4indicators-MFI_RSI_OBV_EMI.ipynb

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    "colab": {
      "name": "Using EMA 30/50/100 + RSI + MFI.ipynb",
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    {
      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/gaditi123/Technical-Indicators-FA-Project/blob/main/Using_EMA_30_50_100_%2B_RSI_%2B_MFI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.72-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.0-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 428 kB/s \n",
            "\u001b[?25hCollecting lxml>=4.5.1\n",
            "  Downloading lxml-4.9.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
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            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.9.0 requests-2.28.0 yfinance-0.1.72\n"
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      ],
      "source": [
        "!pip install yfinance"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas_ta"
      ],
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        "id": "i_pB2Wr3uMaL",
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          "base_uri": "https://localhost:8080/"
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      "execution_count": 4,
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pandas_ta\n",
            "  Downloading pandas_ta-0.3.14b.tar.gz (115 kB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pandas_ta) (1.3.5)\n",
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            "Building wheels for collected packages: pandas-ta\n",
            "  Building wheel for pandas-ta (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pandas-ta: filename=pandas_ta-0.3.14b0-py3-none-any.whl size=218923 sha256=1c67871a91fd22240e90d77fb4e8531b996003a5aa533d79c73a3dafe3b75553\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/81/f0/cca85757840e4616a2c6b9fe12569d97d324c27cac60724c58\n",
            "Successfully built pandas-ta\n",
            "Installing collected packages: pandas-ta\n",
            "Successfully installed pandas-ta-0.3.14b0\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import pandas_datareader.data as web\n",
        "import pandas_ta as ta\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import date\n",
        "plt.style.use('fivethirtyeight')\n",
        "yf.pdr_override()"
      ],
      "metadata": {
        "id": "BNd5E8VwuN0s"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stocksymbols = ['SBIN.NS']\n",
        "startdate = date(2015,1,1)\n",
        "end_date = date(2019,12,31)\n",
        "print(end_date)\n",
        "def getMyPortfolio(stocks = stocksymbols ,start = startdate , end = end_date):\n",
        "    data = web.get_data_yahoo(stocks , data_source='yahoo' , start = start ,end= end )\n",
        "    return data"
      ],
      "metadata": {
        "id": "vaxoo9RhugUC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f93cbe2-e11e-483c-f8f7-f967eabe1481"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2019-12-31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = getMyPortfolio(stocksymbols)\n"
      ],
      "metadata": {
