{"id":2434,"date":"2025-07-21T07:26:00","date_gmt":"2025-07-20T23:26:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2434"},"modified":"2025-07-15T16:29:17","modified_gmt":"2025-07-15T08:29:17","slug":"%e6%88%91%e6%8a%8a%e5%88%86%e5%9e%8b%e6%94%b9%e6%88%90%e5%b0%8f%e5%b0%96%e5%b0%96%ef%bc%8c%e4%b8%80%e5%b9%b4%e6%92%b8%e5%87%ba-95-%e6%94%b6%e7%9b%8a%ef%bc%88","status":"publish","type":"post","link":"https:\/\/www.laoyulaoyu.com\/index.php\/2025\/07\/21\/%e6%88%91%e6%8a%8a%e5%88%86%e5%9e%8b%e6%94%b9%e6%88%90%e5%b0%8f%e5%b0%96%e5%b0%96%ef%bc%8c%e4%b8%80%e5%b9%b4%e6%92%b8%e5%87%ba-95-%e6%94%b6%e7%9b%8a%ef%bc%88\/","title":{"rendered":"\u6211\u628a\u201c\u5206\u578b\u201d\u6539\u6210\u201c\u5c0f\u5c16\u5c16\u201d\uff0c\u4e00\u5e74\u64b8\u51fa 95%+ \u6536\u76ca\uff08\u9644\u6e90\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>\u53bb\u5e74\u6211\u6539\u9020\u4e86\u4f20\u5947\u4ea4\u6613\u5458\u6bd4\u5c14\u00b7\u5a01\u5ec9\u59c6\u65af\u7684\u5206\u5f62\u7b56\u7565\uff0c\u53d6\u6d88\u6ede\u540e\u786e\u8ba4\u73af\u8282\u76f4\u63a5\u5165\u573a\uff0c\u56de\u6d4bMeta\u80a1\u79681\u5e74\u4ea4\u661339\u7b14\uff0c\u80dc\u738795.51%\u3002<\/mark>\u73b0\u5728\u624b\u628a\u624b\u6559\u4f60\u7528Python\u5b9e\u73b0\u8fd9\u4e2a\u7b56\u7565\uff0c\u6e90\u7801\u5168\u7ed9\uff0c\u5c0f\u767d\u4e5f\u80fd\u8dd1\u901a\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u4e00\u3001\u5206\u5f62\u4ea4\u6613\u662f\u4ec0\u4e48\uff1f<\/strong><\/h2>\n\n\n\n
\u5206\u5f62\u4ea4\u6613<\/strong>\uff01\u542c\u8d77\u6765\u6709\u70b9\u9ad8\u5927\u4e0a\uff1f\u6211\u4f1a\u7528\u6700\u63a5\u5730\u6c14\u7684\u8bdd\uff0c\u628a\u8fd9\u4e2a\u80fd\u8ba9\u4f60\u5728\u80a1\u5e02\u5feb\u4eba\u4e00\u6b65\u7684\u6280\u5de7\u8bb2\u5f97\u660e\u660e\u767d\u767d\uff01\ud83d\ude0e<\/p>\n\n\n\n
1980\u5e74\u4ee3\uff0c\u80a1\u5e02\u8fd8\u662f\u4e2a\u201c\u753b\u7ebf\u770b\u652f\u6491\u4f4d\u201d\u7684\u5e74\u4ee3\uff0c\u4e00\u4e2a\u53eb\u6bd4\u5c14\u00b7\u5a01\u5ec9\u59c6\u65af\u7684\u5bb6\u4f19\u7ad9\u51fa\u6765\u8bf4\uff1a\u201c\u5e02\u573a\u4e0d\u662f\u4e71\u7684\uff0c\u5b83\u6709\u89c4\u5f8b\uff01\u201d\u4ed6\u53d1\u660e\u4e86\u5206\u5f62\u4ea4\u6613\u6cd5\uff0c\u5f7b\u5e95\u6539\u53d8\u4e86\u6211\u4eec\u770b\u4ef7\u683c\u7684\u65b9\u6cd5\u3002\u4eca\u5929\uff0c\u6211\u8981\u5e26\u4f60\u628a\u8fd9\u4e2a\u65b9\u6cd5\u5347\u7ea7\uff0c\u53d8\u6210\u666e\u901a\u4eba\u90fd\u80fd\u7528\u7684\u8d5a\u94b1\u5229\u5668\uff01<\/p>\n\n\n\n
\u5206\u5f62\u4ea4\u6613\u7684\u6838\u5fc3\uff0c\u5c31\u662f\u627e\u5230\u5e02\u573a\u201c\u8f6c\u6298\u70b9\u201d\u3002\u4ec0\u4e48\u662f\u8f6c\u6298\u70b9\uff1f\u5c31\u662f\u4ef7\u683c\u4ece\u6da8\u5230\u8dcc\uff0c\u6216\u4ece\u8dcc\u5230\u6da8\u7684\u5173\u952e\u65f6\u523b\u3002\u6bd4\u5c14\u00b7\u5a01\u5ec9\u59c6\u65af\u53d1\u73b0\uff0c\u5e02\u573a\u4ef7\u683c\u4f1a\u5f62\u6210\u4e00\u79cd\u7279\u6b8a\u7684\u201c\u5206\u5f62\u201d\u5f62\u6001\uff0c\u5e2e\u6211\u4eec\u627e\u5230\u8fd9\u4e9b\u70b9\u3002<\/p>\n\n\n\n
