{"id":1186852,"date":"2025-01-15T19:57:57","date_gmt":"2025-01-15T11:57:57","guid":{"rendered":""},"modified":"2025-01-15T19:58:01","modified_gmt":"2025-01-15T11:58:01","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97%e5%9b%9e%e6%92%a4%e7%8e%87","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1186852.html","title":{"rendered":"python\u5982\u4f55\u8ba1\u7b97\u56de\u64a4\u7387"},"content":{"rendered":"

\"python\u5982\u4f55\u8ba1\u7b97\u56de\u64a4\u7387\"<\/p>\n

Python\u8ba1\u7b97\u56de\u64a4\u7387\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u8ba1\u7b97\u6bcf\u4e2a\u65f6\u95f4\u70b9\u7684\u7d2f\u8ba1\u6536\u76ca\u3001\u627e\u5230\u7d2f\u8ba1\u6536\u76ca\u7684\u6700\u5927\u503c\u3001\u8ba1\u7b97\u6bcf\u4e2a\u65f6\u95f4\u70b9\u7684\u56de\u64a4\u7387\u3002<\/strong> \u5728\u8fd9\u51e0\u4e2a\u65b9\u6cd5\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406<\/strong>\u662f\u6700\u4e3a\u5e38\u7528\u7684\uff0c\u56e0\u4e3a\u5b83\u80fd\u591f\u9ad8\u6548\u5730\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5e76\u4e14\u4fbf\u4e8e\u8fdb\u884c\u5404\u79cd\u6570\u636e\u5206\u6790\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93\u8ba1\u7b97\u56de\u64a4\u7387<\/h3>\n<\/p>\n

1. \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u8ba1\u7b97\u56de\u64a4\u7387\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u6240\u9700\u7684\u5e93\uff0c\u5305\u62ecPandas\u548cNumPy\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u4ef7\u683c\u6570\u636e\u7684CSV\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u8bfb\u53d6\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u6570\u636e<\/p>\n
data = pd.read_csv('price_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u8ba1\u7b97\u7d2f\u8ba1\u6536\u76ca<\/h4>\n<\/p>\n
\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u6bcf\u4e2a\u65f6\u95f4\u70b9\u7684\u7d2f\u8ba1\u6536\u76ca\uff0c\u4ee5\u4fbf\u540e\u7eed\u8ba1\u7b97\u56de\u64a4\u7387\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387<\/p>\n
data['dAI<\/a>ly_return'] = data['price'].pct_change()<\/p>\n
\u8ba1\u7b97\u7d2f\u8ba1\u6536\u76ca\u7387<\/strong><\/h2>\n
data['cumulative_return'] = (1 + data['daily_return']).cumprod()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u627e\u5230\u7d2f\u8ba1\u6536\u76ca\u7684\u6700\u5927\u503c<\/h4>\n<\/p>\n
\u4e3a\u4e86\u8ba1\u7b97\u56de\u64a4\u7387\uff0c\u6211\u4eec\u9700\u8981\u627e\u5230\u6bcf\u4e2a\u65f6\u95f4\u70b9\u4e4b\u524d\u7684\u7d2f\u8ba1\u6536\u76ca\u7684\u6700\u5927\u503c\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u7d2f\u8ba1\u6536\u76ca\u7684\u6eda\u52a8\u6700\u5927\u503c<\/p>\n
data['cumulative_max'] = data['cumulative_return'].cummax()<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u8ba1\u7b97\u56de\u64a4\u7387<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u65f6\u95f4\u70b9\u7684\u56de\u64a4\u7387\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u56de\u64a4\u7387<\/p>\n
