{"id":1056451,"date":"2024-12-31T14:59:48","date_gmt":"2024-12-31T06:59:48","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1056451.html"},"modified":"2024-12-31T14:59:50","modified_gmt":"2024-12-31T06:59:50","slug":"%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1%e4%ba%a4%e6%98%93%e6%88%90%e5%8a%9f%e7%8e%87python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1056451.html","title":{"rendered":"\u5982\u4f55\u7edf\u8ba1\u4ea4\u6613\u6210\u529f\u7387python"},"content":{"rendered":"

\"\u5982\u4f55\u7edf\u8ba1\u4ea4\u6613\u6210\u529f\u7387python\"<\/p>\n

\u5728Python\u4e2d\uff0c\u7edf\u8ba1\u4ea4\u6613\u6210\u529f\u7387\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\u3002\u5177\u4f53\u7684\u65b9\u6cd5\u5305\u62ec\u8bfb\u53d6\u4ea4\u6613\u6570\u636e\u3001\u8ba1\u7b97\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf\u3001\u8ba1\u7b97\u603b\u4ea4\u6613\u7684\u6570\u91cf\u3001\u6700\u7ec8\u8ba1\u7b97\u6210\u529f\u7387\u3002<\/strong> \u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\u6765\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n

    \n
  1. \u8bfb\u53d6\u4ea4\u6613\u6570\u636e<\/li>\n
  2. \u8ba1\u7b97\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf<\/li>\n
  3. \u8ba1\u7b97\u603b\u4ea4\u6613\u7684\u6570\u91cf<\/li>\n
  4. \u8ba1\u7b97\u6210\u529f\u7387<\/li>\n<\/ol>\n

    \u4e0b\u9762\u8be6\u7ec6\u63cf\u8ff0\u8fd9\u4e9b\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