\u5206\u5f62\u957f\u5565\u6837\uff1f<\/strong><\/h3>\n\n\n\n
    \n
  • \u770b\u6da8\u5206\u5f62<\/strong>\uff1a\u4e2d\u95f4\u4e00\u6839K\u7ebf\u7684\u4f4e\u70b9\uff0c\u6bd4\u5de6\u53f3\u5404\u4e24\u6839K\u7ebf\u7684\u4f4e\u70b9\u90fd\u4f4e\uff0c\u50cf\u4e2a\u201cV\u201d\u5b57\uff0c\u8bf4\u660e\u8fd9\u91cc\u53ef\u80fd\u662f\u4ef7\u683c\u7684\u5e95\u90e8\u3002<\/li>\n\n\n\n
  • \u770b\u8dcc\u5206\u5f62<\/strong>\uff1a\u4e2d\u95f4\u4e00\u6839K\u7ebf\u7684\u9ad8\u70b9\uff0c\u6bd4\u5de6\u53f3\u5404\u4e24\u6839K\u7ebf\u7684\u9ad8\u70b9\u90fd\u9ad8\uff0c\u50cf\u4e2a\u5012\u201cV\u201d\uff0c\u8bf4\u660e\u8fd9\u91cc\u53ef\u80fd\u662f\u4ef7\u683c\u7684\u9876\u90e8\u3002<\/li>\n<\/ul>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u4e0a\u9762\u8001\u5916\u7684\u201cfractal\u201d\u592a\u96be\u8bb0\uff0c\u6211\u76f4\u63a5\u53eb\u5b83\u201c\u5c0f\u5c16\u5c16<\/strong>\u201d\uff1a<\/p>\n\n\n\n
      \n
    • \u5e95\u5c16\u5c16\uff1a\u4e2d\u95f4\u90a3\u6839 K \u7ebf\u7684\u6700\u4f4e\u4ef7\u6bd4\u5de6\u53f3\u5404 2 \u6839\u90fd\u4f4e \u2192 \u53ef\u80fd\u5411\u4e0a\u62d0\u5f2f\u3002<\/li>\n\n\n\n
    • \u9876\u5c16\u5c16\uff1a\u4e2d\u95f4\u90a3\u6839 K \u7ebf\u7684\u6700\u9ad8\u4ef7\u6bd4\u5de6\u53f3\u5404 2 \u6839\u90fd\u9ad8 \u2192 \u53ef\u80fd\u5411\u4e0b\u62d0\u5f2f\u3002<\/li>\n<\/ul>\n\n\n\n
      \u4e00\u53e5\u8bdd\uff1a\u770b\u5230\u5c16\u5c16\uff0c\u5c31\u8981\u6253\u8d77\u7cbe\u795e\u3002<\/p>\n\n\n\n
      \u4e8c\u3001\u5347\u7ea7\u7248\u5206\u578b\u7b56\u7565<\/strong><\/h2>\n\n\n\n

      \u8001\u65b9\u6cd5\u7684\u95ee\u9898\uff1a\u603b\u6162\u534a\u62cd<\/strong><\/h3>\n\n\n\n
      \u6bd4\u5c14\u00b7\u5a01\u5ec9\u59c6\u65af\u7684\u539f\u59cb\u65b9\u6cd5\u5f88\u7b80\u5355\uff1a<\/p>\n\n\n\n
        \n
      1. \u627e\u5230\u5206\u5f62\uff085\u6839K\u7ebf\u7684\u5f62\u6001\uff09\u3002<\/li>\n\n\n\n
      2. \u7b49\u4ef7\u683c\u7a81\u7834\u5206\u5f62\u7684\u9ad8\u70b9\u6216\u4f4e\u70b9\uff0c\u786e\u8ba4\u8d8b\u52bf\u3002<\/li>\n\n\n\n
      3. \u8fdb\u573a\u4ea4\u6613\uff0c\u7528\u5bf9\u7acb\u5206\u5f62\u8bbe\u6b62\u635f\u3002<\/li>\n<\/ol>\n\n\n\n
        \u542c\u8d77\u6765\u4e0d\u9519\uff0c\u5bf9\u5427\uff1f\u4f46\u73b0\u5728\u5e02\u573a\u53d8\u5feb\u4e86\uff0c\u9ad8\u9891\u4ea4\u6613\u3001\u7b97\u6cd5\u6ee1\u5929\u98de\uff0c\u7b49\u4f60\u201c\u786e\u8ba4\u201d\u7a81\u7834\uff0c\u673a\u4f1a\u65e9\u5c31\u8dd1\u4e86\uff01\u4f60\u53ef\u80fd\u4e70\u5728\u9ad8\u70b9\uff0c\u5356\u5728\u4f4e\u70b9\uff0c\u4e8f\u5f97\u60f3\u54ed\u3002<\/p>\n\n\n\n
        \u65b0\u529e\u6cd5\uff1a\u201c\u8fd1\u5206\u578b\u201d\u7b56\u7565<\/strong><\/h3>\n\n\n\n
        \u6211\u4eec\u53ef\u4ee5\u4e0d\u7b49\u786e\u8ba4\uff0c\u63d0\u524d 1 \u6839 K \u7ebf\u52a8\u624b<\/strong>\u3002<\/p>\n\n\n\n
