{"id":2545,"date":"2025-09-15T07:05:00","date_gmt":"2025-09-14T23:05:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2545"},"modified":"2025-09-08T21:19:28","modified_gmt":"2025-09-08T13:19:28","slug":"%e6%88%91%e6%8b%86%e8%a7%a3%e4%ba%8615%e5%8f%aa%e7%be%8e%e8%82%a1%e7%a7%91%e6%8a%80%e5%b7%a8%e5%a4%b4%ef%bc%8c%e7%94%a8%e9%87%8f%e5%8c%96%e6%96%b9%e6%b3%95%e5%8f%91%e7%8e%b0%e6%8a%95%e8%b5%84ai","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/09\/15\/%e6%88%91%e6%8b%86%e8%a7%a3%e4%ba%8615%e5%8f%aa%e7%be%8e%e8%82%a1%e7%a7%91%e6%8a%80%e5%b7%a8%e5%a4%b4%ef%bc%8c%e7%94%a8%e9%87%8f%e5%8c%96%e6%96%b9%e6%b3%95%e5%8f%91%e7%8e%b0%e6%8a%95%e8%b5%84ai\/","title":{"rendered":"\u6211\u62c6\u89e3\u4e8615\u53ea\u7f8e\u80a1\u79d1\u6280\u5de8\u5934\uff0c\u7528\u91cf\u5316\u65b9\u6cd5\u53d1\u73b0\u6295\u8d44AI\u80a1\u7684\u771f\u76f8\uff08\u4e0a\uff09"},"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>\u6700\u8fd1AI\u6982\u5ff5\u706b\u5230\u4e0d\u884c\uff0c\u4f46\u4f60\u662f\u4e0d\u662f\u4e5f\u5728\u7591\u60d1\uff1a\u8fd9\u4e9b\u79d1\u6280\u516c\u53f8\u5230\u5e95\u503c\u4e0d\u503c\u5f97\u6295\u8d44\uff0c\u8fd8\u53ea\u662f\u5e02\u573a\u7092\u4f5c\uff1f\u4eca\u5929\u6211\u5c31\u7528Python\u91cf\u5316\u65b9\u6cd5\u6765\u62c6\u89e3\u7f8e\u56fd\u524d15\u5927\u79d1\u6280\u80a1\uff0c<\/mark>\u4ece\u8d22\u52a1\u5206\u6790\u5230\u5e02\u573a\u4f30\u503c\uff0c\u4ece\u4e1a\u7ee9\u8868\u73b0\u5230\u98ce\u9669\u6c34\u5e73\u548c\u516c\u4f17\u70ed\u5ea6\uff0c\u53ea\u9700\u516d\u6b65\u62c6\u89e3\u5b83\u4eec\u7684\u4ef7\u503c\uff0c\u5e2e\u4f60\u907f\u6ce1\u6cab\u3001\u6295\u771f\u91d1\uff0c\u6295\u524d\u5fc5\u770b\u3002<\/mark><\/pre>\n<\/blockquote>\n\n\n\n
\u516d\u6b65\u5206\u6790\u6cd5\uff0c\u79d1\u5b66\u8bc4\u4f30\u4f01\u4e1a\u4ef7\u503c<\/strong><\/h2>\n\n\n\n
\u770b\u516c\u53f8\u4e0d\u80fd\u53ea\u770b\u70ed\u95f9\uff0c\u5f97\u770b\u95e8\u9053\u3002\u7528\u6570\u636e\u79d1\u5b66\u63ed\u79d8\u79d1\u6280\u5de8\u5934\u7684\u771f\u5b9e\u4ef7\u503c\uff0c\u522b\u518d\u88ab\u7092\u4f5c\u5ffd\u60a0\u4e86\uff01\u6211\u4eca\u5929\u4f1a\u7528\u516d\u6b65\u5206\u6790\u6cd5<\/strong>\u6765\u5168\u9762\u8bc4\u4f30\u8fd9\u4e9b\u79d1\u6280\u5de8\u5934\uff1a<\/p>\n\n\n\n
    \n
  1. \u8d22\u52a1\u5065\u5eb7\u5ea6<\/strong>\uff1a\u770b\u770b\u8fd9\u4e9b\u516c\u53f8\u8d5a\u94b1\u80fd\u529b\u600e\u4e48\u6837\uff1f<\/li>\n\n\n\n
  2. \u5e02\u573a\u4f30\u503c<\/strong>\uff1a\u5206\u6790\u80a1\u4ef7\u662f\u5426\u865a\u9ad8\uff1f<\/li>\n\n\n\n
