ch08_stock_picking.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFQ17AmL0Yd0"
      },
      "source": [
        "# CH-08 建構投資組合：讓 AI 輔助選股"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjLwn9j5zzG0"
      },
      "source": [
        "## 8-1 建構投資組合"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JafBjysbhZAD"
      },
      "source": [
        "### 1️⃣ 匯入套件 & 掛載雲端硬碟\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPNGbMfnWCRe",
        "outputId": "a3b0d532-56d9-416b-dd78-cc6bfe6c69de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.2.0)\n",
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            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "!pip install gdown\n",
        "import gdown\n",
        "import os\n",
        "import datetime as dt\n",
        "import time\n",
        "from IPython.display import display\n",
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "from tqdm import tqdm\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKxidmqYC_Yk"
      },
      "source": [
        "### 2️⃣ 下載資料庫\n",
        "若資料庫未下載成功或出現 bug，請手動點擊連結下載，並放入 **我的雲端硬碟>StockGPT** 資料夾中\n",
        "\n",
        "下載連結：https://drive.usercontent.google.com/download?id=1S5JE9ZF2hohRpvO8FikgLhQmN2DJrVgW"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RrLWU70aWj0h",
        "outputId": "eb604a56-e7ad-4dbf-d316-2c426c4a592e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "無需下載\n"
          ]
        }
      ],
      "source": [
        "# 指定下載路徑\n",
        "!mkdir -p \"/content/drive/MyDrive/StockGPT/\"\n",
        "output_path = '/content/drive/MyDrive/StockGPT/'\n",
        "\n",
        "# 檢查資料庫是否存在\n",
        "stock_db_path = output_path + 'stock.db'\n",
        "if not os.path.exists(stock_db_path):\n",
        "    print(\"下載資料庫中...\")\n",
        "    id = '1S5JE9ZF2hohRpvO8FikgLhQmN2DJrVgW'\n",
        "\n",
        "    gdown.download(id=id, output=stock_db_path)\n",
        "    print(\"下載完成\")\n",
        "else:\n",
        "    print(\"無需下載\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ux67S5AvDImT"
      },
      "source": [
        "### 3️⃣ 安裝旗標套件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G78l4AslDI1z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ec8aacd-ee32-4ec7-f97d-ae7aace8ac45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'F3933' already exists and is not an empty directory.\n",
            "/content/F3933\n",
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            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json<0.7,>=0.5.7->langchain==0.0.335->-r requirements.txt (line 2)) (24.2)\n",
            "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain==0.0.335->-r requirements.txt (line 2)) (1.0.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six==20221105->pdfplumber==0.10.2->-r requirements.txt (line 6)) (2.22)\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/FlagTech/F3933.git\n",
        "%cd F3933\n",
        "!pip install -r requirements.txt\n",
        "from Ch06 import StockAnalysis, StockInfo\n",
        "from Ch07 import PdfLoader\n",
        "from Stock_DB import StockDB\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2iXo618D-Yf"
      },
      "source": [
        "### 4️⃣ 更新資料庫\n",
        "資料庫更新需耗費較長時間, 若無法更新，請刪除我的雲端硬碟>StockGPT>stock.db 檔案，並至第二個儲存格的連結手動下載"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M2m9aT3pbrhw"
      },
      "outputs": [],
      "source": [
        "stock_db = StockDB()\n",
        "stock_db.renew()\n",
        "stock_db.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMqMw_FW8Ies"
      },
      "source": [
        "###5️⃣ 檢視資料表格式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "k7zqLuAB8IsA",
        "outputId": "b96beae6-fff4-4a34-cf17-8055b25869d4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     股號  股名   產業別            股本            市值\n",
              "0  1101  台泥  水泥工業  7.136180e+09  2.501231e+11\n",
              "1  1102  亞泥  水泥工業  3.546560e+09  1.425717e+11\n",
