market_anomaly.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('FinancialMarketDataFormatted.csv')\n",
        "\n",
        "df.drop(columns=['LLL1 Index'], inplace=True)\n",
        "\n",
        "print(\"Shape\", df.shape)\n",
        "print(\"First few rows\")\n",
        "display(df.head())\n",
        "print(\"Info\")\n",
        "df.info()"
      ],
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      "execution_count": 4,
      "outputs": [
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          "output_type": "stream",
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          "text": [
            "Shape (1148, 57)\n",
            "First few rows\n"
          ]
        },
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          "data": {
            "text/plain": [
              "        Date  XAU BGNL Curncy  ECSURPUS Index  BDIY Index  CRY Index  \\\n",
              "0  4/27/1999           282.25             NaN       995.0     134.65   \n",
              "1   5/4/1999           285.55             NaN      1095.0     139.04   \n",
              "2  5/11/1999           278.25             NaN      1121.0     136.78   \n",
              "3  5/18/1999           273.95             NaN      1061.0     133.90   \n",
              "4  5/25/1999           271.45             NaN      1017.0     132.09   \n",
              "\n",
              "   DXY Curncy  JPY Curncy  GBP Curncy  Cl1 Comdty  VIX Index  ...  RX1 Comdty  \\\n",
              "0      100.63      120.44       1.617       17.81      23.36  ...      115.76   \n",
              "1      100.68      120.77       1.626       18.92      25.62  ...      115.11   \n",
              "2      100.24      121.07       1.621       18.06      25.62  ...      114.60   \n",
              "3      100.72      123.21       1.621       17.11      27.26  ...      114.24   \n",
              "4      101.38      122.85       1.602       17.14      28.90  ...      114.79   \n",
              "\n",
              "   TY1 Comdty  GC1 Comdty  CO1 Comdty  ES1 Index  VG1 Index  NQ1 Index  \\\n",
              "0     115.563       282.3       15.80    1376.50     3726.0        NaN   \n",
              "1     114.313       286.9       16.93    1343.00     3740.0        NaN   \n",
              "2     113.344       278.8       15.96    1358.25     3690.0        NaN   \n",
              "3     112.344       274.2       15.14    1341.25     3595.0        NaN   \n",
              "4     113.125       270.8       15.43    1284.50     3631.0        NaN   \n",
              "\n",
              "   TP1 Index  DU1 Comdty  TU2 Comdty  \n",
              "0     1347.5      105.69     104.898  \n",
              "1     1328.5      105.58     104.664  \n",
              "2     1343.5      105.67     104.406  \n",
              "3     1323.5      105.60     104.141  \n",
              "4     1307.0      105.68     104.289  \n",
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              "[5 rows x 57 columns]"
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              "      <th>XAU BGNL Curncy</th>\n",
              "      <th>ECSURPUS Index</th>\n",
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              "      <th>RX1 Comdty</th>\n",
              "      <th>TY1 Comdty</th>\n",
              "      <th>GC1 Comdty</th>\n",
              "      <th>CO1 Comdty</th>\n",
              "      <th>ES1 Index</th>\n",
              "      <th>VG1 Index</th>\n",
              "      <th>NQ1 Index</th>\n",
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              "      <td>4/27/1999</td>\n",
              "      <td>282.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>995.0</td>\n",
              "      <td>134.65</td>\n",
              "      <td>100.63</td>\n",
              "      <td>120.44</td>\n",
              "      <td>1.617</td>\n",
              "      <td>17.81</td>\n",
              "      <td>23.36</td>\n",
              "      <td>...</td>\n",
              "      <td>115.76</td>\n",
              "      <td>115.563</td>\n",
              "      <td>282.3</td>\n",
              "      <td>15.80</td>\n",
              "      <td>1376.50</td>\n",
              "      <td>3726.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1347.5</td>\n",
              "      <td>105.69</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5/4/1999</td>\n",
              "      <td>285.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1095.0</td>\n",
              "      <td>139.04</td>\n",
              "      <td>100.68</td>\n",
              "      <td>120.77</td>\n",
              "      <td>1.626</td>\n",
              "      <td>18.92</td>\n",
              "      <td>25.62</td>\n",
              "      <td>...</td>\n",
              "      <td>115.11</td>\n",
              "      <td>114.313</td>\n",
              "      <td>286.9</td>\n",
              "      <td>16.93</td>\n",
              "      <td>1343.00</td>\n",
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              "      <td>1328.5</td>\n",
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              "      <td>1121.0</td>\n",
              "      <td>136.78</td>\n",
              "      <td>100.24</td>\n",
              "      <td>121.07</td>\n",
              "      <td>1.621</td>\n",
              "      <td>18.06</td>\n",
              "      <td>25.62</td>\n",
              "      <td>...</td>\n",
              "      <td>114.60</td>\n",
              "      <td>113.344</td>\n",
              "      <td>278.8</td>\n",
              "      <td>15.96</td>\n",
              "      <td>1358.25</td>\n",
              "      <td>3690.0</td>\n",
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              "      <td>1343.5</td>\n",
              "      <td>105.67</td>\n",
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              "      <th>3</th>\n",
              "      <td>5/18/1999</td>\n",
              "      <td>273.95</td>\n",
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              "      <td>1061.0</td>\n",
              "      <td>133.90</td>\n",
              "      <td>100.72</td>\n",
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              "      <td>17.11</td>\n",
              "      <td>27.26</td>\n",
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              "      <td>274.2</td>\n",
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              "      <td>5/25/1999</td>\n",
              "      <td>271.45</td>\n",
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              "      <td>1017.0</td>\n",
              "      <td>132.09</td>\n",
              "      <td>101.38</td>\n",
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              "      <td>1.602</td>\n",
              "      <td>17.14</td>\n",
              "      <td>28.90</td>\n",
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              "      <td>114.79</td>\n",
              "      <td>113.125</td>\n",
              "      <td>270.8</td>\n",
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            "Info\n",
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            "RangeIndex: 1148 entries, 0 to 1147\n",
            "Data columns (total 57 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
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            " 6   JPY Curncy       1148 non-null   float64\n",
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            " 8   Cl1 Comdty       1148 non-null   float64\n",
            " 9   VIX Index        1148 non-null   float64\n",
            " 10  USGG30YR Index   1148 non-null   float64\n",
            " 11  GT10 Govt        1148 non-null   float64\n",
            " 12  USGG2YR Index    1148 non-null   float64\n",
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            " 16  GTDEM10Y Govt    1148 non-null   float64\n",
            " 17  GTDEM2Y Govt     1148 non-null   float64\n",
            " 18  EONIA Index      1148 non-null   float64\n",
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            " 24  GTJPY2YR Corp    1148 non-null   float64\n",
            " 25  GTGBP30Y Govt    1148 non-null   float64\n",
            " 26  GTGBP20Y Govt    1148 non-null   float64\n",
            " 27  GTGBP2Y Govt     1148 non-null   float64\n",
            " 28  LUMSTRUU Index   1148 non-null   float64\n",
            " 29  LMBITR Index     1148 non-null   float64\n",
            " 30  LUACTRUU Index   1148 non-null   float64\n",
            " 31  LF98TRUU Index   1148 non-null   float64\n",
            " 32  LG30TRUU Index   1148 non-null   float64\n",
            " 33  LP01TREU Index   1148 non-null   float64\n",
            " 34  EMUSTRUU Index   1148 non-null   float64\n",
            " 35  LF94TRUU Index   1147 non-null   float64\n",
            " 36  MXUS Index       1148 non-null   float64\n",
            " 37  MXEU Index       1148 non-null   float64\n",
            " 38  MXJP Index       1148 non-null   float64\n",
            " 39  MXBR Index       1148 non-null   float64\n",
            " 40  MXRU Index       1148 non-null   float64\n",
            " 41  MXIN Index       1148 non-null   float64\n",
            " 42  MXCN Index       1148 non-null   float64\n",
            " 43  MXWO Index       1148 non-null   float64\n",
            " 44  MXWD Index       1148 non-null   float64\n",
            " 45  LEGATRUU Index   1148 non-null   float64\n",
            " 46  HFRXGL Index     1147 non-null   float64\n",
            " 47  RX1 Comdty       1148 non-null   float64\n",
