ch09_portfolio_risk.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFQ17AmL0Yd0"
      },
      "source": [
        "# CH-09 讓 AI 評估投資組合風險"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a42M4mg1qNQ5"
      },
      "source": [
        "## 9-1 資金管理"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwGTl75BLNwu"
      },
      "source": [
        "### 1️⃣ 單次賭局的期望資產"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "以下為賠率1的賭局"
      ],
      "metadata": {
        "id": "9E8hN0SA8E4K"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9x1F86C4T9u",
        "outputId": "86be0f32-6fa8-441e-d9ce-5429f7ed7b7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "這次賭局期望獲利為: 300.0\n",
            "期望總資產為: 1300.0\n"
          ]
        }
      ],
      "source": [
        "bet = 500 # 下注金額\n",
        "win_rate = 0.8 # 勝率\n",
        "wealth = 1000 # 資產\n",
        "\n",
        "# 期望獲利\n",
        "gain = win_rate * bet + (1-win_rate) * -bet\n",
        "wealth += gain\n",
        "print(\"這次賭局期望獲利為:\",gain)\n",
        "print(\"期望總資產為:\",wealth)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2️⃣ 單一賭局的隨機結果"
      ],
      "metadata": {
        "id": "ebiRubdMv_AZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def single_bet(bet, win_rate, wealth, odds=1, verbose=True):\n",
        "  # 單一賭局獲利\n",
        "  if random.uniform(0,1) <= win_rate:\n",
        "    gain = bet * odds\n",
        "  else:\n",
        "    gain = -bet\n",
        "  wealth += gain\n",
        "\n",
        "  if verbose:\n",
        "    print(\"這次賭局的獲利為:\", gain)\n",
        "    print(\"總資產為:\", wealth)\n",
        "\n",
        "  return wealth\n",
        "\n",
        "single_bet(bet=1000, win_rate=0.8, wealth=1000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t30pt68amGmN",
        "outputId": "8afada57-9b26-439f-9dcf-901806c26b3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "這次賭局的獲利為: 1000\n",
            "總資產為: 2000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2000"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3️⃣ 重複賭局的資產變化"
      ],
      "metadata": {
        "id": "T59rbTgDwoAZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def simulate_bets(initial_wealth, bet_ratio,\n",
        "                  win_rate, num_bets=100, odds=1, verbose=True):\n",
        "  wealths = [initial_wealth]\n",
        "  wealth = initial_wealth\n",
        "  for i in range(num_bets):\n",
        "    bet = wealth * bet_ratio\n",
        "    wealth = single_bet(bet=bet, win_rate=win_rate,\n",
        "                        wealth=wealth, odds=odds, verbose=verbose)\n",
        "    wealths.append(wealth)\n",
        "    # 輸光就跳出迴圈\n",
        "    if wealth <= 0:\n",
        "        break\n",
        "  return wealths\n",
        "\n",
        "simulate_bets(initial_wealth=1000, bet_ratio=1 ,win_rate=0.8)"
      ],
      "metadata": {
        "id": "4C7n0hxlwx-x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da730d5b-0909-4d4b-86e1-cbe4c72c7899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "這次賭局的獲利為: 1000\n",
            "總資產為: 2000\n",
            "這次賭局的獲利為: -2000\n",
            "總資產為: 0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1000, 2000, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC1gzHDLiqhT"
      },
      "source": [
        "### 4️⃣ 不同下注量的資產成長幅度"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OBkpRomPEXd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "57da6fea-abb5-4f03-9405-c0633e621aa6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "最好的下注比例為: Ratio 0.5, 最終資產：255005527446580.88\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# 設定變數\n",
        "initial_wealth = 1000\n",
        "bet_ratios = [i/10 for i in range(1, 11)]  # 從10%到全押的下注比例\n",
        "num_bets = 100\n",
        "win_rate =0.8\n",
        "\n",
        "df = pd.DataFrame()\n",
        "# 模擬各比例下注\n",
        "for bet_ratio in bet_ratios:\n",
        "  wealths = simulate_bets(initial_wealth, bet_ratio, win_rate, num_bets, verbose=False)\n",
        "  df[f'Ratio {bet_ratio}'] = pd.Series(wealths)\n",
        "\n",
        "final_wealths = df.iloc[-1]\n",
        "max_ratio = final_wealths.idxmax() # 找到最好的下注比例\n",
        "max_value = final_wealths.max()  # 最高資產\n",
        "\n",
        "print(f\"最好的下注比例為: {max_ratio}, 最終資產：{max_value}\")\n",
        "\n",
        "# 繪製圖表\n",
        "ax = df.plot(figsize=(10,6), legend=True, title='Wealth for Different Bet Ratios')\n",
        "ax.set_xlabel('Number of Bets')\n",
        "ax.set_ylabel('Wealth')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5️⃣ 倍倍下注法"
      ],
      "metadata": {
        "id": "0jqJLS9TSqyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def double_bet(initial_wealth, win_rate, num_bets=100, odds=1):\n",
        "  wealths = [initial_wealth]\n",
        "  wealth = initial_wealth\n",
        "  initial_bet = 1\n",
        "  bet = initial_bet\n",
        "  for i in range(num_bets):\n",
        "    if random.uniform(0, 1) <= win_rate:\n",
        "      # 若贏了，則下注初始金額\n",
        "      wealth += bet * odds\n",
        "      bet = initial_bet\n",
        "    else:\n",
        "      # 若輸了，則加倍下注金額\n",
        "      wealth -= bet\n",
        "      bet *= 2\n",
        "    wealths.append(wealth)\n",
        "\n",
        "    # 輸光就跳出迴圈\n",
        "    if wealth <= 0:\n",
        "      break\n",
        "\n",
        "  return wealths\n",
        "\n",
        "# 設定變數\n",
        "initial_wealth = 1000\n",
        "bet_ratios = [i/10 for i in range(1, 11)]  # 從10%到全押的下注比例\n",
        "num_bets = 100\n",
        "win_rate =0.8\n",
        "\n",
        "df = pd.DataFrame()\n",