        "id": "9ImmXNtBu1ON",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a9339d9-be63-48c9-fd16-abb36de6692f"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CustomStrategy = ta.Strategy(\n",
        "    name=\"Defining Various TI to be used\",\n",
        "    description=\"MFI,RSI & EMA 30,50,100\",\n",
        "    ta=[\n",
        "        {\"kind\": \"mfi\",}, #volume\n",
        "        {\"kind\": \"rsi\"}, #momentum\n",
        "        {\"kind\": \"ema\", \"length\": 14}, #trend\n",
        "        {\"kind\": \"ema\", \"length\": 50}, #trend\n",
        "        {\"kind\": \"ema\", \"length\": 100}, #trend\n",
        "        {\"kind\" : \"obv\"},\n",
        "    ]\n",
        ")\n",
        "df.ta.strategy(CustomStrategy)"
      ],
      "metadata": {
        "id": "gzQxgnlnvBMR"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns = {'EMA_14':'Short', 'EMA_50':'Middle', 'EMA_100':'Long'}, inplace = True)\n",
        "df['OBV_EMA'] = df['OBV'].ewm(span=14).mean()"
      ],
      "metadata": {
        "id": "erK3U7Qv0Vev"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(200)"
      ],
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        "id": "eOSyUEs9vGdE",
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          "height": 693
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        "outputId": "5d6fec6c-9b17-4981-a8e1-a9ed765078a1"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2019-03-01  270.549988  274.600006  270.100006  272.950012  266.297180   \n",
              "2019-03-05  271.700012  277.850006  271.200012  276.450012  269.711884   \n",
              "2019-03-06  277.299988  282.549988  276.450012  278.100006  271.321655   \n",
              "2019-03-07  279.450012  282.500000  277.649994  281.750000  274.882660   \n",
              "2019-03-08  281.850006  284.250000  279.600006  281.200012  274.346100   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2019-12-23  335.700012  338.850006  330.350006  332.399994  324.298157   \n",
              "2019-12-24  333.200012  334.200012  329.200012  331.450012  323.371307   \n",
              "2019-12-26  333.000000  333.450012  328.100006  329.850006  321.810303   \n",
              "2019-12-27  333.399994  338.350006  333.000000  337.250000  329.029938   \n",
              "2019-12-30  337.750000  337.850006  332.500000  334.399994  326.249390   \n",
              "\n",
              "              Volume     MFI_14     RSI_14       Short      Middle  \\\n",
              "Date                                                                 \n",
              "2019-03-01  20024726  50.291082  46.140961  271.371383  280.383889   \n",
              "2019-03-05  21533972  57.355134  50.430160  272.048533  280.229619   \n",
              "2019-03-06  22161376  64.158665  52.356446  272.855396  280.146105   \n",
              "2019-03-07  14899105  72.527764  56.393368  274.041344  280.209003   \n",
              "2019-03-08  19943319  80.987763  55.628495  274.995833  280.247866   \n",
              "...              ...        ...        ...         ...         ...   \n",
              "2019-12-23  30150335  36.291109  55.111574  329.482358  317.397523   \n",
              "2019-12-24  21747782  31.458883  54.357410  329.744712  317.948601   \n",
