HM_week_2.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "## Imports and Installs"
      ],
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      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
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            "Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.38)\n",
            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.0.3)\n",
            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.25.2)\n",
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      "source": [
        "# IMPORTS\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "#Fin Data Sources\n",
        "import yfinance as yf\n",
        "import pandas_datareader as pdr\n",
        "\n",
        "import time\n",
        "from datetime import date"
      ],
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        "id": "hF2-gtPG0sZe"
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      "execution_count": 3,
      "outputs": []
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    {
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      "source": [
        "## Question 1: IPO Filings Web Scraping and Data Processing\n",
        "\n",
        "**What's the total sum ($m) of 2023 filings that happened on Fridays?**"
      ],
      "metadata": {
        "id": "KiD27EofUiu2"
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      "source": [
        "import requests\n",
        "\n",
        "headers = {\n",
        "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3',\n",
        "}\n",
        "\n",
        "url = \"https://stockanalysis.com/ipos/filings/\"\n",
        "response = requests.get(url, headers=headers)\n",
        "\n",
        "ipo_dfs = pd.read_html(response.text)"
      ],
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      "execution_count": 4,
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      "source": [
        "ipos_df = ipo_dfs[0]\n",
        "ipos_df.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 326 entries, 0 to 325\n",
            "Data columns (total 7 columns):\n",
            " #   Column                Non-Null Count  Dtype         \n",
            "---  ------                --------------  -----         \n",
            " 0   Filing Date           326 non-null    datetime64[ns]\n",
            " 1   Symbol                326 non-null    object        \n",
            " 2   Company Name          326 non-null    object        \n",
            " 3   Price Range           326 non-null    object        \n",
            " 4   Shares Offered        253 non-null    float64       \n",
            " 5   Avg_price             259 non-null    float64       \n",
            " 6   Shares_offered_value  250 non-null    float64       \n",
            "dtypes: datetime64[ns](1), float64(3), object(3)\n",
            "memory usage: 18.0+ KB\n"
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        "ipos_df.head()"
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              "  Filing Date Symbol                           Company Name Price Range  \\\n",
              "0  2024-05-03    TBN         Tamboran Resources Corporation           -   \n",
              "1  2024-04-29   HWEC                   HW Electro Co., Ltd.       $3.00   \n",
              "2  2024-04-29   DTSQ  DT Cloud Star Acquisition Corporation      $10.00   \n",
              "3  2024-04-26   EURK                Eureka Acquisition Corp      $10.00   \n",
              "4  2024-04-26    HDL    Super Hi International Holding Ltd.           -   \n",
              "\n",
              "   Shares Offered  Avg_price  Shares_offered_value  \n",
              "0             NaN        NaN                   NaN  \n",
              "1       3750000.0        3.0            11250000.0  \n",
              "2       6000000.0       10.0            60000000.0  \n",
              "3       5000000.0       10.0            50000000.0  \n",
              "4             NaN        NaN                   NaN  "
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              "summary": "{\n  \"name\": \"ipos_df\",\n  \"rows\": 326,\n  \"fields\": [\n    {\n      \"column\": \"Filing Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-12-27 00:00:00\",\n        \"max\": \"2024-05-03 00:00:00\",\n        \"num_unique_values\": 257,\n        \"samples\": [\n          \"2022-06-03 00:00:00\",\n          \"2022-01-10 00:00:00\",\n          \"2023-08-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 326,\n        \"samples\": [\n          \"LEDG\",\n          \"ZXTY\",\n          \"BXHA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 326,\n        \"samples\": [\n          \"Ledger Acquisition Company\",\n          \"Zhongxing Holding Group Ltd\",\n          \"Bombax Healthcare Acquisition Corporation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Range\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 45,\n        \"samples\": [\n          \"$7.00 - $9.00\",\n          \"$4.35 - $6.35\",\n          \"$20.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shares Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10818911.411974603,\n        \"min\": 360000.0,\n        \"max\": 100000000.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          800000.0,\n          3750000.0,\n          1500000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8096448246327212,\n        \"min\": 3.0,\n        \"max\": 20.0,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          19.0,\n          6.25,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shares_offered_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111415991.71367534,\n        \"min\": 4000000.0,\n        \"max\": 1000000000.0,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          5500000.0,\n          17500000.0,\n          9000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert to datetime\n",
        "ipos_df['Filing Date'] = pd.to_datetime(ipos_df['Filing Date'], format=\"%b %d, %Y\")"
      ],
      "metadata": {
        "id": "MdvFDz2NSI12"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Problem --> not always the columns are filled\n",
        "missing_prices_df = ipos_df[ipos_df['Shares Offered'].astype(str).str.find('-') >= 0]\n",
        "missing_prices_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "E21pj1n5Sos8",
        "outputId": "4e4a8e8a-72b7-43a6-f508-3fd15c432a75"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Filing Date Symbol                           Company Name Price Range  \\\n",
              "0    2024-05-03    TBN         Tamboran Resources Corporation           -   \n",
              "4    2024-04-26    HDL    Super Hi International Holding Ltd.           -   \n",
              "5    2024-04-22   DRJT                        Derun Group Inc       $5.00   \n",
              "7    2024-04-16   JLJT                  Jialiang Holdings Ltd       $5.00   \n",
              "8    2024-04-15   GAUZ                             Gauzy Ltd.           -   \n",
              "..          ...    ...                                    ...         ...   \n",
              "301  2021-03-02   NSAL  Navios South American Logistics, Inc.           -   \n",
              "315  2021-01-19   DECI             Decipher Biosciences, Inc.           -   \n",
              "317  2020-09-08   GMWX                        GameWorks, Inc.           -   \n",
              "318  2020-09-04   SCIT              Sancai Holding Group Ltd.           -   \n",
              "320  2020-02-18   CAST                         FreeCast, Inc.           -   \n",
              "\n",
              "    Shares Offered  \n",
              "0                -  \n",
              "4                -  \n",
              "5                -  \n",
              "7                -  \n",
              "8                -  \n",
              "..             ...  \n",
              "301              -  \n",
              "315              -  \n",
              "317              -  \n",
              "318              -  \n",
              "320              -  \n",
              "\n",
              "[73 rows x 5 columns]"
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              "      <th>Filing Date</th>\n",
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              "      <td>HDL</td>\n",
              "      <td>Super Hi International Holding Ltd.</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024-04-22</td>\n",
              "      <td>DRJT</td>\n",
              "      <td>Derun Group Inc</td>\n",
              "      <td>$5.00</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>JLJT</td>\n",
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              "      <td>-</td>\n",
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              "      <th>315</th>\n",
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              "      <td>-</td>\n",
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              "      <td>2020-09-08</td>\n",
              "      <td>GMWX</td>\n",
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              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>2020-09-04</td>\n",
              "      <td>SCIT</td>\n",
              "      <td>Sancai Holding Group Ltd.</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>2020-02-18</td>\n",
              "      <td>CAST</td>\n",
              "      <td>FreeCast, Inc.</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "type": "dataframe",
              "variable_name": "missing_prices_df",
              "summary": "{\n  \"name\": \"missing_prices_df\",\n  \"rows\": 73,\n  \"fields\": [\n    {\n      \"column\": \"Filing Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-02-18 00:00:00\",\n        \"max\": \"2024-05-03 00:00:00\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"2023-12-07 00:00:00\",\n          \"2024-05-03 00:00:00\",\n          \"2022-12-20 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          \"GAUZ\",\n          \"AGDX\",\n          \"EPSM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          \"Gauzy Ltd.\",\n          \"Agendia N.V.\",\n          \"Epsium Enterprise Limited\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Range\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"$5.00\",\n          \"$6.00\",\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shares Offered\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipos_df['Shares Offered'] = pd.to_numeric(ipos_df['Shares Offered'], errors='coerce')"
      ],
      "metadata": {
