{"id":4841,"date":"2026-09-09T07:16:00","date_gmt":"2026-09-08T23:16:00","guid":{"rendered":"https:\/\/laoyulaoyu.com\/?p=4841"},"modified":"2026-09-02T16:16:47","modified_gmt":"2026-09-02T08:16:47","slug":"%e4%bd%a0%e7%9a%84%e5%8a%a8%e9%87%8f%e7%ad%96%e7%95%a5%e4%b8%ba%e4%bb%80%e4%b9%88%e6%80%bb%e6%98%af%e4%ba%8f%ef%bc%9f%e6%98%af%e5%9b%a0%e4%b8%ba%e4%bb%a3%e7%a0%81%e9%87%8c%e5%b8%b8%e5%b8%b8%e6%9c%89","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2026\/09\/09\/%e4%bd%a0%e7%9a%84%e5%8a%a8%e9%87%8f%e7%ad%96%e7%95%a5%e4%b8%ba%e4%bb%80%e4%b9%88%e6%80%bb%e6%98%af%e4%ba%8f%ef%bc%9f%e6%98%af%e5%9b%a0%e4%b8%ba%e4%bb%a3%e7%a0%81%e9%87%8c%e5%b8%b8%e5%b8%b8%e6%9c%89\/","title":{"rendered":"\u4f60\u7684\u52a8\u91cf\u7b56\u7565\u4e3a\u4ec0\u4e48\u603b\u662f\u4e8f\uff1f\u662f\u56e0\u4e3a\u4ee3\u7801\u91cc\u5e38\u5e38\u6709\u8fd9\u4e2a\u5751"},"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

\u6587\u4e2d\u793a\u4f8b\u4ec5\u7528\u4e8e\u6280\u672f\u8ba8\u8bba\uff0c\u4e0d\u6784\u6210\u4efb\u4f55\u64cd\u4f5c\u5efa\u8bae\u3002
\u91cf\u5316\u7b56\u7565\u5f00\u53d1\u5e94\u4ee5\u5b66\u4e60\u548c\u6280\u672f\u4ea4\u6d41\u4e3a\u76ee\u7684\u3002
\u672c\u53f7\u4e0d\u8350\u80a1\u3001\u4e0d\u5356\u8bfe\u3001\u4e0d\u627f\u8bfa\u6536\u76ca\u3002
\u5e02\u573a\u6709\u98ce\u9669\uff0c\u8bf7\u5408\u6cd5\u5408\u89c4\u6295\u8d44\u3002<\/p>\n<\/blockquote>\n\n\n\n

\u672c\u6587\u7ea6 3800 \u5b57 | \u9884\u8ba1\u9605\u8bfb 7-10 \u5206\u949f\uff0c\u5185\u6709\u5b9e\u4f8b\u4ee3\u7801\uff0c\u5efa\u8bae\u5148\u6536\u85cf\u3002<\/p>\n\n\n\n

\u00b7 \u00b7 \u00b7<\/p>\n\n\n\n

\u5148\u8bf4\u4e00\u4e2a\u53cd\u76f4\u89c9\u7684\u73b0\u8c61<\/strong><\/h2>\n\n\n\n

\u5927\u591a\u6570\u8001\u6563\u6237\u4f1a\u544a\u8bc9\u4f60\uff1a\u8ffd\u6da8\u6740\u8dcc\u662f\u4e8f\u94b1\u7684\u6839\u6e90\u3002<\/strong><\/p>\n\n\n\n

\u8fd9\u8bdd\u6709\u4e00\u5b9a\u9053\u7406\uff0c\u4f46\u4e0d\u591f\u5b8c\u6574\u3002\u5728\u67d0\u4e9b\u7279\u5b9a\u7684\u65f6\u95f4\u6846\u67b6\u5185\uff0c”\u8ffd\u6da8\u6740\u8dcc”\u5176\u5b9e\u662f\u4e00\u79cd\u6709\u5b66\u672f\u80cc\u4e66\u7684\u7cfb\u7edf\u5316\u7b56\u7565\uff0c\u5b66\u672f\u754c\u628a\u5b83\u53eb\u505a\u65f6\u95f4\u5e8f\u5217\u52a8\u91cf\uff08Time Series Momentum\uff0cTSMOM\uff09<\/strong>\u3002<\/p>\n\n\n\n

\u6838\u5fc3\u903b\u8f91\u5f88\u6734\u7d20\uff1a\u8d44\u4ea7\u8fc7\u53bb\u51e0\u4e2a\u6708\u7684\u8868\u73b0\uff0c\u5728\u672a\u6765\u4e00\u6bb5\u65f6\u95f4\u5185\u503e\u5411\u4e8e\u5ef6\u7eed\u3002\u6da8\u7684\u7ee7\u7eed\u6da8\uff0c\u8dcc\u7684\u7ee7\u7eed\u8dcc\u3002\u8fd9\u4e0d\u662f\u7384\u5b66\uff0c\u800c\u662f\u4e00\u5957\u6709\u5b9e\u8bc1\u6570\u636e\u652f\u6491\u7684\u4ea4\u6613\u601d\u8def\u3002<\/p>\n\n\n\n