        "# 模擬各比例下注\n",
        "for bet_ratio in bet_ratios:\n",
        "  wealths = simulate_bets(initial_wealth, bet_ratio, win_rate, num_bets, verbose=False)\n",
        "  df[f'Ratio {bet_ratio}'] = pd.Series(wealths)\n",
        "\n",
        "# 倍倍下注法模擬結果\n",
        "wealths_double = double_bet(initial_wealth, win_rate, num_bets)\n",
        "df['double_bet'] = pd.Series(wealths_double)\n",
        "\n",
        "# 下注法排名\n",
        "final_wealths = df.iloc[-1]\n",
        "sorted_wealths = final_wealths.sort_values(ascending=False)\n",
        "print(\"下注方法排名：\")\n",
        "i = 1\n",
        "for index, value in zip(sorted_wealths.index, sorted_wealths.values):\n",
        "    print(f\"第{i}名:{index}, 最終資產：{value}\")\n",
        "    i += 1\n",
        "\n",
        "# 繪製圖表\n",
        "ax = df.plot(figsize=(10,6), legend=True, title='Wealth for Different Bet Ratios')\n",
        "ax.set_xlabel('Number of Bets')\n",
        "ax.set_ylabel('Wealth')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791
        },
        "id": "PeCJ5-XHSrU9",
        "outputId": "e68a18fa-4cb0-48c2-f3dc-17b6b1a6b3f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "下注方法排名：\n",
            "第1名:Ratio 0.6, 最終資產：234854258277.38318\n",
            "第2名:Ratio 0.5, 最終資產：116600606971.45903\n",
            "第3名:Ratio 0.3, 最終資產：1933188919.0797477\n",
            "第4名:Ratio 0.4, 最終資產：604450471.5910275\n",
            "第5名:Ratio 0.9, 最終資產：72076763.6778686\n",
            "第6名:Ratio 0.7, 最終資產：16281727.105879573\n",
            "第7名:Ratio 0.2, 最終資產：11069217.105402032\n",
            "第8名:Ratio 0.1, 最終資產：203758.07346974063\n",
            "第9名:Ratio 0.8, 最終資產：46901.6125132311\n",
            "第10名:double_bet, 最終資產：1080.0\n",
            "第11名:Ratio 1.0, 最終資產：nan\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6️⃣ 凱利公式 Kelly formula"
      ],
      "metadata": {
        "id": "vNt1zCZ5A2X0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def kelly_formula(p,b):\n",
        "  # 最佳下注比例\n",
        "  best_bet = (b * p - (1 - p)) / b\n",
        "  # 如果下注比例小於等於 0，則設為 0\n",
        "  if best_bet <= 0:\n",
        "      return 0\n",
        "  # 取到小數點後兩位\n",
        "  best_bet = round(best_bet,2)\n",
        "  return best_bet\n",
        "\n",
        "kelly_formula(p=0.8,b=1)\n",
        "\n",
        "best_bet = kelly_formula(p=0.8, b=1)\n",
        "print(\"最佳下注比例為:\", best_bet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lp9jQF2ZA2md",
        "outputId": "e76d908f-35a2-4c2e-c763-2440447f265c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "最佳下注比例為: 0.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7️⃣ 安裝及匯入套件"
      ],
      "metadata": {
        "id": "xKNkqNWSoAQe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance==0.2.38\n",
        "!pip install backtesting\n",
        "!pip install bokeh==2.4.3 # 繪圖套件\n",
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import pandas as pd # 資料處理套件\n",
        "from scipy.stats import norm\n",
        "import datetime as dt # 時間套件\n",
        "from backtesting import Backtest, Strategy\n",
        "from backtesting.lib import crossover"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZXQFFxZn2rf",
        "outputId": "164d95d4-af2a-4780-89a6-555b3ec0d30a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.31)\n",
            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.5.3)\n",
            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.23.5)\n",
            "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.31.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.10/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.9.4)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.4.4)\n",
            "Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2023.3.post1)\n",
            "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.4.0)\n",
            "Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.10/dist-packages (from yfinance) (3.17.0)\n",
            "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.11.2)\n",
            "Requirement already satisfied: html5lib>=1.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.1)\n",
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            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (2023.11.17)\n",
            "Requirement already satisfied: backtesting in /usr/local/lib/python3.10/dist-packages (0.3.3)\n",
            "Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from backtesting) (1.23.5)\n",
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            "Requirement already satisfied: bokeh>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from backtesting) (2.4.3)\n",
            "Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.4.0->backtesting) (3.1.3)\n",
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            "Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.4.0->backtesting) (9.4.0)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.4.0->backtesting) (6.0.1)\n",
            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.4.0->backtesting) (6.3.2)\n",
            "Requirement already satisfied: typing-extensions>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.4.0->backtesting) (4.9.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=0.25.0,>=0.25.0->backtesting) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=0.25.0,>=0.25.0->backtesting) (2023.3.post1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=2.9->bokeh>=1.4.0->backtesting) (2.1.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas!=0.25.0,>=0.25.0->backtesting) (1.16.0)\n",
