{"id":3303,"date":"2026-01-09T07:14:00","date_gmt":"2026-01-08T23:14:00","guid":{"rendered":"https:\/\/www.laoyulaoyu.com\/?p=3303"},"modified":"2025-12-28T14:45:20","modified_gmt":"2025-12-28T06:45:20","slug":"%e9%87%8f%e5%8c%96%e7%a8%8b%e5%ba%8f%e4%bd%93%e6%a3%80%e6%8a%a5%e5%91%8a%ef%bc%9a%e7%94%a8%e8%bf%998%e4%b8%aa%e6%8a%80%e6%9c%af%e6%8c%87%e6%a0%87%e7%9c%8b%e7%ad%96%e7%95%a5%e9%9d%a0","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2026\/01\/09\/%e9%87%8f%e5%8c%96%e7%a8%8b%e5%ba%8f%e4%bd%93%e6%a3%80%e6%8a%a5%e5%91%8a%ef%bc%9a%e7%94%a8%e8%bf%998%e4%b8%aa%e6%8a%80%e6%9c%af%e6%8c%87%e6%a0%87%e7%9c%8b%e7%ad%96%e7%95%a5%e9%9d%a0\/","title":{"rendered":"\u91cf\u5316\u7a0b\u5e8f\u201c\u4f53\u68c0\u201d\u62a5\u544a\uff1a\u7528\u8fd98\u4e2a\u6280\u672f\u6307\u6807\u770b\u7b56\u7565\u9760\u4e0d\u9760\u8c31"},"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>\u672c\u6587\u57fa\u4e8e\u591a\u4f4d\u91cf\u5316\u9886\u57df\u4e13\u5bb6\u7684\u7ecf\u9a8c\u5206\u4eab\u6536\u96c6\u800c\u6210\uff0c\u5c06\u7cfb\u7edf\u6027\u5730\u4ecb\u7ecd\u7a0b\u5e8f\u4ea4\u6613\u7b56\u7565\u76848\u4e2a\u6838\u5fc3\u98ce\u9669\u6307\u6807\u3002\u6211\u4f1a\u4ee5\u771f\u5b9e\u6570\u636e\u4e3a\u6848\u4f8b\uff0c\u8bb2\u89e3\u6bcf\u4e2a\u6307\u6807\u7684\u542b\u4e49\u3001\u8ba1\u7b97\u65b9\u6cd5\u548c\u5b9e\u6218\u5e94\u7528\u6807\u51c6\uff0c\u5e0c\u671b\u8fd9\u4e9b\u5de5\u5177\u80fd\u5e2e\u4f60\u770b\u7a7f\u7b56\u7565\u7684\u771f\u5b9e\u98ce\u9669\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u524d\u8a00\uff1a\u522b\u88ab\u9ad8\u6536\u76ca\u8ff7\u4e86\u773c<\/strong><\/h2>\n\n\n\n
\u5f88\u591a\u521a\u63a5\u89e6\u7a0b\u5e8f\u4ea4\u6613\u7684\u670b\u53cb\uff0c\u770b\u5230\u4e00\u4e2a\u7b56\u7565\u56de\u6d4b\u4e09\u5e74\u7ffb\u500d\uff0c\u5c31\u89c9\u5f97\u81ea\u5df1\u627e\u5230\u4e86\u6447\u94b1\u6811\u3002\u7ed3\u679c\u771f\u5230\u5e02\u573a\u91cc\uff0c\u7b56\u7565\u521a\u8dcc25%\u5c31\u614c\u4e86\u795e\uff0c\u5728\u6700\u5dee\u7684\u65f6\u673a\u780d\u6389\uff0c\u7136\u540e\u7eb3\u95f7\uff1a”\u8fd9\u7834\u7cfb\u7edf\u600e\u4e48\u4e0d\u7ba1\u7528\u4e86\uff1f”<\/p>\n\n\n\n
\u8001\u4f59\u4eca\u5929\u8981\u544a\u8bc9\u5927\u5bb6\uff1a\u4e13\u4e1a\u4ea4\u6613\u5458\u4ece\u6765\u4e0d\u53ea\u770b\u6536\u76ca\u3002\u4ed6\u4eec\u5173\u6ce8\u7684\u662f\u4e00\u7ec4\u80fd\u770b\u7a7f\u7b56\u7565\u672c\u8d28\u7684\u6307\u6807\u2014\u2014\u98ce\u9669\u5927\u4e0d\u5927\uff1f\u6297\u4e0d\u6297\u63cd\uff1f\u9047\u5230\u6781\u7aef\u884c\u60c5\u4f1a\u600e\u6837\uff1f\u80fd\u4e0d\u80fd\u957f\u671f\u6d3b\u4e0b\u6765\uff1f<\/p>\n\n\n\n
