showstart.ipynb

showstart.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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    {
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      "metadata": {
        "id": "dZU3GWggY4uX"
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      "source": [
        "from pyqlib import init\n",
        "from qlib.data import D\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# 初始化\n",
        "provider = r\"D:\\investmentresearch\\qlibsystem\\data_commod_daily\\qlib_data_demo\"\n",
        "init(provider_uri=provider)\n",
        "\n",
        "symbol = \"CU_DOMINANT_DAILY\"\n",
        "\n",
        "# 取收盘价\n",
        "df = D.features([symbol], [\"$close\"], start_time=\"2018-01-02\", end_time=\"2019-12-31\")\n",
        "close = df[\"$close\"]\n",
        "\n",
        "# 计算短期/长期均线\n",
        "ma5 = close.rolling(5).mean()\n",
        "ma20 = close.rolling(20).mean()\n",
        "\n",
        "# 策略信号\n",
        "signal = (ma5 > ma20).astype(int)\n",
        "\n",
        "# 日收益率\n",
        "ret = close.pct_change().fillna(0)\n",
        "\n",
        "# 策略收益\n",
        "strategy_ret = signal.shift(1) * ret\n",
        "nav_strategy = (1 + strategy_ret).cumprod()\n",
        "\n",
        "# 基准：买入持有\n",
        "nav_bh = (1 + ret).cumprod()\n",
        "\n",
        "# 夏普比率计算函数\n",
        "def sharpe_ratio(returns, freq=252):\n",
        "    mean_ret = returns.mean()\n",
        "    vol = returns.std()\n",
        "    return (mean_ret / vol) * np.sqrt(freq) if vol > 0 else np.nan\n",
        "\n",
        "# 计算夏普\n",
        "sharpe_strategy = sharpe_ratio(strategy_ret)\n",
        "sharpe_bh = sharpe_ratio(ret)\n",
        "\n",
        "print(f\"Sharpe Ratio (MA策略): {sharpe_strategy:.2f}\")\n",
        "print(f\"Sharpe Ratio (买入持有): {sharpe_bh:.2f}\")\n",
        "\n",
        "# 对比画图\n",
        "plt.figure(figsize=(10,5))\n",
        "nav_strategy.plot(label=\"MA Crossover Strategy\")\n",
        "nav_bh.plot(label=\"Buy & Hold\")\n",
        "plt.title(f\"Strategy vs Buy&Hold - {symbol}\")\n",
        "plt.ylabel(\"Net Value\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    }
  ]
}