{"id":1600,"date":"2024-11-06T07:00:00","date_gmt":"2024-11-05T23:00:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1600"},"modified":"2024-10-12T18:02:28","modified_gmt":"2024-10-12T10:02:28","slug":"%e7%94%a8-python-%e8%87%aa%e5%8a%a8%e6%a3%80%e6%b5%8b%e4%ba%a4%e6%98%93%e5%9b%be%e5%bd%a2%e6%80%81%e7%9a%84%e5%ae%9e%e7%94%a8%e6%8c%87%e5%8d%97%e8%af%b7%e6%9f%a5%e6%94%b6","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2024\/11\/06\/%e7%94%a8-python-%e8%87%aa%e5%8a%a8%e6%a3%80%e6%b5%8b%e4%ba%a4%e6%98%93%e5%9b%be%e5%bd%a2%e6%80%81%e7%9a%84%e5%ae%9e%e7%94%a8%e6%8c%87%e5%8d%97%e8%af%b7%e6%9f%a5%e6%94%b6\/","title":{"rendered":"\u7528 Python \u81ea\u52a8\u68c0\u6d4b\u4ea4\u6613\u56fe\u5f62\u6001\u7684\u5b9e\u7528\u6307\u5357\u8bf7\u67e5\u6536"},"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
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\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5229\u7528 Python \u548c EODHD API \u6765\u81ea\u52a8\u5316\u68c0\u6d4b\u80a1\u7968\u4ea4\u6613\u5e02\u573a\u4e2d\u7684\u8721\u70db\u56fe\u5f62\u6001<\/mark>\u3002\u6211\u4f1a\u89e3\u91ca\u4f5c\u4e3a\u4ea4\u6613\u7b56\u7565\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u8721\u70db\u56fe\u7684\u57fa\u672c\u6982\u5ff5\uff0c\u5e76\u8bf4\u660e\u8fd9\u4e9b\u6570\u636e\u5982\u4f55\u5728\u56fe\u8868\u4e0a\u5c55\u73b0\u5f62\u6001\uff0c\u6700\u540e\u8fd8\u4f1a\u4e3e\u4f8b\u8fdb\u884c\u5c55\u793a\u8bf4\u660e\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u4e3a\u4e86\u63a2\u8ba8\u4eca\u5929\u8fd9\u4e2a\u4e3b\u9898\uff0c\u6211\u4eec\u9700\u8981\u5148\u4ece\u57fa\u7840\u8bb2\u8d77\u3002\u591a\u6570\u4eba\u90fd\u719f\u6089\u4ea4\u6613\u56fe\u8868\uff0c\u5b83\u901a\u5e38\u7531\u7eff\u7ea2\u67f1\u5f62\u56fe\u7ec4\u6210\u7ebf\u5f62\u56fe\uff0c\u867d\u7b80\u5355\u5374\u8574\u542b\u5927\u91cf\u6570\u636e\u4fe1\u606f\u3002\u4e00\u6839\u8721\u70db\u4ee3\u8868\u4e00\u4e2a\u6570\u636e\u533a\u95f4\uff0c\u5982\u4e00\u5c0f\u65f6\u56fe\u8868\uff0c\u5305\u542b\u5f00\u76d8\u4ef7\u3001\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u548c\u6536\u76d8\u4ef7\u56db\u4e2a\u5173\u952e\u4fe1\u606f\uff0c\u7b80\u79f0 OHLC \u6570\u636e\u3002\u82e5\u6536\u76d8\u4ef7\u9ad8\u4e8e\u5f00\u76d8\u4ef7\uff0c\u8721\u70db\u56fe\u4e3a\u7eff\u8272\uff1b\u53cd\u4e4b\u5219\u4e3a\u7ea2\u8272\uff08\u7f8e\u80a1\u4e2d\u7684\u7eff\u6da8\u7ea2\u8dcc\u548c\u6211\u4eecA\u80a1\u662f\u53cd\u7684\uff0c\u9700\u8981\u5927\u5bb6\u6ce8\u610f\u8fd9\u4e2a\u7ec6\u8282\uff09\u3002\u4e0b\u56fe\u66f4\u6e05\u6670\u5730\u8bf4\u660e\u4e86\u8fd9\u4e00\u6982\u5ff5\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u4e3e\u4f8b\u8bf4\u660e\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u60a8\u53ef\u80fd\u4f1a\u6ce8\u610f\u5230\u8721\u70db\u5e8f\u5217\u4e2d\u51fa\u73b0\u7684\u7279\u5b9a\u5f62\u6001\uff0c\u5373\u8721\u70db\u56fe\u5f62\u6001\u3002\u4ea4\u6613\u7b56\u7565\u901a\u5e38\u5c31\u662f\u6839\u636e\u8fd9\u4e9b\u5f62\u6001\u5236\u5b9a\u7684\u3002<\/p>\n\n\n\n