data['drawdown'] = data['cumulative_return'] \/ data['cumulative_max'] - 1<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u56de\u64a4\u7387\u7684\u610f\u4e49\u4e0e\u5e94\u7528<\/h3>\n<\/p>\n
1. \u56de\u64a4\u7387\u7684\u5b9a\u4e49<\/h4>\n<\/p>\n
\u56de\u64a4\u7387\uff08Drawdown\uff09\u662f\u6307\u4e00\u9879\u6295\u8d44\u5728\u67d0\u4e00\u65f6\u95f4\u6bb5\u5185\uff0c\u4ece\u4e00\u4e2a\u5cf0\u503c\u5230\u4e00\u4e2a\u4f4e\u8c37\u7684\u6700\u5927\u4e0b\u964d\u5e45\u5ea6\uff0c\u662f\u8861\u91cf\u6295\u8d44\u98ce\u9669\u7684\u91cd\u8981\u6307\u6807\u3002\u56de\u64a4\u7387\u8d8a\u5927\uff0c\u610f\u5473\u7740\u6295\u8d44\u5728\u67d0\u4e00\u6bb5\u65f6\u95f4\u5185\u7ecf\u5386\u4e86\u8f83\u5927\u7684\u635f\u5931\u3002<\/p>\n<\/p>\n
2. \u56de\u64a4\u7387\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u56de\u64a4\u7387\u5728\u5b9e\u9645\u6295\u8d44\u4e2d\u7684\u5e94\u7528\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a<\/p>\n<\/p>\n
    \n
  • \u98ce\u9669\u7ba1\u7406<\/strong>\uff1a\u901a\u8fc7\u56de\u64a4\u7387\uff0c\u6295\u8d44\u8005\u53ef\u4ee5\u4e86\u89e3\u6295\u8d44\u7ec4\u5408\u5728\u7279\u5b9a\u65f6\u95f4\u6bb5\u5185\u7684\u6700\u5927\u635f\u5931\uff0c\u4ece\u800c\u5236\u5b9a\u76f8\u5e94\u7684\u98ce\u9669\u7ba1\u7406\u7b56\u7565\u3002<\/li>\n
  • \u4e1a\u7ee9\u8bc4\u4f30<\/strong>\uff1a\u56de\u64a4\u7387\u53ef\u4ee5\u4f5c\u4e3a\u8861\u91cf\u6295\u8d44\u7ee9\u6548\u7684\u91cd\u8981\u6307\u6807\u4e4b\u4e00\uff0c\u4e0e\u6536\u76ca\u7387\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u66f4\u5168\u9762\u5730\u8bc4\u4f30\u6295\u8d44\u7684\u98ce\u9669\u548c\u6536\u76ca\u3002<\/li>\n
  • \u6295\u8d44\u51b3\u7b56<\/strong>\uff1a\u6295\u8d44\u8005\u53ef\u4ee5\u6839\u636e\u5386\u53f2\u56de\u64a4\u7387\uff0c\u9009\u62e9\u9002\u5408\u81ea\u5df1\u7684\u6295\u8d44\u4ea7\u54c1\u6216\u8c03\u6574\u6295\u8d44\u7b56\u7565\u3002<\/li>\n<\/ul>\n
    \u4e09\u3001\u56de\u64a4\u7387\u7684\u4f18\u5316\u4e0e\u6539\u8fdb<\/h3>\n<\/p>\n
    1. \u4f18\u5316\u8ba1\u7b97\u65b9\u6cd5<\/h4>\n<\/p>\n
    \u867d\u7136\u4f7f\u7528Pandas\u8ba1\u7b97\u56de\u64a4\u7387\u5df2\u7ecf\u6bd4\u8f83\u9ad8\u6548\uff0c\u4f46\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u4ecd\u7136\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u8fdb\u884c\u4f18\u5316\uff1a<\/p>\n<\/p>\n
      \n
    • \u5e76\u884c\u8ba1\u7b97<\/strong>\uff1a\u5229\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u6280\u672f\uff0c\u52a0\u5feb\u8ba1\u7b97\u901f\u5ea6\u3002<\/li>\n
    • \u5206\u5757\u5904\u7406<\/strong>\uff1a\u5c06\u5927\u89c4\u6a21\u6570\u636e\u5206\u6210\u591a\u4e2a\u5c0f\u5757\uff0c\u9010\u5757\u8ba1\u7b97\u56de\u64a4\u7387\uff0c\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002<\/li>\n<\/ul>\n
      2. \u589e\u52a0\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