        "id": "4L06PBlFTkj7"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def extract_numbers(input_string):\n",
        "    price_range = re.search(r'^\\$([0-9]+\\.[0-9]{2}) - \\$([0-9]+\\.[0-9]{2})$', input_string)\n",
        "    if price_range:\n",
        "        min = float(price_range.group(1))\n",
        "        max = float(price_range.group(2))\n",
        "        return round((float(min) + float(max)) / 2, 2)\n",
        "    pr = re.search(r'^\\$([0-9]+)\\.[0-9]{2}$', input_string)\n",
        "    if pr:\n",
        "        return float(input_string[1:])\n",
        "    return float('nan')\n",
        "\n",
        "# Examples\n",
        "examples = [\"Y10_M2\", \"$9.50 - $10.43\", \"$39.56\"]\n",
        "for example in examples:\n",
        "    p1 = extract_numbers(example)\n",
        "    print(f\"{example} ==> {p1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4x3Zj8lUA0g",
        "outputId": "5500f7bb-ae08-481e-e316-062472aa24ec"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y10_M2 ==> nan\n",
            "$9.50 - $10.43 ==> 9.96\n",
            "$39.56 ==> 39.56\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipos_df['Avg_price'] = ipos_df['Price Range'].apply(extract_numbers)"
      ],
      "metadata": {
        "id": "7rs6VVJliZlY"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipos_df['Shares_offered_value'] = ipos_df['Avg_price'] * ipos_df['Shares Offered']"
      ],
      "metadata": {
        "id": "xC27UYDNi0NB"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# filter ipos by day of week friday and year\n",
        "friday_ipos = ipos_df[ipos_df['Filing Date'].dt.dayofweek == 4]\n",
        "friday_ipos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "h-ZqmBMckdXi",
        "outputId": "5e2ce61d-ca5f-4aea-9ceb-645857dcb2fb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Filing Date Symbol                               Company Name  \\\n",
              "0    2024-05-03    TBN             Tamboran Resources Corporation   \n",
              "3    2024-04-26   EURK                    Eureka Acquisition Corp   \n",
              "4    2024-04-26    HDL        Super Hi International Holding Ltd.   \n",
              "6    2024-04-19   GPAT               GP-Act III Acquisition Corp.   \n",
              "9    2024-04-12    BOW            Bowhead Specialty Holdings Inc.   \n",
              "..          ...    ...                                        ...   \n",
              "299  2021-03-05   HRDG  Huarui International New Material Limited   \n",
              "311  2021-02-19   EOCO                Elliott Opportunity I Corp.   \n",
              "314  2021-01-22   TONY                             Tony Fun, Inc.   \n",
              "318  2020-09-04   SCIT                  Sancai Holding Group Ltd.   \n",
              "324  2019-10-04   ZGHB      China Eco-Materials Group Co. Limited   \n",
              "\n",
              "       Price Range  Shares Offered  Avg_price  Shares_offered_value  \n",
              "0                -             NaN        NaN                   NaN  \n",
              "3           $10.00       5000000.0      10.00          5.000000e+07  \n",
              "4                -             NaN        NaN                   NaN  \n",
              "6           $10.00      25000000.0      10.00          2.500000e+08  \n",
              "9                -             NaN        NaN                   NaN  \n",
              "..             ...             ...        ...                   ...  \n",
              "299  $4.00 - $6.00       2400000.0       5.00          1.200000e+07  \n",
              "311         $10.00     100000000.0      10.00          1.000000e+09  \n",
              "314  $7.00 - $7.50       4177500.0       7.25          3.028688e+07  \n",
              "318              -             NaN        NaN                   NaN  \n",
              "324          $4.00       4300000.0       4.00          1.720000e+07  \n",
              "\n",
              "[90 rows x 7 columns]"
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Filing Date</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Company Name</th>\n",
              "      <th>Price Range</th>\n",
              "      <th>Shares Offered</th>\n",
              "      <th>Avg_price</th>\n",
              "      <th>Shares_offered_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-05-03</td>\n",
              "      <td>TBN</td>\n",
              "      <td>Tamboran Resources Corporation</td>\n",
              "      <td>-</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-04-26</td>\n",
              "      <td>EURK</td>\n",
              "      <td>Eureka Acquisition Corp</td>\n",
              "      <td>$10.00</td>\n",
              "      <td>5000000.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>5.000000e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-04-26</td>\n",
              "      <td>HDL</td>\n",
              "      <td>Super Hi International Holding Ltd.</td>\n",
              "      <td>-</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2024-04-19</td>\n",
              "      <td>GPAT</td>\n",
              "      <td>GP-Act III Acquisition Corp.</td>\n",
              "      <td>$10.00</td>\n",
              "      <td>25000000.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>2.500000e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2024-04-12</td>\n",
              "      <td>BOW</td>\n",
              "      <td>Bowhead Specialty Holdings Inc.</td>\n",
              "      <td>-</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>2021-03-05</td>\n",
              "      <td>HRDG</td>\n",
              "      <td>Huarui International New Material Limited</td>\n",
              "      <td>$4.00 - $6.00</td>\n",
              "      <td>2400000.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>1.200000e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>2021-02-19</td>\n",
              "      <td>EOCO</td>\n",
              "      <td>Elliott Opportunity I Corp.</td>\n",
              "      <td>$10.00</td>\n",
              "      <td>100000000.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>1.000000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>2021-01-22</td>\n",
              "      <td>TONY</td>\n",
              "      <td>Tony Fun, Inc.</td>\n",
              "      <td>$7.00 - $7.50</td>\n",
              "      <td>4177500.0</td>\n",
              "      <td>7.25</td>\n",
              "      <td>3.028688e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>2020-09-04</td>\n",
              "      <td>SCIT</td>\n",
              "      <td>Sancai Holding Group Ltd.</td>\n",
              "      <td>-</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>2019-10-04</td>\n",
              "      <td>ZGHB</td>\n",
              "      <td>China Eco-Materials Group Co. Limited</td>\n",
              "      <td>$4.00</td>\n",
              "      <td>4300000.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1.720000e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
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              "        document.querySelector('#df-1183c9ea-32cd-4e30-a04e-f2a52e3b8f5e 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-1183c9ea-32cd-4e30-a04e-f2a52e3b8f5e');\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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              "  </button>\n",
              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    }\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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
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              "        fill: #D2E3FC;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('friday_ipos')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('friday_ipos');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "friday_ipos",
              "summary": "{\n  \"name\": \"friday_ipos\",\n  \"rows\": 90,\n  \"fields\": [\n    {\n      \"column\": \"Filing Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-10-04 00:00:00\",\n        \"max\": \"2024-05-03 00:00:00\",\n        \"num_unique_values\": 69,\n        \"samples\": [\n          \"2023-07-07 00:00:00\",\n          \"2024-05-03 00:00:00\",\n          \"2022-03-18 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"USEE\",\n          \"ORIS\",\n          \"IBG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"USEE Electronic Commerce Ltd\",\n          \"Oriental Rise Holdings Limited\",\n          \"Innovation Beverage Group Limited\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Range\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"-\",\n          \"$4.25 - $6.25\",\n          \"$6.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shares Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13152050.287589641,\n        \"min\": 360000.0,\n        \"max\": 100000000.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          4177500.0,\n          360000.0,\n          1596154.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0625469427653007,\n        \"min\": 4.0,\n        \"max\": 20.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          8.0,\n          4.13,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shares_offered_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 134220034.8807108,\n        \"min\": 4800000.0,\n        \"max\": 1000000000.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          60000000.0,\n          42500000.0,\n          20000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "friday_ipos = friday_ipos[ipos_df['Filing Date'].dt.year == 2023]\n",
        "sum_ipos_fill = friday_ipos['Shares_offered_value'].sum()\n",
        "print(f\"{sum_ipos_fill:,}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0r0cr2kl42r",
        "outputId": "86cee7c7-4545-4ce7-ed61-60e8d943196d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "285,700,000.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-8dc74d05295d>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  friday_ipos = friday_ipos[ipos_df['Filing Date'].dt.year == 2023]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question 2:  IPOs \"Fixed days hold\" strategy\n",
        "\n",
        "\n",
        "**Find the optimal number of days X (between 1 and 30), where 75% quantile growth is the highest?**"
      ],
      "metadata": {
        "id": "IorzZa748apQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://stockanalysis.com/ipos/2023/\"\n",
        "response = requests.get(url, headers=headers)\n",
        "\n",
        "ipo_dfs = pd.read_html(response.text)\n",