  3. \u98ce\u9669\u6c34\u5e73<\/strong>\uff1a\u8861\u91cf\u6295\u8d44\u8fd9\u4e9b\u516c\u53f8\u7684\u98ce\u9669\uff1b<\/li>\n\n\n\n
  4. \u4e1a\u7ee9\u8868\u73b0<\/strong>\uff1a\u56de\u987e\u5386\u53f2\u6536\u76ca\u60c5\u51b5\uff1b<\/li>\n\n\n\n
  5. \u6700\u5927\u56de\u64a4\uff1a<\/strong>\u4f60\u80fd\u627f\u53d7\u591a\u5927\u4e8f\u635f\uff1f<\/li>\n\n\n\n
  6. \u516c\u4f17\u5173\u6ce8\u5ea6<\/strong>\uff1a\u4e86\u89e3\u5e02\u573a\u60c5\u7eea\u3002<\/li>\n<\/ol>\n\n\n\n
    \u5206\u6790\u4e86\u4ece2020\u5e741\u6708\u52302025\u5e748\u6708\u6574\u65741413\u5929\u7684\u6570\u636e<\/strong>\uff0c\u5305\u62ec\u80a1\u4ef7\u3001\u8d22\u52a1\u6307\u6807\u548c\u5e02\u573a\u6570\u636e\u3002<\/p>\n\n\n\n
    \u8fd915\u5bb6\u516c\u53f8\u662f\uff1a<\/strong>\u82f1\u4f1f\u8fbe\uff08NVDA\uff09\u3001\u5fae\u8f6f\uff08MSFT\uff09\u3001\u82f9\u679c\uff08AAPL\uff09\u3001\u8c37\u6b4c\uff08GOOG\uff09\u3001\u4e9a\u9a6c\u900a\uff08AMZN\uff09\u3001Meta\uff08META\uff09\u3001\u535a\u901a\uff08AVGO\uff09\u3001\u7279\u65af\u62c9\uff08TSLA\uff09\u3001\u7532\u9aa8\u6587\uff08ORCL\uff09\u3001\u7f51\u98de\uff08NFLX\uff09\u3001Palantir\uff08PLTR\uff09\u3001AMD\u3001\u601d\u79d1\uff08CSCO\uff09\u3001Salesforce\uff08CRM\uff09\u548cIBM\u3002<\/p>\n\n\n\n
    \u7b80\u5355\u8bf4\uff0c\u903b\u8f91\u662f\u4ece\u8fd915\u5bb6\u516c\u53f8\u6cd5\u5f8b\u57fa\u672c\u8d5a\u94b1\u80fd\u529b\uff0c\u5230\u4f30\u503c\u98ce\u9669\uff0c\u518d\u5230\u8868\u73b0\u548c\u70ed\u5ea6\uff0c\u6700\u540e\u7ed9\u5efa\u8bae\u3002\u8d70\u8d77\uff01<\/p>\n\n\n\n
    \u7b2c\u4e00\u6b65\uff1a\u8d22\u52a1\u5065\u5eb7\u5ea6\uff0c\u8c01\u5728\u771f\u6b63\u8d5a\u94b1\uff1f<\/strong><\/h2>\n\n\n\n
    \u5148\u770b\u76c8\u5229\u80fd\u529b\u3002\u6211\u8ba1\u7b97\u4e86\u8fd9\u4e9b\u516c\u53f8\u4e94\u5e74\u7684\u6bdb\u5229\u7387\u3001\u8425\u4e1a\u5229\u6da6\u7387\u548c\u51c0\u5229\u6da6\u7387<\/strong>\uff0c\u53d1\u73b0\u4e86\u4e0d\u5c11\u6709\u610f\u601d\u7684\u73b0\u8c61\u3002<\/p>\n\n\n\n
    \u9996\u5148\u9700\u8981\u83b7\u53d6\u80a1\u7968\u8d22\u52a1\u6570\u636e<\/strong><\/p>\n\n\n\n
    import yfinance as yf\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n \n# Define the stock ticker\nticker_list = ['NVDA','MSFT','AAPL','GOOG','AMZN','META','AVGO','TSLA','ORCL','NFLX','PLTR','AMD','CSCO','CRM','IBM']\nall_symbols  = \" \".join(ticker_list)\n# Fetch the stock's financial data\ntickers = yf.Tickers(all_symbols)<\/code><\/pre>\n\n\n\n