      \u901a\u8fc7\u7ed8\u5236\u56de\u64a4\u7387\u66f2\u7ebf\uff0c\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u6295\u8d44\u5728\u4e0d\u540c\u65f6\u95f4\u6bb5\u5185\u7684\u56de\u64a4\u60c5\u51b5\uff0c\u4ece\u800c\u5e2e\u52a9\u6295\u8d44\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u6295\u8d44\u98ce\u9669\u3002<\/p>\n<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      plt.figure(figsize=(10, 6))<\/p>\n
      plt.plot(data['date'], data['drawdown'], label='Drawdown')<\/p>\n
      plt.xlabel('Date')<\/p>\n
      plt.ylabel('Drawdown')<\/p>\n
      plt.title('Drawdown Over Time')<\/p>\n
      plt.legend()<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u56db\u3001\u56de\u64a4\u7387\u5728\u4e0d\u540c\u5e02\u573a\u548c\u7b56\u7565\u4e2d\u7684\u8868\u73b0<\/h3>\n<\/p>\n
      1. \u80a1\u7968\u5e02\u573a\u4e2d\u7684\u56de\u64a4\u7387<\/h4>\n<\/p>\n
      \u5728\u80a1\u7968\u5e02\u573a\u4e2d\uff0c\u56de\u64a4\u7387\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u8bc4\u4f30\u4e2a\u80a1\u6216\u6295\u8d44\u7ec4\u5408\u7684\u98ce\u9669\u3002\u4f8b\u5982\uff0c\u5728\u725b\u5e02\u4e2d\uff0c\u80a1\u7968\u7684\u56de\u64a4\u7387\u901a\u5e38\u8f83\u4f4e\uff0c\u800c\u5728\u718a\u5e02\u4e2d\uff0c\u56de\u64a4\u7387\u5219\u53ef\u80fd\u663e\u8457\u589e\u52a0\u3002<\/p>\n<\/p>\n
      2. \u56fa\u5b9a\u6536\u76ca\u5e02\u573a\u4e2d\u7684\u56de\u64a4\u7387<\/h4>\n<\/p>\n
      \u5728\u56fa\u5b9a\u6536\u76ca\u5e02\u573a\u4e2d\uff0c\u56de\u64a4\u7387\u4e3b\u8981\u7528\u4e8e\u8bc4\u4f30\u503a\u5238\u6216\u503a\u5238\u57fa\u91d1\u7684\u98ce\u9669\u3002\u7531\u4e8e\u503a\u5238\u4ef7\u683c\u901a\u5e38\u8f83\u4e3a\u7a33\u5b9a\uff0c\u56e0\u6b64\u56de\u64a4\u7387\u901a\u5e38\u8f83\u4f4e\uff0c\u4f46\u5728\u5229\u7387\u6ce2\u52a8\u8f83\u5927\u7684\u5e02\u573a\u73af\u5883\u4e2d\uff0c\u56de\u64a4\u7387\u4e5f\u53ef\u80fd\u663e\u8457\u589e\u52a0\u3002<\/p>\n<\/p>\n
      3. \u5bf9\u51b2\u57fa\u91d1\u4e2d\u7684\u56de\u64a4\u7387<\/h4>\n<\/p>\n
      \u5bf9\u51b2\u57fa\u91d1\u901a\u5e38\u91c7\u7528\u591a\u79cd\u6295\u8d44\u7b56\u7565\uff0c\u5305\u62ec\u80a1\u7968\u3001\u503a\u5238\u3001\u884d\u751f\u54c1\u7b49\uff0c\u56e0\u6b64\u5176\u56de\u64a4\u7387\u53ef\u80fd\u8f83\u9ad8\u3002\u6295\u8d44\u8005\u53ef\u4ee5\u901a\u8fc7\u56de\u64a4\u7387\u4e86\u89e3\u5bf9\u51b2\u57fa\u91d1\u5728\u4e0d\u540c\u5e02\u573a\u73af\u5883\u4e0b\u7684\u8868\u73b0\uff0c\u4ece\u800c\u8fdb\u884c\u98ce\u9669\u7ba1\u7406\u548c\u6295\u8d44\u51b3\u7b56\u3002<\/p>\n<\/p>\n
      \u4e94\u3001\u56de\u64a4\u7387\u7684\u5c40\u9650\u6027\u4e0e\u6539\u8fdb<\/h3>\n<\/p>\n
      1. \u56de\u64a4\u7387\u7684\u5c40\u9650\u6027<\/h4>\n<\/p>\n
        \n
      • \u53ea\u8003\u8651\u5386\u53f2\u6570\u636e<\/strong>\uff1a\u56de\u64a4\u7387\u662f\u57fa\u4e8e\u5386\u53f2\u6570\u636e\u8ba1\u7b97\u7684\uff0c\u4e0d\u80fd\u9884\u6d4b\u672a\u6765\u7684\u5e02\u573a\u98ce\u9669\u3002<\/li>\n
      • \u5ffd\u7565\u5176\u4ed6\u98ce\u9669\u56e0\u7d20<\/strong>\uff1a\u56de\u64a4\u7387\u53ea\u8003\u8651\u4e86\u5e02\u573a\u98ce\u9669\uff0c\u5ffd\u7565\u4e86\u6d41\u52a8\u6027\u98ce\u9669\u3001\u4fe1\u7528\u98ce\u9669\u7b49\u5176\u4ed6\u98ce\u9669\u56e0\u7d20\u3002<\/li>\n<\/ul>\n
