ch05_stock_backtesting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFQ17AmL0Yd0"
      },
      "source": [
        "# CH-05 AI 技術指標回測系統"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a42M4mg1qNQ5"
      },
      "source": [
        "## 5-2 強大的回測工具：backtesting.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwGTl75BLNwu"
      },
      "source": [
        "### 1️⃣ 安裝及匯入套件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        "id": "V9x1F86C4T9u",
        "outputId": "b35efd4f-0054-451d-b24a-c6ace32c3095"
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      "outputs": [
        {
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          "name": "stdout",
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          ]
        },
        {
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          "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"
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        }
      ],
      "source": [
        "!pip install openai\n",
        "!pip install yfinance==0.2.38\n",
        "!pip install backtesting\n",
        "!pip install bokeh==2.4.3 # 繪圖套件\n",
        "from  openai import OpenAI, OpenAIError # 串接 OpenAI API\n",
        "import yfinance as yf\n",
        "import pandas as pd # 資料處理套件\n",
        "import datetime as dt # 時間套件\n",
        "from backtesting import Backtest, Strategy\n",
        "from backtesting.lib import crossover"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2️⃣ 取得股價資料"
      ],
      "metadata": {
        "id": "ebiRubdMv_AZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 輸入股票代號\n",
        "stock_id = \"2330.tw\"\n",
        "# 抓取 5 年資料\n",
        "df = yf.download(stock_id, period=\"5y\")\n",
        "# 計算指標\n",
        "df['ma1'] = df['Close'].rolling(window=5).mean()\n",
        "df['ma2'] = df['Close'].rolling(window=10).mean()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
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        "id": "t30pt68amGmN",
        "outputId": "4e22cc6f-47ef-4dd6-edbe-37eac41f7e07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open   High    Low  Close   Adj Close    Volume    ma1  ma2\n",
              "Date                                                                    \n",
              "2018-11-07  233.0  236.0  232.5  234.0  205.401077  34424652    NaN  NaN\n",
              "2018-11-08  237.5  237.5  236.0  236.5  207.595581  24739146    NaN  NaN\n",
              "2018-11-09  234.5  234.5  231.0  231.0  202.767746  23219081    NaN  NaN\n",
              "2018-11-12  230.0  234.5  230.0  231.5  203.206650  23228445    NaN  NaN\n",
              "2018-11-13  224.5  228.0  224.5  227.5  199.695480  36104127  232.1  NaN"
            ],
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              "      <th>2018-11-12</th>\n",
              "      <td>230.0</td>\n",
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              "  .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-b29d4de7-264d-4960-93e4-6966b5f5af3c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3️⃣ 定義回測策略"
      ],
      "metadata": {
        "id": "T59rbTgDwoAZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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)"
      ],
      "metadata": {
        "id": "4C7n0hxlwx-x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC1gzHDLiqhT"
      },
      "source": [
        "### 4️⃣ 回測結果"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OBkpRomPEXd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "c52f6ec9-622f-4918-b17c-bde3d1f2024a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start                     2018-11-07 00:00:00\n",
            "End                       2023-11-07 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   76.853377\n",
            "Equity Final [$]                   100527.042\n",
            "Equity Peak [$]                    100666.618\n",
            "Return [%]                           0.527042\n",
            "Buy & Hold Return [%]              136.752137\n",
            "Return (Ann.) [%]                    0.109175\n",
            "Volatility (Ann.) [%]                0.138633\n",
            "Sharpe Ratio                         0.787508\n",
            "Sortino Ratio                        1.209536\n",
            "Calmar Ratio                         0.465253\n",
            "Max. Drawdown [%]                   -0.234657\n",
            "Avg. Drawdown [%]                   -0.032216\n",
            "Max. Drawdown Duration      617 days 00:00:00\n",
            "Avg. Drawdown Duration       50 days 00:00:00\n",
            "# Trades                                   63\n",
            "Win Rate [%]                        49.206349\n",
            "Best Trade [%]                      44.828848\n",
            "Worst Trade [%]                     -13.44591\n",
            "Avg. Trade [%]                       2.117056\n",
            "Max. Trade Duration          80 days 00:00:00\n",
            "Avg. Trade Duration          28 days 00:00:00\n",
            "Profit Factor                        2.575874\n",
            "Expectancy [%]                        2.50118\n",
            "SQN                                    1.8211\n",
            "_strategy                       CrossStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                       Size  EntryB...\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Size  EntryBar  ExitBar  EntryPrice  ExitPrice     PnL  ReturnPct  \\\n",
              "0     1        17       24     230.418      220.0 -10.418  -0.045213   \n",
              "1     1        29       76     221.884      231.0   9.116   0.041085   \n",
              "2     1        82       92     240.458      251.0  10.542   0.043841   \n",
              "3     1        93       94     250.498      249.0  -1.498  -0.005980   \n",
              "4     1        95      112     252.004      261.5   9.496   0.037682   \n",
              "\n",
              "   EntryTime   ExitTime Duration  \n",
              "0 2018-11-30 2018-12-11  11 days  \n",
              "1 2018-12-18 2019-03-08  80 days  \n",
              "2 2019-03-18 2019-04-01  14 days  \n",
              "3 2019-04-02 2019-04-03   1 days  \n",
              "4 2019-04-08 2019-05-02  24 days  "
            ],
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              "      <th>Size</th>\n",
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              "      <td>2018-11-30</td>\n",
              "      <td>2018-12-11</td>\n",
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              "      <td>80 days</td>\n",
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              "      <td>2019-03-18</td>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>14 days</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>250.498</td>\n",
              "      <td>249.0</td>\n",
              "      <td>-1.498</td>\n",
              "      <td>-0.005980</td>\n",
              "      <td>2019-04-02</td>\n",
              "      <td>2019-04-03</td>\n",
              "      <td>1 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>95</td>\n",
              "      <td>112</td>\n",
              "      <td>252.004</td>\n",
              "      <td>261.5</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.037682</td>\n",
              "      <td>2019-04-08</td>\n",
              "      <td>2019-05-02</td>\n",
              "      <td>24 days</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e28b7f2c-9fbf-4307-a450-2fa0a0e7522b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e28b7f2c-9fbf-4307-a450-2fa0a0e7522b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-747fb43b-d68d-4a40-bada-e5147163c395\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-747fb43b-d68d-4a40-bada-e5147163c395')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\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",