    1.1 \u6bdb\u5229\u7387<\/strong>\u662f\u770b\u516c\u53f8\u5356\u7684\u4e1c\u897f\u6263\u9664\u6210\u672c\u540e\u8fd8\u80fd\u8d5a\u591a\u5c11\u3002\u79d1\u6280\u516c\u53f8\uff0c\u7279\u522b\u662f\u8f6f\u4ef6\u670d\u52a1\u7c7b\u516c\u53f8\uff0c\u6bdb\u5229\u7387\u901a\u5e38\u90fd\u572870%\u4ee5\u4e0a\u3002\u8fc7\u53bb\u4e94\u5e74\u7f8e\u56fd\u5341\u4e94\u5927\u79d1\u6280\u516c\u53f8\u7684\u6bdb\u5229\u7387\u5982\u4e0b\u56fe\uff1a<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u4e0b\u9762\u662f\u5b9e\u73b0\u4ee3\u7801\uff1a<\/p>\n\n\n\n
    from matplotlib.pyplot import cm\nplt.figure(figsize=(10, 6))\ncolor = iter(cm.tab20(np.linspace(0, 1, len(ticker_list))))\nfor ticker in ticker_list:\n    income_stmt = tickers.tickers[ticker].financials.T\n    # Calculate Gross Profit Margin\n    income_stmt['Gross Profit Margin'] = (income_stmt['Gross Profit'] \/ income_stmt['Total Revenue']) * 100\n    c = next(color)\n    plt.plot(income_stmt.index, income_stmt['Gross Profit Margin'], marker='o', linestyle='-', color=c,label=ticker)\n    plt.legend()\nplt.title('Gross Profit Margin of Top 15 US Techs Over the Last Five Years')\nplt.xlabel('Year')\nplt.ylabel('Gross Profit Margin (%)')\nplt.grid()\nplt.show()<\/code><\/pre>\n\n\n\n
    1.2 \u8425\u4e1a\u5229\u6da6\u7387<\/strong>\u5219\u8003\u8651\u4e86\u66f4\u591a\u8fd0\u8425\u6210\u672c\uff0c\u80fd\u66f4\u597d\u53cd\u6620\u516c\u53f8\u7684\u5b9e\u9645\u8fd0\u8425\u6548\u7387\u3002\u8fd9\u91cc\u6211\u53d1\u73b0\u4e86\u4e00\u4e9b\u6709\u8da3\u7684\u73b0\u8c61\uff1a\u6709\u4e9b\u516c\u53f8\u6bdb\u5229\u7387\u5f88\u9ad8\uff0c\u4f46\u8425\u4e1a\u5229\u6da6\u7387\u5374\u4e00\u822c\uff0c\u8bf4\u660e\u8fd0\u8425\u6210\u672c\u5403\u6389\u4e86\u4e0d\u5c11\u5229\u6da6\u3002\u8425\u4e1a\u5229\u6da6\u7387\u5982\u4e0b\u56fe\uff1a<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u5b9e\u73b0\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    plt.figure(figsize=(10, 6))\ncolor = iter(cm.tab20(np.linspace(0, 1, len(ticker_list))))\nfor ticker in ticker_list:\n    income_stmt = tickers.tickers[ticker].financials.T\n    # Calculate Operating Profit Margin\n    income_stmt['Operating Profit Margin'] = (income_stmt['Operating Income'] \/ income_stmt['Total Revenue']) * 100\n    c = next(color)\n    plt.plot(income_stmt.index, income_stmt['Operating Profit Margin'], marker='o', linestyle='-', color=c,label=ticker)\n    plt.legend()\nplt.title('Operating Profit Margin of Top 15 US Techs Over the Last Five Years')\nplt.xlabel('Year')\nplt.ylabel('Operating Profit Margin (%)')\nplt.grid()\nplt.show()<\/code><\/pre>\n\n\n\n
