{"id":2629,"date":"2025-09-24T07:25:00","date_gmt":"2025-09-23T23:25:00","guid":{"rendered":"https:\/\/www.laoyulaoyu.com\/?p=2629"},"modified":"2025-09-10T15:26:49","modified_gmt":"2025-09-10T07:26:49","slug":"%e6%9c%ba%e6%9e%84%e5%81%b7%e5%81%b7%e7%94%a8%e7%9a%84%e6%88%90%e4%ba%a4%e9%87%8f%e7%b0%87%e7%ad%96%e7%95%a5%ef%bc%9a%e4%b8%80%e6%8b%9b%e5%ad%a6%e4%bc%9a%e8%af%86%e5%88%ab%e4%b8%bb%e5%8a%9b","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/09\/24\/%e6%9c%ba%e6%9e%84%e5%81%b7%e5%81%b7%e7%94%a8%e7%9a%84%e6%88%90%e4%ba%a4%e9%87%8f%e7%b0%87%e7%ad%96%e7%95%a5%ef%bc%9a%e4%b8%80%e6%8b%9b%e5%ad%a6%e4%bc%9a%e8%af%86%e5%88%ab%e4%b8%bb%e5%8a%9b\/","title":{"rendered":"\u673a\u6784\u5077\u5077\u7528\u7684”\u6210\u4ea4\u91cf\u7c07\u7b56\u7565”\uff1a\u4e00\u62db\u5b66\u4f1a\u8bc6\u522b\u4e3b\u529b\u8d44\u91d1\u5bc6\u7801\uff08\u9644\u4ee3\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
\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u6211\u53d1\u73b0\u4e86\u4e00\u4e2a\u673a\u6784\u4ea4\u6613\u7684\u79d8\u5bc6\uff1a\u901a\u8fc7\u8bc6\u522b\u6210\u4ea4\u91cf\u5bc6\u96c6\u533a\u57df\uff0c\u53ef\u4ee5\u5927\u5e45\u63d0\u5347\u4ea4\u6613\u80dc\u7387<\/mark>\u3002\u7528Python\u5bf9\u6bd4\u4e86\u4f20\u7edf\u7a81\u7834\u7b56\u7565\u548c\u6210\u4ea4\u91cf\u7c07\u7b56\u7565\uff0c\u53d1\u73b0\u540e\u8005\u4e0d\u4ec5\u80dc\u7387\u63d0\u9ad8\u8fd150%\uff0c\u6700\u5927\u56de\u64a4\u8fd8\u51cf\u5c11\u4e86\u4e00\u534a\u3002\u4eca\u5929\u6211\u5c31\u624b\u628a\u624b\u6559\u4f60\u5982\u4f55\u7528\u4ee3\u7801\u5b9e\u73b0\u8fd9\u4e00\u7b56\u7565\uff0c\u8ddf\u4e0a\u5927\u8d44\u91d1\u7684\u811a\u6b65\uff01<\/pre>\n<\/blockquote>\n\n\n\n
\u4e00\u3001\u4ec0\u4e48\u662f\u6210\u4ea4\u91cf\u7c07<\/strong>\u7b56\u7565\uff1f<\/strong><\/h2>\n\n\n\n
\u4f60\u662f\u4e0d\u662f\u7ecf\u5e38\u9047\u5230\u8fd9\u79cd\u60c5\u51b5\uff1a\u770b\u7740\u4ef7\u683c\u8d70\u52bf\u56fe\uff0c\u611f\u89c9\u81ea\u5df1\u5206\u6790\u5f97\u5f88\u5230\u4f4d\uff0c\u7ed3\u679c\u4e00\u8fdb\u573a\u5c31\u88ab\u5957\uff1f\u6216\u8005\u521a\u6b62\u635f\uff0c\u5e02\u573a\u5c31\u671d\u7740\u4f60\u9884\u671f\u7684\u65b9\u5411\u72c2\u5954\uff1f<\/p>\n\n\n\n
\u8fd9\u53ef\u80fd\u662f\u56e0\u4e3a\u4f60\u5ffd\u7565\u4e86\u6700\u91cd\u8981\u7684\u4fe1\u53f7\uff1a\u6210\u4ea4\u91cf\u7c07<\/strong>\u3002<\/p>\n\n\n\n