            "Requirement already satisfied: bokeh==2.4.3 in /usr/local/lib/python3.10/dist-packages (2.4.3)\n",
            "Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.4.3) (3.1.3)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.4.3) (1.23.5)\n",
            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.4.3) (23.2)\n",
            "Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.4.3) (9.4.0)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.4.3) (6.0.1)\n",
            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.4.3) (6.3.2)\n",
            "Requirement already satisfied: typing-extensions>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.4.3) (4.9.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=2.9->bokeh==2.4.3) (2.1.4)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/backtesting/_plotting.py:50: UserWarning: Jupyter Notebook detected. Setting Bokeh output to notebook. This may not work in Jupyter clients without JavaScript support (e.g. PyCharm, Spyder IDE). Reset with `backtesting.set_bokeh_output(notebook=False)`.\n",
            "  warnings.warn('Jupyter Notebook detected. '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pyelatvvLiIn"
      },
      "source": [
        "### 8️⃣ 取得回測結果"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aSjownHrGnde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43a580c6-6960-4955-de18-0b4d677de169"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "Start                     2019-01-29 00:00:00\n",
            "End                       2024-01-29 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   73.415638\n",
            "Equity Final [$]                   100058.484\n",
            "Equity Peak [$]                    100473.894\n",
            "Return [%]                           0.058484\n",
            "Buy & Hold Return [%]              190.337079\n",
            "Return (Ann.) [%]                    0.012127\n",
            "Volatility (Ann.) [%]                0.192189\n",
            "Sharpe Ratio                           0.0631\n",
            "Sortino Ratio                        0.089536\n",
            "Calmar Ratio                         0.029332\n",
            "Max. Drawdown [%]                   -0.413451\n",
            "Avg. Drawdown [%]                   -0.064584\n",
            "Max. Drawdown Duration      461 days 00:00:00\n",
            "Avg. Drawdown Duration       84 days 00:00:00\n",
            "# Trades                                   65\n",
            "Win Rate [%]                        43.076923\n",
            "Best Trade [%]                      35.820355\n",
            "Worst Trade [%]                    -17.323225\n",
            "Avg. Trade [%]                      -0.015128\n",
            "Max. Trade Duration         115 days 00:00:00\n",
            "Avg. Trade Duration          32 days 00:00:00\n",
            "Profit Factor                        1.115614\n",
            "Expectancy [%]                       0.301816\n",
            "SQN                                  0.187842\n",
            "_strategy                       CrossStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                       Size  EntryB...\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 取得股價資料\n",
        "stock_id = \"2330.tw\"\n",
        "df = yf.download(stock_id, period=\"5y\")\n",
        "df['ma1'] = df['Close'].rolling(window=5).mean()\n",
        "df['ma2'] = df['Close'].rolling(window=10).mean()\n",
        "\n",
        "\n",
        "# 定義回測策略\n",
        "class CrossStrategy(Strategy):\n",
        "  def init(self):\n",
        "    super().init()\n",
        "\n",
        "  def next(self):\n",
        "    if crossover(self.data.ma1, self.data.ma2):\n",
        "      self.buy(size=1)\n",
        "    elif crossover(self.data.ma2, self.data.ma1):\n",
        "      self.sell(size=1)\n",
        "\n",
        "\n",
        "# 回測結果\n",
        "backtest = Backtest(df,\n",
        "        CrossStrategy,\n",
        "        cash=100000,\n",
        "        commission=0.004,\n",
        "        margin=1,\n",
        "        hedging=False,\n",
        "        trade_on_close=False,\n",
        "        exclusive_orders=False,\n",
        "        )\n",
        "stats = backtest.run()\n",
        "print(stats)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BP5FkDxALp4e"
      },
      "source": [
        "### 9️⃣ 計算賠率、取得勝率及最佳下注比例"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 先計算出獲利及虧損時的平均報酬\n",
        "def trades_returns(returns):\n",
        "    profits = returns[returns > 0].tolist()\n",
        "    losses = returns[returns < 0].tolist()\n",
        "\n",
        "    # 確保分母不為零\n",
        "    avg_profit = sum(profits) / len(profits) if profits else 0\n",
        "    avg_loss = sum(losses) / len(losses) if losses else 0\n",
        "\n",
        "    return avg_profit, avg_loss\n",
        "\n",
        "avg_profit, avg_loss = trades_returns(stats['_trades']['ReturnPct'])\n",
        "print(f\"獲利時的平均報酬:{avg_profit*100:.2f}%\")\n",
        "print(f\"虧損時的平均報酬:{avg_loss*100:.2f}%\")\n",
        "print(\"--------------------------\")\n",
        "\n",
        "# 用平均獲利除以平均虧損來推估賠率\n",
        "b = -avg_profit/avg_loss\n",
        "p = stats['Win Rate [%]']/100\n",
        "print(f\"賠率為:{b:.2f}\")\n",
        "print(f\"勝率為:{p*100:.2f}%\")\n",
        "print(\"--------------------------\")\n",
        "\n",
        "# 代入凱利公式\n",
        "best_bet = kelly_formula(p=p, b=b)\n",
        "print(\"最佳下注比例為:\", best_bet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-MP_NM-_riU7",