    1.3 \u51c0\u5229\u6da6\u7387<\/strong>\u662f\u6700\u7ec8\u770b\u516c\u53f8\u80fd\u8d5a\u591a\u5c11\u94b1\u7684\u6307\u6807\u3002\u597d\u7684\u79d1\u6280\u516c\u53f8\u901a\u5e38\u80fd\u4fdd\u6301\u572820%\u4ee5\u4e0a\uff0c10%\u7b97\u662f\u53ca\u683c\u7ebf\uff0c\u4f4e\u4e8e10%\u5c31\u9700\u8981\u8b66\u60d5\u4e86\u3002\u51c0\u5229\u6da6\u7387\u5982\u4e0b\u56fe\uff1a<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u5b9e\u73b0\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    plt.figure(figsize=(10, 6))\ncolor = iter(cm.tab20(np.linspace(0, 1, len(ticker_list))))\nfor ticker in ticker_list:\n    income_stmt = tickers.tickers[ticker].financials.T\n    # Calculate net profit margin\n    net_income = income_stmt['Net Income']\n    total_revenue = income_stmt['Total Revenue']\n    net_profit_margin = (net_income \/ total_revenue) * 100\n \n    c = next(color)\n    plt.plot(income_stmt.index, net_profit_margin, marker='o', linestyle='-', color=c,label=ticker)\n    plt.legend()\nplt.title('Net Profit Margin of Top 15 US Techs Over the Last Five Years')\nplt.xlabel('Year')\nplt.ylabel('Net Profit Margin (%)')\nplt.grid()\nplt.show()<\/code><\/pre>\n\n\n\n
    \u8d5a\u94b1\u80fd\u529b\u770b\u6bdb\u5229\u767e\u5206\u6bd4\uff08\u5356\u4e1c\u897f\u6263\u6210\u672c\u5269\u591a\u5c11\uff09\u3001\u8fd0\u8425\u5229\u6da6\u767e\u5206\u6bd4\uff08\u6263\u65e5\u5e38\u82b1\u9500\u5269\u591a\u5c11\uff09\u548c\u51c0\u5229\u767e\u5206\u6bd4\uff08\u6263\u7a0e\u5269\u7eaf\u8d5a\uff09\u3002\u79d1\u6280\u80a1\uff0c\u5c24\u5176\u662f\u8f6f\u4ef6\u7684\uff0c\u6bdb\u5229\u8d8570%\u7b97\u725b\u3002\u6839\u636e2025\u5e749\u6708\u6700\u65b0\u6570\u636e\uff0cNVDA\u3001MSFT\u548cMETA\u9886\u8dd1\uff0cTSLA\u548cAMZN\u4f4e\uff0c\u53ef\u80fd\u662f\u89c4\u6a21\u5927\u6210\u672c\u9ad8\u3002<\/p>\n\n\n\n
    \u4e0b\u9762\u7684\u8fd9\u5f20\u8868\u683c\u8bf4\u660e\u4e86\u5176\u5e73\u5747\u503c\uff082020-2025\uff09\uff1a<\/p>\n\n\n\n
    \u516c\u53f8<\/th>\u6bdb\u5229\u767e\u5206\u6bd4\uff08%\uff09<\/th>\u8fd0\u8425\u5229\u6da6\u767e\u5206\u6bd4\uff08%\uff09<\/th>\u51c0\u5229\u767e\u5206\u6bd4\uff08%\uff09<\/th>\u7ea7\u522b<\/th><\/tr><\/thead>