            " 48  TY1 Comdty       1148 non-null   float64\n",
            " 49  GC1 Comdty       1148 non-null   float64\n",
            " 50  CO1 Comdty       1148 non-null   float64\n",
            " 51  ES1 Index        1148 non-null   float64\n",
            " 52  VG1 Index        1148 non-null   float64\n",
            " 53  NQ1 Index        1140 non-null   float64\n",
            " 54  TP1 Index        1148 non-null   float64\n",
            " 55  DU1 Comdty       1148 non-null   float64\n",
            " 56  TU2 Comdty       1148 non-null   float64\n",
            "dtypes: float64(56), object(1)\n",
            "memory usage: 511.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Pct_Change'] = df['MXWO Index'].pct_change(periods=-1) * 100\n",
        "df['CRASH'] = (df['Pct_Change'] <= -5).astype(int)"
      ],
      "metadata": {
        "id": "HNx1II1wcsg3"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['CRASH'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TwIpn86Zu6H",
        "outputId": "63907f4f-5f27-42ef-c85c-2a99197df9a3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CRASH\n",
            "0    1132\n",
            "1      16\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkccQOqIeTWb",
        "outputId": "121890f6-87d1-478d-b7a7-e061df682713"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date                0\n",
            "XAU BGNL Curncy     0\n",
            "ECSURPUS Index     37\n",
            "BDIY Index          0\n",
            "CRY Index           0\n",
            "DXY Curncy          0\n",
            "JPY Curncy          0\n",
            "GBP Curncy          0\n",
            "Cl1 Comdty          0\n",
            "VIX Index           0\n",
            "USGG30YR Index      0\n",
            "GT10 Govt           0\n",
            "USGG2YR Index       0\n",
            "USGG3M Index        0\n",
            "US0001M Index       0\n",
            "GTDEM30Y Govt       0\n",
            "GTDEM10Y Govt       0\n",
            "GTDEM2Y Govt        0\n",
            "EONIA Index         0\n",
            "GTITL30YR Corp     27\n",
            "GTITL10YR Corp     27\n",
            "GTITL2YR Corp      27\n",
            "GTJPY30YR Corp     19\n",
            "GTJPY10YR Corp      0\n",
            "GTJPY2YR Corp       0\n",
            "GTGBP30Y Govt       0\n",
            "GTGBP20Y Govt       0\n",
            "GTGBP2Y Govt        0\n",
            "LUMSTRUU Index      0\n",
            "LMBITR Index        0\n",
            "LUACTRUU Index      0\n",
            "LF98TRUU Index      0\n",
            "LG30TRUU Index      0\n",
            "LP01TREU Index      0\n",
            "EMUSTRUU Index      0\n",
            "LF94TRUU Index      1\n",
            "MXUS Index          0\n",
            "MXEU Index          0\n",
            "MXJP Index          0\n",
            "MXBR Index          0\n",
            "MXRU Index          0\n",
            "MXIN Index          0\n",
            "MXCN Index          0\n",
            "MXWO Index          0\n",
            "MXWD Index          0\n",
            "LEGATRUU Index      0\n",
            "HFRXGL Index        1\n",
            "RX1 Comdty          0\n",
            "TY1 Comdty          0\n",
            "GC1 Comdty          0\n",
            "CO1 Comdty          0\n",
            "ES1 Index           0\n",
            "VG1 Index           0\n",
            "NQ1 Index           8\n",
            "TP1 Index           0\n",
            "DU1 Comdty          0\n",
            "TU2 Comdty          0\n",
            "Pct_Change          1\n",
            "CRASH               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ECSURPUS Index'].fillna(0, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etxW7DSQfHmE",
        "outputId": "a674edd8-f18d-48bc-a4d4-8ff4e7e3a588"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-5600619e3a2a>:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['ECSURPUS Index'].fillna(0, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "683QjA-GfSDC",
        "outputId": "273c56b1-0a57-4254-f394-477f498de5f4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date                0\n",
            "XAU BGNL Curncy     0\n",
            "ECSURPUS Index      0\n",
            "BDIY Index          0\n",
            "CRY Index           0\n",
            "DXY Curncy          0\n",
            "JPY Curncy          0\n",
            "GBP Curncy          0\n",
            "Cl1 Comdty          0\n",
            "VIX Index           0\n",
            "USGG30YR Index      0\n",
            "GT10 Govt           0\n",
            "USGG2YR Index       0\n",
            "USGG3M Index        0\n",
            "US0001M Index       0\n",
            "GTDEM30Y Govt       0\n",
            "GTDEM10Y Govt       0\n",
            "GTDEM2Y Govt        0\n",
            "EONIA Index         0\n",
            "GTITL30YR Corp     27\n",
            "GTITL10YR Corp     27\n",
            "GTITL2YR Corp      27\n",
            "GTJPY30YR Corp     19\n",
            "GTJPY10YR Corp      0\n",
            "GTJPY2YR Corp       0\n",
            "GTGBP30Y Govt       0\n",
            "GTGBP20Y Govt       0\n",
            "GTGBP2Y Govt        0\n",
            "LUMSTRUU Index      0\n",
            "LMBITR Index        0\n",
            "LUACTRUU Index      0\n",
            "LF98TRUU Index      0\n",
            "LG30TRUU Index      0\n",
            "LP01TREU Index      0\n",
            "EMUSTRUU Index      0\n",
            "LF94TRUU Index      1\n",
            "MXUS Index          0\n",
            "MXEU Index          0\n",
            "MXJP Index          0\n",
            "MXBR Index          0\n",
            "MXRU Index          0\n",
            "MXIN Index          0\n",
            "MXCN Index          0\n",
            "MXWO Index          0\n",
            "MXWD Index          0\n",
            "LEGATRUU Index      0\n",
            "HFRXGL Index        1\n",
            "RX1 Comdty          0\n",
            "TY1 Comdty          0\n",
            "GC1 Comdty          0\n",
            "CO1 Comdty          0\n",
            "ES1 Index           0\n",
            "VG1 Index           0\n",
            "NQ1 Index           8\n",
            "TP1 Index           0\n",
            "DU1 Comdty          0\n",
            "TU2 Comdty          0\n",
            "Pct_Change          1\n",
            "CRASH               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTrvtpeegQGs",
        "outputId": "64436ec5-dbd2-4dc8-c747-84e0e9d5312e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-e9443599d05e>:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
            "  df.fillna(method='ffill', inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(method='bfill', inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEat7WIjhpQo",
        "outputId": "d953ebc0-db73-4542-aa50-5e6dae03b07b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-168d8f9d469d>:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
            "  df.fillna(method='bfill', inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define columns for which to create lagged features\n",
        "columns_to_lag = ['VIX Index', 'MXWO Index']\n",
        "\n",
        "# Number of lags to create\n",
        "num_lags = 3  # 1-week, 2-week, 3-week lags\n",
        "\n",
        "# Create lagged features\n",
        "for col in columns_to_lag:\n",
        "    for lag in range(1, num_lags + 1):\n",
        "        df[f'{col}_lag_{lag}'] = df[col].shift(lag)\n"
      ],
      "metadata": {
        "id": "_JcSeqaQjH9T"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "gQSw6YSNjm9M",
        "outputId": "1c039b28-652f-4648-fb10-fc3afd8fa156"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  XAU BGNL Curncy  ECSURPUS Index  BDIY Index  CRY Index  \\\n",
              "0     4/27/1999           282.25           0.000       995.0    134.650   \n",
              "1      5/4/1999           285.55           0.000      1095.0    139.040   \n",
              "2     5/11/1999           278.25           0.000      1121.0    136.780   \n",
              "3     5/18/1999           273.95           0.000      1061.0    133.900   \n",
              "4     5/25/1999           271.45           0.000      1017.0    132.090   \n",
              "...         ...              ...             ...         ...        ...   \n",
              "1143  3/23/2021          1727.96           0.339      2271.0    184.502   \n",
              "1144  3/30/2021          1685.56           0.303      2103.0    184.874   \n",
              "1145   4/6/2021          1743.28           0.440      2092.0    185.839   \n",
              "1146  4/13/2021          1742.69           0.467      2140.0    188.137   \n",
              "1147  4/20/2021          1779.72           0.492      2432.0    193.804   \n",
              "\n",
              "      DXY Curncy  JPY Curncy  GBP Curncy  Cl1 Comdty  VIX Index  ...  \\\n",
              "0        100.630      120.44       1.617       17.81      23.36  ...   \n",
              "1        100.680      120.77       1.626       18.92      25.62  ...   \n",
              "2        100.240      121.07       1.621       18.06      25.62  ...   \n",
              "3        100.720      123.21       1.621       17.11      27.26  ...   \n",
              "4        101.380      122.85       1.602       17.14      28.90  ...   \n",
              "...          ...         ...         ...         ...        ...  ...   \n",