    \u4e00\u3001\u8bfb\u53d6\u4ea4\u6613\u6570\u636e<\/h3>\n<\/p>\n

    \u9996\u5148\uff0c\u9700\u8981\u5c06\u4ea4\u6613\u6570\u636e\u8bfb\u53d6\u5230Python\u4e2d\u3002\u4ea4\u6613\u6570\u636e\u53ef\u4ee5\u5b58\u50a8\u5728\u5404\u79cd\u683c\u5f0f\u4e2d\uff0c\u5982CSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u3001\u6570\u636e\u5e93\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u8bfb\u53d6\u8fd9\u4e9b\u6570\u636e\u3002pandas\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u80fd\u591f\u8f7b\u677e\u5730\u5904\u7406\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u4ea4\u6613\u6570\u636e<\/strong><\/h2>\n
    df = pd.read_csv('trades.csv')<\/p>\n
    \u663e\u793a\u524d\u51e0\u884c\u6570\u636e<\/strong><\/h2>\n
    print(df.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u8ba1\u7b97\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf<\/h3>\n<\/p>\n
    \u5728\u4ea4\u6613\u6570\u636e\u4e2d\uff0c\u901a\u5e38\u4f1a\u6709\u4e00\u4e2a\u5217\u6807\u8bb0\u6bcf\u7b14\u4ea4\u6613\u662f\u5426\u6210\u529f\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u7684\u8fc7\u6ee4\u529f\u80fd\u6765\u8ba1\u7b97\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf\u3002<\/p>\n<\/p>\n
    # \u5047\u8bbe\u4ea4\u6613\u6210\u529f\u7684\u6807\u8bb0\u5217\u662f 'status'\uff0c\u6210\u529f\u4ea4\u6613\u6807\u8bb0\u4e3a 'success'<\/p>\n
    successful_trades = df[df['status'] == 'success']<\/p>\n
    \u8ba1\u7b97\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf<\/strong><\/h2>\n
    successful_trades_count = successful_trades.shape[0]<\/p>\n
    print(f"\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf: {successful_trades_count}")<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u8ba1\u7b97\u603b\u4ea4\u6613\u7684\u6570\u91cf<\/h3>\n<\/p>\n
    \u603b\u4ea4\u6613\u7684\u6570\u91cf\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u6846\u7684\u884c\u6570\u6765\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u603b\u4ea4\u6613\u7684\u6570\u91cf<\/p>\n
    total_trades_count = df.shape[0]<\/p>\n
    print(f"\u603b\u4ea4\u6613\u7684\u6570\u91cf: {total_trades_count}")<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u8ba1\u7b97\u6210\u529f\u7387<\/h3>\n<\/p>\n
    \u6210\u529f\u7387\u662f\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf\u9664\u4ee5\u603b\u4ea4\u6613\u7684\u6570\u91cf\u3002<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u6210\u529f\u7387<\/p>\n
    success_rate = successful_trades_count \/ total_trades_count<\/p>\n
    print(f"\u4ea4\u6613\u6210\u529f\u7387: {success_rate:.2%}")<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u66f4\u591a\u8be6\u7ec6\u5185\u5bb9<\/h3>\n<\/p>\n
    1\u3001\u5904\u7406\u7f3a\u5931\u6570\u636e<\/h4>\n<\/p>\n
    \u5728\u5904\u7406\u4ea4\u6613\u6570\u636e\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u7f3a\u5931\u6570\u636e\u7684\u95ee\u9898\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u63d0\u4f9b\u7684\u5404\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\u3002<\/p>\n<\/p>\n
    # \u68c0\u67e5\u7f3a\u5931\u6570\u636e<\/p>\n
    print(df.isnull().sum())<\/p>\n
    \u5220\u9664\u5305\u542b\u7f3a\u5931\u6570\u636e\u7684\u884c<\/strong><\/h2>\n
    df = df.dropna()<\/p>\n
    \u6216\u8005\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u6570\u636e<\/strong><\/h2>\n
    df = df.fillna(value={'status': 'unknown'})<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u66f4\u591a\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u6839\u636e\u5176\u4ed6\u6307\u6807\u8fdb\u884c\u66f4\u591a\u7684\u5206\u6790\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u4e0d\u540c\u7c7b\u578b\u4ea4\u6613\u7684\u6210\u529f\u7387\uff0c\u6216\u8005\u57fa\u4e8e\u65f6\u95f4\u6bb5\u7684\u6210\u529f\u7387\u3002<\/p>\n<\/p>\n
    # \u6309\u4ea4\u6613\u7c7b\u578b\u8ba1\u7b97\u6210\u529f\u7387<\/p>\n
    success_rate_by_type = df.groupby('type').apply(lambda x: (x['status'] == 'success').mean())<\/p>\n
    print(success_rate_by_type)<\/p>\n
    \u6309\u65f6\u95f4\u6bb5\u8ba1\u7b97\u6210\u529f\u7387<\/strong><\/h2>\n
    df['date'] = pd.to_datetime(df['date'])<\/p>\n
    success_rate_by_month = df.groupby(df['date'].dt.to_period('M')).apply(lambda x: (x['status'] == 'success').mean())<\/p>\n
    print(success_rate_by_month)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u4f7f\u7528matplotlib\u6216seaborn\u5e93\u5c06\u6210\u529f\u7387\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u4ee5\u4fbf\u66f4\u76f4\u89c2\u5730\u4e86\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    import seaborn as sns<\/p>\n
    \u53ef\u89c6\u5316\u6309\u4ea4\u6613\u7c7b\u578b\u7684\u6210\u529f\u7387<\/strong><\/h2>\n