\u8fd9\u7bc7\u6587\u7ae0\u8001\u4f59\u7ed9\u5927\u5bb6\u6574\u7406\u4e86\u4e00\u4efd”\u7a0b\u5e8f\u4ea4\u6613\u4f53\u68c0\u8868”\uff0c\u75288\u4e2a\u6838\u5fc3\u6307\u6807\uff0c\u5e2e\u4f60\u5224\u65ad\u4f60\u7684\u4ea4\u6613\u7b56\u7565\u662f\u771f\u91d1\u4e0d\u6015\u706b\u70bc\uff0c\u8fd8\u662f\u7eb8\u8001\u864e\u4e00\u6233\u5c31\u7834\u3002<\/p>\n\n\n\n
\u8bbe\u7f6e\u521d\u59cb\u6570\u636e<\/strong><\/h3>\n\n\n\n
\u63a5\u4e0b\u6765\u6211\u5c06\u4f7f\u7528\u82f9\u679c\u516c\u53f8 (AAPL) \u4ece 2020 \u5e74\u5230 2025 \u5e74\u5e95\u7684\u65e5\u6570\u636e\u4f5c\u4f8b\uff0c\u6765\u6f14\u793a\u6bcf\u4e2a\u6307\u6807\u7684\u8ba1\u7b97\u65b9\u6cd5\u3002<\/p>\n\n\n\n
\u6211\u4eec\u9996\u5148\u52a0\u8f7d\u82f9\u679c\u516c\u53f8\u8c03\u6574\u540e\u7684\u6536\u76d8\u4ef7\uff0c\u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387\uff0c\u5e76\u6839\u636e 4% \u7684\u5e74\u6536\u76ca\u7387\u5b9a\u4e49\u6bcf\u65e5\u65e0\u98ce\u9669\u5229\u7387\u3002<\/p>\n\n\n\n
import yfinance as yf<\/code>import pandas as pd<\/code>import numpy as np<\/code><\/code># \u4e0b\u8f7d\u82f9\u679c\u80a1\u7968\u6570\u636e (2020-2025)<\/code>aapl = yf.download('AAPL', start='2020-01-01', end='2025-12-12', progress=False, auto_adjust=True)['Close']<\/code>returns = aapl.pct_change().dropna()<\/code><\/code># \u65e0\u98ce\u9669\u5229\u7387\uff084%\u5e74\u5229\u7387\u6362\u7b97\u4e3a\u65e5\u5229\u7387\uff09<\/code>risk_free_annual = 0.04<\/code>risk_free_daily = (1 + risk_free_annual)**(1\/252) - 1<\/code>print(f\"Data loaded: {len(returns)} daily returns\")<\/code>print(f\"Annualized return: {returns.mean().values[0] * 252 * 100:.1f}%\")<\/code><\/code><\/pre>\n\n\n\n
\u6211\u4eec\u901a\u8fc7\u4e0a\u9762\u7684\u4ee3\u7801\u83b7\u5f97\u4e86\u7ea61500\u4e2a\u4ea4\u6613\u65e5\u7684\u6570\u636e\u3002\u5728\u6b64\u671f\u95f4\uff0c\u82f9\u679c\u516c\u53f8\uff08AAPL\uff09\u5b9e\u73b0\u4e86\u9ad8\u8fbe27.8%\u7684\u5e74\u5316\u6536\u76ca\u7387\uff0c\u8868\u73b0\u663e\u8457\u5f3a\u4e8e\u5e02\u573a\u3002\u4f5c\u4e3a\u53c2\u7167\uff0c\u6807\u666e500\u6307\u6570\u7684\u957f\u671f\u5e74\u5316\u5e73\u5747\u6536\u76ca\u7387\u901a\u5e38\u7ef4\u6301\u572810%\u5de6\u53f3\u3002<\/p>\n\n\n\n
