Timeseries_Forecasting.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mahdi-rostami/Timeseries-Forecasting/blob/main/Timeseries_Forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vNtv_VRTkE6T"
      },
      "outputs": [],
      "source": [
        "#import libraries\n",
        "from __future__ import (absolute_import, division, print_function,\n",
        "                        unicode_literals)\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import cycler\n",
        "import seaborn as sb\n",
        "import numpy as np\n",
        "import gc\n",
        "from operator import itemgetter\n",
        "import warnings\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.utils import shuffle\n",
        "from operator import itemgetter\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import backend as K\n",
        "from keras.models import load_model, Model, Sequential\n",
        "from keras.layers import Flatten, Dense, Dropout, Activation, Input, LSTM, Reshape, Conv2D, MaxPooling2D, GRU, LeakyReLU\n",
        "from keras.optimizers import Adam\n",
        "from keras.utils import np_utils\n",
        "\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set random seeds\n",
        "np.random.seed(1)\n",
        "tf.random.set_seed(2)"
      ],
      "metadata": {
        "id": "GIWwvcFBb6ON"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "if gpus:\n",
        "    try:\n",
        "    # Currently, memory growth needs to be the same across GPUs\n",
        "        for gpu in gpus:\n",
        "            tf.config.experimental.set_memory_growth(gpu, True)\n",
        "            logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
        "        print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
        "    except RuntimeError as e:\n",
        "    # Memory growth must be set before GPUs have been initialized\n",
        "        print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_16W9UObZl_",
        "outputId": "a0436246-45d4-406c-c68e-4b8a5b728da8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 Physical GPUs, 1 Logical GPUs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnLlvDiripMt",
        "outputId": "cd07d5a8-0aa5-489e-ad59-c9afd923b468"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "csv file already existed.\n"
          ]
        }
      ],
      "source": [
        "#download data\n",
        "import os \n",
        "if not os.path.isfile('LOB_NQU22-CME.csv'):\n",
        "    !wget https://raw.githubusercontent.com/0xtweeter/Timeseries-Forecasting/main/data/LOB_NQU22-CME.rar\n",
        "    !pip install unrar\n",
        "    !unrar x \"/content/LOB_NQU22-CME.rar\"\n",
        "    print('data downloaded and exteracted')\n",
        "else :\n",
        "    print('csv file already existed.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZQwBcsBwcQPV"
      },
      "outputs": [],
      "source": [
        "#plt style\n",
        "# colors = cycler('color',\n",
        "#                 ['#669FEE', '#66EE91', '#9988DD',\n",
        "#                  '#EECC55', '#88BB44', '#FFBBBB'])\n",
        "# plt.rc('figure', facecolor='#313233')\n",
        "# plt.rc('axes', facecolor=\"#313233\", edgecolor='none',\n",
        "#        axisbelow=True, grid=True, prop_cycle=colors,\n",
        "#        labelcolor='gray')\n",
        "# plt.rc('grid', color='474A4A', linestyle='solid')\n",
        "# plt.rc('xtick', color='gray')\n",
        "# plt.rc('ytick', direction='out', color='gray')\n",
        "# plt.rc('legend', facecolor=\"#313233\", edgecolor=\"#313233\")\n",
        "# plt.rc(\"text\", color=\"#C9C9C9\")\n",
        "plt.style.use('ggplot')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykMB8lUfgS8Y"
      },
      "source": [
        "# EDA\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2COgAfavkJUc"
      },
      "outputs": [],
      "source": [
        "#reading dataset\n",
        "pd.set_option('display.max_columns',None)  #to show all columns\n",
        "colnames=[\n",
        "    'time', 'open', 'high', 'low', 'close', 'volume',\n",
        "     'numtrades', 'bidvolume', 'askvolume', 'lob_bid', 'lob_ask'\n",
        "     ] #columns name\n",
        "Data = pd.read_csv(\"/content/LOB_NQU22-CME.csv\", names=colnames, header=None, index_col='time' )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = Data.copy()"
      ],
      "metadata": {
        "id": "nJbhktJTVd1z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
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        "outputId": "c5db3682-34dd-4863-b51d-139b6fa7162b"
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              "(72907, 10)"
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        "df.shape"
      ]
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              "                         open      high       low     close  volume  \\\n",
              "time                                                                  \n",
              "2022-03-18  00:00:00  14066.5  14069.75  14061.75  14066.00   145.0   \n",
              "2022-03-18  00:01:00  14064.5  14067.75  14063.00  14066.25    66.0   \n",
              "2022-03-18  00:02:00  14067.0  14068.00  14064.75  14066.25    68.0   \n",
              "\n",
              "                      numtrades  bidvolume  askvolume  \\\n",
              "time                                                    \n",
              "2022-03-18  00:00:00      144.0       60.0       85.0   \n",
              "2022-03-18  00:01:00       64.0       36.0       30.0   \n",
              "2022-03-18  00:02:00       64.0       36.0       32.0   \n",
              "\n",
              "                                                                lob_bid  \\\n",
              "time                                                                      \n",
              "2022-03-18  00:00:00  200.000000|1|578.750000|1|586.500000|1|6490.00...   \n",
              "2022-03-18  00:01:00  200.000000|1|578.750000|1|586.500000|1|6490.00...   \n",
              "2022-03-18  00:02:00  200.000000|1|578.750000|1|586.500000|1|6490.00...   \n",
              "\n",
              "                                                                lob_ask  \n",
              "time                                                                     \n",
              "2022-03-18  00:00:00  14019.500000|1|14019.750000|2|14020.000000|1|1...  \n",
              "2022-03-18  00:01:00  14020.750000|1|14021.000000|1|14021.250000|1|1...  \n",
              "2022-03-18  00:02:00  14022.500000|3|14022.750000|1|14023.000000|1|1...  "
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        "df.head(3)"
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        "id": "2ktWV1m167Pf",
        "outputId": "f4b24f20-b2fc-4438-a975-93ffeb08d4b1"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 72907 entries, 2022-03-18  00:00:00 to 2022-06-29  16:15:00\n",
            "Data columns (total 10 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   open       72907 non-null  float64\n",
            " 1   high       72907 non-null  float64\n",
            " 2   low        72907 non-null  float64\n",
            " 3   close      72907 non-null  float64\n",
            " 4   volume     72907 non-null  float64\n",
            " 5   numtrades  72907 non-null  float64\n",
            " 6   bidvolume  72907 non-null  float64\n",
            " 7   askvolume  72907 non-null  float64\n",
            " 8   lob_bid    72907 non-null  object \n",
            " 9   lob_ask    72907 non-null  object \n",
            "dtypes: float64(8), object(2)\n",
            "memory usage: 6.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
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              "               open          high           low         close        volume  \\\n",
              "count  72907.000000  72907.000000  72907.000000  72907.000000  72907.000000   \n",
              "mean   13339.652328  13343.960775  13335.343534  13339.642065    453.720521   \n",
              "std     1264.345092   1263.763152   1264.884332   1264.338144    702.840828   \n",
              "min    11071.000000  11076.750000  11068.500000  11071.250000      1.000000   \n",
              "25%    12123.000000  12127.250000  12118.000000  12122.875000     60.000000   \n",
              "50%    13458.250000  13464.250000  13453.500000  13458.500000    140.000000   \n",
              "75%    14458.250000  14462.500000  14453.750000  14458.250000    600.000000   \n",
              "max    15310.750000  15311.500000  15307.000000  15310.750000  14469.000000   \n",
              "\n",
              "          numtrades     bidvolume     askvolume  \n",
              "count  72907.000000  72907.000000  72907.000000  \n",
              "mean     413.374491    227.202546    226.517975  \n",
              "std      633.438076    354.099034    354.567693  \n",
              "min        1.000000      0.000000      0.000000  \n",
              "25%       55.000000     29.000000     29.000000  \n",
              "50%      128.000000     70.000000     70.000000  \n",
              "75%      549.000000    299.000000    298.000000  \n",
              "max    11819.000000   7066.000000   7403.000000  "
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              "      <td>72907.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13339.652328</td>\n",
              "      <td>13343.960775</td>\n",
              "      <td>13335.343534</td>\n",
              "      <td>13339.642065</td>\n",
              "      <td>453.720521</td>\n",
              "      <td>413.374491</td>\n",
              "      <td>227.202546</td>\n",
              "      <td>226.517975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1264.345092</td>\n",
              "      <td>1263.763152</td>\n",
              "      <td>1264.884332</td>\n",
              "      <td>1264.338144</td>\n",
              "      <td>702.840828</td>\n",
              "      <td>633.438076</td>\n",
              "      <td>354.099034</td>\n",
              "      <td>354.567693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11071.000000</td>\n",
              "      <td>11076.750000</td>\n",
              "      <td>11068.500000</td>\n",
              "      <td>11071.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12123.000000</td>\n",
              "      <td>12127.250000</td>\n",
              "      <td>12118.000000</td>\n",
              "      <td>12122.875000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13458.250000</td>\n",
              "      <td>13464.250000</td>\n",
              "      <td>13453.500000</td>\n",
              "      <td>13458.500000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>14458.250000</td>\n",
              "      <td>14462.500000</td>\n",
              "      <td>14453.750000</td>\n",
              "      <td>14458.250000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>298.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15310.750000</td>\n",
              "      <td>15311.500000</td>\n",
              "      <td>15307.000000</td>\n",
              "      <td>15310.750000</td>\n",
              "      <td>14469.000000</td>\n",
              "      <td>11819.000000</td>\n",
              "      <td>7066.000000</td>\n",
              "      <td>7403.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32d7b5c4-b088-4208-86d8-733441d3cf1c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-32d7b5c4-b088-4208-86d8-733441d3cf1c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-32d7b5c4-b088-4208-86d8-733441d3cf1c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhOljGL_vClR"
      },
      "source": [
        "## null items\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22lzGASeeMT2",
        "outputId": "8b431ac0-b186-46e8-dd75-c8e89edb9b15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "open         0\n",
              "high         0\n",
              "low          0\n",
              "close        0\n",
              "volume       0\n",
              "numtrades    0\n",
              "bidvolume    0\n",
              "askvolume    0\n",
              "lob_bid      0\n",
              "lob_ask      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mikaunIlu8Ap"
      },
      "source": [
        "## Outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfxnVkvotPsM",
        "outputId": "37a3c626-7e86-4c32-abb9-aece4772bcd7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "open         -1.327697\n",
              "high         -1.327039\n",
              "low          -1.328361\n",
              "close        -1.327696\n",
              "volume       24.174307\n",
              "numtrades    18.861598\n",
              "bidvolume    23.645504\n",
              "askvolume    25.204776\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Return unbiased kurtosis over requested axis using Fisher's definition of kurtosis (kurtosis of normal == 0.0).\n",
        "# The result is normalized by N-1\n",
        "kurt = df.kurt(numeric_only=True)[:]\n",
        "kurt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kypeqr1xtsbw",
        "outputId": "8e554d17-f49a-456b-a1e9-6606faf1cf34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "outliers = [\"volume\", \"numtrades\", \"bidvolume\", \"askvolume\"]\n",
        "for col in outliers:\n",
        "    plt.figure(figsize=(4, 4))\n",
        "    df.boxplot(column=[col])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3AYCnLuTuAZR"
      },
      "outputs": [],
      "source": [
        "def outliers_iqr(ys):\n",
        "    quartile_1, quartile_3 = np.percentile(ys, [25, 75])\n",
        "    iqr = quartile_3 - quartile_1\n",
        "    lower_bound = quartile_1 - (iqr * 1.5)\n",
        "    upper_bound = quartile_3 + (iqr * 1.5)\n",
        "    return np.where((ys > upper_bound) | (ys < lower_bound))\n",
        "\n",
        "def outliers_z_score(ys):\n",
        "    threshold = 3\n",
        "    mean_y = np.mean(ys)\n",
        "    stdev_y = np.std(ys)\n",
        "    z_scores = [(y - mean_y) / stdev_y for y in ys]\n",
        "    return np.where(np.abs(z_scores) > threshold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88dTR0gNuFXa",
        "outputId": "e42744fe-25e2-4ec3-8661-39fdf3e8bc88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "volume: 6380\n",
            "numtrades: 6335\n",
            "bidvolume: 6380\n",
            "askvolume: 6403\n"
          ]
        }
      ],
      "source": [
        "for col in outliers:\n",
        "    print(f'{col}: {len(outliers_iqr(df[col])[0])}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiC9mhyluGtn",
