{"id":2085,"date":"2025-04-09T07:22:00","date_gmt":"2025-04-08T23:22:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2085"},"modified":"2025-03-19T10:19:43","modified_gmt":"2025-03-19T02:19:43","slug":"ai%e9%a1%be%e6%8a%95%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%88%90%e4%ba%a4%e9%87%8f%e8%83%8c%e5%90%8e%e9%9a%90%e8%97%8f%e7%9a%84%e7%82%92%e8%82%a1%e8%b5%9a%e9%92%b1%e5%af%86%e7%a0%81","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/04\/09\/ai%e9%a1%be%e6%8a%95%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%88%90%e4%ba%a4%e9%87%8f%e8%83%8c%e5%90%8e%e9%9a%90%e8%97%8f%e7%9a%84%e7%82%92%e8%82%a1%e8%b5%9a%e9%92%b1%e5%af%86%e7%a0%81\/","title":{"rendered":"AI\u987e\u6295\u5b9e\u6218\u6559\u5b66\uff1a\u6210\u4ea4\u91cf\u80cc\u540e\u9690\u85cf\u7684\u7092\u80a1\u8d5a\u94b1\u5bc6\u7801"},"content":{"rendered":"\n

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\"\"<\/figure>\n\n\n\n
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\u6211\u5728\u7814\u7a76\u82f1\u4f1f\u8fbe\uff08NVIDIA\uff09\u80a1\u7968\u7684\u65f6\u5019\uff0c\u80a1\u4ef7\u5927\u6982\u5728 50 \u7f8e\u5143\u3002\u6211\u5728\u6210\u4ea4\u91cf\u6570\u636e\u4e0a\u6dfb\u52a0\u4e86\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u8fd9\u5c31\u597d\u6bd4\u7ed9\u6210\u4ea4\u91cf\u6570\u636e\u52a0\u4e86\u4e2a \u201c\u91cd\u70b9\u6807\u8bb0\u201d\u3002\u5f53\u6210\u4ea4\u91cf\u7684\u67f1\u5b50\u8d85\u8fc7\u8fd9\u6761\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u65f6\u5019\uff0c\u5c31\u8bf4\u660e\u8fd9\u53ea\u80a1\u7968\u6bd4\u5e73\u65f6\u66f4\u70ed\u95e8\uff0c\u4ea4\u6613\u66f4\u6d3b\u8dc3\u3002\u6211\u8fd8\u5199\u4e86\u6bb5 Python \u4ee3\u7801\u6765\u8f85\u52a9\u5206\u6790\uff1a<\/p>\n\n\n\n
import pandas as pd\nimport matplotlib.pyplot as plt\n\n# NVIDIA\u6570\u636e\ndata = {'Day': ['01-10', '01-11', '01-12', '01-13'],\n        'Volume': [40000, 60000, 95000, 45000]}\ndf = pd.DataFrame(data)\n\n# \u8c03\u6574\u79fb\u52a8\u5e73\u5747\u7ebf\u7a97\u53e3\u4e3a3\u5929\ndf['MA'] = df['Volume'].rolling(window=3).mean()\n\n# \u8bc6\u522b\u201c\u70ed\u95e8\u201d\u4ea4\u6613\u65e5\ndf['Hot'] = df['Volume'] > df['MA']\n\n# \u7ed8\u56fe\nplt.figure(figsize=(8, 5))\n\n# \u6210\u4ea4\u91cf\u67f1\u72b6\u56fe\nplt.bar(df['Day'], df['Volume'], color='green', alpha=0.7, label='Volume')\n\n# \u79fb\u52a8\u5e73\u5747\u7ebf\u6298\u7ebf\u56fe\nplt.plot(df['Day'], df['MA'], color='purple', marker='o', linestyle='dashed', linewidth=2, label='3-day MA')\n\n# \u6807\u8bb0\u70ed\u95e8\u4ea4\u6613\u65e5\nfor i in range(len(df)):\n    if df['Hot'][i]:  # \u5982\u679c\u201cHot\u201d\u4e3aTrue\n        plt.text(df['Day'][i], df['Volume'][i] + 2000, '\ud83d\udd25', ha='center', fontsize=12)\n\n# \u6807\u7b7e\u548c\u6807\u9898\nplt.xlabel('Day')\nplt.ylabel('Volume')\nplt.title('NVIDIA Trading Volume with Moving Average')\nplt.legend()\nplt.xticks(rotation=45)\n\n# \u663e\u793a\u56fe\u8868\nplt.show()\n<\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
