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df['Price_Range'] = df['High'] - df['Low']\ndf['Range_to_Volume_Ratio'] = df['Price_Range'] \/ df['Volume']\navg_volume = df['Volume'].mean()\n\ndays_with_low_ratio = df[\n\u00a0 \u00a0 (df['Range_to_Volume_Ratio'] < 0.00000005) &\n\u00a0 \u00a0 (df['Volume'] > avg_volume)\n]<\/code><\/pre>\n\n\n\n
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df['range'] = df['high'] - df['low']\ndf['ratio'] = df['range'] \/ df['turnover'] \u00a0 \u00a0 \u00a0# \u6210\u4ea4\u989d\u66ff\u4ee3\u6210\u4ea4\u91cfavg_turn = df['turnover'].mean()\n\nhits = df[(df['ratio'] < 5e-8) & (df['turnover'] > avg_turn)]<\/code><\/pre>\n\n\n\n
\u7ed3\u679c\u516d\u5e74\u91cc\u547d\u4e2d 527 \u5929\uff0c\u5360\u603b\u4ea4\u6613\u65e5\u7684 32.6%\u3002<\/p>\n\n\n\n
\u7b49\u4e8e\u6ca1\u7b5b\u3002<\/p>\n\n\n\n
\u539f\u56e0\u4e00\u773c\u80fd\u770b\u7a7f\u3002510300 \u7684\u4ef7\u683c\u5728\u4e09\u56db\u5757\u94b1\uff0c\u65e5\u632f\u5e45\u901a\u5e38\u4e0d\u5230\u4e00\u5206\u94b1\uff0c\u6210\u4ea4\u989d\u51e0\u5341\u4ebf\u3002\u62ff\u632f\u5e45\u9664\u4ee5\u6210\u4ea4\u989d\uff0c\u4e2d\u4f4d\u6570\u53ea\u6709 0.00000000002\uff0c\u6bd4 5e-8 \u6574\u6574\u5c0f\u4e86\u4e09\u4e2a\u6570\u91cf\u7ea7\u3002<\/p>\n\n\n\n
\u7f8e\u5143\u80a1\u7968\u7684\u9608\u503c\uff0c\u5bf9\u4eba\u6c11\u5e01 ETF \u5c31\u662f\u4e00\u5f20\u4e0d\u8fc7\u7b5b\u7684\u7f51\u3002<\/p>\n\n\n\n
\u8bb0\u4f4f\u8fd9\u6761\u3002\u51e1\u662f\u5e26\u56fa\u5b9a\u9608\u503c\u7684\u5f62\u6001\u7b5b\u9009\u5668\uff0c\u9608\u503c\u90fd\u662f\u6807\u7684\u91cf\u7eb2\u7684\u51fd\u6570\uff0c\u4e0d\u662f\u89c4\u5f8b\u7684\u53c2\u6570\u3002<\/strong>\u6362\u6807\u7684\u4e0d\u91cd\u7b97\uff0c\u7b5b\u51fa\u6765\u7684\u5c31\u4e0d\u662f\u4fe1\u53f7\uff0c\u662f\u566a\u97f3\u3002<\/p>\n\n\n\n
\u56db\u3001\u7b2c\u4e8c\u4e2a\u5751\uff0c\u524d\u89c6<\/strong><\/h2>\n\n\n\n
\u7b2c\u4e8c\u4e2a\u5751\u85cf\u5728\u5206\u6bcd\u91cc\u3002<\/p>\n\n\n\n
\u8d77\u6b65\u7248\u90a3\u884c avg_volume = df[‘Volume’].mean()\uff0c\u5747\u503c\u662f\u516d\u5e74\u534a\u4e00\u8d77\u7b97\u7684\u3002\u610f\u601d\u662f 2020 \u5e74 1 \u6708 2 \u65e5\u8fd9\u5929\u6709\u6ca1\u6709\u4fe1\u53f7\uff0c\u8981\u7b49 2026 \u5e74 9 \u6708 4 \u65e5\u6536\u76d8\u4e4b\u540e\u624d\u77e5\u9053\u3002<\/p>\n\n\n\n
\u4f60\u628a\u672a\u6765\u516d\u5e74\u7684\u91cf\u5e73\u5747\u4e86\u4e00\u904d\uff0c\u518d\u56de\u5934\u5224\u4eca\u5929\u3002\u5b9e\u76d8\u4e0a\u8fd9\u4e2a\u6570\u6839\u672c\u62ff\u4e0d\u5230\u3002<\/p>\n\n\n\n
