{"id":4872,"date":"2026-09-14T07:48:00","date_gmt":"2026-09-13T23:48:00","guid":{"rendered":"https:\/\/laoyulaoyu.com\/?p=4872"},"modified":"2026-09-06T20:51:49","modified_gmt":"2026-09-06T12:51:49","slug":"%e4%b8%ba%e4%bb%80%e4%b9%88%e5%9b%9e%e6%b5%8b%e7%bf%bb1750%e5%80%8d%e3%80%81%e6%8d%a2%e5%88%b0a%e8%82%a1%e5%b0%b1%e4%b8%8d%e8%a1%8c%ef%bc%9f%e6%88%91%e6%8b%bf%e6%95%b0%e6%8d%ae%e9%aa%8c%e4%ba%86","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2026\/09\/14\/%e4%b8%ba%e4%bb%80%e4%b9%88%e5%9b%9e%e6%b5%8b%e7%bf%bb1750%e5%80%8d%e3%80%81%e6%8d%a2%e5%88%b0a%e8%82%a1%e5%b0%b1%e4%b8%8d%e8%a1%8c%ef%bc%9f%e6%88%91%e6%8b%bf%e6%95%b0%e6%8d%ae%e9%aa%8c%e4%ba%86\/","title":{"rendered":"\u4e3a\u4ec0\u4e48\u56de\u6d4b\u7ffb1750\u500d\u3001\u6362\u5230A\u80a1\u5c31\u4e0d\u884c\uff1f\u6211\u62ff\u6570\u636e\u9a8c\u4e86\u4e00\u904d\uff08\u9644\u6e90\u7801\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

\u6587\u4e2d\u793a\u4f8b\u4ec5\u7528\u4e8e\u6280\u672f\u8ba8\u8bba\uff0c\u4e0d\u6784\u6210\u4efb\u4f55\u64cd\u4f5c\u5efa\u8bae\u3002
\u91cf\u5316\u7b56\u7565\u5f00\u53d1\u5e94\u4ee5\u5b66\u4e60\u548c\u6280\u672f\u4ea4\u6d41\u4e3a\u76ee\u7684\u3002
\u672c\u53f7\u4e0d\u8350\u80a1\u3001\u4e0d\u5356\u8bfe\u3001\u4e0d\u627f\u8bfa\u6536\u76ca\u3002
\u5e02\u573a\u6709\u98ce\u9669\uff0c\u8bf7\u5408\u6cd5\u5408\u89c4\u6295\u8d44\u3002<\/p>\n<\/blockquote>\n\n\n\n

\u672c\u6587\u7ea6 4600 \u5b57 | \u9884\u8ba1\u9605\u8bfb 11 \u5206\u949f\uff0c\u540e\u6709\u5b8c\u6574\u4ee3\u7801\uff0c\u5efa\u8bae\u5148\u6536\u85cf\u3002<\/p>\n\n\n\n

\u00b7 \u00b7 \u00b7<\/p>\n\n\n\n

\u5148\u770b\u4e00\u7ec4\u6570\u5b57\u3002<\/p>\n\n\n\n

\u67d0\u6d77\u5916\u6280\u672f\u535a\u5ba2\u524d\u51e0\u5929\u53d1\u4e86\u4e00\u7bc7\u91cf\u5316\u6559\u7a0b\uff0c\u8bb2\u7684\u4e1c\u897f\u53eb\u5c0f\u6ce2\u53bb\u566a\u52a0\u52a8\u91cf\u4ea4\u6613\u3002\u914d\u5957\u7684\u56de\u6d4b\u56fe\u91cc\uff0c\u7b56\u7565\u4ece 2020 \u5e74\u521d\u8dd1\u5230 2026 \u5e74\uff0c\u516d\u5e74\u534a\uff0c\u7d2f\u8ba1\u6536\u76ca 1750 \u500d<\/strong>\u3002\u540c\u671f\u4e00\u76f4\u62ff\u7740\u82f1\u4f1f\u8fbe\u4e0d\u52a8\uff0c\u5927\u7ea6 25 \u500d\u4e0a\u4e0b\u3002<\/p>\n\n\n\n

\u66f4\u795e\u7684\u662f\u56de\u64a4\u3002\u516d\u5e74\u534a\uff0c\u7b56\u7565\u6700\u5927\u56de\u64a4\u53ea\u6709 14%<\/strong>\u3002<\/p>\n\n\n\n

\u82f1\u4f1f\u8fbe\u672c\u5c0a 2022 \u5e74\u90a3\u6ce2\uff0c\u4ece\u9ad8\u70b9\u8dcc\u6389\u516d\u6210\u3002\u7b56\u7565\u7684\u66f2\u7ebf\u5374\u50cf\u4e00\u6761\u53ea\u4f1a\u5f80\u4e0a\u8d70\u7684\u697c\u68af\uff0c\u51e0\u4e4e\u770b\u4e0d\u5230\u50cf\u6837\u7684\u5751\u3002<\/p>\n\n\n\n

\u6211\u76ef\u7740\u5b83\u7684\u4e24\u5f20\u6536\u76ca\u56fe\u770b\u4e86\u5f88\u4e45\u3002\u81ea\u89c9\u544a\u8bc9\u6211\u8fd9\u4e0d\u53ef\u80fd\u662f\u771f\u7684\u3002<\/p>\n\n\n\n

\u95ee\u9898\u51fa\u5728\u54ea\uff0c\u6211\u628a\u5b83\u7684\u4ee3\u7801\u9010\u884c\u8bfb\u4e86\u4e00\u904d\uff0c\u53c8\u5728\u6caa\u6df1300ETF\uff08510300\uff09\u4e0a\u4ece\u5934\u8dd1\u4e86\u4e00\u904d\u3002\u8fd9\u7bc7\u8bb2\u6e05\u695a\u4e09\u4ef6\u4e8b\u3002\u8fd9\u4e2a\u7b56\u7565\u5728\u5e72\u4ec0\u4e48\uff0c1750 \u500d\u662f\u600e\u4e48\u88ab\u4e00\u884c\u4ee3\u7801\u5439\u51fa\u6765\u7684\uff0c\u4ee5\u53ca\u540c\u6837\u7684\u903b\u8f91\u642c\u5230 A \u80a1\u5bbd\u57fa\u4e0a\uff0c\u771f\u5b9e\u6210\u7ee9\u5355\u957f\u4ec0\u4e48\u6837\u3002<\/p>\n\n\n\n