    NVDA<\/td>78<\/td>45<\/td>35<\/td>\u4f18\u79c0<\/td><\/tr>
    MSFT<\/td>76<\/td>42<\/td>30<\/td>\u4f18\u79c0<\/td><\/tr>
    META<\/td>74<\/td>38<\/td>32<\/td>\u4f18\u79c0<\/td><\/tr>
    ORCL<\/td>73<\/td>35<\/td>25<\/td>\u4f18\u79c0<\/td><\/tr>
    CRM<\/td>72<\/td>15<\/td>10<\/td>\u826f\u597d<\/td><\/tr>
    AAPL<\/td>68<\/td>40<\/td>28<\/td>\u826f\u597d<\/td><\/tr>
    GOOG<\/td>68<\/td>35<\/td>25<\/td>\u826f\u597d<\/td><\/tr>
    AVGO<\/td>60<\/td>30<\/td>25<\/td>\u826f\u597d<\/td><\/tr>
    CSCO<\/td>65<\/td>30<\/td>25<\/td>\u826f\u597d<\/td><\/tr>
    NFLX<\/td>45<\/td>20<\/td>18<\/td>\u4e00\u822c<\/td><\/tr>
    IBM<\/td>55<\/td>15<\/td>10<\/td>\u4e00\u822c<\/td><\/tr>
    PLTR<\/td>75<\/td>5<\/td>2<\/td>\u4e00\u822c<\/td><\/tr>
    AMD<\/td>57<\/td>8<\/td>5<\/td>\u4e00\u822c<\/td><\/tr>
    AMZN<\/td>54<\/td>8<\/td>5<\/td>\u4e00\u822c<\/td><\/tr>
    TSLA<\/td>58<\/td>5<\/td>3<\/td>\u4e00\u822c<\/td><\/tr><\/tbody><\/table>
    \uff08\u6570\u636e\u6e90\uff1aYahoo Finance\u548c\u6700\u65b0\u8d22\u62a5\uff0c\u7ea7\u522b\u57fa\u4e8e\u884c\u4e1a\u6807\u51c6\uff1a\u6bdb\u5229>70%\u4f18\u79c0\uff0c\u51c0\u5229>20%\u4f18\u79c0\u3002\uff09<\/em><\/figcaption><\/figure>\n\n\n\n
    \u4ece\u4e0a\u9762\u91cf\u5316\u7684\u53d8\u5316\u8d8b\u52bf\u5206\u6790\u6765\u770b\uff0cNVDA\u6bdb\u5229\u7a33\u5347\uff0cTSLA\u6ce2\u52a8\u5927\u3002\u63d0\u793a\u70b9\uff1a\u9ad8\u6bdb\u5229\u4e0d\u7b49\u4e8e\u7a33\u8fd0\u8425\uff0c\u50cfPLTR\u6bdb\u5229\u9ad8\u4f46\u8fd0\u8425\u4f4e\uff0c\u6210\u672c\u5403\u5229\u6da6\u3002<\/p>\n\n\n\n
    \u7b2c\u4e8c\u6b65\uff1a\u5e02\u573a\u4f30\u503c\uff0c\u522b\u4e70\u8d35\u4e86<\/strong><\/h2>\n\n\n\n
    \u518d\u770b\u5e02\u573a\u4f30\u503c\u3002\u6211\u5206\u6790\u4e86\u5e02\u76c8\u7387\u3001\u5e02\u51c0\u7387\u548cPEG\u6bd4\u7387<\/strong>\u7b49\u4e00\u7cfb\u5217\u548c\u4f30\u503c\u6709\u5173\u7684\u6307\u6807\uff0c\u76ee\u7684\u5c31\u662f\u5206\u6790\u8fd9\u4e9b\u516c\u53f8\u7684\u80a1\u4ef7\u662f\u5426\u5b58\u5728\u865a\u9ad8\u3002<\/p>\n\n\n\n
    2.1 \u5f53\u524d\u5e02\u503c<\/strong>\u800c\u8a00\uff0c\u8fd9 15 \u5927\u79d1\u6280\u516c\u53f8\u76ee\u524d\u5e02\u503c\u524d\u4e94\u7684\u80a1\u7968\u662f NVDA\u3001MSFT\u3001AAPL\u3001GOOG \u548c AMZN\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u6bd4\u8f83\u7f8e\u56fd 15 \u5927\u79d1\u6280\u516c\u53f8\u5f53\u524d\u5e02\u503c\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    mcall=[]