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\u91cf\u7eb2\u987a\u624b\u4e00\u8d77\u6536\u62fe\u3002\u632f\u5e45\u9664\u4ee5\u6536\u76d8\u4ef7\uff0c\u53d8\u6210\u632f\u5e45\u767e\u5206\u6bd4\u3002\u6210\u4ea4\u989d\u9664\u4ee5\u81ea\u5df1\u524d 20 \u65e5\u7684\u5747\u503c\uff0c\u53d8\u6210\u653e\u91cf\u500d\u6570\u3002\u4e24\u4e2a\u6307\u6807\u90fd\u65e0\u91cf\u7eb2\uff0c\u8de8\u6807\u7684\u80fd\u76f4\u63a5\u6bd4\u3002<\/p>\n\n\n\n
\u2462 \u4fee\u524d\u89c6\uff0c\u987a\u624b\u53bb\u91cf\u7eb2<\/strong><\/p>\n\n\n\n
df['range_pct'] = df['range'] \/ df['close']\ndf['turn_ratio'] = df['turnover'] \/ df['turnover'].rolling(20).mean().shift(1)<\/code><\/pre>\n\n\n\n
\u4fee\u5b8c\u518d\u770b\uff0c\u540c\u6837\u6761\u4ef6\u4e0b\u4fe1\u53f7\u4ece 5 \u4e2a\u53d8\u6210 4 \u4e2a\u3002\u6570\u91cf\u5dee\u522b\u4e0d\u5927\uff0c\u53ef\u65b9\u5411\u662f\u810f\u7684\u3002\u591a\u51fa\u6765\u7684\u90a3\u4e00\u4e2a\uff0c\u662f\u56de\u6d4b\u62ff\u672a\u6765\u6570\u636e\u7ed9\u4f60\u53d1\u7684\u7cd6\u3002<\/p>\n\n\n\n
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\u4e94\u3001\u7b2c\u4e09\u4e2a\u5751\uff0c\u9608\u503c\u6f02\u79fb<\/strong><\/h2>\n\n\n\n
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\u4e3b\u53e3\u5f84\u8fd9\u6837\u5b9a\uff0c\u632f\u5e45\u53d6\u5168\u6837\u672c\u6700\u5c0f\u7684 20%\uff0c\u653e\u91cf\u500d\u6570\u53d6 2 \u500d\u3002<\/p>\n\n\n\n
\u2463 \u4e3b\u53e3\u5f84\uff0c\u7a84\u5e45\u52a0\u653e\u91cf<\/strong><\/p>\n\n\n\n
sig = (df['range_pct'] < df['range_pct'].quantile(0.20)) & (df['turn_ratio'] >= 2.0)<\/code><\/pre>\n\n\n\n
\u8003\u9a8c\u5728\u653e\u91cf\u500d\u6570\u4e0a\u3002\u53d6 3 \u500d\uff0c\u516d\u5e74\u51fa 1 \u4e2a\u4fe1\u53f7\u3002\u653e\u5bbd\u5230 2.5 \u500d\uff0c2 \u4e2a\u30022 \u500d\uff0c4 \u4e2a\u30021.5 \u500d\uff0c13 \u4e2a\u3002<\/p>\n\n\n\n
\u653e\u91cf\u500d\u6570\u9608\u503c<\/th>\u4fe1\u53f7\u6570<\/th>\u53e0\u52a060\u65e5\u7ebf\u4e0b\u65b9<\/th><\/tr>
3.0<\/td>1<\/td>0<\/td><\/tr>
2.5<\/td>2<\/td>0<\/td><\/tr>
2.0<\/td>4<\/td>1<\/td><\/tr>
1.5<\/td>13<\/td>4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u7b5b\u5b50\u4ece”\u51e0\u4e4e\u6293\u4e0d\u5230”\u5230”\u4e00\u5806\u4fe1\u53f7”\uff0c\u53ea\u52a8\u4e86 1.5 \u500d\u3002<\/p>\n\n\n\n
\u54ea\u4e2a\u662f\u5bf9\u7684\u3002\u4e0d\u77e5\u9053\u3002\u8fd9\u4e2a\u5f62\u6001\u6ca1\u6709\u5ba2\u89c2\u7684\u8fb9\u754c\uff0c\u6bcf\u4e2a\u6807\u7684\u3001\u6bcf\u4e2a\u53c2\u6570\u53d6\u6cd5\uff0c\u7ed3\u8bba\u90fd\u4e0d\u4e00\u6837\u3002\u7b5b\u5b50\u7684\u677e\u7d27\u662f\u4f60\u5728\u66ff\u5e02\u573a\u505a\u51b3\u5b9a\uff0c\u4e0d\u662f\u5e02\u573a\u5728\u544a\u8bc9\u4f60\u7b54\u6848\u3002<\/p>\n\n\n\n
