{"id":1737,"date":"2024-12-24T07:34:00","date_gmt":"2024-12-23T23:34:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1737"},"modified":"2024-11-29T12:01:21","modified_gmt":"2024-11-29T04:01:21","slug":"%e7%ae%80%e5%8d%95%e4%b8%89%e6%ad%a5%ef%bc%8c%e6%95%99%e4%bd%a0%e5%bb%ba%e7%ab%8b%e8%87%aa%e5%b7%b1%e7%9a%84%e9%a6%96%e4%b8%aa%e6%8a%95%e8%b5%84%e7%bb%84%e5%90%88","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2024\/12\/24\/%e7%ae%80%e5%8d%95%e4%b8%89%e6%ad%a5%ef%bc%8c%e6%95%99%e4%bd%a0%e5%bb%ba%e7%ab%8b%e8%87%aa%e5%b7%b1%e7%9a%84%e9%a6%96%e4%b8%aa%e6%8a%95%e8%b5%84%e7%bb%84%e5%90%88\/","title":{"rendered":"\u7b80\u5355\u4e09\u6b65\uff0c\u6559\u4f60\u5efa\u7acb\u81ea\u5df1\u7684\u9996\u4e2a\u6295\u8d44\u7ec4\u5408"},"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

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\"\"<\/figure>\n<\/div>\n\n\n

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\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u4eca\u5929\u6211\u8981\u548c\u5927\u5bb6\u5206\u4eab\u4e00\u7bc7\u5b9e\u7528\u6307\u5357\uff0c\u5e26\u4f60\u4e00\u6b65\u6b65\u6784\u5efa\u5c5e\u4e8e\u81ea\u5df1\u7684\u6295\u8d44\u7ec4\u5408\u3002\u8fd9\u7bc7\u6587\u7ae0\u4e13\u4e3a\u6295\u8d44\u65b0\u624b\u8bbe\u8ba1\uff0c\u901a\u8fc7\u4e09\u4e2a\u7b80\u5355\u6b65\u9aa4\uff0c\u8ba9\u4f60\u8f7b\u677e\u4e0a\u624b\u3002\u6211\u4eec\u4f1a\u4e00\u8d77\u63a2\u8ba8\u5982\u4f55\u8bc4\u4f30\u4f60\u7684\u98ce\u9669\u72b6\u51b5\uff0c\u5b9e\u73b0\u6295\u8d44\u7684\u591a\u6837\u5316\uff0c\u4ee5\u53ca\u5982\u4f55\u76d1\u63a7\u548c\u8c03\u6574\u4f60\u7684\u6295\u8d44\u7ec4\u5408\u3002\u6587\u7ae0\u4e2d\u8fd8\u5305\u542b\u4e86\u5b9e\u7528\u7684Python\u5de5\u5177\u96c6\u548c\u6e90\u4ee3\u7801\uff0c\u5e2e\u52a9\u4f60\u66f4\u6709\u6548\u5730\u7ba1\u7406\u6295\u8d44\u3002\u65e0\u8bba\u4f60\u662f\u6295\u8d44\u5c0f\u767d\uff0c\u8fd8\u662f\u5e0c\u671b\u4f18\u5316\u73b0\u6709\u6295\u8d44\u7b56\u7565\uff0c\u8fd9\u7bc7\u6587\u7ae0\u90fd\u4f1a\u4e3a\u4f60\u63d0\u4f9b\u5b9d\u8d35\u7684\u89c1\u89e3\u548c\u5b9e\u8df5\u6307\u5bfc\u3002<\/mark><\/pre>\n<\/blockquote>\n\n\n\n
\u521d\u6b21\u6784\u5efa\u6295\u8d44\u7ec4\u5408\u53ef\u80fd\u4f1a\u8ba9\u4f60\u611f\u5230\u4e00\u5934\u96fe\u6c34\uff0c\u5c24\u5176\u662f\u5f53\u4f60\u5bf9\u91d1\u878d\u77e5\u8bc6\u4e0d\u592a\u719f\u6089\u65f6\u3002\u522b\u62c5\u5fc3\uff0c\u8fd9\u4efd\u6307\u5357\u5c06\u5e26\u4f60\u4e00\u6b65\u6b65\u8d70\u8fc7\u4e09\u4e2a\u5173\u952e\u9636\u6bb5\uff0c\u6253\u9020\u51fa\u7b26\u5408\u4f60\u4e2a\u4eba\u76ee\u6807\u7684\u6295\u8d44\u7ec4\u5408\u3002\u6211\u4eec\u5c06\u4e00\u8d77\u5b66\u4e60\u5982\u4f55\u8bc4\u4f30\u98ce\u9669\u3001\u5b9e\u73b0\u8d44\u4ea7\u5206\u6563\u4ee5\u53ca\u5982\u4f55\u8ddf\u8e2a\u7ba1\u7406\u4f60\u7684\u8d44\u4ea7\u3002\u6b64\u5916\uff0c\u672c\u6307\u5357\u8fd8\u63d0\u4f9b\u4e86Python\u5de5\u5177\u96c6\u548c\u6e90\u4ee3\u7801\uff0c\u8ba9\u4f60\u80fd\u591f\u8f7b\u677e\u81ea\u52a8\u5316\u5904\u7406\u8bf8\u5982\u7b5b\u9009\u5e02\u503c\u80a1\u7968\u3001\u8bc4\u4f30\u589e\u957f\u6f5c\u529b\u4ee5\u53ca\u8bbe\u7f6e\u6295\u8d44\u8b66\u62a5\u7b49\u64cd\u4f5c\u3002<\/p>\n\n\n\n