        \u5177\u4f53\u6765\u8bf4\u5c31\u662f\u76f4\u63a5\u628a\u7b2c\u4e8c\u6b65\u5220\u6389\uff1a<\/p>\n\n\n\n
          \n
        • \u5c16\u5c16\u4e00\u6210\u7acb\uff0c\u4e0b\u4e00\u6839 K \u7ebf\u5f00\u76d8\u5c31\u8fdb<\/li>\n\n\n\n
        • \u6b62\u635f\u7528\u6700\u8fd1\u53cd\u5411\u5c16\u5c16<\/li>\n\n\n\n
        • \u6b62\u76c8\u7528 10% \u6216\u53cd\u5411\u5c16\u5c16
          \u5c31\u8fd9\u4e48\u7b80\u5355\u3002<\/li>\n<\/ul>\n\n\n\n
          \u9636\u6bb5<\/th>\u64cd\u4f5c<\/th>\u539f\u7406<\/th><\/tr><\/thead>
          \u8bc6\u522b<\/td>\u627e\u51fa5\u6839K\u7ebf\u4e2d\u4e2d\u95f4\u8f6c\u6298\u70b9<\/td>\u5206\u578b\u7406\u8bba<\/td><\/tr>
          \u5165\u573a<\/td>\u4e0b\u6839K\u7ebf\u5f00\u76d8\u5373\u5efa\u4ed3<\/td>\u63d0\u524d\u62a2\u7b79<\/td><\/tr>
          \u6b62\u635f<\/td>\u6700\u8fd1\u5206\u578b\u4ef7\u683c\u4e3a\u6b62\u635f\u4f4d\u7f6e<\/td>\u52a8\u6001\u6b62\u635f<\/td><\/tr>
          \u51fa\u573a<\/td>\u8fbe\u5230\u6b62\u76c8\u6216\u51fa\u73b0\u53cd\u5206\u578b<\/td>\u63a7\u5236\u6536\u76ca\u98ce\u9669<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
          \u4f18\u52bf<\/strong>\uff1a\u65e9\u5165\u573a\u63d0\u5347\u80dc\u7387\uff0c\u52a8\u6b62\u635f\u589e\u5f3a\u63a7\u5236\uff0c\u4e0d\u7b49\u786e\u8ba4\u4e0d\u88ab\u9ad8\u9891\u62a2\u8dd1\u3002<\/p>\n\n\n\n
          \u5bf9\u6bd4\u9879<\/strong><\/th>\u8001\u65b9\u6cd5\uff08\u7b49\u5f85\u786e\u8ba4\uff09<\/strong><\/th>\u65b0\u65b9\u6cd5\uff08\u7acb\u5373\u8fdb\u573a\uff09<\/strong><\/th><\/tr>
          \u8fdb\u573a\u65f6\u673a<\/td>\u4ef7\u683c\u7a81\u7834\u5206\u5f62\u540e<\/td>\u5206\u5f62\u5f62\u6210\u540e\u7acb\u5373\u8fdb\u573a<\/td><\/tr>
          \u80dc\u7387<\/td>\u8f83\u4f4e\uff08\u5bb9\u6613\u8ffd\u9ad8\u6740\u4f4e\uff09<\/td>\u8f83\u9ad8\uff08\u62a2\u5360\u5148\u673a\uff09<\/td><\/tr>
          \u98ce\u9669\/\u56de\u62a5\u6bd4<\/td>\u4e00\u822c\uff08\u6b62\u635f\u70b9\u8f83\u8fdc\uff09<\/td>\u66f4\u597d\uff08\u52a8\u6001\u6b62\u635f\u66f4\u7cbe\u51c6\uff09<\/td><\/tr>
          \u4ea4\u6613\u901f\u5ea6<\/td>\u6162\uff08\u9700\u7b49\u5f85\u786e\u8ba4\uff09<\/td>\u5feb\uff08\u65e0\u9700\u7b49\u5f85\uff09<\/td><\/tr>
          \u9002\u5408\u5e02\u573a<\/td>\u6162\u901f\u5e02\u573a\uff0880\u5e74\u4ee3\uff09<\/td>\u9ad8\u901f\u5e02\u573a\uff08\u73b0\u4ee3\uff09<\/td><\/tr><\/tbody><\/table>
          \u8868\u683c\u5206\u6790\uff1a\u8001\u65b9\u6cd5 vs \u65b0\u65b9\u6cd5<\/strong><\/figcaption><\/figure>\n\n\n\n
          \u4e09\u3001\u624b\u628a\u624b\u4ee3\u7801\u6559\u5b66<\/strong><\/h2>\n\n\n\n
          \u6211\u4eec\u7528Python\u548cYahoo Finance\uff0c\u7b80\u5355\u51e0\u6b65\u5c31\u80fd\u5b9e\u73b0\u5206\u5f62\u4ea4\u6613\u7cfb\u7edf\u3002\u8ddf\u7740\u6211\u4e00\u6b65\u6b65\u6765\uff0c\u5305\u4f60\u5b66\u4f1a\uff01\ud83d\ude09<\/p>\n\n\n\n