              "2019-12-26  24616942  31.856480  53.040899  329.758751  318.415323   \n",
              "2019-12-27  33477173  39.337531  58.095855  330.757584  319.153938   \n",
              "2019-12-30  22775554  41.604710  55.612881  331.243239  319.751822   \n",
              "\n",
              "                  Long          OBV       OBV_EMA  \n",
              "Date                                               \n",
              "2019-03-01  281.899108  136852144.0  1.050424e+08  \n",
              "2019-03-05  281.791205  158386116.0  1.121549e+08  \n",
              "2019-03-06  281.718112  180547492.0  1.212739e+08  \n",
              "2019-03-07  281.718743  195446597.0  1.311636e+08  \n",
              "2019-03-08  281.708471  175503278.0  1.370755e+08  \n",
              "...                ...          ...           ...  \n",
              "2019-12-23  311.298579  848034653.0  8.530043e+08  \n",
              "2019-12-24  311.697617  826286871.0  8.494420e+08  \n",
              "2019-12-26  312.057070  801669929.0  8.430724e+08  \n",
              "2019-12-27  312.555940  835147102.0  8.420157e+08  \n",
              "2019-12-30  312.988495  812371548.0  8.380631e+08  \n",
              "\n",
              "[200 rows x 13 columns]"
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "      <th>MFI_14</th>\n",
              "      <th>RSI_14</th>\n",
              "      <th>Short</th>\n",
              "      <th>Middle</th>\n",
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              "      <th>OBV</th>\n",
              "      <th>OBV_EMA</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
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              "    <tr>\n",
              "      <th>2019-03-01</th>\n",
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              "      <td>20024726</td>\n",
              "      <td>50.291082</td>\n",
              "      <td>46.140961</td>\n",
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              "      <td>276.450012</td>\n",
              "      <td>269.711884</td>\n",
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              "      <td>57.355134</td>\n",
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              "      <td>1.121549e+08</td>\n",
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              "      <th>2019-03-06</th>\n",
              "      <td>277.299988</td>\n",
              "      <td>282.549988</td>\n",
              "      <td>276.450012</td>\n",
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              "      <td>280.146105</td>\n",
              "      <td>281.718112</td>\n",
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              "      <td>1.212739e+08</td>\n",
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              "      <th>2019-03-07</th>\n",
              "      <td>279.450012</td>\n",
              "      <td>282.500000</td>\n",
              "      <td>277.649994</td>\n",
              "      <td>281.750000</td>\n",
              "      <td>274.882660</td>\n",
              "      <td>14899105</td>\n",
              "      <td>72.527764</td>\n",
              "      <td>56.393368</td>\n",