              "1143      92.336      108.72       1.376       57.76      20.30  ...   \n",
              "1144      93.297      110.29       1.372       60.55      19.61  ...   \n",
              "1145      92.335      109.88       1.382       59.33      18.12  ...   \n",
              "1146      91.852      109.19       1.374       60.18      16.65  ...   \n",
              "1147      91.147      108.09       1.395       62.50      18.46  ...   \n",
              "\n",
              "      DU1 Comdty  TU2 Comdty  Pct_Change  CRASH  VIX Index_lag_1  \\\n",
              "0        105.690     104.898    1.549891      0              NaN   \n",
              "1        105.580     104.664   -0.548480      0            23.36   \n",
              "2        105.670     104.406    2.276027      0            25.62   \n",
              "3        105.600     104.141    2.262566      0            25.62   \n",
              "4        105.680     104.289   -0.105340      0            27.26   \n",
              "...          ...         ...         ...    ...              ...   \n",
              "1143     112.140     110.383   -0.609247      0            19.79   \n",
              "1144     112.090     110.387   -2.473029      0            20.30   \n",
              "1145     112.120     110.281   -1.437878      0            19.61   \n",
              "1146     112.115     110.293   -0.878382      0            18.12   \n",
              "1147     112.100     110.293   -0.878382      0            16.65   \n",
              "\n",
              "      VIX Index_lag_2  VIX Index_lag_3  MXWO Index_lag_1  MXWO Index_lag_2  \\\n",
              "0                 NaN              NaN               NaN               NaN   \n",
              "1                 NaN              NaN           1252.10               NaN   \n",
              "2               23.36              NaN           1232.99           1252.10   \n",
              "3               25.62            23.36           1239.79           1232.99   \n",
              "4               25.62            25.62           1212.20           1239.79   \n",
              "...               ...              ...               ...               ...   \n",
              "1143            24.03            24.10           2823.69           2761.82   \n",
              "1144            19.79            24.03           2788.01           2823.69   \n",
              "1145            20.30            19.79           2805.10           2788.01   \n",
              "1146            19.61            20.30           2876.23           2805.10   \n",
              "1147            18.12            19.61           2918.19           2876.23   \n",
              "\n",
              "      MXWO Index_lag_3  \n",
              "0                  NaN  \n",
              "1                  NaN  \n",
              "2                  NaN  \n",
              "3              1252.10  \n",
              "4              1232.99  \n",
              "...                ...  \n",
              "1143           2768.25  \n",
              "1144           2761.82  \n",
              "1145           2823.69  \n",
              "1146           2788.01  \n",
              "1147           2805.10  \n",
              "\n",
              "[1148 rows x 65 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-706379a3-2577-4be5-84dc-9ceb0d610f10\" 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>Date</th>\n",
              "      <th>XAU BGNL Curncy</th>\n",
              "      <th>ECSURPUS Index</th>\n",
              "      <th>BDIY Index</th>\n",
              "      <th>CRY Index</th>\n",
              "      <th>DXY Curncy</th>\n",
              "      <th>JPY Curncy</th>\n",
              "      <th>GBP Curncy</th>\n",
              "      <th>Cl1 Comdty</th>\n",
              "      <th>VIX Index</th>\n",
              "      <th>...</th>\n",
              "      <th>DU1 Comdty</th>\n",
              "      <th>TU2 Comdty</th>\n",
              "      <th>Pct_Change</th>\n",
              "      <th>CRASH</th>\n",
              "      <th>VIX Index_lag_1</th>\n",
              "      <th>VIX Index_lag_2</th>\n",
              "      <th>VIX Index_lag_3</th>\n",
              "      <th>MXWO Index_lag_1</th>\n",
              "      <th>MXWO Index_lag_2</th>\n",
              "      <th>MXWO Index_lag_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4/27/1999</td>\n",
              "      <td>282.25</td>\n",
              "      <td>0.000</td>\n",
              "      <td>995.0</td>\n",
              "      <td>134.650</td>\n",
              "      <td>100.630</td>\n",
              "      <td>120.44</td>\n",
              "      <td>1.617</td>\n",
              "      <td>17.81</td>\n",
              "      <td>23.36</td>\n",
              "      <td>...</td>\n",
              "      <td>105.690</td>\n",
              "      <td>104.898</td>\n",
              "      <td>1.549891</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5/4/1999</td>\n",
              "      <td>285.55</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1095.0</td>\n",
              "      <td>139.040</td>\n",
              "      <td>100.680</td>\n",
              "      <td>120.77</td>\n",
              "      <td>1.626</td>\n",
              "      <td>18.92</td>\n",
              "      <td>25.62</td>\n",
              "      <td>...</td>\n",
              "      <td>105.580</td>\n",
              "      <td>104.664</td>\n",
              "      <td>-0.548480</td>\n",
              "      <td>0</td>\n",
              "      <td>23.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1252.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5/11/1999</td>\n",
              "      <td>278.25</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1121.0</td>\n",
              "      <td>136.780</td>\n",
              "      <td>100.240</td>\n",
              "      <td>121.07</td>\n",
              "      <td>1.621</td>\n",
              "      <td>18.06</td>\n",
              "      <td>25.62</td>\n",
              "      <td>...</td>\n",
              "      <td>105.670</td>\n",
              "      <td>104.406</td>\n",
              "      <td>2.276027</td>\n",
              "      <td>0</td>\n",
              "      <td>25.62</td>\n",
              "      <td>23.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1232.99</td>\n",
              "      <td>1252.10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5/18/1999</td>\n",
              "      <td>273.95</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1061.0</td>\n",
              "      <td>133.900</td>\n",
              "      <td>100.720</td>\n",
              "      <td>123.21</td>\n",
              "      <td>1.621</td>\n",
              "      <td>17.11</td>\n",
              "      <td>27.26</td>\n",
              "      <td>...</td>\n",
              "      <td>105.600</td>\n",
              "      <td>104.141</td>\n",
              "      <td>2.262566</td>\n",
              "      <td>0</td>\n",
              "      <td>25.62</td>\n",
              "      <td>25.62</td>\n",
              "      <td>23.36</td>\n",
              "      <td>1239.79</td>\n",
              "      <td>1232.99</td>\n",
              "      <td>1252.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5/25/1999</td>\n",
              "      <td>271.45</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>132.090</td>\n",
              "      <td>101.380</td>\n",
              "      <td>122.85</td>\n",
              "      <td>1.602</td>\n",
              "      <td>17.14</td>\n",
              "      <td>28.90</td>\n",
              "      <td>...</td>\n",
              "      <td>105.680</td>\n",
              "      <td>104.289</td>\n",
              "      <td>-0.105340</td>\n",
              "      <td>0</td>\n",
              "      <td>27.26</td>\n",
              "      <td>25.62</td>\n",
              "      <td>25.62</td>\n",
              "      <td>1212.20</td>\n",
              "      <td>1239.79</td>\n",
              "      <td>1232.99</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>1143</th>\n",
              "      <td>3/23/2021</td>\n",
              "      <td>1727.96</td>\n",
              "      <td>0.339</td>\n",
              "      <td>2271.0</td>\n",
              "      <td>184.502</td>\n",
              "      <td>92.336</td>\n",
              "      <td>108.72</td>\n",
              "      <td>1.376</td>\n",
              "      <td>57.76</td>\n",
              "      <td>20.30</td>\n",
              "      <td>...</td>\n",
              "      <td>112.140</td>\n",
              "      <td>110.383</td>\n",
              "      <td>-0.609247</td>\n",
              "      <td>0</td>\n",
              "      <td>19.79</td>\n",
              "      <td>24.03</td>\n",
              "      <td>24.10</td>\n",
              "      <td>2823.69</td>\n",
              "      <td>2761.82</td>\n",
              "      <td>2768.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1144</th>\n",
              "      <td>3/30/2021</td>\n",
              "      <td>1685.56</td>\n",
              "      <td>0.303</td>\n",
              "      <td>2103.0</td>\n",
              "      <td>184.874</td>\n",
              "      <td>93.297</td>\n",
              "      <td>110.29</td>\n",
              "      <td>1.372</td>\n",
              "      <td>60.55</td>\n",
              "      <td>19.61</td>\n",
              "      <td>...</td>\n",
              "      <td>112.090</td>\n",
              "      <td>110.387</td>\n",