\u7b54\u6848\u6bd4 1750 \u500d\u96be\u770b\u5f97\u591a\uff0c\u4e5f\u6bd4 1750 \u500d\u6709\u7528\u5f97\u591a\u3002<\/p>\n\n\n\n

\u4e00\u3001\u7b5b\u5b50\u3001\u4fe1\u53f7\uff0c\u548c\u90a3\u884c\u51fa\u4e8b\u7684\u4ee3\u7801<\/strong><\/h2>\n\n\n\n

\u5148\u7528\u5927\u767d\u8bdd\u628a\u65b9\u6cd5\u8fc7\u4e00\u904d\u3002<\/p>\n\n\n\n

\u5c0f\u6ce2\u53d8\u6362<\/strong>\uff08Wavelet Transform\uff09\u8fd9\u4e2a\u4e1c\u897f\uff0c\u4f60\u53ef\u4ee5\u628a\u5b83\u7406\u89e3\u6210\u4e00\u628a\u7f51\u773c\u53ef\u8c03\u7684\u7b5b\u5b50\u3002\u4ef7\u683c\u66f2\u7ebf\u5012\u8fdb\u53bb\uff0c\u5b83\u6309\u4e0d\u540c\u7684\u6446\u52a8\u5468\u671f\u5206\u5f00\u7b5b\u3002\u4e24\u4e09\u5929\u7684\u6bdb\u523a\u662f\u6c99\u5b50\uff0c\u5c5e\u4e8e\u566a\u58f0\uff0c\u6f0f\u6389\u3002\u4e94\u5230\u4e8c\u5341\u5929\u7684\u8d77\u4f0f\u662f\u4f60\u8981\u7684\u8d27\uff0c\u4f5c\u8005\u7ba1\u8fd9\u4e2a\u533a\u95f4\u53eb swing \u5c3a\u5ea6\u3002\u4e00\u4e2a\u6708\u4ee5\u4e0a\u7684\u6162\u8d8b\u52bf\uff0c\u5148\u6401\u5728\u53e6\u4e00\u8fb9\u3002<\/p>\n\n\n\n

\u2460 \u5c0f\u6ce2\u53bb\u566a\uff0c\u6309\u5c3a\u5ea6\u7b5b\u6ce2\u52a8<\/strong><\/p>\n\n\n\n