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              "      <td>280.209003</td>\n",
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              "      <th>2019-03-08</th>\n",
              "      <td>281.850006</td>\n",
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              "      <th>2019-12-23</th>\n",
              "      <td>335.700012</td>\n",
              "      <td>338.850006</td>\n",
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              "      <th>2019-12-24</th>\n",
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              "      <th>2019-12-26</th>\n",
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              "      <td>321.810303</td>\n",
              "      <td>24616942</td>\n",
              "      <td>31.856480</td>\n",
              "      <td>53.040899</td>\n",
              "      <td>329.758751</td>\n",
              "      <td>318.415323</td>\n",
              "      <td>312.057070</td>\n",
              "      <td>801669929.0</td>\n",
              "      <td>8.430724e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-27</th>\n",
              "      <td>333.399994</td>\n",
              "      <td>338.350006</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>337.250000</td>\n",
              "      <td>329.029938</td>\n",
              "      <td>33477173</td>\n",
              "      <td>39.337531</td>\n",
              "      <td>58.095855</td>\n",
              "      <td>330.757584</td>\n",
              "      <td>319.153938</td>\n",
              "      <td>312.555940</td>\n",
              "      <td>835147102.0</td>\n",
              "      <td>8.420157e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-30</th>\n",
              "      <td>337.750000</td>\n",
              "      <td>337.850006</td>\n",
              "      <td>332.500000</td>\n",
              "      <td>334.399994</td>\n",
              "      <td>326.249390</td>\n",
              "      <td>22775554</td>\n",
              "      <td>41.604710</td>\n",
              "      <td>55.612881</td>\n",
              "      <td>331.243239</td>\n",
              "      <td>319.751822</td>\n",
              "      <td>312.988495</td>\n",
              "      <td>812371548.0</td>\n",
              "      <td>8.380631e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a25e9c72-285a-4b95-b136-d05b36a3dd33')\"\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",
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              "  </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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a25e9c72-285a-4b95-b136-d05b36a3dd33 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-a25e9c72-285a-4b95-b136-d05b36a3dd33');\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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['MFI_14'], label='MFI', color='green')\n",
        "plt.plot(df['RSI_14'], label='RSI', color='blue')\n",
        "\n",
        "#add legend\n",
        "plt.legend(title='Group')\n",
        "\n",
        "#add axes labels and a title\n",
        "plt.ylabel('SHARE PRICE', fontsize=14)\n",
        "plt.xlabel('Time', fontsize=14)\n",