        "\n",
        "ipos_2023_df = ipo_dfs[0]\n",
        "ipos_2023_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBXoqEw18jjx",
        "outputId": "51ca7bdf-0a8c-48ce-bbce-c13e18d04cdd"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 154 entries, 0 to 153\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   IPO Date      154 non-null    object\n",
            " 1   Symbol        154 non-null    object\n",
            " 2   Company Name  154 non-null    object\n",
            " 3   IPO Price     154 non-null    object\n",
            " 4   Current       154 non-null    object\n",
            " 5   Return        154 non-null    object\n",
            "dtypes: object(6)\n",
            "memory usage: 7.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://stockanalysis.com/ipos/2024/\"\n",
        "response = requests.get(url, headers=headers)\n",
        "\n",
        "ipo_2024_dfs = pd.read_html(response.text)\n",
        "\n",
        "ipos_2024_df = ipo_2024_dfs[0]\n",
        "ipos_2024_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ay8Mx3i_9RX6",
        "outputId": "6fd34c6d-fb2e-4076-f419-197c9f847822"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 64 entries, 0 to 63\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   IPO Date      64 non-null     object\n",
            " 1   Symbol        64 non-null     object\n",
            " 2   Company Name  64 non-null     object\n",
            " 3   IPO Price     64 non-null     object\n",
            " 4   Current       64 non-null     object\n",
            " 5   Return        64 non-null     object\n",
            "dtypes: object(6)\n",
            "memory usage: 3.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipos_2024_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vtke2CgA_bBB",
        "outputId": "3bf9740f-1b91-4b69-fd53-450b5901e226"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       IPO Date Symbol               Company Name IPO Price Current   Return\n",
              "0   May 1, 2024    VIK       Viking Holdings Ltd.    $24.00  $28.94   18.04%\n",
              "1  Apr 26, 2024   ZONE  CleanCore Solutions, Inc.     $4.00   $3.11  -21.50%\n",
              "2  Apr 25, 2024   RBRK               Rubrik, Inc.    $32.00  $34.87    1.28%\n",
              "3  Apr 25, 2024   LOAR         Loar Holdings Inc.    $28.00  $51.10   82.50%\n",
              "4  Apr 25, 2024    MRX            Marex Group plc    $19.00  $19.15    0.84%"
            ],
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              "\n",
              "  <div id=\"df-ebb559e5-d540-4aa5-a616-0c0af75ffef1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th>IPO Date</th>\n",
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              "      <th>1</th>\n",
              "      <td>Apr 26, 2024</td>\n",
              "      <td>ZONE</td>\n",
              "      <td>CleanCore Solutions, Inc.</td>\n",
              "      <td>$4.00</td>\n",
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              "      <td>Apr 25, 2024</td>\n",
              "      <td>RBRK</td>\n",
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              "      <td>$32.00</td>\n",
              "      <td>$34.87</td>\n",
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              "      <th>3</th>\n",
              "      <td>Apr 25, 2024</td>\n",
              "      <td>LOAR</td>\n",
              "      <td>Loar Holdings Inc.</td>\n",
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              "      <td>$51.10</td>\n",
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              "      <td>Apr 25, 2024</td>\n",
              "      <td>MRX</td>\n",
              "      <td>Marex Group plc</td>\n",
              "      <td>$19.00</td>\n",
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              "        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-e977a199-31c2-4d63-94bd-5e23806d6bb0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ipos_2024_df",
              "summary": "{\n  \"name\": \"ipos_2024_df\",\n  \"rows\": 64,\n  \"fields\": [\n    {\n      \"column\": \"IPO Date\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"Feb 16, 2024\",\n          \"Apr 2, 2024\",\n          \"Apr 16, 2024\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 64,\n        \"samples\": [\n          \"HAO\",\n          \"JVSA\",\n          \"VIK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 64,\n        \"samples\": [\n          \"Haoxi Health Technology Ltd.\",\n          \"JVSPAC Acquisition Corp.\",\n          \"Viking Holdings Ltd.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPO Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"$16.00\",\n          \"$17.50\",\n          \"$24.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Current\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 61,\n        \"samples\": [\n          \"$28.94\",\n          \"$1.05\",\n          \"$15.98\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Return\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"45.88%\",\n          \"31.08%\",\n          \"18.04%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert IPO Date to date type\n",
        "ipos_2023_df['IPO Date'] = pd.to_datetime(ipos_2023_df['IPO Date'], format=\"%b %d, %Y\")\n",
        "ipos_2024_df['IPO Date'] = pd.to_datetime(ipos_2024_df['IPO Date'], format=\"%b %d, %Y\")\n",
        "# get ipos_2024_df where IPO Date is \"< 2024-03-01\"\n",
        "ipos_2024_df = ipos_2024_df[ipos_2024_df['IPO Date'] < pd.to_datetime('2024-03-01')]\n",
        "ipos_2024_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hx4nOIL79jOJ",
        "outputId": "68fee509-4336-4454-ba91-a91ba1527056"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 31 entries, 33 to 63\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   IPO Date      31 non-null     datetime64[ns]\n",
            " 1   Symbol        31 non-null     object        \n",
            " 2   Company Name  31 non-null     object        \n",
            " 3   IPO Price     31 non-null     object        \n",
            " 4   Current       31 non-null     object        \n",
            " 5   Return        31 non-null     object        \n",
            "dtypes: datetime64[ns](1), object(5)\n",
            "memory usage: 1.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# delete ipos 2023 row by Symbol == 'RYZB'\n",
        "ipos_2023_df = ipos_2023_df[ipos_2023_df['Symbol'] != 'RYZB']\n",
        "ipos_2023_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXP36eZt9j0K",
        "outputId": "33d6b694-0832-462f-eb8e-efafe3bd1755"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 153 entries, 0 to 153\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   IPO Date      153 non-null    datetime64[ns]\n",
            " 1   Symbol        153 non-null    object        \n",
            " 2   Company Name  153 non-null    object        \n",
            " 3   IPO Price     153 non-null    object        \n",
            " 4   Current       153 non-null    object        \n",
            " 5   Return        153 non-null    object        \n",
            "dtypes: datetime64[ns](1), object(5)\n",
            "memory usage: 8.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# join ipos_2023 and ipos_2024\n",
        "stacked_ipos_df = pd.concat([ipos_2023_df, ipos_2024_df], ignore_index=True)\n",
        "stacked_ipos_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEF4SUZUCULs",
        "outputId": "2401133e-c16c-4eb7-c686-70901d8dc5e5"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 184 entries, 0 to 183\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   IPO Date      184 non-null    datetime64[ns]\n",
            " 1   Symbol        184 non-null    object        \n",
            " 2   Company Name  184 non-null    object        \n",
            " 3   IPO Price     184 non-null    object        \n",
            " 4   Current       184 non-null    object        \n",
            " 5   Return        184 non-null    object        \n",
            "dtypes: datetime64[ns](1), object(5)\n",
            "memory usage: 8.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipos_tickers = stacked_ipos_df[['Symbol']]\n",
        "\n",
        "# change value of a row 'PTHR' to 'PTHRU'\n",
        "ipos_tickers.loc[ipos_tickers['Symbol'] == 'PTHR', 'Symbol'] = 'PTHRU'\n",
        "ipos_tickers.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UT6FM3_LLm1v",
        "outputId": "83207edb-4d96-4bd3-8ec9-07864e9dd0d3"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 184 entries, 0 to 183\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   Symbol  184 non-null    object\n",
            "dtypes: object(1)\n",
            "memory usage: 1.6+ KB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-9735ddfc9865>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  ipos_tickers.loc[ipos_tickers['Symbol'] == 'PTHR', 'Symbol'] = 'PTHRU'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipos_tickers_list = ipos_tickers['Symbol'].to_list()"
      ],
      "metadata": {
        "id": "Guw8NI0Z4HJk"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "stocks_df = pd.DataFrame({'A' : []})\n",
        "\n",
        "for i,ticker in enumerate(ipos_tickers_list):\n",
        "  print(i, ticker)\n",
        "\n",
        "  # Work with stock prices\n",
        "  historyPrices = yf.download(tickers = ticker,\n",
        "                     period='max',\n",
        "                     interval = \"1d\")\n",
        "\n",
        "  historyPrices['Ticker'] = ticker\n",
        "  historyPrices['Date'] = historyPrices.index.date\n",
        "\n",
        "  # historical returns\n",
        "  for i in range(1, 31):\n",
        "    historyPrices['growth_future_'+str(i)+'d'] = historyPrices['Adj Close'].shift(-i) / historyPrices['Adj Close']\n",
        "\n",
        "\n",
        "  if stocks_df.empty:\n",
        "    stocks_df = historyPrices[historyPrices['Date'] == historyPrices['Date'].min()]\n",
        "  else:\n",
        "    stocks_df = pd.concat([stocks_df, historyPrices[historyPrices['Date'] == historyPrices['Date'].min()]], ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGVZuF3QDPwh",
        "outputId": "c1f798ee-b0af-4fd1-9e04-ab7a3c705a31"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 IROH\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 LGCB\n",
            "2 ZKH\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 BAYA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 INHD\n",
            "5 AFJK\n",
            "6 GSIW\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7 FEBO\n",