              "2  1103  嘉泥  水泥工業  6.588940e+08  1.242015e+10\n",
              "3  1104  環泥  水泥工業  6.732170e+08  1.908570e+10\n",
              "4  1108  幸福  水泥工業  4.047380e+08  6.212728e+09"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b2cb9f1-2499-4f8d-a23a-a4e77e5dac80\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>股號</th>\n",
              "      <th>股名</th>\n",
              "      <th>產業別</th>\n",
              "      <th>股本</th>\n",
              "      <th>市值</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1101</td>\n",
              "      <td>台泥</td>\n",
              "      <td>水泥工業</td>\n",
              "      <td>7.136180e+09</td>\n",
              "      <td>2.501231e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1102</td>\n",
              "      <td>亞泥</td>\n",
              "      <td>水泥工業</td>\n",
              "      <td>3.546560e+09</td>\n",
              "      <td>1.425717e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1103</td>\n",
              "      <td>嘉泥</td>\n",
              "      <td>水泥工業</td>\n",
              "      <td>6.588940e+08</td>\n",
              "      <td>1.242015e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1104</td>\n",
              "      <td>環泥</td>\n",
              "      <td>水泥工業</td>\n",
              "      <td>6.732170e+08</td>\n",
              "      <td>1.908570e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1108</td>\n",
              "      <td>幸福</td>\n",
              "      <td>水泥工業</td>\n",
              "      <td>4.047380e+08</td>\n",
              "      <td>6.212728e+09</td>\n",
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              "     股號         日期        開盤價        最高價        最低價        收盤價        還原價  \\\n",
              "0  1101 2015-01-05  31.923370  31.923370  31.482035  31.629147  20.498331   \n",
              "1  1101 2015-01-06  31.261366  31.334923  30.967140  31.077475  20.140799   \n",
              "2  1101 2015-01-07  31.077475  31.298143  30.783251  30.967140  20.069292   \n",
              "3  1101 2015-01-08  31.077475  31.371700  30.967140  31.077475  20.140799   \n",
              "4  1101 2015-01-09  31.114252  31.334923  30.746471  30.746471  19.926281   \n",
              "\n",
              "          成交量   殖利率   日本益比  股價淨值比 三大法人買賣超股數   融資買入  融卷賣出  \n",
              "0   3847400.0  5.35  14.88   1.42  -1005483  211.0  20.0  \n",
              "1  10386622.0  5.44  14.62   1.40  -2524874  208.0  32.0  \n",
              "2  11841293.0  5.46  14.57   1.39  -5742000  264.0  13.0  \n",
              "3  11815462.0  5.44  14.62   1.40  -5120668  245.0  38.0  \n",
              "4  14240820.0  5.50  14.46   1.38  -7993555  566.0  56.0  "
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              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5fb9e05a-efb0-4da2-b7ea-345c6dbe2c06 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-5fb9e05a-efb0-4da2-b7ea-345c6dbe2c06');\n",
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              "                                                    [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",
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              "      --bg-color: #E8F0FE;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e9ff6da2-678a-4cc5-9259-459d07032151 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            "text/plain": [
              "     股號         日期        營業收入       營業費用       稅後淨利  每股盈餘\n",
              "0  1101 2024-03-01  25544599.0  2687318.0  2239346.0  0.26\n",
              "1  1101 2023-12-01  28347871.0  2871134.0  2499914.0  0.25\n",
              "2  1101 2023-09-01  27002293.0  2712611.0  2292372.0  0.17\n",
              "3  1101 2023-06-01  27668242.0  2616710.0  4206683.0  0.45\n",
              "4  1101 2023-03-01  26295929.0  2303154.0  1005644.0  0.20"
            ],
            "text/html": [
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              "      <th>股號</th>\n",