              "      <td>-2.473029</td>\n",
              "      <td>0</td>\n",
              "      <td>20.30</td>\n",
              "      <td>19.79</td>\n",
              "      <td>24.03</td>\n",
              "      <td>2788.01</td>\n",
              "      <td>2823.69</td>\n",
              "      <td>2761.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1145</th>\n",
              "      <td>4/6/2021</td>\n",
              "      <td>1743.28</td>\n",
              "      <td>0.440</td>\n",
              "      <td>2092.0</td>\n",
              "      <td>185.839</td>\n",
              "      <td>92.335</td>\n",
              "      <td>109.88</td>\n",
              "      <td>1.382</td>\n",
              "      <td>59.33</td>\n",
              "      <td>18.12</td>\n",
              "      <td>...</td>\n",
              "      <td>112.120</td>\n",
              "      <td>110.281</td>\n",
              "      <td>-1.437878</td>\n",
              "      <td>0</td>\n",
              "      <td>19.61</td>\n",
              "      <td>20.30</td>\n",
              "      <td>19.79</td>\n",
              "      <td>2805.10</td>\n",
              "      <td>2788.01</td>\n",
              "      <td>2823.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1146</th>\n",
              "      <td>4/13/2021</td>\n",
              "      <td>1742.69</td>\n",
              "      <td>0.467</td>\n",
              "      <td>2140.0</td>\n",
              "      <td>188.137</td>\n",
              "      <td>91.852</td>\n",
              "      <td>109.19</td>\n",
              "      <td>1.374</td>\n",
              "      <td>60.18</td>\n",
              "      <td>16.65</td>\n",
              "      <td>...</td>\n",
              "      <td>112.115</td>\n",
              "      <td>110.293</td>\n",
              "      <td>-0.878382</td>\n",
              "      <td>0</td>\n",
              "      <td>18.12</td>\n",
              "      <td>19.61</td>\n",
              "      <td>20.30</td>\n",
              "      <td>2876.23</td>\n",
              "      <td>2805.10</td>\n",
              "      <td>2788.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1147</th>\n",
              "      <td>4/20/2021</td>\n",
              "      <td>1779.72</td>\n",
              "      <td>0.492</td>\n",
              "      <td>2432.0</td>\n",
              "      <td>193.804</td>\n",
              "      <td>91.147</td>\n",
              "      <td>108.09</td>\n",
              "      <td>1.395</td>\n",
              "      <td>62.50</td>\n",
              "      <td>18.46</td>\n",
              "      <td>...</td>\n",
              "      <td>112.100</td>\n",
              "      <td>110.293</td>\n",
              "      <td>-0.878382</td>\n",
              "      <td>0</td>\n",
              "      <td>16.65</td>\n",
              "      <td>18.12</td>\n",
              "      <td>19.61</td>\n",
              "      <td>2918.19</td>\n",
              "      <td>2876.23</td>\n",
              "      <td>2805.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1148 rows × 65 columns</p>\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now time to normalize with Min-Max Scaling\n",
        "We choose Min-Max scaling because our data has a defined range"
      ],
      "metadata": {
        "id": "M6V0SNcAXPNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Columns to normalize (exclude 'CRASH' and 'Date')\n",
        "columns_to_normalize = [col for col in df.columns if col not in ['Date', 'CRASH']]\n",
        "\n",
        "# Initialize the scaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply scaling to the selected columns\n",
        "df[columns_to_normalize] = scaler.fit_transform(df[columns_to_normalize])\n"
      ],
      "metadata": {
        "id": "fYj12elFXVaK"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "iLKi4xoIXaB3",
        "outputId": "3862c7f2-32ed-44b4-dbd2-941e5072c42c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  XAU BGNL Curncy  ECSURPUS Index  BDIY Index  CRY Index  \\\n",
              "0     4/27/1999         0.016425        0.495674    0.061207   0.078491   \n",
              "1      5/4/1999         0.018307        0.495674    0.069901   0.090642   \n",
              "2     5/11/1999         0.014143        0.495674    0.072161   0.084387   \n",
              "3     5/18/1999         0.011691        0.495674    0.066945   0.076415   \n",
              "4     5/25/1999         0.010265        0.495674    0.063119   0.071405   \n",
              "...         ...              ...             ...         ...        ...   \n",
              "1143  3/23/2021         0.840915        0.668193    0.172144   0.216479   \n",
              "1144  3/30/2021         0.816734        0.649873    0.157538   0.217509   \n",
              "1145   4/6/2021         0.849652        0.719593    0.156581   0.220180   \n",
              "1146  4/13/2021         0.849315        0.733333    0.160755   0.226541   \n",
              "1147  4/20/2021         0.870433        0.746056    0.186142   0.242227   \n",
              "\n",
              "      DXY Curncy  JPY Curncy  GBP Curncy  Cl1 Comdty  VIX Index  ...  \\\n",
              "0       0.604256    0.758221    0.487404    0.059560   0.209537  ...   \n",
              "1       0.605288    0.763844    0.497262    0.068036   0.243532  ...   \n",
              "2       0.596214    0.768956    0.491785    0.061469   0.243532  ...   \n",
              "3       0.606112    0.805418    0.491785    0.054215   0.268201  ...   \n",
              "4       0.619723    0.799284    0.470975    0.054444   0.292870  ...   \n",
              "...          ...         ...         ...         ...        ...  ...   \n",
              "1143    0.433214    0.558528    0.223439    0.364615   0.163508  ...   \n",
              "1144    0.453033    0.585279    0.219058    0.385919   0.153129  ...   \n",
              "1145    0.433194    0.578293    0.230011    0.376604   0.130716  ...   \n",
              "1146    0.423233    0.566536    0.221249    0.383094   0.108604  ...   \n",
              "1147    0.408694    0.547793    0.244250    0.400809   0.135830  ...   \n",
              "\n",
              "      DU1 Comdty  TU2 Comdty  Pct_Change  CRASH  VIX Index_lag_1  \\\n",
              "0       0.390286    0.542358    0.478313      0              NaN   \n",
              "1       0.380746    0.523333    0.401156      0         0.209537   \n",
              "2       0.388552    0.502358    0.505014      0         0.243532   \n",
              "3       0.382480    0.480813    0.504519      0         0.243532   \n",
              "4       0.389419    0.492846    0.417451      0         0.268201   \n",
              "...          ...         ...         ...    ...              ...   \n",
              "1143    0.949696    0.988293    0.398922      0         0.155836   \n",
              "1144    0.945360    0.988618    0.330390      0         0.163508   \n",
              "1145    0.947962    0.980000    0.368453      0         0.153129   \n",
              "1146    0.947528    0.980976    0.389026      0         0.130716   \n",
              "1147    0.946227    0.980976    0.389026      0         0.108604   \n",
              "\n",
              "      VIX Index_lag_2  VIX Index_lag_3  MXWO Index_lag_1  MXWO Index_lag_2  \\\n",
              "0                 NaN              NaN               NaN               NaN   \n",
              "1                 NaN              NaN          0.247081               NaN   \n",
              "2            0.209537              NaN          0.238445          0.251856   \n",
              "3            0.243532         0.209537          0.241518          0.243054   \n",
              "4            0.243532         0.243532          0.229050          0.246186   \n",
              "...               ...              ...               ...               ...   \n",
              "1143         0.219615         0.220668          0.957295          0.947298   \n",
              "1144         0.155836         0.219615          0.941171          0.975798   \n",
              "1145         0.163508         0.155836          0.948894          0.959362   \n",
              "1146         0.153129         0.163508          0.981038          0.967234   \n",
              "1147         0.130716         0.153129          1.000000          1.000000   \n",
              "\n",
              "      MXWO Index_lag_3  \n",
              "0                  NaN  \n",
              "1                  NaN  \n",
              "2                  NaN  \n",
              "3             0.257561  \n",
              "4             0.248559  \n",
              "...                ...  \n",
              "1143          0.971783  \n",
              "1144          0.968754  \n",
              "1145          0.997899  \n",
              "1146          0.981091  \n",
              "1147          0.989142  \n",
              "\n",
              "[1148 rows x 65 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>XAU BGNL Curncy</th>\n",
              "      <th>ECSURPUS Index</th>\n",
              "      <th>BDIY Index</th>\n",
              "      <th>CRY Index</th>\n",
              "      <th>DXY Curncy</th>\n",
              "      <th>JPY Curncy</th>\n",
              "      <th>GBP Curncy</th>\n",
              "      <th>Cl1 Comdty</th>\n",
              "      <th>VIX Index</th>\n",
              "      <th>...</th>\n",
              "      <th>DU1 Comdty</th>\n",
              "      <th>TU2 Comdty</th>\n",