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    \u6211\u8fd8\u5199\u4e86\u4ee3\u7801\u6765\u63d0\u524d\u53d1\u73b0\u8fd9\u79cd\u89c4\u5f8b\uff0c\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    import pandas as pd\nimport matplotlib.pyplot as plt\n\n# \u793a\u4f8b\u6570\u636e\uff08\u8bf7\u7528\u5b9e\u9645\u6570\u636e\u66ff\u6362\uff09\ndata = {\n    'Day': [1, 2, 3, 4, 5, 6, 7, 8],\n    'Volume': [100, 250, 80, 300, 90, 400, 110, 50],\n    'MA': [90, 100, 110, 120, 130, 140, 150, 160]\n}\ndf = pd.DataFrame(data)\n\n# \u8bc6\u522b\u542f\u52a8\u548c\u56de\u8c03\u70b9\nspark_days = []\ndip_days = []\nspark_volumes = []\ndip_volumes = []\nfor i in range(1, len(df) - 1):\n    if df['Volume'][i] > df['MA'][i] * 2:  # \u6210\u4ea4\u91cf\u5927\u5e45\u653e\u5927\u7684\u65e5\u5b50\n        if df['Volume'][i + 1] < df['MA'][i + 1]:  # \u6210\u4ea4\u91cf\u56de\u8c03\u7684\u65e5\u5b50\n            spark_days.append(df['Day'][i])\n            dip_days.append(df['Day'][i + 1])\n            spark_volumes.append(df['Volume'][i])\n            dip_volumes.append(df['Volume'][i + 1])\n\n# \u7ed8\u5236\u6210\u4ea4\u91cf\u548c\u79fb\u52a8\u5e73\u5747\u7ebf\nplt.figure(figsize=(10, 5))\nplt.plot(df['Day'], df['Volume'], marker='o', linestyle='-', label='Volume', color='blue')\nplt.plot(df['Day'], df['MA'], marker='s', linestyle='--', label='Moving Average (MA)', color='orange')\n\n# \u6807\u8bb0\u542f\u52a8\u70b9\nplt.scatter(spark_days, spark_volumes, color='red', label='Spark (Big Day)', s=100, marker='^')\n\n# \u6807\u8bb0\u56de\u8c03\u70b9\nplt.scatter(dip_days, dip_volumes, color='green', label='Dip (Quiet Dip)', s=100, marker='v')\n\n# \u6807\u7b7e\u548c\u56fe\u4f8b\nplt.xlabel('Day')\nplt.ylabel('Volume')\nplt.title('Spark and Dip Detection')\nplt.legend()\nplt.grid(True)\n\n# \u663e\u793a\u56fe\u8868\nplt.show()<\/code><\/pre>\n\n\n\n
    \u7b2c\u56db\u6b65\uff1a\u7ed3\u5408\u80a1\u4ef7\u8d70\u52bf\u5206\u6790<\/strong><\/h2>\n\n\n\n
    \u5149\u76ef\u7740\u6210\u4ea4\u91cf\u8fd8\u4e0d\u591f\uff0c\u5fc5\u987b\u7ed3\u5408K\u7ebf\u5f62\u6001\u4e00\u8d77\u770b\u3002<\/p>\n\n\n\n