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              "  }\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",
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              "    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-747fb43b-d68d-4a40-bada-e5147163c395 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "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",
        "\n",
        "# 印出回測績效\n",
        "print(stats)\n",
        "\n",
        "# 查看詳細的交易紀錄\n",
        "stats[\"_trades\"].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5️⃣ 回測繪圖"
      ],
      "metadata": {
        "id": "0jqJLS9TSqyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "backtest.plot(plot_equity=True,\n",
        "       plot_return=False,\n",
        "       plot_pl=True,\n",
        "       plot_volume=True,\n",
        "       plot_drawdown=False,\n",
        "       superimpose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "PeCJ5-XHSrU9",
        "outputId": "67250f91-edfe-45d2-d457-3463207b552c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
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              "\n",
              "  const force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "const JS_MIME_TYPE = 'application/javascript';\n",
              "  const HTML_MIME_TYPE = 'text/html';\n",
              "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    const script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    const cell = handle.cell;\n",
              "\n",
              "    const id = cell.output_area._bokeh_element_id;\n",
              "    const server_id = cell.output_area._bokeh_server_id;\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null && id in Bokeh.index) {\n",
              "      Bokeh.index[id].model.document.clear();\n",
              "      delete Bokeh.index[id];\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd_clean, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            const id = msg.content.text.trim();\n",
              "            if (id in Bokeh.index) {\n",
              "              Bokeh.index[id].model.document.clear();\n",
              "              delete Bokeh.index[id];\n",
              "            }\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd_destroy);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    const output_area = handle.output_area;\n",
              "    const output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      const bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      const script_attrs = bk_div.children[0].attributes;\n",
              "      for (let i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      const toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    const events = require('base/js/events');\n",
              "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    const el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error(url) {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < css_urls.length; i++) {\n",
              "      const url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < js_urls.length; i++) {\n",
              "      const url = js_urls[i];\n",
              "      const element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n",
              "  const css_urls = [];\n",
              "\n",
              "  const inline_js = [    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "function(Bokeh) {\n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "          for (let i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "} else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div class=\"bk-root\" id=\"a02440d6-5a42-4893-bfe9-e1aa589c11a7\" data-root-id=\"1407\"></div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "  const docs_json = {\"5b6c10c0-5a72-4953-a622-c0f47a9be151\":{\"defs\":[],\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"1404\"},{\"id\":\"1406\"}],\"sizing_mode\":\"stretch_width\"},\"id\":\"1407\",\"type\":\"Row\"},{\"attributes\":{\"coordinates\":null,\"group\":null},\"id\":\"1098\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1259\",\"type\":\"UndoTool\"},{\"attributes\":{\"callback\":null,\"formatters\":{\"@datetime\":\"datetime\"},\"mode\":\"vline\",\"point_policy\":\"follow_mouse\",\"renderers\":[{\"id\":\"1307\"}],\"tooltips\":[[\"Date\",\"@datetime{%c}\"],[\"x, y\",\"$index\\u00a0\\u00a0\\u00a0\\u00a0$y{0,0.0[0000]}\"],[\"OHLC\",\"@Open{0,0.0[0000]}\\u00a0\\u00a0\\u00a0\\u00a0@High{0,0.0[0000]}\\u00a0\\u00a0\\u00a0\\u00a0@Low{0,0.0[0000]}\\u00a0\\u00a0\\u00a0\\u00a0@Close{0,0.0[0000]}\"],[\"Volume\",\"@Volume{0,0}\"]]},\"id\":\"1331\",\"type\":\"HoverTool\"},{\"attributes\":{\"toolbar\":{\"id\":\"1405\"}},\"id\":\"1406\",\"type\":\"ToolbarBox\"},{\"attributes\":{\"days\":[\"%d %b\",\"%a %d\"],\"months\":[\"%m/%Y\",\"%b'%y\"]},\"id\":\"1040\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{},\"id\":\"1261\",\"type\":\"ResetTool\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"1256\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1395\",\"type\":\"AllLabels\"},{\"attributes\":{\"overlay\":{\"id\":\"1263\"}},\"id\":\"1258\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"args\":{\"ohlc_range\":{\"id\":\"1005\"},\"source\":{\"id\":\"1035\"},\"volume_range\":{\"id\":\"1242\"}},\"code\":\"if (!window._bt_scale_range) {\\n    window._bt_scale_range = function (range, min, max, pad) {\\n        \\\"use strict\\\";\\n        if (min !== Infinity && max !== -Infinity) {\\n            pad = pad ? 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J2QgAAgG2ww3ZCAABA0wLEdkIAAAA5VcR2QgAAwJ6nxHZCAACABPrEdkIAAMA18cV2QgAAgJtDxnZCAABAAZbGdkIAAABn6MZ2QgAAAP4xyHZCAADAY4TIdkIAAIDJ1sh2QgAAQC8pyXZCAAAAlXvJdkIAAEDGcsp2QgAAACzFynZCAADAkRfLdkIAAID3act2QgAAQF28y3ZCAACAjrPMdkIAAED0Bc12QgAAAFpYzXZCAADAv6rNdkIAAIAl/c12QgAAwFb0znZCAACAvEbPdkIAAEAimc92QgAAAIjrz3ZCAADA7T3QdkIAAMCEh9F2QgAAgOrZ0XZCAABAUCzSdkIAAEDnddN2QgAAAE3I03ZCAADAshrUdkIAAIAYbdR2QgAAQH6/1HZCAACAr7bVdkIAAEAVCdZ2QgAAAHtb1nZCAADA4K3WdkIAAIBGANd2QgAAgN1J2HZCAABAQ5zYdkIAAACp7th2QgAAwA5B2XZCAAAAQDjadkIAAMClitp2QgAAgAvd2nZCAABACHncdkIAAABuy9x2QgAAwNMd3XZCAACAOXDddkIAAECfwt12QgAAgNC53nZCAABANgzfdkIAAACcXt92QgAAwAGx33ZCAACAZwPgdkIAAMCY+uB2QgAAgP5M4XZCAABAZJ/hdkIAAADK8eF2QgAAwC9E4nZCAAAAYTvjdkIAAMDGjeN2QgAAgCzg43ZCAABAkjLkdkIAAAD4hOR2QgAAQCl85XZCAAAAj87ldkIAAMD0IOZ2QgAAgFpz5nZCAABAwMXmdkIAAIDxvOd2QgAAQFcP6HZCAAAAvWHodkIAAMAitOh2QgAAgIgG6XZCAADAuf3pdkIAAIAfUOp2QgAAQIWi6nZCAAAA6/TqdkIAAMBQR+t2QgAAAII+7HZCAADA55DsdkIAAIBN4+x2QgAAQLM17XZCAAAAGYjtdkIAAEBKf+52QgAAALDR7nZCAADAFSTvdkIAAIB7du92QgAAQOHI73ZCAACAEsDwdkIAAEB4EvF2QgAAAN5k8XZCAADAQ7fxdkIAAICpCfJ2QgAAwNoA83ZCAACAQFPzdkIAAECmpfN2QgAAAAz483ZCAADAcUr0dkIAAACjQfV2QgAAwAiU9XZCAABA1Dj2dkIAAAA6i/Z2QgAAQGuC93ZCAAAA0dT3dkIAAMA2J/h2QgAAgJx5+HZCAABAAsz4dkIAAIAzw/l2QgAAQJkV+nZCAAAA/2f6dkIAAMBkuvp2QgAAgMoM+3ZCAADA+wP8dkIAAED1O/92QgAAAFuO/3ZCAABAjIUAd0IAAADy1wB3QgAAwFcqAXdCAACAvXwBd0IAAEAjzwF3QgAAgFTGAndCAABAuhgDd0IAAAAgawN3QgAAwIW9A3dCAACA6w8Ed0IAAMAcBwV3QgAAgIJZBXdCAABA6KsFd0IAAABO/gV3QgAAwLNQBndCAAAA5UcHd0IAAMBKmgd3QgAAgLDsB3dCAABAFj8Id0IAAECtiAl3QgAAABPbCXdCAADAeC0Kd0IAAIDefwp3QgAAQETSCndCAACAdckLd0IAAEDbGwx3QgAAAEFuDHdCAADApsAMd0IAAIAMEw13QgAAwD0KDndCAACAo1wOd0IAAEAJrw53QgAAAG8BD3dCAADA1FMPd0IAAAAGSxB3QgAAwGudEHdCAACA0e8Qd0IAAEA3QhF3QgAAAJ2UEXdCAABAzosSd0IAAAA03hJ3QgAAwJkwE3dCAACAlswUd0IAAED8HhV3QgAAAGJxFXdCAADAx8MVd0IAAIAtFhZ3QgAAwF4NF3dCAACAxF8Xd0IAAEAqshd3QgAAAJAEGHdCAADA9VYYd0IAAAAnThl3QgAAwIygGXdCAACA8vIZd0IAAEBYRRp3QgAAAL6XGndCAABA744bd0IAAABV4Rt3QgAAwLozHHdCAACAIIYcd0IAAIC3zx13QgAAQB0iHndCAAAAg3Qed0IAAMDoxh53QgAAgE4ZH3dCAADAfxAgd0IAAIDlYiB3QgAAQEu1IHdCAAAAsQchd0IAAMAWWiF3QgAAAEhRIndCAADAraMid0IAAIAT9iJ3QgAAQHlII3dCAAAA35ojd0IAAEAQkiR3QgAAAHbkJHdCAADA2zYld0IAAIBBiSV3QgAAQKfbJXdCAACA2NImd0IAAEA+JSd3QgAAAKR3J3dCAADACcond0IAAIBvHCh3QgAAwKATKXdCAACABmYpd0IAAEBsuCl3QgAAANIKKndCAADAN10qd0IAAABpVCt3QgAAwM6mK3dCAACANPkrd0IAAECaSyx3QgAAAACeLHdCAABAMZUtd0IAAACX5y13QgAAwPw5LndCAACA+dUvd0IAAEBfKDB3QgAAAMV6MHdCAADAKs0wd0IAAICQHzF3QgAAwMEWMndCAACAJ2kyd0IAAECNuzJ3QgAAAPMNM3dCAADAWGAzd0IAAACKVzR3QgAAwO+pNHdCAACAVfw0d0IAAEC7TjV3QgAAACGhNXdCAABAUpg2d0IAAAC46jZ3QgAAwB09N3dCAACAg483d0IAAEDp4Td3QgAAgBrZOHdCAABAgCs5d0IAAADmfTl3QgAAwEvQOXdCAACAsSI6d0IAAMDiGTt3QgAAgEhsO3dCAABArr47d0IAAAAUETx3QgAAwHljPHdCAAAAq1o9d0IAAMAQrT13QgAAgHb/PXdCAABA3FE+d0IAAABCpD53QgAAQHObP3dCAAAA2e0/d0IAAMA+QEB3QgAAgKSSQHdCAABACuVAd0IAAIA73EF3QgAAQKEuQndCAAAAB4FCd0IAAMBs00J3QgAAgNIlQ3dCAADAAx1Ed0IAAIBpb0R3QgAAQM/BRHdCAAAANRRFd0IAAMCaZkV3QgAAAMxdRndCAADAMbBGd0IAAICXAkd3QgAAQP1UR3dCAAAAY6dHd0IAAECUnkh3QgAAAPrwSHdCAADAX0NJd0IAAIDFlUl3QgAAQCvoSXdCAACAXN9Kd0IAAEDCMUt3QgAAACiES3dCAADAjdZLd0IAAIDzKEx3QgAAwCQgTXdCAACAinJNd0IAAEDwxE13QgAAAO1gT3dCAADAUrNPd0IAAIC4BVB3QgAAQB5YUHdCAABAtaFRd0IAAAAb9FF3QgAAwIBGUndCAACA5phSd0IAAEBM61J3QgAAgH3iU3dCAABA4zRUd0IAAABJh1R3QgAAwK7ZVHdCAACAFCxVd0IAAMBFI1Z3QgAAgKt1VndCAABAEchWd0IAAAB3Gld3QgAAwNxsV3dCAAAADmRYd0IAAMBztlh3QgAAgNkIWXdCAABAP1tZd0IAAAClrVl3QgAAQNakWndCAAAAPPdad0IAAMChSVt3QgAAgAecW3dCAABAbe5bd0IAAICe5Vx3QgAAQAQ4XXdCAAAAaopdd0IAAMDP3F13QgAAgDUvXndCAADAZiZfd0IAAIDMeF93QgAAQDLLX3dCAAAAmB1gd0IAAMD9b2B3QgAAAC9nYXdCAADAlLlhd0IAAID6C2J3QgAAQGBeYndCAAAAxrBid0IAAED3p2N3QgAAAF36Y3dCAADAwkxkd0IAAIAon2R3QgAAQI7xZHdCAACAv+hld0IAAEAlO2Z3QgAAAIuNZndCAADA8N9md0IAAIBWMmd3QgAAwIcpaHdCAACA7Xtod0IAAEBTzmh3QgAAALkgaXdCAADAHnNpd0IAAABQamp3QgAAwLW8andCAACAGw9rd0IAAECBYWt3QgAAQBirbHdCAAAAfv1sd0IAAMDjT213QgAAgEmibXdCAABAr/Rtd0IAAIDg6253QgAAQEY+b3dCAAAArJBvd0IAAMAR4293QgAAgHc1cHdCAADAqCxxd0IAAIAOf3F3QgAAQHTRcXdCAAAA2iNyd0IAAMA/dnJ3QgAAAHFtc3dCAADA1r9zd0IAAIA8EnR3QgAAQKJkdHdCAAAACLd0d0IAAEA5rnV3QgAAAJ8AdndCAADABFN2d0IAAIBqpXZ3QgAAQND3dndCAABAldR6d0IAAAD7Jnt3QgAAwGB5e3dCAAAAknB8d0IAAMD3wnx3QgAAgF0VfXdCAABAw2d9d0IAAAApun13QgAAAMADf3dCAADAJVZ/d0IAAICLqH93QgAAQPH6f3dCAACAIvKAd0IAAECIRIF3QgAAAO6WgXdCAADAU+mBd0IAAIC5O4J3QgAAwOoyg3dCAACAUIWDd0IAAEC214N3QgAAABwqhHdCAADAgXyEd0IAAACzc4V3QgAAwBjGhXdCAACAfhiGd0IAAEDkaoZ3QgAAAEq9hndCAABAe7SHd0IAAADhBoh3QgAAwEZZiHdCAACArKuId0IAAAAPmop3QgAAwHTsindCAACA2j6Ld0IAAMALNox3QgAAgHGIjHdCAABA19qMd0IAAAA9LY13QgAAwKJ/jXdCAAAA1HaOd0IAAMA5yY53QgAAgJ8bj3dCAABABW6Pd0IAAABrwI93QgAAQJy3kHdCAAAAAgqRd0IAAMBnXJF3QgAAgM2ukXdCAACAZPiSd0IAAEDKSpN3QgAAADCdk3dCAADAle+Td0IAAID7QZR3QgAAwCw5lXdCAACAkouVd0IAAED43ZV3QgAAAF4wlndCAADAw4KWd0IAAAD1eZd3QgAAwFrMl3dCAACAwB6Yd0IAAEAmcZh3QgAAAIzDmHdCAABAvbqZd0IAAAAjDZp3QgAAwIhfmndCAACA7rGad0IAAEBUBJt3QgAAgIX7m3dCAABA602cd0IAAABRoJx3QgAAwLbynHdCAACAHEWdd0IAAMBNPJ53QgAAgLOOnndCAABAGeGed0IAAAB/M593QgAAwOSFn3dCAADAe8+gd0IAAIDhIaF3QgAAQEd0oXdCAAAArcahd0IAAEDevaJ3QgAAAEQQo3dCAADAqWKjd0IAAIAPtaN3QgAAQHUHpHdCAACApv6kd0IAAEAMUaV3QgAAAHKjpXdCAADA1/Wld0IAAIA9SKZ3QgAAwG4/p3dCAACA1JGnd0IAAEA65Kd3QgAAAKA2qHdCAADABYmod0IAAAA3gKl3QgAAwJzSqXdCAACAAiWqd0IAAEBod6p3QgAAAM7JqndCAABA/8Crd0IAAABlE6x3QgAAwMplrHdCAACAMLisd0IAAECWCq13QgAAgMcBrndCAABALVSud0IAAACTpq53QgAAwPj4rndCAACAXkuvd0IAAMCPQrB3QgAAgPWUsHdCAABAW+ewd0IAAADBObF3QgAAwCaMsXdCAAAAWIOyd0IAAMC91bJ3QgAAgCMos3dCAABAiXqzd0IAAADvzLN3QgAAQCDEtHdCAAAAhha1d0IAAMDraLV3QgAAgFG7tXdCAABAtw22d0IAAIDoBLd3QgAAQE5Xt3dCAAAAtKm3d0IAAMAZ/Ld3QgAAgH9OuHdCAADAsEW5d0IAAIAWmLl3QgAAQHzquXdCAAAA4jy6d0IAAMBHj7p3QgAAAHmGu3dCAADA3ti7d0IAAIBEK7x3QgAAQKp9vHdCAAAAENC8d0IAAEBBx713QgAAAKcZvndCAADADGy+d0IAAIByvr53QgAAQNgQv3dCAAAA1azAd0IAAMA6/8B3QgAAgKBRwXdCAADA0UjCd0IAAIA3m8J3QgAAQJ3twndCAAAAA0DDd0IAAMBoksN3QgAAAJqJxHdCAADA/9vEd0IAAIBlLsV3QgAAQMuAxXdCAAAAMdPFd0IAAADIHMd3QgAAwC1vx3dCAACAk8HHd0IAAED5E8h3QgAAgCoLyXdCAABAkF3Jd0IAAAD2r8l3QgAAwFsCyndCAACAwVTKd0IAAMDyS8t3QgAAgFiey3dCAABAvvDLd0IAAAAkQ8x3QgAAwImVzHdCAAAAu4zNd0IAAMAg3813QgAAgIYxzndCAABA7IPOd0IAAABS1s53QgAAQIPNz3dCAAAA6R/Qd0IAAMBOctB3QgAAgLTE0HdCAABAGhfRd0IAAIBLDtJ3QgAAQLFg0ndCAAAAF7PSd0IAAMB8BdN3QgAAgOJX03dCAADAE0/Ud0IAAIB5odR3QgAAQN/z1HdCAAAARUbVd0IAAMCqmNV3QgAAANyP1ndCAADAQeLWd0IAAICnNNd3QgAAQA2H13dCAAAAc9nXd0IAAECk0Nh3QgAAAAoj2XdCAADAb3XZd0IAAIDVx9l3QgAAQDsa2ndCAACAbBHbd0IAAEDSY9t3QgAAADi223dCAADAnQjcd0IAAIADW9x3QgAAwDRS3XdCAACAmqTdd0IAAEAA9913QgAAAGZJ3ndCAADAy5ved0IAAAD9kt93QgAAwGLl33dCAACAyDfgd0IAAEAuiuB3QgAAQMXT4XdCAAAAKybid0IAAMCQeOJ3QgAAgPbK4ndCAABAXB3jd0IAAICNFOR3QgAAQPNm5HdCAAAAWbnkd0IAAMC+C+V3QgAAgCRe5XdCAADAVVXmd0IAAIC7p+Z3QgAAQCH65ndCAAAAh0znd0IAAMDsnud3QgAAAB6W6HdCAADAg+jod0IAAIDpOul3QgAAgK4X7XdCAABAFGrtd0IAAAB6vO13QgAAwN8O7ndCAACARWHud0IAAMB2WO93QgAAgNyq73dCAABAQv3vd0IAAACoT/B3QgAAwA2i8HdCAAAAP5nxd0IAAMCk6/F3QgAAgAo+8ndCAABAcJDyd0IAAADW4vJ3QgAAAG0s9HdCAADA0n70d0IAAIA40fR3QgAAQJ4j9XdCAACAzxr2d0IAAEA1bfZ3QgAAAJu/9ndCAADAABL3d0IAAIBmZPd3QgAAwJdb+HdCAACA/a34d0IAAEBjAPl3QgAAAMlS+XdCAADALqX5d0IAAABgnPp3QgAAwMXu+ndCAACAK0H7d0IAAECRk/t3QgAAAPfl+3dCAABAKN38d0IAAACOL/13QgAAwPOB/XdCAACAWdT9d0IAAEC/Jv53QgAAALzC/3dCAADAIRUAeEIAAICHZwB4QgAAwLheAXhCAACAHrEBeEIAAECEAwJ4QgAAAOpVAnhCAADAT6gCeEIAAACBnwN4QgAAwObxA3hCAACATEQEeEIAAECylgR4QgAAABjpBHhCAABASeAFeEIAAACvMgZ4QgAAwBSFBnhCAACAetcGeEIAAEDgKQd4QgAAQHdzCHhCAAAA3cUIeEIAAMBCGAl4QgAAgKhqCXhCAADA2WEKeEIAAIA/tAp4QgAAQKUGC3hCAAAAC1kLeEIAAMBwqwt4QgAAAKKiDHhCAADAB/UMeEIAAIBtRw14QgAAQNOZDXhCAAAAOewNeEIAAEBq4w54QgAAANA1D3hCAADANYgPeEIAAICb2g94QgAAQAEtEHhCAACAMiQReEIAAECYdhF4QgAAAP7IEXhCAADAYxsSeEIAAMD6ZBN4QgAAgGC3E3hCAABAxgkUeEIAAAAsXBR4QgAAwJGuFHhCAAAAw6UVeEIAAMAo+BV4QgAAgI5KFnhCAABA9JwWeEIAAABa7xZ4QgAAQIvmF3hCAAAA8TgYeEIAAMBWixh4QgAAgLzdGHhCAABAIjAZeEIAAIBTJxp4QgAAQLl5GnhCAAAAH8waeEIAAMCEHht4QgAAgOpwG3hCAADAG2gceEIAAICBuhx4QgAAQOcMHXhCAAAATV8deEIAAMCysR14QgAAAOSoHnhCAADASfseeEIAAICvTR94QgAAQBWgH3hCAAAAe/IfeEIAAECs6SB4QgAAABI8IXhCAADAd44heEIAAIDd4CF4QgAAQEMzInhCAACAdCojeEIAAEDafCN4QgAAAEDPI3hCAADApSEkeEIAAIALdCR4QgAAwDxrJXhCAACAor0leEIAAEAIECZ4QgAAAG5iJnhCAADA07QmeEIAAAAFrCd4QgAAwGr+J3hCAACA0FAoeEIAAEA2oyh4QgAAAJz1KHhCAABAzewpeEIAAAAzPyp4QgAAwJiRKnhCAACA/uMqeEIAAEBkNit4QgAAgJUtLHhCAABA+38seEIAAABh0ix4QgAAwMYkLXhCAACALHcteEIAAMBdbi54QgAAgMPALnhCAABAKRMveEIAAACPZS94QgAAwPS3L3hCAAAAJq8weEIAAMCLATF4QgAAgPFTMXhCAABAV6YxeEIAAEDu7zJ4QgAAAFRCM3hCAADAuZQzeEIAAIAf5zN4QgAAQIU5NHhCAACAtjA1eEIAAEAcgzV4QgAAAILVNXhCAADA5yc2eEIAAIBNejZ4QgAAwH5xN3hCAACA5MM3eEIAAEBKFjh4QgAAALBoOHhCAADAFbs4eEIAAABHsjl4QgAAwKwEOnhCAACAElc6eEIAAEB4qTp4QgAAAN77OnhCAAAAdUU8eEIAAMDalzx4QgAAgEDqPHhCAABApjw9eEIAAIDXMz54QgAAQD2GPnhCAAAAo9g+eEIAAMAIKz94QgAAgG59P3hCAADAn3RAeEIAAIAFx0B4QgAAQGsZQXhCAAAA0WtBeEIAAMA2vkF4QgAAAGi1QnhCAADAzQdDeEIAAIAzWkN4QgAAQJmsQ3hCAAAA//5DeEIAAEAw9kR4QgAAAJZIRXhCAADA+5pFeEIAAIBh7UV4QgAAQMc/RnhCAACA+DZHeEIAAEBeiUd4QgAAAMTbR3hCAADAKS5IeEIAAICPgEh4QgAAwMB3SXhCAACAJspJeEIAAECMHEp4QgAAAPJuSnhCAADAV8FKeEIAAACJuEt4QgAAwO4KTHhCAACAVF1MeEIAAEC6r0x4QgAAACACTXhCAABAUflNeEIAAAC3S054QgAAwByeTnhCAACAgvBOeEIAAEDoQk94QgAAgBk6UHhCAABAf4xQeEIAAADl3lB4QgAAwEoxUXhCAACAsINReEIAAMDhelJ4QgAAgEfNUnhCAABArR9TeEIAAAATclN4QgAAwHjEU3hCAAAAqrtUeEIAAMAPDlV4QgAAgHVgVXhCAABA27JVeEIAAABBBVZ4QgAAANhOV3hCAADAPaFXeEIAAICj81d4QgAAQAlGWHhCAACAOj1ZeEIAAECgj1l4QgAAAAbiWXhCAADAazRaeEIAAIDRhlp4QgAAwAJ+W3hCAACAaNBbeEIAAECT/194QgAAAPlRYHhCAADAXqRgeEIAAIDE9mB4QgAAQCpJYXhCAACAW0BieEIAAEDBkmJ4QgAAACflYnhCAADAjDdjeEIAAIDyiWN4QgAAwCOBZHhCAACAidNkeEIAAEDvJWV4QgAAAFV4ZXhCAADAuspleEIAAADswWZ4QgAAwFEUZ3hCAACAt2ZneEIAAEAduWd4QgAAAIMLaHhCAADAf6dpeEIAAIDl+Wl4QgAAQEtManhCAACAfENreEIAAEDilWt4QgAAAEjoa3hCAADArTpseEIAAIATjWx4QgAAwESEbXhCAACAqtZteEIAAEAQKW54QgAAAHZ7bnhCAADA281ueEIAAAANxW94QgAAwHIXcHhCAACA2GlweEIAAEA+vHB4QgAAAKQOcXhCAABA1QVyeEIAAAA7WHJ4QgAAwKCqcnhCAACABv1yeEIAAEBsT3N4QgAAwM49dXhCAACANJB1eEIAAMBlh3Z4QgAAgMvZdnhCAABAMSx3eEIAAACXfnd4QgAAwPzQd3hCAAAALsh4eEIAAMCTGnl4QgAAgPlseXhCAABAX795eEIAAADFEXp4QgAAQPYIe3hCAAAAXFt7eEIAAMDBrXt4QgAAgCcAfHhCAABAjVJ8eEIAAEAknH14QgAAAIrufXhCAADA