        2. \u6539\u8fdb\u65b9\u6cd5<\/h4>\n<\/p>\n
          \n
        • \u7ed3\u5408\u5176\u4ed6\u98ce\u9669\u6307\u6807<\/strong>\uff1a\u5c06\u56de\u64a4\u7387\u4e0e\u5176\u4ed6\u98ce\u9669\u6307\u6807\uff08\u5982\u6ce2\u52a8\u7387\u3001VaR\u7b49\uff09\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u66f4\u5168\u9762\u5730\u8bc4\u4f30\u6295\u8d44\u98ce\u9669\u3002<\/li>\n
        • \u52a8\u6001\u8c03\u6574\u6295\u8d44\u7b56\u7565<\/strong>\uff1a\u6839\u636e\u56de\u64a4\u7387\u7684\u53d8\u5316\uff0c\u52a8\u6001\u8c03\u6574\u6295\u8d44\u7b56\u7565\uff0c\u4ee5\u5e94\u5bf9\u5e02\u573a\u73af\u5883\u7684\u53d8\u5316\u3002<\/li>\n<\/ul>\n
          \u516d\u3001\u56de\u64a4\u7387\u7684\u8ba1\u7b97\u5b9e\u4f8b<\/h3>\n<\/p>\n
          1. \u793a\u4f8b\u6570\u636e<\/h4>\n<\/p>\n
          \u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u80a1\u7968\u4ef7\u683c\u6570\u636e\uff1a<\/p>\n<\/p>\n\n\n\n
          \n\n
          \n
          \n
          \n
          \n
          \u65e5\u671f<\/th>\n<\/p>\n
          		\u4ef7\u683c<\/th>\n<\/p>\n<\/tr>\n<\/thead>\n
          2023-01-01<\/td>\n<\/p>\n
          		100<\/td>\n<\/p>\n<\/tr>\n
          2023-01-02<\/td>\n<\/p>\n
          		105<\/td>\n<\/p>\n<\/tr>\n
          2023-01-03<\/td>\n<\/p>\n
          		102<\/td>\n<\/p>\n<\/tr>\n
          2023-01-04<\/td>\n<\/p>\n
          		108<\/td>\n<\/p>\n<\/tr>\n
          2023-01-05<\/td>\n<\/p>\n
          		104<\/td>\n<\/p>\n<\/tr>\n<\/tbody>\n<\/table>\n
          2. \u8ba1\u7b97\u56de\u64a4\u7387<\/h4>\n<\/p>\n
          import pandas as pd<\/p>\n
          \u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
          data = pd.DataFrame({<\/p>\n
              'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'],<\/p>\n
              'price': [100, 105, 102, 108, 104]<\/p>\n
          })<\/p>\n
          \u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387<\/strong><\/h2>\n
          data['daily_return'] = data['price'].pct_change()<\/p>\n
          \u8ba1\u7b97\u7d2f\u8ba1\u6536\u76ca\u7387<\/strong><\/h2>\n
          data['cumulative_return'] = (1 + data['daily_return']).cumprod()<\/p>\n
          \u8ba1\u7b97\u7d2f\u8ba1\u6536\u76ca\u7684\u6eda\u52a8\u6700\u5927\u503c<\/strong><\/h2>\n
          data['cumulative_max'] = data['cumulative_return'].cummax()<\/p>\n
          \u8ba1\u7b97\u56de\u64a4\u7387<\/strong><\/h2>\n
          data['drawdown'] = data['cumulative_return'] \/ data['cumulative_max'] - 1<\/p>\n
          print(data)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n\n\n\n
          \n\n
          \n
          \n
          \n
          \n
          \u65e5\u671f<\/th>\n<\/p>\n
          		\u4ef7\u683c<\/th>\n<\/p>\n
          		daily_return<\/th>\n<\/p>\n
          		cumulative_return<\/th>\n<\/p>\n
          		cumulative_max<\/th>\n<\/p>\n
          		drawdown<\/th>\n<\/p>\n<\/tr>\n<\/thead>\n
          2023-01-01<\/td>\n<\/p>\n