    for ticker in ticker_list:
        market_cap = tickers.tickers[ticker].info[\"marketCap\"]
        mcall.append(market_cap)
        print(market_cap)

    4385865990144
    3903083511808
    3422047698944
    2498944303104
    2457588465664
    1980520988672
    1418143334400
    1068121325568
    699005403136
    526360084480
    420117676032
    287826903040
    261597200384
    232489648128
    223238520832


    plt.figure(figsize=(12, 6))
    plt.bar(ticker_list, mcall)
    plt.title('Market Cap USD')
    plt.xlabel('Company')
    plt.ylabel('MC USD')
    plt.grid()
    plt.show()<\/pre>\n\n\n\n
    2.2 PEG\u6bd4\u7387<\/strong>\uff08\u5e02\u76c8\u7387\u76f8\u5bf9\u76c8\u5229\u589e\u957f\u6bd4\u7387\uff09\u7279\u522b\u6709\u7528\uff0c\u56e0\u4e3a\u5b83\u8003\u8651\u4e86\u6210\u957f\u6027\u3002\u4e00\u822c\u6765\u8bf4\uff1a<\/p>\n\n\n\n
      \n
    • PEG\u4f4e\u4e8e1\uff1a\u53ef\u80fd\u88ab\u4f4e\u4f30\uff1b<\/li>\n\n\n\n
    • PEG\u57281-2\u4e4b\u95f4\uff1a\u5408\u7406\u4f30\u503c\uff1b<\/li>\n\n\n\n
    • PEG\u9ad8\u4e8e2\uff1a\u53ef\u80fd\u88ab\u9ad8\u4f30\u3002<\/li>\n<\/ul>\n\n\n\n
      \"\u7f8e\u56fd\u524d<\/figure>\n\n\n\n
      \u7ed8\u5236 PEG \u6bd4\u7387\u7684\u4ee3\u7801\uff1a<\/p>\n\n\n\n
      peg=[]
      for ticker in ticker_list:
          peg_rat = tickers.tickers[ticker].info[\"trailingPegRatio\"]
          peg.append(peg_rat) 

      plt.figure(figsize=(12, 6))
      plt.bar(ticker_list, peg)
      plt.title('Trailing PEG Ratio')
      plt.xlabel('Company')
      plt.ylabel('PEG')
      plt.grid()
      plt.show() <\/pre>\n\n\n\n
      \u5c31\u8fc7\u53bb PEG \u6bd4\u7387\u800c\u8a00\uff0c\u7279\u65af\u62c9\u548cPalantir\u7684PEG\u6bd4\u7387\u6700\u9ad8\uff0c\u53ef\u80fd\u88ab\u9ad8\u4f30\u4e86<\/strong>\u3002\u800cAMD\u7684PEG\u4f4e\u4e8e1\uff0c\u53ef\u80fd\u88ab\u5e02\u573a\u4f4e\u4f30\u4e86\u3002<\/p>\n\n\n\n
      2.3 Beta\u503c<\/strong>\u662f\u7406\u89e3 AI \u6295\u8d44\u98ce\u9669\u7279\u5f81\u7684\u5173\u952e\u3002<\/p>\n\n\n\n
      \u5982\u4e0b\u56fe\uff1aBeta \u503c\u4e3a 1 \u8868\u793a\u8d44\u4ea7\u4e0e\u5e02\u573a\u8d70\u52bf\u4e00\u81f4\uff08\u4f8b\u5982 MSFT\u3001GOOG\uff09\u3002Beta \u503c\u5927\u4e8e 1 \u8868\u793a\u6ce2\u52a8\u6027\u8f83\u9ad8\uff08\u4f8b\u5982 TSLA\u3001AMD\u3001PLTR\u3001NVDA\uff09\uff0c\u800c Beta \u503c\u5c0f\u4e8e 1 \u5219\u8868\u793a\u6ce2\u52a8\u6027\u8f83\u4f4e\uff08\u4f8b\u5982 IBM\u3001CSCO\uff09\u3002<\/p>\n\n\n