\u4e00\u3001\u4f7f\u7528 EODHD API \u4e2d\u7684\u6807\u51c6\u666e\u5c14 500 \u6307\u6570\u8fdb\u884c\u6f14\u793a<\/strong><\/h3>\n\n\n\n
\u7b2c\u4e00\u6b65\u662f\u83b7\u53d6\u5206\u6790\u6240\u9700\u7684\u6570\u636e\u96c6\uff0c\u4e3a\u6b64\u6211\u4eec\u5c06\u4f7f\u7528\u5b98\u65b9\u7684 EODHD API Python<\/a>  \u5e93\u3002\u4e0b\u9762\u63d0\u4f9b\u7684\u4ee3\u7801\u7247\u6bb5\u53ef\u83b7\u53d6 720 \u5c0f\u65f6\uff0830 \u5929\uff09\u7684\u6570\u636e\u3002<\/p>\n\n\n\n
\u5b98\u65b9\u5e93\u5730\u5740\uff1ahttps:\/\/eodhd.com\/financial-apis\/python-financial-libraries-and-code-samples\/<\/a><\/p>\n\n\n\n
import config as cfg\nfrom eodhd import APIClient\n\napi = APIClient(cfg.API_KEY)\n\n\ndef get_ohlc_data():\n    df = api.get_historical_data(\"AAPL.US\", \"1h\", results=(24*30))\n    return df\n\n\nif __name__ == \"__main__\":\n    df = get_ohlc_data()\n    print(df)<\/code><\/code><\/pre>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u6211\u4eec\u5148\u6765\u770b\u4e00\u4e2a\u7b80\u5355\u660e\u4e86\u7684\u8721\u70db\u56fe\u5f62\u6001\uff0c\u5373\u9524\u5b50\u6216\u9524\u5b50\u5f62\u6001\u3002\u8fd9\u79cd\u5f62\u6001\u662f\u4e00\u79cd\u770b\u6da8\u53cd\u8f6c\u6307\u6807\uff0c\u901a\u5e38\u51fa\u73b0\u5728\u4e0b\u8dcc\u8d8b\u52bf\u7684\u672b\u7aef\u3002\u5b83\u7684\u7279\u70b9\u662f\u5f00\u76d8\u4ef7\u548c\u6536\u76d8\u4ef7\u5728\u9876\u90e8\u51e0\u4e4e\u5b8c\u5168\u76f8\u540c\uff0c\u52a0\u4e0a\u8f83\u957f\u7684\u4f4e\u4f4d\u706f\u82af\uff0c\u5176\u957f\u5ea6\u81f3\u5c11\u662f\u7a7a\u5934\u4e3b\u4f53\u7684\u4e24\u500d\u3002<\/p>\n\n\n\n
\u4e3a\u8bf4\u660e\u8fd9\u79cd\u5f62\u6001\uff0c\u73b0\u5c06\u8721\u70db\u56fe\u7684\u4fee\u6539\u7248\u4ecb\u7ecd\u5982\u4e0b\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u603b\u4e4b\uff0c\u9524\u5b50\u5f62\u6001\u662f\u770b\u6da8\u8f6c\u6298\u7684\u4fe1\u53f7\uff0c\u88ab\u89c6\u4e3a\u6e29\u548c\u7684\u53cd\u8f6c\u5f62\u6001\uff0c\u8868\u660e\u5e02\u573a\u65b9\u5411\u53ef\u80fd\u4ece\u4e0b\u884c\u8f6c\u5411\u4e0a\u884c\u3002<\/p>\n\n\n\n
\u6211\u4eec\u5f00\u53d1\u4e86\u4e00\u4e9b\u4ee3\u7801\uff0c\u4ee5\u4fbf\u5728\u6570\u636e\u96c6\u4e2d\u627e\u51fa\u8fd9\u4e9b\u8721\u70db\u3002<\/p>\n\n\n\n
