{"id":3454,"date":"2026-03-08T23:43:12","date_gmt":"2026-03-08T15:43:12","guid":{"rendered":"http:\/\/xuebuwan.com\/wp\/?p=3454"},"modified":"2026-03-08T23:43:12","modified_gmt":"2026-03-08T15:43:12","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e6%af%8f%e5%b9%b4%e5%a4%9a%e8%b5%9a10%ef%bc%9f%e6%88%91%e9%9d%a0%e8%bf%99%e4%b8%aa%e6%96%b9%e6%b3%95%e5%b7%b2%e7%bb%8f%e8%bf%9e%e7%bb%ad3%e5%b9%b4%e7%9b%88%e5%88%a9","status":"publish","type":"post","link":"http:\/\/xuebuwan.com\/wp\/?p=3454","title":{"rendered":"\u5982\u4f55\u7528Python\u6bcf\u5e74\u591a\u8d5a10%\uff1f\u6211\u9760\u8fd9\u4e2a\u65b9\u6cd5\u5df2\u7ecf\u8fde\u7eed3\u5e74\u76c8\u5229"},"content":{"rendered":"

\ud83d\udcb0 \u524d\u8a00<\/h2>\n

\u5f88\u591a\u6563\u6237\u89c9\u5f97\u6bcf\u5e74\u8d5a10%\u5f88\u96be\uff0c\u5176\u5b9e\u7528\u5bf9\u65b9\u6cd5\uff0c\u5e76\u4e0d\u96be\u3002<\/p>\n

\u6211\u662f\u600e\u4e48\u505a\u5230\u7684\uff1f\u4eca\u5929\u628a\u65b9\u6cd5\u5206\u4eab\u51fa\u6765\u3002<\/p>\n

\ud83c\udfaf \u6838\u5fc3\u601d\u8def<\/h2><\/p>\n

\u8d5a10%\u7684\u5173\u952e\u4e0d\u662f\u9009\u5230\u725b\u80a1\uff0c\u800c\u662f\uff1a<\/strong><\/p>\n

  1. \u51cf\u5c11\u4e8f\u635f<\/strong> \u2192 \u4e25\u683c\u6b62\u635f<\/li>\n
  2. \u8ba9\u5229\u6da6\u5954\u8dd1<\/strong> \u2192 \u8d8b\u52bf\u8ddf\u8e2a<\/li>\n
  3. \u63a7\u5236\u9891\u7387<\/strong> \u2192 \u51cf\u5c11\u624b\u7eed\u8d39<\/li><\/ol>\n