        "outputId": "a1d8dd1e-4cb1-4b93-9b9d-56dc9f563d17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "volume: 1487\n",
            "numtrades: 1530\n",
            "bidvolume: 1495\n",
            "askvolume: 1493\n"
          ]
        }
      ],
      "source": [
        "for col in outliers:\n",
        "    print(f'{col}: {len(outliers_z_score(df[col])[0])}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gJ8V5BIMuXHa"
      },
      "outputs": [],
      "source": [
        "for i in outliers:\n",
        "    mean_y = np.mean(df[i])\n",
        "    stdev_y = np.std(df[i])\n",
        "    lower_bound = mean_y - (3 * stdev_y)\n",
        "    upper_bound = mean_y + (3 * stdev_y)\n",
        "    df = df[df[i]<upper_bound]\n",
        "    df = df[df[i]>lower_bound]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vw-hajRCuhlJ",
        "outputId": "04c2e080-7e27-45a5-9a64-02d4b102c3b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67121, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcjTEERPgCcI"
      },
      "source": [
        "## visualizing stock prices history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "SY_2xn1kafuM",
        "outputId": "f6321d54-37b5-4f43-fd0a-7832d139a952"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'the total volume of stock being traded for E-mini Nasdaq-100')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(18,5))\n",
        "df['close'].plot()\n",
        "plt.ylabel('Volume')\n",
        "plt.xlabel(None)\n",
        "plt.title(f\"the total volume of stock being traded for E-mini Nasdaq-100\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = [\"open\", \"high\",\t\"low\", \"close\",\t\"volume\",\t\"numtrades\",\t\"bidvolume\",\t\"askvolume\"]\n",
        " \n",
        "plt.subplots(figsize=(20,10))\n",
        " \n",
        "for i, col in enumerate(features):\n",
        "  plt.subplot(2,4,i+1)\n",
        "  sb.distplot(df[col])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "9n2cG-LVuj0G",
        "outputId": "087b3fa8-90e7-4f1c-a45c-2237748ba4fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sMXmaHtgdmt"
      },
      "source": [
        "### The total volume of stock"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "FtskSXs1ghFe",
        "outputId": "f809c65d-b8dd-47c9-89a4-3d6ebfd3e555"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'the total volume of stock being traded each day for E-mini Nasdaq-100')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(18,5))\n",
        "df['volume'].plot()\n",
        "plt.ylabel('Volume')\n",
        "plt.xlabel(None)\n",
        "plt.title(f\"the total volume of stock being traded each day for E-mini Nasdaq-100\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdUHw77pgyed"
      },
      "source": [
        "### Moving averages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "WR6yJwgGmlaJ",
        "outputId": "72a87107-f94f-486b-c151-9f384ea64dfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    open          high           low         close  \\\n",
              "time                                                                 \n",
              "2022-03-18  14141.586726  14144.865140  14138.221374  14141.714801   \n",
              "2022-03-20  14453.708333  14456.345833  14451.579167  14453.681250   \n",
              "2022-03-21  14396.346698  14399.521030  14392.863404  14396.116352   \n",
              "\n",
              "                volume   numtrades   bidvolume   askvolume  \n",
              "time                                                        \n",
              "2022-03-18  304.256997  275.206107  151.964377  152.292621  \n",
              "2022-03-20   72.683333   65.491667   35.941667   36.741667  \n",
              "2022-03-21  282.197327  259.839623  142.043239  140.154088  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-836951a2-c7d1-49d9-8683-8ad129d604a4\">\n",
              "    <div 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>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>numtrades</th>\n",
              "      <th>bidvolume</th>\n",
              "      <th>askvolume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-03-18</th>\n",
              "      <td>14141.586726</td>\n",
              "      <td>14144.865140</td>\n",
              "      <td>14138.221374</td>\n",
              "      <td>14141.714801</td>\n",
              "      <td>304.256997</td>\n",
              "      <td>275.206107</td>\n",
              "      <td>151.964377</td>\n",
              "      <td>152.292621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-20</th>\n",
              "      <td>14453.708333</td>\n",
              "      <td>14456.345833</td>\n",
              "      <td>14451.579167</td>\n",
              "      <td>14453.681250</td>\n",
              "      <td>72.683333</td>\n",
              "      <td>65.491667</td>\n",
              "      <td>35.941667</td>\n",
              "      <td>36.741667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-21</th>\n",
              "      <td>14396.346698</td>\n",
              "      <td>14399.521030</td>\n",
              "      <td>14392.863404</td>\n",
              "      <td>14396.116352</td>\n",
              "      <td>282.197327</td>\n",
              "      <td>259.839623</td>\n",
              "      <td>142.043239</td>\n",
              "      <td>140.154088</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-836951a2-c7d1-49d9-8683-8ad129d604a4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-836951a2-c7d1-49d9-8683-8ad129d604a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-836951a2-c7d1-49d9-8683-8ad129d604a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "#dataframe daily\n",
        "df_daily = df.copy()\n",
        "df_daily.index = pd.to_datetime(df_daily.index)\n",
        "df_daily = df_daily.resample('D').mean()\n",
        "df_daily.dropna(axis = 0, inplace=True)\n",
        "df_daily.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fELVyi9Cg1hd"
      },
      "outputs": [],
      "source": [
        "df_daily['ma for 5 days'] = df_daily['close'].rolling(5).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "UfNCML9BhTC1",
        "outputId": "a83c2fe8-5b93-4d24-ed9d-24827439efed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0d67ef650>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(18,5))\n",
        "\n",
        "df_daily[['close', 'ma for 5 days']].plot()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del df_daily\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emIyt9SCN6dR",
        "outputId": "62067e36-0971-4050-8326-e679b246cff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41688"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFW8FmDagf7X"
      },
      "source": [
        "# DATA LABELING"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## close price labeling"
      ],
      "metadata": {
        "id": "wewDLBTplZMa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vZK3Yc-f0b1H"
      },
      "outputs": [],
      "source": [
        "alpha_up = 0.5\n",
        "alpha_down = -0.5\n",
        "def filter(x):\n",
        "    if x > alpha_up:\n",
        "      x = +1\n",
        "      return x\n",
        "    elif x < alpha_down:\n",
        "      x=-1\n",
        "      return x\n",
        "    else:\n",
        "        return 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DGiEFMLWvobn"
      },
      "outputs": [],
      "source": [
        "df['close_change'] = df['close'].diff()\n",
        "df['close_lable'] = df[\"close_change\"].apply(filter)\n",
        "df.fillna(0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "i7nsZx7S0guI",
        "outputId": "790d9972-8338-4b98-a8c4-fd84331415a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         open      high       low     close  volume  \\\n",
              "time                                                                  \n",
              "2022-03-18  00:00:00  14066.5  14069.75  14061.75  14066.00   145.0   \n",
              "2022-03-18  00:01:00  14064.5  14067.75  14063.00  14066.25    66.0   \n",
              "2022-03-18  00:02:00  14067.0  14068.00  14064.75  14066.25    68.0   \n",
              "\n",
              "                      numtrades  bidvolume  askvolume  \\\n",
              "time                                                    \n",
              "2022-03-18  00:00:00      144.0       60.0       85.0   \n",
              "2022-03-18  00:01:00       64.0       36.0       30.0   \n",
              "2022-03-18  00:02:00       64.0       36.0       32.0   \n",
              "\n",
              "                                                                lob_bid  \\\n",
              "time                                                                      \n",
              "2022-03-18  00:00:00  200.000000|1|578.750000|1|586.500000|1|6490.00...   \n",
              "2022-03-18  00:01:00  200.000000|1|578.750000|1|586.500000|1|6490.00...   \n",
              "2022-03-18  00:02:00  200.000000|1|578.750000|1|586.500000|1|6490.00...   \n",
              "\n",
              "                                                                lob_ask  \\\n",
              "time                                                                      \n",
              "2022-03-18  00:00:00  14019.500000|1|14019.750000|2|14020.000000|1|1...   \n",
              "2022-03-18  00:01:00  14020.750000|1|14021.000000|1|14021.250000|1|1...   \n",
              "2022-03-18  00:02:00  14022.500000|3|14022.750000|1|14023.000000|1|1...   \n",
              "\n",
              "                      close_change  close_lable  \n",
              "time                                             \n",
              "2022-03-18  00:00:00          0.00            0  \n",
              "2022-03-18  00:01:00          0.25            0  \n",
              "2022-03-18  00:02:00          0.00            0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3aabd9c3-24a5-4ccc-82da-c98a822ca22b\">\n",
              "    <div 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>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>numtrades</th>\n",
              "      <th>bidvolume</th>\n",
              "      <th>askvolume</th>\n",
              "      <th>lob_bid</th>\n",
              "      <th>lob_ask</th>\n",
              "      <th>close_change</th>\n",
              "      <th>close_lable</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-03-18  00:00:00</th>\n",
              "      <td>14066.5</td>\n",
              "      <td>14069.75</td>\n",
              "      <td>14061.75</td>\n",
              "      <td>14066.00</td>\n",
              "      <td>145.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>200.000000|1|578.750000|1|586.500000|1|6490.00...</td>\n",
              "      <td>14019.500000|1|14019.750000|2|14020.000000|1|1...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-18  00:01:00</th>\n",
              "      <td>14064.5</td>\n",
              "      <td>14067.75</td>\n",
              "      <td>14063.00</td>\n",
              "      <td>14066.25</td>\n",
              "      <td>66.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>200.000000|1|578.750000|1|586.500000|1|6490.00...</td>\n",
              "      <td>14020.750000|1|14021.000000|1|14021.250000|1|1...</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-18  00:02:00</th>\n",
              "      <td>14067.0</td>\n",
              "      <td>14068.00</td>\n",
              "      <td>14064.75</td>\n",
              "      <td>14066.25</td>\n",
              "      <td>68.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>200.000000|1|578.750000|1|586.500000|1|6490.00...</td>\n",
              "      <td>14022.500000|3|14022.750000|1|14023.000000|1|1...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3aabd9c3-24a5-4ccc-82da-c98a822ca22b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3aabd9c3-24a5-4ccc-82da-c98a822ca22b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3aabd9c3-24a5-4ccc-82da-c98a822ca22b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(df['close_lable'].value_counts().values,\n",
        "        labels=[-1, 0, 1], autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "e1vKuqK3v5Ct",
        "outputId": "dabc3572-3640-4de2-bf41-7a6373fde9a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ask-bid orders labeling [DEEPLOB](https://ieeexplore.ieee.org/document/8673598/references#references)"
      ],
      "metadata": {
        "id": "IWEJ6XuGleNm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ask column"
      ],
      "metadata": {
        "id": "XrwdxNtSd9jZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['lob_ask'] = df[\"lob_ask\"].str.split('|')"
      ],
      "metadata": {
        "id": "m9Y4Ocj9RWHy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for row in df.index:\n",
        "  close = df.loc[row, \"close\"]\n",
        "  lenght = len(df.loc[row, \"lob_ask\"])\n",
        "  for index in range(lenght):\n",
        "    number = float( df.loc[row, \"lob_ask\"][index])\n",
        "    if number > close:\n",
        "      df.loc[row, \"lob_ask\"] = str(df.loc[row, \"lob_ask\"][index:index+20])\n",
        "      break"
      ],
      "metadata": {
        "id": "QE9YoQQQRqJt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"lob_ask\"] = df[\"lob_ask\"].str.replace('[',\"\")\n",
        "df[\"lob_ask\"] = df[\"lob_ask\"].str.replace(']',\"\")\n",
        "\n",
        "df_ask = df[\"lob_ask\"].str.split(',', expand=True)\n",
        "df.drop(columns=[\"lob_ask\"], inplace=True)"
      ],
      "metadata": {
        "id": "Qd6VDUFnXAOk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(20):\n",
        "  df_ask[i] = df_ask[i].str.replace(\"'\",\"\")\n",
        "  df_ask[i] = df_ask[i].astype(float)"
      ],
      "metadata": {
        "id": "jS_MSZYsbG-d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ask_cols = [f'ask{i}' for i in range(20, 40)]\n",
        "df_ask.columns = ask_cols"
      ],
      "metadata": {
        "id": "LPR-3KmobV8F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ask.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "UWv_pKdKaP8x",
        "outputId": "2b01e6e8-e551-4ee3-bd47-0f93c288daec"
      },
      "execution_count": null,
      "outputs": [
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              "                        ask20  ask21    ask22  ask23    ask24  ask25    ask26  \\\n",
              "time                                                                            \n",
              "2022-03-18  00:00:00  14066.5    2.0  14067.0    2.0  14067.5    2.0  14068.0   \n",