              "      <th>日期</th>\n",
              "      <th>營業收入</th>\n",
              "      <th>營業費用</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2239346.0</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1101</td>\n",
              "      <td>2023-12-01</td>\n",
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              "      <th>2</th>\n",
              "      <td>1101</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1101</td>\n",
              "      <td>2023-06-01</td>\n",
              "      <td>27668242.0</td>\n",
              "      <td>2616710.0</td>\n",
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              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1101</td>\n",
              "      <td>2023-03-01</td>\n",
              "      <td>26295929.0</td>\n",
              "      <td>2303154.0</td>\n",
              "      <td>1005644.0</td>\n",
              "      <td>0.20</td>\n",
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              "  </svg>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\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",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-8441fe5a-c57d-4ce1-a1dd-b4d6b6b21611 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-8441fe5a-c57d-4ce1-a1dd-b4d6b6b21611');\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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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
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              "  }\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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_quarterly\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"\\u80a1\\u865f\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1101\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u65e5\\u671f\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-03-01 00:00:00\",\n        \"max\": \"2024-03-01 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2023-12-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u71df\\u696d\\u6536\\u5165\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1103770.4677179037,\n        \"min\": 25544599.0,\n        \"max\": 28347871.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28347871.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u71df\\u696d\\u8cbb\\u7528\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 209120.45831960105,\n        \"min\": 2303154.0,\n        \"max\": 2871134.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2871134.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u7a05\\u5f8c\\u6de8\\u5229\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1144967.8183233798,\n        \"min\": 1005644.0,\n        \"max\": 4206683.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2499914.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6bcf\\u80a1\\u76c8\\u9918\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10922453936730518,\n        \"min\": 0.17,\n        \"max\": 0.45,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 從資料庫中取得表格\n",
        "stock_db = StockDB()\n",
        "df_company = stock_db.get(\"公司\")\n",
        "df_daily = stock_db.get(\"日頻\",psdate=True)\n",
        "df_quarterly = stock_db.get(\"季頻\",psdate=True)\n",
        "\n",
        "# 去除空值\n",
        "df_company = df_company.dropna()\n",
        "df_daily = df_daily.dropna()\n",
        "df_quarterly = df_quarterly.dropna()\n",
        "\n",
        "# 檢視資料\n",
        "display(df_company.head())\n",
        "display(df_daily.head())\n",
        "display(df_quarterly.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwGTl75BLNwu"
      },
      "source": [
        "###6️⃣ 輸入 OpenAI API KEY & 建立物件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziQJGYEH8PKu",
        "outputId": "4d722903-6ced-487f-9f8f-50cce593d893"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "請輸入金鑰：··········\n"
          ]
        }
      ],
      "source": [
        "import getpass\n",