import pandas as pd\nimport config as cfg\nfrom eodhd import APIClient\n\napi = APIClient(cfg.API_KEY)\n\n\ndef candle_hammer(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"* Candlestick Detected: Hammer (\"Weak - Reversal - Bullish Signal - Up\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"high\"] - df[\"low\"]) > 3 * (df[\"open\"] - df[\"close\"]))\n        & (((df[\"close\"] - df[\"low\"]) \/ (0.001 + df[\"high\"] - df[\"low\"])) > 0.6)\n        & (((df[\"open\"] - df[\"low\"]) \/ (0.001 + df[\"high\"] - df[\"low\"])) > 0.6)\n    )\n\n\ndef get_ohlc_data():\n    df = api.get_historical_data(\"AAPL.US\", \"1h\", results=(24*30))\n    return df\n\n\nif __name__ == \"__main__\":\n    df = get_ohlc_data()\n    df[\"hammer\"] = candle_hammer(df)\n    print(df)\n    print(df[df[\"hammer\"] == True])<\/code><\/code><\/pre>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u4e3a\u4e86\u4fbf\u4e8e\u6f14\u793a\uff0c\u6211\u4eec\u5c06\u6570\u636e\u96c6\u663e\u793a\u4e24\u6b21\u3002\u521d\u59cb\u6570\u636e\u96c6\u663e\u793a\u9524\u5f62\u8721\u70db\u7684\u8bc6\u522b\u3002\u968f\u540e\u7684\u6570\u636e\u96c6\u53ea\u663e\u793a\u68c0\u6d4b\u5230\u9524\u5b50\u5f62\u6001\u7684\u60c5\u51b5\u3002\u5728\u8fc7\u53bb\u7684 720 \u4e2a\u5c0f\u65f6\u4e2d\uff0c\u9524\u5b50\u5f62\u6001\u51fa\u73b0\u4e86 67 \u6b21\u3002<\/p>\n\n\n\n
\u5728\u6b64\u57fa\u7840\u4e0a\uff0c\u81ea\u7136\u4f1a\u51fa\u73b0\u4e00\u79cd\u76f8\u5173\u7684\u8721\u70db\u56fe\u5f62\u6001–\u5012\u9524\u5b50\u5f62\u6001\u3002\u4f5c\u4e3a\u9524\u5b50\u7684\u5bf9\u5e94\u5f62\u6001\uff0c\u5b83\u5728\u56fe\u5f62\u4e0a\u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u5012\u8f6c\u7684\u9524\u5b50\u3002\u5b83\u4ecd\u7136\u662f\u4e00\u4e2a\u770b\u6da8\u4fe1\u53f7\uff0c\u901a\u5e38\u51fa\u73b0\u5728\u4e0b\u8dcc\u8d8b\u52bf\u7684\u672b\u7aef\u3002<\/p>\n\n\n\n
def candle_inverted_hammer(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"* Candlestick Detected: Inverted Hammer (\"Weak - Continuation - Bullish Pattern - Up\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"high\"] - df[\"low\"]) > 3 * (df[\"open\"] - df[\"close\"]))\n        & ((df[\"high\"] - df[\"close\"]) \/ (0.001 + df[\"high\"] - df[\"low\"]) > 0.6)\n        & ((df[\"high\"] - df[\"open\"]) \/ (0.001 + df[\"high\"] - df[\"low\"]) > 0.6)\n    )<\/code><\/code><\/pre>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n

\u4e8c\u3001\u70db\u53f0\u5f62\u6001<\/strong><\/h3>\n\n\n\n
\u8721\u70db\u56fe\u5f62\u6001\u57fa\u672c\u4e0a\u53ef\u5206\u4e3a\u4e24\u7c7b\uff1a<\/p>\n\n\n\n
    \n
  1. \u770b\u6da8\u6216\u770b\u8dcc<\/li>\n\n\n\n
  2. \u72b9\u8c6b\u4e0d\u51b3\/\u4e2d\u6027\u3001\u5f31\u3001\u53ef\u9760\u6216\u5f3a<\/li>\n<\/ol>\n\n\n\n
    \u4e0b\u9762\u7b80\u8981\u4ecb\u7ecd\u4e00\u4e0b\u6d41\u884c\u7684\u8721\u70db\u56fe\u5f62\u6001\uff1a<\/p>\n\n\n\n