\u6b65\u9aa4 1\uff1a\u8bc4\u4f30\u60a8\u7684\u98ce\u9669\u72b6\u51b5<\/strong><\/h2>\n\n\n\n
\u6253\u9020\u6295\u8d44\u7ec4\u5408\u7684\u9996\u8981\u6b65\u9aa4\u662f\u8ba4\u6e05\u81ea\u5df1\u7684\u98ce\u9669\u627f\u53d7\u80fd\u529b\u3002\u8fd9\u4e00\u6b65\u9700\u8981\u8003\u8651\u5e74\u9f84\u3001\u6536\u5165\u3001\u98ce\u9669\u504f\u597d\u4ee5\u53ca\u4f60\u7684\u8d22\u52a1\u76ee\u6807\u7b49\u56e0\u7d20\uff0c\u4ee5\u6b64\u6765\u754c\u5b9a\u4f60\u80fd\u63a5\u53d7\u7684\u98ce\u9669\u6c34\u5e73\u3002\u901a\u8fc7\u81ea\u6211\u98ce\u9669\u8bc4\u4f30\uff0c\u4f60\u80fd\u591f\u51b3\u5b9a\u80a1\u7968\u548c\u503a\u5238\u7b49\u8d44\u4ea7\u7684\u6700\u4f73\u914d\u6bd4\u3002\u5408\u7406\u5206\u914d\u8fd9\u4e9b\u4e0d\u540c\u7c7b\u578b\u7684\u8d44\u4ea7\uff0c\u6709\u52a9\u4e8e\u4f60\u7684\u6295\u8d44\u7ec4\u5408\u66f4\u597d\u5730\u5339\u914d\u4f60\u7684\u6295\u8d44\u5468\u671f\u548c\u8d22\u52a1\u7a33\u5065\u76ee\u6807\u3002<\/p>\n\n\n
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\"\"<\/figure>\n<\/div>\n\n\n

\u6b65\u9aa4 2\uff1a\u6709\u6548\u591a\u6837\u5316<\/strong><\/h2>\n\n\n\n
\u6295\u8d44\u591a\u6837\u5316\u5bf9\u4e8e\u589e\u5f3a\u6295\u8d44\u7ec4\u5408\u7684\u97e7\u6027\u81f3\u5173\u91cd\u8981\u3002\u901a\u8fc7\u6d89\u8db3\u4e0d\u540c\u7684\u8d44\u4ea7\u7c7b\u522b\u548c\u533a\u57df\u5e02\u573a\uff0c\u4f60\u53ef\u4ee5\u6709\u6548\u964d\u4f4e\u6295\u8d44\u6ce2\u52a8\u5e76\u51cf\u5c11\u98ce\u9669\u3002ETF\uff08\u4ea4\u6613\u6240\u4ea4\u6613\u57fa\u91d1\uff09\u3001\u503a\u5377\u548c\u671f\u6743\u7b49\u90fd\u5c5e\u4e8e\u666e\u901a\u4eba\u80fd\u591f\u6d89\u53ca\u5230\u7684\u91d1\u878d\u591a\u6837\u5316\u6295\u8d44\u5de5\u5177\uff0c\u80fd\u591f\u8ba9\u4f60\u901a\u8fc7\u5355\u6b21\u6295\u8d44\u63a5\u89e6\u5230\u591a\u5143\u5316\u7684\u8d44\u4ea7\u7ec4\u5408\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u6b65\u9aa4 3\uff1a\u76d1\u63a7\u548c\u8c03\u6574\u6295\u8d44\u7ec4\u5408<\/strong><\/h2>\n\n\n\n
\u5efa\u7acb\u521d\u59cb\u6295\u8d44\u7ec4\u5408\u540e\uff0c\u5b9a\u671f\u5ba1\u67e5\u548c\u8c03\u6574\u6295\u8d44\u7ec4\u5408\u81f3\u5173\u91cd\u8981\u3002\u901a\u8fc7\u76d1\u6d4b\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u5e02\u573a\u53d8\u5316\u8c03\u6574\u6295\u8d44\u7ec4\u5408\uff0c\u5e76\u4fdd\u6301\u6218\u7565\u4e0e\u76ee\u6807\u4e00\u81f4\u3002<\/p>\n\n\n
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\"\"<\/figure>\n<\/div>\n\n\n
\u6295\u8d44\u7ec4\u5408\u5206\u6790\u5de5\u5177\u96c6<\/strong><\/h2>\n\n\n\n