        "outputId": "53f4df31-68bf-4714-89bd-860fa2b882af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "獲利時的平均報酬:6.76%\n",
            "虧損時的平均報酬:-4.59%\n",
            "--------------------------\n",
            "賠率為:1.47\n",
            "勝率為:43.08%\n",
            "--------------------------\n",
            "最佳下注比例為: 0.04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 🔟 用凱利公式來更改策略"
      ],
      "metadata": {
        "id": "VP2kpv8Q_SUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 定義回測策略\n",
        "class CrossStrategy(Strategy):\n",
        "  kelly_ratio = 0.3  # 凱利公式的下注比率\n",
        "\n",
        "  def init(self):\n",
        "    super().init()\n",
        "\n",
        "  def next(self):\n",
        "\n",
        "    size = (self.equity * self.kelly_ratio) / self.data.Close[-1]\n",
        "    size = max(round(size), 1) # 確保交易股數為整數\n",
        "\n",
        "    if crossover(self.data.ma1, self.data.ma2):\n",
        "        self.buy(size=size)\n",
        "    elif crossover(self.data.ma2, self.data.ma1):\n",
        "        self.sell(size=size)\n",
        "\n",
        "\n",
        "# 回測結果\n",
        "backtest = Backtest(df,\n",
        "        CrossStrategy,\n",
        "        cash=100000,\n",
        "        commission=0.004,\n",
        "        margin=1,\n",
        "        hedging=False,\n",
        "        trade_on_close=False,\n",
        "        exclusive_orders=False,\n",
        "        )\n",
        "stats = backtest.run()\n",
        "print(stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pYDV3Kw_SoE",
        "outputId": "6587ac07-1ab3-4bb3-9e59-30b474862022"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start                     2019-01-29 00:00:00\n",
            "End                       2024-01-29 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   97.119342\n",
            "Equity Final [$]                   121074.244\n",
            "Equity Peak [$]                    134354.484\n",
            "Return [%]                          21.074244\n",
            "Buy & Hold Return [%]              190.337079\n",
            "Return (Ann.) [%]                    4.046039\n",
            "Volatility (Ann.) [%]                7.943343\n",
            "Sharpe Ratio                         0.509362\n",
            "Sortino Ratio                        0.805746\n",
            "Calmar Ratio                         0.357368\n",
            "Max. Drawdown [%]                  -11.321771\n",
            "Avg. Drawdown [%]                   -2.114875\n",
            "Max. Drawdown Duration      476 days 00:00:00\n",
            "Avg. Drawdown Duration       57 days 00:00:00\n",
            "# Trades                                  127\n",
            "Win Rate [%]                        44.094488\n",
            "Best Trade [%]                      44.828848\n",
            "Worst Trade [%]                     -13.44591\n",
            "Avg. Trade [%]                       1.527368\n",
            "Max. Trade Duration         115 days 00:00:00\n",
            "Avg. Trade Duration          25 days 00:00:00\n",
            "Profit Factor                        2.065513\n",
            "Expectancy [%]                       1.870983\n",
            "SQN                                  1.374426\n",
            "_strategy                       CrossStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                        Size  Entry...\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9-2 投資組合資金分配與風險管理"
      ],
      "metadata": {
        "id": "ZNDnWo14Xydt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1️⃣1️⃣ 掛載雲端硬碟 & 安裝套件"
      ],
      "metadata": {
        "id": "e9jZNwf8Ykcs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gdown\n",
        "import gdown\n",
        "import os\n",
        "!git clone https://github.com/FlagTech/F3933.git\n",
        "%cd F3933\n",
        "from Stock_DB import StockDB\n",
        "%cd ..\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "x7FHKcoQYjhq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c9626aa-de5a-4b05-f825-328fb6d2675b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.7.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
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            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "fatal: destination path 'Document_loader' already exists and is not an empty directory.\n",
            "/content/Document_loader\n",
            "Requirement already satisfied: faiss-cpu==1.7.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (1.7.4)\n",
            "Requirement already satisfied: langchain==0.0.316 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (0.0.316)\n",
            "Requirement already satisfied: openai==0.28.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (0.28.1)\n",
            "Requirement already satisfied: pdfplumber==0.10.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (0.10.2)\n",
            "Requirement already satisfied: tiktoken==0.5.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (0.5.1)\n",
            "Requirement already satisfied: yfinance==0.2.31 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (0.2.31)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain==0.0.316->-r requirements.txt (line 2)) (6.0.1)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain==0.0.316->-r requirements.txt (line 2)) (2.0.24)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain==0.0.316->-r requirements.txt (line 2)) (3.9.1)\n",
            "Requirement already satisfied: anyio<4.0 in /usr/local/lib/python3.10/dist-packages (from langchain==0.0.316->-r requirements.txt (line 2)) (3.7.1)\n",