        "openai_api_key = getpass.getpass(\"請輸入金鑰：\")\n",
        "six = StockAnalysis(openai_api_key)\n",
        "seven = PdfLoader(openai_api_key)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebiRubdMv_AZ"
      },
      "source": [
        "### 7️⃣ AI 自動化選股機器人\n",
        "\n",
        "<輸入範例>\n",
        "```\n",
        "請選出近一週漲幅最高的10檔股票\n",
        "請選出大市值股(前10%)且近期營收成長最高的10檔股票\n",
        "請選出半導體業且近期每股盈餘成長率最高的10檔股票\n",
        "請選出近一年股價淨值比最低的10檔股票\n",
        "請從近一個月成交量前10%的股票中找出日本益比最低的5檔不同的股票\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "M4BhX9-uuFE1",
        "outputId": "25c41c3c-c0e5-4714-ac7f-65353263e661"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "請輸入選股需求:請從近一個月成交量前10%的股票中找出日本益比最低的5檔不同的股票\n",
            "\n",
            "import pandas as pd\n",
            "\n",
            "def calculate(table_company, table_daily, table_quarterly):\n",
            "    table_daily['日本益比'] = pd.to_numeric(table_daily['日本益比'], errors='coerce')\n",
            "    table_daily['成交量'] = pd.to_numeric(table_daily['成交量'], errors='coerce')\n",
            "    recent_month_volume = table_daily['日期'].max() - pd.DateOffset(months=1)\n",
            "    top_10_percent_volume = table_daily[table_daily['日期'] > recent_month_volume].nlargest(int(len(table_daily) * 0.1), '成交量')\n",
            "    top_10_percent_volume = top_10_percent_volume.groupby('股號').last().reset_index()\n",
            "    top_5_low_pe_stocks = top_10_percent_volume.nsmallest(5, '日本益比')\n",
            "\n",
            "    return top_5_low_pe_stocks[['股號', '股名', '日本益比']].drop_duplicates()\n",
            "\n",
            "AI 除錯中...\n",
            "import pandas as pd\n",
            "\n",
            "def calculate(table_company, table_daily, table_quarterly):\n",
            "    table_daily['日本益比'] = pd.to_numeric(table_daily['日本益比'], errors='coerce')\n",
            "    table_daily['成交量'] = pd.to_numeric(table_daily['成交量'], errors='coerce')\n",
            "    recent_month_volume = table_daily['日期'].max() - pd.DateOffset(months=1)\n",
            "    top_10_percent_volume = table_daily[table_daily['日期'] > recent_month_volume].nlargest(int(len(table_daily) * 0.1), '成交量')\n",
            "    top_10_percent_volume = top_10_percent_volume.groupby('股號').last().reset_index()\n",
            "    top_5_low_pe_stocks = top_10_percent_volume.nsmallest(5, '日本益比')\n",
            "\n",
            "    return top_5_low_pe_stocks[['股號', '日本益比']].drop_duplicates()\n",
            "-------------------------\n",
            "import pandas as pd\n",
            "\n",
            "def calculate(table_company, table_daily, table_quarterly):\n",
            "    table_daily['日本益比'] = pd.to_numeric(table_daily['日本益比'], errors='coerce')\n",
            "    table_daily['成交量'] = pd.to_numeric(table_daily['成交量'], errors='coerce')\n",
            "    recent_month_volume = table_daily['日期'].max() - pd.DateOffset(months=1)\n",
            "    top_10_percent_volume = table_daily[table_daily['日期'] > recent_month_volume].nlargest(int(len(table_daily) * 0.1), '成交量')\n",
            "    top_10_percent_volume = top_10_percent_volume.groupby('股號').last().reset_index()\n",
            "    top_5_low_pe_stocks = top_10_percent_volume.nsmallest(5, '日本益比')\n",
            "\n",
            "    return top_5_low_pe_stocks[['股號', '日本益比']].drop_duplicates()\n"
          ]
        },
        {
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c8313ebc-2283-4427-85e3-22f425288c94 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       股號  日本益比\n",
              "421  2603  1.59\n",
              "13   1218  1.93\n",
              "426  2609  2.17\n",
              "351  2443  2.33\n",
              "215  2025  2.65"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 輸入使用者需求\n",
        "user_msg = input(\"請輸入選股需求:\")\n",
        "\n",
        "# 自動處理 3 個資料表並生成程式碼\n",
        "history, code_str = six.ai_helper(user_msg)\n",
        "\n",
        "# AI 自動選股 & 除錯\n",
        "success = False\n",