70B+eEIAAIBVk354QgAAwIaKf3hCAACA7Nx/eEIAAEBSL4B4QgAAALiBgHhCAADAHdSAeEIAAABPy4F4QgAAwLQdgnhCAACAGnCCeEIAAECAwoJ4QgAAAOYUg3hCAABAFwyEeEIAAAB9XoR4QgAAwOKwhHhCAACASAOFeEIAAECuVYV4QgAAgN9MhnhCAABARZ+GeEIAAACr8YZ4QgAAwBBEh3hCAACAdpaHeEIAAMCnjYh4QgAAgA3giHhCAA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8D//shBXrRDwP/+yEFetEPA/SxgHdoAQ8D/1mli/HRHwP9gjDrgyEfA/g9b9QhMR8D8R+tMlZxHwPy1ZT4ywEfA/nx2qCLsR8D9J0JmTmxHwP7uU9A+mEfA/SdCZk5sR8D/0gokefBHwP581ealcEfA/SuhoND0R8D+Dvi6icRHwP4O+LqJxEfA/g74uonER8D+UxMSsOBHwPwWJHylDEfA/sDsPtCMR8D/O4vf8URDwP5YMMo8dEPA/zuL3/FEQ8D/O4vf8URDwP87i9/xREPA/zuL3/FEQ8D/O4vf8URDwP87i9/xREPA/zuL3/FEQ8D9Fsy5FJBDwP5oAP7pDEPA/DMWZNk4Q8D8oPOQ9ORDwPyg85D05EPA/KDzkPTkQ8D8oPOQ9ORDwPyg85D05EPA/Warahx8Q8D/LbjUEKhDwP7A/WN8jD/A/lODceNoO8D8iHIL8zw7wP+kt7e35DvA/sD9Y3yMP8D+wP1jfIw/wP7A/WN8jD/A/sD9Y3yMP8D+wP1jfIw/wP7A/WN8jD/A/sD9Y3yMP8D+wP1jfIw/wP7A/WN8jD/A/ntSo0fUO8D+e1KjR9Q7wP2X+4mPBDvA/LBBOVesO8D9l/uJjwQ7wP/M5iOe2DvA/LSgd9owO8D+7Y8J5gg7wP7tjwnmCDvA/u2PCeYIO8D+7Y8J5gg7wP7tjwnmCDvA/u2PCeYIO8D+7Y8J5gg7wP7tjwnmCDvA/u2PCeYIO8D+7Y8J5gg7wP7tjwnmCDvA/HbHlrogO8D8cmRYO5w7wP6vUu5HcDvA/ADqbp50O8D/HMzf4JQ/wP6mkHVCZD/A/HIFHbUUP8D9Vb9x7Gw/wP1Vv3HsbD/A/qrzs8DoP8D+OXXGK8Q7wP6vUu5HcDvA/VoerHL0O8D9Wh6scvQ7wP1aHqxy9DvA/VoerHL0O8D9Wh6scvQ7wP1aHqxy9DvA/VoerHL0O8D9Wh6scvQ7wP1aHqxy9DvA/VoerHL0O8D9Wh6scvQ7wP1aHqxy9DvA/VoerHL0O8D9Wh6scvQ7wP1aHqxy9DvA/VoerHL0O8D9Wh6scvQ7wP1aHqxy9DvA/VoerHL0O8D9bdfQq+A7wP+rIaE+PDvA/W3X0KvgO8D947D4y4w7wP5RjiTnODvA/W3X0KvgO8D8/FnnErg7wP+mwma7tDvA/6bCZru0O8D8iny69ww7wP+rIaE+PDvA/XI3Dy5kO8D9cjcPLmQ7wP1yNw8uZDvA/XI3Dy5kO8D9cjcPLmQ7wP1yNw8uZDvA/XI3Dy5kO8D9cjcPLmQ7wP1yNw8uZDvA/XI3Dy5kO8D9cjcPLmQ7wP1yNw8uZDvA/XI3Dy5kO8D9cjcPLmQ7wP1yNw8uZDvA/XI3Dy5kO8D9cjcPLmQ7wP1yNw8uZDvA/XI3Dy5kO8D9cjcPLmQ7wP2ONF16LDvA/DkAH6WsO8D9HLpz3QQ7wP52rSq6kDfA/uQrGFO4N8D9HLpz3QQ7wP2Sl5v4sDvA/ZKXm/iwO8D9kpeb+LA7wP2Sl5v4sDvA/ZKXm/iwO8D9kpeb+LA7wP1E9mX/0DfA/GGfTEcAN8D813h0Zqw3wPzTGTngJDvA/poqp9BMO8D9RPZl/9A3wP6aieJW1DfA/iisujsoN8D+moniVtQ3wP9+QDaSLDfA/35ANpIsN8D/fkA2kiw3wP9+QDaSLDfA/35ANpIsN8D/fkA2kiw3wP9+QDaSLDfA/35ANpIsN8D/fkA2kiw3wP9+QDaSLDfA/35ANpIsN8D/fkA2kiw3wP9+QDaSLDfA/35ANpIsN8D/fkA2kiw3wP9+QDaSLDfA/35ANpIsN8D/fkA2kiw3wP9+QDaSLDfA/syiapIUN8D/r/l8Sug3wP13Duo7EDfA/eSI29Q0O8D9BTHCH2Q3wPyUFxMExDfA/tEBpRScN8D96OgWWrw3wP0FMcIfZDfA/7BYvs1sN8D/sFi+zWw3wP+wWL7NbDfA/7BYvs1sN8D/umXDYSw3wP9IiJtFgDfA/JUCYBD0O8D8lQJgEPQ7wPyVAmAQ9DvA/eo2oeVwO8D8JyU39UQ7wPyUoyWObDvA/lbyFnmIP8D9btiHv6g/wPyPgW4G2D/A/skufRu8O8D8HgeAabQ/wP1zO8I+MD/A/lIznXB8Q8D+UjOdcHxDwP+shZbQjD/A/Xea/MC4P8D8kEPrC+Q7wP3ldCjgZD/A/Xf6O0c8O8D/sOTRVxQ7wP9DauO57DvA/B5mvuw4P8D/PwulN2g7wP+xRA/ZmDvA/QZ8Ta4YO8D/PwulN2g7wP7Iz0KVND/A/BmkRessP8D8Y6Zg1qQ/wPxe5+vNlEPA/+kGw7HoQ8D/Ds1dhKw/wP8OzV2ErD/A/iZUkERIQ8D9sHtoJJxDwP9/6AyfTD/A/bk54S2oP8D+mJD65ng/wPz/LPrjQD/A/Ba0LaLcQ8D/oNcFgzBDwP6zn785vEvA/Oztk8wYS8D8DfW0mdBHwPzs7ZPMGEvA/kaBDCcgR8D92WZdDIBHwP+XVhN1FEvA/AR0xo+0S8D/HLpyUFxPwPx2Ue6rYEvA/VCLUNSgU8D//7JJhqhPwPwEdMaPtEvA/HsQZ7BsS8D8DfW0mdBHwPzoLxrHDEvA/yV461loS8D+s5+/ObxLwPzoLxrHDEvA/c/lawJkS8D/kpeabAhPwP6GIsfEFEvA/h3GjbaEQ8D/7fWvMkA/wP06b3f9sEPA/TYMOX8sQ8D9Ngw5fyxDwP4ZZ1Mz/EPA/h3GjbaEQ8D/cvrPiwBDwP74vmjo0EfA/9u2QB8cR8D9LO6F85hHwPxJNDG4QEvA/hBFn6hoS8D+gcOJQZBLwP/W98sWDEvA/2V53XzoS8D+DbTB5qBLwP4JVYdgGE/A/EZEGXPwS8D+8W8WHfhLwPy4gIASJEvA/ER1uLM0S8D+7n791ahPwP7njuWNWFPA/uD+D8uMU8D8OpWIIpRTwP7izGyITFfA/8tFOciwU8D+6h/DUyBPwP/JdtkL9E/A/gPUkVYAU8D8s2LIhpBPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwPyxMS1HTE/A/LExLUdMT8D8sTEtR0xPwP72Q1a2eE/A/935qvHQT8D/22jNLAhTwPxLGFoIcFPA/E97lIr4T8D+D/gkuVhTwP/U2/dmPFPA/g3KiXYUU8D8uJZLoZRTwP4LOa+wSFfA/ZuOItfgU8D9KEHUfgBTwP2cTJ/c7FPA/LQ3DR8QU8D+7vAD76BTwP7swmSoYFfA/uzCZKhgV8D+2CISExRTwPydBdzD/FPA/Cj7FWEMV8D9CiCP2phXwPwkOJxcAFvA/XueeXPAV8D/Pkyo4WRbwPyThOq14FvA/6trW/QAX8D8jsZxrNRfwP897W5e3FvA/szSv0Q8W8D8Jmo7n0BXwP8+TKjhZFvA/BZYb89cX8D8FCrQiBxjwPwg+UQIwGPA/ezJKwH0X8D8ltZsJGxjwP7QIEC6yF/A/z9sjxCoY8D/sUm7LFRjwP3l2RK5pGPA/lmEn5YMY8D+xqNOqKxnwP3jSDT33GPA/QIivn5MY8D/qItCJ0hjwP0EUF3BkGPA/lu2OtVQY8D8IPlECMBjwP3nq3N2YGPA/eerc3ZgY8D956tzdmBjwP3nq3N2YGPA/eerc3ZgY8D956tzdmBjwP3nq3N2YGPA/eerc3ZgY8D956tzdmBjwP3nq3N2YGPA/eerc3ZgY8D956tzdmBjwP3nq3N2YGPA/eerc3ZgY8D956tzdmBjwP3nq3N2YGPA/eerc3ZgY8D956tzdmBjwP3nq3N2YGPA/eerc3ZgY8D956tzdmBjwP3nq3N2YGPA/8+tOd54Y8D/WXDXPERnwPyuqRUQxGfA/uVmD91UZ8D9Irfcb7RjwPyuqRUQxGfA/uVmD91UZ8D+6cVKY9xjwP7pxUpj3GPA/8ruwNVsZ8D/yL0llihnwP/EXesToGfA/RvHxCdkZ8D9HCcGqehnwPw0DXfsCGvA/Y9zUQPMZ8D8qeqcC7hnwP9QUyOwsGvA/DeuNWmEa8D/w/6ojRxrwP/GLEvQXGvA/fztQpzwa8D9/Ux9I3hnwP39TH0jeGfA/RwnBqnoZ8D9HCcGqehnwP0cJwap6GfA/RwnBqnoZ8D9HCcGqehnwP0cJwap6GfA/RwnBqnoZ8D9HCcGqehnwP0cJwap6GfA/RwnBqnoZ8D9HCcGqehnwP0cJwap6GfA/RwnBqnoZ8D9HCcGqehnwP0cJwap6GfA/RwnBqnoZ8D9HCcGqehnwP0iGqU6DGfA/8ziZ2WMZ8D/zOJnZYxnwP/M4mdljGfA/8ziZ2WMZ8D+LeoDVLhnwP/0+21E5GfA//CYMsZcZ8D8atiVZJBnwPzYVob9tGfA/U6S6Z/oY8D+o8crcGRnwP/0+21E5GfA/xWgV5AQZ8D9vGwVv5RjwP8VoFeQEGfA/U6S6Z/oY8D9SjOvGWBnwP/0+21E5GfA/p9n7O3gZ8D957kYXghnwP80jiOv/GfA/QTBQSu8Y8D+VZZEebRnwPyS5BUMEGfA/7iqtt7QX8D/uKq23tBfwP3xmUjuqF/A/CoooHv4X8D+0JEkIPRjwP3tOg5oIGPA/Q3i9LNQX8D8mAXMl6RfwP3tOg5oIGPA/d84OnxEY8D8+4HmQOxjwP6+MBWykGPA/PLDbTvgY8D8eCfMFyhnwP8rTsTFMGfA/PLDbTvgY8D+tXGcqYRnwPx4J8wXKGfA/dG7SG4sZ8D87gD0NtRnwP8mjE/AIGvA/x3N1rsUa8D+NbRH/TRvwP8dbpg0kG/A/OlCfy3Ea8D8d2VTEhhrwP46F4J/vGvA/x3N1rsUa8D8AYgq9mxrwP3M+NNpHGvA/dFYDe+kZ8D+PtX7hMhrwP1bH6dJcGvA/qxT6R3wa8D/kdieGgRrwP6wsyegdGvA/JTlQdQIZ8D9fV4PFGxjwP0T4B1/SF/A/tbxi29wX8D8ngb1X5xfwP19Xg8UbGPA/YYchB18X8D8q4fnabRbwP0ZYROJYFvA/KuH52m0W8D+3BNC9wRbwP9NjSyQLF/A/8PJkzJcW8D8MUuAy4RbwP9R7GsWsFvA/RlhE4lgW8D9UtxckvxbwP/+B1k9BFvA/ALJ0kYQV8D+PHbhWvRTwP+VqyMvcFPA/c6ZtT9IU8D9WLyNI5xTwPwDKQzImFfA/AMpDMiYV8D/kUvkqOxXwPwCydJGEFfA/MKAgqWwV8D9pjrW3QhXwP0wXa7BXFfA/onxKxhgV8D/44Snc2RTwP6Ss6AdcFPA/TkcJ8poU8D/aOkGTqxXwP9o6QZOrFfA/2jpBk6sV8D+ciKg29hTwP7n/8j3hFPA/gUH8cE4U8D8sDLuc0BPwP4FZyxHwE/A/8gVX7VgU8D/yBVftWBTwP9aODOZtFPA/RiPJIDUV8D+m9mVAKBXwP+AUmZBBFPA/i6+5eoAU8D/gFJmQQRTwP6cmBIJrFPA/bjhvc5UU8D+nJgSCaxTwP/xbRVbpFPA/NBo8I3wV8D+m9mVAKBXwP/orpxSmFfA/NBo8I3wV8D/etFwNuxXwP09h6OgjFvA/wA10xIwW8D/dhL7LdxbwP92Evst3FvA/FnNT2k0W8D+HH9+1thbwP04xSqfgFvA/wA10xIwW8D/6K6cUphXwP1LBJGyqFPA/+hPYcwQW8D+lxsf+5BXwP1B5t4nFFfA/+iunFKYV8D+lxsf+5BXwPxaLInvvFfA/F6PxG5EV8D9QebeJxRXwPzxzjBoCFvA/WLo44KkW8D8CVVnK6BbwP6zvebQnF/A/kajN7n8W8D87Q+7YvhbwPzxzjBoCFvA/ISzgVFoV8D8ezKPR0xbwP3QZtEbzFvA/yWbEuxIX8D9WipqeZhfwP/SwD5mPF/A/ShbvrlAX8D+eSzCDzhfwP/SwD5mPF/A/EChaoHoX8D8uz0LpqBbwP7zyGMz8FvA/u8J6irkX8D+fe87EERfwP/XgrdrSFvA/oatsBlUW8D/2EEwcFhbwP6HDO6f2FfA/L+cRikoW8D+FTPGfCxbwP2e91/d+FvA/vSK3DUAW8D9Mdisy1xXwP6GrbAZVFvA/oatsBlUW8D9N20Q1PhbwP9rOfNZOF/A/E70R5SQX8D/2RcfdORfwP6Hg58d4F/A/hGmdwI0X8D+jQCRL/xXwP9wuuVnVFfA/+aUDYcAV8D8xlJhvlhXwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/jW0R/00b8D+NbRH/TRvwP41tEf9NG/A/x3N1rsUa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D8N641aYRrwPw3rjVphGvA/DeuNWmEa8D/UFMjsLBrwPw0DXfsCGvA/DQNd+wIa8D8NA137AhrwP/EXesToGfA/8Rd6xOgZ8D/xF3rE6BnwP/IvSWWKGfA/8ruwNVsZ8D+5WYP3VRnwP7lZg/dVGfA/uVmD91UZ8D+5WYP3VRnwP7lZg/dVGfA/uVmD91UZ8D8rqkVEMRnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/sajTqisZ8D+xqNOqKxnwP7Go06orGfA/lmEn5YMY8D95dkSuaRjwPwg+UQIwGPA/CD5RAjAY8D8IPlECMBjwPwg+UQIwGPA/CD5RAjAY8D8IPlECMBjwPwUKtCIHGPA/BZYb89cX8D8jsZxrNRfwPyOxnGs1F/A/I7GcazUX8D8jsZxrNRfwPyOxnGs1F/A/6trW/QAX8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45Wc4jRFvA/jlZziNEW8D+OVnOI0RbwP45W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              "  });\n",
              "})();\n",
              "</script>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6️⃣ 設定停利、停損點"
      ],
      "metadata": {
        "id": "vNt1zCZ5A2X0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "        # 買入時設置停損與停利價格\n",
        "        self.buy(size=1,\n",
        "            sl=self.data.Close[-1] * 0.90,\n",
        "            tp=self.data.Close[-1] * 1.10)\n",
        "    elif crossover(self.data.ma2, self.data.ma1):\n",
        "        # 賣出時時設置停損與停利價格\n",