\u7136\u800c\uff0c\u4ec5\u5173\u6ce8\u6700\u7ec8\u56de\u62a5\u5e76\u4e0d\u8db3\u4ee5\u7406\u89e3\u5176\u80cc\u540e\u7684\u6ce2\u52a8\u4e0e\u98ce\u9669\uff0c\u8fd9\u4e5f\u662f\u6211\u4eec\u9700\u8981\u501f\u52a9\u66f4\u591a\u6307\u6807\u8fdb\u884c\u5206\u6790\u7684\u539f\u56e0\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u9010\u4e00\u5ba1\u89c6\u8fd98\u9879\u5173\u952e\u6307\u6807\u3002<\/p>\n\n\n\n
1. \u590f\u666e\u6bd4\u7387\uff1a\u503c\u4e0d\u503c\uff1f<\/strong><\/h2>\n\n\n\n
\u590f\u666e\u6bd4\u7387<\/strong>\u56de\u7b54\u4e00\u4e2a\u7b80\u5355\u95ee\u9898\uff1a\u4f60\u627f\u53d7\u4e00\u4efd\u6ce2\u52a8\uff0c\u80fd\u6362\u6765\u591a\u5c11\u8d85\u989d\u6536\u76ca\uff1f<\/p>\n\n\n\n
\u5f88\u591a\u65b0\u624b\u4f4e\u4f30\u4e86\u7b56\u7565\u7684\u6ce2\u52a8\u6709\u591a\u5927\u3002\u590f\u666e\u6bd4\u7387\u5e2e\u4f60\u628a\u98ce\u9669\u548c\u6536\u76ca\u653e\u5728\u5929\u5e73\u4e24\u7aef\uff0c\u770b\u770b\u5230\u5e95\u5212\u4e0d\u5212\u7b97\u3002<\/p>\n\n\n\n
\u5b9e\u6218\u6848\u4f8b\uff08\u4ee5\u82f9\u679c\u80a1\u7968\u4e3a\u4f8b\uff09<\/strong><\/h3>\n\n\n\n
\u75282020-2025\u5e74\u7684\u82f9\u679c\u80a1\u7968\u6570\u636e\u6d4b\u7b97\uff0c\u590f\u666e\u6bd4\u7387\u662f0.747\u3002\u4e0b\u9762\u662f\u4ee3\u7801\uff1a<\/p>\n\n\n\n
excess_returns = returns - risk_free_daily<\/code>sharpe_ratio = excess_returns.mean() \/ excess_returns.std() * np.sqrt(252)<\/code>print(f\"Sharpe Ratio: {sharpe_ratio.values[0]:.3f}\")<\/code><\/code><\/pre>\n\n\n\n
\u8fd9\u8bf4\u660e\uff1a<\/p>\n\n\n\n
    \n
  • \u6536\u76ca\u786e\u5b9e\u4e0d\u9519\u3002<\/li>\n\n\n\n
  • \u4f46\u6ce2\u52a8\u4e5f\u5f88\u5927\u3002<\/li>\n\n\n\n
  • \u5bf9\u5355\u53ea\u80a1\u7968\u6765\u8bf4\uff0c0.7\u7b97\u662f\u6b63\u5e38\u6c34\u5e73\u3002<\/li>\n<\/ul>\n\n\n\n
    \u5982\u4f55\u89e3\u8bfb\u590f\u666e\u6bd4\u7387\uff1f<\/h3>\n\n\n\n
    \u590f\u666e\u6bd4\u7387<\/th>\u7b56\u7565\u8d28\u91cf<\/th><\/tr>
    \u4f4e\u4e8e0.8<\/td>\u611f\u89c9\u50cf\u968f\u673a\u4ea4\u6613<\/td><\/tr>
    1.0-1.5<\/td>\u666e\u901a\u6295\u8d44\u8005\u80fd\u63a5\u53d7\u7684\u597d\u7b56\u7565<\/td><\/tr>
    \u8d85\u8fc72.0<\/td>\u4e13\u4e1a\u673a\u6784\u6c34\u5e73\uff0c\u4e0d\u9760\u6760\u6746\u5f88\u96be\u8fbe\u5230<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u6ce8\u610f<\/strong>\uff1a\u590f\u666e\u6bd4\u7387\u6709\u4e2a\u6bdb\u75c5\uff1a\u5b83\u628a\u4e0a\u6da8\u6ce2\u52a8\u4e5f\u7b97\u4f5c”\u98ce\u9669”\u3002\u6240\u4ee5\u54b1\u4eec\u8fd8\u9700\u8981\u7b2c\u4e8c\u4e2a\u6307\u6807\u3002<\/p>\n\n\n\n