            "8 CLBR\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9 ELAB\n",
            "10 RR\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11 DDC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12 SHIM\n",
            "13 GLAC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14 SGN\n",
            "15 HG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16 CRGX\n",
            "17 ANSC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18 AITR\n",
            "19 GVH\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20 LXEO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21 PAPL\n",
            "22 ATGL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23 MNR\n",
            "24 WBUY\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25 NCL\n",
            "26 BIRK\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27 GMM\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "28 PMEC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29 LRHC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30 GPAK\n",
            "31 SPKL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32 QETA\n",
            "33 MSS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34 ANL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35 SYRA\n",
            "36 VSME\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37 LRE\n",
            "38 TURB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "39 MDBH\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40 KVYO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41 CART\n",
            "42 DTCK\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "43 NMRA\n",
            "44 ARM\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45 SPPL\n",
            "46 NWGL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47 SWIN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "48 IVP\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49 NNAG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50 SRM\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51 SPGC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "52 LQR\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "53 NRXS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54 FTEL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "55 MIRA\n",
            "56 PXDT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "57 CTNT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "58 HRYU\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "59 SRFM\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60 PRZO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "61 HYAC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62 KVAC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63 JNVR\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64 ELWS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "65 WRNT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "66 TSBX\n",
            "67 ODD\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "68 APGE\n",
            "69 NETD\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "70 SGMT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71 BOWN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "72 SXTP\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "73 PWM\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "74 VTMX\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75 INTS\n",
            "76 SVV\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "77 KGS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "78 FIHL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79 GENK\n",
            "80 BUJA\n",
            "81 BOF\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82 AZTR\n",
            "83 CAVA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "84 ESHA\n",
            "85 ATMU\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86 ATS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "87 IPXX\n",
            "88 CWD\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "89 SGE\n",
            "90 SLRN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "91 ALCY\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "92 KVUE\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "93 GODN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94 TRNR\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95 AACT\n",
            "96"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " JYD\n",
            "97 USGO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "98 UCAR\n",
            "99 WLGS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100 TPET\n",
            "101 TCJH\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102 GDTC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "103 VCIG\n",
            "104 GDHG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "105 ARBB\n",
            "106 ISPR\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "107 MGIH\n",
            "108 MWG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "109 HSHP\n",
            "110 SFWL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "111 SYT\n",
            "112 HKIT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "113 CHSN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "114 TBMC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "115 HLP\n",
            "116 ZJYL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "117 TMTC\n",
            "118 YGFGF\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "119 OAKU\n",
            "120 BANL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "121 OMH\n",
            "122 MGRX\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "123 FORL\n",
            "124 ICG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125 IZM\n",
            "126 AESI\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127 AIXI\n",
            "128 SBXC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "129 BMR\n",
            "130 DIST\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "131 GXAI\n",
            "132 MARX\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "133 BFRG\n",
            "134 ENLT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "135 MLYS\n",
            "136 PTHRU\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "137 BLAC\n",
            "138 NXT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "139 HSAI\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "140 LSDI\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "141 LICN\n",
            "142"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " GPCR\n",
            "143"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " ASST\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "144 CETU\n",
            "145 TXO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "146 BREA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "147 GNLX\n",
            "148 QSG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "149 CVKD\n",
            "150 SKWD\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "151 ISRL\n",
            "152 MGOL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "153 SMXT\n",
            "154 VHAI\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "155 DYCQ\n",
            "156 CHRO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157 UMAC\n",
            "158 TBBB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159 MGX\n",
            "160 HLXB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "161 TELO\n",
            "162 KYTX\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "163 PMNT\n",
            "164 AHR\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "165 LEGT\n",
            "166 ANRO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "167 GUTS\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "168 AS\n",
            "169 FBLG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "170 BTSG\n",
            "171 AVBP\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "172 HAO\n",
            "173 CGON\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "174 YIBO\n",
            "175 SUGP\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "176 JL\n",
            "177 KSPI\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "178 JVSA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "179 PSBD\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "180 CCTG\n",
            "181"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " SYNX\n",
            "182 SDHC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "183 ROMA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "growth_future = stocks_df[['growth_future_1d', 'growth_future_2d', 'growth_future_3d',\n",
        "       'growth_future_4d', 'growth_future_5d', 'growth_future_6d',\n",
        "       'growth_future_7d', 'growth_future_8d', 'growth_future_9d',\n",
        "       'growth_future_10d', 'growth_future_11d', 'growth_future_12d',\n",
        "       'growth_future_13d', 'growth_future_14d', 'growth_future_15d',\n",
        "       'growth_future_16d', 'growth_future_17d', 'growth_future_18d',\n",
        "       'growth_future_28d', 'growth_future_29d', 'growth_future_30d']].describe()\n",
        "\n",
        "growth_future"
      ],
      "metadata": {
        "id": "aPbfjrL0Fv48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "77b5150f-06ba-4b70-ef59-8ca3968e315c"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       growth_future_1d  growth_future_2d  growth_future_3d  growth_future_4d  \\\n",
              "count        184.000000        184.000000        184.000000        184.000000   \n",
              "mean           0.945820          0.937103          0.930279          0.919575   \n",
              "std            0.170230          0.205919          0.247641          0.263790   \n",
              "min            0.153569          0.108733          0.086641          0.094257   \n",
              "25%            0.921392          0.876712          0.848790          0.832387   \n",
              "50%            1.000000          0.999058          0.997823          0.995105   \n",
              "75%            1.011792          1.017794          1.009551          1.008924   \n",
              "max            1.362069          1.464015          2.380000          2.083710   \n",
              "\n",
              "       growth_future_5d  growth_future_6d  growth_future_7d  growth_future_8d  \\\n",
              "count        184.000000        184.000000        184.000000        184.000000   \n",
              "mean           0.913155          0.906276          0.896519          0.896256   \n",