    2.1 \u72b9\u8c6b\u4e0d\u51b3\/\u4e2d\u6027<\/strong>\uff08Indecision \/ Neutral\uff09<\/p>\n\n\n\n
      \n
    • Doji<\/li>\n<\/ul>\n\n\n\n
      2.2 \u5f31<\/strong>\uff08Weak\uff09<\/p>\n\n\n\n
        \n
      • \u9524\u5b50\uff08\u770b\u6da8\uff09<\/li>\n\n\n\n
      • \u5012\u9524\uff08\u770b\u6da8\uff09<\/li>\n\n\n\n
      • Shooting Star \uff08\u770b\u8dcc\uff09<\/li>\n<\/ul>\n\n\n\n
        2.3 \u53ef\u9760<\/strong>\uff08Reliable\uff09<\/p>\n\n\n\n
          \n
        • Hanging Man (\u770b\u8dcc)<\/li>\n\n\n\n
        • Three Line Strike (\u770b\u6da8)<\/li>\n\n\n\n
        • Two Black Gapping (\u770b\u8dcc)<\/li>\n\n\n\n
        • Abandoned Baby (\u770b\u6da8)<\/li>\n\n\n\n
        • Morning Doji Star (\u770b\u6da8)<\/li>\n\n\n\n
        • Evening Doji Star (\u770b\u8dcc)<\/li>\n<\/ul>\n\n\n\n
          2.4<\/strong> \u5f3a<\/strong>\uff08Strong\uff09<\/p>\n\n\n\n
            \n
          • Three White Soldiers \uff08\u4e09\u4e2a\u767d\u5175\uff09(\u770b\u6da8)<\/li>\n\n\n\n
          • Three Black Crows\uff08\u4e09\u53ea\u9ed1\u9e26\uff09 (\u770b\u8dcc)<\/li>\n\n\n\n
          • Morning Star (\u770b\u6da8)<\/li>\n\n\n\n
          • Evening Star (\u770b\u8dcc)<\/li>\n<\/ul>\n\n\n\n
            \u6211\u4eec\u5c06\u8fd9\u4e9b\u70db\u53f0\u5f62\u6001\u7f16\u7801\u6210 Python \u51fd\u6570\uff0c\u5e76\u4f7f\u7528 Numpy \u6765\u5904\u7406\u4e00\u4e9b\u66f4\u590d\u6742\u7684\u5f62\u6001\u3002<\/p>\n\n\n\n
            import numpy as np\n\ndef candle_hammer(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"* Candlestick Detected: Hammer (\"Weak - Reversal - Bullish Signal - Up\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"high\"] - df[\"low\"]) > 3 * (df[\"open\"] - df[\"close\"]))\n        & (((df[\"close\"] - df[\"low\"]) \/ (0.001 + df[\"high\"] - df[\"low\"])) > 0.6)\n        & (((df[\"open\"] - df[\"low\"]) \/ (0.001 + df[\"high\"] - df[\"low\"])) > 0.6)\n    )\n\n\ndef candle_inverted_hammer(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"* Candlestick Detected: Inverted Hammer (\"Weak - Reversal - Bullish Pattern - Up\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"high\"] - df[\"low\"]) > 3 * (df[\"open\"] - df[\"close\"]))\n        & ((df[\"high\"] - df[\"close\"]) \/ (0.001 + df[\"high\"] - df[\"low\"]) > 0.6)\n        & ((df[\"high\"] - df[\"open\"]) \/ (0.001 + df[\"high\"] - df[\"low\"]) > 0.6)\n    )\n\n\ndef candle_shooting_star(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"* Candlestick Detected: Shooting Star (\"Weak - Reversal - Bearish Pattern - Down\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"open\"].shift(1) < df[\"close\"].shift(1)) & (df[\"close\"].shift(1) < df[\"open\"]))\n        & (df[\"high\"] - np.maximum(df[\"open\"], df[\"close\"]) >= (abs(df[\"open\"] - df[\"close\"]) * 3))\n        & ((np.minimum(df[\"close\"], df[\"open\"]) - df[\"low\"]) <= abs(df[\"open\"] - df[\"close\"]))\n    )\n\n\ndef candle_hanging_man(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"* Candlestick Detected: Hanging Man (\"Weak - Reliable - Bearish Pattern - Down\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"high\"] - df[\"low\"]) > (4 * (df[\"open\"] - df[\"close\"])))\n        & (((df[\"close\"] - df[\"low\"]) \/ (0.001 + df[\"high\"] - df[\"low\"])) >= 0.75)\n        & (((df[\"open\"] - df[\"low\"]) \/ (0.001 + df[\"high\"] - df[\"low\"])) >= 0.75)\n        & (df[\"high\"].shift(1) < df[\"open\"])\n        & (df[\"high\"].shift(2) < df[\"open\"])\n    )\n\n\ndef candle_three_white_soldiers(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"*** Candlestick Detected: Three White Soldiers (\"Strong - Reversal - Bullish Pattern - Up\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"open\"] > df[\"open\"].shift(1)) & (df[\"open\"] < df[\"close\"].shift(1)))\n        & (df[\"close\"] > df[\"high\"].shift(1))\n        & (df[\"high\"] - np.maximum(df[\"open\"], df[\"close\"]) < (abs(df[\"open\"] - df[\"close\"])))\n        & ((df[\"open\"].shift(1) > df[\"open\"].shift(2)) & (df[\"open\"].shift(1) < df[\"close\"].shift(2)))\n        & (df[\"close\"].shift(1) > df[\"high\"].shift(2))\n        & (\n            df[\"high\"].shift(1) - np.maximum(df[\"open\"].shift(1), df[\"close\"].shift(1))\n            < (abs(df[\"open\"].shift(1) - df[\"close\"].shift(1)))\n        )\n    )\n\n\ndef candle_three_black_crows(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"* Candlestick Detected: Three Black Crows (\"Strong - Reversal - Bearish Pattern - Down\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"open\"] < df[\"open\"].shift(1)) & (df[\"open\"] > df[\"close\"].shift(1)))\n        & (df[\"close\"] < df[\"low\"].shift(1))\n        & (df[\"low\"] - np.maximum(df[\"open\"], df[\"close\"]) < (abs(df[\"open\"] - df[\"close\"])))\n        & ((df[\"open\"].shift(1) < df[\"open\"].shift(2)) & (df[\"open\"].shift(1) > df[\"close\"].shift(2)))\n        & (df[\"close\"].shift(1) < df[\"low\"].shift(2))\n        & (\n            df[\"low\"].shift(1) - np.maximum(df[\"open\"].shift(1), df[\"close\"].shift(1))\n            < (abs(df[\"open\"].shift(1) - df[\"close\"].shift(1)))\n        )\n    )\n\n\ndef candle_doji(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"! Candlestick Detected: Doji (\"Indecision \/ Neutral\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((abs(df[\"close\"] - df[\"open\"]) \/ (df[\"high\"] - df[\"low\"])) < 