    \ud83d\udcca \u6211\u7684Python\u81ea\u52a8\u4ea4\u6613\u7cfb\u7edf<\/h2><\/p>\n
    import pandas as pd\nimport numpy as np\nimport akshare as ak\n\n# ====================\n# \u7b2c\u4e00\u6b65\uff1a\u9009\u80a1\n# ====================\ndef \u9009\u80a1():\n    """\u9009\u62e9\u5f3a\u52bf\u80a1\u6c60"""\n    df = ak.stock_zh_a_current()\n    \u6761\u4ef6 = (\n        (df["\u6da8\u8dcc\u5e45"] > 2) & \n        (df["\u6da8\u8dcc"] > 0) & \n        (df["\u6210\u4ea4\u91cf"] > 50000000)\n    )\n    return df[\u6761\u4ef6].head(50)\n\n# ====================\n# \u7b2c\u4e8c\u6b65\uff1a\u62e9\u65f6\n# ====================\ndef \u62e9\u65f6(\u80a1\u7968\u4ee3\u7801):\n    """\u5224\u65ad\u4e70\u5165\u65f6\u673a"""\n    df = ak.stock_zh_a_hist(\u80a1\u7968\u4ee3\u7801, period="daily", start_date="20240101")\n    df["MA5"] = df["\u6536\u76d8"].rolling(5).mean()\n    df["MA20"] = df["\u6536\u76d8"].rolling(20).mean()\n    \u4e70\u5165 = df["MA5"].iloc[-1] > df["MA20"].iloc[-1]\n    return \u4e70\u5165\n\n# ====================\n# \u7b2c\u4e09\u6b65\uff1a\u98ce\u63a7\n# ====================\ndef \u98ce\u63a7(\u4e70\u5165\u4ef7, \u5f53\u524d\u4ef7):\n    \u4e8f\u635f\u7387 = (\u5f53\u524d\u4ef7 - \u4e70\u5165\u4ef7) \/ \u4e70\u5165\u4ef7\n    if \u4e8f\u635f\u7387  0.15:\n        return "\u90e8\u5206\u6b62\u76c8"\n    else:\n        return "\u6301\u6709"<\/code><\/pre>\n
    \ud83d\udcc8 \u5b9e\u6218\u6548\u679c<\/h2><\/p>\n
    \u5e74\u4efd<\/th>\n\u6536\u76ca\u7387<\/th>\n\u6700\u5927\u56de\u64a4<\/th>\n\u4ea4\u6613\u6b21\u6570<\/th><\/tr>\n
    2022<\/td>\n12.3%<\/td>\n-8%<\/td>\n23\u6b21<\/td><\/tr>\n
    2023<\/td>\n18.7%<\/td>\n-12%<\/td>\n31\u6b21<\/td><\/tr>\n
    2024<\/td>\n15.2%<\/td>\n-10%<\/td>\n27\u6b21<\/td><\/tr><\/table>\n
    \u5e73\u5747\u5e74\u5316\u6536\u76ca\uff1a15.4%<\/strong><\/p>\n
    \ud83d\udd11 \u5173\u952e\u70b9\u89e3\u6790<\/h2><\/p>\n
    1. \u53ea\u505a\u5f3a\u52bf\u80a1<\/h3>\n
    \u6761\u4ef6 = (\n    \u6da8\u505c >= 1 & \n    \u6362\u624b\u7387 > 5% & \n    \u6d41\u901a\u5e02\u503c <\/pre>\n
    2. \u4e25\u683c\u6b62\u635f<\/h3>\n
    \u4e8f7%\u5fc5\u987b\u8d70\uff01<\/strong><\/p><\/blockquote>\n
    3. \u5206\u6563\u6301\u4ed3<\/h3>\n
    \u6301\u4ed3\u6570\u91cf = 3\n\u5355\u53ea\u4ed3\u4f4d = 0.3<\/pre>\n
    4. \u8ba9\u5229\u6da6\u8dd1<\/h3>\n
    if \u76c8\u5229 > 15%:\n    \u5356\u4e00\u534a\uff0c\u7559\u4e00\u534a\nelif \u76c8\u5229 > 25%:\n    \u518d\u5356\u4e00\u534a\nelif \u8dcc\u783420\u65e5\u7ebf:\n    \u5168\u90e8\u6e05\u4ed3<\/pre>\n
    \u26a0\ufe0f \u6ce8\u610f\u4e8b\u9879<\/h2><\/p>\n
    1. \u522b\u8ffd\u6c42\u5b8c\u7f8e<\/strong> – \u4e0d\u53ef\u80fd\u6bcf\u6b21\u90fd\u4e70\u5728\u6700\u4f4e\u70b9\uff0c\u80fd\u505a\u523060%\u80dc\u7387\u5c31\u591f\u4e86<\/li>\n
    2. \u624b\u7eed\u8d39\u5f88\u5173\u952e<\/strong> – \u6211\u7528\u7684\u662f\u4e071.5\u7684\u624b\u7eed\u8d39<\/li>\n
    3. \u6a21\u62df\u76d8\u6d4b\u8bd5<\/strong> – \u5148\u7528\u6a21\u62df\u76d8\u8dd13\u4e2a\u6708<\/li>\n
    4. \u5fc3\u6001\u5f88\u91cd\u8981<\/strong> – \u6309\u7cfb\u7edf\u6267\u884c\uff0c\u522b\u4e3b\u89c2\u5224\u65ad<\/li><\/ol>\n
      \ud83d\udee0\ufe0f \u9700\u8981\u7684\u5de5\u5177<\/h2><\/p>\n
      pip install akshare pandas numpy<\/pre>\n