              "2022-03-18  00:01:00  14066.5    2.0  14067.0    2.0  14067.5    2.0  14068.0   \n",
              "2022-03-18  00:02:00  14066.5    2.0  14067.0    2.0  14067.5    2.0  14068.0   \n",
              "\n",
              "                      ask27     ask28  ask29    ask30  ask31    ask32  ask33  \\\n",
              "time                                                                           \n",
              "2022-03-18  00:00:00    3.0  14068.25    1.0  14068.5    2.0  14069.0    4.0   \n",
              "2022-03-18  00:01:00    3.0  14068.25    1.0  14068.5    2.0  14069.0    4.0   \n",
              "2022-03-18  00:02:00    3.0  14068.25    1.0  14068.5    2.0  14069.0    4.0   \n",
              "\n",
              "                        ask34  ask35    ask36  ask37     ask38  ask39  \n",
              "time                                                                   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### bid cloumn"
      ],
      "metadata": {
        "id": "CQLPrQCweAJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['lob_bid'] = df[\"lob_bid\"].str.split('|')"
      ],
      "metadata": {
        "id": "vlLyY0GFbgg_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keys = [i for i in range(20)]\n",
        "values = [itemgetter(-i) for i in range(2, 22)] \n",
        " \n",
        "myDict = { k:v for (k,v) in zip(keys, values)} \n",
        "df_bid = df['lob_bid'].transform(myDict)\n",
        "df.drop(columns=[\"lob_bid\"], inplace=True)"
      ],
      "metadata": {
        "id": "OkU6WkTpyV3t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(20):\n",
        "  df_bid[i] = df_bid[i].astype(float)"
      ],
      "metadata": {
        "id": "isTADQ7wTJWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bid_cols = [f'bid{i}' for i in range(19,-1,-1)]\n",
        "df_bid.columns = bid_cols"
      ],
      "metadata": {
        "id": "BdsbwiRTaHRg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_bid = df_bid[[f'bid{i}' for i in range(20)]]"
      ],
      "metadata": {
        "id": "ePPH3bdpfWmQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_bid.head(3)"
      ],
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        "id": "17dw543nUVTr",
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        },
        "outputId": "2465aa6e-15bf-43aa-e97e-0fc7bd65d2c9"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "                          bid0  bid1      bid2  bid3      bid4  bid5  \\\n",
              "time                                                                   \n",
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              "2022-03-18  00:01:00  14022.25   2.0  14022.50   2.0  14022.75   1.0   \n",
              "2022-03-18  00:02:00  14023.00   2.0  14023.25   1.0  14023.50   1.0   \n",
              "\n",
              "                          bid6  bid7      bid8  bid9     bid10  bid11  \\\n",
              "time                                                                    \n",
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              "\n",
              "                         bid12  bid13     bid14  bid15     bid16  bid17  \\\n",
              "time                                                                      \n",
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              "\n",
              "                         bid18  bid19  \n",
              "time                                   \n",
              "2022-03-18  00:00:00  14026.50    1.0  \n",
              "2022-03-18  00:01:00  14024.50    2.0  \n",
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              "      <td>14023.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14023.25</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14023.50</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14023.75</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14024.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14024.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14024.50</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-18  00:02:00</th>\n",
              "      <td>14023.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14023.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14023.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14023.75</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14024.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14024.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14024.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14024.75</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14025.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14025.25</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-107a3f51-6d5c-402b-a834-8f661f994d57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-107a3f51-6d5c-402b-a834-8f661f994d57 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-107a3f51-6d5c-402b-a834-8f661f994d57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### bid & ask"
      ],
      "metadata": {
        "id": "R7_OO5oDhzVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_bidask =  pd.concat([df_bid, df_ask], axis=1)\n",
        "df_bidask.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "XPN7_Is6fqzE",
        "outputId": "f5497440-564c-4cd8-82f9-1a995c88ab85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          bid0  bid1      bid2  bid3      bid4  bid5  \\\n",
              "time                                                                   \n",
              "2022-03-18  00:00:00  14024.25   2.0  14024.50   1.0  14024.75   1.0   \n",
              "2022-03-18  00:01:00  14022.25   2.0  14022.50   2.0  14022.75   1.0   \n",
              "2022-03-18  00:02:00  14023.00   2.0  14023.25   1.0  14023.50   1.0   \n",
              "\n",
              "                          bid6  bid7      bid8  bid9     bid10  bid11  \\\n",
              "time                                                                    \n",
              "2022-03-18  00:00:00  14025.00   1.0  14025.25   2.0  14025.50    1.0   \n",
              "2022-03-18  00:01:00  14023.00   1.0  14023.25   3.0  14023.50    2.0   \n",
              "2022-03-18  00:02:00  14023.75   2.0  14024.00   1.0  14024.25    1.0   \n",
              "\n",
              "                         bid12  bid13     bid14  bid15     bid16  bid17  \\\n",
              "time                                                                      \n",
              "2022-03-18  00:00:00  14025.75    1.0  14026.00    1.0  14026.25    2.0   \n",
              "2022-03-18  00:01:00  14023.75    1.0  14024.00    1.0  14024.25    1.0   \n",
              "2022-03-18  00:02:00  14024.50    1.0  14024.75    1.0  14025.00    2.0   \n",
              "\n",
              "                         bid18  bid19    ask20  ask21    ask22  ask23  \\\n",
              "time                                                                    \n",
              "2022-03-18  00:00:00  14026.50    1.0  14066.5    2.0  14067.0    2.0   \n",
              "2022-03-18  00:01:00  14024.50    2.0  14066.5    2.0  14067.0    2.0   \n",
              "2022-03-18  00:02:00  14025.25    1.0  14066.5    2.0  14067.0    2.0   \n",
              "\n",
              "                        ask24  ask25    ask26  ask27     ask28  ask29  \\\n",
              "time                                                                    \n",
              "2022-03-18  00:00:00  14067.5    2.0  14068.0    3.0  14068.25    1.0   \n",
              "2022-03-18  00:01:00  14067.5    2.0  14068.0    3.0  14068.25    1.0   \n",
              "2022-03-18  00:02:00  14067.5    2.0  14068.0    3.0  14068.25    1.0   \n",
              "\n",
              "                        ask30  ask31    ask32  ask33    ask34  ask35    ask36  \\\n",
              "time                                                                            \n",
              "2022-03-18  00:00:00  14068.5    2.0  14069.0    4.0  14069.5    2.0  14070.0   \n",
              "2022-03-18  00:01:00  14068.5    2.0  14069.0    4.0  14069.5    2.0  14070.0   \n",
              "2022-03-18  00:02:00  14068.5    2.0  14069.0    4.0  14069.5    2.0  14070.0   \n",
              "\n",
              "                      ask37     ask38  ask39  \n",
              "time                                          \n",
              "2022-03-18  00:00:00    8.0  14070.25    2.0  \n",
              "2022-03-18  00:01:00    8.0  14070.25    2.0  \n",
              "2022-03-18  00:02:00    8.0  14070.25    2.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-66d212df-299f-4ab8-8d73-84dd4474621c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bid0</th>\n",
              "      <th>bid1</th>\n",
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              "      <th>ask38</th>\n",
              "      <th>ask39</th>\n",
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              "    <tr>\n",
              "      <th>time</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2022-03-18  00:00:00</th>\n",
              "      <td>14024.25</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>14024.75</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>14025.25</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14025.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14025.75</td>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-18  00:01:00</th>\n",
              "      <td>14022.25</td>\n",
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              "  </tbody>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-66d212df-299f-4ab8-8d73-84dd4474621c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-66d212df-299f-4ab8-8d73-84dd4474621c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  \n",
        "\n",
        "bidask = scaler.fit_transform(df_bidask)\n",
        "df_bidask = pd.DataFrame(bidask, index=df_bidask.index, columns=df_bidask.columns)\n",
        "# df_bidask=pd.DataFrame(df_bidask, columns=['bid0', 'bid1', 'bid2',\n",
        "#        'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
        "#        'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
        "#        'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
        "#        'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
        "#        'ask35', 'ask36', 'ask37', 'ask38', 'ask39'])"
      ],
      "metadata": {
        "id": "XJmyUW4KjD1_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alpha_up = 0.0005\n",
        "alpha_down = -0.0005\n",
        "def filter(x):\n",
        "    if x > alpha_up:\n",
        "      x = +1\n",
        "      return x\n",
        "    elif x < alpha_down:\n",
        "      x=-1\n",
        "      return x\n",
        "    else:\n",
        "        return 0"
      ],
      "metadata": {
        "id": "5pGPZ4yNj3m3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fNPj3v9zzwM_"
      },
      "outputs": [],
      "source": [
        "df_bidask['p_b'] = df_bidask[\"bid18\"]\n",
        "df_bidask['p_a'] = df_bidask[\"ask20\"]\n",
        "df_bidask['p_t'] = (df_bidask['p_b']+df_bidask['p_a'])/2\n",
        "df_bidask['m_+'] = (df_bidask['p_b'].rolling(2).sum())/2\n",
        "df_bidask['l_t'] = (df_bidask['m_+'] - df_bidask['p_t']) / df_bidask['p_t']\n",
        "df_bidask.fillna(0, inplace=True)\n",
        "df_bidask['l_t_lable'] = df_bidask[\"l_t\"].apply(filter)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(df_bidask['l_t_lable'].value_counts().values,\n",
        "        labels=[-1,0,1], autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "8AusjhJcyKBF",
        "outputId": "e3057a97-6f23-4577-9b87-591875df5984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bidask.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "PgBFCigooLD8",
        "outputId": "6cb09ef3-fe17-4727-892f-cf0b60bdb877"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          bid0      bid1      bid2      bid3      bid4  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00  0.562161 -0.199037  0.562161 -0.587085  0.562161   \n",
              "2022-03-18  00:01:00  0.560569 -0.199037  0.560569 -0.150239  0.560569   \n",
              "2022-03-18  00:02:00  0.561166 -0.199037  0.561166 -0.587085  0.561166   \n",
              "\n",
              "                          bid5      bid6      bid7      bid8      bid9  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00 -0.507191  0.562160 -0.492321  0.562160 -0.035384   \n",
              "2022-03-18  00:01:00 -0.507191  0.560568 -0.492321  0.560568  0.445878   \n",
              "2022-03-18  00:02:00 -0.507191  0.561165 -0.063754  0.561165 -0.516646   \n",
              "\n",
              "                         bid10     bid11     bid12     bid13     bid14  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00  0.562160 -0.523799  0.562159 -0.600543  0.562158   \n",
              "2022-03-18  00:01:00  0.560568 -0.004901  0.560567 -0.600543  0.560566   \n",
              "2022-03-18  00:02:00  0.561165 -0.523799  0.561164 -0.600543  0.561163   \n",
              "\n",
              "                         bid15     bid16     bid17     bid18     bid19  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00 -0.569687  0.562158  0.154312  0.562153 -0.344443   \n",
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              "\n",
              "                         ask20     ask21     ask22     ask23     ask24  \\\n",
              "time                                                                     \n",
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              "\n",
              "                         ask25     ask26     ask27     ask28    ask29  \\\n",
              "time                                                                    \n",
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              "\n",
              "                         ask30     ask31    ask32     ask33     ask34  \\\n",
              "time                                                                    \n",
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              "\n",
              "                         ask35     ask36     ask37     ask38     ask39  \\\n",
              "time                                                                     \n",
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              "\n",
              "                           p_b       p_a       p_t       m_+       l_t  \\\n",
              "time                                                                     \n",
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              "2022-03-18  00:02:00  0.561158  0.563979  0.562569  0.560859 -0.003039   \n",