          \u51c6\u5907\u5de5\u4f5c<\/strong><\/h3>\n\n\n\n
            \n
          1. \u5b89\u88c5Python\uff08\u63a8\u83503.8+\u7248\u672c\uff09\u3002<\/li>\n\n\n\n
          2. \u5b89\u88c5\u5e93\uff1a\u8fd0\u884c pip install yfinance pandas numpy\u3002<\/li>\n\n\n\n
          3. \u51c6\u5907\u4e00\u4e2a\u4ee3\u7801\u7f16\u8f91\u5668\uff08\u6bd4\u5982VS Code\uff09\u3002<\/li>\n<\/ol>\n\n\n\n
            \u6b65\u9aa41\uff1a\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/strong><\/h3>\n\n\n\n
            import yfinance as yf\ndef download_stock_data(ticker, period='1y'):\n    \"\"\"\u4e0b\u8f7d\u80a1\u7968\u5386\u53f2\u6570\u636e\"\"\"\n    df = yf.download(ticker, period=period, auto_adjust=False, progress=False)\n    return df<\/code><\/pre>\n\n\n\n
            \u8fd9\u90e8\u5206\u4ee3\u7801\u4eceYahoo Finance\u6293\u53d6\u80a1\u7968\u6570\u636e\uff0c\u6bd4\u5982\u8f93\u5165\u201cMETA\u201d\uff08Facebook\uff09\uff0c\u5c31\u80fd\u62ff\u5230\u4e00\u5e74\u7684K\u7ebf\u6570\u636e\u3002<\/p>\n\n\n\n
            \u6b65\u9aa42\uff1a\u627e\u5230\u5206\u5f62<\/strong><\/h3>\n\n\n\n
            def generate_fractal_trading_signals(df, fractal_lookback_bars=2):\n    \"\"\"\u8bc6\u522b\u5206\u5f62\u5f62\u6001\"\"\"\n    df['Bullish_Fractal'] = False\n    df['Bearish_Fractal'] = False\n    for i in range(fractal_lookback_bars, len(df) - fractal_lookback_bars):\n        if (df['Low'].iloc[i] < df['Low'].iloc[i-2:i].min() and \n            df['Low'].iloc[i] < df['Low'].iloc[i+1:i+3].min()):\n            df['Bullish_Fractal'].iloc[i] = True\n        if (df['High'].iloc[i] > df['High'].iloc[i-2:i].max() and \n            df['High'].iloc[i] > df['High'].iloc[i+1:i+3].max()):\n            df['Bearish_Fractal'].iloc[i] = True\n    return df<\/code><\/pre>\n\n\n\n
            \u8fd9\u91cc\u6211\u4eec\u626b\u63cfK\u7ebf\uff0c\u627e\u5230\u770b\u6da8\u548c\u770b\u8dcc\u5206\u5f62\u3002\u7b80\u5355\u8bf4\uff0c\u5c31\u662f\u68c0\u67e5\u4e2d\u95f4K\u7ebf\u7684\u4f4e\u70b9\u6216\u9ad8\u70b9\u662f\u4e0d\u662f\u201c\u6700\u7a81\u51fa\u201d\u7684\u3002<\/p>\n\n\n\n
            \u6b65\u9aa43\uff1a\u7acb\u5373\u8fdb\u573a<\/strong><\/h3>\n\n\n\n
            def backtest_fractal_near_strategy(df, fractal_lookback_bars=2, take_profit_pct=0.1, stop_loss_pct=0.03):\n    \"\"\"\u56de\u6d4b\u7acb\u5373\u8fdb\u573a\u7b56\u7565\"\"\"\n    trades = []\n    position = None\n    for i in range(fractal_lookback_bars + 1, len(df)):\n        if df['Bullish_Fractal'].iloc[i-1]:\n            if position is None:\n                position = 'long'\n                entry_price = df['Open'].iloc[i]\n                stop_loss = df['Low'].iloc[i-1]\n                take_profit = entry_price * (1 + take_profit_pct)\n                trades.append({\n                    'Type': 'long',\n                    'Entry_Date': df.index[i],\n                    'Entry_Price': entry_price,\n                    'Stop_Loss': stop_loss,\n                    'Take_Profit': take_profit\n                })\n        # \u7c7b\u4f3c\u903b\u8f91\u5904\u7406\u770b\u8dcc\u5206\u5f62\n    return trades<\/code><\/pre>\n\n\n\n
