{"id":6336,"date":"2025-06-01T15:07:10","date_gmt":"2025-06-04T07:07:10","guid":{"rendered":"https:\/\/badgameshow.com\/steven\/?p=6336"},"modified":"2025-06-04T15:07:10","modified_gmt":"2025-06-04T07:07:10","slug":"%e5%88%a9%e7%94%a8%e8%82%a1%e7%a5%a8%e5%b8%82%e5%80%bc%e6%af%94%e5%80%92%e5%82%b3%e9%81%9e%e5%b8%b6stock-to-marketratioechobands%e4%ba%a4%e6%98%93%e6%8c%87%e6%a8%99%ef%bc%8c%e5%ad%b8%e7%bf%92python","status":"publish","type":"post","link":"https:\/\/badgameshow.com\/steven\/python\/%e5%88%a9%e7%94%a8%e8%82%a1%e7%a5%a8%e5%b8%82%e5%80%bc%e6%af%94%e5%80%92%e5%82%b3%e9%81%9e%e5%b8%b6stock-to-marketratioechobands%e4%ba%a4%e6%98%93%e6%8c%87%e6%a8%99%ef%bc%8c%e5%ad%b8%e7%bf%92python\/","title":{"rendered":"2025 \u6700\u65b0Python\u6559\u5b78\uff1a\u5229\u7528\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36\u6307\u6a19\u63d0\u5347\u4ea4\u6613\u6c7a\u7b56"},"content":{"rendered":"<p><meta name=\"keywords\" content=\"Python, \u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36, Stock-to-Market Ratio Echo Bands, \u4ea4\u6613\u6307\u6a19, \u4f7f\u7528\u6642\u6a5f, Python \u7a0b\u5f0f\u6559\u5b78\"><\/p>\n<h1>2025 \u6700\u65b0Python\u6559\u5b78\uff1a\u5229\u7528\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36\u6307\u6a19\u63d0\u5347\u4ea4\u6613\u6c7a\u7b56<\/h1>\n<p>\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36(Stock-to-Market Ratio Echo Bands)\u662f\u4e00\u7a2e\u6709\u6548\u7684\u4ea4\u6613\u6307\u6a19\uff0c\u5e6b\u52a9\u6295\u8cc7\u8005\u5206\u6790\u80a1\u7968\u5e02\u5834\u8da8\u52e2\u4e26\u6355\u6349\u6f5b\u5728\u7684\u4ea4\u6613\u6a5f\u6703\u3002\u672c\u6587\u5c07\u6df1\u5165\u63a2\u8a0e\u8a72\u6307\u6a19\u7684\u904b\u4f5c\u65b9\u5f0f\uff0c\u63d0\u4f9b\u5be6\u4f5cPython\u7bc4\u4f8b\uff0c\u4ee5\u53ca\u6700\u4f73\u4f7f\u7528\u6642\u6a5f\uff0c\u8b93\u6295\u8cc7\u8005\u80fd\u66f4\u6709\u6548\u7387\u5730\u9032\u884c\u5e02\u5834\u5206\u6790\u3002<\/p>\n<h2>\u4ec0\u9ebc\u662f\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36\uff1f<\/h2>\n<p>\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36\u662f\u4e00\u7a2e\u6280\u8853\u6307\u6a19\uff0c\u57fa\u65bc\u80a1\u7968\u7684\u5e02\u503c\u6bd4\u7387\uff0c\u65e8\u5728\u6aa2\u6e2c\u80a1\u7968\u5e02\u5834\u7684\u8da8\u52e2\u3002\u9019\u500b\u6307\u6a19\u80fd\u63d0\u4f9b\u6295\u8cc7\u8005\u6709\u95dc\u5e02\u5834\u52d5\u80fd\u7684\u8cc7\u8a0a\uff0c\u4e26\u5e6b\u52a9\u8b58\u5225\u9032\u51fa\u5e02\u5834\u7684\u6700\u4f73\u6642\u6a5f\u3002\u900f\u904e\u5206\u6790\u5e02\u503c\u6bd4\u7387\uff0c\u6295\u8cc7\u8005\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u5e02\u5834\u7684\u5065\u5eb7\u72c0\u6cc1\uff0c\u4e26\u505a\u51fa\u66f4\u5177\u6230\u7565\u6027\u7684\u4ea4\u6613\u6c7a\u7b56\u3002<\/p>\n<h2>\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36\u7684\u4f7f\u7528\u6642\u6a5f<\/h2>\n<p>\u4f7f\u7528\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36\u7684\u6700\u4f73\u6642\u6a5f\u5305\u62ec\uff1a<br \/>\n&#8211; \u7576\u5e02\u5834\u986f\u793a\u51fa\u660e\u986f\u7684\u4e0a\u5347\u6216\u4e0b\u964d\u8da8\u52e2\u6642\uff0c\u9019\u500b\u6307\u6a19\u80fd\u63d0\u4f9b\u9032\u5165\u6216\u9000\u51fa\u5e02\u5834\u7684\u4fe1\u865f\u3002<br \/>\n&#8211; \u5728\u5e02\u5834\u6ce2\u52d5\u6027\u589e\u52a0\u7684\u6642\u5019\uff0c\u8a72\u6307\u6a19\u80fd\u5e6b\u52a9\u6295\u8cc7\u8005\u8fa8\u8b58\u6f5b\u5728\u7684\u53cd\u8f49\u8da8\u52e2\u3002<\/p>\n<p>\u6295\u8cc7\u8005\u61c9\u8a72\u7d50\u5408\u5176\u4ed6\u6280\u8853\u6307\u6a19\u4f86\u589e\u5f37\u4ea4\u6613\u6c7a\u7b56\u7684\u6e96\u78ba\u6027\uff0c\u4f8b\u5982\u79fb\u52d5\u5e73\u5747\u7dda\u6216\u76f8\u5c0d\u5f37\u5f31\u6307\u6a19(RSI)\u3002\u9019\u6a23\u7684\u7d44\u5408\u53ef\u4ee5\u63d0\u4f9b\u66f4\u5168\u9762\u7684\u5e02\u5834\u8996\u89d2\uff0c\u4e26\u964d\u4f4e\u55ae\u7368\u4f9d\u8cf4\u67d0\u4e00\u6307\u6a19\u7684\u98a8\u96aa\u3002<\/p>\n<h2>\u4f7f\u7528Python\u5be6\u4f5c\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36<\/h2>\n<p>\u4ee5\u4e0b\u662f\u4f7f\u7528Python\u8a08\u7b97\u80a1\u7968\u5e02\u503c\u6bd4\u5012\u50b3\u905e\u5e36\u7684\u57fa\u672c\u7bc4\u4f8b\uff1a<\/p>\n<p>&#8220;`python<br \/>\nimport pandas as pd<br \/>\nimport numpy as np<br \/>\nimport matplotlib.pyplot as plt<\/p>\n<p># \u5047\u8a2d\u6211\u5011\u6709\u4e00\u500b\u5305\u542b\u80a1\u7968\u5e02\u503c\u548c\u50f9\u683c\u7684\u6578\u64da\u6846<br \/>\ndata = {<br \/>\n    &#8216;Stock Price&#8217;: [100, 102, 105, 103, 101, 104],<br \/>\n    &#8216;Market Cap&#8217;: [1e9, 1.02e9, 1.05e9, 1.03e9, 1.01e9, 1.04e9]\n}<\/p>\n<p>df = pd.DataFrame(data)<\/p>\n<p># \u8a08\u7b97\u80a1\u7968\u5e02\u503c\u6bd4<br \/>\ndf[&#8216;Stock-to-Market Ratio&#8217;] = df[&#8216;Stock Price&#8217;] \/ df[&#8216;Market Cap&#8217;]\nprint(df)<\/p>\n<p># \u53ef\u8996\u5316<br \/>\nplt.plot(df[&#8216;Stock-to-Market Ratio&#8217;])<br \/>\nplt.title(&#8216;Stock-to-Market Ratio Over Time&#8217;)<br \/>\nplt.xlabel(&#8216;Time&#8217;)<br \/>\nplt.ylabel(&#8216;Stock-to-Market Ratio&#8217;)<br \/>\nplt.show()<br \/>\n&#8220;`<\/p>\n<p>\u9019\u6bb5\u7a0b\u5f0f\u78bc\u5c55\u793a\u4e86\u5982\u4f55\u8a08\u7b97\u80a1\u7968\u5e02\u503c\u6bd4\u4e26\u5c07\u5176\u53ef\u8996\u5316\u3002\u6295\u8cc7\u8005\u53ef\u4ee5\u6839\u64da\u9019\u4e9b\u6578\u64da\u9032\u884c\u66f4\u6df1\u5165\u7684\u5206\u6790\u3002<\/p>\n<h2>\u932f\u8aa4\u6392\u9664\u8207\u5ef6\u4f38\u61c9\u7528<\/h2>\n<p>\u5728\u4f7f\u7528\u4e0a\u8ff0\u7a0b\u5f0f\u78bc\u6642\uff0c\u53ef\u80fd\u6703\u9047\u5230\u4ee5\u4e0b\u5e7e\u500b\u5e38\u898b\u932f\u8aa4\uff1a<br \/>\n&#8211; \u78ba\u4fdd\u6578\u64da\u683c\u5f0f\u6b63\u78ba\uff1a\u5982\u679c\u6578\u64da\u6846\u4e2d\u6c92\u6709\u6b63\u78ba\u7684\u6578\u64da\u985e\u578b\uff0c\u53ef\u80fd\u6703\u5c0e\u81f4\u8a08\u7b97\u932f\u8aa4\u3002<br \/>\n&#8211; 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