        "for _ in range(3):\n",
        "    try:\n",
        "        # 新建 df 表格\n",
        "        df1 = df_company\n",
        "        df2 = df_daily\n",
        "        df3 = df_quarterly\n",
        "\n",
        "        print(code_str)\n",
        "        exec(code_str)\n",
        "        new_df = calculate(df1, df2, df3)\n",
        "        success = True\n",
        "        break\n",
        "    except Exception as e:\n",
        "        print(f\"AI 除錯中...\")\n",
        "        code_str = six.ai_debug(history, code_str, str(e))\n",
        "        print(code_str)\n",
        "        print(\"-------------------------\")\n",
        "\n",
        "if not success:\n",
        "    print(\"請更換或重新輸入選股需求\")\n",
        "else:\n",
        "    display(new_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a42M4mg1qNQ5"
      },
      "source": [
        "## 8-2 AI 趨勢報告推薦系統"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T59rbTgDwoAZ"
      },
      "source": [
        "### 8️⃣ 取得個股分析報告"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXV-VIEOkYF6",
        "outputId": "0104a6f9-7866-4c9e-bd06-e2fb76290619"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "台積電（2330-TW）近期的趨勢報告如下：\n",
            "\n",
            "1. 股價漲幅分析：\n",
            "根據提供的近期價格資訊，台積電的股價在2023年10月30日至11月13日之間有所波動。從收盤價的變化來看，股價從532.0元上升至573.0元，漲幅約為7.72%。每日報酬方面，股價的波動幅度較小，整體呈現穩定的趨勢。\n",
            "\n",
            "2. 基本面分析：\n",
            "根據提供的每季營收資訊，台積電在2023年第三季度的營收成長率為1136.03%，顯示公司業績有顯著的增長。然而，需要注意的是，前三個季度的營收成長率分別為-0.05%、-0.19%和0.02%，顯示公司在前兩個季度的業績表現較為疲弱。此外，EPS（每股盈餘）在最近四個季度中呈現波動，但整體趨勢仍然穩定。\n",
            "\n",
            "3. 新聞資訊分析：\n",
            "根據提供的近期新聞資訊，台積電的股價受到了一些影響。例如，有報導指出台積電的五大客戶追單，其中包括蘋果、超威、博通等重量級客戶，這可能對台積電的業績產生積極影響。此外，還有一些其他股票的漲跌速報，但這些報導對台積電的整體趨勢影響有限。\n",
            "\n",
            "綜合以上分析，台積電的股價在近期呈現穩定的上升趨勢，並且公司的基本面表現也相對穩定。然而，需要注意的是，公司在前兩個季度的營收成長率較低，可能需要密切關注業績的發展。此外，公司與重要客戶的合作關係對業績也具有重要影響，需要關注相關新聞資訊的變化。\n",
            "\n",
            "以上報告僅供參考，投資者在做出任何投資決策前應進一步進行自己的研究和分析。\n"
          ]
        }
      ],
      "source": [
        "reply = six.stock_gpt(stock_id=\"2330\")\n",
        "print(reply)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC1gzHDLiqhT"
      },
      "source": [
        "### 9️⃣ 收集多檔股票的趨勢報告\n",
        "10 檔股票約會跑 5 分鐘, 若跑太久為 API 當掉"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdSmGArMx6ia",
        "outputId": "216d3da7-027c-412b-d882-f6691d03dc41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2364 檔案已存在\n",
            "2535 檔案已存在\n",
            "3041 檔案已存在\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "# 建立股票清單\n",
        "stock_list = ['2364', '2535', '3041', '5215', '2363',\n",
        "              '1568', '2369', '2816', '9955', '2233']\n",
        "\n",
        "# 設定儲存路徑\n",
        "today_time = dt.date.today().strftime(\"%Y%m%d\")\n",
        "path = '/content/drive/MyDrive/StockGPT/TrendReport/'\n",
        "os.makedirs(path, exist_ok=True)\n",
        "\n",
        "# 建立多檔股票的趨勢報告並儲存\n",
        "content = {}\n",
        "\n",
        "for stock in stock_list:\n",
        "  file_path = f\"{path}trend_{stock}_{today_time}.txt\"\n",
        "\n",
        "  if os.path.exists(file_path):\n",
        "    print(f\"{stock} 檔案已存在\")\n",
        "  else:\n",
        "    with open(file_path, \"w\", encoding=\"utf-8\") as f:\n",
        "      f.write(six.stock_gpt(stock_id=stock))\n",
        "\n",
        "  with open(file_path, \"r\", encoding=\"utf-8\") as f:\n",
        "      content[stock] = f.read()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h9Aj6vrrYxci"
      },
      "source": [
        "### 🔟 推薦出一檔股票"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2STlwryzwdW",
        "outputId": "9cbbbf84-329d-4ffd-9733-eb5d4b7f7166"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "根據提供的資料，我對以上股票的趨勢進行了分析。在這些股票中，我認為科嘉-KY（代號5215-TW）是一檔值得投資的股票。\n",
            "\n",
            "首先，科嘉-KY的股價在近期呈現波動，但整體呈現上升趨勢。雖然股價波動較大，但公司在市場上仍被認為是值得持續關注的強勢股之一。\n",
            "\n",