              "      <th>Pct_Change</th>\n",
              "      <th>CRASH</th>\n",
              "      <th>VIX Index_lag_1</th>\n",
              "      <th>VIX Index_lag_2</th>\n",
              "      <th>VIX Index_lag_3</th>\n",
              "      <th>MXWO Index_lag_1</th>\n",
              "      <th>MXWO Index_lag_2</th>\n",
              "      <th>MXWO Index_lag_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4/27/1999</td>\n",
              "      <td>0.016425</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.061207</td>\n",
              "      <td>0.078491</td>\n",
              "      <td>0.604256</td>\n",
              "      <td>0.758221</td>\n",
              "      <td>0.487404</td>\n",
              "      <td>0.059560</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.390286</td>\n",
              "      <td>0.542358</td>\n",
              "      <td>0.478313</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5/4/1999</td>\n",
              "      <td>0.018307</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.069901</td>\n",
              "      <td>0.090642</td>\n",
              "      <td>0.605288</td>\n",
              "      <td>0.763844</td>\n",
              "      <td>0.497262</td>\n",
              "      <td>0.068036</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>...</td>\n",
              "      <td>0.380746</td>\n",
              "      <td>0.523333</td>\n",
              "      <td>0.401156</td>\n",
              "      <td>0</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.247081</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5/11/1999</td>\n",
              "      <td>0.014143</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.072161</td>\n",
              "      <td>0.084387</td>\n",
              "      <td>0.596214</td>\n",
              "      <td>0.768956</td>\n",
              "      <td>0.491785</td>\n",
              "      <td>0.061469</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>...</td>\n",
              "      <td>0.388552</td>\n",
              "      <td>0.502358</td>\n",
              "      <td>0.505014</td>\n",
              "      <td>0</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.238445</td>\n",
              "      <td>0.251856</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5/18/1999</td>\n",
              "      <td>0.011691</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.066945</td>\n",
              "      <td>0.076415</td>\n",
              "      <td>0.606112</td>\n",
              "      <td>0.805418</td>\n",
              "      <td>0.491785</td>\n",
              "      <td>0.054215</td>\n",
              "      <td>0.268201</td>\n",
              "      <td>...</td>\n",
              "      <td>0.382480</td>\n",
              "      <td>0.480813</td>\n",
              "      <td>0.504519</td>\n",
              "      <td>0</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>0.241518</td>\n",
              "      <td>0.243054</td>\n",
              "      <td>0.257561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5/25/1999</td>\n",
              "      <td>0.010265</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>0.071405</td>\n",
              "      <td>0.619723</td>\n",
              "      <td>0.799284</td>\n",
              "      <td>0.470975</td>\n",
              "      <td>0.054444</td>\n",
              "      <td>0.292870</td>\n",
              "      <td>...</td>\n",
              "      <td>0.389419</td>\n",
              "      <td>0.492846</td>\n",
              "      <td>0.417451</td>\n",
              "      <td>0</td>\n",
              "      <td>0.268201</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.229050</td>\n",
              "      <td>0.246186</td>\n",
              "      <td>0.248559</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>3/23/2021</td>\n",
              "      <td>0.840915</td>\n",
              "      <td>0.668193</td>\n",
              "      <td>0.172144</td>\n",
              "      <td>0.216479</td>\n",
              "      <td>0.433214</td>\n",
              "      <td>0.558528</td>\n",
              "      <td>0.223439</td>\n",
              "      <td>0.364615</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>...</td>\n",
              "      <td>0.949696</td>\n",
              "      <td>0.988293</td>\n",
              "      <td>0.398922</td>\n",
              "      <td>0</td>\n",
              "      <td>0.155836</td>\n",
              "      <td>0.219615</td>\n",
              "      <td>0.220668</td>\n",
              "      <td>0.957295</td>\n",
              "      <td>0.947298</td>\n",
              "      <td>0.971783</td>\n",
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              "    <tr>\n",
              "      <th>1144</th>\n",
              "      <td>3/30/2021</td>\n",
              "      <td>0.816734</td>\n",
              "      <td>0.649873</td>\n",
              "      <td>0.157538</td>\n",
              "      <td>0.217509</td>\n",
              "      <td>0.453033</td>\n",
              "      <td>0.585279</td>\n",
              "      <td>0.219058</td>\n",
              "      <td>0.385919</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>...</td>\n",
              "      <td>0.945360</td>\n",
              "      <td>0.988618</td>\n",
              "      <td>0.330390</td>\n",
              "      <td>0</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>0.155836</td>\n",
              "      <td>0.219615</td>\n",
              "      <td>0.941171</td>\n",
              "      <td>0.975798</td>\n",
              "      <td>0.968754</td>\n",
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              "    <tr>\n",
              "      <th>1145</th>\n",
              "      <td>4/6/2021</td>\n",
              "      <td>0.849652</td>\n",
              "      <td>0.719593</td>\n",
              "      <td>0.156581</td>\n",
              "      <td>0.220180</td>\n",
              "      <td>0.433194</td>\n",
              "      <td>0.578293</td>\n",
              "      <td>0.230011</td>\n",
              "      <td>0.376604</td>\n",
              "      <td>0.130716</td>\n",
              "      <td>...</td>\n",
              "      <td>0.947962</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.368453</td>\n",
              "      <td>0</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>0.155836</td>\n",
              "      <td>0.948894</td>\n",
              "      <td>0.959362</td>\n",
              "      <td>0.997899</td>\n",
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              "    <tr>\n",
              "      <th>1146</th>\n",
              "      <td>4/13/2021</td>\n",
              "      <td>0.849315</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.160755</td>\n",
              "      <td>0.226541</td>\n",
              "      <td>0.423233</td>\n",
              "      <td>0.566536</td>\n",
              "      <td>0.221249</td>\n",
              "      <td>0.383094</td>\n",
              "      <td>0.108604</td>\n",
              "      <td>...</td>\n",
              "      <td>0.947528</td>\n",
              "      <td>0.980976</td>\n",
              "      <td>0.389026</td>\n",
              "      <td>0</td>\n",
              "      <td>0.130716</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>0.981038</td>\n",
              "      <td>0.967234</td>\n",
              "      <td>0.981091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1147</th>\n",
              "      <td>4/20/2021</td>\n",
              "      <td>0.870433</td>\n",
              "      <td>0.746056</td>\n",
              "      <td>0.186142</td>\n",
              "      <td>0.242227</td>\n",
              "      <td>0.408694</td>\n",
              "      <td>0.547793</td>\n",
              "      <td>0.244250</td>\n",
              "      <td>0.400809</td>\n",
              "      <td>0.135830</td>\n",
              "      <td>...</td>\n",
              "      <td>0.946227</td>\n",
              "      <td>0.980976</td>\n",
              "      <td>0.389026</td>\n",
              "      <td>0</td>\n",
              "      <td>0.108604</td>\n",
              "      <td>0.130716</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.989142</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1148 rows × 65 columns</p>\n",
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              "  </button>\n",
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              "\n",
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              "    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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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    60% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\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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              "        document.querySelector('#df-1349edd1-1583-46c2-bc27-b57481db64e5 button');\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [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",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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",
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              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with NaN values\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "# Reset the index after dropping rows\n",
        "df.reset_index(drop=True, inplace=True)\n"
      ],