2011\u5e74\uff0c\u4e09\u4f4d\u5b66\u8005 Moskowitz\u3001Ooi \u548cPedersen\u5728\u9876\u520a\u53d1\u8868\u4e86\u4e00\u7bc7\u7814\u7a76\uff0c\u68c0\u9a8c\u4e86\u8986\u76d6\u80a1\u6307\u671f\u8d27\u3001\u5916\u6c47\u671f\u8d27\u3001\u5927\u5b97\u5546\u54c1\u671f\u8d27\u548c\u56fd\u503a\u671f\u8d27\u5728\u5185\u768458\u79cd\u6d41\u52a8\u6027\u8d44\u4ea7<\/strong>\u3002\u51e0\u4e4e\u6bcf\u4e00\u79cd\u8d44\u4ea7\u7684\u5386\u53f2\u6536\u76ca\u90fd\u80fd\u9884\u6d4b\u81ea\u5df1\u672a\u6765\u7684\u65b9\u5411\u3002<\/p>\n\n\n\n

\u8fd9\u4e2a\u7ed3\u8bba\u542c\u8d77\u6765\u6709\u70b9\u53cd\u5e38\u8bc6\uff0c\u4f46\u80cc\u540e\u7684\u884c\u4e3a\u91d1\u878d\u5b66\u89e3\u91ca\u5f88\u76f4\u767d\uff1a\u5e02\u573a\u5bf9\u4fe1\u606f\u5b58\u5728\u53cd\u5e94\u8fdf\u6ede<\/strong>\u3002\u597d\u6d88\u606f\u6765\u4e86\uff0c\u5148\u77e5\u5148\u89c9\u7684\u4eba\u5148\u8fdb\u573a\uff0c\u540e\u77e5\u540e\u89c9\u7684\u4eba\u8ddf\u7740\u8ffd\uff0c\u5f62\u6210\u6301\u7eed\u7684\u8d8b\u52bf\u3002\u7b49\u8d8b\u52bf\u8d70\u5b8c\u4e00\u7a0b\uff0c\u624d\u53ef\u80fd\u51fa\u73b0\u53cd\u8f6c\u3002<\/p>\n\n\n\n

\u4eca\u5929\u8fd9\u7bc7\u6587\u7ae0\uff0c\u6211\u7528 Python \u628a\u65f6\u95f4\u5e8f\u5217\u52a8\u91cf\u7684\u5b8c\u6574\u5b9e\u73b0\u8dd1\u4e00\u904d\uff0c\u4ece\u4fe1\u53f7\u8ba1\u7b97\u5230\u4ed3\u4f4d\u7ba1\u7406\u5230\u56de\u6d4b\u8bc4\u4f30\uff0c\u5168\u90e8\u7ed9\u4f60\u62c6\u5f00\u6765\u8bb2\u3002<\/p>\n\n\n\n

\u4e00\u3001\u5b66\u672f\u754c\u600e\u4e48\u9a8c\u8bc1\u8fd9\u4e2a\u89c4\u5f8b<\/strong><\/h2>\n\n\n\n

\u4e0a\u9762\u8bf4\u5230\u7684\u8bba\u6587\uff08\u94fe\u63a5\u9644\u5728\u6587\u672b\uff09\u91cc\u7528\u4e86\u76f8\u5f53\u5168\u9762\u7684\u6570\u636e\uff1a<\/p>\n\n\n\n

\"\"
\u25b2 \u8de8\u8d44\u4ea7\u7c7b\u522b\u7684\u52a8\u91cf\u6548\u5e94\uff1a58\u79cd\u671f\u8d27\u7684\u5386\u53f2\u6570\u636e\u9a8c\u8bc1\u4e86\u8d8b\u52bf\u8ddf\u8e2a\u7684\u53ef\u884c\u6027\uff08\u6765\u6e90\uff1aMoskowitz, Ooi & Pedersen, 2011\uff09<\/figcaption><\/figure>\n\n\n\n
    \n
  • 58\u79cd\u671f\u8d27\u5408\u7ea6<\/strong>\uff0c\u8986\u76d6\u56db\u5927\u8d44\u4ea7\u7c7b\u522b\uff1a\u80a1\u6307\u3001\u5916\u6c47\u3001\u5927\u5b97\u5546\u54c1\u3001\u56fd\u503a\u3002<\/li>\n\n\n\n
  • \u68c0\u9a8c\u5468\u671f\u4ece\u4e0a\u4e16\u7eaa\u4e03\u516b\u5341\u5e74\u4ee3\u52302009\u5e74\uff0c\u65f6\u95f4\u8de8\u5ea6\u8db3\u591f\u957f\u3002<\/li>\n\n\n\n
  • \u7ed3\u679c\u663e\u793a\uff1a\u51e0\u4e4e\u6bcf\u4e00\u79cd\u8d44\u4ea7\u90fd\u80fd\u4ece\u81ea\u5df1\u7684\u5386\u53f2\u6536\u76ca\u4e2d\u9884\u6d4b\u672a\u6765\u65b9\u5411\u3002<\/strong><\/li>\n<\/ul>\n\n\n\n