              "\n",
              "                      l_t_lable  \n",
              "time                             \n",
              "2022-03-18  00:00:00          0  \n",
              "2022-03-18  00:01:00         -1  \n",
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          },
          "metadata": {},
          "execution_count": 49
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    },
    {
      "cell_type": "code",
      "source": [
        "total_df = pd.concat([df, df_bidask], axis=1)\n",
        "total_df.head(3)"
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      "metadata": {
        "id": "u6zfXhuvkbxt",
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          "height": 343
        },
        "outputId": "a0886463-8d0d-4936-f9be-633bceae5d03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         open      high       low     close  volume  \\\n",
              "time                                                                  \n",
              "2022-03-18  00:00:00  14066.5  14069.75  14061.75  14066.00   145.0   \n",
              "2022-03-18  00:01:00  14064.5  14067.75  14063.00  14066.25    66.0   \n",
              "2022-03-18  00:02:00  14067.0  14068.00  14064.75  14066.25    68.0   \n",
              "\n",
              "                      numtrades  bidvolume  askvolume  close_change  \\\n",
              "time                                                                  \n",
              "2022-03-18  00:00:00      144.0       60.0       85.0          0.00   \n",
              "2022-03-18  00:01:00       64.0       36.0       30.0          0.25   \n",
              "2022-03-18  00:02:00       64.0       36.0       32.0          0.00   \n",
              "\n",
              "                      close_lable      bid0      bid1      bid2      bid3  \\\n",
              "time                                                                        \n",
              "2022-03-18  00:00:00            0  0.562161 -0.199037  0.562161 -0.587085   \n",
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              "2022-03-18  00:02:00            0  0.561166 -0.199037  0.561166 -0.587085   \n",
              "\n",
              "                          bid4      bid5      bid6      bid7      bid8  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00  0.562161 -0.507191  0.562160 -0.492321  0.562160   \n",
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              "\n",
              "                          bid9     bid10     bid11     bid12     bid13  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00 -0.035384  0.562160 -0.523799  0.562159 -0.600543   \n",
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              "\n",
              "                         bid14     bid15     bid16     bid17     bid18  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00  0.562158 -0.569687  0.562158  0.154312  0.562153   \n",
              "2022-03-18  00:01:00  0.560566 -0.569687  0.560566 -0.515952  0.560561   \n",
              "2022-03-18  00:02:00  0.561163 -0.569687  0.561163  0.154312  0.561158   \n",
              "\n",
              "                         bid19     ask20     ask21     ask22     ask23  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00 -0.344443  0.563979 -0.319181  0.564166 -0.332552   \n",
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              "\n",
              "                         ask24     ask25     ask26     ask27     ask28  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00  0.564354 -0.321784  0.564541 -0.151661  0.564532   \n",
              "2022-03-18  00:01:00  0.564354 -0.321784  0.564541 -0.151661  0.564532   \n",
              "2022-03-18  00:02:00  0.564354 -0.321784  0.564541 -0.151661  0.564532   \n",
              "\n",
              "                        ask29     ask30     ask31    ask32     ask33  \\\n",
              "time                                                                   \n",
              "2022-03-18  00:00:00 -0.54633  0.564522 -0.366536  0.56471 -0.021388   \n",
              "2022-03-18  00:01:00 -0.54633  0.564522 -0.366536  0.56471 -0.021388   \n",
              "2022-03-18  00:02:00 -0.54633  0.564522 -0.366536  0.56471 -0.021388   \n",
              "\n",
              "                         ask34     ask35     ask36     ask37     ask38  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00  0.564897 -0.436125  0.565084  0.573427  0.565074   \n",
              "2022-03-18  00:01:00  0.564897 -0.436125  0.565084  0.573427  0.565074   \n",
              "2022-03-18  00:02:00  0.564897 -0.436125  0.565084  0.573427  0.565074   \n",
              "\n",
              "                         ask39       p_b       p_a       p_t       m_+  \\\n",
              "time                                                                     \n",
              "2022-03-18  00:00:00 -0.426986  0.562153  0.563979  0.563066  0.000000   \n",
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              "2022-03-18  00:02:00 -0.426986  0.561158  0.563979  0.562569  0.560859   \n",
              "\n",
              "                           l_t  l_t_lable  \n",
              "time                                       \n",
              "2022-03-18  00:00:00  0.000000          0  \n",
              "2022-03-18  00:01:00 -0.001624         -1  \n",
              "2022-03-18  00:02:00 -0.003039         -1  "
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              "      <th>2022-03-18  00:00:00</th>\n",
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              "      <td>14061.75</td>\n",
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              "      <th>2022-03-18  00:01:00</th>\n",
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              "      <th>2022-03-18  00:02:00</th>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a760619-32f4-406e-b3d6-51fd2c9b2488')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        " \n",
        "# As our concern is with the highly\n",
        "# correlated features only so, we will visualize\n",
        "# our heatmap as per that criteria only.\n",
        "sb.heatmap(total_df.corr() > 0.7, annot=True, cbar=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "id": "BPXSv2Wix2bF",
        "outputId": "713a3b78-fb70-4a45-d7ec-8ea0c701e768"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_df.to_csv(\"total.csv\")"
      ],
      "metadata": {
        "id": "9ju-RHdqNwWO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCGGSEoDmYrv",
        "outputId": "45b561be-5694-4710-b6dd-ef646a375c08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "158416"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DmcOGehGfQnI"
      },
      "source": [
        "# ML MODELING, BACKTESTING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Rs7_lSvguAo"
      },
      "source": [
        "### CLASSIC ML use classification "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fflvgoJ9n3bT",
        "outputId": "abcb328e-1cd1-4b04-c092-0c9a7cd017c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67121, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bidask.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lF8CZu_n6Ur",
        "outputId": "af7b8a0f-c9d1-41d9-ae68-166e0d62eab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67121, 46)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zoaNPHBIwiFl",
        "outputId": "be3ceb94-2096-44fd-876f-ff7d29fe061c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67121, 56)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_horizon = 67121\n",
        "67121 // 5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TzYD2GpyaBk",
        "outputId": "1025bacd-107d-4acb-9af7-a30ecd49d17c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13424"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_horizon = 13424\n",
        "train = total_df.head(-valid_horizon).copy()\n",
        "test = total_df.tail(valid_horizon)"
      ],
      "metadata": {
        "id": "r6zJjkXewhUV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yg1A-QeUndiL",
        "outputId": "4b76ec5e-e5d7-41ca-dac1-3e34e1821e3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['open', 'high', 'low', 'close', 'volume', 'numtrades', 'bidvolume',\n",
              "       'askvolume', 'close_change', 'close_lable', 'bid0', 'bid1', 'bid2',\n",
              "       'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
              "       'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
              "       'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
              "       'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
              "       'ask35', 'ask36', 'ask37', 'ask38', 'ask39', 'p_b', 'p_a', 'p_t', 'm_+',\n",
              "       'l_t', 'l_t_lable'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# x_train = train[['open', 'high', 'low',\t\"volume\", \"numtrades\", \"p_t\", \"bidvolume\",\t\"askvolume\", \"bid18\", \"ask20\"]]\n",
        "y_train = train['close_lable']\n",
        "x_train = train[['bid0', 'bid1', 'bid2',\n",
        "       'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
        "       'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
        "       'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
        "       'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
        "       'ask35', 'ask36', 'ask37', 'ask38', 'ask39']]\n",
        "# x_test = test[['open', 'high', 'low',\t\"volume\", \"numtrades\", \"p_t\", \"bidvolume\",\t\"askvolume\", \"bid18\", \"ask20\"]]\n",
        "x_test = test[['bid0', 'bid1', 'bid2',\n",
        "       'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
        "       'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
        "       'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
        "       'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
        "       'ask35', 'ask36', 'ask37', 'ask38', 'ask39']]\n",
        "y_test = test['close_lable']\n",
        "y_train = y_train.to_frame()\n",
        "y_test = y_test.to_frame()"
      ],
      "metadata": {
        "id": "KxlPNfiuzh4S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'x_train shape: {x_train.shape}, x_test shape: {x_test.shape}, y_train shape: {y_train.shape}, y_test shape: {y_test.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_RVCSACyyFs",
        "outputId": "6d84af7e-5847-4a3f-ccce-099712f454e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (53697, 40), x_test shape: (13424, 40), y_train shape: (53697, 1), y_test shape: (13424, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "aiEaKWpWzSTk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = scaler.fit(x_train)\n",
        "x_train = scaler.transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "2ebih1kD07ds"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = y_train.to_numpy()\n",
        "y_test = y_test.to_numpy()"
      ],
      "metadata": {
        "id": "q-G9B9q8-q_o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [LogisticRegression(), XGBClassifier()]\n",
        "# SVC(kernel='poly', probability=True)\n",
        "\n",
        "for i in range(2):\n",
        "  models[i].fit(x_train, y_train )\n",
        " \n",
        "  print(f'{models[i]} : ')\n",
        "  y_train_predict = models[i].predict(x_train)\n",
        "  y_train_predict = y_train_predict.reshape(-1,1)\n",
        "  y_test_predict = models[i].predict(x_test)\n",
        "  y_test_predict = y_test_predict.reshape(-1,1)\n",
        "  \n",
        "  print('Training f1 Score : ', metrics.f1_score(y_train , y_train_predict, average=None))\n",
        "  print('Test f1 Score : ', metrics.f1_score(y_test , y_test_predict, average=None))\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aIwO20jU1jMI",
        "outputId": "9c7d8e64-13ee-4276-b427-54fba6acc5af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression() : \n",
            "Training f1 Score :  [0.63438039 0.         0.58921991]\n",
            "Test f1 Score :  [0.62870006 0.         0.28999168]\n",
            "\n",
            "XGBClassifier(objective='multi:softprob') : \n",
            "Training f1 Score :  [0.62893646 0.01099164 0.55568746]\n",
            "Test f1 Score :  [0.61006146 0.00107009 0.56440678]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Backtest XGBClassifier "
      ],
      "metadata": {
        "id": "yVpoYzny5DuB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "backtest_data = total_df[['bid0', 'bid1', 'bid2',\n",
        "       'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
        "       'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
        "       'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
        "       'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
        "       'ask35', 'ask36', 'ask37', 'ask38', 'ask39', \"close\"]]\n",
        "featuers = backtest_data[['bid0', 'bid1', 'bid2',\n",
        "       'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
        "       'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
        "       'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
        "       'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
        "       'ask35', 'ask36', 'ask37', 'ask38', 'ask39']]\n",
        "featuers = StandardScaler().fit_transform(featuers)\n",
        "featuers = pd.DataFrame(featuers, columns = ['bid0', 'bid1', 'bid2',\n",
        "       'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
        "       'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
        "       'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
        "       'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
        "       'ask35', 'ask36', 'ask37', 'ask38', 'ask39'])"
      ],
      "metadata": {
        "id": "6mfxbaO962sR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "buy_signals = []\n",
        "sell_signals =[]\n",
        "trigger = 0\n",
        "for x in range(len(backtest_data)):\n",
        "  featuer = featuers.iloc[x].to_numpy()\n",
        "  predict = models[0].predict(featuer.reshape(1, -1))\n",
        "  if int(predict[0]) == 1 and trigger != 1:\n",
        "    buy_signals.append(backtest_data[\"close\"].iloc[x])\n",
        "    sell_signals.append(float('nan'))\n",
        "    trigger = 1\n",
        "  elif predict[0] == -1 and trigger != -1:\n",
        "    sell_signals.append(backtest_data[\"close\"].iloc[x])\n",
        "    buy_signals.append(float('nan'))\n",
        "    trigger = -1\n",
        "  else:\n",
        "    buy_signals.append(float('nan'))\n",