    2023\u5e74\u7279\u65af\u62c9\u5c31\u662f\u4e2a\u5f88\u597d\u7684\u4f8b\u5b50\uff1a\u5f53\u65f6\u80a1\u4ef7\u5728270\u7f8e\u5143\u9644\u8fd1\uff0c\u6210\u4ea4\u91cf\u7a81\u7136\u653e\u5927\uff0c\u968f\u540e\u8d70\u51fa\u4e86\u4e00\u4e2a\u5178\u578b\u7684“\u725b\u65d7”\u5f62\u6001<\/strong>\u2014\u2014\u5c31\u50cf\u4e00\u9762\u8fce\u98ce\u98d8\u626c\u7684\u65d7\u5e1c\uff0c\u5148\u5feb\u901f\u62c9\u5347\u5f62\u6210\u65d7\u6746\uff0c\u7136\u540e\u5c0f\u5e45\u9707\u8361\u6574\u7406\u5f62\u6210\u65d7\u9762\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u8fd9\u79cd\u5f62\u6001\u5f80\u5f80\u9884\u793a\u7740\u4e0a\u6da8\u4e2d\u7ee7\uff0c\u5c31\u50cf\u8fd0\u52a8\u5458\u8d77\u8df3\u524d\u7684\u4e0b\u8e72\u84c4\u529b\u3002<\/p>\n\n\n\n
    \u9664\u6b64\u4e4b\u5916\uff0c\u8fd8\u6709\u4e0a\u5347\u4e09\u89d2\u5f62\u3001\u6954\u5f62\u7b49\u6574\u7406\u5f62\u6001\uff0c\u90fd\u50cf\u662f\u80a1\u7968\u5728”\u6df1\u547c\u5438”\uff0c\u4e3a\u4e0b\u4e00\u6ce2\u884c\u60c5\u505a\u51c6\u5907\u3002\u638c\u63e1\u8fd9\u4e9b\u5f62\u6001\u7279\u5f81\uff0c\u80fd\u5e2e\u6211\u4eec\u66f4\u597d\u5730\u628a\u63e1\u4e70\u5356\u65f6\u673a\u3002<\/p>\n\n\n\n
    \u7b2c\u4e94\u6b65\uff1a\u9009\u62e9\u5408\u9002\u7684\u5165\u573a\u65f6\u673a<\/strong><\/h2>\n\n\n\n
    \u627e\u51c6\u4e70\u70b9\u5f88\u5173\u952e\u3002\u4ee5MSTR\u4e3a\u4f8b\uff0c146\u7f8e\u5143\u8fd9\u4e2a\u4f4d\u7f6e\u4e24\u6b21\u90fd\u6ca1\u8dcc\u7834\uff0c\u5f62\u6210\u4e86\u660e\u663e\u7684\u652f\u6491\u4f4d\u3002\u5f53\u80a1\u4ef7\u653e\u91cf\u7a81\u7834146\u7f8e\u5143\u65f6\uff0c\u6211\u5c31\u77e5\u9053\u673a\u4f1a\u6765\u4e86\u3002\u6211\u4f1a\u76ef\u77405\u5206\u949fK\u7ebf\u56fe\uff0c\u53ea\u8981\u6536\u76d8\u4ef7\u7ad9\u7a33146\u7f8e\u5143\u5c31\u7acb\u5373\u8fdb\u573a\u3002\u540c\u65f6\uff0c\u6211\u4f1a\u628a\u6b62\u635f\u8bbe\u5728\u5f53\u5929\u6700\u4f4e\u4ef7\u4e0b\u65b9\uff0c\u6bd4\u5982143\u7f8e\u5143\uff0c\u8fd9\u6837\u5373\u4f7f\u5224\u65ad\u9519\u8bef\uff0c\u635f\u5931\u4e5f\u6709\u9650\u3002<\/p>\n\n\n\n
    \u8fd9\u4e2a\u4ea4\u6613\u7b56\u7565\u6309\u4e0b\u9762\u7684\u6d41\u7a0b\u6765\uff1a<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u8be6\u7ec6\u903b\u8f91\u6d41\u7a0b\u56fe\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    \u5f00\u59cb\n\u2502\n\u25bc\n\u521d\u59cb\u5316\uff1a\n\u89e6\u53d1\u4ef7 = 146\n\u4ef7\u683c\u5e8f\u5217 = [144, 145, 146.2, 147]\n\u6b62\u635f\u4ef7 = 143\n\u2502\n\u25bc\n\u904d\u5386\u4ef7\u683c\u5e8f\u5217\uff1a\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u5f53\u524d\u4ef7\u683c = 144 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\u2502\n\u25bc\n144 > 146 ? \u2717 (\u5426) \u2192 \u7ee7\u7eed\u5faa\u73af\n\u2502\n\u25bc\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u5f53\u524d\u4ef7\u683c = 145 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\u2502\n\u25bc\n145 > 146 ? \u2717 (\u5426) \u2192 \u7ee7\u7eed\u5faa\u73af\n\u2502\n\u25bc\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u5f53\u524d\u4ef7\u683c = 146.2 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\u2502\n\u25bc\n146.2 > 146 ? \u2713 (\u662f) \u2192 \u8f93\u51fa\uff1a\n\"\u5728146.2\u8fdb\u573a\uff0c\u6b62\u635f\u8bbe\u5728143\"\n\u2502\n\u25bc\n\u7ed3\u675f\u5faa\u73af\n\u2502\n\u25bc\n\u7a0b\u5e8f\u7ed3\u675f<\/code><\/pre>\n\n\n\n