        "        self.sell(size=1,\n",
        "             sl=self.data.Close[-1] * 1.10,\n",
        "             tp=self.data.Close[-1] * 0.90)\n",
        "\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": "lp9jQF2ZA2md",
        "outputId": "e3522661-9cd0-4c2b-c386-c4805544dde1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start                     2018-11-07 00:00:00\n",
            "End                       2023-11-07 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   56.177924\n",
            "Equity Final [$]                    99901.398\n",
            "Equity Peak [$]                    100056.974\n",
            "Return [%]                          -0.098602\n",
            "Buy & Hold Return [%]              136.752137\n",
            "Return (Ann.) [%]                   -0.020476\n",
            "Volatility (Ann.) [%]                0.084842\n",
            "Sharpe Ratio                              0.0\n",
            "Sortino Ratio                             0.0\n",
            "Calmar Ratio                              0.0\n",
            "Max. Drawdown [%]                   -0.368459\n",
            "Avg. Drawdown [%]                   -0.052725\n",
            "Max. Drawdown Duration     1279 days 00:00:00\n",
            "Avg. Drawdown Duration      148 days 00:00:00\n",
            "# Trades                                   69\n",
            "Win Rate [%]                        34.782609\n",
            "Best Trade [%]                      14.218451\n",
            "Worst Trade [%]                    -12.260064\n",
            "Avg. Trade [%]                      -0.133241\n",
            "Max. Trade Duration          80 days 00:00:00\n",
            "Avg. Trade Duration          15 days 00:00:00\n",
            "Profit Factor                        0.996009\n",
            "Expectancy [%]                      -0.007308\n",
            "SQN                                 -0.483592\n",
            "_strategy                       CrossStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                       Size  EntryB...\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5-3 讓 AI 產生回測策略"
      ],
      "metadata": {
        "id": "Kt6-OJregDbS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7️⃣ 輸入 OpenAI API KEY"
      ],
      "metadata": {
        "id": "xKNkqNWSoAQe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "api_key = getpass.getpass(\"請輸入金鑰：\")\n",
        "client = OpenAI(api_key=api_key)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZXQFFxZn2rf",
        "outputId": "d827d591-c028-47d3-8554-7d59c0203999"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "請輸入金鑰：··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pyelatvvLiIn"
      },
      "source": [
        "### 8️⃣ 創建 GPT 3.5 模型函式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aSjownHrGnde"
      },
      "outputs": [],
      "source": [
        "# GPT 3.5 模型\n",
        "def get_reply(messages):\n",
        "  try:\n",
        "    response = client.chat.completions.create(model=\"gpt-3.5-turbo\",\n",
        "                         messages=messages)\n",
        "    reply = response.choices[0].message.content\n",
        "  except OpenAIError as err:\n",
        "    reply = f\"發生 {err.type} 錯誤\\n{err.message}\"\n",
        "  return reply\n",
        "\n",
        "# 設定 AI 角色, 使其依據使用者需求進行 df 處理\n",
        "def ai_helper(df, user_msg):\n",
        "\n",
        "  msg = [{\n",
        "    \"role\":\n",
        "    \"system\",\n",
        "    \"content\":\n",
        "    f\"As a professional code generation robot, \\\n",
        "      I require your assistance in generating Python code \\\n",
        "      based on specific user requirements. To proceed, \\\n",
        "      I will provide you with a dataframe (df) that follows the \\\n",
        "      format {df.columns}. Your task is to carefully analyze the \\\n",
        "      user's requirements and generate the Python code \\\n",
        "      accordingly.Please note that your response should solely \\\n",
        "      consist of the code itself, \\\n",
        "      and no additional information should be included.\"\n",
        "  }, {\n",
        "    \"role\":\n",
        "    \"user\",\n",
        "    \"content\":\n",
        "    f\"The user requirement:{user_msg} \\n\\\n",
        "      Your task is to develop a Python function named \\\n",
        "      'calculate(df)'. This function should accept a dataframe as \\\n",
        "      its parameter. Ensure that you only utilize the columns \\\n",
        "      present in the dataset, specifically {df.columns}.\\\n",
        "      After processing, the function should return the processed \\\n",
        "      dataframe. Your response should strictly contain the Python \\\n",
        "      code for the 'calculate(df)' function \\\n",
        "      and exclude any unrelated content.\"\n",
        "  }]\n",
        "\n",
        "  reply_data = get_reply(msg)\n",
        "  return reply_data\n",
        "\n",
        "# 產生技術指標策略\n",
        "def ai_strategy(df, user_msg, add_msg=\"無\"):\n",
        "\n",
        "  code_example ='''\n",
        "class AiStrategy(Strategy):\n",
        "  def init(self):\n",
        "    super().init()\n",
        "\n",
        "  def next(self):\n",
        "    if crossover(self.data.short_ma, self.data.long_ma):\n",
        "        self.buy(size=1,\n",
        "            sl=self.data.Close[-1] * 0.90,\n",
        "            tp=self.data.Close[-1] * 1.10)\n",
        "    elif crossover(self.data.long_ma, self.data.short_ma):\n",
        "        self.sell(size=1,\n",
        "             sl=self.data.Close[-1] * 1.10,\n",
        "             tp=self.data.Close[-1] * 0.90)\n",
        "        '''\n",
        "\n",
        "  msg = [{\n",
        "    \"role\":\n",
        "    \"system\",\n",
        "    \"content\":\n",
        "     \"As a Python code generation bot, your task is to generate \\\n",
        "     code for a stock strategy based on user requirements and df. \\\n",
        "     Please note that your response should solely \\\n",
        "     consist of the code itself, \\\n",
        "     and no additional information should be included.\"\n",
        "  }, {\n",
        "    \"role\":\n",
        "    \"user\",\n",
        "    \"content\":\n",
        "     \"The user requirement:計算 ma,\\n\\\n",
        "     The additional requirement: 請設置 10% 的停利與停損點\\n\\\n",
        "     The df.columns =['Open',\t'High', 'Low',\t'Close',\t'Adj Close',\t'Volume', 'short_ma',\t'long_ma']\\n\\\n",
        "     Please using the crossover() function in next(self)\\\n",
        "     Your response should strictly contain the Python \\\n",
        "     code for the 'AiStrategy(Strategy)' class \\\n",
        "     and exclude any unrelated content.\"\n",
        "  }, {\n",
        "    \"role\":\n",
        "    \"assistant\",\n",
        "    \"content\":f\"{code_example}\"\n",
        "  }, {\n",
        "    \"role\":\n",
        "    \"user\",\n",
        "    \"content\":\n",
        "    f\"The user requirement:{user_msg}\\n\\\n",
        "     The additional requirement:{add_msg}\\n\\\n",
        "     The df.columns ={df.columns}\\n\\\n",
        "     Your task is to develop a Python class named \\\n",
        "     'AiStrategy(Strategy)'\\\n",
        "     Please using the crossover() function in next(self).\"\n",
        "\n",
        "  }]\n",
        "\n",
        "  reply_data = get_reply(msg)\n",
        "  return reply_data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BP5FkDxALp4e"
      },
      "source": [
        "### 9️⃣ 計算技術指標"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "j1x0glPsNJe2",