        "    sell_signals.append(float('nan'))\n"
      ],
      "metadata": {
        "id": "nB1tDhj-6h_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "backtest_data['Buy Signals'] = buy_signals\n",
        "backtest_data['Sell Signals'] = sell_signals"
      ],
      "metadata": {
        "id": "Qjan-pokA0Bq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.plot(backtest_data['close'], label='Share Price', color='lightgray', alpha=0.5)\n",
        "# plt.scatter(backtest_data.index, backtest_data['Buy Signals'], label='Buy Signal', marker=\"^\", color=\"#00ff00\", lw=1)\n",
        "# plt.scatter(backtest_data.index, backtest_data['Sell Signals'], label='Sell Signal', marker=\"v\", color=\"#ff0000\", lw=1)\n",
        "# plt.legend(loc=\"upper left\")\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "jDz6hf0f9tYb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the percentage of variation of the asset\n",
        "backtest_data[\"pct\"] = backtest_data[\"close\"].pct_change(1)\n",
        "backtest_data.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "O6HGhvkiCen2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_up(x):\n",
        "    if x > 0:\n",
        "      return 1\n",
        "    else:\n",
        "      return 0\n",
        "def filter_down(x):\n",
        "    if x > 0:\n",
        "      return -1\n",
        "    else:\n",
        "      return 0"
      ],
      "metadata": {
        "id": "BVSKca_8NnJh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "backtest_data[\"signal_long\"] = backtest_data[\"Buy Signals\"].apply(filter_up)\n",
        "backtest_data[\"signal_short\"] = backtest_data[\"Sell Signals\"].apply(filter_down)"
      ],
      "metadata": {
        "id": "QbMmE3O5N2_V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the positions\n",
        "backtest_data[\"Position\"] = (backtest_data[\"signal_short\"].fillna(method=\"ffill\")+ backtest_data[\"signal_long\"].fillna(method=\"ffill\"))\n",
        "\n",
        "# Compute the return of the strategy\n",
        "backtest_data[\"return\"] = backtest_data[\"pct\"]*(backtest_data[\"Position\"].shift(1))"
      ],
      "metadata": {
        "id": "bF93DeXVFv1F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "backtest_data[\"return\"].cumsum().plot(figsize=(15,6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "TwMugpmEOiJ5",
        "outputId": "5a4abff2-4769-4e4c-817f-1c7140477583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17f48fb3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def drawdown_function(serie):\n",
        "# We compute Cumsum of the returns\n",
        "  cum = serie.dropna().cumsum()+1\n",
        "# We compute max of the cumsum on the period (accumulate max)\n",
        "  running_max = np.maximum.accumulate(cum)\n",
        "# We compute drawdown\n",
        "  drawdown  = cum/running_max - 1 \n",
        "  return drawdown"
      ],
      "metadata": {
        "id": "cc6RMnX_GFbH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We compute drawdown\n",
        "drawdown = drawdown_function(backtest_data[\"return\"])\n",
        "backtest_data[\"drawdown\"] = drawdown \n",
        "# Computation Max drawdown\n",
        "max_drawdown =  -np.min(drawdown)*100\n",
        "print(f\"Max drawdown: {np.round(max_drawdown,1)} %\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DMXApLGGIRx",
        "outputId": "8fd1b9a9-b768-4d1e-ada2-817bb9ecd586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max drawdown: 7.5 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "backtest_data[\"drawdown\"].plot()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "rHgUUOZMPSL9",
        "outputId": "e6be0712-4e31-43db-b0cc-8abb6fb6b946"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NoT3C_fgtxC"
      },
      "source": [
        "## DNN"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [DEEPLOB](https://ieeexplore.ieee.org/document/8673598/references#references) \n"
      ],
      "metadata": {
        "id": "kdqJDRgiplyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_x(data):\n",
        "    df1 = data[['bid0', 'bid1', 'bid2',\n",
        "       'bid3', 'bid4', 'bid5', 'bid6', 'bid7', 'bid8', 'bid9', 'bid10',\n",
        "       'bid11', 'bid12', 'bid13', 'bid14', 'bid15', 'bid16', 'bid17', 'bid18',\n",
        "       'bid19', 'ask20', 'ask21', 'ask22', 'ask23', 'ask24', 'ask25', 'ask26',\n",
        "       'ask27', 'ask28', 'ask29', 'ask30', 'ask31', 'ask32', 'ask33', 'ask34',\n",
        "       'ask35', 'ask36', 'ask37', 'ask38', 'ask39']]\n",
        "    return np.array(df1)\n",
        "\n",
        "def get_label(data):\n",
        "    lob = data[['close_lable', 'l_t_lable']]\n",
        "    return lob\n",
        "\n",
        "def data_classification(X, Y, T):\n",
        "    [N, D] = X.shape\n",
        "    df = np.array(X)\n",
        "    dY = np.array(Y)\n",
        "    dataY = dY[T - 1:N]\n",
        "    dataX = np.zeros((N - T + 1, T, D))\n",
        "    for i in range(T, N + 1):\n",
        "        dataX[i - T] = df[i - T:i, :]\n",
        "    return dataX.reshape(dataX.shape + (1,)), dataY\n",
        "\n",
        "def prepare_x_y(data, k, T):\n",
        "    x = prepare_x(data)\n",
        "    y = get_label(data)\n",
        "    x, y = data_classification(x, y, T=T)\n",
        "    y = y[:,k]\n",
        "    y = np_utils.to_categorical(y, 3)\n",
        "    return x, y"
      ],
      "metadata": {
        "id": "L-mCeNSrcJaW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k = 0 # which prediction horizon\n",
        "T = 100 # the length of a single input\n",
        "n_hiddens = 64\n",
        "checkpoint_filepath = './model_tensorflow2/weights'"
      ],
      "metadata": {
        "id": "j9ji9ln1cI84"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_horizon = 13424\n",
        "data = pd.read_csv(\"total.csv\")\n",
        "train = data.head(-valid_horizon).copy()\n",
        "test = data.tail(valid_horizon)\n",
        "print(train.shape, test.shape)"
      ],
      "metadata": {
        "id": "uJO0kMr5vciV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9a7b6e1-6a3a-417a-b167-5ff883fd2d97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(53697, 57) (13424, 57)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainX_CNN, trainY_CNN = prepare_x_y(train, k, T)\n",
        "valX_CNN, valY_CNN = prepare_x_y(test, k, T)\n",
        "# testX_CNN, testY_CNN = prepare_x_y(dec_test, k, T)\n",
        "\n",
        "print(trainX_CNN.shape, trainY_CNN.shape)\n",
        "print(valX_CNN.shape, valY_CNN.shape)\n",
        "# print(testX_CNN.shape, testY_CNN.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWV9M4DBcFYF",
        "outputId": "770c66f6-e675-42a4-bb36-264492f13ac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(53598, 100, 40, 1) (53598, 3)\n",
            "(13325, 100, 40, 1) (13325, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_deeplob(T, NF, number_of_lstm):\n",
        "    input_lmd = Input(shape=(T, NF, 1))\n",
        "    \n",
        "    # build the convolutional block\n",
        "    conv_first1 = Conv2D(32, (1, 2), strides=(1, 2))(input_lmd)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "    conv_first1 = Conv2D(32, (4, 1), padding='same')(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "    conv_first1 = Conv2D(32, (4, 1), padding='same')(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "\n",
        "    conv_first1 = Conv2D(32, (1, 2), strides=(1, 2))(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "    conv_first1 = Conv2D(32, (4, 1), padding='same')(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "    conv_first1 = Conv2D(32, (4, 1), padding='same')(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "\n",
        "    conv_first1 = Conv2D(32, (1, 10))(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "    conv_first1 = Conv2D(32, (4, 1), padding='same')(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "    conv_first1 = Conv2D(32, (4, 1), padding='same')(conv_first1)\n",
        "    conv_first1 = keras.layers.LeakyReLU(alpha=0.01)(conv_first1)\n",
        "    \n",
        "    # build the inception module\n",
        "    convsecond_1 = Conv2D(64, (1, 1), padding='same')(conv_first1)\n",
        "    convsecond_1 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_1)\n",
        "    convsecond_1 = Conv2D(64, (3, 1), padding='same')(convsecond_1)\n",
        "    convsecond_1 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_1)\n",
        "\n",
        "    convsecond_2 = Conv2D(64, (1, 1), padding='same')(conv_first1)\n",
        "    convsecond_2 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_2)\n",
        "    convsecond_2 = Conv2D(64, (5, 1), padding='same')(convsecond_2)\n",
        "    convsecond_2 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_2)\n",
        "\n",
        "    convsecond_3 = MaxPooling2D((3, 1), strides=(1, 1), padding='same')(conv_first1)\n",
        "    convsecond_3 = Conv2D(64, (1, 1), padding='same')(convsecond_3)\n",
        "    convsecond_3 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_3)\n",
        "    \n",
        "    convsecond_output = keras.layers.concatenate([convsecond_1, convsecond_2, convsecond_3], axis=3)\n",
        "    conv_reshape = Reshape((int(convsecond_output.shape[1]), int(convsecond_output.shape[3])))(convsecond_output)\n",
        "    conv_reshape = keras.layers.Dropout(0.2, noise_shape=(None, 1, int(conv_reshape.shape[2])))(conv_reshape, training=True)\n",
        "\n",
        "    # build the last LSTM layer\n",
        "    conv_lstm = LSTM(number_of_lstm)(conv_reshape)\n",
        "\n",
        "    # build the output layer\n",
        "    out = Dense(3, activation='softmax')(conv_lstm)\n",
        "    model = Model(inputs=input_lmd, outputs=out)\n",
        "    adam = keras.optimizers.Adam(lr=0.0001)\n",
        "    model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "deeplob = create_deeplob(trainX_CNN.shape[1], trainX_CNN.shape[2], n_hiddens)\n",
        "deeplob.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FkenfnCcOjm",
        "outputId": "05cf0c81-79d7-4e92-fa47-2e34737a82dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 100, 40, 1)  0           []                               \n",
            "                                ]                                                                 \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)                (None, 100, 20, 32)  96          ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " leaky_re_lu (LeakyReLU)        (None, 100, 20, 32)  0           ['conv2d[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)              (None, 100, 20, 32)  4128        ['leaky_re_lu[0][0]']            \n",
            "                                                                                                  \n",
            " leaky_re_lu_1 (LeakyReLU)      (None, 100, 20, 32)  0           ['conv2d_1[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)              (None, 100, 20, 32)  4128        ['leaky_re_lu_1[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_2 (LeakyReLU)      (None, 100, 20, 32)  0           ['conv2d_2[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)              (None, 100, 10, 32)  2080        ['leaky_re_lu_2[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_3 (LeakyReLU)      (None, 100, 10, 32)  0           ['conv2d_3[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)              (None, 100, 10, 32)  4128        ['leaky_re_lu_3[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_4 (LeakyReLU)      (None, 100, 10, 32)  0           ['conv2d_4[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)              (None, 100, 10, 32)  4128        ['leaky_re_lu_4[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_5 (LeakyReLU)      (None, 100, 10, 32)  0           ['conv2d_5[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)              (None, 100, 1, 32)   10272       ['leaky_re_lu_5[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_6 (LeakyReLU)      (None, 100, 1, 32)   0           ['conv2d_6[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)              (None, 100, 1, 32)   4128        ['leaky_re_lu_6[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_7 (LeakyReLU)      (None, 100, 1, 32)   0           ['conv2d_7[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)              (None, 100, 1, 32)   4128        ['leaky_re_lu_7[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_8 (LeakyReLU)      (None, 100, 1, 32)   0           ['conv2d_8[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)              (None, 100, 1, 64)   2112        ['leaky_re_lu_8[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)             (None, 100, 1, 64)   2112        ['leaky_re_lu_8[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_9 (LeakyReLU)      (None, 100, 1, 64)   0           ['conv2d_9[0][0]']               \n",
            "                                                                                                  \n",
            " leaky_re_lu_11 (LeakyReLU)     (None, 100, 1, 64)   0           ['conv2d_11[0][0]']              \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2D)   (None, 100, 1, 32)   0           ['leaky_re_lu_8[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)             (None, 100, 1, 64)   12352       ['leaky_re_lu_9[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 100, 1, 64)   20544       ['leaky_re_lu_11[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 100, 1, 64)   2112        ['max_pooling2d[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_10 (LeakyReLU)     (None, 100, 1, 64)   0           ['conv2d_10[0][0]']              \n",
            "                                                                                                  \n",
            " leaky_re_lu_12 (LeakyReLU)     (None, 100, 1, 64)   0           ['conv2d_12[0][0]']              \n",
            "                                                                                                  \n",
            " leaky_re_lu_13 (LeakyReLU)     (None, 100, 1, 64)   0           ['conv2d_13[0][0]']              \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 100, 1, 192)  0           ['leaky_re_lu_10[0][0]',         \n",
            "                                                                  'leaky_re_lu_12[0][0]',         \n",
            "                                                                  'leaky_re_lu_13[0][0]']         \n",
            "                                                                                                  \n",