    \u66f4\u5173\u952e\u7684\u662f\uff0c\u5f53\u628a\u8fd958\u4e2a\u54c1\u79cd\u7684\u52a8\u91cf\u4fe1\u53f7\u505a\u6210\u4e00\u4e2a\u7ec4\u5408\u65f6\uff0c\u4ea7\u751f\u4e86\u76f8\u5f53\u53ef\u89c2\u7684\u98ce\u9669\u8c03\u6574\u540e\u6536\u76ca<\/strong>\uff0c\u800c\u8fd9\u4e2a\u6536\u76ca\u548c\u4f20\u7edf\u7684\u80a1\u7968\u5e02\u573a\u56e0\u5b50\u3001\u4ef7\u503c\u56e0\u5b50\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u5f88\u4f4e\u3002<\/p>\n\n\n\n

    \u7ffb\u8bd1\u6210\u4eba\u8bdd\u5c31\u662f\uff1a\u8fd9\u4e2a\u7b56\u7565\u8d5a\u7684\u94b1\uff0c\u4e0d\u662f\u9760”\u5927\u76d8\u6da8\u4e86\u6211\u8ddf\u7740\u6da8”\u6765\u7684\uff0c\u5b83\u662f\u72ec\u7acb\u7684\u3002\u4e0d\u8fc7\uff0c\u8fd9\u91cc\u6709\u4e2a\u91cd\u8981\u7684\u80cc\u666f\u9700\u8981\u77e5\u9053\uff1a\u8d8b\u52bf\u8ddf\u8e2a\u7b56\u7565\u7684\u5229\u6da6\uff0c\u4e00\u90e8\u5206\u6765\u81ea\u4e3a\u4ed6\u4eba\u63d0\u4f9b\u4fdd\u9669\u3002<\/strong> \u505a\u8d8b\u52bf\u8ddf\u8e2a\u7684\u4eba\uff0c\u5728\u5e02\u573a\u5e73\u7a33\u65f6\u4e0d\u65ad\u5c0f\u989d\u4e8f\u635f\uff0c\u4f46\u5f53\u6781\u7aef\u884c\u60c5\u6765\u4e34\u65f6\u8d5a\u5927\u94b1\u3002\u53cd\u8fc7\u6765\uff0c\u90a3\u4e9b\u60f3\u8981\u5bf9\u51b2\u98ce\u9669\u7684\u4eba\uff08\u5957\u4fdd\u8005\uff09\uff0c\u4e3a\u6b64\u4ed8\u51fa\u4e86\u4fdd\u9669\u8d39\u3002\u8d44\u91d1\u6d41\u5411\u662f\uff1a\u5957\u4fdd\u8005\u4ed8\u94b1\uff0c\u8d8b\u52bf\u8ddf\u8e2a\u8005\u6536\u94b1\u3002<\/p>\n\n\n\n

    \u4e8c\u3001\u57fa\u7840\u52a8\u91cf\u4fe1\u53f7\u600e\u4e48\u7b97<\/strong><\/h2>\n\n\n\n

    \u8bf4\u5b8c\u4e86\u7406\u8bba\uff0c\u6765\u770b\u4ee3\u7801\u5b9e\u73b0\u3002<\/p>\n\n\n\n

    \u52a8\u91cf\u4fe1\u53f7\u7684\u6838\u5fc3\u53ea\u6709\u4e00\u6b65\uff1a\u6bd4\u8f83\u5f53\u524d\u4ef7\u683c\u548c\u67d0\u4e2a\u5386\u53f2\u7a97\u53e3\u7684\u4ef7\u683c\uff0c\u770b\u65b9\u5411\u3002<\/strong><\/p>\n\n\n\n