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            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain==0.0.316->-r requirements.txt (line 2)) (4.9.0)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain==0.0.316->-r requirements.txt (line 2)) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain==0.0.316->-r requirements.txt (line 2)) (2023.11.17)\n",
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            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=36.0.0->pdfminer.six==20221105->pdfplumber==0.10.2->-r requirements.txt (line 4)) (1.16.0)\n",
            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json<0.7,>=0.5.7->langchain==0.0.316->-r requirements.txt (line 2)) (23.2)\n",
            "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain==0.0.316->-r requirements.txt (line 2)) (1.0.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six==20221105->pdfplumber==0.10.2->-r requirements.txt (line 4)) (2.21)\n",
            "/content\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1️⃣2️⃣ 下載資料庫"
      ],
      "metadata": {
        "id": "jG7R2MIOgcEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 指定下載路徑\n",
        "!mkdir -p \"/content/drive/MyDrive/StockGPT/\"\n",
        "output_path = '/content/drive/MyDrive/StockGPT/'\n",
        "\n",
        "# 檢查資料庫是否存在\n",
        "stock_db_path = output_path + 'stock.db'\n",
        "if not os.path.exists(stock_db_path):\n",
        "    print(\"下載資料庫中...\")\n",
        "    id = '1S5JE9ZF2hohRpvO8FikgLhQmN2DJrVgW'\n",
        "\n",
        "    gdown.download(id=id, output=stock_db_path)\n",
        "    print(\"下載完成\")\n",
        "else:\n",
        "    print(\"無需下載\")"
      ],
      "metadata": {
        "id": "rBRvifEsgcOP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "286b8885-3f50-4413-9a45-fca42f2d31d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "無需下載\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "若需更新資料庫, 可執行以下程式碼\n",
        "\n",
        "```\n",
        "stock_db = StockDB()\n",
        "stock_db.renew()\n",
        "stock_db.close()\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "pAcdsiHS_bOz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1️⃣3️⃣ 設定投資組合"
      ],
      "metadata": {
        "id": "isKciWtDVIon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 以10檔股票為例\n",
        "stock_list = [1101, 1203, 1216, 1402, 1722,\n",
        "               1762, 2330, 2608, 2884, 6405]\n",
        "\n",
        "condition = f\"股號 IN ({','.join(map(str, stock_list))})\"\n",
        "\n",
        "# 從資料庫取出資料\n",
        "stock_db = StockDB()\n",
        "df = stock_db.get(table=\"日頻\", where=condition)\n",
        "df = df.dropna()\n",
        "df.tail()"
      ],
      "metadata": {
        "id": "YwObTGQiVCP6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "39d0dfce-9b04-44cb-eaa3-60561da73ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     股號         日期        開盤價        最高價        最低價        收盤價        還原價  \\\n",
              "0  1101 2015-01-05  31.923370  31.923370  31.482035  31.629147  20.498331   \n",
              "1  1101 2015-01-06  31.261366  31.334923  30.967140  31.077475  20.140799   \n",
              "2  1101 2015-01-07  31.077475  31.298143  30.783251  30.967140  20.069292   \n",
              "3  1101 2015-01-08  31.077475  31.371700  30.967140  31.077475  20.140799   \n",
              "4  1101 2015-01-09  31.114252  31.334923  30.746471  30.746471  19.926281   \n",
              "\n",
              "          成交量   殖利率   日本益比  股價淨值比 三大法人買賣超股數   融資買入  融卷賣出  \n",
              "0   3847400.0  5.35  14.88   1.42  -1005483  211.0  20.0  \n",
              "1  10386622.0  5.44  14.62   1.40  -2524874  208.0  32.0  \n",
              "2  11841293.0  5.46  14.57   1.39  -5742000  264.0  13.0  \n",
              "3  11815462.0  5.44  14.62   1.40  -5120668  245.0  38.0  \n",
              "4  14240820.0  5.50  14.46   1.38  -7993555  566.0  56.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25661562-f745-4256-9b77-688f3719d150\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>股號</th>\n",
              "      <th>日期</th>\n",
              "      <th>開盤價</th>\n",
              "      <th>最高價</th>\n",
              "      <th>最低價</th>\n",
              "      <th>收盤價</th>\n",
              "      <th>還原價</th>\n",
              "      <th>成交量</th>\n",
              "      <th>殖利率</th>\n",
              "      <th>日本益比</th>\n",
              "      <th>股價淨值比</th>\n",
              "      <th>三大法人買賣超股數</th>\n",
              "      <th>融資買入</th>\n",
              "      <th>融卷賣出</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1101</td>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>31.923370</td>\n",
              "      <td>31.923370</td>\n",
              "      <td>31.482035</td>\n",
              "      <td>31.629147</td>\n",
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              "      <td>211.0</td>\n",
              "      <td>20.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1101</td>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>31.261366</td>\n",
              "      <td>31.334923</td>\n",
              "      <td>30.967140</td>\n",
              "      <td>31.077475</td>\n",
              "      <td>20.140799</td>\n",
              "      <td>10386622.0</td>\n",
              "      <td>5.44</td>\n",
              "      <td>14.62</td>\n",
              "      <td>1.40</td>\n",
              "      <td>-2524874</td>\n",
              "      <td>208.0</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1101</td>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>31.077475</td>\n",