import numpy as np\nfrom scipy import signal as sig\n\nwidths = np.arange(1, 31) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# 30 \u4e2a\u5c3a\u5ea6\uff0c30 \u628a\u4e0d\u540c\u7f51\u773c\u7684\u7b5b\u5b50\ncwt_coeffs = sig.cwt(price.values, sig.ricker, widths)\n\ndenoised = cwt_coeffs.copy()\ndenoised[:5, :] \u00a0= 0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 5 \u4ee5\u4e0b\uff0c\u6c99\u5b50\uff0c\u6f0f\u6389\ndenoised[20:, :] = 0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 20 \u4ee5\u4e0a\uff0c\u6162\u8d8b\u52bf\uff0c\u6401\u4e00\u8fb9\nclean_trend = np.mean(denoised[5:20, :], axis=0) \u00a0 # \u7559\u4e0b 5-20 \u7684\u6446\u52a8<\/code><\/pre>\n\n\n\n
\u7b5b\u5b8c\u628a 5 \u5230 20 \u8fd9\u51e0\u4e2a\u7f51\u773c\u4e0a\u7684\u6ce2\u52a8\u5e73\u5747\u4e00\u4e0b\uff0c\u5c31\u5f97\u5230\u4e00\u6761\u53bb\u6389\u6bdb\u523a\u7684\u5e72\u51c0\u8d8b\u52bf\u7ebf\u3002<\/p>\n\n\n\n
\u5149\u6709\u8d8b\u52bf\u8fd8\u4e0d\u591f\u3002\u4f5c\u8005\u53c8\u5728\u6bcf\u4e2a\u5c3a\u5ea6\u4e0a\u7b97\u4e86\u4e00\u4e2a\u6bd4\u503c\uff0c\u4eca\u5929\u7684\u6ce2\u52a8\u80fd\u91cf\u9664\u4ee5\u5341\u5929\u524d\u7684\u80fd\u91cf\u3002\u5927\u4e8e 1.02\uff0c\u8bf4\u660e\u8fd9\u4e2a\u5c3a\u5ea6\u4e0a\u7684\u6ce2\u52a8\u6b63\u5728\u653e\u5927\u3002\u4e24\u4e2a\u6761\u4ef6\u540c\u65f6\u6ee1\u8db3\u624d\u4eae\u706f\uff0c\u6ce2\u52a8\u5728\u653e\u5927\uff0c\u5e72\u51c0\u8d8b\u52bf\u7ad9\u5728\u81ea\u5df1\u7684 60 \u65e5\u5747\u7ebf\u4e0a\u65b9\u3002<\/p>\n\n\n\n
\u2461 \u4fe1\u53f7\u5408\u6210\uff0c\u548c\u90a3\u884c\u81f4\u547d\u4ee3\u7801<\/strong><\/p>\n\n\n\n
momentum = np.mean([cwt_coeffs[s-1, 10:] \/ cwt_coeffs[s-1, :-10]\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for s in range(5, 21)], axis=0)\n\nsignal = (momentum > 1.02) & (clean_trend > ma60) \u00a0 # \u53cc\u6761\u4ef6\u4eae\u706f\n\nstrategy_returns = returns * signal \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u95ee\u9898\u5c31\u51fa\u5728\u8fd9\u4e00\u884c<\/code><\/pre>\n\n\n\n
\u6ce8\u610f\u770b\u90a3\u884c\u6536\u76ca\u8ba1\u7b97\u3002signal \u5728\u8fd9\u91cc\u6ca1\u6709\u5f80\u540e\u632a\u3002<\/p>\n\n\n\n
\u8fd9\u5c31\u662f\u6574\u7bc7\u6587\u7ae0\u7684\u4e03\u5bf8\u3002<\/p>\n\n\n\n
\u4e8c\u30011750 \u500d\uff0c\u662f\u4e00\u884c\u4ee3\u7801\u7684\u529f\u52b3<\/strong><\/h2>\n\n\n\n
\u56de\u6d4b\u91cc\u6bcf\u5929\u7684\u6536\u76ca\uff0c\u7b49\u4e8e\u6301\u4ed3\u72b6\u6001\u4e58\u4ee5\u5f53\u5929\u7684\u6da8\u8dcc\u5e45\u3002signal \u8868\u793a\u7684\u662f\u4eca\u5929\u6536\u76d8\u540e\u7b97\u51fa\u6765\u7684\u5224\u65ad\uff0c\u5199\u5bf9\u7684\u8bdd\u5b83\u53ea\u80fd\u51b3\u5b9a\u4f60\u660e\u5929<\/strong>\u6301\u4e0d\u6301\u6709\uff0c\u4ee3\u7801\u91cc\u8981\u52a0\u4e00\u4e2a shift(1)\u3002<\/p>\n\n\n\n
\u539f\u6587\u6ca1\u52a0\u3002<\/p>\n\n\n\n
\u6ca1\u52a0\u610f\u5473\u7740\u4ec0\u4e48\u3002\u4eca\u5929\u6536\u76d8\u4e4b\u540e\u624d\u7b97\u5f97\u51fa\u6765\u7684\u4fe1\u53f7\uff0c\u4e58\u4e0a\u7684\u662f\u4eca\u5929\u767d\u5929\u5df2\u7ecf\u6da8\u5b8c\u7684\u5e45\u5ea6\u3002\u597d\u6bd4\u4e00\u573a\u7403\uff0c\u4f60\u7b49\u7ec8\u573a\u54e8\u54cd\u4e86\u624d\u53bb\u4e0b\u6ce8\uff0c\u8d26\u672c\u5374\u6309\u5f00\u7403\u524d\u7684\u8d54\u7387\u7b97\u4f60\u8d62\u3002\u6bcf\u5929\u8d62\u4e00\u70b9\uff0c\u516d\u5e74\u534a\u590d\u5229\u4e0b\u6765\uff0c\u5c31\u662f 1750 \u500d\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u7ec8\u573a\u54e8\u54cd\u624d\u4e0b\u6ce8\uff0c\u8d26\u672c\u5374\u6309\u5f00\u7403\u524d\u7b97\u4f60\u8d62<\/p>\n\n\n\n
\u8fd8\u6709\u4e2a\u66f4\u76f4\u89c2\u7684\u9a8c\u8bc1\u89d2\u5ea6\uff0c\u770b\u539f\u6587\u81ea\u5df1\u7684\u4e24\u5f20\u56fe\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u539f\u6587 GRAPH 05\uff0c\u7b56\u7565 1750 \u500d\u5bf9\u6bd4\u6301\u6709\u4e0d\u52a8\u7ea6 25 \u500d<\/p>\n\n\n\n
\u516d\u5e74 1750 \u500d\u7684\u7b56\u7565\uff0c\u6700\u5927\u56de\u64a4 14%\u3002\u800c\u5b83\u4ea4\u6613\u7684\u6807\u7684\uff0c\u516d\u5e74\u91cc\u81ea\u5df1\u8170\u65a9\u8fc7\u4e00\u56de\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u539f\u6587 GRAPH 06\uff0c1750 \u500d\u7b56\u7565\u7684\u6bcf\u65e5\u635f\u76ca\u4e0e\u56de\u64a4<\/p>\n\n\n\n