            " reshape (Reshape)              (None, 100, 192)     0           ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 100, 192)     0           ['reshape[0][0]']                \n",
            "                                                                                                  \n",
            " lstm (LSTM)                    (None, 64)           65792       ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 3)            195         ['lstm[0][0]']                   \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 142,435\n",
            "Trainable params: 142,435\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=checkpoint_filepath,\n",
        "    save_weights_only=True,\n",
        "    monitor='val_loss',\n",
        "    mode='auto',\n",
        "    save_best_only=True)\n",
        "#epocs = 200\n",
        "deeplob.fit(trainX_CNN, trainY_CNN, validation_data=(valX_CNN, valY_CNN), \n",
        "            epochs=10, batch_size=128, verbose=2, callbacks=[model_checkpoint_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gld0-kTEcW8F",
        "outputId": "d857c628-b495-4a3b-acb5-9c1a97112fec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "419/419 - 34s - loss: 1.0174 - accuracy: 0.4252 - val_loss: 1.0057 - val_accuracy: 0.4353 - 34s/epoch - 80ms/step\n",
            "Epoch 2/10\n",
            "419/419 - 18s - loss: 0.9846 - accuracy: 0.4878 - val_loss: 0.9453 - val_accuracy: 0.5481 - 18s/epoch - 44ms/step\n",
            "Epoch 3/10\n",
            "419/419 - 19s - loss: 0.9437 - accuracy: 0.5417 - val_loss: 0.9346 - val_accuracy: 0.5509 - 19s/epoch - 45ms/step\n",
            "Epoch 4/10\n",
            "419/419 - 19s - loss: 0.9371 - accuracy: 0.5476 - val_loss: 0.9317 - val_accuracy: 0.5544 - 19s/epoch - 45ms/step\n",
            "Epoch 5/10\n",
            "419/419 - 20s - loss: 0.9338 - accuracy: 0.5503 - val_loss: 0.9409 - val_accuracy: 0.5593 - 20s/epoch - 47ms/step\n",
            "Epoch 6/10\n",
            "419/419 - 18s - loss: 0.9318 - accuracy: 0.5515 - val_loss: 0.9279 - val_accuracy: 0.5595 - 18s/epoch - 44ms/step\n",
            "Epoch 7/10\n",
            "419/419 - 19s - loss: 0.9293 - accuracy: 0.5537 - val_loss: 0.9286 - val_accuracy: 0.5638 - 19s/epoch - 46ms/step\n",
            "Epoch 8/10\n",
            "419/419 - 19s - loss: 0.9284 - accuracy: 0.5556 - val_loss: 0.9303 - val_accuracy: 0.5626 - 19s/epoch - 44ms/step\n",
            "Epoch 9/10\n",
            "419/419 - 19s - loss: 0.9272 - accuracy: 0.5549 - val_loss: 0.9234 - val_accuracy: 0.5639 - 19s/epoch - 44ms/step\n",
            "Epoch 10/10\n",
            "419/419 - 19s - loss: 0.9250 - accuracy: 0.5577 - val_loss: 0.9332 - val_accuracy: 0.5644 - 19s/epoch - 44ms/step\n",
            "CPU times: user 2min 39s, sys: 8.28 s, total: 2min 48s\n",
            "Wall time: 3min 25s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f17f3d0a510>"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deeplob.load_weights(checkpoint_filepath)\n",
        "pred = deeplob.predict(valX_CNN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ya7pSsOUcirE",
        "outputId": "82f4fade-c374-4c36-b2a3-b70f4c35d7ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "417/417 [==============================] - 10s 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('accuracy_score:', metrics.accuracy_score(np.argmax(valY_CNN, axis=1), np.argmax(pred, axis=1)))\n",
        "print(metrics.classification_report(np.argmax(valY_CNN, axis=1), np.argmax(pred, axis=1), digits=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nTxqm6iJcjHI",
        "outputId": "b83be28c-e2dd-49ff-a89e-b10e419e3efc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score: 0.5636022514071295\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.2857    0.0011    0.0022      1844\n",
            "           1     0.5761    0.5914    0.5837      5754\n",
            "           2     0.5539    0.7168    0.6249      5727\n",
            "\n",
            "    accuracy                         0.5636     13325\n",
            "   macro avg     0.4719    0.4364    0.4036     13325\n",
            "weighted avg     0.5264    0.5636    0.5209     13325\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Backtest DEEPLOB"
      ],
      "metadata": {
        "id": "XOCmFZCSV1s6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total = pd.read_csv(\"total.csv\")\n",
        "trainX_CNN, trainY_CNN = prepare_x_y(total, 0, 100)\n",
        "deeplob.load_weights(checkpoint_filepath)\n",
        "predict = deeplob.predict(trainX_CNN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8AK0PNRIM-ky",
        "outputId": "a6b16fc5-6863-48a4-e924-6a2bc0e02a82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2095/2095 [==============================] - 12s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pr = np.argmax(predict, axis=1).reshape(-1,1)\n",
        "a = np.zeros((99,1))\n",
        "pre = np.concatenate((a, pr), axis=0)\n",
        "total['Position'] = pre"
      ],
      "metadata": {
        "id": "a3mAz-6sTJHl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def filter(x):\n",
        "  if x==2:\n",
        "    return -1\n",
        "  else:\n",
        "    return x"
      ],
      "metadata": {
        "id": "-JWLCr3PSFII"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total['Position'] = total['Position'].apply(filter)\n",
        "# Compute the percentage of variation of the asset\n",
        "total[\"pct\"] = total[\"close\"].pct_change(1)\n",
        "total.fillna(0, inplace=True)\n",
        "# Compute the return of the strategy\n",
        "total[\"return\"] = total[\"pct\"]*(total[\"Position\"].shift(1))"
      ],
      "metadata": {
        "id": "lf8YCvneRsIe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total[\"return\"].cumsum().plot(figsize=(15,6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "XZv7kv1QU6GL",
        "outputId": "edbd5c2a-be9d-4ece-89e5-e3639d0b4c7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f586c046cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def drawdown_function(serie):\n",
        "# We compute Cumsum of the returns\n",
        "  cum = serie.dropna().cumsum()+1\n",
        "# We compute max of the cumsum on the period (accumulate max)\n",
        "  running_max = np.maximum.accumulate(cum)\n",
        "# We compute drawdown\n",
        "  drawdown  = cum/running_max - 1 \n",
        "  return drawdown"
      ],
      "metadata": {
        "id": "fTFdjAAIWGOK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We compute drawdown\n",
        "drawdown = drawdown_function(total[\"return\"])\n",
        "total[\"drawdown\"] = drawdown \n",
        "# Computation Max drawdown\n",
        "max_drawdown =  -np.min(drawdown)*100\n",
        "print(f\"Max drawdown: {np.round(max_drawdown,1)} %\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88bccc14-5766-4bcf-a188-143866db12cf",
        "id": "aas8n7WrWGOL"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max drawdown: 13.7 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "total[\"drawdown\"].plot()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "d9a62601-cee8-42fb-e576-694059b0c54e",
        "id": "xVnn6jEDWGOM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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N3AyMUZ8uQNvWwlm7MTPwwu/+F0gJ5/U2Rt6cuQ1P/ugoeFGvmnY9a1qS+cqeiiZu1vE4eyG7teFjmTXcH0d9Yn1Zq1qq51nXmMXGWHB7j0zUTE1weW5sjMXewqLMy6m2JTvz0ealGfZ1r3lnCrEy13NPs9KycuSfLUL3CXN8rl4IAEV+zJRVRu/M2aEJyPepLs7Ex1r8+rszWgsbTB0aWFsIqWemlZk5varEei1W6qUlab4jUhI8Zy6o9Wyagd7NMjD7SWvBFaWab3xs5f86V35W/nK3FnOjn8XAnMNVdWEhIqJIVPm/PSLAe7d0AeBoZjxHp3Fyx7Ez0ealGQCAc0Wl+HmVa28lZ87rr4K5vkE5n4uxncRkZriezH/h4zoYAC7VKL3x9dJ9KDU4qfzkDuNWFt0yHalaRmt7wonzLMR/5+5EgU511rdvtK458uX90qJWqub+I5c1c0nx3TF+GHJzsjGyTxP0aZ7hUnEWAHbkncXJc8W6BYMA4IJqxvfD2xzFicyeTCon296ui3Ren3vNe0vwtw//QuaoKdiZp18Y5VxRKZqNnoYsP4JAxT9+jqxCRp/8uQdNnp+KT/+0zrDcbEvf690sA8Wl5SEJ6IyYaUNisc0a3X9pU1zSJB2vXt1OUxDHWdVE10DvatUaOcD7WfLEuBh8PbI7mtn+Fv9+WTMAQIcGgenhGUzqvov+MProeu7XDV6tSzbj4W9Xe/2cO9wseyAiqmwYDIbAfjf9i06cK0bbMTPw9E/rvD5uMGcVlCux7pYG9m9Zy2Vbbk62xwIxAPDG9dp2D411gk13tum0ktg1Ybjbwjbf3NsdG14eDMC/tUuVhVGwAgBLRl1mvx0b41ugBLhekR/ZpwkeG9gCgDVtePurwzQB21f3dMfEu7vhEdsJq1rncbPQK2euZhynzhfjQnGZ5nXUMx6xJk+cY30MBtXKyqUmWB341gJNkKrQa4fiztmiUoz6Zb1uMY305HjvBxoGXp2yBQBQYJvxXXfAURzog/m7QjImtdKyclMXhA6fdrwfvr+vB2675CJc7qY/YStbhcy3b+yEYe3q4OcHerjM4G09cgZf3Kl/Ie0tpzY4eno0rQEASNEJPCsbX9PDnRn9qr5b7n8RHW8+M/y9AHu+uBQjPl+OvWGw3pOIIheDwRBwnq1Rp8R10WnLYJa3J6XeUFK9LEEq1d+6rrXdwwe3dsGHt3XBTw/0wGcjsuyzqgDQRBUg3te3ieb5Mze5zrbGeLjSHxdjsadolUTAzOBNH1tnXZrUTHYpolJLlU6pBEq+zIYuV6VM9m9ZE2lJcUhPjkduTjbu69vUMFXN3W9ii62Qze2fLUOnsbPQ+qXp9kqi6tlbwPzMYIzJYHBP/jk8/O1q3fW5en1BV+ikjB467ToTsWRXvuG6w48X7sb3K/bj0z+t6eLqtW8DW7teUAm0y//7JzJHTfFpPaUn7k6OS8rKcabI+hnVt7ljtvif07f6PJZ1OhVnfWH2QtrRAtc1gH10iobVS0sE4EhljbEIfHDbxcjKdC0c0yi9Cvq3quVSJRcAaqUmehxTOKwVVHj6TDbrgoe1qGqBuLB4tkj/9YzWrJv9bJ239RgWbD+Gf05noRoiCh0Gg0GyZexQrLfNOqnN26rts5Z3ptDtTCEATLimvcs2veIJV767yFSFUl84p4macVevTK9fZ1j7uhjari4yUhIwoHVttKtf1f7Y7vxz+Nd1HfDeLV3w/HDterS6aY6Tpq/u6YaNrwwx9XpCCMRahD3N9Kr3FuOHFZW/RLs7/7quA0YNa4Vv73X0JItVBVExtlStLYcLsP3oGWSOmoJNJtfZnDpvvXDRuVE1POtFBdZujWsYPrbtyBm8N2+nZt3o1A3WdTr557Qn32ZS+az7Wf+Nnk7Khv1nIaasP6ybnvnypE0u235Y6ZrWeO/ElS7bbvlkmT0l0tk7c3YAAAptAWirFx1rg81UvvWX0kbliyW52HDAv/VVzk6ed1zY6tFE+zt/Y+Y2++305HjNLKi3azsV136wxKfnPfnDWsxX9bz0J6uidlXXgC3GNgOmBBHurqG5e4t6lVUZBtezApUmarYwGBCY5RNbDCovHzujXyyqrMy714yEKsJEFL4YDAZJUnwMqia6FuhwnjnJKyjyWEThpq4NNfdX7zupO4N4+HQhbg1ST8KScusYPZ2KfzvSEYDorRXzlvqE8Z7ejXFD14b2gjxqo351FJHp07ymV0UZYmMESsslcvPPYd3+U3j2F/8L0lS05XscM1YX26ob9mxqnbFQF2ABHAHVkz+uw/SN1nWsSvBl1m8P9UKrOlU972jjruXKUz+tw+sztmm2ldpOppyDv1gvZwZ3uUmdBYBCWzuAP9YewpHT2gspWw+7zhDoFZMwmq301Ix6x9GzOOc041CRhYzGTd6MK95dpFvx0heHT1+wfy51algN3913iea999ECR2VGIYBLmqjW7Hp58qzwNQ341zUHcecXK5AzbSu2Himw/x6a10qxp3aapfc5F+MU/QmdT87EOOv7xl2w4nwcPUr1SxkG0aD6YuLFF1W3txZyR1n3rZdq7kmD6kkB+amoWxupHdbJCgCML0JdKC7Dte8vtqeIK79eBoNEFEoMBivIxLu7AXANBvedOI+NB42vzs9+si8sFqGpzHjt+0s0vbzUduSd9frE3oy3Zlp7enk6we3ZzHHSX72K/+ufUhPj0MlWkW94+zp+H09PnMVa1bDfG/ODcvxgGvPHRnyxeA9u+Ogv+zZ1afTcnGx87rQeSX01W5kRNXo/OevYsBo62ioqBlNL2wn5cDdl591Rgsif3BRi+mPtQc39K52qQZ5RBWr39mlsv71q70nNfk8OauHTGC/vUBezNmvTm9+c6XvvPE/rjvLPFqHbeNciN/6sq1S764sV9ttNa1pbxbx7S2fdfculRLwqiFYuNnnSv6U28BrZu7HBnsZ25jk+wz5csAtD3/7TniZ8e4+LMP3xvprG456o/95OGKQG601oP9zPGtw4r8PtoPr7MrNGVtkj3AKKXx7siXFXt/O436A2tZGbk42nBrd0u5/ehZSODaoF5OfSuq7rha/CkjK8PWuH7v7/W39Id/vD367G6n2n0P7lmQBUv7swCOSJKHIxGKwgibYgUN0EHAAe+mY1XvzDNR1NoZxU3dVL/6TnmSGuX5APfeN99TNPNtgCVufx69kydig+vO1i3bLqvlDSTT21I/DVmaJSfLE4NyjHDqb/zN6BiX/txSv/2+zV875Ykmu//c7cndZttn9/UWkZOo+dibu+sF4JX7wzX1OpNT5GINnLUvi+6GpbW6XMbs58oi/evN5zMQ2FunR/SVk53pq13T7788nC3bj106V47Pu1mufkGaR8VYmPwb19HGtUr3NKTVzj47q1uBgLHv9BO4bNBulonvywYh8ufX2+S4ALWFPTr31/MbJena37b3xz5jaXbb5QXyi6IcvaU9Uo7bW0TGoujP2f6j3pjgSQoHqeMgvujU2HXH/Gz9kyC5RMhAf6NXHZxwxlZtTiFP3pTfC1qWcNMJRAQy8cMLNGO5pml+7smWn4mF4qZ1yM8DrQck5vBrRrrhWtXpyuWUOtVlxajl3HXLMS1GMsKCzBg0H4riYi8haDwQoSZzuBeeDrVYb7/PM617WBylVno7V6D/dvZliJLhjMXKlOio/B0HaBm8W7qlN95OZko0aK9gv5g1u7IClCm8Wb8e/ZrrNIG3TWqTozSt99e/Z2tHxhOk6eL8G8bceQOWoKbv10Gdq9PMO+T7kMThEh56qvygmc8lItaqfiuosb+HTsP9YewjtzduCNmdvw2aI9GD91i9velc7OF5ehqlNPRnVqn5kKhjvzzrjMXPiSEiqlRM60rZp1xsWl5fYUTOcsgy8X78FdX67A6n3GAetcp3XMgVCoWoM356lLXR7/fsV+JKgar+86Zq6aopTQpHH6stbQ+SIAAKzItc72KmmZV3eqj//c1Mllv6FtzX2uqZvKW7n+zQxoXRtzn7oUl3eo57Kn0j/TzBptvRTUykqZBX3x8jY+Pb9WVdegTHFcNSu76oWByM3JhsUiYHLS2U7vO67YhzWlepWHD6tS0TvYZgeB6AjkiajyYjBYQcwUvmhpW4OVYLJ58MwnrE2HOznNwLXRSWkJlEBVgwuEYe3rYsu4oZptvswURIohbWsjVWedqrNnh+qnW709Wz/lqbCkHC1fmAbAmiK5aGe+7n6e3Nytkf32Tw/0wJeqvpQ3ZDXUvLeUkyZ/320DWtVCme1scMH2Yxg32btZVEWi00UHpVXCeRNVDVftPYmBby3Ekz+uw79UVQONgjDndYRq0zcewYcLdqHPv+bZt93yyVLszrcGU6dUBVwyR03ByyZmjfNNzPZ7q2dTx+yKkt3gTP37nq3Te1WPBAAhMMv22efLSXRGinFAoYxJCIGrOtV3eVyvv6oe5zopRtdPmuj8bCQc73tvLryEQzxRYFsrd1F6FZ+eH+emAE2Bqiq3cuEwUIGyL595/5mzA1sOFyDr1dlYtVd/BlERDr87IopcDAYriJk1WR3qp+G9W7pgxQsDTR2zhW1dS3Wn3mTHAlQQQo9eGfXK5ImBvq3fUgtmMB0sGSkJLjOnRoSJE0znIhpFpeXIHDXFp7Ep1DNocTEWNFSdEO49fk6zdk2p7ulPA+mmNZORGBdj//fuNjn7ZET93vps0R6UlJXb20O4o6SV/m/dIbyv6qmX4DJ7ZOW8jlBN3eD822XWn+dK1RpGJeVQr1WG2lyd2TpvqWdH1QFsbk62xxYgr1zZFnlnHLMkXXVaLhi9poAjaHNeb2dk3tY8+wn5jV2NZ5jVwbQeo76lyizird0b2capfdzX2XRTM4P2NNHKH1I8+LU1LbLQw/vTyEU1tEHk0t3H0StnLj79czdW5p502V8I738uejP213R2vTDgyazNRzHsP38i/2wRxuhUJlYLg18dEUUwBoMVxKiwgJrFIpDdoS6qJsZh4t3d8JSPhSmMyl0HQvNa3lXaq2jKOhx/lHqbV6RSXFpuv/odTNtU67PiYy2QUuoWqTCy5sVBLtueHdoKqQmx2D1hOKY/3ldTtCjQ4mKEZsbo7l6NMbC164n2mn2uJ3hmxcfGWFsGBOhE67GBzbFVNRP90Der8dYs9wVfPv1zt+Fj87bpzwwqQZ6eBduP2W8//5tr1VvlmC1fmO7ymFqTmin48LaLAQDXdq6PzFFTdNcbutP4ual47Ps1AIC2Y2Z42Nvqzp6ZmPPUpRjRMxNTNxyxb6/qRcN0IRxBktkqpHd9uQLXfWAtsqRXDERxaUv3FZCNMg+u6lQftVITcKG4zNaqRbt+zZtQUL2vN5kY4RBPKD11F2w75mFPfYNVabqHT1/ATR8vxcFTF/DqlC34aulel/0FAvNz8XQx96PbL3b7uNLOxblvqkM4/PaIKFIxGKwg7eu7r8D4f7Zqo4pLW9TEIwOamz5+18zqeFS1f7CuEseZWDNY0b5RtbNIT/a/gun2o+7bEbgz4vPlmrUgwaJOq2tdJxXlUnqVEuU8m5ybk40H+zXFhleG2ItfjOzTBGlJntNOfRHvNHNksQjdE6oEP9aExsdaUFxWjk/cBGR65j3dz/Axdbqouxk8hZJOqkd9gUjdksWoKAUA7HXqr1bqNIuh7tWolpuTbb/dyDYj288W+Py6xhoE6q2lM6J8vvyxVr9qopGXr2yrmzZq9n0mpfUEX5lp+3SR55lZZ+6qp+r1DDQrPtZimE5oZmJQ7zM7UgvIVIn3f613j9fmetzHOjPo3XH1fuaT1h3Cwu3GAWyWyeUJRmseZ28J/LpdIiKzgl8WkABAkxLn7NHLmqGvnz35fnqgJwBHQ+ui0nKXdU6BYCbFsKL1apaBnx7oEZSToZKycjQfPQ0XX1QdvzzY0+P+6jS+YJqnWm+2M+8szhWX+VTkwJN1YwZj25EzGPL2woAeV5nx2DpuqP3kXG8W5LbunvuQGTlw4jzWmZiRV3RsWA3PDmmJxh7WhT03rBVem7bV7T7e6tksAxkpCfaefzvzzqCZiVn4nU4VC1MSYvHXLsd7MCMlHrOftKaETn+8DyxC2NPL/flTLlHNyJlJH8MH4EUAACAASURBVL6nd2Ms26P922heKwU7bH0gTQeDkBBC4EKJdabGqBm4O+eKHLM8nRtVwxo3xXW8kRBrMWxeb+ZnXc3Wiqdt/TT7mlkza83DqYCMoltj14qdgfLHw73stwW8ryZqZGXuCfv39G6nvzuzF60m6/QpJSIKNc4Mhsh9fR2ly5/00D/JG0oj58IS39ZkuNMwPSngxwyUrpnp6NbY3LojZ8o6HzXlKv3HC62zSqv2njQsFlJaVo5fVh1AeYD6tZmhDvTP2VKYFri5cu2PlnVS8amt8TMA7J4w3KfjjBrWyn5bWQuYGBejaVXhvCa1fnXf33PHTQaCSt/EPx7uZe+Tmd3BuL9hrk4/v58f6OHDCK2U9UgrRg+wb/txpbY/4p78c5riM4qhb/+puX+2qBQ3f7LUfv/bey+xBxmt6lS1B4KAf0GE0Vq9z+/M0t3+4uVtMPmRPpptLVTrUpUWJ55Iae3Z50/48948x2v1be64CGecwmdOfGyMYYq+mYtojTOS8dtDPTHmCkelTTNposrv4vPF3s+ShkojHwvI6HG+kKBuaeTLzKCRd+buxHHbxZoVTrP3ZoJ2IqLKisFgiPxDpz9gIFzZ0XpieSGAwaDSL2n/Cd+LeVRm469xtPS4rou1uMT4KVuQOWoKXp/h6MH26Hf6aXSfLdqDp35ap1mzctyPIj7HzxZpWgeobThwGmv3n9JNRztSUKjzDGPepPwOVBXOcO6hZtYQ1XqfVnX1Z71GO61TDFb1WuXCwYuXt8EP9/fAaqc1lH2aGRdKGjXMdS1lKy+KDt2Y1VBzX0kXVQcMyszQ2v2nkDlqCvq/Md9efKZzI/P9O1u4aZ7uz4/W6AS7X4tapo/hnCpshpIOXa+a7xcJDp5yfI7tU/2djezjfQN7NXezlGZ/1J0bVdcUFjLzt6YUPFm+x33FysqkfQP3yya8Me2xPljwTD/8/EAP7HlNe6FKCPOr8TYePI2r31vsdn3gxa/OBuDa89ZMMHj49AXdHoZERKHGYDBEYiwC3993ib1EeqD8b511DY+Z9RTeHjMaKAVo9NYizd5yFIUlZZi8XvvzUFIG1RXjlJMGb2w9UoCx/9uMruNna1oHqF3x7iJc/d5ir4+tRy+ocee7ey/Bh7d18fn11E3GjdZCtapTFTvHD/P5NcyItQj8eH8PrBg9EPf0bozEuBiXtaZt6xmfrKYlxaFrpnaNULKHNVBfqNpo/PNvHTSP/cvpPuAIRvV+181qpqClmyDPLH9Svo1S77y5UODLelRp67ugnk32R96ZQtS3BZbuCsv4a76XBVOUXw0nnNx7/9YuqFctCRfVSEZWZrrOe1pASmD70TOaolt6Xp2yGWv3n8LmQ55Tj537W8aauLBRUioDepGWiChQGAyGiBAClzSpgeYBOKlTC8aataMFwatOWtl4amJ/7/+txN+/XeOSJqTHuSDEqr0n0Xz0VHvRDyklJq07ZL9/+TuL8PniPaiobNO7e2V6tX+PpjUwtJ1x+qQniapgsJqbQCA2xoLcnGxN0ZNAWPa8NQ1z1QvWWUClsbeedvWruq2m+sVd2oJP7gKrPs0z0L9lLfRrWVM3nTRVp5Lm6zO3uWxT/LTqAB7s19TwcbOcAw29aq5GAvEedf6RKRdZ/lh7EM/9ul73OeoefIoDJ/Vn0XWf7/Q3+ezQVljwTD/MeqKv23XdgPViiK+yMn3rf2omlTeM6sYE1KoXBmJ4e/efR9b3uMTgfy/0uO556W7rZ7pe03lnvhScLi4rx9r9gVmfSkQUSAwGI0ww6rtsOnQ68AetpJLiXf8khqrSG5VqjWcLPTcbL7hg3ed8cSkyR03BdR8sQUmZtM8gTlp3CI9+twYf26pdljqdYfuTamqGEAKfjcjC4lGXBfV1FOo+iGaupPvLefaudtVE5OZkI62K5xkpIQRG9mmCmU/0xSqdvp8pCbGmg1VlreSXd3VDls66NL1+g+7aw6QmxOLqzvXx4uVtDPcBrH0W3XEOYL2ZhTLb38+dGk6zsX//dg2mbTiMx75fi++W78ftny1zfZJ0/Yzz1BtQ83TVsHNzstGhQTXExlhMXZTr0dT3FD9fW/KYKX5SVBL4wlGV2a4Jw7F7wnBTfVWF0F64kFLirZnbcPi08ZKHkrJyTRGpyY/01r7+sbP2FhkAsOHlwQCs1XmfHmzcDmrkxBX22/Of7me4vpaIqKIxGAyB/97c2afnmUljauBHwQ0jzg3II9mSnY6Z1e6N05Gbk40PdVoeuCtPr+g4dibOFJagzUvaHmzf2PrIKWvF8gxmXvPOFGkac3tqJO6LAa1r29PkKsLWcUODngaq6NGkht8XR1rUTvV40pkY5/5jVC/lVL1e02hd5GlVkPOyqqjIQ/2bAXC0hjDy/q3ue585c74Y4U4ginJcd7Fr8/cHv1ltv/3njnw89+sGTN3gqMAo4dpC5edV2mI77ijDfmKgdz1cBxs0m3dnxWjHRQRf0z3N/Jz96YsajmIswnQ6soDQzAZvPFiAd+budLuMIi0pTvMOU68xBYABby7AT6v2AwBqV01AaqL14tKXd3XD3y9zbQd1Z89MAECuqi1MZkYyLmvl/XuKiCgYGAyGwBUd6/n0vNdVa4uu1zmRAoDs9o5jB6rX4KA21pmxjz001o0E6tTBxwY6vtj/ek47e7b+gDXdx9PPuL2JnoNGxxj2nz/RbfwcjJy4AsWl5fjOTTPycJEYF1Mhs4IAEGOxQEpr0ORpTZ+vFj7TH4uf9X5m9Y3rO3rcp+NYx3vnzl6NseHlwfjpgR6431aJuI4fPfH0eBcM+v/ZUi3Jc0/Q75bvw0OqAFGqZgZv6motxPPlklyX6snrD1gL7xQUamcNlXF7e5GgfyvPhXGU30tGSjxyc7I1nyVGLSeMpCdbnxusAkrRYtbmozipuqhyxbuLNI8rWRvq9ij5Z4s1ucj10pLwwa3atdJK+5Z/DGkFZ0ucMi2cf/f+Vq0lIgo0BoNhRF1047nh+uuZqqtS4M4WeU5lNOPt2dsB+FfmP1z0VrU2qJfm+PdWTdSmFiql8Bs/NzVgr13LYA3b7C15aPHCNLz8v80ujz01yLsZjmiirP2Zv+2Yvf1GoDWqUcVl5lAvrdRZjWT937XeOkUl6ElNjEPXzHT7rIheEZW3b+xkv+3tjFGZF/ur48bHB7rOhpiRFB9jOtX2F9vsn4QjkFOnbc7fpm3afeW71sI7T3yvrQCsDNvbEEtp/+GOMtNZVWc9rDqt0Iwv7uyKcVe3Q20TAX84NZuvaJ4qLOeY6Bfask4qhrWvi7FXtbVvUwK8uFjXU6h61ZLwvi14nP54H5w4p838mHCto3r1k/z8JqJKgMFgBfrvzZ3xwKW+F35QL2yPMbi0fVcvR3n0PDfrjryxyVZdLRquUqvL3atPtvVOvP+p0/fNDKO02+4my47PeNxRgbZ2muNkcdLfe+ntHrVC1fvLzFqmlrb3wOtOlURH9mnisu/1WfpZAM5uzGqIqzvXx6S/90LvZhler1MrLXMfVZSVS3vhJPXM4GMDfAsGFZteGYIv7uyq2TasXR3N/ad+Wofc/HOQ0pEmOkiVupmaqL8O9GxRqWb9pTJsb2cGE3RO+p01zkjGoDa18c5NrssAvJl1BYA6aYm4/ZKLvHoOubqjh/uf4TqDgi7qt4dSBdm5nQQAdGqg3+ZlePu6yM3JRqs6VfGv67RZAFVUWQo3dnW0mXGexSYiqigMBivQFR3raRpve0s9M2gx+M2py/dP33jE59fSYxSARpKWqkAt3sMJ4Ae2vm/ObuneyGUd3jOqvpLKekPnn6ZSlOOtG9ynEKpPJpQ1ounJ8ehgcGISrSr64sXqFwdh7UvWSqU/2aqGNkzXn02vmZqA3JxsXO/Uc1CPu9/r+Gva2W8/bXuPdWhQDV+P7O7x/auWWaOKx4BlzKSNuP7Dv7D+wCnNzKA/LSoA64WW/q1q2Qs1zXi8L6bpfHb1e2O+ZmawSrzjAs0vBusGl+05ga7jHW1elIIs3o7ZzP5xMRZ8ckcW2tV3XSPqzayrtzgxaKx6FfepyOsO6BdH23XsHNrW067R79uiJv4xVNsfOC7W8/siOUGboq6uWK1+dp6XfWKJiAKFwWAYUZ/cmjnRVTdM95V6BiAaWkzEqWYGnWcDnBsaG5lwTXv8+Y/+9vuzn+yLh/s3Q25ONjo2rIYdeWfR+sXp9seVE+vycokWtVM0zdm/vbe7y/GTVMGgMpujFKMhh7gKWpuoSE+ORzXbyWerOqlIiovB2CvbeXiWsQXP9MPy5we4/XfM2nzUfttdqwxPjhQUYtXek/b7w/7zJzJHTcEkVY/Rr5da16xuPlRg/1xQB6P++uC2Llj4TH+0rJOKTa8M0d2nuLRcE5gpxbh+XXPQ1GuEKqWyac2UoB07UGvDI5G778kLHlLHv733Ek0WBgA81K+Z5n68ic8Y5zXSmtYVmuFF/sVWIqqcGAyGEfXMnJn+U/669v3F6PCKo4iFc5GGSKT+cnf+ohdCaII8dywWgSWjLsPWcUPRTJWup6QlXSgps68B/GrpXgDAtI1HsP3oWSQnxGLxqMuQc2179Gya4dKcOyUhFk1spc+V9UmXNGFRAmehTGtOTYzDlnFDTRUeMXJRjWTU8rBm7OBJ4xL53ii0tSdQ2plsOWxNDX/0uzUu+1qEsF/AUD6H1KX4fSWEQKMa1l5/Rk3lNx0q0HzyqduvKOvyzFT6tVRwloOZtX++YihozN1HwJki92mZaUlxmkwRPWZSwgFrj9OWtvYlyaoZ7Yr4Hici8oTBYBgpU10BdlfO/t83OtIMx012LTpi1up9p3BG1U/Pm7SzcKUuWa5XvtxTY2p1AZB61ZKQ6KGJvZH61ZJwU7dGAFz7sSXGxeCH+3vgm5Hd0bFBGp4Y2EJ3nVK0C9WawYrk74xTdaeei+d1ZktKy7Qpjv9bf8iebqn8iH9+oAd+ebCnX2Mxa7+qybx6ne2C7ccAuL9opaTCVnTGezBfr3mt4M06hjt36b3/nrXDr2M7/+24U7tqImY80Re5Odma7xXt8BjWE1FoRP7ZfYRy9yV3dSdH5bvPFu3x+7UyUqzBSK9mGR72jA7znu6nu/3dWzrjblUBHz1NPDQC1/P0kJYu22qmJqBXswwIIfDYwOYeZ5Ci0bI9J+y3/UmhrGiLR12Gpc8NMLXv+04l7731/q0X4+H+jqJWes3k52zVVuosLZP2mUFlhq1GSgIuvqi6X2Mxa/exc/bb6oq/yizmA1+vMnxuv9fnA6j4hDxPxXn80aC64wLVoVOBmSmOFO6C8O+W+9aqR+n3u9Bklog76uFV9Gw1EZGCwWAE8regg7PWdauiU8NqUVFN1AyjlLjLO9Tz2Az5TRP95ZwpawirV4kzXYqfgN9U68gKg9RaIhjqV0tCnTRzwb3Z5ttGejStgWdUvdLWHTiNzFFT7IWJAOBdWxuVurYx/bX7OHrl2Jp2V8BHwq4Jw3GtQWuHNJ3ZmWW7T+jsaZVvSyut6PNuXzMEvHUgQGnDkUIvwJpvcDHPrGmP9UFuTrZhBVtvqL+rK3qNMxGRQn9hBlVKzmvHzCorl34FcqVlEnExkR0Ivv63DvYU0J3jh3ndJNqszo2qwyK0fdo8ibEIBoE+aFe/KjYetK59OxOgnpuRalCb2pi1+ah9dk0dVNxgK39/+LRrtcPvl+/DDSYqovrii7u6IjUhFjEWgQnXtjcsEtOjSQ38tfu4/X5xmee/3Ypeq1VRF9L0Znajmd6PPcHNEouKFtnfqkQULirPpyJ5lJ7svky2kRITJ0fulJaXI9aol0WEuD6rIS6xrT+KjbEYFrBQTH6kN+7t0xi7JgzHfX2bmE7rA4Dlo12bkrOseOApRVGiwb193Kcne3JzN9eATkkPLyktNyzKsnqffp+2QOjfshayMq2FkdzNrE28u5v9ttk10pGakVfuZT9DM+Zvy0PmqCnYk3/O886VjF7QXzdNv92Lkv5ZkdTvQwbyRBQqfp3hCyHShRCzhBA7bP/XXTQihBhh22eHEGKEbVsVIcQUIcRWIcQmIUSOP2OJJh0buPaxcjawtaOKYZGfJ8UlZVJbDpvQrn4aRme3QYxF4PnhrU2n9QFAhk4Fut22E63OjdgrMFCGqxqXz3yir5s9w1tuTjZGZ7fx6xh6F3vyz1rblYydvBkrc/VTLwe2rq27PRhu7d7I9praCq3qwlaBWCMdSN+M7I77+japsNdTt50JlKd+XAfA9zV2zrIqaG0pYC7on/xIb8x4vC9+uP+S4A/IiTpYDUIcT0Rkir/TPaMAzJFSNgcwx3ZfQwiRDmAMgO4AugEYowoa35BStgLQGUAvIcQwP8cT8eY+dSm+Hunae87ZpyO64uUrrCeIHcfO9LC3e2v3n4qKyoyh9OZMa0/INUGcaYk2D/RzFEZpUdt9ifho52m90o0fL9Xd/uYN3q+B9VV2+7oAgLt7+zcLCni3rjrJj/V+vZpl4PnhrT3vGCAbDxUE/JjHbT1Mf11trpej2p87jqHDyzNw1pamXb9aEi6q4X8bErOc1wxOfqS3yz7VqlhbSFRVrQF86XL/Lq6YxplBIqoE/A0GrwIw0XZ7IoCrdfYZAmCWlPKElPIkgFkAhkopz0sp5wGAlLIYwGoADfwcT8RrUjPF9ML1Uj8uNa7Zd1Jzf962Yz4fizxbkXvS807kFX9O4qNNfKxvF3sqci1xz2YZ2PjKEPRs6lrVuFND1xn18de0MzyW2Wtb68YMxooXXNO6K5t/XtceAJDh41ICM/JVPR3NemPmdhQUlmL70TMAACllhaboOr9Wu/rWrBp1M3l1NVZFRWXCqMcnGQwSUYj4GwzWllIett0+AkAvZ6g+gP2q+wds2+yEENUAXAHr7CIFyHvzdvr83B9XHgjgSMjZ8tEDNCckiopMoYp0Qgj0aZ6Bt2/sFOqhVHrOaaKvXdseX9/THQ3T9ddXKSq6wnCKwVreD2+7WHN/ePs6uLX7RYaFl8w0pgesRbuMXrMySbI1Mn/wm9UBPe5ZPwovLdmZj3X7rZkOSpwjUbFFU4xeq2WdVKQmxuL+S7UpvHf0uAiAY7wViWmiRBQqHr/lhBCzAdTReWi0+o6UUgohvP44E0LEAvgOwDtSyt1u9rsPwH0A0KhRI29fJio1qZmCVXt9m3EK1PoQ0lcrNRG1Ul3XGVZkClU0+OoezynVpE0THd6+Dm7uZv2Mnf5YX7QdM8PweQmxlWP21XnN7js3dbbfnv90P5w4X4xr319i37buwOkKG1tF+HHFfs87+WDo2wvdPi6lxJr9p9ClketFrAnTtthvz9uah+a1UyBlxRbv2WabkdSz4eUhLtuUoVVUymaq6kID00SJKFQ8zgxKKQdKKdvp/PcHgKNCiLoAYPt/ns4hDgJQl6prYNum+BjADinl2x7G8bGUMktKmVWzZk1PwybAfkJHlZdzFdLS8uipgEmVhzotTl25MzkhFl/e1dV+f+Ez/bFuzOAKHZtZPWzVgO+/tAliVcFtZkayS7Cy/kBkrc29xqAPo7889S38ceV+XPv+EkzfeNjlMaWtCwC8O28n7p24EhKyQtt6zNp81Kv9m9vWFjfUSR0NBvXaVX70E1Go+JsmOgnACNvtEQD+0NlnBoDBQojqtsIxg23bIIR4FUAagMf9HAfp6GYry06VV520RNSv5kjF87cNCJEvDqpO+p3bOHRr7PgcaZie5HO/02D77r5LMP/pfnh2SCuP++49fr4CRlRxOqrWTGaOmoJfVvmW5r/96Bmc8yI1dNcxaxXkXBM/z2V7TlT4zKBSERcAnh/u+X1xa/dG+PWhnhjYpuKq5Co4M0hEoeJvMJgDYJAQYgeAgbb7EEJkCSE+BQAp5QkA4wCssP03Vkp5QgjRANZU0zYAVgsh1gohRvo5HlJpVKMK7undOCzWvESzh/s3s98uLeMJAVW8Hk1r2G8nxGq/FtSFeJSZjMmP9MbqFwdVzOC8kJmRDIuJdYyRVh3ZuZDPUz+tw4Lt3hX9Ki+XGPzvhbj9s2Wmn+PtTzHvTFHIUnSrxHv+HhRC6Ka8VgTGgkQUKn5FCVLK4wBcum1LKVcCGKm6/zmAz532OYCKXUselSyCVxwruz7NHdURzRa2IAok9Wyg88ygXhsGpSpjuBrg1Ksw3MXqtAYZ8fly7J4w3FRwDADFtqyE1bb2Nt40sPdmNnHL4cC3vzDj8g51Q/K6ZvlTrIeIyB/+zgxSJWcRwutgsKi0DLVSXRujU3A0TK+CL+60rsvypx0IUSAkVpKiMIG2MgxaRPiqhkFLibVerI1Up6hnjpqCt+fsMP3c/87dWSkvZKl/LpWl2JGRnOlbQz0EIopSDAYjnBDCq5LVk9YdQssXpiPvjLanVIPq7kvMk39qV7VWQ2xVl83RKbTiY12/FqY91gef3pEVgtEETkZKgr11S6QlSyTGxejOfL3w20bTxygu1a5XfkcnGHTphaeadHxt6hYs2ZUPADh5rhiVQWdVymdFt0Hx1qnzleNnRkTRh8FghLMIxxf4i79vxOjfNhjuW1JWjke/W6P72A/39wjK+MiqTb2q+O2hnnh6cMtQD4XIReu6VUNSVCPQRvZp4nmnMBWvkyq62YuUzBKD9cq3XeKoSv3jSuMWFp8u2oNbPlmGgsISdB43y/TrBtPdvTPttyv7OlGuFyeiUGEwGOEsQtjTd75auhffLDPuH7h4Z77hY+qKlxQcnRtV1/R7I6pIVROtS8g7qSpTRpqKrGRZ0fz97DCqZHzoVKH99u5j55B/tgjvzduJjQdPw6LzA+30ykyXbXOfutSvsflKnRpqdu1kqLCSNBGFCstMRjhrARlz++pdWSai6LD2pcHYkXcWLetEfqpyJM7BxMX6F+wUGwQjQ9rWxtyt1hbCHy3cjY8W7gYAvD5jGx7q19Rlf+fvmw9u7YKEuNCs1+scRhc2uF6ciEKFZ/8RTrka6rLWQ8ekdYeCPRwiqqQsFhHxgaASLkXamkHAeGaw1OSMk9HM1DWdGxg+5+Ap903p5zx1KYa1r4s6tjXRFc1iEZj8SG+MGua5x2CocWaQiEKFM4MRTknjafzcVI/7Lt5lnCZKRBTuHG0yIi8aXLRD//P75PkS1DRRHdpozZpeQSFFwYUSt8dsWjMFgLZ4yzWd63scSyC1q58WFq1Q9FJuiYgqAmcGI5w3yyT2n3B/lZeIKJwpa5/b1K0a4pEE3o68s7rbu46fjSvfXYRPFu7Gc7+uN8wS+X3NQa9fc9427xrbA8C/b+zk9XMi2Z//6A8AeOBS15RbIqKKwGAwwuk1jNZz5LSjSMCNWQ1NXUkmIgonbepVxe8P98LjA1uEeigBlxhn/HW+/sBpjJ+6Bd8t34+T5/Vn8z5dtMd+e5CtcuywdnUAAJ+YaCvy6tXtvBku2VSJt66nTE6o3H0QiShyMRiMcHqpJxeKy1y2jZuy2X77H0NbYsVoR4PmrpnVXfYnIgpHnRpWq/SVJX0x4Zr2mvvv3tLZ52MNbF0LX9/THf+5yXqMQR7aiky4pj1u7d5Is40VqM1RvqPLWECGiEKEwWCE05sYzH7nT/vtotIyvDVrO6asP2zfViNFOyv4w33sMUhEVJlVideWAEhJ0C8JoP5KeG3qFnyxeI9Lw/nOjaqjd/MMt+sFAaCVreBQ/1Y1XbJQBrSupbk/+ZHe+O/NvgeokUq5MMFYkIhChcFghNO7AL47/5z99ld/7cU7c3Y4Hpsw3H57eHtrilAkXkUnIookCU6BW2qifjD4v/WOqtEfLdyNV/63GeWqdYSTH+mNFrVdq8pe28W18MvWI2cA6LclempQS839dvXTcEXHem7+BdFJ+Xo1U/GbiCgYWE00whlVKCspK8egtxYg9/h57f6qwO+9W7rwaiURURhw/qjPP1usu9+/Z23HHT0yNdvUKYpG3xktdQJE5+dkpCQg/2wRMmtUQVqVOBOjJqaJElGocWYwwhkVkHl/3i6XQFDvuTGcFSQiqvR2Hzunud/KoGekXgEZ9cxgabl+v7tVe08avnZhqXUd+pW2mT+jIjXkKoZpokQUYgwGI5xRLPfv2dtdthmdPBARUeW2J18bDFarEu92f3VaokH8pzFz81HDx5RZLSWo9LTWkByU67XlTBMlohDhJ3aEM2ok7OztGzth6qN9gjwaIiIKhhu7NtTcT0tyn6apTktUByLpye6DSD2pCdbXUqqOjhrayutjRCslTbScU4NEFCIMBiPc+KlbTO13def6LBRDRBSm2tVPw7qXBpvev1QVfJTZgsHrujRAg+pVvH5tZX1gr2YZmPd0P91iM6QvRjBNlIhCi8FgFGlfP013+6wn+lbwSIiIKNDSqsRhRI+L7D3/Pr79Ykx+pLfuvpqZQdvtzo2qGR57w8vWQDO7fV23Y2ickWy4Vp1cMU2UiEKN1USjyEP9muLBb1ZrtuXmZIdoNEREFGivXNXOfntw2zqG+50876g2qsSFRpVEASA1Mc7+fTFl1BQ/R0kKIQSEYDBIRKHDmcEIV79akv02v2qIiKLPF3d1xaS/97Lfl1IiKS7Gfv9CibUaqE67QKoAMUIwGCSikOFHf4Qbf43jKnG3xulIjnecADSvlRKKIRERUQXq37IWOjRwpICeLy7TXBz81/StANzPDFLwWITgmkEiChmmiUa44lJHzfDUxFiseGEg8gqKkJmRHMJRERFRqNz8yVJ8NqKr/X6hbWaQwWBoCMFqokQUOgwGI1xVVXnxWIsFCbECmRn8tRMRRav1B06j6/jZ9vsJsdaMEcaCoVFUWo4DJy+EehhEFKWYJhrhOqpSg2LYOoKIiJys3ncSADBt45EQjyR6TdlwONRDIKIoxWAwwsXFMAAkIiIgu4N+W4imNa3rx2/Maqj7OBERRS4GgxEuluXhiIgIwJT1+rNPf+0+DgBItkWJ7QAAIABJREFUT4n3+phG/WuJiCg8MFIgIiIixPiwaLBTQ+NG9UREVPkxGCQiIooCn9yR5fbxc8WlXh8zOYEFyYiIwhmDQSIioigwqE1tjB7e2vDxXXlnvT5mg+pJ/gyJADTJSEb3xumhHgYRRSkGg1GCXzRERHRP78YBPd4lTWoE9HjRqGpSHOJjeTpGRKHB/I4osGP8MDYTJiIiWCwCo4e3xvipW3Qf81azWimBGFZU49czEYUSL0VFgbgYC3sMEhERAODevk10tzevlVrBIyEiolBjMEhERBRlburq2lOwm5fLCT64tUughkNERCHCYJCIiCjKpASgCmiLOpxJDATm7RBRKDEYJCIiijKBWDpwUXqVAIyEAEDKUI+AiKKV38GgECJdCDFLCLHD9v/qBvuNsO2zQwgxQufxSUKIjf6Oh4iIiNwTTlVLPrzNfMrnWzd0RJ/mGYiN4fVkIqJwF4hP8lEA5kgpmwOYY7uvIYRIBzAGQHcA3QCMUQeNQohrAXjf4IiIiIi85jwx2KC6+Vm+a7s0wFf3dA/wiKKXc2BORFSRAhEMXgVgou32RABX6+wzBMAsKeUJKeVJALMADAUAIUQKgCcBvBqAsRAREZEHzmmiddMSQzQSAgAJ5okSUWgEIhisLaU8bLt9BEBtnX3qA9ivun/Atg0AxgF4E8B5dy8ihLhPCLFSCLHy2LFjfg6ZiIgoejnPRsWx6XnIcF6QiELJVDkxIcRsAHV0HhqtviOllEII05e3hBCdADSVUj4hhMh0t6+U8mMAHwNAVlYWL6ERERH5yDlNNM7CYJCIKBqZCgallAONHhNCHBVC1JVSHhZC1AWQp7PbQQD9VPcbAJgPoAeALCFErm0stYQQ86WU/UBERERBMXn9Yc39uBjOT4USq4kSUagE4lLgJABKddARAP7Q2WcGgMFCiOq2wjGDAcyQUn4gpawnpcwE0BvAdgaCREREwTWsnTXZp139qrj9kosC0mqCfMP6MUQUSoEIBnMADBJC7AAw0HYfQogsIcSnACClPAHr2sAVtv/G2rYRERFRBUtPjgcA9G9ZC+OubseKlkREUcpUmqg7UsrjAAbobF8JYKTq/ucAPndznFwA7fwdDxEREbl3becGWLPvFG7vcVGoh0JgmigRhY7fwSARERGFl7QqcXjn5s6hHgYBEKwnSkQhxPJhREREREREUYjBIBEREVEIsek8EYUKg0EiIiKiUGGWKBGFEINBIiIiohBiARkiChUGg0REREQh4m5icGfeGZSXM1IkouBhMEhERERUyazdfwoD31qIj//cHeqhEFEEY2sJIiIiohBSz/29NnULMlIS8NOq/QCAnGlb8cClTUMzMCKKeAwGiYiIiEJECO2awY8WciaQiCoO00SJiIiIwsSgtxbgxd83hnoYRBQhGAwSERERhZIXNWJ25J3FV0v3Yt7WvOCNh4iiBoNBIiIiohARXjQaPHz6gv32wh3HgjEcIooyDAaJiIiIKoENB067ffzU+RL77ZKy8mAPh4iiAINBIiIiohCStjzR6z9a4nY/izA/i0hEZAaDQSIiIqIQUcd3hSXuZ/ssqn0le9ETUQAwGCQiIiIKIU+B3aR1hwBoA8dzRaVBHBERRQsGg0REREQhogR40k1EeOJskcu239ceCtaQiCiKMBgkIiIiCrG1+08ZPlZSZg0UWTOGiAKNwSARERFRCEkAz/6y3n5/14Th2PPacPv9L5fkAgDKyrlQkIgCi8EgERERUYgofQbVWaIxFgGhWiDYpGYyAKDcKZW0qLQs+AMkoojGYJCIiIgoxJRZv9SEWJfHkuOt25yDwX3Hzwd/YEQU0RgMEhEREYXQheIy7M4/BwDo3TzDvr1WagIAYM3+kwBc00T3MhgkIj8xGCQiIiIKESGAzYcL7PeLSx1VYh65rBkA4GiBtZqo85LBGZuOBH+ARBTRGAwSERERVRKZGcn22x0aVNM85pwmWsbO80TkJwaDRERERJVErMVROKZetSTNY85potWS4itkTEQUuRgMEhEREVUSI3pm2m/Hx2pP05xnBiU4M0hE/mEwSERERFRJqGcDE5yDQaem8+XsO0hEfmIwSERERBQi6n6CzuJjtKdpzmsEuWaQiPzFYJCIiIgoRBZuP2b4mEW1fjD/bJFrAZly52cE1saDp/Hg16tQGuwXIqKQce1sSkRERESVypnCUjzw1SrNtmCniT7y3RrsyT+HvSfOo2nNlKC+FhGFBoNBIiIiokqu/xvzXbaVBjkYlLaZSONEViIKd0wTJSIiIgpDzmmjgaasSYyxMBwkilScGSQiIiIKscFtauP+S5t49Zxgzwwq1UstborcEFF448wgERERUYi9d2sXXHxRusv2b0Z2N3xOmXOviQCzp4kyFiSKWH4Fg0KIdCHELCHEDtv/qxvsN8K2zw4hxAjV9nghxMdCiO1CiK1CiOv8GQ8RERFROIqL0T8l69Usw/A5ZcFeMxjUoxNRZeDvzOAoAHOklM0BzLHd1xBCpAMYA6A7gG4AxqiCxtEA8qSULQC0AbDAz/EQERERRQV3HR8+X7QHmaOm4PT5Ep+Pr6xJZDtDosjlbzB4FYCJttsTAVyts88QALOklCeklCcBzAIw1PbY3QBeAwApZbmUMt/P8RARERFFBXcFZMZO3gwA2HjotE/HXrIzH0cLigAwGCSKZP4Gg7WllIdtt48AqK2zT30A+1X3DwCoL4SoZrs/TgixWgjxkxBC7/lEREREZNOqTioAoGZKgu7jhSVl9tvxsb6d6t3y6TL77WBXLSWi0PH4CSGEmC2E2Kjz31Xq/aR1lbE3nxaxABoAWCKl7ALgLwBvuBnHfUKIlUKIlceOHfPiZYiIiIgqp8/vzMI7N3d2u8/I3o0191+8vA0AYEDrWrr7T5i6xX47EG0hvlu+z+9jEFHl5DEYlFIOlFK20/nvDwBHhRB1AcD2/zydQxwE0FB1v4Ft23EA5wH8atv+E4AubsbxsZQyS0qZVbNmTVP/OCIiIqLK7LJWtXFlx3pu98nK1NbnUwI8oyvw246csd/ed/y812M6crpQc/+jhbu9PgYRhQd/00QnAVCqg44A8IfOPjMADBZCVLcVjhkMYIZtJvF/APrZ9hsAYLOf4yEiIiKKKLEW7emaPRg0iAaX7Tlhv/34D2uROWoKJi7JNf16f+1mCQeiaOFvMJgDYJAQYgeAgbb7EEJkCSE+BQAp5QkA4wCssP031rYNAJ4F8LIQYj2A2wE85ed4iIiIiCLKBdUaQMDRBF56sZZvzKRNpvd94od1mvsN05NMP5eIwkusP0+WUh6HdUbPeftKACNV9z8H8LnOfnsB9PVnDERERESRbNme45r7ysygt20GV+SewKuTN2Nw2zp4uH8z089rkpHi3QsRUdjwKxgkIiIiouBKjteersUIJRj0Lhq8/sO/AADrDpw2DAb/2nXcZVtBoe+9ComocvM3TZSIiIiIgqhBdW2apmNmMLAtHzYfKsDNnyx12b5m36mAvg4RVR4MBomIiIgqsRu7NsKjA5rb7wcrGBw5cYXm/nVdGgT0+ERU+TAYJCIiIqrE4mMteHJQC/v9GNvZW3m5676bDxWYOuaU9YcBABeKy9B9wmxkjpqCQ04tJR4dYH5dIRGFJwaDRERERGHEorNm8K2Z2/D7moM4dOqCfdvkR3obHuOjhbsAAJ3HzcTRgiLdfRpWrwIAuL9vE7/HTESVEwvIEBEREYURpe+gEgvuP3Ee78zd6bJfu/pphsdIiLUeo7BEZ3rRxhZzIiEuxseRElFlx5lBIiIiojCi9KBXZgb7/Guex+d0bKANDHs0qeHxOUIIWIR3/QyJKLwwGCQiIiIKI0oBmW+W7fMYqI25og0AoGktba/AKgnmksMsQgS8UA0RVR4MBomIiIjCiNJncMPB09h4UL9gzCtXtgUA3NWrMaomxiLOoj3ly5m2FZPWHfL4WtZg0M8BE1GlxWCQiIiIKIxYbDODAHDFu4s0j3VokIbaVRNwR4+L7NuEEPhh5X6X4zz63RrD11j94iDbc4FyRoNEEYsFZIiIiIjCiFJNVM/6A6cBWANAhdHuVeJjcL64zGV7bk62/XaMhWmiRJGMM4NEREREEcwodNQLBJ0xTZQosjEYJCIiIgoDt3RvBCDw1T2zO9TFnteG48ashpj95KWax4QAZwaJIhiDQSIiIqIw8OpV7bDt1aFez9QJIZCWFGf4+JT1hyGEwD//1gHNnKqOWoTgmkFy8eh3a5A5akqoh0EBwGCQiIiIKAxYLAIJsTFIindtAq80kQeAAa1qaR4TAOJifDvlswgwTZRcmKlES+GBwSARERFRGElJiMVLl7fRbFv4j/722zd0bah5TAigpKzcp9eKsQiUMU2UDJw4VxzqIZCfGAwSERERhZm7ezfGnT0zAQAzHu+L2lUTse6lwXhiYAsMaVvHaW/hczDINFFy52hBYaiHQH5iMEhEREQUhl6+si1yc7LRsk4qACCtShweG9jcZb/8s0WmKofqYWsJclZY4ngvXf3e4hCOhAKBwSARERFRlHj9bx1ctn0zsrvh/hYhsCL3JNMBye6pH9fZbxeV+jbjTJUHm84TERERRYl+LWth4t3dkJESj7b10lBaVo5YN8VlSsvLcTD/ArqMm6VpRk/Ra8qGw6EeAgUQg0EiIiKiKBEXI3Bpi5r2++4CQQA4WlAU7CERUQgxTZSIiIgoSsRYRKiHQBGkYXpSqIdAfmIwSERERBQl4mN56keBc23nBqEeAvmJnwhEREREUSIh1rVhPZGvSstZQCbcMRgkIiIiIiKvlbIHZdhjMEhERERERF4rLWMwGO4YDBIRERERkdc+W7QHpWVMFQ1nDAaJiIiIiMiUm7o21NxvNnpaiEZCgcBgkIiIiIg8KuEMEAFIqxJX4VVpi0vL8e7cHSgsKavQ140GDAaJiIiIyKOVuSdDPQSqDCRQ0d0qv166F2/M3I5PFu6u4FeOfAwGiYiIiCLYsucHAABi/Ww4f/MnSwMxHApzEoDw8a309dK92HXsrNfPKyy1zgieK+bMYKDFhnoARERERBQ8tasmYv7T/ZCc4P9p3/GzRaiRkuCyfdOh0zh9oQQ9m2b4/RpUuUkpIZzmBsvKJWJMXGx44feNSIqLwZZxQ716TYst+pSS1UsDjTODRERERBEuMyMZNVNdgzhvfWyQppf9ziLc8skyv49PlZ+UrjOD3qwnveDDuj8lziwLcl/Ds