      "metadata": {
        "id": "bBLT0MbFXwlb"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "KB2-condXye_",
        "outputId": "824e882d-76a2-46f3-9aac-f96afbe480c8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  XAU BGNL Curncy  ECSURPUS Index  BDIY Index  CRY Index  \\\n",
              "0     5/18/1999         0.011691        0.495674    0.066945   0.076415   \n",
              "1     5/25/1999         0.010265        0.495674    0.063119   0.071405   \n",
              "2      6/1/1999         0.007813        0.495674    0.059468   0.067641   \n",
              "3      6/8/1999         0.004676        0.495674    0.062859   0.082588   \n",
              "4     6/15/1999         0.003593        0.495674    0.062859   0.086186   \n",
              "...         ...              ...             ...         ...        ...   \n",
              "1140  3/23/2021         0.840915        0.668193    0.172144   0.216479   \n",
              "1141  3/30/2021         0.816734        0.649873    0.157538   0.217509   \n",
              "1142   4/6/2021         0.849652        0.719593    0.156581   0.220180   \n",
              "1143  4/13/2021         0.849315        0.733333    0.160755   0.226541   \n",
              "1144  4/20/2021         0.870433        0.746056    0.186142   0.242227   \n",
              "\n",
              "      DXY Curncy  JPY Curncy  GBP Curncy  Cl1 Comdty  VIX Index  ...  \\\n",
              "0       0.606112    0.805418    0.491785    0.054215   0.268201  ...   \n",
              "1       0.619723    0.799284    0.470975    0.054444   0.292870  ...   \n",
              "2       0.633953    0.758392    0.481928    0.048335   0.258574  ...   \n",
              "3       0.627560    0.737945    0.474261    0.058415   0.219465  ...   \n",
              "4       0.636840    0.762651    0.458927    0.065211   0.247894  ...   \n",
              "...          ...         ...         ...         ...        ...  ...   \n",
              "1140    0.433214    0.558528    0.223439    0.364615   0.163508  ...   \n",
              "1141    0.453033    0.585279    0.219058    0.385919   0.153129  ...   \n",
              "1142    0.433194    0.578293    0.230011    0.376604   0.130716  ...   \n",
              "1143    0.423233    0.566536    0.221249    0.383094   0.108604  ...   \n",
              "1144    0.408694    0.547793    0.244250    0.400809   0.135830  ...   \n",
              "\n",
              "      DU1 Comdty  TU2 Comdty  Pct_Change  CRASH  VIX Index_lag_1  \\\n",
              "0       0.382480    0.480813    0.504519      0         0.243532   \n",
              "1       0.389419    0.492846    0.417451      0         0.268201   \n",
              "2       0.353859    0.456016    0.338040      0         0.292870   \n",
              "3       0.356461    0.451545    0.436590      0         0.258574   \n",
              "4       0.326106    0.438211    0.346166      0         0.219465   \n",
              "...          ...         ...         ...    ...              ...   \n",
              "1140    0.949696    0.988293    0.398922      0         0.155836   \n",
              "1141    0.945360    0.988618    0.330390      0         0.163508   \n",
              "1142    0.947962    0.980000    0.368453      0         0.153129   \n",
              "1143    0.947528    0.980976    0.389026      0         0.130716   \n",
              "1144    0.946227    0.980976    0.389026      0         0.108604   \n",
              "\n",
              "      VIX Index_lag_2  VIX Index_lag_3  MXWO Index_lag_1  MXWO Index_lag_2  \\\n",
              "0            0.243532         0.209537          0.241518          0.243054   \n",
              "1            0.243532         0.243532          0.229050          0.246186   \n",
              "2            0.268201         0.243532          0.216929          0.233477   \n",
              "3            0.292870         0.268201          0.217494          0.221122   \n",
              "4            0.258574         0.292870          0.229922          0.221698   \n",
              "...               ...              ...               ...               ...   \n",
              "1140         0.219615         0.220668          0.957295          0.947298   \n",
              "1141         0.155836         0.219615          0.941171          0.975798   \n",
              "1142         0.163508         0.155836          0.948894          0.959362   \n",
              "1143         0.153129         0.163508          0.981038          0.967234   \n",
              "1144         0.130716         0.153129          1.000000          1.000000   \n",
              "\n",
              "      MXWO Index_lag_3  \n",
              "0             0.257561  \n",
              "1             0.248559  \n",
              "2             0.251762  \n",
              "3             0.238765  \n",
              "4             0.226131  \n",
              "...                ...  \n",
              "1140          0.971783  \n",
              "1141          0.968754  \n",
              "1142          0.997899  \n",
              "1143          0.981091  \n",
              "1144          0.989142  \n",
              "\n",
              "[1145 rows x 65 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>XAU BGNL Curncy</th>\n",
              "      <th>ECSURPUS Index</th>\n",
              "      <th>BDIY Index</th>\n",
              "      <th>CRY Index</th>\n",
              "      <th>DXY Curncy</th>\n",
              "      <th>JPY Curncy</th>\n",
              "      <th>GBP Curncy</th>\n",
              "      <th>Cl1 Comdty</th>\n",
              "      <th>VIX Index</th>\n",
              "      <th>...</th>\n",
              "      <th>DU1 Comdty</th>\n",
              "      <th>TU2 Comdty</th>\n",
              "      <th>Pct_Change</th>\n",
              "      <th>CRASH</th>\n",
              "      <th>VIX Index_lag_1</th>\n",
              "      <th>VIX Index_lag_2</th>\n",
              "      <th>VIX Index_lag_3</th>\n",
              "      <th>MXWO Index_lag_1</th>\n",
              "      <th>MXWO Index_lag_2</th>\n",
              "      <th>MXWO Index_lag_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5/18/1999</td>\n",
              "      <td>0.011691</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.066945</td>\n",
              "      <td>0.076415</td>\n",
              "      <td>0.606112</td>\n",
              "      <td>0.805418</td>\n",
              "      <td>0.491785</td>\n",
              "      <td>0.054215</td>\n",
              "      <td>0.268201</td>\n",
              "      <td>...</td>\n",
              "      <td>0.382480</td>\n",
              "      <td>0.480813</td>\n",
              "      <td>0.504519</td>\n",
              "      <td>0</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>0.241518</td>\n",
              "      <td>0.243054</td>\n",
              "      <td>0.257561</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5/25/1999</td>\n",
              "      <td>0.010265</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>0.071405</td>\n",
              "      <td>0.619723</td>\n",
              "      <td>0.799284</td>\n",
              "      <td>0.470975</td>\n",
              "      <td>0.054444</td>\n",
              "      <td>0.292870</td>\n",
              "      <td>...</td>\n",
              "      <td>0.389419</td>\n",
              "      <td>0.492846</td>\n",
              "      <td>0.417451</td>\n",
              "      <td>0</td>\n",
              "      <td>0.268201</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.229050</td>\n",
              "      <td>0.246186</td>\n",
              "      <td>0.248559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6/1/1999</td>\n",
              "      <td>0.007813</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.059468</td>\n",
              "      <td>0.067641</td>\n",
              "      <td>0.633953</td>\n",
              "      <td>0.758392</td>\n",
              "      <td>0.481928</td>\n",
              "      <td>0.048335</td>\n",
              "      <td>0.258574</td>\n",
              "      <td>...</td>\n",
              "      <td>0.353859</td>\n",
              "      <td>0.456016</td>\n",
              "      <td>0.338040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.292870</td>\n",
              "      <td>0.268201</td>\n",
              "      <td>0.243532</td>\n",
              "      <td>0.216929</td>\n",
              "      <td>0.233477</td>\n",
              "      <td>0.251762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6/8/1999</td>\n",
              "      <td>0.004676</td>\n",
              "      <td>0.495674</td>\n",
              "      <td>0.062859</td>\n",
              "      <td>0.082588</td>\n",
              "      <td>0.627560</td>\n",
              "      <td>0.737945</td>\n",
              "      <td>0.474261</td>\n",
              "      <td>0.058415</td>\n",
              "      <td>0.219465</td>\n",
              "      <td>...</td>\n",
              "      <td>0.356461</td>\n",
              "      <td>0.451545</td>\n",
              "      <td>0.436590</td>\n",
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              "      <td>0.258574</td>\n",
              "      <td>0.292870</td>\n",
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              "      <td>0.217494</td>\n",
              "      <td>0.221122</td>\n",
              "      <td>0.238765</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6/15/1999</td>\n",