    \u770b\u4e0b\u9762\u8fd9\u4e2a\u51fd\u6570\uff1a<\/p>\n\n\n\n

    import yfinance as yf\nimport pandas as pd\nimport numpy as np\n\ndef calculate_tsmom_signal(ticker, lookback=20,\n                           start=\"2010-01-01\",\n                           end=\"2025-12-31\"):\n    data = yf.download(ticker, start=start, end=end,\n                       progress=False)\n\n    # \u63d0\u53d6\u6536\u76d8\u4ef7\n    prices = data[\"Close\"]\n    if isinstance(prices, pd.DataFrame):\n        prices = prices.squeeze()\n    prices = prices.dropna()\n\n    # \u7b80\u5355\u52a8\u91cf\u6536\u76ca\uff1a\u7528\u5f53\u524d\u4ef7\u683c\u9664\u4ee5 lookback \u5929\u524d\u7684\u4ef7\u683c - 1\n    momentum = prices \/ prices.shift(lookback) - 1\n\n    # \u4fe1\u53f7\uff1a\u6536\u76ca\u4e3a\u6b63\u5219\u591a\u5934\uff0c\u4e3a\u8d1f\u5219\u7a7a\u5934\n    signal = np.sign(momentum)\n\n    # \u4fe1\u53f7\u8981\u63d0\u524d\u4e00\u5929\u5bf9\u9f50\uff1a\u6536\u76d8\u65f6\u4ea7\u751f\u4fe1\u53f7\uff0c\u6b21\u65e5\u5f00\u76d8\u6267\u884c\n    signal = signal.shift(1)\n\n    return prices, signal<\/code><\/pre>\n\n\n\n
    \u9010\u6b65\u89e3\u91ca\u4e00\u4e0b\uff1a<\/p>\n\n\n\n
    \u7b2c\u4e00\u6b65<\/strong>\uff1a\u7528 yfinance \u4e0b\u8f7d\u5386\u53f2\u6536\u76d8\u4ef7\uff0c\u6570\u636e\u6765\u6e90\u662f Yahoo Finance\uff0cA\u80a1\u4e5f\u53ef\u4ee5\u6362\u6210 akshare\u3002<\/p>\n\n\n\n
    \u7b2c\u4e8c\u6b65<\/strong>\uff1a\u8ba1\u7b97\u7a97\u53e3\u671f\u6536\u76ca\u3002\u6bd4\u5982 lookback=20\uff0c\u5c31\u662f\u7528\u4eca\u5929\u7684\u4ef7\u683c\u9664\u4ee520\u4e2a\u4ea4\u6613\u65e5\u524d\u7684\u4ef7\u683c\uff0c\u518d\u51cf1\u3002\u8fd9\u4e2a\u6570\u503c\u53ef\u80fd\u662f\u6b63\u7684\uff0c\u4e5f\u53ef\u80fd\u662f\u8d1f\u7684\u3002<\/p>\n\n\n\n
    \u7b2c\u4e09\u6b65<\/strong>\uff1a\u53d6\u65b9\u5411\u3002\u6536\u76ca\u4e3a\u6b63\u5219\u4fe1\u53f7\u4e3a+1\uff08\u505a\u591a\uff09\uff0c\u6536\u76ca\u4e3a\u8d1f\u5219\u4fe1\u53f7\u4e3a-1\uff08\u505a\u7a7a\uff09\u3002\u7b80\u5355\u7c97\u66b4\uff0c\u4f46\u6709\u6548\u3002<\/p>\n\n\n\n
    \u7b2c\u56db\u6b65<\/strong>\uff1a\u4fe1\u53f7\u504f\u79fb\u4e00\u4f4d\u3002\u8fd9\u662f\u91cf\u5316\u56de\u6d4b\u91cc\u6700\u91cd\u8981\u7684\u539f\u5219\u4e4b\u4e00\uff0c\u4fe1\u53f7\u5728\u6536\u76d8\u65f6\u4ea7\u751f\uff0c\u5b9e\u9645\u4ed3\u4f4d\u4ece\u6b21\u65e5\u624d\u5f00\u59cb\u751f\u6548<\/strong>\uff0c\u4e0d\u80fd\u5077\u770b\u672a\u6765\u6570\u636e\u3002<\/p>\n\n\n\n
    \u4e09\u3001\u4e3a\u4ec0\u4e48\u8981\u505a\u6ce2\u52a8\u7387\u7f29\u653e<\/strong><\/h2>\n\n\n\n
    \u5149\u6709\u65b9\u5411\u4fe1\u53f7\u8fd8\u4e0d\u884c\u3002\u5982\u679c\u4e0d\u52a0\u63a7\u5236\uff0c\u540c\u6837\u7684\u4fe1\u53f7\u5728\u4e0d\u540c\u8d44\u4ea7\u4e0a\u4ea7\u751f\u7684\u98ce\u9669\u655e\u53e3\u53ef\u80fd\u76f8\u5dee\u5341\u500d\uff0c\u6ce2\u52a8\u5927\u7684\u54c1\u79cd\u4ed3\u4f4d\u592a\u91cd\uff0c\u6ce2\u52a8\u5c0f\u7684\u54c1\u79cd\u4ed3\u4f4d\u592a\u8f7b\u3002<\/p>\n\n\n\n