              "      <td>31.298143</td>\n",
              "      <td>30.783251</td>\n",
              "      <td>30.967140</td>\n",
              "      <td>20.069292</td>\n",
              "      <td>11841293.0</td>\n",
              "      <td>5.46</td>\n",
              "      <td>14.57</td>\n",
              "      <td>1.39</td>\n",
              "      <td>-5742000</td>\n",
              "      <td>264.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1101</td>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>31.077475</td>\n",
              "      <td>31.371700</td>\n",
              "      <td>30.967140</td>\n",
              "      <td>31.077475</td>\n",
              "      <td>20.140799</td>\n",
              "      <td>11815462.0</td>\n",
              "      <td>5.44</td>\n",
              "      <td>14.62</td>\n",
              "      <td>1.40</td>\n",
              "      <td>-5120668</td>\n",
              "      <td>245.0</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1101</td>\n",
              "      <td>2015-01-09</td>\n",
              "      <td>31.114252</td>\n",
              "      <td>31.334923</td>\n",
              "      <td>30.746471</td>\n",
              "      <td>30.746471</td>\n",
              "      <td>19.926281</td>\n",
              "      <td>14240820.0</td>\n",
              "      <td>5.50</td>\n",
              "      <td>14.46</td>\n",
              "      <td>1.38</td>\n",
              "      <td>-7993555</td>\n",
              "      <td>566.0</td>\n",
              "      <td>56.0</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3be74abb-3eb9-452a-aca5-2ed50afdfa8a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1️⃣4️⃣ 計算每月的漲幅或跌幅"
      ],
      "metadata": {
        "id": "S5a8TuB5d2oe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 設定日期為索引\n",
        "df['日期'] = pd.to_datetime(df['日期'])\n",
        "df.set_index('日期', inplace=True)\n",
        "df = df[df.index > '2017-01-01']\n",
        "\n",
        "# 訓練資料與測試資料\n",
        "start = \"2021-01-01\"\n",
        "end = \"2023-10-10\"\n",
        "train_df = df[df.index <= start]\n",
        "test_df = df[(df.index > start) & (df.index <= end)]\n",
        "\n",
        "# 取出每月最後一個交易日的收盤價\n",
        "monthly_closing = train_df.groupby('股號')\\\n",
        "                          .resample('M')['收盤價'].last()\n",
        "\n",
        "# 計算每月的漲幅或跌幅\n",
        "monthly_return = monthly_closing.groupby(level=0)\\\n",
        "                                .pct_change().fillna(0)\n",
        "\n",
        "print(monthly_return)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0IfypV1Ej-v",
        "outputId": "2c816ac2-f7e6-4d16-8c70-c45bb35af657"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "股號    日期        \n",
            "1101  2017-01-31    0.000000\n",
            "      2017-02-28    0.063559\n",
            "      2017-03-31   -0.035857\n",
            "      2017-04-30   -0.033058\n",
            "      2017-05-31   -0.019943\n",
            "                      ...   \n",
            "6405  2020-08-31   -0.044855\n",
            "      2020-09-30    0.038674\n",
            "      2020-10-31   -0.061170\n",
            "      2020-11-30    0.084986\n",
            "      2020-12-31    0.120105\n",
            "Name: 收盤價, Length: 480, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1️⃣5️⃣ 計算每檔股票的最佳下注比例"
      ],
      "metadata": {
        "id": "78dlMbp-ABMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = []\n",
        "\n",
        "# 計算每檔股票的最佳下注比例\n",
        "for stock in stock_list:\n",
        "    str_stock = str(stock)\n",
        "    avg_profit, avg_loss = trades_returns(monthly_return[str_stock])\n",
        "    b = -avg_profit/avg_loss # 賠率\n",
        "    p = len(monthly_return[str_stock][monthly_return[str_stock] > 0]\n",
        "            ) / len(monthly_return[str_stock])  # 勝率\n",
        "    best_bet = kelly_formula(p=p, b=b) # 下注比例\n",
        "\n",
        "    results.append([stock, avg_profit, avg_loss, p, b, best_bet])\n",
        "\n",
        "# 合併為 DataFrame\n",
        "df_results = pd.DataFrame(results,\n",
        "              columns=['股號', '平均漲幅', '平均跌幅',\n",
        "                  '勝率', '賠率', '下注比例'])\n",
        "\n",
        "total_bet = df_results['下注比例'].sum()\n",
        "df_results['資金分配'] = df_results['下注比例'] / total_bet\n",
        "\n",
        "df_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "uBLX7R3eMiGd",
        "outputId": "fd3cb81c-2abb-4528-85cc-019c3859ee28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     股號      平均漲幅      平均跌幅        勝率        賠率  下注比例      資金分配\n",
              "0  1101  0.042147 -0.033390  0.562500  1.262293  0.22  0.111111\n",
              "1  1203  0.024024 -0.014281  0.625000  1.682224  0.40  0.202020\n",
              "2  1216  0.040596 -0.033364  0.520833  1.216760  0.13  0.065657\n",
              "3  1402  0.044509 -0.049732  0.562500  0.894973  0.07  0.035354\n",
              "4  1722  0.037341 -0.038913  0.583333  0.959612  0.15  0.075758\n",
              "5  1762  0.099330 -0.060044  0.520833  1.654290  0.23  0.116162\n",
              "6  2330  0.067950 -0.047247  0.583333  1.438197  0.29  0.146465\n",
              "7  2608  0.033360 -0.032416  0.500000  1.029125  0.01  0.005051\n",
              "8  2884  0.036476 -0.043529  0.708333  0.837968  0.36  0.181818\n",
              "9  6405  0.096346 -0.060858  0.458333  1.583135  0.12  0.060606"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e0b6418-bd8b-433d-a69f-05093de860af\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>股號</th>\n",