              "std            0.294112          0.308535          0.297122          0.318985   \n",
              "min            0.081118          0.087677          0.085261          0.090123   \n",
              "25%            0.787687          0.770357          0.788063          0.749143   \n",
              "50%            0.996456          0.971947          0.978291          0.981124   \n",
              "75%            1.009369          1.007147          1.006871          1.008127   \n",
              "max            2.262443          2.529870          2.173913          2.359740   \n",
              "\n",
              "       growth_future_9d  growth_future_10d  ...  growth_future_12d  \\\n",
              "count        184.000000         184.000000  ...         184.000000   \n",
              "mean           0.898742           0.894293  ...           0.926943   \n",
              "std            0.349821           0.363047  ...           0.595602   \n",
              "min            0.095616           0.096997  ...           0.078357   \n",
              "25%            0.732369           0.709589  ...           0.685522   \n",
              "50%            0.990592           0.978063  ...           0.987710   \n",
              "75%            1.011545           1.010670  ...           1.016016   \n",
              "max            2.751948           3.176087  ...           6.779545   \n",
              "\n",
              "       growth_future_13d  growth_future_14d  growth_future_15d  \\\n",
              "count         184.000000         184.000000         184.000000   \n",
              "mean            0.931986           0.923325           0.923139   \n",
              "std             0.541802           0.565505           0.508499   \n",
              "min             0.077321           0.073870           0.064895   \n",
              "25%             0.689244           0.696126           0.702996   \n",
              "50%             0.993697           0.989805           0.979026   \n",
              "75%             1.019795           1.016382           1.016284   \n",
              "max             5.622727           6.406818           4.656818   \n",
              "\n",
              "       growth_future_16d  growth_future_17d  growth_future_18d  \\\n",
              "count         183.000000         183.000000         183.000000   \n",
              "mean            0.912754           0.904623           0.893866   \n",
              "std             0.435756           0.403297           0.427298   \n",
              "min             0.056610           0.056265           0.056921   \n",
              "25%             0.712059           0.713676           0.666029   \n",
              "50%             0.986914           0.988235           0.960938   \n",
              "75%             1.014669           1.010848           1.014167   \n",
              "max             4.072398           3.212670           3.500000   \n",
              "\n",
              "       growth_future_28d  growth_future_29d  growth_future_30d  \n",
              "count         182.000000         182.000000         182.000000  \n",
              "mean            0.932887           0.932601           0.927785  \n",
              "std             0.783521           0.781665           0.772243  \n",
              "min             0.046945           0.042803           0.040387  \n",
              "25%             0.640811           0.627851           0.623529  \n",
              "50%             0.974952           0.974438           0.977535  \n",
              "75%             1.040245           1.026362           1.023718  \n",
              "max             9.081632           9.265306           9.372449  \n",
              "\n",
              "[8 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c1d18235-7c65-4640-b9a2-80593bbe314f\" 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>growth_future_1d</th>\n",
              "      <th>growth_future_2d</th>\n",
              "      <th>growth_future_3d</th>\n",
              "      <th>growth_future_4d</th>\n",
              "      <th>growth_future_5d</th>\n",
              "      <th>growth_future_6d</th>\n",
              "      <th>growth_future_7d</th>\n",
              "      <th>growth_future_8d</th>\n",
              "      <th>growth_future_9d</th>\n",
              "      <th>growth_future_10d</th>\n",
              "      <th>...</th>\n",
              "      <th>growth_future_12d</th>\n",
              "      <th>growth_future_13d</th>\n",
              "      <th>growth_future_14d</th>\n",
              "      <th>growth_future_15d</th>\n",
              "      <th>growth_future_16d</th>\n",
              "      <th>growth_future_17d</th>\n",
              "      <th>growth_future_18d</th>\n",
              "      <th>growth_future_28d</th>\n",
              "      <th>growth_future_29d</th>\n",
              "      <th>growth_future_30d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>182.000000</td>\n",
              "      <td>182.000000</td>\n",
              "      <td>182.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.945820</td>\n",
              "      <td>0.937103</td>\n",
              "      <td>0.930279</td>\n",
              "      <td>0.919575</td>\n",
              "      <td>0.913155</td>\n",
              "      <td>0.906276</td>\n",
              "      <td>0.896519</td>\n",
              "      <td>0.896256</td>\n",
              "      <td>0.898742</td>\n",
              "      <td>0.894293</td>\n",
              "      <td>...</td>\n",
              "      <td>0.926943</td>\n",
              "      <td>0.931986</td>\n",
              "      <td>0.923325</td>\n",
              "      <td>0.923139</td>\n",
              "      <td>0.912754</td>\n",
              "      <td>0.904623</td>\n",
              "      <td>0.893866</td>\n",
              "      <td>0.932887</td>\n",
              "      <td>0.932601</td>\n",
              "      <td>0.927785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.170230</td>\n",
              "      <td>0.205919</td>\n",
              "      <td>0.247641</td>\n",
              "      <td>0.263790</td>\n",
              "      <td>0.294112</td>\n",
              "      <td>0.308535</td>\n",
              "      <td>0.297122</td>\n",
              "      <td>0.318985</td>\n",
              "      <td>0.349821</td>\n",
              "      <td>0.363047</td>\n",
              "      <td>...</td>\n",
              "      <td>0.595602</td>\n",
              "      <td>0.541802</td>\n",
              "      <td>0.565505</td>\n",
              "      <td>0.508499</td>\n",
              "      <td>0.435756</td>\n",
              "      <td>0.403297</td>\n",
              "      <td>0.427298</td>\n",
              "      <td>0.783521</td>\n",
              "      <td>0.781665</td>\n",
              "      <td>0.772243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.153569</td>\n",
              "      <td>0.108733</td>\n",
              "      <td>0.086641</td>\n",
              "      <td>0.094257</td>\n",
              "      <td>0.081118</td>\n",
              "      <td>0.087677</td>\n",
              "      <td>0.085261</td>\n",
              "      <td>0.090123</td>\n",
              "      <td>0.095616</td>\n",
              "      <td>0.096997</td>\n",
              "      <td>...</td>\n",
              "      <td>0.078357</td>\n",
              "      <td>0.077321</td>\n",
              "      <td>0.073870</td>\n",
              "      <td>0.064895</td>\n",
              "      <td>0.056610</td>\n",
              "      <td>0.056265</td>\n",
              "      <td>0.056921</td>\n",
              "      <td>0.046945</td>\n",
              "      <td>0.042803</td>\n",
              "      <td>0.040387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.921392</td>\n",
              "      <td>0.876712</td>\n",
              "      <td>0.848790</td>\n",
              "      <td>0.832387</td>\n",
              "      <td>0.787687</td>\n",
              "      <td>0.770357</td>\n",
              "      <td>0.788063</td>\n",
              "      <td>0.749143</td>\n",
              "      <td>0.732369</td>\n",
              "      <td>0.709589</td>\n",
              "      <td>...</td>\n",
              "      <td>0.685522</td>\n",
              "      <td>0.689244</td>\n",
              "      <td>0.696126</td>\n",
              "      <td>0.702996</td>\n",
              "      <td>0.712059</td>\n",
              "      <td>0.713676</td>\n",
              "      <td>0.666029</td>\n",
              "      <td>0.640811</td>\n",
              "      <td>0.627851</td>\n",
              "      <td>0.623529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999058</td>\n",
              "      <td>0.997823</td>\n",
              "      <td>0.995105</td>\n",
              "      <td>0.996456</td>\n",
              "      <td>0.971947</td>\n",
              "      <td>0.978291</td>\n",
              "      <td>0.981124</td>\n",
              "      <td>0.990592</td>\n",
              "      <td>0.978063</td>\n",
              "      <td>...</td>\n",
              "      <td>0.987710</td>\n",
              "      <td>0.993697</td>\n",
              "      <td>0.989805</td>\n",
              "      <td>0.979026</td>\n",
              "      <td>0.986914</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.960938</td>\n",
              "      <td>0.974952</td>\n",
              "      <td>0.974438</td>\n",
              "      <td>0.977535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.011792</td>\n",
              "      <td>1.017794</td>\n",
              "      <td>1.009551</td>\n",
              "      <td>1.008924</td>\n",
              "      <td>1.009369</td>\n",
              "      <td>1.007147</td>\n",
              "      <td>1.006871</td>\n",
              "      <td>1.008127</td>\n",
              "      <td>1.011545</td>\n",
              "      <td>1.010670</td>\n",
              "      <td>...</td>\n",
              "      <td>1.016016</td>\n",
              "      <td>1.019795</td>\n",
              "      <td>1.016382</td>\n",
              "      <td>1.016284</td>\n",
              "      <td>1.014669</td>\n",
              "      <td>1.010848</td>\n",
              "      <td>1.014167</td>\n",
              "      <td>1.040245</td>\n",
              "      <td>1.026362</td>\n",
              "      <td>1.023718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.362069</td>\n",
              "      <td>1.464015</td>\n",
              "      <td>2.380000</td>\n",
              "      <td>2.083710</td>\n",