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              "      <td>0.062859</td>\n",
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              "      <td>0.346166</td>\n",
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              "      <td>0.221698</td>\n",
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              "    <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>1140</th>\n",
              "      <td>3/23/2021</td>\n",
              "      <td>0.840915</td>\n",
              "      <td>0.668193</td>\n",
              "      <td>0.172144</td>\n",
              "      <td>0.216479</td>\n",
              "      <td>0.433214</td>\n",
              "      <td>0.558528</td>\n",
              "      <td>0.223439</td>\n",
              "      <td>0.364615</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>...</td>\n",
              "      <td>0.949696</td>\n",
              "      <td>0.988293</td>\n",
              "      <td>0.398922</td>\n",
              "      <td>0</td>\n",
              "      <td>0.155836</td>\n",
              "      <td>0.219615</td>\n",
              "      <td>0.220668</td>\n",
              "      <td>0.957295</td>\n",
              "      <td>0.947298</td>\n",
              "      <td>0.971783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>3/30/2021</td>\n",
              "      <td>0.816734</td>\n",
              "      <td>0.649873</td>\n",
              "      <td>0.157538</td>\n",
              "      <td>0.217509</td>\n",
              "      <td>0.453033</td>\n",
              "      <td>0.585279</td>\n",
              "      <td>0.219058</td>\n",
              "      <td>0.385919</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>...</td>\n",
              "      <td>0.945360</td>\n",
              "      <td>0.988618</td>\n",
              "      <td>0.330390</td>\n",
              "      <td>0</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>0.155836</td>\n",
              "      <td>0.219615</td>\n",
              "      <td>0.941171</td>\n",
              "      <td>0.975798</td>\n",
              "      <td>0.968754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>4/6/2021</td>\n",
              "      <td>0.849652</td>\n",
              "      <td>0.719593</td>\n",
              "      <td>0.156581</td>\n",
              "      <td>0.220180</td>\n",
              "      <td>0.433194</td>\n",
              "      <td>0.578293</td>\n",
              "      <td>0.230011</td>\n",
              "      <td>0.376604</td>\n",
              "      <td>0.130716</td>\n",
              "      <td>...</td>\n",
              "      <td>0.947962</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.368453</td>\n",
              "      <td>0</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>0.155836</td>\n",
              "      <td>0.948894</td>\n",
              "      <td>0.959362</td>\n",
              "      <td>0.997899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>4/13/2021</td>\n",
              "      <td>0.849315</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.160755</td>\n",
              "      <td>0.226541</td>\n",
              "      <td>0.423233</td>\n",
              "      <td>0.566536</td>\n",
              "      <td>0.221249</td>\n",
              "      <td>0.383094</td>\n",
              "      <td>0.108604</td>\n",
              "      <td>...</td>\n",
              "      <td>0.947528</td>\n",
              "      <td>0.980976</td>\n",
              "      <td>0.389026</td>\n",
              "      <td>0</td>\n",
              "      <td>0.130716</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>0.163508</td>\n",
              "      <td>0.981038</td>\n",
              "      <td>0.967234</td>\n",
              "      <td>0.981091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1144</th>\n",
              "      <td>4/20/2021</td>\n",
              "      <td>0.870433</td>\n",
              "      <td>0.746056</td>\n",
              "      <td>0.186142</td>\n",
              "      <td>0.242227</td>\n",
              "      <td>0.408694</td>\n",
              "      <td>0.547793</td>\n",
              "      <td>0.244250</td>\n",
              "      <td>0.400809</td>\n",
              "      <td>0.135830</td>\n",
              "      <td>...</td>\n",
              "      <td>0.946227</td>\n",
              "      <td>0.980976</td>\n",
              "      <td>0.389026</td>\n",
              "      <td>0</td>\n",
              "      <td>0.108604</td>\n",
              "      <td>0.130716</td>\n",
              "      <td>0.153129</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.989142</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1145 rows × 65 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time to split the data"
      ],
      "metadata": {
        "id": "AulsULZqYDr_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the split ratio (80% training, 20% testing)\n",
        "split_ratio = 0.8\n",
        "\n",
        "# Determine the split index\n",
        "split_index = int(len(df) * split_ratio)\n",
        "\n",
        "# Split the data\n",
        "train_data = df.iloc[:split_index]\n",
        "test_data = df.iloc[split_index:]\n",
        "\n",
        "# Separate features and target variable\n",
        "X_train = train_data.drop(columns=['Date', 'CRASH'])\n",
        "y_train = train_data['CRASH']\n",
        "\n",
        "X_test = test_data.drop(columns=['Date', 'CRASH'])\n",
        "y_test = test_data['CRASH']\n"
      ],
      "metadata": {
        "id": "Aw-U52rEYE2D"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Training set size: {X_train.shape[0]} rows\")\n",
        "print(f\"Testing set size: {X_test.shape[0]} rows\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHb5a5TtYIat",
        "outputId": "86032801-ac1e-4b5b-99c5-43323cf8b01b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 916 rows\n",
            "Testing set size: 229 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time to train the model"
      ],
      "metadata": {
        "id": "bhzFKKUDYTOw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.drop(columns=['Pct_Change'])\n",
        "X_test = X_test.drop(columns=['Pct_Change'])\n"
      ],
      "metadata": {
        "id": "xKCOpnNva4i-"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import classification_report, roc_auc_score\n",
        "\n",
        "# Initialize the XGBoost classifier\n",
        "model = XGBClassifier(\n",
        "    n_estimators=100,  # Number of trees\n",
        "    learning_rate=0.1,  # Step size\n",
        "    max_depth=3,  # Maximum tree depth\n",
        "    random_state=42  # Reproducibility\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_proba = model.predict_proba(X_test)[:, 1]  # Confidence scores for CRASH (1)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Compute and display the ROC-AUC score\n",
        "roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
        "print(f\"ROC-AUC Score: {roc_auc:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KknayolBYUcr",
        "outputId": "e75ef889-4c41-48dc-f8ad-49410ec1e526"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99       226\n",
            "           1       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.99       229\n",
            "   macro avg       0.49      0.50      0.50       229\n",
            "weighted avg       0.97      0.99      0.98       229\n",
            "\n",
            "ROC-AUC Score: 0.99\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['CRASH'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEelHLi-Y56n",
        "outputId": "6730d466-d8df-45ea-f623-6476e5d6218f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CRASH\n",
            "0    1129\n",
            "1      16\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "crash_row = df[df['CRASH'] == 1]\n",
        "print(crash_row)\n"
      ],
      "metadata": {
        "id": "7kuBYvjqZPxe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "427651e0-0d2f-4bbc-8b12-a761581acce3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Date  XAU BGNL Curncy  ECSURPUS Index  BDIY Index  CRY Index  \\\n",
            "43     3/14/2000         0.020616        0.744020    0.117719   0.192033   \n",
            "166    7/23/2002         0.034389        0.679389    0.059207   0.173598   \n",
            "177    10/8/2002         0.037127        0.425954    0.091897   0.210412   \n",
            "199    3/11/2003         0.055348        0.435115    0.128152   0.311941   \n",
            "493   10/28/2008         0.274936        0.296183    0.060077   0.422742   \n",
            "512    3/10/2009         0.365352        0.084478    0.174491   0.280524   \n",
            "513    3/17/2009         0.377482        0.096692    0.146322   0.304938   \n",
            "519    4/28/2009         0.364993        0.396947    0.130325   0.307041   \n",
            "530    7/14/2009         0.382843        0.745547    0.243958   0.359577   \n",
            "577     6/8/2010         0.564855        0.708397    0.285863   0.397997   \n",
            "581     7/6/2010         0.536283        0.482952    0.159624   0.408293   \n",
            "646    10/4/2011         0.780195        0.366921    0.138585   0.517572   \n",
            "854    9/29/2015         0.500365        0.227990    0.055208   0.241593   \n",
            "1088   3/24/2020         0.783827        0.656997    0.027126   0.060513   \n",