    2. \u7d22\u63d0\u8bfa\u6bd4\u7387\uff1a\u53ea\u60e9\u7f5a\u4e0b\u8dcc\u6ce2\u52a8<\/strong><\/h2>\n\n\n\n
    \u7d22\u63d0\u8bfa\u6bd4\u7387<\/strong>\u662f\u590f\u666e\u6bd4\u7387\u7684\u5347\u7ea7\u7248\u3002\u5b83\u53ea\u5173\u5fc3\u8ba9\u4f60\u4e8f\u94b1\u7684\u6ce2\u52a8\uff0c\u4e0d\u7ba1\u4e0a\u6da8\u7684\u90a3\u90e8\u5206\u3002\u8ba1\u7b97\u4ee3\u7801\uff1a<\/p>\n\n\n\n
    downside_returns = excess_returns.copy()<\/code>downside_returns[downside_returns > 0] = 0<\/code>downside_deviation = np.sqrt((downside_returns**2).mean())<\/code>sortino_ratio = excess_returns.mean() \/ downside_deviation * np.sqrt(252)<\/code>print(f\"Sortino Ratio: {sortino_ratio.values[0]:.3f}\")<\/code><\/code><\/pre>\n\n\n\n
    \u82f9\u679c\u80a1\u7968\u7684\u7d22\u63d0\u8bfa\u6bd4\u7387\u662f1.105\uff0c\u6bd4\u590f\u666e\u6bd4\u7387\u9ad8\u51fa\u4e0d\u5c11\u3002\u8fd9\u8bf4\u660e\uff1a<\/p>\n\n\n\n
      \n
    • \u5927\u90e8\u5206\u6ce2\u52a8\u6765\u81ea\u4e0a\u6da8\uff082020-2021\u5e74\u7684\u66b4\u6da8\uff09\uff1b<\/li>\n\n\n\n
    • \u771f\u6b63\u4e0b\u8dcc\u7684\u65f6\u5019\u53cd\u800c\u6ca1\u90a3\u4e48\u5413\u4eba\uff1b<\/li>\n\n\n\n
    • \u8fd9\u4e2a\u4e0d\u5bf9\u79f0\u6027\u5bf9\u6295\u8d44\u8005\u662f\u6709\u5229\u7684\u3002<\/li>\n<\/ul>\n\n\n\n
      \u7d22\u63d0\u8bfa\u6bd4\u7387\u53c2\u8003\u6807\u51c6<\/strong><\/h3>\n\n\n\n
        \n
      • \u6700\u4f4e\u8981\u6c42\uff1a2.0\u4ee5\u4e0a<\/strong><\/li>\n\n\n\n
      • \u7406\u60f3\u6c34\u5e73\uff1a3.0+\uff08\u8bf4\u660e\u7b56\u7565\u8d8b\u52bf\u6bd4\u8f83\u5e72\u51c0\uff09<\/strong><\/li>\n<\/ul>\n\n\n\n
        \u5982\u679c\u7d22\u63d0\u8bfa\u6bd4\u7387\u6bd4\u590f\u666e\u6bd4\u7387\u9ad8\u5f88\u591a\uff0c\u8bf4\u660e\u4f60\u7684\u7b56\u7565”\u6da8\u8d77\u6765\u731b\uff0c\u8dcc\u8d77\u6765\u6e29\u67d4”\uff0c\u8fd9\u662f\u597d\u4e8b\u3002<\/p>\n\n\n\n
        3. \u98ce\u9669\u4ef7\u503c\uff08VaR\uff09\uff1a\u5e95\u7ebf\u5728\u54ea\uff1f<\/strong><\/h2>\n\n\n\n
        VaR<\/strong>\u544a\u8bc9\u4f60\uff1a\u5728\u6b63\u5e38\u5e02\u573a\u60c5\u51b5\u4e0b\uff0c\u670995%\u7684\u628a\u63e1\u8bf4\uff0c\u4eca\u5929\u7684\u6700\u5927\u635f\u5931\u4e0d\u4f1a\u8d85\u8fc7\u591a\u5c11\u3002<\/p>\n\n\n\n
        var_95 = np.percentile(returns, 5)<\/code>print(f\"95% Daily VaR: {var_95*100:.2f}%\")<\/code><\/code><\/pre>\n\n\n\n