    \u8fd9\u79cd\u7b56\u7565\u9002\u7528\u4e8e\u7a81\u7834\u4ea4\u6613\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u5728\u5173\u952e\u4ef7\u4f4d\u53ca\u65f6\u8fdb\u573a\uff0c\u540c\u65f6\u63a7\u5236\u98ce\u9669\u3002<\/p>\n\n\n\n
      \n
    1. <\/li>\n<\/ol>\n\n\n\n
      \u4e3a\u4e86\u9a8c\u8bc1\u8fd9\u4e2a\u65b9\u6cd5\u7684\u53ef\u9760\u6027\uff0c\u6211\u8fd8\u4e13\u95e8\u5199\u4e86\u7a0b\u5e8f\u56de\u6d4b\uff1a<\/p>\n\n\n\n
      import matplotlib.pyplot as plt\n\n# \u6570\u636e\ntrigger = 146\nprices = [144, 145, 146.2, 147]  # 5\u5206\u949f\u6536\u76d8\u4ef7\nstop = 143\nentry_price = None\ntime_intervals = list(range(len(prices)))  # \u5047\u8bbe\u65f6\u95f4\u70b9\u4e3a\u7d22\u5f15\n\n# \u5bfb\u627e\u5165\u573a\u4ef7\u683c\nfor price in prices:\n    if price > trigger:\n        entry_price = price\n        break\n\n# \u7ed8\u5236\u80a1\u4ef7\u8d70\u52bf\nplt.figure(figsize=(8, 5))\nplt.plot(time_intervals, prices, marker='o', linestyle='-', label='Price Movement')\n\n# \u6807\u8bb0\u5165\u573a\u70b9\nif entry_price:\n    entry_index = prices.index(entry_price)\n    plt.scatter(entry_index, entry_price, color='red', s=100, label=f'Entry at {entry_price}')\n    plt.axhline(y=stop, color='blue', linestyle='dashed', label=f'Stop at {stop}')\n\n# \u6807\u7b7e\u548c\u6807\u9898\nplt.xlabel(\"Time Intervals\")\nplt.ylabel(\"Price\")\nplt.title(\"Trade Entry Visualization\")\nplt.legend()\nplt.grid()\nplt.show()<\/code><\/pre>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
      \u8fd9\u8ba9\u6211\u66f4\u6709\u4fe1\u5fc3\u5728\u5b9e\u9645\u4ea4\u6613\u4e2d\u8fd0\u7528\u8fd9\u4e2a\u65b9\u6cd5\uff0c\u800c\u5b9e\u9645\u4e0a\u901a\u8fc7\u8fd9\u6b21\u4ea4\u6613\uff0cMSTR\u7684\u80a1\u4ef7\u4e00\u8def\u6da8\u5230 450 \u7f8e\u5143\uff0c\u6536\u76ca\u76f8\u5f53\u53ef\u89c2\u3002<\/p>\n\n\n\n
      \u7b2c\u516d\u6b65\uff1a\u9501\u5b9a\u5229\u6da6<\/strong><\/h2>\n\n\n\n