            "其次，從基本面來看，科嘉-KY在2023年6月30日的營收成長率為46%，顯示公司營收呈現正向增長。儘管在此之前的兩個季度營收成長率呈現下降趨勢，但整體來說，公司的營收表現穩定。\n",
            "\n",
            "最後，根據新聞資訊，科嘉-KY在市場上被認為是「疾風下的勁草」，並且被列為值得持續關注的強勢股之一。這表明市場對於公司的潛在價值持有樂觀態度。\n",
            "\n",
            "綜合以上分析，我認為科嘉-KY是一檔值得投資的股票。公司的營收表現穩定，並且在市場上受到肯定。投資者應該密切關注公司的營收和盈利表現，並謹慎評估投資風險。希望這份分析對您有所幫助。\n"
          ]
        }
      ],
      "source": [
        "def stock_choice(data):\n",
        "    # 設定提示模板\n",
        "    msg = [{\n",
        "        \"role\": \"system\",\n",
        "        \"content\":\n",
        "        \"你現在是一位專業的證券分析師, 你會針對各股的專業趨勢分析報告, \\n\\\n",
        "         選擇出最適合投資的一檔股票,並說明選擇它的理由。\"\n",
        "    }, {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": str(data)\n",
        "    }]\n",
        "\n",
        "    reply_data = six.get_reply(msg)\n",
        "    return reply_data\n",
        "\n",
        "print(stock_choice(content))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jqJLS9TSqyU"
      },
      "source": [
        "### 1️⃣1️⃣ 推薦股票的評分排序"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8Kpk7J_YY1q",
        "outputId": "54fc381d-e79b-4057-f15e-2fdc124f1397"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "根據提供的資料，我將對各股票的趨勢進行分析並給予評分。\n",
            "\n",
            "1. 2364 (倫飛): 股價波動較大，營收成長率為6%，但EPS季增率波動較大，且受市場環境和產業消息影響。評分: 45\n",
            "\n",
            "2. 2535 (未提供資料): 無法提供評分。\n",
            "\n",
            "3. 3041 (未提供資料): 無法提供評分。\n",
            "\n",
            "4. 5215 (科嘉-KY): 股價波動較大，營收成長率在不同季度呈現波動，且市場上被認為是值得持續關注的強勢股之一。評分: 55\n",
            "\n",
            "5. 2363 (矽統): 股價呈現明顯的下跌趨勢，營收成長率有一定程度的增長，但EPS季增率波動較大，且受外資賣超情況影響。評分: 40\n",
            "\n",
            "6. 1568 (倉佑): 股價呈現下跌趨勢，營收成長率輕微增長，但EPS季增率呈現波動，且受多方面因素影響。評分: 42\n",
            "\n",
            "7. 2369 (菱生): 股價呈現下跌趨勢，營收成長率有所增長，但EPS呈現下滑趨勢，且受市場情緒影響。評分: 45\n",
            "\n",
            "8. 2816 (旺旺保): 股價波動較大，營收成長率輕微增長，但EPS季增率呈現下降，且受多項因素影響。評分: 48\n",
            "\n",
            "9. 9955 (佳龍公司): 股價呈現下跌趨勢，營收成長不穩定，且EPS波動較大，受多項因素影響。評分: 40\n",
            "\n",
            "10. 2233 (宇隆): 股價波動較大，營收有一定程度的增長，且受營運週轉資金需求和產業投資價值影響。評分: 50\n",
            "\n",
            "根據以上分析，我將這些股票依照評分排序如下：\n",
            "1. 2816 (旺旺保) - 48\n",
            "2. 2364 (倫飛) - 45\n",
            "3. 2369 (菱生) - 45\n",
            "4. 2233 (宇隆) - 50\n",
            "5. 5215 (科嘉-KY) - 55\n",
            "6. 1568 (倉佑) - 42\n",
            "7. 2363 (矽統) - 40\n",
            "8. 9955 (佳龍公司) - 40\n"
          ]
        }
      ],
      "source": [
        "def stock_sort(data):\n",
        "    # 設定提示模板\n",
        "    msg = [{\n",
        "        \"role\": \"system\",\n",
        "        \"content\":\n",
        "        \"你現在是一位專業的股票分析師, 會根據各股的專業趨勢分析報告去評斷\\\n",
        "         適不適合投資, 並給予0-100之間的評分。\\n\\\n",
        "         以 50 分為基準, 有任何正面消息可以加分如:\\n\\\n",
        "         股價整體上升、法人買超、營收成長上升、新聞有正面消息；\\n\\\n",
        "         若有任何負面消息必須扣分如:\\n\\\n",
        "         股價整體下降、法人賣超、營收成長下降、新聞有負面消息。\\n\\\n",
        "         最後請將所有股票依照評分排序出來。\"\n",
        "    }, {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": str(data)\n",
        "    }]\n",
        "    reply_data = six.get_reply(msg)\n",
        "    return reply_data\n",
        "\n",
        "print(stock_sort(content))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyhMRREM78Y5"
      },
      "source": [
        "## 8-3 AI 年報分析推薦系統"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MkoAWO2GjnlT"
      },
      "source": [
        "### 1️⃣2️⃣ 取得多檔股票的年報檔案"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xCESGymy3Cq7"
      },
      "outputs": [],
      "source": [
        "# 建立股票清單\n",
        "stock_list = ['2364', '2535', '3041', '5215', '2363',\n",
        "              '1568', '2369', '2816', '9955', '2233']\n",
        "\n",
        "# 取得並儲存年報資料\n",
        "for stock in stock_list:\n",
        "  if not os.path.exists(f'/content/drive/MyDrive/StockGPT/PDF/113_{stock}.pdf'):\n",
        "    try:\n",