        \u901a\u8fc7\u4e0a\u9762\u4ee3\u7801\u7684\u8ba1\u7b97\uff0c\u82f9\u679c\u80a1\u7968\u768495% VaR\u662f-2.85%\uff0c\u610f\u601d\u662f\uff1a<\/p>\n\n\n\n
          \n
        • 100\u5929\u91cc\uff0c\u670995\u5929\u7684\u635f\u5931\u4e0d\u8d85\u8fc72.85%\uff1b<\/li>\n\n\n\n
        • \u53e6\u59165\u5929\u53ef\u80fd\u66f4\u60e8\u3002<\/li>\n<\/ul>\n\n\n\n
          VaR\u4f7f\u7528\u8981\u70b9<\/strong><\/h3>\n\n\n\n
          VaR\u5c31\u50cf\u5929\u6c14\u9884\u62a5\u8bf4”\u660e\u5929\u67095%\u6982\u7387\u4f1a\u4e0b\u5927\u96e8”\uff0c\u4f46\u5b83\u6ca1\u544a\u8bc9\u4f60\u5982\u679c\u771f\u4e0b\u5927\u96e8\uff0c\u662f\u6bdb\u6bdb\u96e8\u8fd8\u662f\u503e\u76c6\u5927\u96e8\u3002\u6240\u4ee5\u8fd8\u9700\u8981\u4e0b\u4e00\u4e2a\u6307\u6807\u3002<\/p>\n\n\n\n
          \"\u98ce\u9669\u7ba1\u7406\u53ef\u89c6\u5316\"\/<\/figure>\n\n\n\n
          \u98ce\u9669\u7ba1\u7406\u4e0e\u6295\u8d44\u7ec4\u5408\u53ef\u89c6\u5316\u5206\u6790\uff08\u6765\u6e90\uff1a\u7f51\u7edc\uff09<\/p>\n\n\n\n
          4. \u6761\u4ef6\u98ce\u9669\u4ef7\u503c\uff08CVaR\uff09\uff1a\u6700\u574f\u60c5\u51b5\u662f\u4ec0\u4e48<\/strong><\/h2>\n\n\n\n
          CVaR<\/strong>\uff08\u4e5f\u53eb\u9884\u671f\u635f\u5931\uff09\u56de\u7b54\uff1a\u5982\u679c\u5012\u9709\u78b0\u4e0a\u4e86\u90a35%\u7684\u6781\u7aef\u60c5\u51b5\uff0c\u5e73\u5747\u4f1a\u4e8f\u591a\u5c11\uff1f<\/p>\n\n\n\n
          cvar_95 = returns[returns <= var_95].mean()<\/code>print(f\"95% CVaR: {cvar_95.values[0]*100:.2f}%\")<\/code><\/code><\/pre>\n\n\n\n
          \u82f9\u679c\u80a1\u7968\u7684CVaR\u662f-4.48%\uff0c\u610f\u601d\u662f\uff1a<\/p>\n\n\n\n
            \n
          • \u5728\u6700\u5dee\u76845%\u65e5\u5b50\u91cc\uff0c\u5e73\u5747\u635f\u5931\u63a5\u8fd14.5%\uff1b<\/li>\n\n\n\n
          • \u8fd9\u4e2a\u6570\u503c\u53cd\u6620\u4e86\u75ab\u60c5\u66b4\u8dcc\u30012022\u5e74\u79d1\u6280\u80a1\u56de\u8c03\u7b49\u6781\u7aef\u60c5\u51b5\u3002<\/li>\n<\/ul>\n\n\n\n
            \u5173\u952e\u5224\u65ad<\/strong><\/h3>\n\n\n\n
            \u5982\u679cCVaR\u662fVaR\u7684\u4e24\u500d\u4ee5\u4e0a\uff08\u6bd4\u5982VaR-2%\uff0cCVaR-5%\uff09\uff0c\u8bf4\u660e\u7b56\u7565\u6709”\u5c3e\u5df4\u98ce\u9669”\u2014\u2014\u5e73\u65f6\u6ca1\u4e8b\uff0c\u4e00\u51fa\u4e8b\u5c31\u662f\u5927\u4e8b\u3002\u8fd9\u79cd\u7b56\u7565\u8981\u614e\u7528\uff0c\u9664\u975e\u4f60\u6709\u5bf9\u51b2\u624b\u6bb5\u3002<\/p>\n\n\n\n