    \u89e3\u51b3\u529e\u6cd5\u662f\u6ce2\u52a8\u7387\u7f29\u653e\uff08Volatility Scaling\uff09<\/strong>\uff1a<\/p>\n\n\n\n
    def volatility_scaled_position(prices, signal,\n                               target_vol=0.15,\n                               vol_lookback=60):\n    returns = prices.pct_change()\n    # \u8ba1\u7b97\u5386\u53f2\u5b9e\u73b0\u6ce2\u52a8\u7387\uff0c\u5e74\u5316\n    realized_vol = returns.rolling(vol_lookback).std() \\\n                   * np.sqrt(252)\n\n    # \u907f\u514d\u9664\u96f6\n    realized_vol = realized_vol.replace(0, np.nan).ffill()\n\n    # \u76ee\u6807\u6ce2\u52a8\u7387 \/ \u5b9e\u9645\u6ce2\u52a8\u7387\uff0c\u5f97\u5230\u7f29\u653e\u7cfb\u6570\n    position = signal * (target_vol \/ realized_vol)\n\n    # \u9650\u5236\u6781\u7aef\u4ed3\u4f4d\uff0c\u6700\u591a\u53ea\u5141\u8bb83\u500d\u6760\u6746\n    position = position.clip(-3, 3)\n\n    return position<\/code><\/pre>\n\n\n\n
    \u6838\u5fc3\u601d\u8def\uff1a\u628a\u4ed3\u4f4d\u5927\u5c0f\u8c03\u6574\u6210\u6bcf\u7b14\u4ea4\u6613\u5bf9\u7ec4\u5408\u7684\u98ce\u9669\u8d21\u732e\u5927\u81f4\u76f8\u7b49<\/strong>\u3002\u6ce2\u52a8\u7387\u9ad8\u7684\u8d44\u4ea7\u5c11\u914d\uff0c\u6ce2\u52a8\u7387\u4f4e\u7684\u8d44\u4ea7\u591a\u914d\u3002<\/p>\n\n\n\n
    \u8fd9\u91cc\u768415%\u662f\u5e74\u5316\u76ee\u6807\u6ce2\u52a8\u7387\uff0c\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u7ec4\u5408\u98ce\u9669\u53c2\u6570\uff0c\u53ef\u4ee5\u6839\u636e\u4f60\u81ea\u5df1\u7684\u98ce\u9669\u504f\u597d\u8c03\u6574\u3002<\/p>\n\n\n\n
    clip(-3, 3) \u8fd9\u4e00\u6b65\u662f\u98ce\u63a7\u5e95\u7ebf\uff0c\u6700\u591a\u53ea\u5141\u8bb83\u500d\u6760\u6746\uff0c\u4e0d\u80fd\u8ba9\u5355\u4e00\u4ed3\u4f4d\u628a\u6574\u4e2a\u7ec4\u5408\u62d6\u4e0b\u6c34\u3002<\/p>\n\n\n\n
    \u56db\u3001\u5355\u54c1\u79cd\u56de\u6d4b\uff1a\u6211\u8dd1\u4e86SPY\u7684\u6570\u636e<\/strong><\/h2>\n\n\n\n
    \u6709\u4e86\u4ed3\u4f4d\uff0c\u56de\u6d4b\u5c31\u987a\u7406\u6210\u7ae0\u4e86\uff1a<\/p>\n\n\n\n
    def backtest_tsmom(prices, position, cost_bps=5):\n    asset_returns = prices.pct_change()\n\n    # \u7b56\u7565\u6536\u76ca = \u524d\u4e00\u5929\u4ed3\u4f4d \u00d7 \u5f53\u5929\u8d44\u4ea7\u6536\u76ca\n    strategy_returns = position.shift(1) * asset_returns\n\n    # \u4f30\u7b97\u4ea4\u6613\u6210\u672c\uff1a\u4ed3\u4f4d\u53d8\u52a8 \u00d7 \u624b\u7eed\u8d39\u7387\n    position_change = position.diff().abs()\n    costs = position_change * (cost_bps \/ 10000)\n\n    net_returns = strategy_returns - costs\n    if isinstance(net_returns, pd.DataFrame):\n        net_returns = net_returns.squeeze()\n    net_returns = net_returns.dropna()\n\n    # \u8ba1\u7b97\u5173\u952e\u6307\u6807\n    cumulative = (1 + net_returns).cumprod()\n    total_return = cumulative.iloc[-1] - 1\n    ann_return = (1 + total_return) ** (252 \/ len(net_returns)) - 1\n    ann_vol = net_returns.std() * np.sqrt(252)\n    sharpe = ann_return \/ ann_vol if ann_vol > 0 else np.nan\n\n    return {\n        \"cumulative\": cumulative,\n        \"ann_return\": ann_return,\n        \"ann_vol\": ann_vol,\n        \"sharpe\": sharpe,\n        \"net_returns\": net_returns\n    }<\/code><\/pre>\n\n\n\n