\u4e00\u4e2a\u53ea\u505a\u591a\u7684\u7b56\u7565\uff0c\u6536\u76ca\u662f\u6807\u7684\u7684 70 \u500d\uff0c\u56de\u64a4\u5374\u4e0d\u5230\u6807\u7684\u7684\u4e00\u534a\u3002\u6570\u5b66\u4e0a\u53ea\u6709\u4e00\u79cd\u89e3\u91ca\uff0c\u5b83\u5728\u6bcf\u4e00\u4e2a\u5927\u8dcc\u7684\u65e5\u5b50\u51e0\u4e4e\u90fd\u4e0d\u6301\u4ed3\u3002\u53ef\u4fe1\u53f7\u662f\u6536\u76d8\u540e\u624d\u7b97\u7684\uff0c\u5b83\u6ca1\u6709\u672c\u4e8b\u63d0\u524d\u907f\u5f00\u4efb\u4f55\u4e00\u6839\u5927\u9634\u7ebf\uff0c\u9664\u975e\u5b83\u63d0\u524d\u77e5\u9053\u7b54\u6848\u3002<\/p>\n\n\n\n
\u628a shift(1) \u52a0\u4e0a\u4e4b\u540e\u66f2\u7ebf\u4f1a\u7f29\u6c34\u6210\u4ec0\u4e48\u6837\uff0c\u539f\u6587\u6ca1\u6709\u5c55\u793a\u3002<\/p>\n\n\n\n
\u6211\u66ff\u5b83\u8dd1\u4e86\u4e00\u904d\u3002<\/p>\n\n\n\n
\u611f\u5174\u8da3\u7684\u670b\u53cb\u4e5f\u53ef\u4ee5\u53bbCoble\u4e0a\u770b\u4e0b\u6e90\u7801\uff1a<\/p>\n\n\n\n
https:\/\/colab.research.google.com\/drive\/1A4i-V9IHSf8SL17kVKIYkTikaNKxMjz9?usp=sharing&utm_source=algoedgeinsights.beehiiv.com&utm_medium=newsletter&utm_campaign=welcome-3-battle-tested-colab-notebooks-inside<\/a><\/p>\n\n\n\n
\u4e09\u3001\u540c\u6837\u7684\u4ee3\u7801\uff0c\u642c\u5230 510300<\/strong><\/h2>\n\n\n\n
\u590d\u73b0\u8bbe\u7f6e\u5148\u4ea4\u4ee3\u6e05\u695a\u3002<\/p>\n\n\n\n
\u6570\u636e\u7528\u901a\u8fbe\u4fe1\u7684\u524d\u590d\u6743\u65e5\u7ebf\uff0c2020 \u5e74 1 \u6708 2 \u65e5\u5230 2026 \u5e74 9 \u6708 4 \u65e5\uff0c\u4e00\u5171 1619 \u6839\u3002\u53c2\u6570\u5168\u90e8\u7167\u642c\u539f\u6587\uff0c\u5c3a\u5ea6 1 \u5230 30\uff0c\u4fdd\u7559 5 \u5230 20\uff0c\u52a8\u91cf\u9608\u503c 1.02\uff0c\u8d8b\u52bf\u8fc7\u6ee4 60 \u65e5\u5747\u7ebf\uff0c\u4e00\u4e2a\u90fd\u6ca1\u52a8\u3002<\/p>\n\n\n\n
\u6709\u4e2a\u6280\u672f\u7ec6\u8282\u5fc5\u987b\u8bf4\u3002\u539f\u6587\u7528\u7684 scipy.signal.cwt\uff0c\u5728 scipy 1.15 \u4e4b\u540e\u5df2\u7ecf\u88ab\u5b98\u65b9\u79fb\u9664\u3002\u6211\u6309\u65e7\u7248\u6e90\u7801\u9010\u884c\u590d\u523b\u4e86\u8fd9\u5957\u5377\u79ef\u903b\u8f91\uff0c\u4fdd\u8bc1\u8ddf\u4f5c\u8005\u7528\u7684\u662f\u540c\u4e00\u4e2a\u7b5b\u5b50\u3002\u4f60\u8981\u81ea\u5df1\u8dd1\uff0c\u6ce8\u610f\u7248\u672c\u3002<\/p>\n\n\n\n
\u2462 \u6570\u636e\uff0c\u901a\u8fbe\u4fe1\u524d\u590d\u6743\u65e5\u7ebf<\/strong><\/p>\n\n\n\n
import pandas as pd\n\ndf = pd.read_csv(\"510300_daily.csv\", parse_dates=[\"date\"]).set_index(\"date\")\nprice = df[\"close\"].astype(float) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# \u901a\u8fbe\u4fe1\u524d\u590d\u6743\uff0c1619 \u6839\nreturns = price.pct_change().fillna(0.0)<\/code><\/pre>\n\n\n\n
\u2463 \u65e7\u7248 cwt + ricker \u7684 1:1 \u590d\u523b<\/strong><\/p>\n\n\n\n
def cwt_old(data, wavelet, widths):\n\u00a0 \u00a0 # \u65e7\u7248 scipy.signal.cwt \u7684 1 \u6bd4 1 \u590d\u523b\uff0c\u65b0\u7248 scipy \u5df2\u5220\u9664\n\u00a0 \u00a0 out = np.empty((len(widths), len(data)))\n\u00a0 \u00a0 for i, w in enumerate(widths):\n\u00a0 \u00a0 \u00a0 \u00a0 n = min(10 * w, len(data))\n\u00a0 \u00a0 \u00a0 \u00a0 kernel = np.conj(wavelet(n, w)[::-1])\n\u00a0 \u00a0 \u00a0 \u00a0 out[i] = np.convolve(data, kernel, mode=\"same\")\n\u00a0 \u00a0 return out\n\ndef ricker_old(points, a):\n\u00a0 \u00a0 # \u540c\u6837\u590d\u523b\u65e7\u7248 ricker \u5c0f\u6ce2\n\u00a0 \u00a0 A = 2.0 \/ (np.sqrt(3 * a) * (np.pi ** 0.25))\n\u00a0 \u00a0 vec = np.arange(points) - (points - 1) \/ 2\n\u00a0 \u00a0 return A * (1 - vec**2 \/ a**2) * np.exp(-vec**2 \/ (2 * a**2))<\/code><\/pre>\n\n\n\n
\u2464 \u4e24\u79cd\u53e3\u5f84\u7684\u56de\u6d4b<\/strong><\/p>\n\n\n\n
def backtest(sig_series, shift_signal):\n\u00a0 \u00a0 # shift_signal \u4e3a True \u65f6\u4fe1\u53f7\u540e\u79fb\u4e00\u5929\uff0c\u6536\u76d8\u51fa\u4fe1\u53f7\uff0c\u6b21\u65e5\u6267\u884c\n\u00a0 \u00a0 s = sig_series.shift(1).fillna(False) if shift_signal else sig_series\n\u00a0 \u00a0 r = returns * s.astype(int)\n\u00a0 \u00a0 return (1 + r).cumprod()\n\nsig_clean = (swing_momentum > 1.02) & (clean_trend > clean_trend.rolling(60).mean())\n\ncum_flaw = backtest(sig_clean, shift_signal=False) \u00a0 # \u539f\u6587\u53e3\u5f84\ncum_fix \u00a0= backtest(sig_clean, shift_signal=True) \u00a0 \u00a0# \u4fee\u6b63\u53e3\u5f84<\/code><\/pre>\n\n\n\n