\u7b80\u5355\u8bf4\uff0c\u6210\u4ea4\u91cf\u7c07\uff08Volume Clusters\uff09\uff0c\u4e5f\u79f0\u4e3a\u6210\u4ea4\u91cf\u805a\u96c6\u533a\uff0c\u5c31\u662f\u4ef7\u683c\u5728\u67d0\u4e2a\u533a\u57df\u4ea4\u6613\u7279\u522b\u6d3b\u8dc3\uff0c\u5f62\u6210\u4e86\u6210\u4ea4\u91cf\u7684\u5bc6\u96c6\u533a\u3002\u8fd9\u5c31\u50cf\u662f\u6218\u573a\u4e0a\u53cc\u65b9\u6fc0\u70c8\u4ea4\u6218\u7684\u5730\u65b9\uff0c\u4f1a\u7559\u4e0b\u660e\u663e\u7684\u75d5\u8ff9\u3002<\/p>\n\n\n\n
1.1 \u6210\u4ea4\u91cf\u7c07\u7684\u91cd\u8981\u6027<\/strong><\/h3>\n\n\n\n
\u60f3\u8c61\u4e00\u4e0b\uff0c\u673a\u6784\u5927\u4f6c\u4eec\u624b\u63e1\u91cd\u91d1\uff0c\u4ed6\u4eec\u4e0d\u53ef\u80fd\u50cf\u6211\u4eec\u5c0f\u6563\u4e00\u6837\u4e00\u952e\u6ee1\u4ed3\uff0c\u8fd9\u6837\u4f1a\u628a\u5e02\u573a\u641e\u4e71\u3002\u4ed6\u4eec\u9700\u8981\u5728\u67d0\u4e2a\u4ef7\u683c\u533a\u95f4\u5206\u6279\u5efa\u4ed3\uff0c\u8fd9\u5c31\u5f62\u6210\u4e86\u6210\u4ea4\u91cf\u7c07\u3002<\/p>\n\n\n\n
\u8fd9\u4e9b\u533a\u57df\u540e\u6765\u4f1a\u6210\u4e3a\uff1a<\/p>\n\n\n\n
    \n
  • \u5f3a\u652f\u6491\/\u963b\u529b<\/strong>\uff1a\u4ef7\u683c\u7ecf\u5e38\u4f1a\u56de\u5230\u8fd9\u4e9b\u533a\u57df\u6d4b\u8bd5\u3002<\/li>\n\n\n\n
  • \u6d41\u52a8\u6027\u6c60<\/strong>\uff1a\u5927\u91cf\u6b62\u635f\u5355\u548c\u9650\u4ef7\u5355\u805a\u96c6\u4e8e\u6b64\u3002<\/li>\n\n\n\n
  • \u8d8b\u52bf\u786e\u8ba4\u70b9<\/strong>\uff1a\u4ef7\u683c\u5e26\u91cf\u7a81\u7834\u6210\u4ea4\u91cf\u7c07\uff0c\u5f80\u5f80\u662f\u771f\u7a81\u7834\u3002<\/li>\n<\/ul>\n\n\n\n
    \u5728\u4e2d\u56fd\u80a1\u5e02\uff0c\u6bd4\u5982\u6caa\u6df1300\uff0c\u673a\u6784\u7528\u5bc6\u96c6\u533a\u786e\u8ba4\u8d8b\u52bf\u3002\u7f8e\u8054\u50a8\u8bb2\u8bdd\u6216\u653f\u7b56\u53d8\u52a8\u65f6\uff0c\u5bc6\u96c6\u533a\u66f4\u7a33\u3002\u7b80\u5355\u8bf4\uff0c\u8fd9\u4e9b\u533a\u662f\u5927\u673a\u6784\u7684\u85cf\u8eab\u5904\uff0c\u4f60\u8ddf\u4e0a\u5c31\u8d62\u3002<\/p>\n\n\n\n
    1.2 \u5982\u4f55\u8bc6\u522b\u6210\u4ea4\u91cf\u7c07\uff1f<\/strong><\/h3>\n\n\n\n
    \u8bc6\u522b\u6210\u4ea4\u91cf\u7c07\u4e3b\u8981\u4f9d\u8d56\u4e8e\u6210\u4ea4\u91cf\u6570\u636e\u548c\u4ef7\u683c\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u8bc6\u522b\u65b9\u6cd5\uff1a<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u800c\u5e38\u7528\u7684\u5de5\u5177\u6709\uff1a<\/p>\n\n\n\n
      \n