0.1)\n        & ((df[\"high\"] - np.maximum(df[\"close\"], df[\"open\"])) > (3 * abs(df[\"close\"] - df[\"open\"])))\n        & ((np.minimum(df[\"close\"], df[\"open\"]) - df[\"low\"]) > (3 * abs(df[\"close\"] - df[\"open\"])))\n    )\n\n\ndef candle_three_line_strike(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"** Candlestick Detected: Three Line Strike (\"Reliable - Reversal - Bullish Pattern - Up\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"open\"].shift(1) < df[\"open\"].shift(2)) & (df[\"open\"].shift(1) > df[\"close\"].shift(2)))\n        & (df[\"close\"].shift(1) < df[\"low\"].shift(2))\n        & (\n            df[\"low\"].shift(1) - np.maximum(df[\"open\"].shift(1), df[\"close\"].shift(1))\n            < (abs(df[\"open\"].shift(1) - df[\"close\"].shift(1)))\n        )\n        & ((df[\"open\"].shift(2) < df[\"open\"].shift(3)) & (df[\"open\"].shift(2) > df[\"close\"].shift(3)))\n        & (df[\"close\"].shift(2) < df[\"low\"].shift(3))\n        & (\n            df[\"low\"].shift(2) - np.maximum(df[\"open\"].shift(2), df[\"close\"].shift(2))\n            < (abs(df[\"open\"].shift(2) - df[\"close\"].shift(2)))\n        )\n        & ((df[\"open\"] < df[\"low\"].shift(1)) & (df[\"close\"] > df[\"high\"].shift(3)))\n    )\n\n\ndef candle_two_black_gapping(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"*** Candlestick Detected: Two Black Gapping (\"Reliable - Reversal - Bearish Pattern - Down\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        ((df[\"open\"] < df[\"open\"].shift(1)) & (df[\"open\"] > df[\"close\"].shift(1)))\n        & (df[\"close\"] < df[\"low\"].shift(1))\n        & (df[\"low\"] - np.maximum(df[\"open\"], df[\"close\"]) < (abs(df[\"open\"] - df[\"close\"])))\n        & (df[\"high\"].shift(1) < df[\"low\"].shift(2))\n    )\n\n\ndef candle_morning_star(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"*** Candlestick Detected: Morning Star (\"Strong - Reversal - Bullish Pattern - Up\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        (np.maximum(df[\"open\"].shift(1), df[\"close\"].shift(1)) < df[\"close\"].shift(2)) & (df[\"close\"].shift(2) < df[\"open\"].shift(2))\n    ) & ((df[\"close\"] > df[\"open\"]) & (df[\"open\"] > np.maximum(df[\"open\"].shift(1), df[\"close\"].shift(1))))\n\n\ndef candle_evening_star(df: pd.DataFrame = None) -> np.ndarray:\n    \"\"\"*** Candlestick Detected: Evening Star (\"Strong - Reversal - Bearish Pattern - Down\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        (np.minimum(df[\"open\"].shift(1), df[\"close\"].shift(1)) > df[\"close\"].shift(2)) & (df[\"close\"].shift(2) > df[\"open\"].shift(2))\n    ) & ((df[\"close\"] < df[\"open\"]) & (df[\"open\"] < np.minimum(df[\"open\"].shift(1), df[\"close\"].shift(1))))\n\n\ndef candle_abandoned_baby(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"** Candlestick Detected: Abandoned Baby (\"Reliable - Reversal - Bullish Pattern - Up\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (\n        (df[\"open\"] < df[\"close\"])\n        & (df[\"high\"].shift(1) < df[\"low\"])\n        & (df[\"open\"].shift(2) > df[\"close\"].shift(2))\n        & (df[\"high\"].shift(1) < df[\"low\"].shift(2))\n    )\n\n\ndef candle_morning_doji_star(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"** Candlestick Detected: Morning Doji Star (\"Reliable - Reversal - Bullish Pattern - Up\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (df[\"close\"].shift(2) < df[\"open\"].shift(2)) & (\n        abs(df[\"close\"].shift(2) - df[\"open\"].shift(2)) \/ (df[\"high\"].shift(2) - df[\"low\"].shift(2)) >= 0.7\n    ) & (abs(df[\"close\"].shift(1) - df[\"open\"].shift(1)) \/ (df[\"high\"].shift(1) - df[\"low\"].shift(1)) < 0.1) & (\n        df[\"close\"] > df[\"open\"]\n    ) & (\n        abs(df[\"close\"] - df[\"open\"]) \/ (df[\"high\"] - df[\"low\"]) >= 0.7\n    ) & (\n        df[\"close\"].shift(2) > df[\"close\"].shift(1)\n    ) & (\n        df[\"close\"].shift(2) > df[\"open\"].shift(1)\n    ) & (\n        df[\"close\"].shift(1) < df[\"open\"]\n    ) & (\n        df[\"open\"].shift(1) < df[\"open\"]\n    ) & (\n        df[\"close\"] > df[\"close\"].shift(2)\n    ) & (\n        (df[\"high\"].shift(1) - np.maximum(df[\"close\"].shift(1), df[\"open\"].shift(1)))\n        > (3 * abs(df[\"close\"].shift(1) - df[\"open\"].shift(1)))\n    ) & (\n        np.minimum(df[\"close\"].shift(1), df[\"open\"].shift(1)) - df[\"low\"].shift(1)\n    ) > (\n        3 * abs(df[\"close\"].shift(1) - df[\"open\"].shift(1))\n    )\n\n\ndef candle_evening_doji_star(df: pd.DataFrame = None) -> pd.Series:\n    \"\"\"** Candlestick Detected: Evening Doji Star (\"Reliable - Reversal - Bearish Pattern - Down\")\"\"\"\n\n    # Fill NaN values with 0\n    df = df.fillna(0)\n\n    return (df[\"close\"].shift(2) > df[\"open\"].shift(2)) & (\n        abs(df[\"close\"].shift(2) - df[\"open\"].shift(2)) \/ (df[\"high\"].shift(2) - df[\"low\"].shift(2)) >= 0.7\n    ) & (abs(df[\"close\"].shift(1) - df[\"open\"].shift(1)) \/ (df[\"high\"].shift(1) - df[\"low\"].shift(1)) < 0.1) & (\n        df[\"close\"] < df[\"open\"]\n    ) & (\n        abs(df[\"close\"] - df[\"open\"]) \/ (df[\"high\"] - df[\"low\"]) >= 0.7\n    ) & (\n        df[\"close\"].shift(2) < df[\"close\"].shift(1)\n    ) & (\n        df[\"close\"].shift(2) < df[\"open\"].shift(1)\n    ) & (\n        df[\"close\"].shift(1) > df[\"open\"]\n    ) & (\n        df[\"open\"].shift(1) > df[\"open\"]\n    ) & (\n        df[\"close\"] < df[\"close\"].shift(2)\n    ) & (\n        (df[\"high\"].shift(1) - np.maximum(df[\"close\"].shift(1), df[\"open\"].shift(1)))\n        > (3 * abs(df[\"close\"].shift(1) - df[\"open\"].shift(1)))\n    ) & (\n        np.minimum(df[\"close\"].shift(1), df[\"open\"].shift(1)) - df[\"low\"].shift(1)\n    ) > (\n        3 * abs(df[\"close\"].shift(1) - df[\"open\"].shift(1))\n    )<\/code><\/code><\/pre>\n\n\n\n
            \u63a2\u7d22\u6211\u4eec\u7684\u6570\u636e\u96c6\uff0c\u770b\u770b\u80fd\u5426\u8bc6\u522b\u51fa\u4efb\u4f55\u5f3a\u52bf\u8721\u70db\u56fe\u5f62\u6001…<\/p>\n\n\n\n
            if __name__ == \"__main__\":\n    df = get_ohlc_data()\n\n    df[\"three_white_soldiers\"] = candle_three_white_soldiers(df)\n    df[\"three_black_crows\"] = candle_three_black_crows(df)\n    df[\"morning_star\"] = candle_morning_star(df)\n    df[\"evening_star\"] = candle_evening_star(df)\n\n    print(df[(df[\"three_white_soldiers\"] == True) | (df[\"three_black_crows\"] == True) | (df[\"morning_star\"] == True) | (df[\"evening_star\"] == True)])<\/code><\/code><\/pre>\n\n\n\n
            \"\"\/<\/figure>\n\n\n\n
            \u4e8b\u5b9e\u4e0a\uff0c\u5728\u8fc7\u53bb\u7684 30 \u5929\u91cc\uff0c\u5f3a\u52bf\u5f62\u6001\u5df2\u7ecf\u51fa\u73b0\u4e86\u5f88\u591a\u6b21\u3002\u4ece\u5206\u4eab\u7684\u6570\u636e\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\u8721\u70db\u56fe\u5f62\u6001\u51fa\u73b0\u7684\u65f6\u95f4\u548c\u7c7b\u578b\u3002\u4e00\u4e2a\u6709\u8da3\u7684\u7ec3\u4e60\u662f\u67e5\u770b\u6807\u51c6\u666e\u5c14 500 \u6307\u6570\u5c0f\u65f6\u56fe\u4e2d\u7684\u8fd9\u4e9b\u7279\u5b9a\u65f6\u95f4\u6bb5\uff0c\u5e76\u5c1d\u8bd5\u627e\u51fa\u8fd9\u4e9b\u5f62\u6001\u3002<\/p>\n\n\n\n
            \u89c2\u70b9\u56de\u987e<\/strong><\/h3>\n\n\n\n
            \u8bf7\u5927\u5bb6\u8bb0\u4f4f\uff0c\u6ca1\u6709\u4efb\u4f55\u4e00\u79cd\u7b56\u7565\u80fd\u4fdd\u8bc1\u4ea4\u6613\u6210\u529f\u3002\u4e0e\u5176\u4ed6\u4ea4\u6613\u65b9\u6cd5\u4e00\u6837\uff0c\u57fa\u4e8e\u8721\u70db\u56fe\u5f62\u6001\u7684\u7b56\u7565\u7684\u53ef\u884c\u6027\u53d7\u5e02\u573a\u52a8\u6001\u3001\u6d41\u52a8\u6027\u548c\u6ce2\u52a8\u6027\u7684\u5f71\u54cd\u3002\u56e0\u6b64\uff0c\u5efa\u8bae\u4ea4\u6613\u8005\u5c06\u8fd9\u4e9b\u7b56\u7565\u7eb3\u5165\u66f4\u5168\u9762\u3001\u66f4\u591a\u6837\u5316\u7684\u4ea4\u6613\u65b9\u6cd5\u4e2d\uff0c\u5e76\u901a\u8fc7\u5176\u4ed6\u5206\u6790\u548c\u5de5\u5177\u5bf9\u5176\u8fdb\u884c\u5f3a\u5316\uff0c\u4ee5\u83b7\u5f97\u66f4\u5168\u9762\u7684\u89c6\u89d2\u3002<\/p>\n\n\n\n