rates\n\n\ndef compute_squeeze_score(\n    cot: pd.DataFrame,\n    px: pd.DataFrame,\n    rates: pd.DataFrame\n) -> Tuple[float, Dict[str, float]]:\n    \"\"\"\n    \u57fa\u4e8e\u6700\u65b0\u6570\u636e\u8ba1\u7b97\u6324\u538b\u8bc4\u5206\u53ca\u5176\u5b50\u6210\u5206\n    \"\"\"\n    # \u53d6\u6700\u65b0\u4e00\u884c\n    c = cot.iloc[-1]\n    p = px.iloc[-1]\n    r = rates.iloc[-1]\n    \n    # 1. \u6301\u4ed3\u62e5\u6324\u5ea6\uff08\u7a7a\u5934\u5360\u6bd4\uff09\n    positioning = np.clip(c[\"short_pct_oi\"] \/ 0.50, 0, 1)\n    \n    # 2. \u4ef7\u683c\u8d8b\u52bf\uff08TLT\u76f8\u5bf950\u65e5\u5747\u7ebf\u7684\u504f\u79bb\uff09\n    trend = np.clip((p[\"TLT_vs_50D\"] + 0.03) \/ 0.04, 0, 1)\n    \n    # 3. \u6536\u76ca\u7387\u52a8\u91cf\uff08\u8d1f\u5411\u53d8\u5316\uff09\n    yield_momentum = np.clip(-r[\"10Y_5D\"] \/ 0.20, 0, 1)\n    \n    # 4. \u4e45\u671f\u786e\u8ba4\uff08EDV\u548cZROZ\u540c\u65f6\u4e0a\u6da8\uff09\n    duration_confirmation = np.mean([p[\"EDV_5D\"] > 0, p[\"ZROZ_5D\"] > 0])\n    \n    # \u52a0\u6743\u603b\u5206\n    weights = {\n        \"positioning\": 0.40,\n        \"trend\": 0.30,\n        \"yield_momentum\": 0.20,\n        \"duration_confirmation\": 0.10\n    }\n    score = 100 * (\n        weights[\"positioning\"] * positioning +\n        weights[\"trend\"] * trend +\n        weights[\"yield_momentum\"] * yield_momentum +\n        weights[\"duration_confirmation\"] * duration_confirmation\n    )\n    \n    components = {\n        \"positioning\": positioning,\n        \"trend\": trend,\n        \"yield_momentum\": yield_momentum,\n        \"duration_confirmation\": duration_confirmation\n    }\n    return score, components\n\n\ndef main(start_date: str = \"2025-01-01\") -> None:\n    \"\"\"\n    \u4e3b\u6d41\u7a0b\uff1a\u62c9\u53d6\u6570\u636e\u3001\u8ba1\u7b97\u8bc4\u5206\u5e76\u6253\u5370\u7ed3\u679c\n    \"\"\"\n    try:\n        print(\"\u6b63\u5728\u83b7\u53d6CFTC\u6301\u4ed3\u6570\u636e...\")\n        cot = fetch_cot_data(start_date=start_date)\n        \n        print(\"\u6b63\u5728\u83b7\u53d6ETF\u4ef7\u683c\u6570\u636e...\")\n        px = fetch_etf_prices(symbols=[\"TLT\", \"EDV\", \"ZROZ\"], start_date=start_date)\n        \n        print(\"\u6b63\u5728\u83b7\u53d6\u56fd\u503a\u6536\u76ca\u7387\u6570\u636e...\")\n        rates = fetch_treasury_rates(start_date=start_date)\n        \n        score, components = compute_squeeze_score(cot, px, rates)\n        \n        print(\"\\n=== Treasury Squeeze Score ===\")\n        print(f\"\u7efc\u5408\u8bc4\u5206: {score:.1f}\/100\")\n        print(\"\\n\u5b50\u6210\u5206\u660e\u7ec6:\")\n        for name, value in components.items():\n            print(f\"  {name:<20}: {value:.3f}\")\n            \n    except Exception as e:\n        print(f\"\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\u53d1\u751f\u9519\u8bef: {e}\")\n        raise\n\n\nif __name__ == \"__main__\":\n    main()<\/code><\/pre>\n\n\n\n