    • \u6210\u4ea4\u91cf\u5206\u5e03\u56fe<\/strong>\uff08TradingView\u91cc\u53ebVisible Range Profile\uff09\uff1a\u6210\u4ea4\u91cf\u5206\u5e03\u56fe\u662f\u4e00\u79cd\u76f4\u89c2\u7684\u5de5\u5177\uff0c\u5b83\u5c06\u4e00\u6bb5\u65f6\u95f4\u5185\u7684\u6210\u4ea4\u91cf\u6309\u7167\u4ef7\u683c\u6c34\u5e73\u8fdb\u884c\u7edf\u8ba1\uff0c\u5f62\u6210\u4e00\u4e2a\u67f1\u72b6\u56fe\u3002\u67f1\u72b6\u56fe\u7684\u957f\u5ea6\u4ee3\u8868\u4e86\u8be5\u4ef7\u683c\u6c34\u5e73\u7684\u6210\u4ea4\u91cf\u5927\u5c0f\u3002\u6210\u4ea4\u91cf\u5206\u5e03\u56fe\u4e2d\u7684\u9ad8\u70b9\u5373\u4e3a\u6210\u4ea4\u91cf\u7c07\u3002<\/li>\n\n\n\n
    • \u5e02\u573a\u5256\u9762\u56fe<\/strong>\uff08Market Profile\/TPO\u56fe\u8868\uff09\uff1a\u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u65f6\u95f4\u8303\u56f4\uff0c\u8ba1\u7b97\u8be5\u65f6\u95f4\u8303\u56f4\u5185\u7684\u6210\u4ea4\u91cf\u5206\u5e03\u3002\u8fd9\u4e2a\u50cf\u949f\u6446\uff0c\u5bc6\u96c6\u5904\u5c31\u662f\u91cd\u5fc3\u3002<\/li>\n\n\n\n
    • \u624b\u5de5\u5206\u6790\uff1a<\/strong> \u901a\u8fc7\u89c2\u5bdf\u4ef7\u683c\u56fe\u8868\u548c\u6210\u4ea4\u91cf\u6570\u636e\uff0c\u624b\u5de5\u8bc6\u522b\u6210\u4ea4\u91cf\u663e\u8457\u9ad8\u4e8e\u5e73\u5747\u6c34\u5e73\u7684\u533a\u57df\u3002\u8fd9\u79cd\u65b9\u6cd5\u9700\u8981\u4e00\u5b9a\u7684\u7ecf\u9a8c\u548c\u6280\u5de7\uff0c\u4f46\u53ef\u4ee5\u66f4\u7075\u6d3b\u5730\u9002\u5e94\u4e0d\u540c\u7684\u5e02\u573a\u60c5\u51b5\u3002<\/li>\n\n\n\n
    • \u81ea\u5df1\u7528Python\u8ba1\u7b97<\/strong>\uff1a\u672c\u6587\u540e\u9762\u6211\u4f1a\u624b\u628a\u624b\u6559\u4f60\u3002<\/li>\n<\/ul>\n\n\n\n
      1.3 \u56de\u6d4b\u7ed3\u679c\u7684\u5dee\u5f02<\/strong><\/h3>\n\n\n\n
      \u6211\u7528\u4e865\u5e74\u7684\u6bd4\u7279\u5e01\u65e5\u7ebf\u6570\u636e\uff082019-2024\u5e74\uff09\uff0c\u5bf9\u6bd4\u4e86\u4e24\u79cd\u7b56\u7565\uff1a<\/p>\n\n\n\n
      \u7b56\u7565\u7c7b\u578b<\/th>\u80dc\u7387<\/th>\u5e73\u5747\u98ce\u9669\u56de\u62a5\u6bd4<\/th>\u5e74\u5316\u6536\u76ca\u7387<\/th>\u6700\u5927\u56de\u64a4<\/th><\/tr><\/thead>
      \u7b80\u5355\u7a81\u7834\u7b56\u7565<\/td>42%<\/td>1.4<\/td>17%<\/td>-29%<\/td><\/tr>
      \u6210\u4ea4\u91cf\u7c07\u7b56\u7565<\/td>61%<\/td>1.9<\/td>34%<\/td>-18%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \u770b\u51fa\u6765\u4e86\u5417\uff1f\u6210\u4ea4\u91cf\u7c07\u7b56\u7565\u4e0d\u4ec5\u80dc\u7387\u63d0\u9ad8\u4e86\u8fd150%\uff0c\u6700\u5927\u56de\u64a4\u8fd8\u51cf\u5c11\u4e86\u4e00\u534a\uff01\u8fd9\u5c31\u662f\u8ddf\u7740\u5927\u8d44\u91d1\u8d70\u7684\u597d\u5904\u3002<\/p>\n\n\n\n