              "      <th>平均漲幅</th>\n",
              "      <th>平均跌幅</th>\n",
              "      <th>勝率</th>\n",
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              "      <td>-0.033364</td>\n",
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              "      <td>0.500000</td>\n",
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              "      <td>0.01</td>\n",
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              "      <td>-0.043529</td>\n",
              "      <td>0.708333</td>\n",
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              "      <td>0.36</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "    background-color: var(--bg-color);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1️⃣6️⃣ 比較平均分配與使用下注比例的報酬"
      ],
      "metadata": {
        "id": "vtbnVrn6eAA5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算測試資料的每月漲幅或跌幅\n",
        "monthly_closing_test = test_df.groupby('股號')\\\n",
        "                .resample('M')['收盤價'].last()\n",
        "monthly_return_test = monthly_closing_test.groupby(level=0)\\\n",
        "                                .pct_change().fillna(0)\n",
        "first_price = monthly_closing_test.groupby('股號').first()\n",
        "last_price = monthly_closing_test.groupby('股號').last()\n",
        "\n",
        "\n",
        "# 計算報酬率\n",
        "returns = (last_price /first_price)\n",
        "df_results['股號'] = df_results['股號'].astype(str)\n",
        "df_results_test = df_results.merge(\n",
        "    returns.rename('報酬率'), left_on='股號', right_index=True)\n",
        "display(df_results_test)\n",
        "\n",
        "# 設定初始資金\n",
        "initial_capital = 100000\n",
        "\n",
        "# 平均分配策略的結果\n",
        "avg = initial_capital / len(stock_list)\n",
        "avg_strategy = sum(df_results_test['報酬率'] * avg)\n",
        "\n",
        "# 使用下注比例的策略結果\n",
        "bet_strategy = sum(df_results_test['報酬率'] *(\n",
        "    df_results_test['資金分配'] * initial_capital))\n",
        "\n",
        "print(f\"平均分配策略的最終資金: {avg_strategy}\")\n",
        "print(f\"下注比例策略的最終資金: {bet_strategy}\")"
      ],
      "metadata": {
        "id": "7g3vYcYMXQCm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "1a8c26a6-2efd-4f29-cab7-035590f1e3de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     股號      平均漲幅      平均跌幅        勝率        賠率  下注比例      資金分配       報酬率\n",
              "0  1101  0.042147 -0.033390  0.562500  1.262293  0.22  0.111111  0.898572\n",
              "1  1203  0.024024 -0.014281  0.625000  1.682224  0.40  0.202020  1.166667\n",
              "2  1216  0.040596 -0.033364  0.520833  1.216760  0.13  0.065657  1.010294\n",
              "3  1402  0.044509 -0.049732  0.562500  0.894973  0.07  0.035354  1.098077\n",
              "4  1722  0.037341 -0.038913  0.583333  0.959612  0.15  0.075758  1.208748\n",
              "5  1762  0.099330 -0.060044  0.520833  1.654290  0.23  0.116162  1.099251\n",
              "6  2330  0.067950 -0.047247  0.583333  1.438197  0.29  0.146465  0.900169\n",
              "7  2608  0.033360 -0.032416  0.500000  1.029125  0.01  0.005051  0.899149\n",
              "8  2884  0.036476 -0.043529  0.708333  0.837968  0.36  0.181818  1.215912\n",
              "9  6405  0.096346 -0.060858  0.458333  1.583135  0.12  0.060606  1.284289"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>股號</th>\n",
              "      <th>平均漲幅</th>\n",
              "      <th>平均跌幅</th>\n",
              "      <th>勝率</th>\n",
              "      <th>賠率</th>\n",
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              "      <th>0</th>\n",
              "      <td>1101</td>\n",
              "      <td>0.042147</td>\n",
              "      <td>-0.033390</td>\n",
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              "      <td>1.262293</td>\n",
              "      <td>0.22</td>\n",
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              "      <td>0.116162</td>\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ac1047a8-52bf-493c-a328-529775f6253e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0eab4f22-6420-4c7a-ad54-8fe556ed03df\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_results_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0eab4f22-6420-4c7a-ad54-8fe556ed03df button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_results_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "平均分配策略的最終資金: 107811.2775886113\n",
            "下注比例策略的最終資金: 109524.21874729404\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1️⃣7️⃣ 與大盤績效進行比較"
      ],
      "metadata": {
        "id": "OyV5GHiLqASo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 投組每月報酬\n",
        "returns_df = pd.concat([monthly_return_test[str(stock)]\n",
        "                         for stock in stock_list], axis=1)\n",
        "returns_df.columns = stock_list\n",
        "weights = df_results['資金分配'].values\n",
        "returns_df['投組報酬'] = returns_df[stock_list].dot(weights)\n",
        "\n",
        "# 計算大盤報酬\n",
        "market_index = yf.download(\"^TWII\",start=start,end=end)\n",
        "market_closing = market_index.resample('M')['Close'].last()\n",
        "market_return = market_closing.pct_change().fillna(0)\n",
        "returns_df['大盤報酬'] = market_return\n",
        "\n",
        "# 累積報酬\n",
        "returns_df['投組累積報酬'] = (1 + returns_df['投組報酬']).cumprod() - 1\n",
        "returns_df['大盤累積報酬'] = (1 + returns_df['大盤報酬']).cumprod() - 1\n",
        "\n",
        "# 繪製大盤與投組績效\n",
        "returns_df = returns_df.drop(returns_df.index[0])\n",