              "      <td>2.262443</td>\n",
              "      <td>2.529870</td>\n",
              "      <td>2.173913</td>\n",
              "      <td>2.359740</td>\n",
              "      <td>2.751948</td>\n",
              "      <td>3.176087</td>\n",
              "      <td>...</td>\n",
              "      <td>6.779545</td>\n",
              "      <td>5.622727</td>\n",
              "      <td>6.406818</td>\n",
              "      <td>4.656818</td>\n",
              "      <td>4.072398</td>\n",
              "      <td>3.212670</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>9.081632</td>\n",
              "      <td>9.265306</td>\n",
              "      <td>9.372449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    }\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        width: 32px;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('growth_future')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('growth_future');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "growth_future"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get max from growth_future.loc['75%']\n",
        "p75 = growth_future.loc['75%']\n",
        "p75.idxmax(), p75.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDJBSv0vTVtj",
        "outputId": "072e64c9-88fd-4ae0-a541-7acf3712b597"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('growth_future_28d', 1.040244654702553)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question 3: Is Growth Concentrated in the Largest Stocks?\n",
        "\n",
        "**Get the share of days (percentage as int) when Large Stocks outperform (growth_7d - growth over 7 periods back) the Largest stocks?**"
      ],
      "metadata": {
        "id": "ZonL7ixfA4u1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "US_STOCKS = ['MSFT', 'AAPL', 'GOOG', 'NVDA', 'AMZN', 'META', 'BRK-B', 'LLY', 'AVGO','V', 'JPM']\n",
        "\n",
        "EU_STOCKS = ['NVO','MC.PA', 'ASML', 'RMS.PA', 'OR.PA', 'SAP', 'ACN', 'TTE', 'SIE.DE','IDEXY','CDI.PA']\n",
        "\n",
        "INDIA_STOCKS = ['RELIANCE.NS','TCS.NS','HDB','BHARTIARTL.NS','IBN','SBIN.NS','LICI.NS','INFY','ITC.NS','HINDUNILVR.NS','LT.NS']\n",
        "\n",
        "LARGEST_STOCKS = US_STOCKS + EU_STOCKS + INDIA_STOCKS\n",
        "\n",
        "NEW_US = ['TSLA','WMT','XOM','UNH','MA','PG','JNJ','MRK','HD','COST','ORCL']\n",
        "\n",
        "NEW_EU = ['PRX.AS','CDI.PA','AIR.PA','SU.PA','ETN','SNY','BUD','DTE.DE','ALV.DE','MDT','AI.PA','EL.PA']\n",
        "\n",
        "NEW_INDIA = ['BAJFINANCE.NS','MARUTI.NS','HCLTECH.NS','TATAMOTORS.NS','SUNPHARMA.NS','ONGC.NS','ADANIENT.NS','ADANIENT.NS','NTPC.NS','KOTAKBANK.NS','TITAN.NS']\n",
        "\n",
        "LARGE_STOCKS = NEW_EU + NEW_US + NEW_INDIA"
      ],
      "metadata": {
        "id": "HDXABcVhDFd0"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "stocks_df = pd.DataFrame({'A' : []})\n",
        "\n",
        "ALL_STOCKS = LARGEST_STOCKS + LARGE_STOCKS\n",
        "\n",
        "for i,ticker in enumerate(ALL_STOCKS):\n",
        "  print(i,ticker)\n",
        "\n",
        "  # Work with stock prices\n",
        "  historyPrices = yf.download(tickers = ticker,\n",
        "                     start = \"2013-12-22\",\n",
        "                     end= \"2023-12-31\",\n",
        "                     interval = \"1d\")\n",
        "\n",
        "  historyPrices['ticker'] = ticker\n",
        "  # check if a stock is in largest o large list and set ticker_category\n",
        "  if ticker in LARGEST_STOCKS:\n",
        "    historyPrices['ticker_category'] = 'LARGEST'\n",
        "  else:\n",
        "    historyPrices['ticker_category'] = 'LARGE'\n",
        "\n",
        "  historyPrices['date'] = historyPrices.index.date\n",
        "\n",
        "  # historical returns\n",
        "  historyPrices['growth_'+str(7)+'d'] = historyPrices['Adj Close'] / historyPrices['Adj Close'].shift(7)\n",
        "\n",
        "  # sleep 1 sec between downloads - not to overload the API server\n",
        "  time.sleep(1)\n",
        "\n",
        "\n",
        "  if stocks_df.empty:\n",
        "    stocks_df = historyPrices\n",
        "  else:\n",
        "    stocks_df = pd.concat([stocks_df, historyPrices], ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7oaXiALCyq1",
        "outputId": "f644ea5b-0ff0-4622-9d83-fa4e8bc91c34"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 MSFT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 AAPL\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 GOOG\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 NVDA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 AMZN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5 META\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6 BRK-B\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7 LLY\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8 AVGO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9 V\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 JPM\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11 NVO\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12 MC.PA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13 ASML\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14 RMS.PA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15 OR.PA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16 SAP\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17 ACN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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              "         date ticker_category ticker  growth_7d\n",
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              "            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",
              "      --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",
              "    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-80da6180-50b1-47ce-a2cd-bc2fe36e11ca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"stocks_df[['date', 'ticker_category', 'ticker', 'growth_7d']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-12-23\",\n        \"max\": \"2013-12-30\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2013-12-24\",\n          \"2013-12-30\",\n          \"2013-12-26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ticker_category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"LARGEST\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ticker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"MSFT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"growth_7d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stocks_7d_growth = pd.pivot_table(stocks_df,\n",
        "                       values='growth_7d',\n",
        "                       index=['date'],\n",
        "                       columns=['ticker_category'],\n",
        "                       aggfunc=\"mean\"\n",
        ")\n",
        "stocks_7d_growth.head(9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "MudfpBNS3C43",
        "outputId": "ed7d9b21-6375-4b7b-c280-9ea9a8058c34"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ticker_category     LARGE   LARGEST\n",
              "date                               \n",
              "2014-01-02       0.980160  0.992433\n",
              "2014-01-03       0.996904  0.994669\n",
              "2014-01-06       0.995651  0.993711\n",
              "2014-01-07       0.990978  0.991773\n",
              "2014-01-08       0.989366  0.987206\n",
              "2014-01-09       0.991875  0.980598\n",
              "2014-01-10       0.989286  0.981773\n",
              "2014-01-13       0.998538  0.992337\n",
              "2014-01-14       1.008288  0.998351"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44d02b37-c5d1-4f35-a8de-a4d729886533\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th>ticker_category</th>\n",
              "      <th>LARGE</th>\n",
              "      <th>LARGEST</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>0.980160</td>\n",
              "      <td>0.992433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>0.996904</td>\n",
              "      <td>0.994669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>0.995651</td>\n",
              "      <td>0.993711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>0.990978</td>\n",
              "      <td>0.991773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>0.989366</td>\n",
              "      <td>0.987206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-09</th>\n",
              "      <td>0.991875</td>\n",
              "      <td>0.980598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-10</th>\n",
              "      <td>0.989286</td>\n",
              "      <td>0.981773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-13</th>\n",
              "      <td>0.998538</td>\n",
              "      <td>0.992337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-14</th>\n",
              "      <td>1.008288</td>\n",
              "      <td>0.998351</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44d02b37-c5d1-4f35-a8de-a4d729886533')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\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-44d02b37-c5d1-4f35-a8de-a4d729886533 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-44d02b37-c5d1-4f35-a8de-a4d729886533');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-93323ae0-b56b-4309-9163-f201d0635b9d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93323ae0-b56b-4309-9163-f201d0635b9d')\"\n",
              "            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",
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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",
              "    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-93323ae0-b56b-4309-9163-f201d0635b9d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "stocks_7d_growth",
              "summary": "{\n  \"name\": \"stocks_7d_growth\",\n  \"rows\": 2594,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-01-02\",\n        \"max\": \"2023-12-29\",\n        \"num_unique_values\": 2594,\n        \"samples\": [\n          \"2014-10-27\",\n          \"2022-03-04\",\n          \"2020-09-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LARGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02512773191733296,\n        \"min\": 0.7760420043579758,\n        \"max\": 1.119997949115588,\n        \"num_unique_values\": 2594,\n        \"samples\": [\n          1.0378889902118713,\n          0.9746720503892368,\n          0.979168312472261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LARGEST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02650748176100666,\n        \"min\": 0.7952957892949821,\n        \"max\": 1.1233855926996932,\n        \"num_unique_values\": 2594,\n        \"samples\": [\n          1.0372724938755633,\n          0.9598594169409204,\n          0.9638317452981403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stocks_7d_growth['LARGE_outperforms_LARGEST'] = np.where(stocks_7d_growth['LARGE'] > stocks_7d_growth['LARGEST'], True, False)\n",