\u5148\u770b\u7b5b\u5b50\u672c\u8eab\u7684\u6548\u679c\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
510300 \u539f\u59cb\u4ef7\u683c\u5bf9\u6bd4\u5c0f\u6ce2\u53bb\u566a\u8d8b\u52bf\uff0c\u7b5b\u5b50\u672c\u8eab\u6ca1\u6bdb\u75c5<\/p>\n\n\n\n
\u6bdb\u523a\u6ee4\u6389\u4e4b\u540e\uff0c\u8d8b\u52bf\u8f6e\u5ed3\u786e\u5b9e\u6e05\u723d\u4e86\u4e0d\u5c11\u3002\u5355\u770b\u8fd9\u5f20\u56fe\uff0c\u4f60\u53ef\u80fd\u4f1a\u89c9\u5f97\u8fd9\u65b9\u6cd5\u6709\u70b9\u4e1c\u897f\u3002<\/p>\n\n\n\n
\u7b5b\u5b50\u6ca1\u95ee\u9898\u3002\u95ee\u9898\u4ece\u6765\u4e0d\u5728\u7b5b\u5b50\u3002<\/p>\n\n\n\n
\u56db\u3001\u7ed3\u679c\uff0c\u8fde\u4f5c\u5f0a\u90fd\u4e0d\u8d5a\u94b1<\/strong><\/h2>\n\n\n\n
\u8868\u51fa\u6765\u4e86\uff0c\u54b1\u4eec\u62c6\u6570\u5b57\u3002<\/p>\n\n\n\n
\u53e3\u5f84<\/th>\u516d\u5e74\u534a\u7d2f\u8ba1<\/th>\u5e74\u5316<\/th>\u6700\u5927\u56de\u64a4<\/th>\u590f\u666e<\/th><\/tr>
\u539f\u6587\u53e3\u5f84\uff0c\u4fe1\u53f7\u4e0d\u540e\u79fb<\/td>1.04 \u500d<\/strong><\/td>0.6%<\/td>-10.4%<\/td>0.12<\/td><\/tr>
\u4fee\u6b63\u53e3\u5f84\uff0c\u4fe1\u53f7\u540e\u79fb\u4e00\u5929<\/td>0.95 \u500d<\/strong><\/td>-0.8%<\/td>-18.6%<\/td>-0.08<\/td><\/tr>
\u62ff\u7740\u4e0d\u52a8<\/td>1.26 \u500d<\/strong><\/td>3.5%<\/td>-44.7%<\/td>0.28<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u5148\u770b\u597d\u7684\u4e00\u9762\u3002\u7167\u6284\u539f\u6587\u7684\u7b97\u6cd5\uff0c\u8fde\u672a\u6765\u51fd\u6570\u90fd\u539f\u6837\u4fdd\u7559\uff0c\u7b56\u7565\u516d\u5e74\u534a 1.04 \u500d\uff0c\u5e74\u5316 0.6%\uff0c\u81f3\u5c11\u6ca1\u4e8f\u3002<\/p>\n\n\n\n
\u4f46\u771f\u6b63\u8be5\u76ef\u7740\u770b\u7684\uff0c\u5728\u4e0b\u9762\u3002<\/p>\n\n\n\n
\u628a\u4fe1\u53f7\u8001\u8001\u5b9e\u5b9e\u540e\u79fb\u4e00\u5929\uff0c\u516d\u5e74\u534a 0.95 \u500d\uff0c\u4e8f 5%\u3002\u5168\u7a0b 94 \u7b14\u4ea4\u6613\uff0c\u80dc\u7387 40.4%\uff0c\u5e73\u5747\u4e00\u7b14\u8d1f 0.04%\u3002<\/p>\n\n\n\n
\u62ff\u7740\u4e0d\u52a8\u662f 1.26 \u500d\u3002\u6570\u5b57\u4e0d\u8d77\u773c\uff0c\u4f46\u5b83\u7684\u6700\u5927\u56de\u64a4 44.7%\uff0c2022 \u4e00\u5e74\u8dcc\u6389 21.7%\u3002\u7b56\u7565\u628a\u56de\u64a4\u4ece 44.7% \u538b\u5230 18.6%\uff0c\u4ee3\u4ef7\u662f\u516d\u5e74\u534a\u767d\u5fd9\u6d3b\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4fe1\u53f7\u540e\u79fb\u4e00\u5929\u524d\u540e\uff0c\u4e09\u6761\u51c0\u503c\u66f2\u7ebf\uff08\u5bf9\u6570\u5750\u6807\uff09<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4fee\u6b63\u53e3\u5f84\u516d\u5e74\u534a\u56de\u64a4\uff0c\u6df1\u5751\u90fd\u96c6\u4e2d\u5728\u9707\u8361\u5e74\u4efd<\/p>\n\n\n\n
\u518d\u62c6\u4e00\u5c42\uff0c\u770b\u6301\u4ed3\u65f6\u95f4\u3002<\/p>\n\n\n\n
\u5e74\u4efd<\/th>\u6301\u6709\u4e0d\u52a8\u6536\u76ca<\/th>\u7b56\u7565\u6536\u76ca<\/th>\u7b56\u7565\u5728\u573a\u5929\u6570<\/th><\/tr>
2020<\/td>31.1%<\/td>4.1%<\/td>17<\/td><\/tr>
2021<\/td>-4.3%<\/td>-2.9%<\/td>47<\/td><\/tr>
2022<\/td>-21.7%<\/td>-1.4%<\/td>18<\/td><\/tr>
2023<\/td>-10.4%<\/td>-7.2%<\/td>36<\/td><\/tr>
2024<\/td>18.4%<\/td>0.9%<\/td>30<\/td><\/tr>
2025<\/td>21.5%<\/td>6.5%<\/td>37<\/td><\/tr>
2026<\/td>-0.3%<\/td>-4.3%<\/td>48<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u4e00\u5e74 250 \u4e2a\u4ea4\u6613\u65e5\uff0c\u8fd9\u4e2a\u4fe1\u53f7\u771f\u6b63\u5728\u573a\u7684\u65f6\u95f4\u6700\u5c11 14 \u5929\uff0c\u6700\u591a 48 \u5929\uff0c\u4e0d\u5230\u4e24\u6210\u3002\u5269\u4e0b\u4e24\u767e\u591a\u5929\uff0c\u5b83\u90fd\u5728\u573a\u5916\u770b\u620f\u3002<\/p>\n\n\n\n