1. \u968f\u65f6\u4e86\u89e3\u5e02\u573a\u8d8b\u52bf<\/strong><\/h3>\n\n\n\n
\u901a\u8fc7\u76d1\u63a7\u4e3b\u8981\u6307\u6570\uff08\u5982\u6807\u51c6\u666e\u5c14 500 \u6307\u6570\u548c\u7eb3\u65af\u8fbe\u514b\u6307\u6570\uff09\u4e86\u89e3\u6700\u65b0\u4fe1\u606f\u3002\u8be5\u529f\u80fd\u53ef\u68c0\u7d22\u4e1a\u7ee9\u6570\u636e\uff0c\u8ba9\u60a8\u968f\u65f6\u4e86\u89e3\u6700\u65b0\u8d8b\u52bf\uff1a<\/p>\n\n\n\n
def market_trends(indices=[\"^GSPC\", \"^IXIC\"]):  # S&P 500 and Nasdaq\n    trends = {}\n    for index in indices:\n        index_data = yf.Ticker(index)\n        history = index_data.history(period=\"1y\")['Close']\n        performance = (history[-1] \/ history[0]) - 1\n        trends[index] = performance * 100\n    return trends\n\n# Example usage:\nprint(market_trends())<\/code><\/pre>\n\n\n\n
2. \u4e3a\u6295\u8d44\u673a\u4f1a\u8bbe\u5b9a\u660e\u786e\u7684\u6807\u51c6<\/strong><\/h3>\n\n\n\n
\u5236\u5b9a\u8bc4\u4f30\u6295\u8d44\u7684\u6807\u51c6\u3002\u4f8b\u5982\uff0c\u4f60\u53ef\u4ee5\u5c06\u5e02\u76c8\u7387\u5c0f\u4e8e 20\u3001\u9500\u552e\u989d\u589e\u957f\u7387\u5927\u4e8e 10%\u3001\u5206\u6790\u5e08\u63a8\u8350\u79ef\u6781\u7684\u80a1\u7968\u8bbe\u5b9a\u4e3a “\u4e70\u5165 “\u6807\u51c6\u3002<\/p>\n\n\n\n
def evaluate_investment_opportunity(ticker):\n    stock = yf.Ticker(ticker)\n    pe_ratio = stock.info.get(\"trailingPE\", 0)\n    sales_growth = sales_growth_5_years(ticker)\n    recommendation = stock.info.get(\"recommendationMean\", 5)\n    if pe_ratio < 20 and \"increased\" in sales_growth and recommendation < 3:\n        return f\"{ticker} meets the investment criteria.\"\n    else:\n        return f\"{ticker} does not meet the investment criteria.\"\n# Example usage:\nprint(evaluate_investment_opportunity(\"AAPL\"))<\/code><\/pre>\n\n\n\n
3. \u6309\u5e02\u503c\u7b5b\u9009\u80a1\u7968<\/strong><\/h3>\n\n\n\n
\u60a8\u8fd8\u53ef\u4ee5\u805a\u7126\u5927\u516c\u53f8\uff0c\u6709\u52a9\u4e8e\u907f\u514d\u5c0f\u76d8\u80a1\u7ecf\u5e38\u51fa\u73b0\u7684\u6ce2\u52a8\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u7b5b\u9009\u6700\u4f4e\u5e02\u503c\u80a1\u7968\u7684\u65b9\u6cd5\uff1a<\/p>\n\n\n\n
import yfinance as yf\n\ndef filter_by_market_cap(ticker, min_market_cap=10e9):\n    stock = yf.Ticker(ticker)\n    market_cap = stock.info.get(\"marketCap\", 0)\n    if market_cap >= min_market_cap:\n        return f\"{ticker} has a market cap of {market_cap:.2f}\"\n    else:\n        return f\"{ticker} is too small for the market cap filter\"\n# Example usage:\nprint(filter_by_market_cap(\"AAPL\"))  # Minimum market cap of $10B<\/pre>\n\n\n\n
4. \u68c0\u67e5\u6f5c\u5728\u516c\u53f8\u8fd15\u5e74\u9500\u552e\u589e\u957f\u60c5\u51b5<\/strong><\/h3>\n\n\n\n