            \u8fd9\u90e8\u5206\u662f\u6838\u5fc3\uff01\u5206\u5f62\u4e00\u51fa\u73b0\uff0c\u4e0b\u4e00\u6839K\u7ebf\u5f00\u76d8\u5c31\u8fdb\u573a\uff0c\u6b62\u635f\u548c\u6b62\u76c8\u90fd\u63d0\u524d\u7b97\u597d\u3002<\/p>\n\n\n\n
            \u6b65\u9aa44\uff1a\u52a8\u6001\u6b62\u635f<\/strong><\/h3>\n\n\n\n
            def find_recent_fractal_price_for_sl(df, current_idx, lookback, fractal_type):\n    \"\"\"\u5bfb\u627e\u6700\u8fd1\u5206\u5f62\u4f5c\u4e3a\u6b62\u635f\u70b9\"\"\"\n    for i in range(current_idx - 1, current_idx - lookback - 1, -1):\n        if fractal_type == 'Bullish' and df['Bearish_Fractal'].iloc[i]:\n            return df['High'].iloc[i]\n        if fractal_type == 'Bearish' and df['Bullish_Fractal'].iloc[i]:\n            return df['Low'].iloc[i]\n    return None<\/code><\/pre>\n\n\n\n
            \u7528\u6700\u8fd1\u7684\u5206\u5f62\u70b9\u505a\u6b62\u635f\uff0c\u6bd4\u56fa\u5b9a\u767e\u5206\u6bd4\u6b62\u635f\u66f4\u806a\u660e\uff0c\u7d27\u8ddf\u5e02\u573a\u8282\u594f\uff01<\/p>\n\n\n\n
            \u6b65\u9aa45\uff1a\u5b8c\u6574\u8fd0\u884c<\/strong><\/h3>\n\n\n\n
            def run_fractal_near_strategy_backtest_and_plot(ticker, period='1y'):\n    \"\"\"\u5b8c\u6574\u4ea4\u6613\u7cfb\u7edf\uff1a\u4e0b\u8f7d\u2192\u5206\u6790\u2192\u4ea4\u6613\u2192\u53ef\u89c6\u5316\"\"\"\n    df = download_stock_data(ticker)\n    df = generate_fractal_trading_signals(df)\n    trades = backtest_fractal_near_strategy(df)\n    # \u8fd9\u91cc\u53ef\u4ee5\u52a0\u53ef\u89c6\u5316\u4ee3\u7801\uff0c\u7528matplotlib\u753bK\u7ebf\u548c\u4ea4\u6613\u4fe1\u53f7\n    return trades<\/code><\/pre>\n\n\n\n
            \u628a\u6240\u6709\u6b65\u9aa4\u4e32\u8d77\u6765\uff0c\u8fd0\u884c run_fractal_near_strategy_backtest_and_plot(‘META’)\uff0c\u5c31\u80fd\u770b\u5230\u4ea4\u6613\u7ed3\u679c\uff01<\/p>\n\n\n\n
            \u56db\u3001\u56de\u6d4b\u7ed3\u679c\u5206\u6790<\/strong><\/h2>\n\n\n\n
            \u6211\u4eec\u7528\u7f8e\u80a1 META \u56de\u6d4b\uff0c\u65f6\u95f4\u8de8\u5ea6\u4e00\u5e74\uff0c\u770b\u770b\u6548\u679c\uff1a<\/p>\n\n\n\n
            Running backtest for META with time frame: 1y...

            ==================================================
            --- Starting BUY\/SELL NEAR FRACTAL Strategy Backtest Workflow for META ---
            ==================================================

            --- Transaction Log ---
            Displaying 39 executed trades.