        "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(15, 4))\n",
        "\n",
        "ax1.plot(returns_df['大盤報酬'], label=\"market\")\n",
        "ax1.plot(returns_df['投組報酬'], label=\"portfolio\")\n",
        "ax1.set_title(\"Monthly Returns: Portfolio vs Market\")\n",
        "ax1.legend()\n",
        "\n",
        "ax2.plot(returns_df['大盤累積報酬'], label=\"market\")\n",
        "ax2.plot(returns_df['投組累積報酬'], label=\"portfolio\")\n",
        "ax2.set_title(\"Cumulative Returns: Portfolio vs Market\")\n",
        "ax2.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "Tp99WT69qAgI",
        "outputId": "8390355b-0840-4e89-94c3-293198db4691"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1️⃣8️⃣ 投資組合標準差 (σ)"
      ],
      "metadata": {
        "id": "sR1HfwW7eAFy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算共變異數矩陣\n",
        "stk_returns_df = returns_df.iloc[:, :-4]\n",
        "cov_matrix = stk_returns_df.cov()\n",
        "display(cov_matrix)\n",
        "\n",
        "# 計算投資組合的報酬與標準差\n",
        "portfolio_return = np.dot(weights, stk_returns_df.mean().values)\n",
        "portfolio_std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix.values, weights)))\n",
        "\n",
        "# 轉換成年化\n",
        "annualized_portfolio_return = (1 + portfolio_return)**12 - 1\n",
        "annualized_portfolio_std = portfolio_std * (12**0.5)\n",
        "annualized_market_return = (1 + market_return.mean())**12 - 1\n",
        "annualized_market_std = market_return.std() * (12**0.5)\n",
        "\n",
        "print(f\"投資組合的年化報酬率:{annualized_portfolio_return*100:.2f}%\")\n",
        "print(f\"投資組合的年化標準差:{annualized_portfolio_std*100:.2f}%\")\n",
        "\n",
        "print(f\"大盤指數的年化報酬率:{annualized_market_return*100:.2f}%\")\n",
        "print(f\"大盤指數的年化標準差:{annualized_market_std*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "wAQ6aVO7Oawn",
        "outputId": "e222658f-e274-4570-8e01-598033aed51d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          1101      1203      1216      1402      1722      1762      2330  \\\n",
              "1101  0.004561  0.000414  0.000602  0.001047  0.002989  0.002175  0.002499   \n",
              "1203  0.000414  0.001502  0.000350  0.000535  0.000050  0.000820  0.001437   \n",
              "1216  0.000602  0.000350  0.001127  0.000170  0.000113  0.001444  0.000276   \n",
              "1402  0.001047  0.000535  0.000170  0.002178  0.000612  0.001772  0.000339   \n",
              "1722  0.002989  0.000050  0.000113  0.000612  0.004117  0.001316  0.001476   \n",
              "1762  0.002175  0.000820  0.001444  0.001772  0.001316  0.014119  0.002504   \n",
              "2330  0.002499  0.001437  0.000276  0.000339  0.001476  0.002504  0.006524   \n",
              "2608  0.001317  0.000246 -0.000052  0.000033 -0.000731  0.000869  0.001485   \n",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "投資組合的年化報酬率:6.42%\n",
            "投資組合的年化標準差:14.12%\n",
            "大盤指數的年化報酬率:4.70%\n",
            "大盤指數的年化標準差:17.68%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1️⃣9️⃣ 風險值 (Value at Risk, VaR)"
      ],
      "metadata": {
        "id": "7RgHhuVReAIu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confidence_level = 0.95\n",
        "\n",
        "# 月頻 VaR\n",
        "VaR = portfolio_return - portfolio_std * norm.ppf(confidence_level)\n",
        "# 年頻 VaR\n",
        "VaR_annualized = VaR * (12**0.5)\n",
        "\n",
        "print(f\"在 {confidence_level*100:.0f}% 的信心水準下,\\\n",
        " 下個月的最大可能損失為：{VaR*100:.2f}%\")\n",
        "print(f\"在 {confidence_level*100:.0f}% 的信心水準下,\\\n",
        " 明年最大可能損失為：{VaR_annualized*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bg-TnxJcYHsr",
        "outputId": "7b6a20f2-6750-44f5-9795-3d939c8f5eba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "在 95% 的信心水準下, 下個月的最大可能損失為：-6.19%\n",
            "在 95% 的信心水準下, 明年最大可能損失為：-21.43%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2️⃣0️⃣ beta 係數 (β)\n"
      ],
      "metadata": {
        "id": "BZt3Q-SdeALY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算β係數\n",
        "portfolio_market_cov = returns_df[['投組報酬', '大盤報酬']].cov()\\\n",
        "                               .iloc[0, 1]\n",
        "market_var = returns_df['大盤報酬'].var()\n",
        "portfolio_beta = portfolio_market_cov / market_var\n",
        "\n",
        "print(f\"投組與大盤的共變異數為：{portfolio_market_cov:.5f},\\\n",
        "    大盤變異數為：{market_var:.5f}\")\n",
        "print(f\"投資組合的β係數：{portfolio_beta:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jV9indGrgSGg",
        "outputId": "ba717d0e-fe20-4bdb-d547-f3fa099289bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "投組與大盤的共變異數為：0.00177,    大盤變異數為：0.00269\n",
            "投資組合的β係數：0.66\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2️⃣1️⃣ 夏普比率 (Sharpe Ratio)"
      ],
      "metadata": {
        "id": "5qVe-laAeANx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 設定無風險利率\n",
        "risk_free_rate = 0.015/12  # 轉換成月頻 (算術平均)\n",
        "\n",
        "average_return = returns_df['投組報酬'].mean()\n",
        "portfolio_std = returns_df['投組報酬'].std()\n",
        "\n",
        "sharpe_ratio = (average_return - risk_free_rate) / portfolio_std\n",
        "sharpe_ratio_annualized = sharpe_ratio * (12**0.5)\n",
        "\n",
        "print(f\"投資組合的夏普比率：{sharpe_ratio:.2f}\")\n",
        "print(f\"年化的夏普比率：{sharpe_ratio_annualized:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xq5ZY8CQ-FIr",
        "outputId": "1ee8d2e6-3d1f-49f3-a85d-20933760db06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "投資組合的夏普比率：0.10\n",
            "年化的夏普比率：0.34\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}