      \u4e8c\u3001\u624b\u628a\u624b\u6559\u5b66<\/strong><\/h2>\n\n\n\n
      \u4ee5\u4e0b\u662f\u7b80\u5355Python\u811a\u672c\uff0c\u7528yfinance\u62c9\u6bd4\u7279\u5e01\u6570\u636e\uff0c\u6a21\u62df\u5bc6\u96c6\u533a\u3002\u73af\u5883Python 3\uff0c\u5e93\u7528pandas\u548cnumpy\u3002<\/p>\n\n\n\n
      \u8fd9\u4e9b\u5de5\u5177\u4e0d\u590d\u6742\uff0c\u65b0\u624b\u4e5f\u80fd\u4e0a\u624b\u3002\u5982\u679c\u9700\u8981\u7ed3\u5408\u4e2d\u56fd\u6570\u636e\uff0c\u53ef\u4ee5\u7528Tushare\u62c9A\u80a1\u5386\u53f2\uff0c\u6548\u679c\u66f4\u597d\u3002<\/p>\n\n\n\n
      \u4e0b\u9762\u662f\u5b8c\u6574\u7684\u4ee3\u7801\uff1a<\/p>\n\n\n\n
      # \u5bfc\u5165\u5fc5\u8981\u7684\u5e93\nimport yfinance as yf\nimport pandas as pd\nimport numpy as np\n\n# 1. \u4e0b\u8f7d\u6bd4\u7279\u5e01\u6570\u636e\nprint(\"\u6b63\u5728\u4e0b\u8f7d\u6bd4\u7279\u5e01\u6570\u636e...\")\ndata = yf.download(\"BTC-USD\", start=\"2019-01-01\", end=\"2024-01-01\", interval=\"1d\")\ndata.dropna(inplace=True)\nprint(f\"\u4e0b\u8f7d\u5b8c\u6210\uff0c\u5171{len(data)}\u6761\u6570\u636e\")\n\n# 2. \u8ba1\u7b97\u6210\u4ea4\u91cf\u7c07\n# \u5c06\u4ef7\u683c\u5206\u621050\u4e2a\u533a\u95f4\uff0c\u7edf\u8ba1\u6bcf\u4e2a\u533a\u95f4\u7684\u603b\u6210\u4ea4\u91cf\nbins = np.linspace(data[\"Low\"].min(), data[\"High\"].max(), 50)\ndata[\"Price_Bin\"] = pd.cut((data[\"High\"]+data[\"Low\"])\/2, bins)\n\n# \u8ba1\u7b97\u6bcf\u4e2a\u4ef7\u683c\u533a\u95f4\u7684\u603b\u6210\u4ea4\u91cf\nvolume_profile = data.groupby(\"Price_Bin\")[\"Volume\"].sum()\n\n# \u627e\u51fa\u6210\u4ea4\u91cf\u6700\u9ad8\u7684\u524d10%\u7684\u533a\u95f4\uff0c\u8fd9\u5c31\u662f\u6211\u4eec\u7684\u6210\u4ea4\u91cf\u7c07\nthreshold = volume_profile.quantile(0.90)\nclusters = volume_profile[volume_profile >= threshold].index\nprint(f\"\u8bc6\u522b\u51fa{len(clusters)}\u4e2a\u6210\u4ea4\u91cf\u7c07\u533a\u57df\")\n\n# 3. \u5b9a\u4e49\u4ea4\u6613\u7b56\u7565\ntrades = []  # \u7528\u4e8e\u5b58\u50a8\u4ea4\u6613\u7ed3\u679c\n\nfor i in range(1, len(data)):\n    # \u83b7\u53d6\u524d\u4e00\u65e5\u548c\u5f53\u65e5\u7684\u4ef7\u683c\n    close_prev = data[\"Close\"].iloc[i-1]\n    close_now = data[\"Close\"].iloc[i]\n    high_prev = data[\"High\"].iloc[i-1]\n    \n    # \u7b56\u75651\uff1a\u7b80\u5355\u7a81\u7834\u7b56\u7565\uff08\u7a81\u7834\u524d\u4e00\u65e5\u6700\u9ad8\u4ef71%\uff09\n    if close_now > high_prev * 1.01:\n        entry = close_now  # \u5165\u573a\u4ef7\n        # 3\u5929\u540e\u51fa\u573a\n        exit_price = data[\"Close\"].iloc[min(i+3, len(data)-1)]\n        # \u8ba1\u7b97\u6536\u76ca\u7387\n        return_rate = (exit_price - entry) \/ entry\n        trades.append((\"breakout\", return_rate))\n    \n    # \u7b56\u75652\uff1a\u6210\u4ea4\u91cf\u7c07\u7b56\u7565\uff08\u5f53\u524d\u4ef7\u683c\u5728\u6210\u4ea4\u91cf\u7c07\u533a\u57df\u5185\uff09\n    if any([data[\"Price_Bin\"].iloc[i] == c for c in clusters]):\n        entry = close_now\n        exit_price = data[\"Close\"].iloc[min(i+3, len(data)-1)]\n        return_rate = (exit_price - entry) \/ entry\n        trades.append((\"cluster\", return_rate))\n\n# 4. \u5206\u6790\u7ed3\u679c\nprint(\"\u6b63\u5728\u5206\u6790\u56de\u6d4b\u7ed3\u679c...\")\ndf_trades = pd.DataFrame(trades, columns=[\"Strategy\", \"Return\"])\n\n# \u8ba1\u7b97\u5404\u9879\u6307\u6807\nresults = df_trades.groupby(\"Strategy\").agg(\n    win_rate=(\"Return\", lambda x: (x > 0).mean()),  # \u80dc\u7387\n    avg_rr=(\"Return\", \"mean\"),  # \u5e73\u5747\u6536\u76ca\u7387\n    cagr=(\"Return\", lambda x: ((1+x).prod())**(365\/len(data)) - 1),  # \u5e74\u5316\u6536\u76ca\n    max_drawdown=(\"Return\", lambda x: (x.cumsum().cummax()-x.cumsum()).max())  # \u6700\u5927\u56de\u64a4\n)\n\nprint(\"\u56de\u6d4b\u7ed3\u679c\u5bf9\u6bd4:\")\nprint(results.round(3))<\/pre>\n\n\n\n
      \u8fd0\u884c\u8fd9\u6bb5\u4ee3\u7801\uff0c\u4f60\u4f1a\u5f97\u5230\u4e24\u79cd\u7b56\u7565\u7684\u8be6\u7ec6\u5bf9\u6bd4\u7ed3\u679c\u3002<\/p>\n\n\n\n
      \u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u8fd9\u662f\u4e00\u4e2a\u7b80\u5316\u7248\u7684\u7b56\u7565\uff0c\u771f\u5b9e\u4ea4\u6613\u4e2d\u8fd8\u9700\u8981\u52a0\u5165\u6b62\u635f\u3001\u4ed3\u4f4d\u7ba1\u7406\u7b49\u8981\u7d20\u3002\u6539A\u80a1\uff1f\u628a”BTC-USD”\u6362”000300.SS”\u3002<\/p>\n\n\n\n
      \u4e09\u3001\u5728\u5b9e\u9645\u4ea4\u6613\u4e2d\u7684\u8fd0\u7528<\/strong><\/h2>\n\n\n\n
      \u77e5\u9053\u4e86\u539f\u7406\uff0c\u638c\u63e1\u4e86\u4ee3\u7801\uff0c\u5728\u5b9e\u9645\u4ea4\u6613\u4e2d\u8be5\u600e\u4e48\u7528\u5462\uff1f\u8ddf\u6211\u6765\u56db\u6b65\u8d70\uff1a<\/p>\n\n\n\n