0WlOH62KKivUdkwGCQiIiIiXR/dfrHmfkUXDqHKR8J1zWCwiwspM4PB7nE/4M35uPjV2ThXVBrcF6pE+BdNRERERLqGtK2D4e3r2O/HWiy484vl+GHFvhCOikLJOjMoMO/pfujdzJoW3P7lmUF9TUcw6F80WFhShtX7jAshHS2wzgq2HTMDE5fkYtzkzTh5rtiv16zs/A4GhRDpQohZQogdtv9XN9hvhG2fHUKIEartNwshNggh1gshpgshmGxOREREVEm0qlPVfvvfs7dj/rZjePaXDSEcEYWaANA4IxmLdubbt+Xmn3P7HH/W+ylpol8uyfX5GABwx+fLce37S3D49AWP+46ZtAmfLdqD7q/N8es1K7tAzAyOAjBHStkcwBzbfQ0hRDqAMQC6A+gGYIwQoroQIhbAfwD0l1J2ALAewN8DMCYiIiIiCoARPTJ1t7OYR3SQUmLTodMe9+v3xvygjWG3h0DTrOV7TgAAzhSaTwMtLo3s/pqBCAavAjDRdnsigKt19hkCYJaU8oSU8iSAWQCGwnphQQBIFkIIAFUBHArAmIiIiIgoANKqxOlu/3xxLgBr0Q2KXD+vOoDsdxZh7tajAAAJ3y4C+HPtID8ARV3UFy+W7TmBeVvzNI9HejqokUAEg7WllIdtt48AqK2zT30A+1X3DwD4//buO07K6t7j+Pe3HZal9yJditgQKVYUVAQNxpJYokTjNdyY5MaYGLwmuRpvEGPiTbwmllhzozcqJspFgwr2ShMEBGUpCkjvnS3n/jHPzE55ZndmZ7bMzuf9es1rn+ec8zzP2T3LML89rZtzrkzSv0paokAQOFjSo34PMbMbzGy+mc3funVrGqoNAACARDxzw8iYtDtnfipJej3qQzWalmVf7ZEkrd12QJJ04HCFDteityxd/ciHy2u31+A1j80NHf/ihaW69ol5Efl/erM0pXplqoSCQTObbWZLfV4Tw8u5QMidcFubWb4CweCJkroqMEz0Vr+yzrmHnXPDnHPDOnTokOgjAAAAkKIRfdpp7bQJMenlFZV6+O1VDVAj1JfoeXrPzF+nI3W8emhlpdPmPYdC50V5uaHjZ+evr9U931m5rdr8fYezc0P7hIJB59xY59wQn9eLkjabWRdJ8r76/Xlog6QeYefdvbQTvPuv8gLJZyWdksL3AwAAgHry/ML1WrphT0NXA/XAb7uFLq2KIs6r67VLZo7pf760XCOmzgkt9DK8d9tQ3szF6ZtRtml3VcA5sHNJ2u6bSdIxTHSGpODqoJMkvehT5hVJ53qLxrSRdK6XtkHSYDMLdvWdI2l5GuoEAACANCv99fm6/8oTQ+c7D5Q1YG1Qn/5r9ufqNeWliLS3bzlLl53UXQM6BQKp4FBSP8kME33svTWSpNc+DcxT7FBSGMqrzRDVeEaGrRT6HzOWpe2+mSQdweA0SeeY2UpJY71zmdkwM3tEkpxzOyTdKWme9/qVt5jMV5LukPS2mX2iQE/h1DTUCQAAAGmWl5ujC47rGjqf9s8VDVgb1Ce/Dd/zc3N0z2XH66yBHSVJ5/3+bX20entKz/n5C1XblixZH1jFtHlBXijtwuO7xlyTqvI6HvbamKUcDDrntjvnxjjn+nvDSXd46fOdc9eHlXvMOdfPez0elv6gc26Qc+4459yFzrnUfoMAAABQ75rl59ZcCBmh15SXdNmD7ydcfnjvqm3Gf+UtLBStplGizjld98Q8/fXDL0Npzy1Yr8n/s0Dfe2pBKK1tsf/qttWZt3ZHxPnLPzw94vzuWVV/1MjPtaTvn8nS0TMIAACALHJc91YxaTnZ9Rm6yZu3dqcq/boDfRSGLfASXH20OrsPxg4vXr5xr+/KtLOWbYoYjlxWkfy6pJc9+EHo+MoRR2lw15YR+X9+Z03o+NcXHZv0/TMZwSAAAACS8o1hPWLSWjVLvscGjU/4Qi+3/19i8+gK82oOKcL3JwxfuCVo/H3vJPSs8loEg+FO6NE64nzW0o0R54X52RUeZdd3CwAAgJR98+SqYPC1m87QJUO7y4yuwaYgvOftLx98kdA1a7btDx2f1q+9b5nwYaJf7ToYk39qv3YJPevf/7Gk5kLVCA4DPXdwJw3sXKLJf10Ykb/RJ1BtyggGAQAAkJT83Bzdd8WJOndwJ/Xr2KKhq4M0OFRWoRFTZ+vjL3fWWPbiod0izs84umoP8P6dav59uPaJeXpu/rqItPdK62bZkCse/jDiPC8nEP68+ulmrdi0N6Z8n/bFdVKPxopgEAAAAEn72vFd9fA1w+gRbCJeWbZJm/cc1jejgqdot54/UP950ZCItNywCaOPv7dWUmAl0IqwOYfRW0L8dPonKda4ZuUVlfoganXTozv57yc4+cy+mjSqp8YM6qT3ppyt//v+aXVev8aAYBAAAADIYpWVTv/2t0UJlf3umX0jtnqQpBaFkefPzlunC+9/V3e9vFwbvCGhkx6bW+19e7ZrLkn68zXDQmkPfuuk0PHYQR0Tql9ZRaX2HAosOLM0ajGbS0/qrgHe5vLR8xynnD9Qd0wcotwcU7fWzXSszyJJTRHBIAAAAJDFNu5JbJ7czycM8k0vys/VijvHhRZnueX5QK/fI++u0anTXtfvZ3+uRet2VXvvozuVaGDnEp3St2ru4LghnfXjc46WJHVt3UySNKhLS9/rg256ZpGOu/1Vbdl7KGbV0nbFBaHj7m2ahY6X3nGe773GDuqowTU8L9Pl1VwEAAAAQFP1Xum2hMpdPapn3Lyi/Ny4Ad/vZ6+Me92CL3aquDBXzklmpuLCPF054iiN6N1WkvS90X1VXul0wxl9tGTD7pheyGgzPwmsDjr813Ni8h56e7VuHR8IaK8/vY9u/XtgMZriAv89MnPMVFnTBokZjmAQAAAAyGLPzFtXbX5hXo4emTQsYj/BdCirqNQlDwQ2tx87qKOCMw+nfr1qr7+83JxQ7+Chskp9/GVigWtNxg/pEgoG4817zTFTE48FCQYBAACAbLbgi/griP7pqqE6vkdrdWvdLG6Z2up/2z9Dx4GewerLL98YmAN435yVuv703jFzF5PRqnm+5t42Rm2bF8Qtk5MjVTTxaJA5gwAAAAAiDD2qtc4a0EHjj+2ScCD49RO71VyoGokuTHvva59r6svLk77/FcN7RJx3LClSXm78cIhhogAAAACarC1xFo/5+/dOTfpe937jeN0x8Ri9X7pdk/+6IKlrkw25tuw5rFVb96lzyyIVe/MI1+04UO01JUX5ST0jG4aJ0jMIAACAlLDVYOY6/TdvpO1eZqaWRfkaN6Sz/nV035j8qV8/VmcN6KAzwzapD/pyxwGZEv9FevXTzRrzu7f07certqxYu31/6HjNXeN1szfXMGhgZ/89BuOZsfgrrdm2v+aCGYxgEAAAAEiTyx/+QNc/Oa+hq5Gw6M3g0+Un5w7Qwl+co++f1S+UNrBLiR6/driO7tQipnzpln21GpI5b23VfMerH60KDM1MPxjTP6JsqsNYmyKCQQAAACBNPly9Q7OXb9HBIxU1DltsSLsOHNHmqCGiT143PG33z80xtS0u0Kn92ofSgv1+l57Uw/eaZVGbxCcjXg/e6f0Dzz+pZ5u4q4ZmM+YMAgAAAGl24p2v6lBZpVbcOU5F+endkiEdhk+doyNhvYKDu7SUq4MJcqPCNpEPbko/IMnhmok467dvVpv/w6hewkRcfnIP/a2GbTcyHT2DAAAAQJodKgsEWqn0dtWlI1HDQ0/v317tWxRKSn5uXU3WTpugtdMm+PbM9W5fnPB9/nD5Cb7X9pryUkT6X8J6OINDT3Nr0SvYqnm+CvKadrjUtL87AAAAoAHtO1ze0FVISEWl05BurTTj+6fqpR+eXm/P/dm4AQmXnXhC5Jy/eEND+3WsmpNYURkIBnNqEfXk5Vjo+qaKYBAAAACoI3k5dTtPbcn63VrwxY6U7xOMeY7r3lq5dVzncCN6t9ObPxktSXrwWyfVWP7Vm84IHQ/r2ca3TNewfRGDwVxtegY/27RPFZUuYwL62iAYBAAAAOpIXQdWF97/ri554IOU77Nm27401CZxnVoGhqQeKq9Qr/bFWjttgsYN6VzjdUd3qhrCeuv4QRF5N5zRR6unjo9ICwWDtWiH2cs3S5Lmrtme9LWZgmAQAAAASAO/BVjqqmdw+77DMXPlUjHplF5pu1cirhrRU5LUorD261me6C1IE/Tv4wcpJ+rnfedFQ3RK33Ya0q1VrZ9TXtF0h4oSDAIAAABp4De9rLyO5pzdMv2TWl/rNw9u9ICOqVQnaT84u59W/vp8lRTl1/oeOTmmswdWX+9jurbS0/8yslYrut40NrBpfTKL3GQagkEAAAAgDdZuj13QpLKOgsE5K7ZEnO9PYl5bWUXdbDSfDDNTfm7qochj3z5ZUvpXQJWkLq2LJElb9x5O+70bC4JBAAAApKwu9qirb5P/Z4F+/OyiWl//8icbY9LS2TN44X+/q+8/vdA374L/fjfh+0TX6dKTuqdUr/r20/MG6JcXDA6dv/XT0Xpu8qi0P+eFjzdIkn70TO1/Jxo7gkEAAACkpP7Wnqxbs5Zt0t8XbtD4P7xTq+v9wr50bU0w9eXlWrJht2b6BJxS/G0W/EQH7tMXrE+pbvXtxrP66brTeofOe7YrTmm4aU220DMIAAAAZIdPN9Zuo/hKn97RdASDh8sr9PDbq0Pny2tZv6DoKk0+s29K92uqmvoegxLBIAAAAKC7Xl5eJ/etbpjo6ys2q9eUl7R0w+5q77HvUOR8wPPj9FwmOj8xumfw2BRW2mzKmvLCMUEEgwAAAMh6D4X1vNWW37TJ6nqXvvPkfEk1z/c7cKQioecfLItfrnTL3lAQGF2lvNymMtA3va4Z1auhq1DnCAYBAACANPBbRKe8Mv7KnYmuuVNdkBfxrDj74b27cpvG3vu2npu/3ntuZLlWzepuvl0mCwbJg7u0bOCa1J3a7/IIAAAAIMQvFPObR+hn5idfac3W/frBmP4xedc+Pi+hexyuqJAUG9gtWrdTkrTwy536xsk9Qj2D/z5+oNq3KNSI3m0Tun+26d+xhb57Zh9dNbxnQ1elztAzCAAAgEbt/tdXavL/LKjXZ27afSjpa/zivni9dZJ05tEdJEk92jbT95/+WL977XPfcht2HUzo+fGGpAb382tekOfVM1CuuDBPFw/tLjOGifoxM916/iAd1a55Q1elzhAMAgAAoFH77aufa9ayTXV2f7/hnQeOJL6Je3WmRi1M8/6qbfqxt29di6JAcLb7QFnMdX/5YK16TXlJkvSjsYHewl9NPEaLfnlOTNlff32IpNjAc+GXO7Vx98HQIjaPvbdGUtWcwRyCwKzHMFEAAABkNb8evbJqevSSsdML9JxzemHRBt30zGJJ0jdO7qFD3sIwe8JWC3XOycz0yxeXSZJun7FMc1ZslhR/QZPmBbmSIucW7j5Qpov/9L4kaUi3yDlvwaGrhIKgZxAAAABZ7VB57AIttek0i3dNZaXTg2+tDgWCknT5wx/6rhL69sptEedPvL9W63ZEDhN99aYz1L5FYej8i+0HJEl3/N+yUNq+sJ7NpRsi9yUMhrn0DCKlYNDM2prZa2a20vvaJk65WWa2y8xmRqX3NrOPzKzUzJ4xs4JU6gMAAICGkcnbc89dsyMm7asE5+mFixdalW7dp7tnrYh9xu6MdkxIAAAagUlEQVTYZ7y4aINvfcId3alEc24+M3S+fd8RSdJ7pdtDaYerWYE0uB8hsSBS7RmcImmOc66/pDneuZ97JF3tk363pP9yzvWTtFPSd1KsDwAAAOpZpgcVfguo/PmdxPYdnLV0k3pNeUkLvtih+14v9S0Tb0HRYI9euNVb9ys3J/EfqJn/PoTV7U3omDMIT6rB4ERJT3rHT0q6yK+Qc26OpL3haRb4V3e2pOk1XQ8AAADUlV0HjsSkDeyc2N5yk/8aWOX0kgc+iFvGJdFvumjdLl3ywPsRaT8bN1BLbj83Ii0Yx+Xn5MTMCZSk90q3xaQFheYMEgtmvVSDwU7OuY3e8SZJnZK4tp2kXc654IDm9ZK6xStsZjeY2Xwzm79169ba1RYAAACI8orPSqWzlqZv9dJq9p3XjWf1rfH6DiWFKimK3D/wSHngpi2K8vTtU3qF0isqnd5YsUV3/TN2WGoQcwYRVGMwaGazzWypz2tieDkXWJO3zoaLO+ceds4Nc84N69ChQ109BgAAAFnmnMFV/RnnD+ksSdqfwtYS3ds0izivbuP5P76xqsb7LV63KyatbfMCXTniKP3luuERw1y37zuslVuqBuR9b3RksHmkvJKeQYTUuLWEc25svDwz22xmXZxzG82si6QtSTx7u6TWZpbn9Q52l7QhiesBAACAlHVuGQje7r/yRHVv01z/XLpJk8+sucduy17/jemP6dpS63dWLQ5zpCLQi9eyKE9OgYVmwreTKMjN0ZGKSnVtVaSvfDa7P+SzGExOjmnq14+NSd+w62DEfME+HVpE5P/s+U9CvZH0DCLVYaIzJE3yjidJejHRC72exDckXVqb6wEAAJBdgkMj0+3+N1ZKkj5cvV1tmgeGY3YsKazuEknxF4bp1LJIktSjbSDI/GBVYJXP753VT0tuP08j+rQLlb1z4jE6uXdgQf6fjhsQcZ9ld5yn607trV9NHJLw95Jjpt/PXhk6P/PoyBF1//h4Q2jTeWJBpBoMTpN0jpmtlDTWO5eZDTOzR4KFzOwdSc9JGmNm683sPC/rZ5J+bGalCswhfDTF+gAAAKCJCvawpVtwH799h8pDK3mWV9Y8++nNz/wHxbVpXqAVd47TLyYMliTd88pnkqQ87975uVVR2LdG9tRDVw/Ts98dpa+f2D2U/vT1I1RcmKdfXjhYzbxN5asz/tjA8NaJf3wvIr24MPba1z4NbGK/c3/swjnILikFg8657c65Mc65/s65sc65HV76fOfc9WHlTnfOdXDONXPOdXfOveKlr3bODXfO9XPOXeacO5zatwMAAICmqrq5d6kY0LlEkjS4a8tQMFiZQDC4YZf/MNFvn9JLRfm5MYvQBIdlvrykKt3M1KIwT8N7t40oe0q/9ol/A5KuHtnLN715QZ7OHRy5xmMwOP3DnJV+lyCLpNozCAAAANSLOooFdcFxXSRJZw/slFTPYLxhq22KCyRJBXmRH7U/XL3dr3iEtdMmaO20CTWWi9aiMHYpkGtP7SVJuu+KE/X+lLMlSZ29IaxS3Q27ReYgGAQAAEBmqKNgMNjjmJdjyssJfDxetzN2Q/hoLaKGYObmWEQg16Nt84j8fC84nHhCV0lScQLDPxO162DskM//uPAYSVJRfq66tg7MX9y0p6o38/whXdL2fGQmgkEAAABkhNeWb66T+5ZXBILB3BxTrjeU86G3Vtd4Xd+olToronoTR0QN/fz0qz2SpI9W75Ak7T8Su0pobY0MW5RGkmb+4LQar2nZrMaNBdDE8RsAAACAlNXVEM5wm3YfrLlQLQSDuLxcU25u4ktsBoeSFuTl+A65HNYrMhgM9kCG986lS35uTtLDS7958lFprwcyCz2DAAAASImpfvYoqKuAs6yyqmcwPyoYnLV0k65+9CM5n4cHg8jurZvF5AX9fMKg0HGnkqK45epDnw7FoeNzB3dSv44tqimNbEAwCAAAgIwQvSBLulR4W1bk5+SoMC9yHt/kvy7QOyu3af4XO2OuC/YM9m5fHJMXFJ7XsWVg78L//ZeRkqRe7Zr7XlNXXr95dOg4uIIqshvBIAAAADJCc58VM9Ph6blfSqra+sHPZQ9+ENo8PqjcCyIvHhrYH3DymX1jrjupZ5vQ8Qk9WkuSOpQU1Pi8utKlVaB38t/G9K/3Z6PxYc4gAAAAMkKfanrgamvLnkP6fPM+SVKFNxR06FGtVVYROyx0+oL1GtW3aqGWYM/gyb3b6NWbzohZUEaSWjcvCB1fe2pvSVXDXRsgFtQHt46p/4ei0aJnEAAAABkherXOdBg+dU7ouGVRoJ9k4Ze7tGTDbq3aui+i7PML14eO1+04oJ+/sFSSlJeTo6M7lYT2KIwnOr+m8kBdo2cQAAAAGaGijpcsjQ7Oxvzurbhl7561Iu51NenboYWuGdVTk07pldR1QLoRDAIAACAjVNZBz2A4S3Dc5meb9mrmJxtD53lJBoM5OaZfTRyS1DVAXSAYBAAAQEaoi2GiyfrGQx9o7podEWkM90SmIhgEAABAo5ZjUqWr2rS9IUUHgpJUkFv9Mhxzbxujw2Wxm9IDDY1gEAAAAI1ajpkqnVNFpbR+5wEdOFKhozvV3T55zQtydeBIReL1q6FnsGMDbzYPxMNqogAAAEiZU9312gW3cLjx6YU67e43dO5/vZ30Pb7Yvl+DfjFLpVv2htLeL90WOn71pjNCx9MnnyKpYbZ+AOoTwSAAAABSUpdB0/7D5Wm5z/QF63WwrEJj760KJK985CNJUrvigoiexuD307sO9jUEGhOGiQIAAKDRuvaJeWm5z5GKyDl7zy+o2jNw54Ejvtes3ro/dHzpSd01PewaSXrhxlOVS/chMhg9gwAAAGi0/BZskaS12/b7psfz0FurQ8dHyit183OLQ+cj+7SLKOsX333bZ0/AE3q01rHdWyVVD6AxIRgEAABAxhn92zeTKn/RCV1Dx0s27IrIu//KoRHnpthocEi3VurbgWGjaFoIBgEAAJBxBnVpmVT5vh1ahI4veeCDiLy2xQUJ3WPOzaOTeibQ2BEMAgAAIOMs37gnqfIdSgoTLltWwZ6AyA4EgwAAAMhIew6VafG6XTUXVHIrnm7cfSji/N5vHB9T5jun9U78hkAjRTAIAACAjHPViKN041MLNfGP7+lQWc0bxFf6bIN47am9tHrq+Jj06D3kLx7aPabMmUd3SLiuQGNFMAgAAIB6sftgme6c+amOlCc/DLNj1DDPpz76Uu+sDGwavy+BvQidTzB48YndlRMd+Um+aUHTJ49Sh5JCndK3XdwyQKYgGAQAAEC9uHvWCj367hrNWPxVQuW/+VDVQi9b9h6OW+7gkQo559Rryksa87s3fcs4xUaD8baFqG7vwGG92mrebWOVl8vHaGQ+fosBAABQLw54PXjVdLyFfL55rz6Ks8dgtINlFSqrCAR7q7b67z/oN0w0nhw2kkeWIBgEAABAyvyGYUbz4jXlJhANbopaxKU62/YdrnkFUK+Cwc3jxw7qFLdoDp+QkSX4VQcAAEBKqutIq6h02n2gTLsOHFGlF5BZmnvervzzR3ri/bXVlgnGqleOOEr9OrbQlPMHprUOQCbKa+gKAAAAoGkq3bJXY+99O3Q+/tjOkmoeJvrpV3t0zWNz4+b369hCpVv2RaTd88pn1d6z0hsn2r5FoWb/+MzqKwBkCXoGAQAAUCfCA0Gpaiipqfpo8KZnFsWkDerSMnQ8qk87nTs4/jBPP8GewWT7JEf2aZvkFUDmIBgEAABAvQgFgzVEZJ9t3huTNqxnm9Dx0J6tlZebXFgXfHayi8N87fhuSZUHMgnBIAAAAOrFwi93SkpsNdFo/Tu1CB1feFzXaucdOp/VbCqruiVrFnb5FcN7JFpFIOMwZxAAAAD1IrhXYLILyFwxvIeuGH6UNu0+pKtG9lRebo5uv/AYvfTJRt/yh8srVZSf65uX7No16V7sBmhM6BkEAABAvUo2vLrr4uOUn5ujW8YNVLfWzSRJHUoKtXbaBP3mkuNiyh8ui91m4jfeAjOuhh0oJCmnNl2XQAZKKRg0s7Zm9pqZrfS+tolTbpaZ7TKzmVHpT5nZZ2a21MweM7P8VOoDAACAxq+meXutmyf+kfCYblULy5w/JLBa6eHyiphyR8oDUWBlAhsiDu/FojHIDqn2DE6RNMc511/SHO/czz2SrvZJf0rSQEnHSmom6foU6wMAAIBGzkzafaAsbv55gzuHjkf0rj4wO6ZrK71zy1lac9d4jfE2kj9cHr/7ryCv5o+/9AwiW6QaDE6U9KR3/KSki/wKOefmSIpZFso597LzSJorqXuK9QEAAEAjMnZQ7BYQk/+6QMf/6lXNW7vD95qyykAw17a4QM98d1SNz+jRtrnMTIVeoOfXMxiU6Cqka6dN0NppExIqC2SqVIPBTs654MzdTZKS2/DF4w0PvVrSrBTrAwAAgEZg2Ve7JUmzl2/Wmz8ZHZFXVhEYqvle6Tbfa8srnFo3z9c7t5yV1DO/2nVQkjRjsf/CMpKUy4IwQEiNwaCZzfbm9EW/JoaX83r3ah6E7e9Pkt52zr1TTT1uMLP5ZjZ/69attXwMAAAA6kL0h8Bt+46EjvPjDM2sjPPJccbir7TrQJmKC5Nb+P7TjXskSS8u2hC3TLL7DAJNWY3/wpxzY+PlmdlmM+vinNtoZl0kbUm2Amb2H5I6SPpuDfV4WNLDkjRs2LDaBp0AAACoA1v3HtY/Pl6vrx3fTbk5pkmPzQ3lFcabp5fAYi7J6NSySJLU2fvqh/mAQJVUh4nOkDTJO54k6cVkLjaz6yWdJ+kK5xJZ6BcAAACNzf/OXSdJuumZxbrkgfdj8tu3KPS9zi8UXLxuV63rcfHQbpKkb43sWet7ANkk1WBwmqRzzGylpLHeucxsmJk9EixkZu9Iek7SGDNbb2bneVkPKjDP8AMzW2Rmv0yxPgAAAKhnR7VtHjpetG6Xbv37Jwld57fNw8Q/vlfregSHgDIUFEhMcgOxozjntksa45M+X2HbRDjnTo9zfUrPBwAAQMPLj1qhM9hTKFW/lUP0nMH1Ow+kVI9gLRLZSxBA6j2DAAAAyHLB1UH9vPezs+PmLVm/O+L8+ifnp1SP/NzAR9vySmYfAYkgGAQAAEBKtu49HDeveUFu3LySoshBYis2xWxLnZTgqqVl5T7DT0/oqtbN81O6P9DUEAwCAAAgJQfLarfJe9fWzeLm3XhW36TrERyuesvzn+j2Gcu0cffBUF5ujqlFkltVAE0dwSAAAADqTF5O/I+bnVpGrjLaLL+qF/HmcwYk/ayC3KpnPfH+Wo266/WqTKYRAjEIBgEAAJAWq6aO18QTukak5Vazr9/Ul1fo/tdX6tn5gQVnhvduG8qrzX6A+bmRH21LCvO0+2CZnvroCzlJLDIKRKKvHAAAAGmRm2Nas22/b97MH5ymC/773Zj03776uSRpWM82euvzrSk9P3pI6t7D5bpl+mK9smyz+nQololoEAhHzyAAAABSUpRf9ZHyhjP6+JYZ0q2V1k6bEPceP3u+am/C96fEX4G0OgW5sR9tt3iL25RXOHoGgSj0DAIAACAlb/xktDbvCQRd447pXKt7zFu7M3Rc3cIy1TEzndavvd4t3RZKq/A2M8wxpg0C0egZBAAAQEq6tGqmE3q0liTl+fTO1ad+HVtEnH/i7WW4dvsBBokCUQgGAQAA0Gg8ed3wlK6P3rswnDFOFIhAMAgAAIB684Oz++mXFwxWh5JC3/ze7YpTun/0XoITju0SOiYUBCIRDAIAACCtTurZJm7ezecO0HWn9dZWb2GXaO1LClJ6dklRfsT5S0s2Vp0QDQIRCAYBAACQVuGbx8fz1k9H+6Y3L0htfcNDZRUpXQ9kE1YTBQAAQFoN6dZK75Zu0+n928ct0zNqOOjj3z5Zh8srU372a59ujptHxyAQiZ5BAAAApFWXVkWSpDP6d6i23HWn9pYU2JvwrIEdNW5I7balCHfliKPi5rGADBCJnkEAAACk1VUjjpKZdMXw+IGZFFhMZu+hMv1obP+0PbuX1+M4uEtLfbpxT0QeoSAQiWAQAAAAaZWXm6NrRvWqsVyb4gLdc9nxaX32sd1badaPTldxQZ5O/80bEXl0DAKRCAYBAADQpAzs3LKhqwBkBOYMAgAAICsYA0WBCASDAAAAyAoMEwUiEQwCAAAAQBYiGAQAAEBWWLFpb0NXAWhUCAYBAADQJHVr3ayhqwA0agSDAAAAaJLalxQ2dBWARo1gEAAAAE3SSUe1aegqAI0awSAAAAAAZCGCQQAAADRJ3zy5h3LYTgKIi2AQAAAATdKAziVafdcEvfTD0xq6KkCjRDAIAACAJu2Yrq0augpAo0QwCAAAAABZiGAQAAAAALJQXkNXAAAAAKhrf7pqqA4cqWjoagCNCsEgAAAAmrzxx3Zp6CoAjQ7DRAEAAAAgCxEMAgAAAEAWSikYNLO2Zvaama30vraJU26Wme0ys5lx8u8zs32p1AUAAAAAkLhUewanSJrjnOsvaY537uceSVf7ZZjZMEm+QSQAAAAAoG6kGgxOlPSkd/ykpIv8Cjnn5kjaG51uZrkKBIq3pFgPAAAAAEASUg0GOznnNnrHmyR1SvL670uaEXaPuMzsBjObb2bzt27dmmw9AQAAAABhatxawsxmS+rsk3Vb+IlzzpmZS/TBZtZV0mWSRidS3jn3sKSHJWnYsGEJPwcAAAAAEKvGYNA5NzZenpltNrMuzrmNZtZF0pYknn2ipH6SSs1MkpqbWalzrl8S9wAAAAAA1EKqw0RnSJrkHU+S9GKiFzrnXnLOdXbO9XLO9ZJ0gEAQAAAAAOpHqsHgNEnnmNlKSWO9c5nZMDN7JFjIzN6R9JykMWa23szOS/G5AAAAAIAU1DhMtDrOue2Sxvikz5d0fdj56Qncq0UqdQEAAAAAJC7VnkEAAAAAQAYiGAQAAACALGTOZd4uDWa2VdIXDV0PH+0lbWvoSiAltGHTQDtmPtow89GGTQPtmPlow8wXrw17Ouc6pHLjjAwGGyszm++cG9bQ9UDt0YZNA+2Y+WjDzEcbNg20Y+ajDTNfXbYhw0QBAAAAIAsRDAIAAABAFiIYTK+HG7oCSBlt2DTQjpmPNsx8tGHTQDtmPtow89VZGzJnEAAAAACyED2DAAAAAJCFCAbTwMzGmdlnZlZqZlMauj6QzOwxM9tiZkvD0tqa2WtmttL72sZLNzO7z2u/T8xsaNg1k7zyK81sUlj6SWa2xLvmPjOz+v0Omz4z62Fmb5jZp2a2zMz+zUunHTOEmRWZ2VwzW+y14R1eem8z+8j7uT9jZgVeeqF3Xurl9wq7161e+mdmdl5YOu+/9cDMcs3sYzOb6Z3ThhnGzNZ673eLzGy+l8b7aQYxs9ZmNt3MVpjZcjMbRRtmFjMb4P0bDL72mNmPGrQdnXO8UnhJypW0SlIfSQWSFksa3ND1yvaXpDMkDZW0NCztN5KmeMdTJN3tHY+X9E9JJmmkpI+89LaSVntf23jHbby8uV5Z8649v6G/56b2ktRF0lDvuETS55IG046Z8/J+ri2843xJH3k/72clXe6lPyjpX73j70l60Du+XNIz3vFg7721UFJv7z03l/ffem3LH0t6WtJM75w2zLCXpLWS2kel8X6aQS9JT0q63jsukNSaNszcl/f+t0lSz4ZsR3oGUzdcUqlzbrVz7oikv0ma2MB1ynrOubcl7YhKnqjAG6m8rxeFpf/FBXwoqbWZdZF0nqTXnHM7nHM7Jb0maZyX19I596EL/Kv7S9i9kCbOuY3OuYXe8V5JyyV1E+2YMby22Oed5nsvJ+lsSdO99Og2DLbtdEljvL9oTpT0N+fcYefcGkmlCrz38v5bD8ysu6QJkh7xzk20YVPB+2mGMLNWCvyh+1FJcs4dcc7tEm2YycZIWuWc+0IN2I4Eg6nrJmld2Pl6Lw2NTyfn3EbveJOkTt5xvDasLn29TzrqiDfU7EQFepZoxwziDS9cJGmLAv9ZrZK0yzlX7hUJ/7mH2srL3y2pnZJvW6TX7yXdIqnSO28n2jATOUmvmtkCM7vBS+P9NHP0lrRV0uPekO1HzKxYtGEmu1zS/3rHDdaOBIPISt5fS1hKNwOYWQtJz0v6kXNuT3ge7dj4OecqnHMnSOquQC/QwAauEpJgZhdI2uKcW9DQdUHKTnPODZV0vqQbzeyM8EzeTxu9PAWmvzzgnDtR0n4FhhOG0IaZw5tn/TVJz0Xn1Xc7EgymboOkHmHn3b00ND6bve5zeV+3eOnx2rC69O4+6UgzM8tXIBB8yjn3dy+ZdsxA3nCmNySNUmCYS56XFf5zD7WVl99K0nYl37ZIn1Mlfc3M1iowhPNsSX8QbZhxnHMbvK9bJP1DgT/O8H6aOdZLWu+c+8g7n65AcEgbZqbzJS10zm32zhusHQkGUzdPUn8LrKxWoECX74wGrhP8zZAUXG1pkqQXw9Kv8VZsGilpt9dV/4qkc82sjbeq07mSXvHy9pjZSG8uzDVh90KaeD/bRyUtd87dG5ZFO2YIM+tgZq2942aSzlFg7ucbki71ikW3YbBtL5X0uvcX0hmSLrfASpW9JfVXYII87791zDl3q3Ouu3OulwI/39edc1eJNswoZlZsZiXBYwXeB5eK99OM4ZzbJGmdmQ3wksZI+lS0Yaa6QlVDRKWGbEe/VWV4Jb0a0HgFVjpcJem2hq4PLyfvH9hGSWUK/DXtOwrMW5kjaaWk2ZLaemVN0h+99lsiaVjYfa5TYKGDUknXhqUPU+A/0lWS7pdkDf09N7WXpNMUGCbxiaRF3ms87Zg5L0nHSfrYa8Olkn7ppfdRIBAoVWCITKGXXuSdl3r5fcLudZvXTp8pbGU03n/rtT1Hq2o1Udowg15eey32XsuCP2feTzPrJekESfO999QXFFhFkjbMsJekYgVGTLQKS2uwdjTvIgAAAABAFmGYKAAAAABkIYJBAAAAAMhCBIMAAAAAkIUIBgEAAAAgCxEMAgAAAEAWIhgEAAAAgCxEMAgAAAAAWYhgEAAAAACy0P8DQnwZeEXhz/QAAAAASUVORK5CYII=\n"
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