        "outputId": "68e4d939-c52a-427a-e2cf-e3580d10fe2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "def calculate(df):\n",
            "    # Calculate the MACD line\n",
            "    df['12-day EMA'] = df['Close'].ewm(span=12, adjust=False).mean()\n",
            "    df['26-day EMA'] = df['Close'].ewm(span=26, adjust=False).mean()\n",
            "    df['MACD Line'] = df['12-day EMA'] - df['26-day EMA']\n",
            "    \n",
            "    # Calculate the signal line\n",
            "    df['Signal Line'] = df['MACD Line'].ewm(span=9, adjust=False).mean()\n",
            "    \n",
            "    # Calculate the MACD histogram\n",
            "    df['MACD Histogram'] = df['MACD Line'] - df['Signal Line']\n",
            "    \n",
            "    return df\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open   High    Low  Close  Adj Close    Volume  12-day EMA  \\\n",
              "Date                                                                      \n",
              "2023-11-01  533.0  533.0  527.0  528.0      528.0  13603347  536.071721   \n",
              "2023-11-02  536.0  547.0  535.0  547.0      547.0  29401438  537.752994   \n",
              "2023-11-03  547.0  549.0  545.0  549.0      549.0  15426176  539.483303   \n",
              "2023-11-06  553.0  556.0  550.0  550.0      550.0  29215201  541.101256   \n",
              "2023-11-07  552.0  555.0  550.0  553.0      553.0   7551711  542.931832   \n",
              "\n",
              "            26-day EMA  MACD Line  Signal Line  MACD Histogram  \n",
              "Date                                                            \n",
              "2023-11-01  538.115217  -2.043496    -0.399727       -1.643769  \n",
              "2023-11-02  538.773349  -1.020354    -0.523852       -0.496502  \n",
              "2023-11-03  539.530878  -0.047575    -0.428597        0.381022  \n",
              "2023-11-06  540.306369   0.794887    -0.183900        0.978788  \n",
              "2023-11-07  541.246638   1.685194     0.189919        1.495276  "
            ],
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              "\n",
              "  <div id=\"df-241158f0-09ac-4da9-9d69-afef3ddf3a38\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "      <th>26-day EMA</th>\n",
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              "      <th>Signal Line</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2023-11-01</th>\n",
              "      <td>533.0</td>\n",
              "      <td>533.0</td>\n",
              "      <td>527.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>528.0</td>\n",
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              "      <td>536.0</td>\n",
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              "      <td>-0.523852</td>\n",
              "      <td>-0.496502</td>\n",
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              "    <tr>\n",
              "      <th>2023-11-03</th>\n",
              "      <td>547.0</td>\n",
              "      <td>549.0</td>\n",
              "      <td>545.0</td>\n",
              "      <td>549.0</td>\n",
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              "      <td>-0.428597</td>\n",
              "      <td>0.381022</td>\n",
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              "    <tr>\n",
              "      <th>2023-11-06</th>\n",
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              "      <th>2023-11-07</th>\n",
              "      <td>552.0</td>\n",
              "      <td>555.0</td>\n",
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              "      <td>0.189919</td>\n",
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              "  <style>\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-241158f0-09ac-4da9-9d69-afef3ddf3a38 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-241158f0-09ac-4da9-9d69-afef3ddf3a38');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\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-8e0feda9-d93e-4bec-ac00-51a2485c26bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# 輸入股票代號\n",
        "stock_id = \"2330.tw\"\n",
        "# 抓取 5 年資料\n",
        "df = yf.download(stock_id, period=\"5y\")\n",
        "# 計算指標\n",
        "user_msg = [\"MACD\", \"請設置10%的停損點與20%的停利點\"]\n",
        "code_str = ai_helper(df, user_msg[0])\n",
        "print(code_str)\n",
        "exec(code_str)\n",
        "new_df = calculate(df)\n",
        "new_df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 🔟 策略生成"
      ],
      "metadata": {
        "id": "VP2kpv8Q_SUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "strategy_str = ai_strategy(new_df, user_msg[0], user_msg[1])\n",
        "print(strategy_str)\n",
        "print(\"-----------------------\")\n",
        "exec(strategy_str)\n",
        "backtest = Backtest(df,\n",
        "        AiStrategy,\n",
        "        cash=100000,\n",
        "        commission=0.004,\n",
        "        trade_on_close=True,\n",
        "        exclusive_orders=True,\n",
        "        )\n",
        "stats = backtest.run()\n",
        "print(stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pYDV3Kw_SoE",
        "outputId": "5abbce5b-dc22-4c0c-a683-874a504c71ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class AiStrategy(Strategy):\n",
            "  def init(self):\n",
            "    super().init()\n",
            "\n",
            "  def next(self):\n",
            "    if crossover(self.data['MACD Line'], self.data['Signal Line']):\n",
            "        self.buy(size=1,\n",
            "                 sl=self.data.Close[-1] * 0.90,\n",
            "                 tp=self.data.Close[-1] * 1.20)\n",
            "    elif crossover(self.data['Signal Line'], self.data['MACD Line']):\n",
            "        self.sell(size=1,\n",
            "                  sl=self.data.Close[-1] * 1.10,\n",
            "                  tp=self.data.Close[-1] * 0.80)\n",
            "-----------------------\n",
            "Start                     2018-11-07 00:00:00\n",
            "End                       2023-11-07 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   97.034596\n",
            "Equity Final [$]                   100248.662\n",
            "Equity Peak [$]                     100352.87\n",
            "Return [%]                           0.248662\n",
            "Buy & Hold Return [%]              136.324786\n",
            "Return (Ann.) [%]                    0.051566\n",
            "Volatility (Ann.) [%]                0.117311\n",
            "Sharpe Ratio                         0.439566\n",
            "Sortino Ratio                        0.646068\n",
            "Calmar Ratio                         0.298157\n",
            "Max. Drawdown [%]                   -0.172949\n",
            "Avg. Drawdown [%]                   -0.028182\n",
            "Max. Drawdown Duration      607 days 00:00:00\n",
            "Avg. Drawdown Duration       53 days 00:00:00\n",
            "# Trades                                   88\n",
            "Win Rate [%]                        39.772727\n",
            "Best Trade [%]                      27.109166\n",
            "Worst Trade [%]                     -8.698539\n",
            "Avg. Trade [%]                       0.591113\n",
            "Max. Trade Duration          59 days 00:00:00\n",
            "Avg. Trade Duration          21 days 00:00:00\n",
            "Profit Factor                        1.406448\n",
            "Expectancy [%]                       0.797836\n",
            "SQN                                  0.933658\n",