        "        seven.annual_report(stock,'113')\n",
        "    except:\n",
        "        time.sleep(10)\n",
        "        while True:\n",
        "            try:\n",
        "                seven.annual_report(stock,'113')\n",
        "                break\n",
        "            except:\n",
        "                time.sleep(10*2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BP5FkDxALp4e"
      },
      "source": [
        "### 1️⃣3️⃣ 建立向量資料庫\n",
        "10 檔股票約需 20 分鐘"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ORuDNVa6FBVF"
      },
      "outputs": [],
      "source": [
        "db_list=[]\n",
        "for i in stock_list:\n",
        "    if not os.path.exists(\n",
        "        f'/content/drive/MyDrive/StockGPT/DB/113_{i}'):\n",
        "        print('start',i)\n",
        "        db_list.append(\n",
        "            seven.pdf_loader(\n",
        "                f'/content/drive/MyDrive/StockGPT/PDF/113_{i}.pdf',\n",
        "                600, 60))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VP2kpv8Q_SUH"
      },
      "source": [
        "### 1️⃣4️⃣ 讀取向量資料庫"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5zTl8dQ038bC"
      },
      "outputs": [],
      "source": [
        "from langchain.embeddings import OpenAIEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "\n",
        "db_list=[]\n",
        "for i in stock_list:\n",
        "    db_list.append(\n",
        "        FAISS.load_local(\n",
        "          folder_path=f'/content/drive/MyDrive/StockGPT/DB/113_{i}',\n",
        "          embeddings=OpenAIEmbeddings()\n",
        "          ))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91HhzWPNYLf_"
      },
      "source": [
        "### 1️⃣5️⃣ 建立問題串列關鍵字"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I9TRgUPnVZLb"
      },
      "outputs": [],
      "source": [
        "key_word = ['公司的財務健全度為何？',\n",
        "            '營運策略和市場定位是什麼？',\n",
        "            '面對哪些主要風險和挑戰？',\n",
        "            '公司在研發和創新方面有哪些成就和計劃？',\n",
        "            '公司治理結構和管理層的組成是？']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykxyKmwMYW7X"
      },
      "source": [
        "### 1️⃣6️⃣ 取得相關資料 & 建立分析機器人"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tcy0halI4mZs"
      },
      "outputs": [],
      "source": [
        "# 建立從向量資料庫取得相關資訊函式\n",
        "def generate_data(db, key_word):\n",
        "    results = []\n",
        "    for word in key_word:\n",
        "        results.append(seven.analyze_chain(db, word))\n",
        "    return results\n",
        "\n",
        "# 建立年報分析機器人\n",
        "def stock_report_summary(data):\n",
        "\n",
        "    msg = [{\n",
        "        \"role\": \"system\",\n",
        "        \"content\": \"你現在是一位專業的年報分析師,\\n\\\n",
        "                    你會針對年報報告彙整出一份專業的分析報告。\\n\\\n",
        "                    請以詳細、嚴謹及專業的角度撰寫此報告,並提及重要的數字\\\n",
        "                    ,reply in 繁體中文\"\n",
        "    }, {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": str(data)\n",
        "    }]\n",
        "\n",
        "    reply_data = six.get_reply(msg)\n",
        "\n",
        "    return reply_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3DFggGZY1X4"
      },
      "source": [
        "### 1️⃣7️⃣ 多檔股票的年報分析報告\n",
        "10 檔股票約需執行 40 分鐘"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjU8jHmKWIte",
        "outputId": "116379f4-fd79-4a1c-ad66-7a7306e8a295"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/10 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2364 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 10%|█         | 1/10 [00:00<00:04,  2.07it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2535 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 20%|██        | 2/10 [00:00<00:03,  2.56it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3041 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 30%|███       | 3/10 [00:01<00:02,  2.66it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5215 