          		100<\/td>\n<\/p>\n
          		NaN<\/td>\n<\/p>\n
          		NaN<\/td>\n<\/p>\n
          		NaN<\/td>\n<\/p>\n
          		NaN<\/td>\n<\/p>\n<\/tr>\n
          2023-01-02<\/td>\n<\/p>\n
          		105<\/td>\n<\/p>\n
          		0.050<\/td>\n<\/p>\n
          		1.050<\/td>\n<\/p>\n
          		1.050<\/td>\n<\/p>\n
          		0.000<\/td>\n<\/p>\n<\/tr>\n
          2023-01-03<\/td>\n<\/p>\n
          		102<\/td>\n<\/p>\n
          		-0.028571<\/td>\n<\/p>\n
          		1.020<\/td>\n<\/p>\n
          		1.050<\/td>\n<\/p>\n
          		-0.0286<\/td>\n<\/p>\n<\/tr>\n
          2023-01-04<\/td>\n<\/p>\n
          		108<\/td>\n<\/p>\n
          		0.058824<\/td>\n<\/p>\n
          		1.080<\/td>\n<\/p>\n
          		1.080<\/td>\n<\/p>\n
          		0.000<\/td>\n<\/p>\n<\/tr>\n
          2023-01-05<\/td>\n<\/p>\n
          		104<\/td>\n<\/p>\n
          		-0.037037<\/td>\n<\/p>\n
          		1.040<\/td>\n<\/p>\n
          		1.080<\/td>\n<\/p>\n
          		-0.0370<\/td>\n<\/p>\n<\/tr>\n<\/tbody>\n<\/table>\n
          \u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
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          \u603b\u4e4b\uff0c\u56de\u64a4\u7387\u662f\u8861\u91cf\u6295\u8d44\u98ce\u9669\u7684\u91cd\u8981\u6307\u6807\uff0c\u901a\u8fc7\u5408\u7406\u8ba1\u7b97\u548c\u5206\u6790\u56de\u64a4\u7387\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u5236\u5b9a\u66f4\u79d1\u5b66\u7684\u6295\u8d44\u7b56\u7565\uff0c\u63d0\u9ad8\u6295\u8d44\u7ee9\u6548\u3002<\/strong><\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u56de\u64a4\u7387\u7684\u5b9a\u4e49\u662f\u4ec0\u4e48\uff1f<\/strong><\/p>\n
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          \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u56de\u64a4\u7387\u7684\u8ba1\u7b97\uff1f<\/strong><\/p>\n
          \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u4ece\u800c\u8ba1\u7b97\u56de\u64a4\u7387\u3002\u4e00\u822c\u6b65\u9aa4\u5305\u62ec\uff1a<\/p>\n
            \n
          1. \u83b7\u53d6\u6295\u8d44\u7ec4\u5408\u7684\u5386\u53f2\u51c0\u503c\u6570\u636e\u3002<\/li>\n
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          3. \u786e\u5b9a\u5386\u53f2\u6700\u9ad8\u503c\uff0c\u8fdb\u800c\u8ba1\u7b97\u5f53\u524d\u503c\u4e0e\u5386\u53f2\u6700\u9ad8\u503c\u4e4b\u95f4\u7684\u5dee\u989d\u3002<\/li>\n
          4. \u5c06\u8fd9\u4e2a\u5dee\u989d\u4e0e\u5386\u53f2\u6700\u9ad8\u503c\u8fdb\u884c\u6bd4\u8f83\uff0c\u5f97\u51fa\u56de\u64a4\u7387\u3002<\/li>\n<\/ol>\n
            \u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n
            import pandas as pd\n\n# \u5047\u8bbedf\u662f\u4e00\u4e2a\u5305\u542b\u51c0\u503c\u6570\u636e\u7684DataFrame\uff0c\u5217\u540d\u4e3a'NetValue'\ndf['MaxValue'] = df['NetValue'].cummax()\ndf['Drawdown'] = (df['NetValue'] - df['MaxValue']) \/ df['MaxValue']\ndrawdown_rate = df['Drawdown'].min()\n<\/code><\/pre>\n
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