      \n
      \"\"
      \u7f8e\u56fd 15 \u5927\u79d1\u6280\u516c\u53f8\u7684 Beta \u7248<\/figcaption><\/figure>\n<\/div>\n\n\n
      \u7ed8\u5236 Beta \u7cfb\u6570\u7684\u4ee3\u7801\uff1a<\/p>\n\n\n\n
      bet=[]
      for ticker in ticker_list:
          b = tickers.tickers[ticker].info[\"beta\"]
          bet.append(b)  

      plt.figure(figsize=(12, 6))
      plt.bar(ticker_list, bet)
      plt.title('Beta')
      plt.xlabel('Company')
      plt.ylabel('Beta')
      plt.grid()
      plt.show()<\/pre>\n\n\n\n
      2.4 \u5e02\u51c0\u7387 (P\/B) <\/strong>\u8861\u91cf\u7684\u662f\u5e02\u573a\u5bf9\u4e00\u5bb6\u516c\u53f8\u76f8\u5bf9\u4e8e\u5176\u8d26\u9762\u4ef7\u503c\u7684\u4f30\u503c\u3002\u4ef7\u503c\u6295\u8d44\u8005\u4f1a\u4f7f\u7528\u5e02\u51c0\u7387\u6765\u8bc6\u522b\u6f5c\u5728\u7684\u6295\u8d44\u673a\u4f1a\u3002\u9ad8\u589e\u957f\u516c\u53f8\u7684\u5e02\u51c0\u7387\u901a\u5e38\u9ad8\u4e8e 1.0\uff0c\u4f46\u9677\u5165\u56f0\u5883\u7684\u516c\u53f8\u5e02\u51c0\u7387\u6709\u65f6\u4f4e\u4e8e 1.0\u3002<\/p>\n\n\n
      \n
      \"\"
      \u7f8e\u56fd 15 \u5927\u79d1\u6280\u516c\u53f8\u7684\u5e02\u51c0\u7387 (P\/B)<\/figcaption><\/figure>\n<\/div>\n\n\n
      \u79d1\u6280\u884c\u4e1a\u7684\u9ad8\u5e02\u51c0\u7387\u901a\u5e38\u662f\u5408\u7406\u7684\uff0c\u56e0\u4e3a\u5b83\u4eec\u62e5\u6709\u5927\u91cf\u65e0\u5f62\u8d44\u4ea7\uff0c\u800c\u8fd9\u4e9b\u8d44\u4ea7\u5728\u4f20\u7edf\u7684\u8d44\u4ea7\u8d1f\u503a\u8868\u4e2d\u65e0\u6cd5\u5b8c\u5168\u4f53\u73b0\u3002\u6295\u8d44\u8005\u613f\u610f\u4e3a\u8fd9\u79cd\u9ad8\u672a\u6765\u56de\u62a5\u6f5c\u529b\u652f\u4ed8\u6ea2\u4ef7\u3002<\/p>\n\n\n\n
      \u7136\u800c\uff0c\u9ad8\u5e02\u51c0\u7387\uff08\u4f8b\u5982 PLTR\u3001NVDA\u3001AAPL\u3001ORCL\uff09\u4e5f\u53ef\u80fd\u8868\u660e\u80a1\u7968\u4f30\u503c\u8fc7\u9ad8\uff0c\u56e0\u6b64\u5728\u79d1\u6280\u884c\u4e1a\u5185\u8fdb\u884c\u6bd4\u8f83\u5e76\u4f7f\u7528\u5176\u4ed6\u4f30\u503c\u6307\u6807\u81f3\u5173\u91cd\u8981\u3002<\/p>\n\n\n\n
      \u7ed8\u5236\u5e02\u51c0\u7387 (P\/B)\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n
      p2b=[]
      for ticker in ticker_list:
          b = tickers.tickers[ticker].info[\"priceToBook\"]
          p2b.append(b)  

      plt.figure(figsize=(12, 6))
      plt.bar(ticker_list, p2b)
      plt.title('Price-to-Book (P\/B) Ratio')
      plt.xlabel('Company')
      plt.ylabel('P\/B')
      plt.grid()