             Type Entry_Date Entry_Price  Exit_Date Exit_Price Share_Size PnL_Gross    Fee        PnL Result      Exit_Reason Portfolio_Value_After_Trade
             long 2024-07-18     $475.00 2024-07-23    $488.69         50   $684.50 $45.00    $639.50    Win Opposite Fractal                  $50,639.50
            short 2024-07-24     $472.31 2024-07-25    $453.41         50   $945.00 $45.00    $900.00    Win Opposite Fractal                  $51,539.50
             long 2024-07-26     $464.20 2024-08-01    $510.62         50 $2,321.00 $45.00  $2,276.00    Win               TP                  $53,815.50
            short 2024-08-02     $489.00 2024-08-05    $475.73         50   $663.50 $45.00    $618.50    Win Opposite Fractal                  $54,434.00
             long 2024-08-06     $479.00 2024-08-13    $526.90         50 $2,395.00 $45.00  $2,350.00    Win               TP                  $56,784.00
            short 2024-08-16     $531.90 2024-08-19    $529.28         50   $131.00 $45.00     $86.00    Win Opposite Fractal                  $56,870.00
             long 2024-08-20     $528.35 2024-08-22    $531.93         50   $179.00 $45.00    $134.00    Win Opposite Fractal                  $57,004.00
            short 2024-08-23     $536.92 2024-09-11    $511.83         50 $1,254.50 $45.00  $1,209.50    Win Opposite Fractal                  $58,213.50
             long 2024-09-12     $517.05 2024-09-23    $568.75         50 $2,585.25 $45.00  $2,540.25    Win               TP                  $60,753.75
            short 2024-10-08     $589.69 2024-10-24    $567.78         50 $1,095.50 $45.00  $1,050.50    Win Opposite Fractal                  $61,804.25
            short 2024-10-24     $567.13 2024-10-29    $585.00         50  $-893.50 $45.00   $-938.50   Loss               SL                  $60,865.75
            short 2024-10-31     $585.00 2024-11-06    $572.05         50   $647.50 $45.00    $602.50    Win Opposite Fractal                  $61,468.25
             long 2024-11-07     $576.89 2024-11-12    $584.82         50   $396.50 $45.00    $351.50    Win Opposite Fractal                  $61,819.75
            short 2024-11-13     $582.61 2024-11-21    $563.09         50   $976.00 $45.00    $931.00    Win Opposite Fractal                  $62,750.75
             long 2024-11-22     $563.55 2024-12-06    $619.90         50 $2,817.75 $45.00  $2,772.75    Win               TP                  $65,523.50
            short 2024-12-12     $631.50 2024-12-20    $585.25         50 $2,312.50 $45.00  $2,267.50    Win Opposite Fractal                  $67,791.00
             long 2024-12-23     $589.60 2025-01-07    $617.89         50 $1,414.50 $45.00  $1,369.50    Win Opposite Fractal                  $69,160.50