        "stocks_7d_growth.head(9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "pfaXiMBiA_Ug",
        "outputId": "ba2692e2-b270-4d2f-aeb1-5b4142dd55cb"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ticker_category     LARGE   LARGEST  LARGE_outperforms_LARGEST\n",
              "date                                                          \n",
              "2014-01-02       0.980160  0.992433                      False\n",
              "2014-01-03       0.996904  0.994669                       True\n",
              "2014-01-06       0.995651  0.993711                       True\n",
              "2014-01-07       0.990978  0.991773                      False\n",
              "2014-01-08       0.989366  0.987206                       True\n",
              "2014-01-09       0.991875  0.980598                       True\n",
              "2014-01-10       0.989286  0.981773                       True\n",
              "2014-01-13       0.998538  0.992337                       True\n",
              "2014-01-14       1.008288  0.998351                       True"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55a8aa17-8382-4508-965f-b683df114633\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>ticker_category</th>\n",
              "      <th>LARGE</th>\n",
              "      <th>LARGEST</th>\n",
              "      <th>LARGE_outperforms_LARGEST</th>\n",
              "    </tr>\n",
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              "      <th>date</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>0.980160</td>\n",
              "      <td>0.992433</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>0.996904</td>\n",
              "      <td>0.994669</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>0.995651</td>\n",
              "      <td>0.993711</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>0.990978</td>\n",
              "      <td>0.991773</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>0.989366</td>\n",
              "      <td>0.987206</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-09</th>\n",
              "      <td>0.991875</td>\n",
              "      <td>0.980598</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-10</th>\n",
              "      <td>0.989286</td>\n",
              "      <td>0.981773</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-13</th>\n",
              "      <td>0.998538</td>\n",
              "      <td>0.992337</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-14</th>\n",
              "      <td>1.008288</td>\n",
              "      <td>0.998351</td>\n",
              "      <td>True</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\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",
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              "    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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              "    }\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-30f95890-4f38-42f7-b9af-8f5ace47d4d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "stocks_7d_growth",
              "summary": "{\n  \"name\": \"stocks_7d_growth\",\n  \"rows\": 2594,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-01-02\",\n        \"max\": \"2023-12-29\",\n        \"num_unique_values\": 2594,\n        \"samples\": [\n          \"2014-10-27\",\n          \"2022-03-04\",\n          \"2020-09-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LARGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02512773191733296,\n        \"min\": 0.7760420043579758,\n        \"max\": 1.119997949115588,\n        \"num_unique_values\": 2594,\n        \"samples\": [\n          1.0378889902118713,\n          0.9746720503892368,\n          0.979168312472261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LARGEST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02650748176100666,\n        \"min\": 0.7952957892949821,\n        \"max\": 1.1233855926996932,\n        \"num_unique_values\": 2594,\n        \"samples\": [\n          1.0372724938755633,\n          0.9598594169409204,\n          0.9638317452981403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LARGE_outperforms_LARGEST\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get the porcentage of days where Large outperforms Largest\n",
        "stocks_7d_growth['LARGE_outperforms_LARGEST'].mean().round(4) * 100\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cu0NXfGvCPg5",
        "outputId": "98f1216d-0e92-47ac-c33c-29a40b8f4501"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47.07"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question 4: Trying Another Technical Indicators strategy\n",
        "\n",
        "**What's the total gross profit (in THOUSANDS of $) you'll get from trading on CCI (no fees assumption)?**"
      ],
      "metadata": {
        "id": "JBUi6sG7C9ML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1m3Qisfs2XfWk6Sw_Uk5kHLWqwQ0q8SKb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZXDV0azZCyW",
        "outputId": "203b162c-8b68-4da4-9ec4-a590ff776c7d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1m3Qisfs2XfWk6Sw_Uk5kHLWqwQ0q8SKb\n",
            "To: /content/stocks_df_combined_trunc_2014_2023.parquet.brotli\n",
            "100% 47.3M/47.3M [00:01<00:00, 39.6MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pandas read df from downloaded file\n",
        "df = pd.read_parquet('stocks_df_combined_trunc_2014_2023.parquet.brotli')\n",
        "df = df.reset_index(drop=True)\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzqXclwrZVZ6",
        "outputId": "70f525ec-d38a-446a-d1d2-901fe1f4fe51"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 80762 entries, 0 to 80761\n",
            "Columns: 202 entries, Open to growth_btc_usd_365d\n",
            "dtypes: datetime64[ns](3), float64(128), int32(64), int64(5), object(2)\n",
            "memory usage: 104.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEGQ3J79hYc1",
        "outputId": "8dd0fe75-0d5d-4529-aa51-ec224cced1ce"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Open', 'High', 'Low', 'Close', 'Adj Close_x', 'Volume', 'Ticker',\n",
              "       'Year', 'Month', 'Weekday', 'Date', 'growth_1d', 'growth_3d',\n",
              "       'growth_7d', 'growth_30d', 'growth_90d', 'growth_365d',\n",
              "       'growth_future_5d', 'SMA10', 'SMA20', 'growing_moving_average',\n",
              "       'high_minus_low_relative', 'volatility',\n",
              "       'is_positive_growth_5d_future', 'ticker_type', 'index_x', 'adx',\n",
              "       'adxr', 'apo', 'aroon_1', 'aroon_2', 'aroonosc', 'bop', 'cci',\n",
              "       'cmo', 'dx', 'macd', 'macdsignal', 'macdhist', 'macd_ext',\n",
              "       'macdsignal_ext', 'macdhist_ext', 'macd_fix', 'macdsignal_fix',\n",
              "       'macdhist_fix', 'mfi', 'minus_di', 'mom', 'plus_di', 'dm', 'ppo',\n",
              "       'roc', 'rocp', 'rocr', 'rocr100', 'rsi', 'slowk', 'slowd', 'fastk',\n",
              "       'fastd', 'fastk_rsi', 'fastd_rsi', 'trix', 'ultosc', 'willr',\n",
              "       'index_y', 'ad', 'adosc', 'obv', 'atr', 'natr', 'ht_dcperiod',\n",
              "       'ht_dcphase', 'ht_phasor_inphase', 'ht_phasor_quadrature',\n",
              "       'ht_sine_sine', 'ht_sine_leadsine', 'ht_trendmod', 'avgprice',\n",
              "       'medprice', 'typprice', 'wclprice', 'index', 'cdl2crows',\n",
              "       'cdl3blackrows', 'cdl3inside', 'cdl3linestrike', 'cdl3outside',\n",
              "       'cdl3starsinsouth', 'cdl3whitesoldiers', 'cdlabandonedbaby',\n",
              "       'cdladvancedblock', 'cdlbelthold', 'cdlbreakaway',\n",
              "       'cdlclosingmarubozu', 'cdlconcealbabyswall', 'cdlcounterattack',\n",
              "       'cdldarkcloudcover', 'cdldoji', 'cdldojistar', 'cdldragonflydoji',\n",
              "       'cdlengulfing', 'cdleveningdojistar', 'cdleveningstar',\n",
              "       'cdlgapsidesidewhite', 'cdlgravestonedoji', 'cdlhammer',\n",
              "       'cdlhangingman', 'cdlharami', 'cdlharamicross', 'cdlhighwave',\n",
              "       'cdlhikkake', 'cdlhikkakemod', 'cdlhomingpigeon',\n",
              "       'cdlidentical3crows', 'cdlinneck', 'cdlinvertedhammer',\n",
              "       'cdlkicking', 'cdlkickingbylength', 'cdlladderbottom',\n",
              "       'cdllongleggeddoji', 'cdllongline', 'cdlmarubozu',\n",
              "       'cdlmatchinglow', 'cdlmathold', 'cdlmorningdojistar',\n",
              "       'cdlmorningstar', 'cdlonneck', 'cdlpiercing', 'cdlrickshawman',\n",
              "       'cdlrisefall3methods', 'cdlseparatinglines', 'cdlshootingstar',\n",
              "       'cdlshortline', 'cdlspinningtop', 'cdlstalledpattern',\n",
              "       'cdlsticksandwich', 'cdltakuru', 'cdltasukigap', 'cdlthrusting',\n",
              "       'cdltristar', 'cdlunique3river', 'cdlupsidegap2crows',\n",
              "       'cdlxsidegap3methods', 'growth_dax_1d', 'growth_dax_3d',\n",
              "       'growth_dax_7d', 'growth_dax_30d', 'growth_dax_90d',\n",
              "       'growth_dax_365d', 'growth_snp500_1d', 'growth_snp500_3d',\n",
              "       'growth_snp500_7d', 'growth_snp500_30d', 'growth_snp500_90d',\n",
              "       'growth_snp500_365d', 'growth_dji_1d', 'growth_dji_3d',\n",
              "       'growth_dji_7d', 'growth_dji_30d', 'growth_dji_90d',\n",
              "       'growth_dji_365d', 'growth_epi_1d', 'growth_epi_3d',\n",
              "       'growth_epi_7d', 'growth_epi_30d', 'growth_epi_90d',\n",
              "       'growth_epi_365d', 'Quarter', 'gdppot_us_yoy', 'gdppot_us_qoq',\n",
              "       'cpi_core_yoy', 'cpi_core_mom', 'FEDFUNDS', 'DGS1', 'DGS5',\n",
              "       'DGS10', 'Adj Close_y', 'growth_gold_1d', 'growth_gold_3d',\n",
              "       'growth_gold_7d', 'growth_gold_30d', 'growth_gold_90d',\n",
              "       'growth_gold_365d', 'growth_wti_oil_1d', 'growth_wti_oil_3d',\n",
              "       'growth_wti_oil_7d', 'growth_wti_oil_30d', 'growth_wti_oil_90d',\n",
              "       'growth_wti_oil_365d', 'growth_brent_oil_1d',\n",