    \u6743\u91cd\u4e0d\u9700\u8981\u8fc7\u5ea6\u4f18\u5316\u3002\u8fd9\u4e2a\u6846\u67b6\u7684\u4ef7\u503c\u5728\u4e8e\uff0c\u628a\u52bf\u80fd\u79ef\u7d2f \u548c \u5b9e\u9645\u89e6\u53d1\u5206\u5f00\u6765\u770b\u3002\u73b0\u5728\u6301\u4ed3\u5206\u9879\u7684\u5f97\u5206\u5f88\u9ad8\uff0c\u8d8b\u52bf\u5206\u9879\u8fd8\u5f88\u4f4e,\u8fd9\u5c31\u662f\u6709\u610f\u601d\u7684\u539f\u56e0\u3002<\/p>\n\n\n\n
    \u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
    \u6211\u4e0d\u4f1a\u7b80\u5355\u8bf4\uff1a\u505a\u591a\u56fd\u503a\uff0c\u56e0\u4e3a\u6536\u76ca\u7387\u8981\u964d\u3002\u8fd9\u4e2a\u7ed3\u8bba\u4e0b\u5f97\u592a\u65e9\uff0c\u8bc1\u636e\u8fd8\u4e0d\u591f\u3002<\/p>\n\n\n\n
    \u66f4\u51c6\u786e\u7684\u8bf4\u6cd5\u662f\uff1a\u56fd\u503a\u5e02\u573a\u73b0\u5728\u6709\u5f02\u5e38\u5927\u7684\u7a7a\u5934\u6301\u4ed3\uff0c\u6709\u65b0\u7684\u8fb9\u9645\u4e70\u5bb6\u8fdb\u5165\uff0c\u4ef7\u683c\u70b9\u4f4d\u79bb\u5173\u952e\u8d8b\u52bf\u7ebf\u5f88\u8fd1\u3002\u4e00\u6b21\u6e29\u548c\u7684\u521d\u59cb\u53cd\u5f39\uff0c\u6709\u53ef\u80fd\u6f14\u53d8\u6210\u81ea\u6211\u5f3a\u5316\u7684\u884c\u60c5\u3002<\/p>\n\n\n\n
    \u8d1d\u68ee\u7279\u4e0d\u9700\u8981\u6c38\u4e45\u89e3\u51b3\u56fd\u503a\u5e02\u573a\u7684\u7ed3\u6784\u6027\u95ee\u9898\uff0c\u4ed6\u505a\u4e0d\u5230\u3002\u4ed6\u53ea\u9700\u8981\u628a\u4ef7\u683c\u63a8\u5230\u8db3\u591f\u8fdc\uff0c\u8ba9\u522b\u4eba\u5f00\u59cb\u4e70\u3002<\/p>\n\n\n\n
    \u5728\u4e00\u4e2a\u6301\u4ed3\u62e5\u6324\u7684\u5e02\u573a\u91cc\uff0c\u8fd9\u5f80\u5f80\u5c31\u8db3\u591f\u5236\u9020\u51fa\u6bd4\u6700\u521d\u50ac\u5316\u5242\u5927\u5f97\u591a\u7684\u884c\u60c5\u4e86\u3002<\/p>\n\n\n\n
    \n\n\n\n
    #\u7f8e\u503a<\/a> #\u7a7a\u5934\u6324\u538b<\/a> #\u8d22\u653f\u90e8\u56de\u8d2d<\/a> #CTA\u8d8b\u52bf\u8ddf\u8e2a<\/a> #TLT<\/a> #DV01<\/a> #\u56fd\u503a\u671f\u8d27<\/a> #CFTC\u6301\u4ed3<\/a> #\u6536\u76ca\u7387<\/a> #\u4e45\u671f\u51f8\u6027<\/a> #\u671f\u6743\u7ed3\u6784<\/a> #QuantJourney<\/a><\/p>\n\n\n\n
    <\/p>\n","protected":false},"excerpt":{"rendered":"
    \u4f5c \u8005\uff1a\u8001\u4f59\u635e\u9c7c \u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005…<\/p>\n
    Continue reading\u7f8e\u503a\u7a7a\u5934\u6301\u4ed3223\u4e07\u624b\uff0c\u8d1d\u68ee\u7279\u4e00\u4e70\u5165\u5c31\u80fd\u903c\u7a7a\uff1f<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[4,5,14,6],"class_list":["post-4789","post","type-post","status-publish","format-standard","hentry","category-aiinvest","tag-ai","tag-14","tag-6","entry"],"_links":{"self":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4789","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/comments?post=4789"}],"version-history":[{"count":1,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4789\/revisions"}],"predecessor-version":[{"id":4796,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4789\/revisions\/4796"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=4789"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=4789"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=4789"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}