\u8bc4\u4f30\u4e00\u5bb6\u516c\u53f8\u5728\u4e94\u5e74\u5185\u7684\u9500\u552e\u989d\u589e\u957f\u662f\u5426\u8d85\u8fc7 10%–\u8fd9\u662f\u4e1a\u7ee9\u7a33\u5b9a\u7684\u6807\u5fd7\u3002Python \u53ef\u4ee5\u8f7b\u677e\u8ba1\u7b97\u8fd9\u79cd\u589e\u957f\uff1a<\/p>\n\n\n\n
def sales_growth_5_years(ticker):\n    stock = yf.Ticker(ticker)\n    financials = stock.financials.T\n    initial_sales = financials['Total Revenue'][-5]\n    final_sales = financials['Total Revenue'][0]\n    growth = ((final_sales - initial_sales) \/ initial_sales) * 100\n    if growth >= 10:\n        return f\"{ticker} has increased sales by {growth:.2f}% over the last five years.\"\n    else:\n        return f\"{ticker} does not meet the 10% sales growth benchmark.\"\n\n# Example usage:\nprint(sales_growth_5_years(\"AAPL\"))<\/pre>\n\n\n\n
5. \u8bc4\u4f30\u4e1a\u52a1\u7406\u89e3\u548c\u7a33\u5b9a\u589e\u957f<\/strong><\/h3>\n\n\n\n
\u7a33\u5b9a\u7684\u5e74\u6536\u5165\u589e\u957f\u53ef\u4ee5\u8868\u660e\u516c\u53f8\u7684\u6297\u98ce\u9669\u80fd\u529b\u3002\u4e0b\u9762\u4ecb\u7ecd\u5982\u4f55\u7528 Python \u8ba1\u7b97\u5e74\u589e\u957f\u7387\uff1a<\/p>\n\n\n\n
def steady_growth(ticker):\n    stock = yf.Ticker(ticker)\n    historical_revenue = stock.financials.T['Total Revenue']\n    growth_rates = historical_revenue.pct_change().dropna() * 100\n    average_growth = growth_rates.mean()\n    if average_growth > 5:  # Example threshold of >5% average annual growth\n        return f\"{ticker} has an average annual growth rate of {average_growth:.2f}%.\"\n    else:\n        return f\"{ticker} shows limited growth potential.\"\n\n# Example usage:\nprint(steady_growth(\"AAPL\"))<\/code><\/pre>\n\n\n\n
6. \u9a8c\u8bc1\u5206\u6790\u5e08\u7684\u9884\u6d4b<\/strong><\/h3>\n\n\n\n
\u60a8\u53ef\u4ee5\u4f7f\u7528\u5e02\u573a\u4e0a\u6295\u8d44\u673a\u6784\u548c\u5206\u6790\u5e08\u7684\u5efa\u8bae\u4f5c\u4e3a\u6295\u8d44\u524d\u7684\u989d\u5916\u68c0\u67e5\u3002Python \u53ef\u4ee5\u9a8c\u8bc1\u80a1\u7968\u7684\u63a8\u8350\u5f97\u5206\uff081 = \u5f3a\u52bf\u4e70\u5165\uff0c5 = \u5356\u51fa\uff09\u3002<\/p>\n\n\n\n
def verify_analyst_forecast(ticker):\n    stock = yf.Ticker(ticker)\n    recommendation = stock.info.get(\"recommendationMean\", None)\n    if recommendation and recommendation < 3:  # Scores under 3 indicate positive forecasts\n        return f\"{ticker} has a positive analyst rating of {recommendation} (1=Strong Buy, 5=Sell)\"\n    else:\n        return f\"{ticker} does not have a favorable analyst rating.\"\n\n# Example usage:\nprint(verify_analyst_forecast(\"AAPL\"))<\/code><\/pre>\n\n\n\n
7. \u5229\u7528\u514d\u8d39\u5728\u7ebf\u5de5\u5177\u8fdb\u884c\u80a1\u7968\u5206\u6790<\/strong><\/h3>\n\n\n\n