    \u8fd0\u884c\u7ed3\u679c\uff08SPY\uff0c2010-2025\uff0c20\u65e5 lookback\uff0c15%\u76ee\u6807\u6ce2\u52a8\u7387\uff09\uff1a<\/p>\n\n\n\n
    \u6307\u6807<\/strong><\/th>\u6570\u503c<\/strong><\/th><\/tr><\/thead>
    \u5e74\u5316\u6536\u76ca\u7387<\/td>-0.10%<\/td><\/tr>
    \u5e74\u5316\u6ce2\u52a8\u7387<\/td>16.36%<\/td><\/tr>
    \u590f\u666e\u6bd4\u7387<\/td>-0.01<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u8fd9\u4e2a\u7ed3\u679c\u6709\u70b9\u624e\u773c\uff0cSharpe \u51e0\u4e4e\u7b49\u4e8e\u96f6\u3002<\/strong> \u8bf4\u660e\u57282020-2025\u5e74\u7684\u7f8e\u80a1\u5e02\u573a\uff0c\u5355\u7eaf\u7684\u65f6\u95f4\u5e8f\u5217\u52a8\u91cf\u7b56\u7565\u5e76\u6ca1\u6709\u663e\u8457\u7684\u8d85\u989d\u6536\u76ca\u3002<\/p>\n\n\n\n
    \u8fd9\u6b63\u597d\u5370\u8bc1\u4e86\u90a3\u53e5\u8001\u8bdd\uff1a\u7b56\u7565\u4e0d\u662f\u5168\u573a\u666f\u901a\u7528\uff0c\u6ca1\u6709\u54ea\u4e2a\u7b56\u7565\u5728\u6240\u6709\u5e02\u573a\u73af\u5883\u4e0b\u90fd\u6709\u6548\u3002<\/strong> \u8d8b\u52bf\u8ddf\u8e2a\u5728\u8d8b\u52bf\u660e\u663e\u7684\u884c\u60c5\u91cc\u8868\u73b0\u597d\uff0c\u5728\u9707\u8361\u5e02\u91cc\u4f1a\u4e0d\u65ad\u88ab\u6765\u56de\u6253\u8138\u3002<\/p>\n\n\n\n
    \u4e94\u3001\u591a\u8d44\u4ea7\u7ec4\u5408\uff1a\u5206\u6563\u5316\u624d\u662f\u5173\u952e<\/strong><\/h2>\n\n\n\n
    \u5355\u4e2a\u54c1\u79cd\u6548\u679c\u4e00\u822c\uff0c\u90a3\u591a\u4e2a\u54c1\u79cd\u7ec4\u5408<\/strong>\u5462\uff1f<\/p>\n\n\n\n
    def multi_asset_tsmom(tickers, lookback=60, target_vol=0.10):\n    all_positions = {}\n    all_returns = {}\n\n    for ticker in tickers:\n        prices, signal = calculate_tsmom_signal(\n            ticker, lookback=lookback)\n        position = volatility_scaled_position(\n            prices, signal, target_vol=target_vol)\n        asset_returns = prices.pct_change()\n\n        all_positions[ticker] = position\n        all_returns[ticker] = asset_returns\n\n    positions_df = pd.DataFrame(all_positions)\n    returns_df = pd.DataFrame(all_returns)\n\n    # \u7b49\u6743\u5e73\u5747\u5404\u8d44\u4ea7\u7684\u4ed3\u4f4d\n    portfolio_position = positions_df.mean(axis=1)\n    portfolio_returns = (positions_df.shift(1) * returns_df).mean(axis=1)\n\n    return portfolio_returns.dropna()<\/code><\/pre>\n\n\n\n
    \u6d4b\u8bd5\u7ec4\u5408\uff1aSPY\uff08\u7f8e\u80a1\uff09\u3001TLT\uff08\u957f\u671f\u56fd\u503a\uff09\u3001GLD\uff08\u9ec4\u91d1\uff09\u3001UUP\uff08\u7f8e\u5143\u6307\u6570\uff09\u3002\u8986\u76d6\u56db\u7c7b\u8d44\u4ea7\uff1a\u80a1\u7968\u3001\u503a\u5238\u3001\u9ec4\u91d1\u3001\u7f8e\u5143\u3002<\/p>\n\n\n\n