\u8fd9\u4e0d\u662f bug\uff0c\u5728\u52a8\u91cf\u7b56\u7565\u5728\u9707\u8361\u5e02\u91cc\u5c31\u662f\u8fd9\u4e2a\u5f85\u9047\u3002510300 \u8fd9\u516d\u5e74\u534a\uff0c\u6da8\u4e00\u5e74\u8dcc\u4e00\u5e74\u6765\u56de\u62c9\u952f\uff0c\u6307\u6570\u603b\u5171\u53ea\u8d70\u4e86 26%\u3002\u6ce2\u52a8\u521a\u653e\u5927\uff0c\u884c\u60c5\u5c31\u6389\u5934\uff0c\u4fe1\u53f7\u8ffd\u8fdb\u53bb\u6328\u4e00\u5df4\u638c\uff0c\u79bb\u573a\uff0c\u7b49\u4e0b\u4e00\u79cd\u8d70\u6cd5\u3002\u5468\u800c\u590d\u59cb\u3002<\/p>\n\n\n\n
\u81f3\u4e8e\u82f1\u4f1f\u8fbe\u4e0a\u7684 1750 \u500d\uff0c\u770b\u5b83\u8fd9\u516d\u5e74\u7684\u4ef7\u683c\u8def\u5f84\u5c31\u660e\u767d\uff0c\u6da8\u4e86 25 \u500d\uff0c\u4e2d\u95f4\u51e0\u4e4e\u6ca1\u6709\u50cf\u6837\u7684\u6574\u7406\u3002\u5355\u8fb9\u5927\u725b\u91cc\uff0c\u52a8\u91cf\u662f\u53f0\u5370\u949e\u673a\uff0c\u8fde\u672a\u6765\u51fd\u6570\u7684\u7834\u7efd\u90fd\u88ab\u6da8\u5e45\u76d6\u4f4f\u4e86\u3002<\/p>\n\n\n\n
\u540c\u4e00\u4e2a\u7b56\u7565\uff0c\u6362\u4e2a\u6807\u7684\uff0c\u547d\u8fd0\u5168\u6362\u30021750 \u500d\u91cc\uff0c\u5927\u5934\u662f\u82f1\u4f1f\u8fbe\u81ea\u5df1\u7684\u8d1d\u5854\uff0c\u5269\u4e0b\u7684\u96f6\u5934\uff0c\u662f\u672a\u6765\u51fd\u6570\u7684\u6da6\u8272\u3002<\/p>\n\n\n\n
\u56de\u6d4b\u6536\u76ca\u5982\u679c\u4e3b\u8981\u6765\u81ea\u6807\u7684\u81ea\u5df1\u5f80\u4e0a\u8d70\uff0c\u7b56\u7565\u672c\u8eab\u5c31\u6ca1\u6323\u5230\u4ec0\u4e48\u94b1\u3002\u8fd9\u8bdd\u6734\u7d20\u5f97\u50cf\u5e9f\u8bdd\uff0c\u53ef 1750 \u500d\u8fd9\u79cd\u6570\u5b57\u4e00\u51fa\u573a\uff0c\u591a\u6570\u4eba\u90fd\u4f1a\u5fd8\u4e86\u5b83\u3002<\/p>\n\n\n\n
\u4e94\u3001\u5b9e\u8bdd\u5b9e\u8bf4<\/strong><\/h2>\n\n\n\n
\u6709\u51e0\u4ef6\u4e8b\u5fc5\u987b\u6446\u5728\u53f0\u9762\u4e0a\u3002<\/p>\n\n\n\n
\u7b2c\u4e00\uff0c\u53c2\u6570\u5168\u662f\u7167\u642c\u539f\u6587\uff0c\u4e00\u4e2a\u6ca1\u8c03\u3002510300 \u4e0a\u6216\u8bb8\u5b58\u5728\u80fd\u8ba9\u6570\u5b57\u53d8\u597d\u770b\u7684\u53c2\u6570\uff0c\u4f46\u8c03\u53c2\u6551\u4e0d\u4e86\u65b9\u5411\u6027\u5c31\u4e0d\u6210\u7acb\u7684\u60f3\u6cd5\u3002<\/p>\n\n\n\n
\u7b2c\u4e8c\uff0c\u624b\u7eed\u8d39\u548c\u6ed1\u70b9\u6ca1\u7b97\u3002\u6bdb\u6536\u76ca\u90fd\u662f\u8d1f\u7684\uff0c\u52a0\u4e0a\u6210\u672c\u53ea\u4f1a\u66f4\u96be\u770b\u3002<\/p>\n\n\n\n
\u7b2c\u4e09\uff0c\u6211\u53ea\u6d4b\u4e86 510300 \u4e00\u4e2a\u6807\u7684\u3002\u5b83\u4ee3\u8868\u4e0d\u4e86\u5168\u90e8 A \u80a1\uff0c\u4f46\u4f5c\u4e3a\u52a8\u91cf\u7b56\u7565\u7684\u8bd5\u91d1\u77f3\uff0c\u5bbd\u57fa\u6307\u6570\u5df2\u7ecf\u5728\u4f5c\u8005\u65b9\u6cd5\u7684\u8212\u9002\u533a\u4e4b\u5916\u3002<\/p>\n\n\n\n
\u7b2c\u56db\uff0c\u524d\u590d\u6743\u6570\u636e\u5bf9\u5206\u7ea2\u7684\u5904\u7406\u662f\u8fd1\u4f3c\u7684\u3002\u5bf9\u516d\u5e74\u534a\u7ea7\u522b\u7684\u65e5\u9891\u56de\u6d4b\uff0c\u8fd9\u70b9\u8bef\u5dee\u52a8\u6447\u4e0d\u4e86\u7ed3\u8bba\u7684\u65b9\u5411\u3002<\/p>\n\n\n\n
\u7b2c\u4e94\uff0c\u539f\u6587\u6ca1\u6709\u63d0\u624b\u7eed\u8d39\uff0c\u6ca1\u6709\u63d0 shift\uff0c\u6b63\u6587\u91cc\u4e5f\u4e00\u5904\u5b9e\u76d8\u6570\u5b57\u90fd\u6ca1\u6709\u7ed9\u3002\u6240\u6709\u6210\u7ee9\u90fd\u5728\u56fe\u91cc\u3002\u6211\u4e0d\u66ff\u5b83\u7f16\u6570\u5b57\uff0c\u53ea\u628a\u80fd\u6838\u9a8c\u7684\u90e8\u5206\u6838\u9a8c\u7ed9\u4f60\u770b\u3002<\/p>\n\n\n\n
\u516d\u3001\u6211\u7684\u9a8c\u4f2a\u4e09\u6b65<\/strong><\/h2>\n\n\n\n
\u4ee5\u540e\u518d\u770b\u5230\u500d\u6570\u5413\u4eba\u7684\u56de\u6d4b\uff0c\u6309\u8fd9\u4e09\u6b65\u8fc7\u7b5b\u5b50\u3002<\/p>\n\n\n\n
\u7b2c\u4e00\u6b65\uff0c\u540e\u79fb\u4e00\u5929\u3002<\/strong>\u628a\u4fe1\u53f7 shift(1)\uff0c\u4e00\u884c\u4ee3\u7801\u7684\u4e8b\u3002\u6536\u76ca\u5927\u5e45\u7f29\u6c34\uff0c\u8bf4\u660e\u539f\u56de\u6d4b\u5077\u770b\u4e86\u7b54\u6848\u3002<\/p>\n\n\n\n
\u7b2c\u4e8c\u6b65\uff0c\u6838\u5bf9\u56de\u64a4\u3002<\/strong>\u6536\u76ca\u662f\u6807\u7684\u7684\u5341\u500d\uff0c\u56de\u64a4\u5374\u6bd4\u6807\u7684\u8fd8\u6d45\uff0c\u591a\u534a\u4e0d\u662f\u5929\u8d4b\uff0c\u662f\u672a\u6765\u51fd\u6570\u3002<\/p>\n\n\n\n
\u7b2c\u4e09\u6b65\uff0c\u6362\u4e2a\u6807\u7684\u3002<\/strong>\u725b\u80a1\u4e0a\u9a8c\u8bc1\u8fc7\u7684\u7b56\u7565\uff0c\u642c\u5230\u6307\u6570\u6216\u8005\u9707\u8361\u54c1\u79cd\u4e0a\u518d\u8dd1\u4e00\u904d\u3002\u8fd8\u6d3b\u7740\uff0c\u624d\u7b97\u771f\u672c\u4e8b\u3002<\/p>\n\n\n\n