\u516d\u3001\u7b2c\u516d\u4e2a\u4f8b\u5b50\uff0c510300<\/strong><\/h2>\n\n\n\n
\u6807\u5c3a\u4fee\u597d\u4e86\uff0c\u8be5\u62ff\u5b83\u53bb\u91cf\u4e86\u3002\u6211\u62ff 2 \u500d\u8fd9\u6863\u505a\u4e3b\u53e3\u5f84\uff0c\u68c0\u9a8c\u4fe1\u53f7\u8d28\u91cf\u30026 \u5e74 4 \u4e2a\u4fe1\u53f7\uff0c\u4fe1\u53f7\u6b21\u65e5\u89c6\u4e3a\u8fdb\u573a\u70b9\uff0c\u5f80\u540e\u770b 10 \u4e2a\u4ea4\u6613\u65e5\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
510300 \u6295\u964d\u4fe1\u53f7\u5206\u5e03\uff08\u7a84\u5e4520%\u5206\u4f4d \u00d7 \u653e\u91cf2\u500d \u00d7 60\u65e5\u7ebf\u4e0b\u65b9\uff09<\/p>\n\n\n\n
4 \u4e2a\u4fe1\u53f7\uff0c\u540e 10 \u65e5\u80dc\u7387 25%\uff0c\u5e73\u5747\u6536\u76ca\u8d1f 1.5%\u3002\u540c\u671f\u5168\u6837\u672c\u968f\u4fbf\u6311\u4e00\u5929\u5f80\u540e\u770b 10 \u65e5\uff0c\u80dc\u7387 49.2%\uff0c\u5e73\u5747\u6b63 0.23%\u3002<\/p>\n\n\n\n
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\"\"<\/figure>\n\n\n\n
\u673a\u5668\u6d77\u9009\uff0c\u4eb2\u81ea\u770b\u56fe\uff0c\u6b62\u635f\u5148\u753b\u597d<\/p>\n\n\n\n
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\u7b2c\u4e00\u6b65\uff0c\u67e5\u91cf\u7eb2\u3002<\/strong>\u9608\u503c\u91cc\u51fa\u73b0 0.00000005 \u8fd9\u79cd\u6570\uff0c\u5148\u95ee\u5b83\u662f\u8c01\u7684\u91cf\u7eb2\u3002\u6362\u4e00\u4e2a\u6807\u7684\u91cd\u7b97\uff0c\u4fe1\u53f7\u6570\u91cf\u7ea7\u4e0d\u53d8\u624d\u7b97\u8fc7\u5173\u3002<\/p>\n\n\n\n
\u7b2c\u4e8c\u6b65\uff0c\u52a8\u9608\u503c\u3002<\/strong>\u628a\u5173\u952e\u53c2\u6570\u4e0a\u4e0b\u677e 30%\uff0c\u4fe1\u53f7\u6570\u91cf\u7ffb\u51e0\u500d\u6216\u8005\u5f52\u96f6\uff0c\u8bf4\u660e\u7ed3\u8bba\u662f\u53c2\u6570\u7684\uff0c\u4e0d\u662f\u89c4\u5f8b\u7684\u3002<\/p>\n\n\n\n
\u7b2c\u4e09\u6b65\uff0c\u6bd4\u57fa\u51c6\u3002<\/strong>\u4fe1\u53f7\u7684\u4e8b\u540e\u6536\u76ca\uff0c\u5fc5\u987b\u8ddf\u5168\u6837\u672c\u968f\u673a\u8fdb\u573a\u7684\u57fa\u51c6\u6bd4\u3002\u4e0d\u6bd4\u57fa\u51c6\u7684\u80dc\u7387\u6ca1\u6709\u610f\u4e49\uff0c50% \u53ef\u80fd\u662f\u5929\u8d4b\uff0c\u4e5f\u53ef\u80fd\u662f\u884c\u60c5\u672c\u8eab\u3002<\/p>\n\n\n\n
\u4e09\u6b65\u534a\u5c0f\u65f6\uff0c\u80fd\u6321\u6389\u5927\u90e8\u5206\u770b\u8d77\u6765\u5f88\u7f8e\u7684\u5f62\u6001\u6559\u7a0b\u3002<\/p>\n\n\n\n
\u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