    \u7ec4\u5408\u903b\u8f91\u4e0d\u590d\u6742\uff1a\u6bcf\u4e2a\u54c1\u79cd\u5206\u522b\u8dd1\u52a8\u91cf\u4fe1\u53f7\uff0c\u505a\u5b8c\u6ce2\u52a8\u7387\u7f29\u653e\u4e4b\u540e\uff0c\u7b49\u6743\u5e73\u5747\u3002<\/strong> \u8fd9\u4e48\u505a\u7684\u597d\u5904\u662f\uff0c\u5f53\u67d0\u4e00\u7c7b\u8d44\u4ea7\u8fdb\u5165\u9707\u8361\u65f6\uff0c\u5176\u4ed6\u8d44\u4ea7\u7684\u8d8b\u52bf\u4fe1\u53f7\u53ef\u80fd\u8fd8\u5728\u751f\u6548\uff0c\u7ec4\u5408\u5c42\u9762\u53ef\u4ee5\u5e73\u6ed1\u6536\u76ca\u3002\u8fd9\u4e5f\u662f\u4e3a\u4ec0\u4e48\u5b66\u672f\u7814\u7a76\u53d1\u73b0\u8d8b\u52bf\u8ddf\u8e2a\u5728\u6781\u7aef\u5e02\u573a\u73af\u5883<\/strong>\u4e0b\u53cd\u800c\u8868\u73b0\u6700\u5f3a\uff0c\u5371\u673a\u6765\u4e34\u65f6\uff0c\u5404\u7c7b\u8d44\u4ea7\u7684\u76f8\u5173\u6027\u98d9\u5347\uff0c\u65b9\u5411\u6027\u8d8b\u52bf\u66f4\u660e\u663e\uff0c\u8d8b\u52bf\u8ddf\u8e2a\u7b56\u7565\u7684\u4fe1\u53f7\u6210\u529f\u7387\u5927\u5e45\u63d0\u5347\u3002<\/p>\n\n\n\n
    \u516d\u3001\u5bf9\u4e2a\u4eba\u6295\u8d44\u8005\u6709\u4ec0\u4e48\u542f\u53d1<\/strong><\/h2>\n\n\n\n
    \u8bf4\u5b8c\u4ee3\u7801\uff0c\u6765\u70b9\u5b9e\u5728\u7684\u3002\u4e2a\u4f53\u6295\u8d44\u8005\u6ca1\u6709\u673a\u6784\u90a3\u4e48\u590d\u6742\u7684\u7cfb\u7edf\uff0c\u4f46\u5b8c\u5168\u53ef\u4ee5\u501f\u9274\u8fd9\u4e2a\u601d\u8def\u3002<\/p>\n\n\n\n
    \u7b2c\u4e00\uff0c\u7528\u4e3b\u6d41ETF\u5c31\u591f\u4e86\u3002<\/strong> \u4e0d\u9700\u8981\u641e\u671f\u8d27\uff0c\u4e0d\u9700\u8981\u6760\u6746\uff0c\u4f60\u624b\u4e0a\u7684 QDII ETF\u3001\u9ec4\u91d1ETF\u3001\u56fd\u503aETF\u3001\u8d27\u5e01\u57fa\u91d1\uff0c\u5c31\u662f\u5929\u7136\u7684\u8d44\u4ea7\u7c7b\u522b\u8986\u76d6\u3002<\/p>\n\n\n\n
    \u7b2c\u4e8c\uff0clookback \u7a97\u53e3\u4e0d\u8981\u4e71\u8bbe\u3002<\/strong> \u5b66\u672f\u754c\u5e38\u7528 1 \u4e2a\u6708\u5230 12 \u4e2a\u6708\uff0c\u6211\u7684\u7ecf\u9a8c\u662f\u4ece 60 \u65e5\uff083\u4e2a\u6708\uff09\u8d77\u6b65\u505a\u6d4b\u8bd5\uff0c\u592a\u77ed\u4e86\u566a\u58f0\u591a\uff0c\u592a\u957f\u4e86\u4fe1\u53f7\u6ede\u540e\u4e25\u91cd\u3002<\/p>\n\n\n\n
    \u7b2c\u4e09\uff0c\u6ce2\u52a8\u7387\u7f29\u653e\u4e00\u5b9a\u8981\u505a\u3002<\/strong> \u8fd9\u662f\u7b56\u7565\u80fd\u4e0d\u80fd\u957f\u671f\u8dd1\u4e0b\u53bb\u7684\u6838\u5fc3\u3002\u4f60\u4e0d\u4e00\u5b9a\u8981\u624b\u5199\u4ee3\u7801\uff0c\u4f46\u73b0\u5728\u4e3b\u6d41\u7684\u667a\u80fd\u5b9a\u6295\u5de5\u5177\u548cFOF\u7ec4\u5408\uff0c\u80cc\u540e\u591a\u5c11\u90fd\u6709\u7c7b\u4f3c\u601d\u8def\u3002<\/p>\n\n\n\n
    \u7b2c\u56db\uff0c\u6210\u672c\u6bd4\u4f60\u60f3\u8c61\u7684\u66f4\u4f24\u3002<\/strong> \u7b56\u7565\u8d8a\u9891\u7e41\u6362\u4ed3\uff0c\u4ea4\u6613\u6210\u672c\u8d8a\u9ad8\u3002\u673a\u6784\u7684\u624b\u7eed\u8d39\u6781\u4f4e\uff0c\u6563\u6237\u5982\u679c\u7528\u9ad8\u4f63\u91d1\u5238\u5546\uff0c\u6210\u672c\u53ef\u4ee5\u628a\u6240\u6709\u8d85\u989d\u6536\u76ca\u5403\u6389\u3002<\/p>\n\n\n\n