            "_strategy                          AiStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                       Size  EntryB...\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1️⃣1️⃣ 寫成函式"
      ],
      "metadata": {
        "id": "e9jZNwf8Ykcs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def ai_backtest(stock_id, period, user_msg, add_msg):\n",
        "\n",
        "  # 下載資料\n",
        "  df = yf.download(stock_id, period=period)\n",
        "\n",
        "  # 獲取和執行指標計算程式碼\n",
        "  code_str = ai_helper(df, user_msg)\n",
        "  local_namespace = {}\n",
        "  exec(code_str, globals(), local_namespace)\n",
        "  calculate = local_namespace['calculate']\n",
        "  new_df = calculate(df)\n",
        "\n",
        "  # 獲取和執行策略程式碼\n",
        "  strategy_str = ai_strategy(new_df, user_msg, add_msg)\n",
        "  print(strategy_str)\n",
        "  print(\"-----------------------\")\n",
        "  exec(strategy_str, globals(), local_namespace)\n",
        "  AiStrategy = local_namespace['AiStrategy']\n",
        "\n",
        "  backtest = Backtest(df,\n",
        "          AiStrategy,\n",
        "          cash=100000,\n",
        "          commission=0.004,\n",
        "          trade_on_close=True,\n",
        "          exclusive_orders=True,\n",
        "          )\n",
        "  stats = backtest.run()\n",
        "  print(stats)\n",
        "  return str(stats)\n"
      ],
      "metadata": {
        "id": "x7FHKcoQYjhq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5-4 讓 AI 解析回測報告"
      ],
      "metadata": {
        "id": "7OU07WxPgeVL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1️⃣2️⃣ 設定 AI 回復內容"
      ],
      "metadata": {
        "id": "jG7R2MIOgcEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest_analysis(*args):\n",
        "\n",
        "  content_list = [f\"策略{i+1}：{report}\"\n",
        "                  for i, report in enumerate(args)]\n",
        "  content = \"\\n\".join(content_list)\n",
        "  content += \"\\n\\n請依資料給我一份約200字的分析報告。若有多個策略, \\\n",
        "                  請選出最好的策略及原因, reply in 繁體中文.\"\n",
        "\n",
        "  msg = [{\n",
        "      \"role\": \"system\",\n",
        "      \"content\": \"你是一位專業的證券分析師, 我會給你交易策略的回測績效,\\\n",
        "                  請幫我進行績效分析.不用詳細講解每個欄位, \\\n",
        "                  重點說明即可, 並回答交易策略的好壞\"\n",
        "  }, {\n",
        "      \"role\": \"user\",\n",
        "      \"content\": content\n",
        "  }]\n",
        "\n",
        "  reply_data = get_reply(msg)\n",
        "  return reply_data\n"
      ],
      "metadata": {
        "id": "rBRvifEsgcOP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1️⃣3️⃣ 回測結果分析"
      ],
      "metadata": {
        "id": "gp0SRd2qgjve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats = ai_backtest(stock_id=\"2330.TW\",\n",
        "           period=\"5y\",\n",
        "           user_msg=\"MACD\",\n",
        "           add_msg=\"請設置10%的停損點與20%的停利點\")\n",
        "reply = backtest_analysis(stats)\n",
        "print(reply)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu2k-YH3gj4e",
        "outputId": "e5a5c795-290e-4f64-a6b0-234b56085354"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "class AiStrategy(Strategy):\n",
            "  def init(self):\n",
            "    super().init()\n",
            "\n",
            "  def next(self):\n",
            "    if crossover(self.data.MACD, self.data.Signal_Line):\n",
            "        self.buy(size=1,\n",
            "            sl=self.data.Close[-1] * 0.90,\n",
            "            tp=self.data.Close[-1] * 1.20)\n",
            "    elif crossover(self.data.Signal_Line, self.data.MACD):\n",
            "        self.sell(size=1,\n",
            "             sl=self.data.Close[-1] * 1.10,\n",
            "             tp=self.data.Close[-1] * 0.80)\n",
            "-----------------------\n",
            "Start                     2018-11-05 00:00:00\n",
            "End                       2023-11-03 00:00:00\n",
            "Duration                   1824 days 00:00:00\n",
            "Exposure Time [%]                   96.952224\n",
            "Equity Final [$]                   100257.416\n",
            "Equity Peak [$]                    100358.616\n",
            "Return [%]                           0.257416\n",
            "Buy & Hold Return [%]              133.617021\n",
            "Return (Ann.) [%]                     0.05338\n",
            "Volatility (Ann.) [%]                0.117346\n",
            "Sharpe Ratio                         0.454892\n",
            "Sortino Ratio                        0.668602\n",
            "Calmar Ratio                         0.308661\n",
            "Max. Drawdown [%]                   -0.172939\n",
            "Avg. Drawdown [%]                   -0.026935\n",
            "Max. Drawdown Duration      607 days 00:00:00\n",
            "Avg. Drawdown Duration       49 days 00:00:00\n",
            "# Trades                                   90\n",
            "Win Rate [%]                        38.888889\n",
            "Best Trade [%]                      27.109166\n",
            "Worst Trade [%]                     -8.698539\n",
            "Avg. Trade [%]                       0.617418\n",
            "Max. Trade Duration          59 days 00:00:00\n",
            "Avg. Trade Duration          20 days 00:00:00\n",
            "Profit Factor                         1.43143\n",
            "Expectancy [%]                       0.817243\n",
            "SQN                                  0.969224\n",
            "_strategy                          AiStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                       Size  EntryB...\n",
            "dtype: object\n",
            "根據提供的交易策略回測績效分析，該策略在回測期間（2018年11月至2023年11月）總共進行了90筆交易，其勝率為38.89％。策略的年化報酬率為0.05338％，僅略高於基準策略（買入並持有）的報酬率（133.62％）。策略的波動性（年化波動率）為0.11735％，表示該策略的風險相對較低。夏普比率為0.45489，這代表每承受一定的風險，該策略的報酬相對較高。Sortino比率為0.66860，它對下行風險給予比夏普比率更大的重視，顯示該策略在應對不利情況上表現較好。\n",
            "\n",
            "最大回撤為17.29％，且平均回撤為2.69％，回撤持續時間較長，最長達到607天，回撤在平均水平的下方。最大單筆交易獲利為27.11％，最大單筆交易虧損為-8.70％。策略的交易間隔相對較短，平均為20天，最長一筆交易持續了59天。策略的盈利因子為1.43，表示策略的盈利交易相對於虧損交易更多。\n",
            "\n",
            "整體而言，該策略的回測績效相對保守，年化報酬率較低，但波動性也較低，且在風險調整指標方面表現良好。然而，交易的勝率和平均交易回報率相對較低，回撤持續時間也較長，這可能顯示該策略有改進的空間。\n",
            "\n",
            "在多個策略中，最好的策略可以根據不同的指標進行選擇，但綜合考慮下，可優先選擇具有較高夏普比率以及Sortino比率的策略。這些指標不僅衡量了回報的水平，還考慮了回撤風險的控制。因此，在這種情況下，最好的策略是具有較高夏普比率和Sortino比率的策略。\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1️⃣4️⃣ 比較多個策略"
      ],
      "metadata": {
        "id": "isKciWtDVIon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 策略1:MACD+停利停損\n",
        "stats1 = ai_backtest(stock_id=\"2330.TW\", period=\"5y\",\n",
        "            user_msg=\"MACD\",\n",
        "            add_msg=\"請設置10%的停損點與20%的停利點\")\n",
        "# 策略2:SMA\n",
        "stats2 = ai_backtest(stock_id=\"2330.TW\", period=\"5y\",\n",
        "            user_msg=\"SMA\",\n",
        "            add_msg=\"無\")\n",
        "# 策略3:RSI+停利停損\n",
        "stats3 = ai_backtest(stock_id=\"2330.TW\", period=\"5y\",\n",
        "            user_msg=\"RSI\",\n",
        "            add_msg=\"請設置10%的停損點與20%的停利點\")\n",
        "\n",
        "reply = backtest_analysis(stats1, stats2, stats3)\n",
        "print(reply)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwObTGQiVCP6",
        "outputId": "11442498-4ae6-4b7b-f21d-8800a3939bd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "class AiStrategy(Strategy):\n",
            "  def init(self):\n",
            "    super().init()\n",
            "\n",
            "  def next(self):\n",
            "    if crossover(self.data.MACD, self.data.Signal):\n",
            "        self.buy(size=1,\n",
            "            sl=self.data.Close[-1] * 0.90,\n",