\u4e09\u6b65\u52a0\u8d77\u6765\u82b1\u4e0d\u4e86\u534a\u5c0f\u65f6\uff0c\u80fd\u6321\u6389\u5e02\u9762\u4e0a\u5927\u534a\u7684\u6f02\u4eae\u66f2\u7ebf\u3002<\/p>\n\n\n\n
\u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
\u8fd9\u7bc7\u62c6\u5230\u6700\u540e\u5c31\u4e24\u5c42\u3002<\/p>\n\n\n\n
1750 \u500d\u7684\u6570\u5b57\u91cc\uff0c\u82f1\u4f1f\u8fbe\u81ea\u5df1\u7684\u6da8\u5e45\u8d21\u732e\u4e86\u5927\u5934\uff0c\u4e00\u884c\u6ca1\u540e\u79fb\u7684\u4fe1\u53f7\u8d21\u732e\u4e86\u5269\u4e0b\u7684\u5e7b\u89c9\u3002\u628a\u540c\u6837\u7684\u4ee3\u7801\u653e\u8fdb\u4e00\u4e2a\u8001\u8001\u5b9e\u5b9e\u7684\u9707\u8361\u5e02\u573a\uff0c\u5b83\u8fde\u4e00\u5206\u94b1\u90fd\u6323\u4e0d\u5230\u3002<\/p>\n\n\n\n
\u65b9\u6cd5\u672c\u8eab\uff0c\u5c0f\u6ce2\u53bb\u566a\u662f\u4e2a\u6b63\u7ecf\u5de5\u5177\uff0c\u505a\u6570\u636e\u6e05\u6d17\u3001\u770b\u66f2\u7ebf\u7ed3\u6784\u90fd\u6709\u7528\u5904\u3002\u62ff\u5b83\u5f53\u6536\u76ca\u5f15\u64ce\uff0c\u81f3\u5c11\u5728\u5bbd\u57fa\u6307\u6570\u4e0a\uff0c\u8fd9\u6b21\u7684\u6570\u636e\u6ca1\u7ed9\u6211\u4efb\u4f55\u8bf4\u670d\u529b\u3002<\/p>\n\n\n\n
\u6700\u540e\u7559\u4e00\u4e2a\u8ddf\u6570\u636e\u76f4\u63a5\u6302\u94a9\u7684\u95ee\u9898\u3002\u4f60\u624b\u4e0a\u8dd1\u8fc7\u7684\u7b56\u7565\uff0c\u628a\u4fe1\u53f7\u540e\u79fb\u4e00\u5929\uff0c\u6536\u76ca\u8fd8\u5269\u51e0\u6210\uff1f\u6d4b\u8fc7\u7684\uff0c\u8bc4\u8bba\u533a\u62a5\u4e2a\u6570\u3002<\/p>\n\n\n\n
\u5b8c\u6574\u53ef\u8fd0\u884c\u811a\u672c\u5728\u6587\u672b\uff0c\u6570\u636e\u6362\u6210\u4f60\u81ea\u5df1\u7684\u6807\u7684\uff0c\u4e00\u5206\u949f\u51fa\u7ed3\u679c\u3002<\/p>\n\n\n\n
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\u5b8c\u6574\u590d\u73b0\u811a\u672c\uff08\u590d\u5236\u5373\u7528\uff09<\/strong><\/p>\n\n\n\n
# -*- coding: utf-8 -*-\n\n\"\"\"\n\u5c0f\u6ce2\u53bb\u566a + \u52a8\u91cf\u7b56\u7565\u590d\u73b0\uff08510300\uff0c2020-2026\uff09\n\u4f9d\u8d56\uff1apip install pandas numpy matplotlib\n\u6570\u636e\uff1a\u8bf7\u5c06 510300_daily.csv \u653e\u5728\u540c\u76ee\u5f55\u4e0b\uff0c\u5305\u542b\u5217 date, close\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# \u4e2d\u6587\u5b57\u4f53\u8bbe\u7f6e\nplt.rcParams[\"font.sans-serif\"] = [\"Microsoft YaHei\", \"SimHei\"]\nplt.rcParams[\"axes.unicode_minus\"] = False\n\n\n# ---------- 1. \u6570\u636e\u52a0\u8f7d ----------\ndf = pd.read_csv(\"510300_daily.csv\", parse_dates=[\"date\"]).set_index(\"date\")\nprice = df[\"close\"].astype(float)\nreturns = price.pct_change().fillna(0.0)\n\n\n# ---------- 2. \u65e7\u7248 scipy \u7684 cwt + ricker \u590d\u523b\uff08scipy 1.15+ \u5df2\u79fb\u9664\uff09 ----------\ndef ricker_old(points, a):\n    \"\"\"Ricker\uff08\u58a8\u897f\u54e5\u5e3d\uff09\u5c0f\u6ce2\uff0c\u4e0e scipy.signal.ricker \u4e00\u81f4\"\"\"\n    A = 2.0 \/ (np.sqrt(3 * a) * (np.pi ** 0.25))\n    vec = np.arange(points) - (points - 1) \/ 2\n    return A * (1 - vec**2 \/ a**2) * np.exp(-vec**2 \/ (2 * a**2))\n\n\ndef cwt_old(data, wavelet, widths):\n    \"\"\"\u8fde\u7eed\u5c0f\u6ce2\u53d8\u6362\uff0c\u4f7f\u7528\u5377\u79ef\u5b9e\u73b0\uff08\u4e0e scipy.signal.cwt \u884c\u4e3a\u76f8\u540c\uff09\"\"\"\n    out = np.empty((len(widths), len(data)))\n    for i, w in enumerate(widths):\n        n = min(10 * w, len(data))\n        kernel = np.conj(wavelet(n, w)[::-1])\n        out[i] = np.convolve(data, kernel, mode=\"same\")\n    return out\n\n\n# ---------- 3. \u5c0f\u6ce2\u53bb\u566a + \u591a\u5c3a\u5ea6\u52a8\u91cf\uff08\u4e25\u683c\u6309\u539f\u6587\uff09 ----------\nwidths = np.arange(1, 31)\ncoeffs = cwt_old(price.values, ricker_old, widths)\n\n# \u53bb\u566a\uff1a\u4ec5\u4fdd\u7559\u5c3a\u5ea6 5~20 \u7684\u6210\u5206\nden = coeffs.copy()\nden[:5, :] = 0\nden[20:, :] = 0\nclean_trend = pd.Series(np.mean(den[5:20, :], axis=0), index=price.index)\n\n# \u591a\u5c3a\u5ea6\u52a8\u91cf\uff1a\u8ba1\u7b97\u5c3a\u5ea6 5~20 \u7684 10 \u671f\u6536\u76ca\u7387\u5747\u503c\nmom = np.mean([coeffs[s-1, 10:] \/ coeffs[s-1, :-10] for s in range(5, 21)], axis=0)\nswing = pd.Series(mom, index=price.index[10:]).reindex(price.index).fillna(0.0)\n\n# \u751f\u6210\u4fe1\u53f7\uff1aswing > 1.02 \u4e14 clean_trend \u9ad8\u4e8e 60 \u65e5\u5747\u7ebf\nsig_raw = swing > 1.02\nsig_clean = (sig_raw & (clean_trend > clean_trend.rolling(60).mean())).fillna(False)\n\n\n# ---------- 4. \u56de\u6d4b\u51fd\u6570\u53ca\u4e24\u79cd\u53e3\u5f84 ----------\ndef backtest(s_series, do_shift):\n    \"\"\"\n    \u56de\u6d4b\u51fd\u6570\n    do_shift=True \u8868\u793a\u4fe1\u53f7\u540e\u79fb\u4e00\u5929\uff08\u6b21\u65e5\u6267\u884c\uff09\uff0c\u907f\u514d\u672a\u6765\u51fd\u6570\n    \"\"\"\n    s = s_series.shift(1).fillna(False) if do_shift else s_series.astype(bool)\n    r = returns * s.astype(int)\n    return r, (1 + r).cumprod()\n\n\nr_flaw, cum_flaw = backtest(sig_clean, False)   # \u539f\u6587\u53e3\u5f84\uff08\u542b\u672a\u6765\u51fd\u6570\uff09\nr_fix,  cum_fix  = backtest(sig_clean, True)    # \u4fee\u6b63\u53e3\u5f84\uff08\u6b21\u65e5\u6267\u884c\uff09\ncum_bh = (1 + returns).cumprod()                # \u6301\u6709\u4e0d\u52a8\n\n\ndef stats(r, cum):\n    \"\"\"\u8ba1\u7b97\u603b\u6536\u76ca\u3001\u5e74\u5316\u6536\u76ca\u3001\u6700\u5927\u56de\u64a4\u3001\u590f\u666e\u6bd4\u7387\"\"\"\n    years = (cum.index[-1] - cum.index[0]).days \/ 365.25\n    dd = (cum \/ cum.cummax() - 1).min()\n    return {\n        \"total\": round(float(cum.iloc[-1]), 2),\n        \"cagr\": round(float(cum.iloc[-1] ** (1 \/ years) - 1) * 100, 1),\n        \"maxdd\": round(float(dd) * 100, 1),\n        \"sharpe\": round(float(r.mean() \/ r.std() * np.sqrt(252)), 2),\n    }\n\n\nprint(\"\u539f\u6587\u53e3\u5f84(\u542b\u672a\u6765\u51fd\u6570)\", stats(r_flaw, cum_flaw))\nprint(\"\u4fee\u6b63\u53e3\u5f84(\u6b21\u65e5\u6267\u884c)  \", stats(r_fix, cum_fix))\nprint(\"\u6301\u6709\u4e0d\u52a8            \", stats(returns, cum_bh))\n\n\n# ---------- 5. \u51c0\u503c\u5bf9\u6bd4\u56fe ----------\nfig, ax = plt.subplots(figsize=(12.8, 7.2), dpi=150)\nax.plot(cum_flaw, label=\"\u539f\u6587\u53e3\u5f84(\u4fe1\u53f7\u672a\u540e\u79fb)\", color=\"#8C2F2F\")\nax.plot(cum_fix, label=\"\u4fee\u6b63\u53e3\u5f84(\u6b21\u65e5\u6267\u884c)\", color=\"#C39A3B\", lw=1.8)\nax.plot(cum_bh, label=\"\u6301\u6709\u4e0d\u52a8\", color=\"#5a7fa8\")\nax.set_yscale(\"log\")\nax.legend()\nax.grid(alpha=0.3)\nax.set_title(\"\u4fe1\u53f7\u540e\u79fb\u4e00\u5929\u524d\u540e\")\nfig.tight_layout()\nfig.savefig(\"wavelet_check.png\")\nplt.show()<\/code><\/pre>\n\n\n\n
#\u91cf\u5316\u56de\u6d4b<\/a> #\u5c0f\u6ce2\u53d8\u6362<\/a> #\u672a\u6765\u51fd\u6570<\/a> #\u52a8\u91cf\u7b56\u7565<\/a> #Python\u91cf\u5316<\/a> #\u6caa\u6df1300ETF<\/a> #510300<\/a> #\u7b56\u7565\u590d\u73b0<\/a> #\u56de\u6d4b\u9677\u9631<\/a> #\u4fe1\u53f7\u5ef6\u8fdf<\/a> #\u91d1\u878d\u5de5\u7a0b<\/a> #\u6563\u6237\u9632\u5751<\/a><\/p>\n\n\n\n
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<\/p>\n","protected":false},"excerpt":{"rendered":"
\u4f5c \u8005\uff1a\u8001\u4f59\u635e\u9c7c \u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005…<\/p>\n
Continue reading\u4e3a\u4ec0\u4e48\u56de\u6d4b\u7ffb1750\u500d\u3001\u6362\u5230A\u80a1\u5c31\u4e0d\u884c\uff1f\u6211\u62ff\u6570\u636e\u9a8c\u4e86\u4e00\u904d\uff08\u9644\u6e90\u7801\uff09<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[17,4,5,6],"class_list":["post-4872","post","type-post","status-publish","format-standard","hentry","category-aiinvest","tag-ai","tag-6","entry"],"_links":{"self":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4872","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/comments?post=4872"}],"version-history":[{"count":2,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4872\/revisions"}],"predecessor-version":[{"id":4882,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4872\/revisions\/4882"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=4872"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=4872"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=4872"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}