    \u7b2c\u4e94\uff0c\u8fd9\u4e2a\u7b56\u7565\u4e5f\u6709\u5f31\u70b9\u3002<\/strong> \u8d8b\u52bf\u8ddf\u8e2a\u5728\u5e02\u573a\u6025\u8f6c\u7684\u65f6\u5019\u4f1a\u6709\u660e\u663e\u6ede\u540e\uff0c\u8ffd\u8fdb\u53bb\u7684\u65f6\u5019\u53ef\u80fd\u8d8b\u52bf\u5df2\u7ecf\u5feb\u7ed3\u675f\u4e86\u3002\u9707\u8361\u884c\u60c5\u91cc\u4fe1\u53f7\u6765\u56de\u5207\u6362\uff0c\u4f1a\u53cd\u590d\u4e8f\u635f\u3002\u4ed3\u4f4d\u7ba1\u7406\u548c\u7ec4\u5408\u5206\u6563\u662f\u5e94\u5bf9\u8fd9\u4e9b\u5f31\u70b9\u7684\u4e3b\u8981\u624b\u6bb5\u3002<\/strong><\/p>\n\n\n\n
    \u4e03\u3001\u8fdb\u9636\u4f18\u5316\uff1a\u516d\u4e2a\u53ef\u4ee5\u63d0\u5347\u7684\u65b9\u5411<\/strong><\/h2>\n\n\n\n
    \u5982\u679c\u4f60\u60f3\u628a\u8fd9\u5957\u6846\u67b6\u7ee7\u7eed\u5b8c\u5584\uff0c\u4e0b\u9762\u51e0\u4e2a\u65b9\u5411\u503c\u5f97\u7814\u7a76\uff1a<\/p>\n\n\n\n
    \u4e00\u3001\u62c9\u957f lookback \u7a97\u53e3<\/strong><\/p>\n\n\n\n
    \u5b66\u672f\u7814\u7a76\u663e\u793a\uff0c6-12\u4e2a\u6708\u7684\u52a8\u91cf\u4fe1\u53f7\u6bd4\u77ed\u5468\u671f\u4fe1\u53f7\u66f4\u7a33\u5b9a\uff0c\u566a\u58f0\u66f4\u5c11\u3002\u77ed\u5468\u671f\uff0820\u65e5\u4ee5\u5185\uff09\u66f4\u9002\u5408\u77ed\u7ebf\u8d8b\u52bf\uff0c\u4f46\u9707\u8361\u5e02\u7684\u4e8f\u635f\u4e5f\u66f4\u9891\u7e41\u3002<\/p>\n\n\n\n
    \u4e8c\u3001\u591a\u5468\u671f\u53e0\u52a0<\/strong><\/p>\n\n\n\n
    \u7528\u4e00\u4e2a lookback \u4fe1\u53f7\u5bb9\u6613\u9519\u8fc7\u5176\u4ed6\u65f6\u95f4\u5c3a\u5ea6\u7684\u8d8b\u52bf\u3002\u5b9e\u8df5\u4e2d\uff0c\u628a 60 \u65e5\u3001120 \u65e5\u3001252 \u65e5\u4e09\u4e2a\u5468\u671f\u7684\u4fe1\u53f7\u53d6\u5e73\u5747\uff0c\u5f97\u5230\u7684\u590d\u5408\u4fe1\u53f7\u66f4\u7a33\u5065\u3002<\/p>\n\n\n\n
    \u4e09\u3001\u8d44\u4ea7\u95f4\u76f8\u5173\u6027\u8c03\u6574<\/strong><\/p>\n\n\n\n
    \u5f53\u591a\u4e2a\u5e02\u573a\u540c\u65f6\u5411\u540c\u4e00\u65b9\u5411\u8fd0\u884c\u65f6\uff0c\u7b49\u6743\u7ec4\u5408\u4f1a\u4ea7\u751f\u8fc7\u5ea6\u96c6\u4e2d\u3002\u901a\u8fc7\u8ba1\u7b97\u8d44\u4ea7\u95f4\u7684\u6eda\u52a8\u76f8\u5173\u6027\uff0c\u964d\u4f4e\u540c\u5411\u4fe1\u53f7\u7684\u4ed3\u4f4d\u6743\u91cd\uff0c\u53ef\u4ee5\u63a7\u5236\u7ec4\u5408\u5c42\u9762\u7684\u6781\u7aef\u98ce\u9669\u3002<\/p>\n\n\n\n
    \u56db\u3001\u673a\u5668\u5b66\u4e60\u8f85\u52a9<\/strong><\/p>\n\n\n\n
    ML\u53ef\u4ee5\u6355\u6349\u6536\u76ca\u9884\u6d4b\u4e2d\u7684\u975e\u7ebf\u6027\u5173\u7cfb\uff0c\u6bd4\u5982\u7528\u968f\u673a\u68ee\u6797\u6216XGBoost\u7ed3\u5408\u591a\u4e2a\u6280\u672f\u6307\u6807\u6765\u9884\u6d4b\u672a\u6765\u6536\u76ca\u3002\u4e0d\u8fc7\uff0c\u7b80\u5355\u7ebf\u6027\u89c4\u5219\u5728\u5f88\u591a\u5e02\u573a\u91cc\u4f9d\u7136\u6709\u7ade\u4e89\u529b\uff0cML\u7684\u4f18\u52bf\u5728\u4e8e\u7279\u5f81\u7ec4\u5408\uff0c\u4e0d\u5728\u4e8e\u66ff\u4ee3\u903b\u8f91\u3002<\/p>\n\n\n\n
    \u4e94\u3001\u98ce\u63a7\u5c42\u53e0\u52a0<\/strong><\/p>\n\n\n\n