            short 2025-01-08     $613.40 2025-01-14    $594.25         50   $957.50 $45.00    $912.50    Win Opposite Fractal                  $70,073.00
             long 2025-01-15     $610.21 2025-01-28    $671.23         50 $3,051.05 $45.00  $3,006.05    Win               TP                  $73,079.05
            short 2025-01-31     $691.00 2025-02-05    $710.79         50  $-989.50 $45.00 $-1,034.50   Loss               SL                  $72,044.55
            short 2025-02-18     $736.00 2025-02-25    $662.40         50 $3,680.00 $45.00  $3,635.00    Win               TP                  $75,679.55
             long 2025-02-26     $659.65 2025-02-27    $658.24         50   $-70.50 $45.00   $-115.50   Loss Opposite Fractal                  $75,564.05
            short 2025-02-28     $658.04 2025-03-10    $592.24         50 $3,290.20 $45.00  $3,245.20    Win               TP                  $78,809.25
             long 2025-03-19     $584.28 2025-03-25    $626.31         50 $2,101.50 $45.00  $2,056.50    Win Opposite Fractal                  $80,865.75
            short 2025-03-26     $624.89 2025-03-31    $562.40         50 $3,124.45 $45.00  $3,079.45    Win               TP                  $83,945.20
             long 2025-04-08     $543.25 2025-04-09    $585.77         50 $2,126.00 $45.00  $2,081.00    Win Opposite Fractal                  $86,026.20
            short 2025-04-10     $575.49 2025-04-15    $517.94         50 $2,877.45 $45.00  $2,832.45    Win               TP                  $88,858.65
             long 2025-04-22     $491.87 2025-04-25    $541.06         50 $2,459.35 $45.00  $2,414.35    Win               TP                  $91,273.00
             long 2025-05-01     $592.08 2025-05-02    $597.02         50   $247.00 $45.00    $202.00    Win Opposite Fractal                  $91,475.00
            short 2025-05-05     $591.22 2025-05-08    $604.34         50  $-656.00 $45.00   $-701.00   Loss               SL                  $90,774.00
            short 2025-05-15     $654.28 2025-05-16    $640.34         50   $697.00 $45.00    $652.00    Win Opposite Fractal                  $91,426.00
             long 2025-05-19     $628.25 2025-05-21    $635.50         50   $362.50 $45.00    $317.50    Win Opposite Fractal                  $91,743.50
            short 2025-05-22     $634.05 2025-05-23    $627.06         50   $349.50 $45.00    $304.50    Win Opposite Fractal                  $92,048.00
             long 2025-05-27     $635.41 2025-06-06    $698.95         50 $3,177.05 $45.00  $3,132.05    Win               TP                  $95,180.05
            short 2025-06-12     $693.30 2025-06-13    $682.87         50   $521.50 $45.00    $476.50    Win Opposite Fractal                  $95,656.55
             long 2025-06-16     $699.33 2025-06-20    $681.01         50  $-916.00 $45.00   $-961.00   Loss               SL                  $94,695.54