      \u6211\u7684\u5356\u51fa\u7b56\u7565\u8bb2\u7a76\u7a33\u624e\u7a33\u6253\u3002\u4ee5MSTR\u4e3a\u4f8b\uff0c\u5f53\u80a1\u4ef7\u6da8\u5230\u524d\u671f\u9ad8\u70b9\uff0c\u6bd4\u5982200\u7f8e\u5143\u3001300\u7f8e\u5143\u8fd9\u4e9b\u5173\u952e\u4f4d\u7f6e\u65f6\uff0c\u6211\u4f1a\u5206\u6279\u51cf\u4ed3\uff0c\u6bcf\u6b21\u5356\u51fa25%\u7684\u6301\u4ed3\u3002\u5bf9\u4e8e\u50cfSMCI\u8fd9\u6837\u521b\u5386\u53f2\u65b0\u9ad8\u7684\u80a1\u7968\uff0c\u6211\u4f1a\u7528\u6590\u6ce2\u90a3\u5951\u56de\u64a4\u4f4d\u4f5c\u4e3a\u53c2\u8003\uff0c\u968f\u7740\u80a1\u4ef7\u4e0a\u6da8\u9010\u6b65\u83b7\u5229\u4e86\u7ed3\u3002\u6bcf\u6b21\u5356\u51fa\u540e\uff0c\u6211\u90fd\u4f1a\u628a\u6b62\u635f\u4f4d\u4e0a\u79fb\u5230\u6210\u672c\u4ef7\uff0c\u786e\u4fdd\u5df2\u7ecf\u5230\u624b\u7684\u5229\u6da6\u4e0d\u4f1a\u56de\u5410\u3002<\/p>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
      \u8fd9\u5957\u65b9\u6cd5\u8ba9\u6211\u6293\u4f4f\u4e86\u4e0d\u5c11\u5927\u725b\u80a1\uff0c\u6bd4\u5982\u82f1\u4f1f\u8fbe\u4ece50\u7f8e\u5143\u6da8\u5230100\u591a\u7f8e\u5143\uff0c\u7279\u65af\u62c9\u4ece270\u7f8e\u5143\u6da8\u5230480\u7f8e\u5143\u3002\u5f53\u7136\uff0c\u6ca1\u6709\u5b8c\u7f8e\u7684\u7b56\u7565\uff0c\u6211\u4e5f\u72af\u8fc7\u9519\u8bef\u2014\u2014\u66fe\u7ecf\u8fc7\u65e9\u5356\u51fa\u4e86\u82f1\u4f1f\u8fbe\uff0c\u9519\u8fc7\u4e86\u540e\u9762\u7684\u7ffb\u500d\u884c\u60c5\u3002\u4f46\u53ea\u8981\u575a\u6301\u7eaa\u5f8b\uff0c\u957f\u671f\u6765\u770b\u6536\u76ca\u8fd8\u662f\u76f8\u5f53\u53ef\u89c2\u3002<\/p>\n\n\n\n
      \u6210\u4ea4\u91cf\u5206\u6790\u5f7b\u5e95\u6539\u53d8\u4e86\u6211\u7684\u4ea4\u6613\u65b9\u5f0f\u3002\u5b83\u8ba9\u6211\u6446\u8131\u4e86\u76f2\u76ee\u8ddf\u98ce\u7684\u574f\u4e60\u60ef\uff0c\u627e\u5230\u4e86\u53ef\u590d\u5236\u7684\u76c8\u5229\u6a21\u5f0f\u3002\u6210\u4ea4\u91cf\u4e0d\u662f\u7b80\u5355\u7684\u67f1\u72b6\u56fe\uff0c\u800c\u662f\u5e02\u573a\u60c5\u7eea\u7684\u6e29\u5ea6\u8ba1\uff0c\u662f\u4e3b\u529b\u52a8\u5411\u7684\u6307\u5357\u9488\u3002\u5efa\u8bae\u5927\u5bb6\u770b\u76d8\u65f6\u591a\u5173\u6ce8\u6210\u4ea4\u91cf\u53d8\u5316\uff0c\u7ed3\u5408\u4ef7\u683c\u8d70\u52bf\uff0c\u76f8\u4fe1\u4f60\u4e5f\u80fd\u53d1\u73b0\u5c5e\u4e8e\u81ea\u5df1\u7684\u4ea4\u6613\u673a\u4f1a\u3002<\/p>\n\n\n\n
      \u6700\u540e\uff0c\u6211\u60f3\u8bf4\u7684\u662f\uff0c\u80a1\u5e02\u6295\u8d44\u9700\u8981\u8010\u5fc3\u548c\u7eaa\u5f8b\u3002\u5e0c\u671b\u6211\u7684\u7ecf\u9a8c\u5bf9\u5927\u5bb6\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u5404\u4f4d\u6295\u8d44\u8005\u90fd\u80fd\u5728\u80a1\u5e02\u4e2d\u627e\u5230\u5c5e\u4e8e\u81ea\u5df1\u7684\u8d22\u5bcc\u5bc6\u7801\uff01\u8bb0\u4f4f\uff0c\u7a33\u624e\u7a33\u6253\uff0c\u65b9\u80fd\u884c\u7a33\u81f4\u8fdc\u3002<\/p>\n\n\n\n
      \u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
      \u672c\u6587\u5206\u4eab\u4e86\u901a\u8fc7\u5206\u6790\u6210\u4ea4\u91cf\u5728\u80a1\u5e02\u76c8\u5229\u7684\u7ecf\u9a8c\uff0c\u5b66\u4f1a\u770b\u6210\u4ea4\u91cf\u8ba9\u7092\u80a1\u80dc\u7387\u7ffb\u500d\u5e76\u8d5a\u5f97\u516d\u4f4d\u6570\u6536\u76ca\u3002<\/p>\n\n\n\n