            "            tp=self.data.Close[-1] * 1.20)\n",
            "    elif crossover(self.data.Signal, self.data.MACD):\n",
            "        self.sell(size=1,\n",
            "             sl=self.data.Close[-1] * 1.10,\n",
            "             tp=self.data.Close[-1] * 0.80)\n",
            "-----------------------\n",
            "Start                     2018-11-07 00:00:00\n",
            "End                       2023-11-07 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   97.034596\n",
            "Equity Final [$]                   100248.662\n",
            "Equity Peak [$]                     100352.87\n",
            "Return [%]                           0.248662\n",
            "Buy & Hold Return [%]              136.752137\n",
            "Return (Ann.) [%]                    0.051566\n",
            "Volatility (Ann.) [%]                0.117311\n",
            "Sharpe Ratio                         0.439566\n",
            "Sortino Ratio                        0.646068\n",
            "Calmar Ratio                         0.298157\n",
            "Max. Drawdown [%]                   -0.172949\n",
            "Avg. Drawdown [%]                   -0.028182\n",
            "Max. Drawdown Duration      607 days 00:00:00\n",
            "Avg. Drawdown Duration       53 days 00:00:00\n",
            "# Trades                                   88\n",
            "Win Rate [%]                        39.772727\n",
            "Best Trade [%]                      27.109166\n",
            "Worst Trade [%]                     -8.698539\n",
            "Avg. Trade [%]                       0.591113\n",
            "Max. Trade Duration          59 days 00:00:00\n",
            "Avg. Trade Duration          21 days 00:00:00\n",
            "Profit Factor                        1.406448\n",
            "Expectancy [%]                       0.797836\n",
            "SQN                                  0.933658\n",
            "_strategy                          AiStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                       Size  EntryB...\n",
            "dtype: object\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "\n",
            "class AiStrategy(Strategy):\n",
            "  def init(self):\n",
            "    super().init()\n",
            "\n",
            "  def next(self):\n",
            "    if crossover(self.data.Close, self.data.SMA):\n",
            "        self.buy(size=1)\n",
            "    elif crossover(self.data.SMA, self.data.Close):\n",
            "        self.sell(size=1)\n",
            "-----------------------\n",
            "Start                     2018-11-07 00:00:00\n",
            "End                       2023-11-07 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   99.423394\n",
            "Equity Final [$]                    99245.536\n",
            "Equity Peak [$]                    100032.298\n",
            "Return [%]                          -0.754464\n",
            "Buy & Hold Return [%]              136.752137\n",
            "Return (Ann.) [%]                   -0.157081\n",
            "Volatility (Ann.) [%]                0.122603\n",
            "Sharpe Ratio                              0.0\n",
            "Sortino Ratio                             0.0\n",
            "Calmar Ratio                              0.0\n",
            "Max. Drawdown [%]                   -0.787508\n",
            "Avg. Drawdown [%]                   -0.144934\n",
            "Max. Drawdown Duration     1064 days 00:00:00\n",
            "Avg. Drawdown Duration      260 days 00:00:00\n",
            "# Trades                                  330\n",
            "Win Rate [%]                         30.30303\n",
            "Best Trade [%]                      33.481821\n",
            "Worst Trade [%]                    -24.860937\n",
            "Avg. Trade [%]                      -0.527728\n",
            "Max. Trade Duration          34 days 00:00:00\n",
            "Avg. Trade Duration           6 days 00:00:00\n",
            "Profit Factor                        0.658074\n",
            "Expectancy [%]                       -0.46279\n",
            "SQN                                 -2.664337\n",
            "_strategy                          AiStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                        Size  Entry...\n",
            "dtype: object\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "class AiStrategy(Strategy):\n",
            "  def init(self):\n",
            "    super().init()\n",
            "\n",
            "  def next(self):\n",
            "    if self.data.RSI[-1] > 70:\n",
            "        self.sell(size=1,\n",
            "                  sl=self.data.Close[-1] * 1.10,\n",
            "                  tp=self.data.Close[-1] * 0.80)\n",
            "    elif self.data.RSI[-1] < 30:\n",
            "        self.buy(size=1,\n",
            "                 sl=self.data.Close[-1] * 0.90,\n",
            "                 tp=self.data.Close[-1] * 1.20)\n",
            "-----------------------\n",
            "Start                     2018-11-07 00:00:00\n",
            "End                       2023-11-07 00:00:00\n",
            "Duration                   1826 days 00:00:00\n",
            "Exposure Time [%]                   95.140033\n",
            "Equity Final [$]                    99157.408\n",
            "Equity Peak [$]                    100007.084\n",
            "Return [%]                          -0.842592\n",
            "Buy & Hold Return [%]              136.324786\n",
            "Return (Ann.) [%]                   -0.175491\n",
            "Volatility (Ann.) [%]                0.123469\n",
            "Sharpe Ratio                              0.0\n",
            "Sortino Ratio                             0.0\n",
            "Calmar Ratio                              0.0\n",
            "Max. Drawdown [%]                   -0.880614\n",
            "Avg. Drawdown [%]                   -0.880614\n",
            "Max. Drawdown Duration     1742 days 00:00:00\n",
            "Avg. Drawdown Duration     1742 days 00:00:00\n",
            "# Trades                                  295\n",
            "Win Rate [%]                        30.847458\n",
            "Best Trade [%]                      16.792383\n",
            "Worst Trade [%]                    -10.415756\n",
            "Avg. Trade [%]                      -0.731708\n",
            "Max. Trade Duration          81 days 00:00:00\n",
            "Avg. Trade Duration           6 days 00:00:00\n",
            "Profit Factor                        0.556131\n",
            "Expectancy [%]                      -0.674601\n",
            "SQN                                 -3.138839\n",
            "_strategy                          AiStrategy\n",
            "_equity_curve                             ...\n",
            "_trades                        Size  Entry...\n",
            "dtype: object\n",
            "根據所提供的交易策略回測績效，我們可以得出以下結論：\n",
            "\n",
            "策略1的績效相對較好，回報率為0.25％，與Buy & Hold的回報率相比相對較低，但風險也更低。策略1具有較高的夏普比率和索提諾比率，顯示其在每承擔一定風險下，能夠產生較高的回報。此外，策略1的最大回撤率為17.29％，相對較低於其他策略，且平均回撤率為2.82％，也較低。策略1進行了88次交易，勝率為39.77％。\n",
            "\n",
            "相比之下，策略2和策略3的績效較差。策略2的回報率為-0.75％，策略3的回報率為-0.84％。兩者的夏普比率和索提諾比率均為0，表示無法為風險承擔帶來較高的回報。此外，策略2和策略3的最大回撤率分別為78.75％和88.06％，平均回撤率也較高。\n",
            "\n",
            "綜合以上分析，策略1是最優選擇。它有較高的回報並能夠承擔較低的風險。儘管策略1的勝率較低，但平均交易績效較高，且具有較低的回撤率和較短的回撤時間。因此，根據所提供的績效數據，我建議選擇策略1作為投資決策的基礎。\n"
          ]
        }
      ]
    }
  ],
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      "display_name": "Python 3",
      "name": "python3"
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