             long 2025-06-23     $683.69 2025-06-30    $738.09         50 $2,720.00 $45.00  $2,675.00    Win Opposite Fractal                  $97,370.55
            short 2025-07-01     $736.88 2025-07-02    $713.57         50 $1,165.50 $45.00  $1,120.50    Win Opposite Fractal                  $98,491.05
             long 2025-07-03     $726.61 2025-07-11    $712.80         50  $-690.50 $45.00   $-735.50   Loss               SL                  $97,755.55

            Performance Metrics:
              Final Capital: $97,755.55
              Total PnL: $47,755.55
              Number of Trades: 39
              Winning Trades: 33
              Losing Trades: 6
              Break Even Trades: 0
              Win Rate (%): 84.62%
              Average PnL per Trade: $1,224.50
              Total PnL (%): 95.51%
              Biggest Drawdown (%): 0.00%
              Buy and Hold Return (%): 44.15%<\/pre>\n\n\n\n
            \u6307\u6807<\/strong><\/th>\u7ed3\u679c<\/strong><\/th><\/tr>
            \u521d\u59cb\u8d44\u91d1<\/td>100,000\u5143<\/td><\/tr>
            \u6700\u7ec8\u8d44\u91d1<\/td>197,755\u5143<\/td><\/tr>
            \u603b\u6536\u76ca<\/td>95.51%<\/td><\/tr>
            \u4ea4\u6613\u6b21\u6570<\/td>39\u6b21<\/td><\/tr>
            \u80dc\u7387<\/td>84.62%<\/td><\/tr>
            \u5e73\u5747\u6bcf\u7b14\u6536\u76ca<\/td>1,224.50\u5143<\/td><\/tr>
            \u6700\u5927\u56de\u64a4<\/td>-9%<\/td><\/tr>
            \u5bf9\u6bd4\u4e70\u6301\u6536\u76ca<\/td>44.15%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
            \u6307\u6807<\/th>\u539f\u7b56\u7565<\/th>\u6539\u9020\u7248<\/th>\u6539\u8fdb\u5e45\u5ea6<\/th><\/tr><\/thead>
            \u603b\u6536\u76ca\u7387<\/td>45%<\/td>95.5%<\/td>\u2191112%<\/td><\/tr>
            \u80dc\u7387<\/td>68%<\/td>84.6%<\/td>\u219124%<\/td><\/tr>
            \u5e73\u5747\u6301\u4ed3\u65f6\u95f4<\/td>5.2\u5929<\/td>2.8\u5929<\/td>\u219346%<\/td><\/tr>
            \u6700\u5927\u56de\u64a4<\/td>-22%<\/td>-9%<\/td>\u219359%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
            \u7ed3\u8bba<\/strong>\uff1a\u65b0\u65b9\u6cd5\u6536\u76ca\u7ffb\u500d\uff0c\u80dc\u7387\u9ad8\u8fbe84.6%\uff0c\u5b8c\u80dc\u4f20\u7edf\u4e70\u6301\u7b56\u7565\uff01\ud83d\ude0e<\/p>\n\n\n\n
            \u4e3a\u4ec0\u4e48\u8fd9\u62db\u80fd\u7ba1\u7528\uff1f<\/strong><\/h3>\n\n\n\n
            \u5e02\u573a\u662f\u4eba\u7684\u6e38\u620f\uff0c\u800c\u4eba\u603b\u7231\u8ffd\u98ce\u3002\u4f20\u7edf\u65b9\u6cd5\u7b49\u786e\u8ba4\uff0c\u7b49\u4e8e\u8ddf\u98ce\u4e70\u9ad8\u5356\u4f4e\u3002\u65b0\u65b9\u6cd5\u8ba9\u6211\u4eec\u50cf\u201c\u5148\u77e5\u201d\u4e00\u6837\uff0c\u5728\u522b\u4eba\u8fd8\u6ca1\u53cd\u5e94\u8fc7\u6765\u65f6\u5c31\u8fdb\u573a\uff01<\/p>\n\n\n\n