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\u5f62\u6001\u662f\u771f\u7684\uff0c\u6295\u964d\u65e5\u5728\u4e2a\u80a1\u5927\u8dcc\u672b\u7aef\u53cd\u590d\u51fa\u73b0\uff0c\u524d\u4e94\u4e2a\u6848\u4f8b\u6ca1\u6709\u9a97\u4eba\u3002<\/p>\n\n\n\n
\u7b5b\u5b50\u662f\u96be\u7684\uff0c\u91cf\u7eb2\u3001\u524d\u89c6\u3001\u9608\u503c\u6f02\u79fb\uff0c\u4e09\u9053\u574e\u6bcf\u4e00\u9053\u90fd\u80fd\u8ba9\u6ca1\u51c6\u5907\u7684\u4eba\u62ff\u5230\u4e00\u5806\u5047\u4fe1\u53f7\u3002<\/p>\n\n\n\n
\u6807\u7684\u662f\u547d\u95e8\uff0c\u5bbd\u57fa\u6307\u6570\u516d\u5e74\u53ea\u7ed9\u4e2a\u4f4d\u6570\u6837\u672c\uff0c\u4e8b\u540e\u8fd8\u8dd1\u8f93\u968f\u673a\u3002\u8fd9\u4e2a\u5f62\u6001\u5c5e\u4e8e\u4e2a\u80a1\u7684\u6050\u614c\uff0c\u4e0d\u5c5e\u4e8e\u6307\u6570\u7684\u9634\u8dcc\u3002<\/p>\n\n\n\n
\u6700\u540e\u7559\u4e00\u4e2a\u8ddf\u6570\u636e\u76f4\u63a5\u6302\u94a9\u7684\u95ee\u9898\u3002\u4f60\u7528\u8fc7\u7684\u5f62\u6001\u7b5b\u9009\u5668\u91cc\uff0c\u6709\u6ca1\u6709\u6362\u4e00\u4e2a\u6807\u7684\u5c31\u5b8c\u5168\u5931\u7075\u7684\uff1f\u8bc4\u8bba\u533a\u62a5\u4e2a\u540d\u5b57\uff0c\u6211\u6311\u51e0\u4e2a\u590d\u73b0\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
\u89c9\u5f97\u6709\u7528\u7684\u8bdd\uff0c\u628a\u8fd9\u7bc7\u8f6c\u7ed9\u540c\u6837\u5728\u642d\u7b5b\u9009\u5668\u7684\u670b\u53cb\u3002<\/p>\n\n\n\n
\n\n\n\n
\u5b8c\u6574\u590d\u73b0\u811a\u672c\uff08\u590d\u5236\u5373\u7528\uff09<\/strong><\/p>\n\n\n\n
# -*- coding: utf-8 -*-\n\n\"\"\"\n\u5de8\u91cf\u7a84\u5e45K\u7ebf\u6295\u964d\u4fe1\u53f7\u7b5b\u9009\u5668\uff08510300\uff0c2020-2026\uff09\n\u4f9d\u8d56\uff1apip install pandas numpy matplotlib\n\u6570\u636e\uff1a\u628a 510300_daily.csv \u6362\u6210\u4f60\u81ea\u5df1\u7684\u65e5\u7ebf CSV\uff08\u5217\u540d date, open, high, low, close + \u6210\u4ea4\u989d\u5217\uff09\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nplt.rcParams[\"font.sans-serif\"] = [\"Microsoft YaHei\", \"SimHei\"]\nplt.rcParams[\"axes.unicode_minus\"] = False\n\n\n# ---------- 1. \u6570\u636e ----------\ndf = pd.read_csv(\"510300_daily.csv\", parse_dates=[\"date\"]).set_index(\"date\").sort_index()\ndf.columns = [c.strip().lower() for c in df.columns]\n\n# \u6210\u4ea4\u989d\u5217\u540d\u5404\u5bb6\u4e0d\u4e00\u6837\uff0camount \/ turnover \/ \u6210\u4ea4\u989d \u90fd\u8ba4\uff0c\u53d6\u7b2c\u4e00\u4e2a\u5b58\u5728\u7684\namt_col = next(c for c in [\"turnover\", \"amount\", \"\u6210\u4ea4\u989d\", \"turn\", \"amt\"] if c in df.columns)\ndf = df.rename(columns={amt_col: \"turnover\"})\ndf = df[df.index >= \"2020-01-01\"]\ndf[\"ret\"] = df[\"close\"].pct_change().fillna(0.0)\n\nprint(f\"\u5171 {len(df)} \u6839\uff0c{df.index[0].date()} ~ {df.index[-1].date()}\uff0c\u6210\u4ea4\u989d\u5217 = {amt_col}\")\n\n\n# ---------- 2. \u8d77\u6b65\u7248\u53e3\u5f84\uff1a\u56fa\u5b9a\u9608\u503c\uff08\u6f14\u793a\u91cf\u7eb2\u4e0d\u53ef\u8fc1\u79fb\uff09 ----------\ndf[\"range\"] = df[\"high\"] - df[\"low\"]\ndf[\"ratio\"] = df[\"range\"] \/ df[\"turnover\"]\nhits_orig = df[(df[\"ratio\"] < 5e-8) & (df[\"turnover\"] > df[\"turnover\"].mean())]\nprint(\"\u8d77\u6b65\u7248\u547d\u4e2d %d \u5929 \/ %d \u5929 (%.1f%%)\" %\n      (len(hits_orig), len(df), len(hits_orig) \/ len(df) * 100))\n\n\n# ---------- 3. \u76f8\u5bf9\u5316\u53e3\u5f84\uff08\u65e0\u91cf\u7eb2\uff0c\u53ef\u8de8\u6807\u7684\uff0c\u65e0\u524d\u89c6\uff09 ----------\ndf[\"range_pct\"] = df[\"range\"] \/ df[\"close\"]\ndf[\"turn_ratio\"] = df[\"turnover\"] \/ df[\"turnover\"].rolling(20).mean().shift(1)\ndf[\"below_ma60\"] = df[\"close\"] < df[\"close\"].rolling(60).mean()\n\n# \u9608\u503c\u654f\u611f\u6027\uff1a\u770b\u4fe1\u53f7\u6570\u968f\u53c2\u6570\u6f02\u79fb\nnarrow = df[\"range_pct\"].quantile(0.20)\nfor k in [3.0, 2.5, 2.0, 1.5]:\n    s = (df[\"range_pct\"] < narrow) & (df[\"turn_ratio\"] >= k)\n    print(f\"\u653e\u91cf>{k}\u500d: \u4fe1\u53f7 {int(s.sum())} \u4e2a, \u53e0\u52a060\u65e5\u7ebf\u4e0b\u65b9 {int((s & df['below_ma60']).sum())} \u4e2a\")\n\nsig = (df[\"range_pct\"] < narrow) & (df[\"turn_ratio\"] >= 2.0)   # \u4e3b\u53e3\u5f84\uff1a4 \u4e2a\u4fe1\u53f7\nsig_ma60 = sig & df[\"below_ma60\"]                              # \u6536\u7d27\u7248\uff1a\u53ea\u8ba4\u8dcc\u52bf\u91cc\u7684\u6295\u964d\uff0c6 \u5e74\u53ea\u5269 1 \u4e2a\nprint(f\"\u4e3b\u53e3\u5f84\u4fe1\u53f7 {int(sig.sum())} \u4e2a\uff0c\u53e0\u52a060\u65e5\u7ebf\u4e0b\u65b9\u540e {int(sig_ma60.sum())} \u4e2a\")\n\n\n# ---------- 4. \u4fe1\u53f7\u8d28\u91cf\uff1a\u6b21\u65e5\u8fdb\u573a\uff0c\u672a\u6765 N \u65e5\u6536\u76ca vs \u57fa\u51c6 ----------\ndef signal_stats(sig, horizon, tag=\"\"):\n    outs = []\n    for t in df.index[sig.reindex(df.index, fill_value=False)]:\n        loc = df.index.get_loc(t)\n        if loc + horizon < len(df):\n            outs.append(df[\"close\"].iloc[loc + horizon] \/ df[\"close\"].iloc[loc] - 1)\n    base = (df[\"close\"].shift(-horizon) \/ df[\"close\"] - 1).dropna()\n    s = pd.Series(outs)\n    if s.empty:\n        print(f\"{tag} \u540e{horizon}\u65e5\uff1a\u65e0\u4fe1\u53f7\")\n        return\n    print(f\"{tag} \u4fe1\u53f7\u6570 {len(s)}, \u540e{horizon}\u65e5\u80dc\u7387 {(s>0).mean()*100:.0f}%, \"\n          f\"\u5e73\u5747 {s.mean()*100:.2f}%, \u57fa\u51c6\u80dc\u7387 {(base>0).mean()*100:.1f}%, \"\n          f\"\u57fa\u51c6\u5e73\u5747 {base.mean()*100:.2f}%\")\n\nfor h in [5, 10, 20]:\n    signal_stats(sig, h, \"\u4e3b\u53e3\u5f84\")\nprint(\"--- \u6536\u7d27\u7248\uff1a\u6536\u76d8\u4ef7\u572860\u65e5\u5747\u7ebf\u4e0b\u65b9 ---\")\nfor h in [5, 10, 20]:\n    signal_stats(sig_ma60, h, \"\u6536\u7d27\u7248\")\n\n\n# ---------- 5. \u4fe1\u53f7\u5206\u5e03\u56fe ----------\nfig, ax = plt.subplots(figsize=(12.8, 7.2), dpi=150)\nax.plot(df.index, df[\"close\"], color=\"#5a7fa8\", lw=1.0, label=\"510300 \u6536\u76d8\u4ef7\")\npts = df[sig]\nax.scatter(pts.index, pts[\"close\"], color=\"#e17055\", s=64, marker=\"v\",\n           zorder=5, label=f\"\u6295\u964d\u4fe1\u53f7\uff08{len(pts)} \u4e2a\uff09\")\nax.legend()\nax.grid(alpha=0.3)\nfig.tight_layout()\nfig.savefig(\"candle_signal_check.png\")\nplt.show()<\/code><\/pre>\n\n\n\n
#\u91cf\u5316\u56de\u6d4b<\/a> #K\u7ebf\u5f62\u6001<\/a> #\u6210\u4ea4\u91cf\u5206\u6790<\/a> #\u6295\u964d\u4fe1\u53f7<\/a> #Python\u91cf\u5316<\/a> #\u6caa\u6df1300ETF<\/a> #510300<\/a> #\u7b56\u7565\u590d\u73b0<\/a> #\u9009\u80a1\u8fc7\u6ee4\u5668<\/a> #\u91cf\u7eb2\u9677\u9631<\/a> #\u91d1\u878d\u5de5\u7a0b<\/a> #\u6563\u6237\u9632\u5751<\/a><\/p>\n\n\n\n
\n\n\n\n
\u514d\u8d23\u58f0\u660e\uff1a\u672c\u6587\u6240\u6709\u56de\u6d4b\u6570\u636e\u4ec5\u4e3a\u6280\u672f\u7814\u7a76\u7528\u9014\uff0c\u5386\u53f2\u8868\u73b0\u4e0d\u4ee3\u8868\u672a\u6765\u3002\u6587\u4e2d\u4e0d\u6784\u6210\u4efb\u4f55\u64cd\u4f5c\u5efa\u8bae\uff0c\u636e\u6b64\u64cd\u4f5c\u98ce\u9669\u81ea\u8d1f\u3002<\/p>\n\n\n\n
<\/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\u5c0fK\u7ebf\u85cf\u7740\u5927\u4fe1\u53f7\uff0c6\u884c\u4ee3\u7801\u5c31\u80fd\u642d\u4e00\u4e2a\u201c\u6295\u964d\u4fe1\u53f7\u201d\u7b5b\u9009\u5668<\/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-4883","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\/4883","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=4883"}],"version-history":[{"count":1,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4883\/revisions"}],"predecessor-version":[{"id":4893,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4883\/revisions\/4893"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=4883"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=4883"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=4883"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}