            "1090    4/7/2020         0.797646        0.675827    0.026517   0.059960   \n",
            "1120   11/3/2020         0.944196        0.937913    0.084507   0.112739   \n",
            "\n",
            "      DXY Curncy  JPY Curncy  GBP Curncy  Cl1 Comdty  VIX Index  ...  \\\n",
            "43      0.698501    0.492418    0.441402    0.165547   0.225331  ...   \n",
            "166     0.728609    0.702505    0.430449    0.124465   0.533845  ...   \n",
            "177     0.757687    0.824672    0.418401    0.148671   0.475181  ...   \n",
            "199     0.548164    0.700971    0.475356    0.203955   0.363718  ...   \n",
            "493     0.323751    0.349293    0.423877    0.402566   0.865373  ...   \n",
            "512     0.362913    0.386948    0.224535    0.272602   0.525572  ...   \n",
            "513     0.321792    0.387119    0.253012    0.298946   0.471871  ...   \n",
            "519     0.285187    0.349804    0.317634    0.304750   0.429001  ...   \n",
            "530     0.182611    0.293065    0.500548    0.378054   0.234507  ...   \n",
            "577     0.351921    0.262566    0.296824    0.473274   0.365072  ...   \n",
            "581     0.263018    0.195945    0.377875    0.473198   0.304152  ...   \n",
            "646     0.170506    0.015335    0.401972    0.501374   0.472172  ...   \n",
            "854     0.505785    0.745442    0.376780    0.268937   0.261733  ...   \n",
            "1088    0.633313    0.605384    0.000000    0.106903   0.785800  ...   \n",
            "1090    0.589202    0.560572    0.067908    0.104001   0.560620  ...   \n",
            "1120    0.458312    0.486625    0.144578    0.211133   0.392900  ...   \n",
            "\n",
            "      DU1 Comdty  TU2 Comdty  Pct_Change  CRASH  VIX Index_lag_1  \\\n",
            "43      0.084996    0.052683    0.217388      1         0.223827   \n",
            "166     0.227233    0.115528    0.109774      1         0.409446   \n",
            "177     0.350390    0.115528    0.093854      1         0.371390   \n",
            "199     0.462272    0.791382    0.196235      1         0.336943   \n",
            "493     0.415438    0.702439    0.000000      1         0.657040   \n",
            "512     0.595403    0.826341    0.169509      1         0.624248   \n",
            "513     0.582827    0.822520    0.218347      1         0.525572   \n",
            "519     0.600173    0.804715    0.197168      1         0.416817   \n",
            "530     0.607112    0.791382    0.205193      1         0.322202   \n",
            "577     0.773634    0.883496    0.212781      1         0.392750   \n",
            "581     0.714657    0.889837    0.230977      1         0.371540   \n",
            "646     0.751518    0.965447    0.152419      1         0.425391   \n",
            "854     0.881180    0.920325    0.218425      1         0.195698   \n",
            "1088    0.939722    0.965691    0.202775      1         1.000000   \n",
            "1090    0.940590    0.971463    0.216532      1         0.663508   \n",
            "1120    0.977450    0.988293    0.219177      1         0.359807   \n",
            "\n",
            "      VIX Index_lag_2  VIX Index_lag_3  MXWO Index_lag_1  MXWO Index_lag_2  \\\n",
            "43           0.209687         0.247142          0.294097          0.292614   \n",
            "166          0.312726         0.293773          0.057532          0.084901   \n",
            "177          0.467659         0.429904          0.022722          0.013580   \n",
            "199          0.335590         0.326113          0.016806          0.019513   \n",
            "493          0.687425         0.665614          0.120555          0.146134   \n",
            "512          0.542419         0.590102          0.000000          0.028763   \n",
            "513          0.624248         0.542419          0.008997          0.000000   \n",
            "519          0.424789         0.465704          0.068252          0.072095   \n",
            "530          0.254513         0.318141          0.101476          0.119168   \n",
            "577          0.378761         0.362816          0.164580          0.158083   \n",
            "581          0.265042         0.247292          0.154887          0.184193   \n",
            "646          0.352437         0.413357          0.196494          0.210325   \n",
            "854          0.197202         0.232702          0.404643          0.427780   \n",
            "1088         0.569645         0.412004          0.482452          0.620306   \n",
            "1090         0.785800         1.000000          0.518510          0.477806   \n",
            "1120         0.299639         0.250301          0.757533          0.791089   \n",
            "\n",
            "      MXWO Index_lag_3  \n",
            "43            0.294446  \n",
            "166           0.081129  \n",
            "177           0.038119  \n",
            "199           0.029551  \n",
            "493           0.157514  \n",
            "512           0.043019  \n",
            "513           0.029414  \n",
            "519           0.060034  \n",
            "530           0.109742  \n",
            "577           0.191521  \n",
            "581           0.189839  \n",
            "646           0.202233  \n",
            "854           0.434210  \n",
            "1088          0.694427  \n",
            "1090          0.502916  \n",
            "1120          0.828542  \n",
            "\n",
            "[16 rows x 65 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import plot_importance\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plot_importance(model)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "OIgHztjaaX8V",
        "outputId": "725a4d0d-c90b-4598-c035-4fc851b54aaf"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We split the data by time because we don't want to predict the future based on future date\n",
        "We created lagged features\n",
        "We removed or filled in data that was NaN\n",
        "We normalized the data\n",
        "We trained the XGBoost model on the data, but had to remove the Pct_Change column\n",
        "Trained again, but now the model is only predicting NO CRASH everytime (and getting accurate resultsl) because there are so few CRASH events\n",
        "- need to use SMOTE to address how CRASH is underrepresented and NO CRASH is overrepresented"
      ],
      "metadata": {
        "id": "XWKbwRxCgM3m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smote = SMOTE(random_state=42)\n",
        "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "gaO0FW8_0zfT"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "rus = RandomUnderSampler(random_state=42)\n",
        "X_train_resampled, y_train_resampled = rus.fit_resample(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "anbd1pEb0_8k"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Train the model with resampled data\n",
        "model = XGBClassifier(random_state=42, eval_metric='aucpr')\n",
        "model.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Evaluate the model\n",
        "from sklearn.metrics import classification_report, roc_auc_score\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(\"ROC-AUC Score:\", roc_auc_score(y_test, y_pred_proba))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ohgDbBxt1zqp",
        "outputId": "91520138-6d48-4da2-d70d-dfbabc494385"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.84      0.91       226\n",
            "           1       0.08      1.00      0.14         3\n",
            "\n",
            "    accuracy                           0.84       229\n",
            "   macro avg       0.54      0.92      0.53       229\n",
            "weighted avg       0.99      0.84      0.90       229\n",
            "\n",
            "ROC-AUC Score: 0.9004424778761062\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "These results tell us that our model doesn't miss any CRASH events (perfect recall score) at the expense of many false positives (low precision)"
      ],
      "metadata": {
        "id": "MjQ6o9o_2PNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import plot_importance\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plot_importance(model)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yQ-drW0H2avC",
        "outputId": "6e732978-5c06-4530-bc22-d9d9f2074d6d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "# Generate confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Display confusion matrix\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
        "disp.plot(cmap='Blues')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "sNE-K-Z12s39",
        "outputId": "4f2096c8-ae3d-488e-e969-a3a56cb19027"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7ce47272a740>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All done, we have our model that predicts the crashes all the time at the expense of relatively high false positive rate (36) in the test data."
      ],
      "metadata": {
        "id": "MDkxqHt83HBU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "# Save the model to a file\n",
        "with open('xgb_model.pkl', 'wb') as file:\n",
        "    pickle.dump(model, file)\n",
        "\n",
        "print(\"Model saved successfully!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNmYKBlg3mRt",
        "outputId": "01c7f79a-9df5-4531-eccb-4c8df68894a6"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved successfully!\n"
          ]
        }
      ]
    }
  ]
}