              "       'growth_brent_oil_3d', 'growth_brent_oil_7d',\n",
              "       'growth_brent_oil_30d', 'growth_brent_oil_90d',\n",
              "       'growth_brent_oil_365d', 'growth_btc_usd_1d', 'growth_btc_usd_3d',\n",
              "       'growth_btc_usd_7d', 'growth_btc_usd_30d', 'growth_btc_usd_90d',\n",
              "       'growth_btc_usd_365d'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Date.min(), df.Date.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7PI5e65aGbm",
        "outputId": "6dc306b2-d3c9-44eb-f120-81d16b2f973c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Timestamp('2014-01-01 00:00:00'), Timestamp('2023-12-29 00:00:00'))"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['Date', 'Ticker', 'Weekday', 'Adj Close_x', 'Adj Close_y', 'cci']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "NZLyfVm2htA1",
        "outputId": "d77c034f-86c4-424f-ae60-0331057a3344"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date Ticker  Weekday  Adj Close_x  Adj Close_y         cci\n",
              "0     2014-01-02   MSFT        3    31.233059        14.23   57.700615\n",
              "1     2014-01-03   MSFT        4    31.022930        13.76    1.373763\n",
              "2     2014-01-06   MSFT        0    30.367352        13.55  -96.631259\n",
              "3     2014-01-07   MSFT        1    30.602673        12.92  -83.904297\n",
              "4     2014-01-08   MSFT        2    30.056356        12.87 -147.855135\n",
              "...          ...    ...      ...          ...          ...         ...\n",
              "80757 2023-12-22  LT.NS        4  3477.949951        13.03   70.767162\n",
              "80758 2023-12-26  LT.NS        1  3490.050049        12.99   99.598220\n",
              "80759 2023-12-27  LT.NS        2  3544.000000        12.43  130.401152\n",
              "80760 2023-12-28  LT.NS        3  3518.050049        12.47  106.774509\n",
              "80761 2023-12-29  LT.NS        4  3526.000000        12.45   87.014290\n",
              "\n",
              "[80762 rows x 6 columns]"
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              "      <th>Date</th>\n",
              "      <th>Ticker</th>\n",
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              "      <td>2014-01-02</td>\n",
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              "      <td>31.233059</td>\n",
              "      <td>14.23</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-01-03</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>4</td>\n",
              "      <td>31.022930</td>\n",
              "      <td>13.76</td>\n",
              "      <td>1.373763</td>\n",
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              "      <th>2</th>\n",
              "      <td>2014-01-06</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>0</td>\n",
              "      <td>30.367352</td>\n",
              "      <td>13.55</td>\n",
              "      <td>-96.631259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-01-07</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>1</td>\n",
              "      <td>30.602673</td>\n",
              "      <td>12.92</td>\n",
              "      <td>-83.904297</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-01-08</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>2</td>\n",
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              "      <td>12.87</td>\n",
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              "      <th>80757</th>\n",
              "      <td>2023-12-22</td>\n",
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              "      <td>3477.949951</td>\n",
              "      <td>13.03</td>\n",
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              "      <th>80758</th>\n",
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              "      <td>LT.NS</td>\n",
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              "      <td>12.99</td>\n",
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              "      <td>LT.NS</td>\n",
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              "      <td>12.43</td>\n",
              "      <td>130.401152</td>\n",
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              "    <tr>\n",
              "      <th>80760</th>\n",
              "      <td>2023-12-28</td>\n",
              "      <td>LT.NS</td>\n",
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              "      <td>3518.050049</td>\n",
              "      <td>12.47</td>\n",
              "      <td>106.774509</td>\n",
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              "      <th>80761</th>\n",
              "      <td>2023-12-29</td>\n",
              "      <td>LT.NS</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['Date', 'Ticker', 'Weekday', 'Adj Close_x', 'Adj Close_y', 'cci']]\",\n  \"rows\": 80762,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-01-01 00:00:00\",\n        \"max\": \"2023-12-29 00:00:00\",\n        \"num_unique_values\": 2595,\n        \"samples\": [\n          \"2014-11-03 00:00:00\",\n          \"2022-06-07 00:00:00\",\n          \"2020-11-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"HINDUNILVR.NS\",\n          \"OR.PA\",\n          \"IBN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekday\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 556.9007558702737,\n        \"min\": 3.6226232051849365,\n        \"max\": 3843.19189453125,\n        \"num_unique_values\": 75003,\n        \"samples\": [\n          24.618900299072266,\n          22.547260284423828,\n          687.1704711914062\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close_y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.311165118630728,\n        \"min\": 9.140000343322754,\n        \"max\": 82.69000244140625,\n        \"num_unique_values\": 1395,\n        \"samples\": [\n          19.6299991607666,\n          16.84000015258789,\n          19.780000686645508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cci\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.3137472426569,\n        \"min\": -435.2892748170835,\n        \"max\": 458.9151019948647,\n        \"num_unique_values\": 80749,\n        \"samples\": [\n          153.23280192982864,\n          340.0271212694879,\n          155.20053366270113\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trading_records = pd.DataFrame(columns=['ticker', 'buy_date', 'buy_price', 'sell_date', 'sell_price', 'result', 'cci'])"
      ],
      "metadata": {
        "id": "klqXAWZ4oHTt"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def buy(df, threshould):\n",
        "  # get df redords where the cci column is over 200 and day of week = 4, avoid dates < '2023-12-22'\n",
        "  buy_df = df[(df['cci'] > threshould) & (df['Weekday'] == 4) & (df['Date'] < '2023-12-22')]\n",
        "\n",
        "  trading_records['ticker'] = buy_df['Ticker']\n",
        "  trading_records['buy_date'] = buy_df['Date']\n",
        "  trading_records['buy_price'] = buy_df['Adj Close_x']\n",
        "  trading_records['cci'] = buy_df['cci']\n",
        "\n",
        "buy(df, 200)\n",
        "trading_records"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "HOdNdpV8qKW9",
        "outputId": "d328c884-8381-4039-cd84-e0c332f1c000"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ticker   buy_date    buy_price sell_date sell_price result         cci\n",
              "102     MSFT 2014-05-30    34.912762       NaN        NaN    NaN  203.518798\n",
              "170     MSFT 2014-09-05    39.395618       NaN        NaN    NaN  257.013330\n",
              "329     MSFT 2015-04-24    41.630741       NaN        NaN    NaN  340.009871\n",
              "441     MSFT 2015-10-02    40.151123       NaN        NaN    NaN  206.125828\n",
              "456     MSFT 2015-10-23    46.583046       NaN        NaN    NaN  431.107024\n",
              "...      ...        ...          ...       ...        ...    ...         ...\n",
              "80491  LT.NS 2022-11-25  2058.108887       NaN        NaN    NaN  210.797547\n",
              "80638  LT.NS 2023-06-30  2469.979980       NaN        NaN    NaN  219.106421\n",
              "80653  LT.NS 2023-07-21  2580.430908       NaN        NaN    NaN  359.236523\n",
              "80687  LT.NS 2023-09-08  2901.600098       NaN        NaN    NaN  303.781710\n",
              "80742  LT.NS 2023-12-01  3190.649902       NaN        NaN    NaN  273.278565\n",
              "\n",
              "[459 rows x 7 columns]"
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              "      <th>102</th>\n",
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              "      <td>34.912762</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>340.009871</td>\n",
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              "      <td>2015-10-02</td>\n",
              "      <td>40.151123</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>206.125828</td>\n",
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              "    <tr>\n",
              "      <th>456</th>\n",
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              "      <td>46.583046</td>\n",
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              "      <th>80687</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>273.278565</td>\n",
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              "<p>459 rows × 7 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "trading_records",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sell(df):\n",
        "  for i, row in trading_records.iterrows():\n",
        "\n",
        "    sell_row = df.iloc[i+5]\n",
        "    #print(type(sell_row))\n",
        "    #print(sell_row)\n",
        "\n",
        "    trading_records.loc[i, 'sell_date'] = sell_row['Date']\n",
        "    trading_records.loc[i, 'sell_price'] = sell_row['Adj Close_x']\n",
        "\n",
        "    result = round((sell_row['Adj Close_x'] - row.buy_price) / row.buy_price * 1000, 2)\n",
        "    #print(type(result))\n",
        "    trading_records.loc[i, 'result'] = result\n",
        "\n",
        "sell(df)"
      ],
      "metadata": {
        "id": "7TEraXVUtHje"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trading_records['result'].sum().round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYZi_gFmRJei",
        "outputId": "a299c4e2-4f2c-4df1-f454-95d78db0026d"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1069.52"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    }
  ]
}