yfinance \u5e93\u63d0\u4f9b\u5f53\u524d\u4ef7\u683c\u3001\u5e02\u503c\u3001\u5e02\u76c8\u7387\u548c\u5206\u6790\u5e08\u5efa\u8bae\u7b49\u57fa\u672c\u80a1\u7968\u6570\u636e\uff1a<\/p>\n\n\n\n
def quick_info(ticker):\n    stock = yf.Ticker(ticker)\n    info = {\n        \"Current Price\": stock.history(period=\"1d\")['Close'][-1],\n        \"Market Cap\": stock.info.get(\"marketCap\"),\n        \"P\/E Ratio\": stock.info.get(\"trailingPE\"),\n        \"Analyst Rating\": stock.info.get(\"recommendationMean\"),\n    }\n    return info\n\n# Example usage:\nprint(quick_info(\"AAPL\"))<\/code><\/pre>\n\n\n\n
8. \u7528 ETF \u8865\u5145\u80a1\u7968\u4ee5\u5b9e\u73b0\u591a\u6837\u5316<\/strong><\/h3>\n\n\n\n
ETF \u63d0\u4f9b\u4e86\u5feb\u901f\u3001\u7b80\u4fbf\u7684\u591a\u6837\u5316\u6295\u8d44\u3002\u8ba9\u6211\u4eec\u5206\u6790\u4e00\u4e0b ETF\uff08\u5982 SPDR S&P 500 ETF Trust (SPY)\uff09\u4f5c\u4e3a\u80a1\u7968\u6295\u8d44\u7ec4\u5408\u8865\u5145\u7684\u5e74\u6536\u76ca\u7387\u3002<\/p>\n\n\n\n
def etf_performance(ticker=\"SPY\"):\n    etf = yf.Ticker(ticker)\n    history = etf.history(period=\"1y\")['Close']\n    return_rate = (history[-1] \/ history[0]) - 1\n    return f\"The annual return for {ticker} is {return_rate * 100:.2f}%.\"\n\n# Example usage:\nprint(etf_performance())<\/code><\/pre>\n\n\n\n
9. \u7cfb\u7edf\u6027\u5ba1\u67e5\u6295\u8d44\u7ec4\u5408<\/strong><\/h3>\n\n\n\n
\u5728 Python \u4e2d\u8ba1\u7b97\u6bcf\u53ea\u80a1\u7968\u7684\u5e74\u6536\u76ca\u7387\uff0c\u5b9e\u73b0\u6295\u8d44\u7ec4\u5408\u5ba1\u67e5\u81ea\u52a8\u5316\u3002<\/p>\n\n\n\n
import numpy as np\n\ndef portfolio_review(portfolio):\n    returns = {}\n    for ticker in portfolio:\n        stock = yf.Ticker(ticker)\n        history = stock.history(period=\"1y\")['Close']\n        return_rate = (history[-1] \/ history[0]) - 1\n        returns[ticker] = return_rate * 100  # Return as a percentage\n    return returns\n# Example usage:\nportfolio = [\"AAPL\", \"MSFT\", \"GOOGL\"]\nprint(\"Portfolio Performance:\", portfolio_review(portfolio))<\/code><\/pre>\n\n\n\n
\u8fd9\u4e9bPython\u51fd\u6570\u4e3a\u4f60\u63d0\u4f9b\u4e86\u4e00\u5957\u6807\u51c6\u5316\u7684\u6d41\u7a0b\u6765\u7b5b\u9009\u548c\u8ffd\u8e2a\u6f5c\u5728\u7684\u6295\u8d44\u673a\u4f1a\u3002\u901a\u8fc7\u81ea\u52a8\u5316\u90e8\u5206\u5206\u6790\u5de5\u4f5c\uff0c\u4f60\u80fd\u591f\u7b80\u5316\u51b3\u7b56\u6b65\u9aa4\u3001\u51cf\u5c11\u98ce\u9669\uff0c\u5e76\u57fa\u4e8e\u66f4\u660e\u667a\u7684\u5224\u65ad\u4e3a\u4f60\u7684\u6295\u8d44\u7ec4\u5408\u505a\u51fa\u9009\u62e9\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u6e90\u4ee3\u7801\u96c6\u5408\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/alexyu2013\/techsenseai\/blob\/main\/APracticalGuidetoBuildingYourFirstInvestmentPortfolioinThreeSteps.ipynb<\/a><\/p>\n\n\n\n
\u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n