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 40%|████      | 4/10 [00:01<00:02,  2.67it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2363 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 5/10 [00:01<00:01,  2.75it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1568 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 60%|██████    | 6/10 [00:02<00:01,  2.85it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2369 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 70%|███████   | 7/10 [00:02<00:01,  2.79it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2816 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 80%|████████  | 8/10 [00:02<00:00,  2.68it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "9955 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 90%|█████████ | 9/10 [00:03<00:00,  2.46it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2233 檔案已存在\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:03<00:00,  2.60it/s]\n"
          ]
        }
      ],
      "source": [
        "# 年報分析檔案的儲存路徑\n",
        "path = \"/content/drive/MyDrive/StockGPT/AnnualReport/\"\n",
        "os.makedirs(path, exist_ok=True)\n",
        "\n",
        "\n",
        "# 建立多檔股票的年報分析報告並儲存\n",
        "content = {}\n",
        "\n",
        "for stock in tqdm(stock_list, total=len(stock_list)):\n",
        "  file_path = f\"{path}annual_{stock}_113.txt\"\n",
        "\n",
        "  if os.path.exists(file_path):\n",
        "    print(f\"{stock} 檔案已存在\")\n",
        "  else:\n",
        "\n",
        "      db = db_list[stock_list.index(stock)]\n",
        "      report = stock_report_summary(generate_data(db, key_word))\n",
        "\n",
        "      with open(file_path, \"w\", encoding=\"utf-8\") as f:\n",
        "        f.write(report)\n",
        "\n",
        "  # 取得股名\n",
        "  name = six.stock_info.get_stock_name(stock, six.name_df)\n",
        "\n",
        "  with open(file_path, \"r\", encoding=\"utf-8\") as f:\n",
        "      content[f\"{name}({stock})\"] = f.read()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMrgmyCsZDkQ"
      },
      "source": [
        "### 1️⃣8️⃣ 根據分析結果推薦出一檔股票"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFHQt-3uV1M9",
        "outputId": "4f577fe6-a2d5-4445-ce12-d15aa61c9516"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "根據所提供的年報分析報告，我會選擇倫飛(2364)作為最適合投資的一檔股票。以下是我選擇它的理由：\n",
            "\n",
            "1. 財務比率表現良好：根據年報的分析，倫飛公司在財務比率方面表現良好，包括利息保障倍數、資產報酬率、權益報酬率、純益率等指標均呈現上升趨勢。這顯示公司有良好的獲利能力和資產運用效率。\n",
            "\n",
            "2. 公司努力和措施：倫飛公司積極強化董事會職能、加強公司治理，並重視產品創新和研發，以提高競爭力和市場佔有率。這些努力有助於提高公司的透明度、獲利能力和競爭力。\n",
            "\n",
            "3. 現金流量允許比率上升：根據年報的分析，倫飛公司的現金流量允許比率上升，顯示公司有足夠的現金流量來應對短期債務，這有助於確保公司的財務穩健性。\n",
            "\n",
            "綜合以上分析，倫飛公司在財務表現、公司治理和市場競爭力方面均表現良好，並且有積極的發展策略和措施。因此，我認為倫飛(2364)是一檔值得投資的股票。\n"
          ]
        }
      ],
      "source": [
        "print(stock_choice(content))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WiGoyIWHavZy"
      },
      "source": [
        "### 1️⃣9️⃣ 年報分析報告評分排序"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "25yhBtuUHv1z"
      },
      "outputs": [],
      "source": [
        "def stock_report_sort(data):\n",
        "    # 設定提示模板\n",
        "    msg = [{\n",
        "        \"role\": \"system\",\n",
        "        \"content\":\n",
        "        \"你現在是一位專業的股票分析師, 會根據各股的年報分析報告去評斷\\\n",
        "         適不適合投資, 並給予0-100之間的評分。\\n\\\n",
        "         以 50 分為基準, 有任何正面消息可以加分如:\\n\\\n",
        "         財務呈現穩定增長的趨勢, 具有競爭優勢；\\n\\\n",
        "         若有任何負面消息必須扣分如:\\n\\\n",
        "         財務呈現下降的趨勢, 不具有競爭優勢。\\n\\\n",
        "         最後請將10檔股票依照評分排序出來。\"\n",
        "    }, {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": str(data)\n",
        "    }]\n",
        "    reply_data = six.get_reply(msg)\n",
        "    return reply_data\n",
        "\n",
        "print(stock_report_sort(content))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}