            5. \u671f\u671b\u503c\uff1a\u957f\u671f\u770b\u597d\uff1f<\/strong><\/h2>\n\n\n\n
            \u671f\u671b\u503c<\/strong>\u544a\u8bc9\u4f60\uff1a\u5e73\u5747\u6bcf\u7b14\u4ea4\u6613\u80fd\u8d5a\u591a\u5c11\uff1f\u5b83\u628a\u80dc\u7387\u548c\u76c8\u4e8f\u6bd4\u653e\u5728\u4e00\u8d77\u7b97\u3002<\/p>\n\n\n\n
            # \u6a21\u62df 20 \u6b21\u4ea4\u6613\uff08\u5982\u6309\u5b63\u5ea6\u6301\u4ed3\uff09<\/code>np.random.seed(42)<\/code>n_trades = 20<\/code>win_rate = 0.55<\/code>avg_win = 0.05<\/code>avg_loss = -0.03<\/code><\/code>wins = np.random.binomial(1, win_rate, n_trades)<\/code>trade_returns = np.where(<\/code>    wins == 1,<\/code>    np.random.normal(avg_win, 0.02, n_trades),<\/code>    np.random.normal(avg_loss, 0.01, n_trades)<\/code>)<\/code><\/code>expectancy = trade_returns.mean()<\/code>expectancy_r = (win_rate * avg_win) - ((1 - win_rate) * abs(avg_loss))<\/code><\/code>print(f\"Expectancy per trade: {expectancy*100:.2f}%\")<\/code>print(f\"Expectancy in R: {expectancy_r:.3f}R\")<\/code><\/code><\/pre>\n\n\n\n
            \u6a21\u62df20\u7b14\u4ea4\u6613\u7684\u7ed3\u679c\uff1a<\/p>\n\n\n\n
              \n
            • \u80dc\u738755%\uff1b<\/li>\n\n\n\n
            • \u5e73\u5747\u76c8\u52295%\uff1b<\/li>\n\n\n\n
            • \u5e73\u5747\u4e8f\u635f3%\u3002<\/li>\n<\/ul>\n\n\n\n
              \u671f\u671b\u503c = 0.55 \u00d7 5% – 0.45 \u00d7 3% = 1.45%<\/strong><\/p>\n\n\n\n
              \u671f\u671b\u503c\u600e\u4e48\u7406\u89e3\uff1f<\/strong><\/h3>\n\n\n\n
                \n
              • \u6b63\u6570\uff1a\u7b56\u7565\u957f\u671f\u80fd\u8d5a\u94b1\uff1b<\/li>\n\n\n\n
              • \u8d1f\u6570\uff1a\u7b56\u7565\u4f1a slowly \u628a\u94b1\u4e8f\u5149\uff1b<\/li>\n\n\n\n
              • \u6570\u503c\u5927\u5c0f\uff1a\u51b3\u5b9a\u8d5a\u94b1\u901f\u5ea6\u3002<\/li>\n<\/ul>\n\n\n\n
                \u80dc\u7387\u4e0d\u662f\u4e00\u5207\uff01\u4e00\u4e2a\u80dc\u738740%\u7684\u7b56\u7565\uff0c\u5982\u679c\u76c8\u5229\u65f6\u8d5a20%\uff0c\u4e8f\u635f\u65f6\u53ea\u4e8f5%\uff0c\u671f\u671b\u503c\u4f9d\u7136\u662f\u6b63\u7684\u3002<\/p>\n\n\n\n
                6. \u6700\u5927\u56de\u64a4\uff1a\u6700\u591a\u4f1a\u4e8f\u591a\u5c11\uff1f<\/strong><\/h2>\n\n\n\n
                \u6700\u5927\u56de\u64a4<\/strong>\u662f\u4f60\u8d26\u6237\u4ece\u9ad8\u70b9\u5230\u4f4e\u70b9\u7684\u6700\u5927\u8dcc\u5e45\u3002\u8fd9\u662f\u6700\u80fd\u53cd\u6620”\u5fc3\u7406\u627f\u53d7\u529b”\u7684\u6307\u6807\u3002<\/p>\n\n\n\n