    plt.figure(figsize=(10, 6))<\/p>\n
    sns.barplot(x=success_rate_by_type.index, y=success_rate_by_type.values)<\/p>\n
    plt.title('\u6210\u529f\u7387\u6309\u4ea4\u6613\u7c7b\u578b')<\/p>\n
    plt.ylabel('\u6210\u529f\u7387')<\/p>\n
    plt.xlabel('\u4ea4\u6613\u7c7b\u578b')<\/p>\n
    plt.show()<\/p>\n
    \u53ef\u89c6\u5316\u6309\u6708\u7684\u6210\u529f\u7387<\/strong><\/h2>\n
    plt.figure(figsize=(14, 7))<\/p>\n
    success_rate_by_month.plot()<\/p>\n
    plt.title('\u6309\u6708\u7684\u6210\u529f\u7387')<\/p>\n
    plt.ylabel('\u6210\u529f\u7387')<\/p>\n
    plt.xlabel('\u6708\u4efd')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4\u3001\u81ea\u52a8\u5316\u5904\u7406\u6d41\u7a0b<\/h4>\n<\/p>\n
    \u5982\u679c\u9700\u8981\u7ecf\u5e38\u8fdb\u884c\u4ea4\u6613\u6210\u529f\u7387\u7684\u7edf\u8ba1\uff0c\u53ef\u4ee5\u5c06\u4e0a\u8ff0\u4ee3\u7801\u5c01\u88c5\u6210\u51fd\u6570\uff0c\u5e76\u8bbe\u7f6e\u4e3a\u5b9a\u65f6\u4efb\u52a1\u81ea\u52a8\u8fd0\u884c\u3002<\/p>\n<\/p>\n
    import schedule<\/p>\n
    import time<\/p>\n
    def calculate_success_rate():<\/p>\n
        df = pd.read_csv('trades.csv')<\/p>\n
        successful_trades_count = df[df['status'] == 'success'].shape[0]<\/p>\n
        total_trades_count = df.shape[0]<\/p>\n
        success_rate = successful_trades_count \/ total_trades_count<\/p>\n
        print(f"\u4ea4\u6613\u6210\u529f\u7387: {success_rate:.2%}")<\/p>\n
    \u6bcf\u5929\u8fd0\u884c\u4e00\u6b21<\/strong><\/h2>\n
    schedule.every().day.at("10:00").do(calculate_success_rate)<\/p>\n
    while True:<\/p>\n
        schedule.run_pending()<\/p>\n
        time.sleep(1)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u4f7f\u7528Python\u7edf\u8ba1\u4ea4\u6613\u6210\u529f\u7387\u3002\u9996\u5148\uff0c\u8bfb\u53d6\u4ea4\u6613\u6570\u636e\uff0c\u7136\u540e\u8ba1\u7b97\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf\u548c\u603b\u4ea4\u6613\u7684\u6570\u91cf\uff0c\u6700\u540e\u8ba1\u7b97\u6210\u529f\u7387\u3002\u5904\u7406\u7f3a\u5931\u6570\u636e\u3001\u8fdb\u884c\u66f4\u591a\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3001\u81ea\u52a8\u5316\u5904\u7406\u6d41\u7a0b\u90fd\u662f\u8fdb\u4e00\u6b65\u63d0\u9ad8\u4ea4\u6613\u6570\u636e\u5206\u6790\u8d28\u91cf\u7684\u6709\u6548\u624b\u6bb5\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u5728Python\u4e2d\u7edf\u8ba1\u4ea4\u6613\u6210\u529f\u7387\uff0c\u5e76\u5e94\u7528\u4e8e\u5b9e\u9645\u7684\u4ea4\u6613\u6570\u636e\u5206\u6790\u4e2d\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97\u4ea4\u6613\u6210\u529f\u7387\uff1f<\/strong>
    \u8981\u8ba1\u7b97\u4ea4\u6613\u6210\u529f\u7387\uff0c\u60a8\u9700\u8981\u9996\u5148\u6536\u96c6\u4ea4\u6613\u7684\u76f8\u5173\u6570\u636e\uff0c\u5305\u62ec\u6210\u529f\u4ea4\u6613\u7684\u6570\u91cf\u548c\u603b\u4ea4\u6613\u7684\u6570\u91cf\u3002\u63a5\u4e0b\u6765\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u7b80\u5355\u7684\u516c\u5f0f\u8fdb\u884c\u8ba1\u7b97\uff1a\u4ea4\u6613\u6210\u529f\u7387 = (\u6210\u529f\u4ea4\u6613\u6570\u91cf \/ \u603b\u4ea4\u6613\u6570\u91cf) * 100%\u3002\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u6216\u6570\u636e\u6846\u67b6\uff08\u5982Pandas\uff09\u6765\u5b58\u50a8\u548c\u5904\u7406\u8fd9\u4e9b\u6570\u636e\uff0c\u4ee5\u4fbf\u8f7b\u677e\u83b7\u53d6\u6210\u529f\u7387\u3002<\/p>\n
    \u9700\u8981\u54ea\u4e9b\u6570\u636e\u6765\u8bc4\u4f30\u4ea4\u6613\u6210\u529f\u7387\uff1f<\/strong>
    \u8bc4\u4f30\u4ea4\u6613\u6210\u529f\u7387\u901a\u5e38\u9700\u8981\u4ee5\u4e0b\u6570\u636e\uff1a\u6210\u529f\u4ea4\u6613\u7684\u603b\u6570\u91cf\u3001\u5931\u8d25\u4ea4\u6613\u7684\u603b\u6570\u91cf\uff0c\u4ee5\u53ca\u53ef\u80fd\u5f71\u54cd\u4ea4\u6613\u7ed3\u679c\u7684\u5176\u4ed6\u56e0\u7d20\uff0c\u5982\u4ea4\u6613\u65f6\u95f4\u3001\u5e02\u573a\u6761\u4ef6\u7b49\u3002\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u662f\u83b7\u5f97\u53ef\u9760\u6210\u529f\u7387\u7684\u5173\u952e\u3002<\/p>\n
    \u5728Python\u4e2d\u5982\u4f55\u53ef\u89c6\u5316\u4ea4\u6613\u6210\u529f\u7387\uff1f<\/strong>
    \u60a8\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6216Seaborn\u7b49\u6570\u636e\u53ef\u89c6\u5316\u5e93\u6765\u5c55\u793a\u4ea4\u6613\u6210\u529f\u7387\u3002\u901a\u8fc7\u521b\u5efa\u67f1\u72b6\u56fe\u6216\u997c\u56fe\uff0c\u60a8\u53ef\u4ee5\u6e05\u6670\u5730\u5c55\u793a\u6210\u529f\u548c\u5931\u8d25\u4ea4\u6613\u7684\u6bd4\u4f8b\u3002\u8fd9\u4e0d\u4ec5\u80fd\u5e2e\u52a9\u60a8\u7406\u89e3\u4ea4\u6613\u8868\u73b0\uff0c\u8fd8\u80fd\u4e3a\u540e\u7eed\u51b3\u7b56\u63d0\u4f9b\u4f9d\u636e\u3002<\/p>\n
    \u600e\u6837\u63d0\u9ad8\u4ea4\u6613\u6210\u529f\u7387\uff1f<\/strong>
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