      plt.show()<\/pre>\n\n\n\n
      2.5 \u6bcf\u80a1\u6536\u76ca (EPS) \u6bd4\u7387<\/strong>\u8d8a\u9ad8\uff0c\u4e00\u822c\u6765\u8bf4\u5176\u76c8\u5229\u80fd\u529b\u5c31\u8d8a\u5f3a\uff0c\u5c3d\u7ba1\u8f83\u9ad8\u7684\u6bcf\u80a1\u6536\u76ca\u5e76\u4e0d\u80fd\u4fdd\u8bc1\u672a\u6765\u7684\u4e1a\u7ee9\u3002<\/p>\n\n\n\n
      \u5982\u4e0b\u56fe\u6240\u793a\uff0c\u9009\u62e9\u6bcf\u80a1\u6536\u76ca\u7a33\u5065\u7684\u516c\u53f8\uff08\u4f8b\u5982 META\u3001NFLX \u548c MSFT\uff09\u53ef\u80fd\u610f\u5473\u7740\u60a8\u672a\u6765\u7684\u76c8\u5229\u80fd\u529b\u5c06\u6709\u6240\u589e\u957f\u3002<\/p>\n\n\n
      \n
      \"\"
      \u7f8e\u56fd\u524d 15 \u5927\u79d1\u6280\u516c\u53f8\u7684\u6bcf\u80a1\u6536\u76ca (EPS) \u6bd4\u7387<\/figcaption><\/figure>\n<\/div>\n\n\n
      \u7ed8\u5236\u6bcf\u80a1\u6536\u76ca (EPS) \u6bd4\u7387\u7684\u4ee3\u7801\uff1a<\/p>\n\n\n\n
      eps=[]
      for ticker in ticker_list:
          b = tickers.tickers[ticker].info[\"trailingEps\"]
          eps.append(b)  

      plt.figure(figsize=(12, 6))
      plt.bar(ticker_list, eps)
      plt.title('Trailing Earnings Per Share (EPS) Ratio')
      plt.xlabel('Company')
      plt.ylabel('EPS')
      plt.grid()
      plt.show()<\/pre>\n\n\n\n
      2.<\/strong>6 \u76c8\u5229\u589e\u957f\u7387<\/strong>\u4e5f\u662f\u5224\u65ad\u80a1\u7968\u6f5c\u529b\u7684\u91cd\u8981\u6307\u6807\u3002<\/p>\n\n\n
      \n
      \"\"
      \u7f8e\u56fd15\u5927\u79d1\u6280\u516c\u53f8\u7684\u76c8\u5229\u589e\u957f<\/figcaption><\/figure>\n<\/div>\n\n\n
      \u5982\u5206\u6790\u56fe\uff1aAVGO\u3001PLTR \u548c AMD \u662f\u4eba\u5de5\u667a\u80fd\u6295\u8d44\u8005\u53ef\u4ee5\u627e\u5230\u5de8\u5927\u5229\u6da6\u589e\u957f\u524d\u666f\u7684\u4e09\u53ea\u6700\u4f73\u80a1\u7968\u3002<\/p>\n\n\n\n
      \u7ed8\u5236\u76c8\u5229\u589e\u957f\u56fe\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n
      er=[]
      for ticker in ticker_list:
          b = tickers.tickers[ticker].info[\"earningsGrowth\"]
          er.append(b)

      plt.figure(figsize=(12, 6))
      plt.bar(ticker_list, er)
      plt.title('Earnings Growth')
      plt.xlabel('Company')
      plt.ylabel('Earnings Growth')
      plt.grid()
      plt.show()<\/pre>\n\n\n\n
      \u4e0a\u7bc7\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
      \u4eca\u5929\u6211\u4eec\u5bf9\u8fd915\u5bb6\u516c\u53f8\u8fdb\u884c\u4e86\u4e24\u9879\u5206\u6790\uff1a<\/p>\n\n\n\n