                cumulative = (1 + returns).cumprod()<\/code>rolling_max = cumulative.cummax()<\/code>drawdown = cumulative \/ rolling_max - 1<\/code>max_drawdown = drawdown.min()<\/code>print(f\"Maximum Drawdown: {max_drawdown.values[0]*100:.2f}%\")<\/code><\/code><\/pre>\n\n\n\n
                \u82f9\u679c\u80a1\u7968\u7684\u6700\u5927\u56de\u64a4\u662f-33.36%\u3002\u8fd9\u610f\u5473\u7740\uff1a<\/p>\n\n\n\n
                  \n
                • \u6700\u60e8\u7684\u65f6\u5019\u4e8f\u6389\u4e86\u4e09\u5206\u4e4b\u4e00\uff1b<\/li>\n\n\n\n
                • \u5f88\u591a\u4eba\u8fd9\u65f6\u5019\u5df2\u7ecf\u5fc3\u6001\u7206\u70b8\u4e86\uff1b<\/li>\n\n\n\n
                • \u9700\u8981\u6781\u5f3a\u7684\u4fe1\u5ff5\u624d\u80fd\u625b\u8fc7\u53bb\u3002<\/li>\n<\/ul>\n\n\n\n
                  \u6700\u5927\u56de\u64a4\u53c2\u8003\u6807\u51c6<\/strong><\/h3>\n\n\n\n
                  \u56de\u64a4\u5e45\u5ea6<\/th>\u7b56\u7565\u8bc4\u4ef7<\/th><\/tr>
                  25%\u4ee5\u4e0a<\/td>\u5f88\u96be\u575a\u6301<\/td><\/tr>
                  20%\u4ee5\u5185<\/td>\u591a\u6570\u6295\u8d44\u8005\u80fd\u63a5\u53d7<\/td><\/tr>
                  10-15%<\/td>\u4f18\u79c0\u6c34\u5e73<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                  \u63a7\u5236\u6700\u5927\u56de\u64a4\uff0c\u6bd4\u8ffd\u6c42\u9ad8\u6536\u76ca\u66f4\u91cd\u8981\u3002\u56e0\u4e3a\u5927\u591a\u6570\u4eba\u90fd\u5728\u56de\u64a4\u671f\u653e\u5f03\u7b56\u7565\uff0c\u5012\u5728\u9ece\u660e\u524d\u3002<\/p>\n\n\n\n
                  7.\u5e73\u5747\u9006\u5411\u6ce2\u52a8\uff08MAE\uff09\uff1a\u6b62\u635f\u7684\u79d1\u5b66<\/strong><\/h2>\n\n\n\n
                  MAE<\/strong>\u8861\u91cf\u4e00\u7b14\u4ea4\u6613\u5728\u76c8\u5229\u524d\uff0c\u901a\u5e38\u4f1a\u5148\u5f80\u574f\u5904\u8d70\u591a\u5c11\u3002<\/p>\n\n\n\n
                  # MAE\uff1a\u6bcf\u7b14\u4ea4\u6613\u7684\u5e73\u5747\u6700\u5927\u4e0d\u5229\u53d8\u52a8<\/code>adverse_excursions = np.minimum(trade_returns, 0) * np.random.uniform(0.5, 1.2, n_trades)<\/code>mae = np.mean(np.abs(adverse_excursions))<\/code>print(f\"Mean Adverse Excursion (MAE): {mae*100:.2f}%\")<\/code><\/code><\/pre>\n\n\n\n
                  \u82f9\u679c\u80a1\u7968\u7684MAE\u662f1.01%\uff0c\u8bf4\u660e\uff1a<\/p>\n\n\n\n