        "plt.title('TA', fontsize=16)\n",
        "\n",
        "#display plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Upu17QGCvfsD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "a0cd8aec-6518-4b3a-b8b9-7fa3afdf7c9c"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Wgq3E7ZE2x+eZ8nAH7ohom7GGprnVU4YOHYr9+/cjLy8PxcXF2LBhAx5++GHRcTk5OWjZsiUGXj8QA68fiCcGPVH7nY0giUZxodZLJUi52llN3SeliUQyFIAmXGMZknCLVc3tWPkx4vb3dryHvErx7wiVizUXI9YWDY7jsKdwD46VkX9TtNE0t3pKRkYG7r77bnz33XdIT09H37590bp1a9Fx7du3x9KlS30vmMHIF2mM1ZmrHDqI152sbitS41ProDfRgySwSb/dSyTX3KSI09e/dZpTladE22JVuFlcFuL2w6WH0X9xf+x/Yj+aJDUJ+zoXzOI0bZHmxY0v4svDXwIAZtw2A6OvGx31awaiaW71mBEjRmDx4sX47rvvfObJYOLj49GhQwe0bNsSLdu2RIuWLWq5l5GFJJRpA0J9huQSLlXEUlJzi6BmG6tCQYpia7FoW6wWBJV6l6scVfgzPzKFZwvNhRFph0a5rdwn2ADg5T9ejur1SGiaWxCmcSbY7XYkJorNX2pwMA6ROp7dODuiZp1bb70VcXFxKC8vx6BBgyLWbixDGlzrq+enFHa3OJBXyilJr6PPUyMpkOqjcKt2iM25I9eMxOqhq+ugN9LITdQi5dAT7aw1pATetY2muUUJNyOOrYm0KVCn02Hbtm04ePAgEhIS1J1cP62SRK9As5Ps2VdXmJ3msLPOkzQ3J0sPvNdLfMqRHMgainDbfmF7zFUod7Nu2eQKLVNbUvetPb0W13xxDXp+3RM7L+6UbEfKxB0JYmHZQxNuUaK2zB6pqalIS0urlWvFAqTBNVbMkja3Dc/89gzazG2Dqz6/CkfLjobclqa5RQ7a+0ELd6grlLzHtIkKy7F4YcMLOF99HicrT2LS5kmR7p4qNOHWgCGW2YhADam5c+fihx9+ULR/8uTJ2LFjB/VYuf2xSCyvub259U0sPLYQLMfigvkCZu+ZHXJbatfcDHoDdd+lrLkxLEP9/bHmZavEvM6y5PtfaC5EocW/jhYYL0ccd6KruMUEmnCLErEwc2mIxLLm5k1T5SWcNEckzU3KZCWluV3KDiWhOuHUBUqEG01Qk7LXeIUa6RyakIwYMTD8acItWhAebiwJvFjqixpIs9BYG6S8hLOobmNUam66WtLcJEIOYhGpCUGslQJSMkkjJYEGyN+F91jSObH226OBJtyiBFF41E95ElOQ7musCjcpbUoOtWtuUpMV1fenpgWwdDHw1Qbg7C3htVXHSE0IaIKirghnzY30O735KEm/M9q/PRYmz5pwq0Vi4YHXd0iDa0OchRLNkhLeklLruWrNksb1M4Gjw4Cz/YEflgNuf+B2rK1TySGVcDjW3ptwzJKkiY9Xa60T4RYB/4JwiSnhxjAM3nnnHVxzzTXIysrCNddcg3feeQdut/9BcByHqVOnomvXrmjRogUGDRqE48eP12GvyWiCLDqQPppIDrh2tx1vb3sbI9eMDDsDvlQWfzlIv0ly/SiCGUrcBx/y/2FrCpz+u/869UxzkzJLkjKX1CUWt7zmRlsrk9LciGtu9ew5hkJMCbdZs2ZhwYIFmD59Onbt2oVp06Zh/vz5+Oijj3zHzJ49G3PmzMH06dOxceNGZGZmYvDgwaipqanDnhOI8TW3+gppEI/khzrtr2n4cPeHWJazDIOXDw7LWSWcWCLSgCRplpTS3MLVUJz+MkOx8g6frTqL4+Xyk1qpezZzd2wVulWiudE0LpJw83rckvZFUnNjWAaz98zGsJXDsPj4YgDk96S2tbmYEm67du3CXXfdhYEDB6Jt27b4xz/+gYEDB2LvXn4GzXEc5s6di3HjxuHee+9Ft27dMHfuXJjNZixbtqyOey8kFh5uQ4SouUXQvDRrzyzfv6ud1YJSIVIsOyF+/8LR3EiDT6jV08PWbBl/iaRYmPF/c+QbdP+qO3p/2xuTN0+WPNbOiM27XqSqH9QFwWVtHr3yUdzf+X7BNtq7TnpfvEHqkXAoOVZ2DL/m/UoUwD+e/BFTtk7BujPrMGbdGBwoORATNfRiSrjddNNN2Lp1K06ePAkAOHHiBLZs2YK//503i5w7dw7FxcXo37+/75ykpCT06dMHO3dKR+Q3FGqcNThffR6l1lLFwpJ0XH0VtKQPJJofzfnq87LHuFk3Rq4dGdHrEjW3EN3aw/ZwjDHh9u/1//b1Y+7+uZKZRmIte40Uwc/cqDeiY+OOksd4IU18ymxlAMJfc1t7ei1u/v5mPPLLI7hj8R2ic4NrDY74ZURMmEJjKrfkuHHjYDab0atXLxgMBrjdbrz00ksYOZIfOIqL+QSomZmZgvMyMzNRWEhPBJqbK87HlpiYKJmyym6nz/iU4HITFnhdTth1obfrYl04VeNfJ2DcDDLi5evP0QSC0t9YXc2nLyLdx9qmqFhcqbiouAi5idHpW35JvuTvzs3NxTETuaQHy7Ah37PCIvH7fKHoAnITyO2VlpVKtpUbr6YfNwj/DBBuTpdT9jfV9nuy+/hutEtpR9yXU5IjeW5t9VXJdQqLhc+8proGcQ5hFYbSslJiW3ml4nI4p/NPI9edi/MW8QTN4XIo/u2j1o3yCatj5ccwf+t83HGZvxZclaNKcHxBTQHO5Z8TtXMy9yTiDf53KZx7n52dLXtMTAm35cuXY/HixViwYAG6du2Kw4cPY9KkSWjTpg0ee+yxkNsl3YiqqipqcuRIJE42uMVxR8Y4Y1jtllSXCP4utBWiRZp8ln/SLI0Dp7gvaWlpsNvtil6oaJPpzBRta5rZNGp90yfpqW3n5uYiOzsbWw5tIe43Go0h96upraloW0bTDGp7TU3i4700y2wW3v0JEG46g06yLe89iRakNbTOHTqjbXpb4vGHWelA+tp4p5Xek6Zm4TNsnNEYzZObA+tuBYquA65YgfTG6cS2jnBHRNuaZzVHdnY22Arx5Fanl36OgdT8KvRnOM+d9527u3A38ZzLWl4m2ta+Y3s0imsEIPrvCRBjZsnXX38dzz77LIYMGYIrr7wSDz30EP71r39h5kx+4TcrKwsAUFoqnKWWlpaiefPmtd5f1YRpCbS77Zg2aRr6demHfl364fZut+Oqq67Ciy++CJPJb5o5fPgwhg8fjs6dOyMrKwvXXn0tXn/udRRd8Gs9t3S+BStXrgyvQ3UAMRQgiu7pS3OWypYHOVFxgrg90g4lUqYkKZMPKZWXus7Ejlmyxil2HJNycpEKBYg1gu+tQWdA7o5uwNd/AOtmAZ/th7WGXE+v0l4p2uZ9h4hmSS4yDiV/nP9DtC0tPg3l1nLR9kt6zc1qtcJgEGo8BoPB5/7atm1bZGVlYdOmTb79drsdO3bsQK9evWq1r3IQHUrClG7el7VHnx74ceuPWLxxMT7++GOsXbsW48ePBwCUlZXh3nvvRUpKCpYsWYJdu3Zhztw5uLz15bCaG15pGCD6H824DeMk9xdUFxC3Sw66bgdW5q7ExnMbiYv0ah1KpK716p+vhrfGyvoH1LoWbiRhJSX0Yy2WTYrgvhr0Bix7Z6h/g70JDq7uQzyXtO7obW9bwTbxvjAmhIGTNtKaZuPExnhqzVOi7Zf0mttdd92FWbNmoW3btujatSsOHTqEOXPm4KGH+LgbnU6HsWPH4qOPPkJ2djY6deqEDz74AMnJyXjggQfquPfRxzu4xcXHoUkmX433+muvx+DBg7Fw4UIAwF9//QWTyYQ5c+YgPp6fcbdo2QLpXdLrptMRRi6I+2jZURwqOYQB7QegaRLdVKeGdWfWSe4nBVwD9AGE4zgMWDIAB0sO+rb9+fCfuKb5Nf5zCYOypHCTEV6nTadFzgmKYf0Tzrp2RFIbxF/XwlgNJM2NcQmH6JI8csmbSgdBc2MZmOwmYqHQSIUCkJyVzlWL19uAS1y4zZgxA++++y7Gjx+PsrIyZGVl4fHHH8fLL/sfzvPPPw+bzYYJEybAZDKhR48eWL58OVJTUyPSh4yMdACREATpAK6QPcpkqpI9xgvp5Th79iw2bNiAuDh+dp2VlQWWZbFy5Uo88MAD0Ol01Fl9XQ9UoSCVW3JbwTbct/w+uFgXmiY1xd7H9yIjUd7hRgkMy1Az76txzwaAQ6WHBIINAP534H+YM2CO4HpK2wPkBw6S2UoxnP9317UmRHqXFd8XU2tglse5Qu9E57fuinT3wiL4mZPyheriyBMcmua26Pgi4vHhCJrAEBc17VzSwi01NRXTpk3DtGnTqMfodDpMnjwZkydLx7fUZ5yME/nV+XCyTrRIboHGiY0F+3dt2YWB1w8Ey7BwOviX/d133wUA3HjjjRg/fjzGjh2Ll156Cd27d0evPr1w7e3XokVLeeeTWOJ4+XHE6+MFGgfpA5mydQrapbfDmtNrfO7y5bZyTN85HVNvnRqRvphdZqQnkCc9UjW2SJDW8LYWbBW2SRBukum3Agf9ww8Bv70PJFUAgx8DLjsYXn3BAM2trjUh0vUlzZKB9/Hz7QENxcO5/Wng2Uj2LjyC3yNSblK9kfwcK+2VwIUewO5/AY3zgL/NAMuxKLeJ176AyGluasybtT0xiqk1Nw2eQnMhqp3VsLvtOFd1zvciem3d195wLRb8tABzl87F6NGjMWDAAIwZM8Z3/muvvYaTJ09i9uzZ6NatGxZ9vwhPDnoSe3cI00nFSrYJEm9ufRO9v+2NG76+AfMPzvdtp/V51NpRvuwIXj4/9Lmqa0p9qCRHBt95KjU3khYVHPBNWvBXZJZ0JQCrPgNqWgEl1wDr+YliWMKN8w8TsSjcpPokeDY1rQT7zq4eFrF+RYLg36HXE4QbRXMrMzmBbzYAB54ENr0DbHkVDMsgyZhEPD5SgkZNDOUl7VCiwVNhr/D9mwPnMzl4hVtCUgJatm2JDl06YMaMGbBarZgxY4agjSZNmuC+++7Du+++i83bNiOrZRa+/fRb4YViVLZZXVbM3MN7yHLgBEGitA+ENPCrzehRaqPHikkJN1q+P9oAcsEsLoVzecrlwnPVmiW9g0z+3wBHgIaZdxe1PcXEkHCTKu1CwvcMDo6IVpcihsihhGCW1BvJ7/S5zf2Ez33zFDAc43O9DyYczS3QoUTN0sYlbZaMBUymKl+cm4tx4UiZP36kVWorZDYSx1mRyK/O92UI8HJZ8mVokaLeNOh96WnpnCZOnIihQ4fiiSeewGWXieNL4uPj0bJ1S5SVlBHOjj1MDnrGCdkP5OQ/gAs9gSuXAM2PwWQC7HYdWrSQ/wgv1lyk7qtxqNfcaNqSz7uSMQAXegFp+diO7XCzbhj1/CdJStqsSHPjyPPV8DQ3/yDrYl3IqchBlyZdQm8vDFSbJTkGsDYBVnxLPSZWCP5tbqtYMMWnV4i2AYD1QgfRNjfrRoKBnKiCAweWY8MqywRIfI9nbgO2vwRknAHumAwkmDWzZCxRbC0W/F1QQ3b5VkqoZkDvwEUrSHnzzTejS5cu+OCDD7B27VqMHj0aa9euxalTp5Cbm4u5c+birz//ws1/v5nYH47jcK7qHA4UH0BuRW6d17kifXDeDO7FlmLRPh8n7gEW/gpsngL8by9w4FFcc00aunZNwyuvyAeskzQqL6GYJQEQ1zxsjA1gdcDXm4AvtgH/PQGc7o83t70JgHevXn9uveg8qTU3Hyz5HYmUWRIAHv754TpzRiJ9Q1L3n+M44GDoCSBqk2Dtuii3tegYQyI5ZrGqUqynMBwjOeaEqs0HjkNE4eZIAb7/FcgdBOx+FvjjDfqxUUQTbhJIZRSXI5LrWV6TE8kG7x1knn32WXz77bdISUlBcnIyXnvtNdxyyy24/fbb8ePSHzH25bF4ZMwjxPZrnDWosFeAAwezy0xdhK4tSPf9nmX3gOM4FFokAqp/mef/N5MI/PQNqqt5bffTTxNQWiodVH3RTNfcqp3V1H1Sg8Qnez8RbXO4HfyHf94z2XA3AtZ95DuWVmqHZv4EAgYOjizcwpqwBAm3PFNeeN6XYUAaICWrbbMMYK8fYTDBQvriCbFwI70CG85uEJQl8h3LsZICJdRAbq91wXsNEQcf5d9pLzvG04+NIppZUoJwsrqTCFdz00GHSdMmCfYxHAOjzoihQ4di6FA+4LNv376CYywuC05WnBRs25SzCVc1u3Xg+DkAACAASURBVAqAWGO5aL6IrOSskPoaCUgDcaGlEPk1+dKDtEXa5HvwoAF33EE/X8osKZVAWUpzmLVnFt7o+4Zgm4NxACf/T3hg8bXC/QSkfrtPk6JpbmFM1EhthqUJhgFpgPx036fo37Y/4WjPs3GT151ijeD3yFQojtNkGfEYMnL+54DzftF2hmWknW1C1dz0MpqbrQnxPE1zayAQzTYhKnPel4JoklHyglKuG2iWjCVoA6fdbQ/LMaJJE+nfKaW5vb/zfeo+tR+tw+0AnCnEfVaXVVXNLi++d4Oy5hZWuiWCNhhLwm39ufV4ccOLxGB6XriRPQZjDcFv2zUW+9ZeJzqGIXyrlcveI7bHcNLCTYk2TxobApcNiBM7lpwiTBNuDYRIpt/yD1zifUoWaeWuG2shAbSB0+w0hzVIT985DfuL91P3S5ljzS566RS1AtfO2KnCrcJeQQ8tkPjtfrNkNBxKxG3W1boscYBkDPhiSTken/OFeBfLAO7wkqDXFr7nbm0MrJ1FPob0agRo/cHthVuhnfTeBAo88vMgCzfNoaQBE6oQCVtzo/WHi7zmxnIs1pxeg6k7piqqlEyCNhDXOGvCGlTX5f2Ou5fdTa3xFWrdM7UfrZNxAjn3EfeV28qpz1TqWfsnQJQ1NyYMYXT0QcAsTEwezWTVUhAH0yU/Aj/8hHVTJuPjj+MFu3jNjV7aKlQYlsGMnTMwePlgfH/0+4i06VtTzf0HwMZTjlHe3rGyY5Lf9olycsLvQEjrmYHfCfF5aJpbPYE1ALbGgEvd7C/SAoNGJGZDkdTc/r3+3xj+83BM3zkddy+7G0UWcf01OWgD8ZCfhoTn7MIaYHFZsOHcBvJuifvcMoWc0w9Q/wzs1cnUfRU2Cc0tnDW3cDQ3aybw2X6BgKsrs6ToXa1sC+Tc6/vz9deFJkiO46Jillx0fBHe2/EeNp3fhH/9/i9Ji4BSfM/dQXeAYVnlfgC/nflNuH5b0BM4OZAPQQGwr3ifbBvEclkBYxtxnKNobvVOuEl5cNV7OB1QegVQ2REo7QY4wstfGarA82lYpLx6Csx01NySEV5z25K/Bd8d/c73d7mtHMtzlqtuhzZwulk3dhXuCrl/Xq2G5rAhpY1IlU5Rq8VYLrSn7jM5TFQhJincvM+YJfiIsbrwhZH5cl6jUNAXgA/ZGPDDADSd3RTP/PZMxDQ90btaRa7j5sXisgBHI5+JZNYeodlQrnKEEnzCjTJBAcSam9S3a2fsqLB54uJ2/gtYsBNYuJrXdCEd3uKFqLlxoWluta3tSwq3rl27oqLCHzQ4atQoQS21kpISNGvWLHq9izJyg7qjJo13KQcA6AGT9IckaDuC2pCUG3o4l/EJN4lG1Ai+Tec3iba98ucruFBDjx8jETWtwDNopMSR17ukzJJSwk1uRhrsqei2060AJrspvDU3hmDO4gyRGViO+z3y5CZVn+3/DLsKd4HhGCw8thA7Lu4I//og3GvKGqOXr498HZHrBrIlf4sv7tJLcCLsUPA9I4ppmT9G+D1aXBbJNn3Cac1//Rtz7gUq2sueC5BjKwOfweFSTzFYDrxGyIGuuYVo9g8VyTejuLgYTMAK5po1a2CxCG9IrHnaKSU5ORkmk0my/25bkDmDCW9hOmyBF+rptPO8k33K4MxxHEwmE5KT6Wa0QEqsJcTtj656VNV7EvK6mlGmKKdHq6EJT6k+SmpuMmbJ4MmJ204wk2XymXAq7ZXU3y+55ubtO0m4scbITBgu85ux5NbwvOnTvHy468Pwrw91wq3CVhF+odYgVp1ahXt+vCeibXrx/TaS9u09JugVCEzVJ4KTeG+r2hDrCAYjZZasdlTjZOVJwJzJJ6V+2w0sWQa4yKEXtW3lCzvOLdKxYLWF0WhEamoqqqvFWlF1dTXS0tKw/1ANrBXCAa9DB2X5Co8VHBO5lrdPb490Tj6gdPcZcen2DGRgf/5+WN3CF5JpwSCuMXmm5KXIUoTd58VtNkZjNE5sLLqeXqdHh0Q+nU9qaiqMRmWvCU1I7iveh0Olh3Btc7JXVzCyA/H5PsCJe4G2fwJdfvVvj7NKr6/ImCWlNDAn4wTHcaL3neM4/wBT2Zb/uAtvAFpvAx7vBxhdogHC7SBMkjx9MzlMaJJEjhNSFApAFG4GVcJNl1ADjmSCj/O/e2q9VhONkfFYFAs3+vgjZ3ZL6XgQQDtV13/818dVHe/l17xf8d+9/0XrtNZ475b30KyR2OLlN0vSvzcu6PdK5k9lDXThZnQo09wkHEqW5SzjNxx4Eijozf/7+BB6d7Qg7trDaDQiPV0sbEpKStC6dWu884kJRQeFgcGjRyurv/bVxq9EKZQeuuIh/P0KcSaBYF7+S1xcEH+Rj51x2wzc0O4G0XaH24E9RXvQLr0dCpwFxDZ7tu+JduntRPvSE9IxstdI2X4GI/XyFtQUKBduUgHHpV2ALzcDnBHY/jLw6B1AR4+DiKzmxgsQ2oAgZ7pzMk4kGIWedyaHyd/e+um8YAP4BMYLVwGP3SkSboyNMLP1DGhSmpuiumUMwTOQjVOnDcfZyOvLAYPuvqJ9uOnymxQ3STMFq0Vk2pLQ3DhwEU8OLpfqK3Dys+TEEry08SU43A44WF7I7Li4A8lxyZh5+0zR+b5neOFG6jWClR9JgcHp+XeTdA8MyoQbaVLkvaYvW9D66bLtBJ5XW0iaJXU6Xb3VzCIB56bbvuUgfQTReLikNl2MC7cvvh2Dlg1Cr2964a9CsmSkfaiNjKFldJD6fZLmkyAkB+IdL/KCzcuKb/z/jpf5WDlp4Sa3JkCaBQu8QYMdF04PACD+PYyDoF2yfs0tFLOk/yCC5uZKUifcaNpQgKPDmaozytsDkBQXGY9FkelYQrixHCu9JhdhwRf4/rsYFyb+MRHVzmqfYPPy5eEvieczLAMwRuAMOdsKADCM8NlImsQ5Pf/Okt4JHadMuBEmmt7fmRafJnt+ILUd5yapuXEch0GDBsFg4F9qm82GoUOH+qo+M8SIwoYDy4Su2JIepJLBSe0aJkmgLMtZ5qtmYHaZqdk1GJYhXi/UgYjYd1cCoHej1EIvJyM6RcqEVthd+Lc5oFSMgb4uBsA3ONNMNZb8TsBXnwHOVGDAS0Dn1YL9pGcqmcgZANxxovMYO2Hy4BG8lfbKgGDeJsDJu4HMo0DLvcpCAYjCrVFk1twCNLc4vbQpPBha0m/VXVCx5sawjLRmJ2HSDAU36/alpjpddVp1/k2G8+TBdNCrx5esHwHAPzlTpLnZGov3sQZFa25SmhuteC+NmDJLTpw4sbb6EZNwTBiaG0GQKZm5qH0BSNrGr3m/Eo4U4+bcxHUJoy40oS7q+6GHgV/+B3B67LB/D/RU1o7kQJxEGTA48MU5JTvI/y5qnbVlLwEXPZ1c+TkwviWg9/+mB1Y8gGm3TcONl/nNRrLCzdZUJJRYoubG981kN/HvjqUZ8NkBoKYloGOA4ffA3TOH/tOkvCVVCzfKoB/gxadGE+dbjIwg8WdiAXCxBx+mQzsW0ppbpIXbpM2T0LFxRzx97dOqawkCnvdSYr0NAFhnEgKFm+SE2au51Vwu3scaYXaFFwqg0+mAi9fLtgHPGBVTmtukSZOkdjd4GCc5S4Cic0mam4KHq/YFIGlLSgcyhmWIrtKhvoQC4cYYgDWzARe/1rLty/uAV5W1I2lCM4rzBwIQa3QkPIMzbUCwnAyQvpYWfGxjlr+e397ivXjo54eQMyrHlxld4CEaX8NrfYE4UsXCzU3QegLMkgzHANsm8ILN2+/DD8N9A/0Gygk3h1tGqw2ENuhveRW4/T8AoK69COKbzP30FXCQ7NzBsoBer8QsGVnh5jU3llnLMLDDQMljSc5JLMdSY8RoSJrSOT3/nKyEcC1OmeYm5S3pdDuBvDvlO5lQLTivtpBcczObzVi0aBHRo7CqqgqLFi0ShQY0JGqKm8sfRIE041FillQrWEiantKYpoKaAry25TVFbSpBEOpQ3hmw+T8qa1km7BS5FExpYQJwsTtAysZAE25nb5VvmJUWbiIIA2O5rRzbLmzz/e1zNedAdoF2i9e7WJJFIMAs6WbdwMkgd/NjD0j22zfIkVJNuZMi5xJvagOAHNMoRaTW7jmOA8qyqYIN8OdflDdLRiFBEwfM+iAVD/fvCyxcCVjEmf0B8jfGCzd1VhNJgeHV3ByEtTHWqEi4/XjyR3Gznu/cyToBO92E6j+Bf7djyqHkiy++wIoVK5CWJr456enp+Omnn/DFF+JkpQ0FUbBtgjJPSYAi3JRoblIDrzkTWPwj8OkhYD//cU/ZOgX51fmCw5RqbtsvbCf3IRKam0VcMsdkkh/gFq+qwNvDRwDz9gKLfhYv+tPW1ZR8ZDJmSRGUmX3gIrvvXjNx5OBbR5roehxD+Ow8fbO4LMgz5YmvzSRKPlfpIG69ooHMf7zEc5p1DrCnwuQwYfP5zYqbjKhZMv9vkse43QHH1qJDCQCg+Grgj7dQXtSIL2u0Zyy5jwSNiHcokdbcEtoeEJ9DI+9OfhwifRusgRqXGtjHb458I9rufddcjEsym4q/k/EAF2OJk5cvX45Ro0ZR948aNQrLli2LeKdiAQdpDG0mn2jUi1zaGhqSL8DWScCJ+4GSq4GfP/fl+ntp00uCw5R6xtEGy1BnWILzCDWdlAi3F57L8Nffyr0beJMD8j0u54wROHWX+KQjQ3lNUbaD/MCh2HOQMjAGOkf4BB0tvu7IQ77rVTuqMWv3LLjchFHV7l/0r7RXqs7E7xvkSMKtoqNKzU3mOR3mi95+uv9T6eMudgf2/hOovjyymttKsrehF6/mxoKVHHyjorl5qk772PQ28TBSnCDDMUBlB8nmg9cJJb/V5d/B4QTwywLxPs9kqtRKd/Q6bTpN3O69ppN1SmZT8cEkAM6UWq8kIfl0T58+ja5du1L3d+nSBWfOqHMJri/k5RFuTTy97EkwpLQ1gbOsPYV7sCV/i8isIPmy/vWi/9+cAdg7GgCw7sw6wWFKXyLabDpaws1skZ8q2yoJnl2f7wB++QxY+IvA1Onjx0VAGf099eGZFSufQZLvT2BFdN8khpKVAbv/5XseD//yMN7Y9gbd9JRzd0Cb4mtLxrlBQnNbNQ8W4mwtRH6dC0D83gk4cxswfyc/sM49JJksWg1K3k2f5sbW7pobf1Fla2ZEzY1jgD/FywSBBGv9DMfwDkeUvpj2ECaDgO8dlKoMQEt67hNujFM2/ZkPS2atJ9uW7BnHcSgrK6PuLysrq7fpt+SYNIkwE1fxMZDiQ7yD6oe7PsQdP9yBe368R5RwVZVguSgO3gbqUHODtHCzWMN4V/Y+DeRRPlTOAJjaybfhNUt6JhlWlxWvbXkN1315He5aQmib8rz1AZ+N7x6e70s8Fq12wM26UeWowtaCrYJ+iPjpKwCAzWIAzOKq4nIBxPxBZCeoiqM9qOeK21L2njdvJLEm/dNX/nhEW1McWSWfvEAJSvITejPny5kl1XpLMiwDOJKB1R8DX2wGDj5CaFRZm0RvapaRNblyQeFJLMcCyXTzYtVvz1Ma4jUuqby1JRZyu6rNkgBgah+SB2k4SAq3K664Ahs3bqTuX79+vaRmV585eJC06K/cjEGKpfIKnbe3+00VXx/5WuCOryrBbUCpj0BEQsucCfw8D1i2kM/w4YEazByqQ0ngRIcQW2M2R3EipGQhnhVqbvMPzscnez/B2aqz+OsiIdCd8rwDTWwuxsV7hi5dSr5m1kEwHCOsIUfrq60pcL4Pdk9aJva6hEKzpIusIbmtkckQEkjHDLobfnC2/qJjEseqQI3mxnDSDiVqNTc7Ywd2PQvseg44fwuw4js+5ZoAZW2SnqUSwc0FCROGk16no+az9byD1Q66cCuySmtuDsahfEz84cfYEm4jRozAhx9+iF9/FcdNrVq1CjNnzsSjjz4atc7FHjrFmqqaNbfAgS8Si66ia/+8ANg3CjgyHPhhhW8hnWQ6BegJkOWQM0uGpbnJoeQjCzJLLj8pXY7HoJOvKOxkncCFXvRGPN6SVpcVKLwW+O5XIHcQ/fg1H4OxkINjXVX0Chy+e19zGXG/PkGFQ4lCSPkRaQTnRA0VRkHyXavHBCuvualbc3O4HcCGacKNOymaUSCMASi62udpCpDX3JQkFg6OveU4TtoUmmAib/cISSlHI6rm5hHCLtalbM0NABwZtS7cJKe7jz32GLZu3YoRI0YgOzsb2dnZAICTJ08iLy8PgwcPxmOPPVYrHY0JOD1YjlWUbYFk8mM4ckaQeEO84JhwsTNB7vIn/8//77Ir+PWpzBN8nAoFlmOh16n7+OWEW43yJUv1KPnIPLNV76xZbg3AqDeC9DQCM+K7Wbe01uhKgot1weyyAMt+AMq70I8FgEK6+ZBldMT4KCBg1k+KaQKgk0tNFogSjYbVqXIQqHJU4ljZMXRr1k15Pwi4FVzSZDMDaBJxb0miU07wWivp3r3t6bTBAQwZDnRbQV9zM1r9DlUEgoWbrOZmpDx3BZ7DtEB9X5ybmjU30CfT0UK2Z/PmzcMXX3yBTp06IS8vD6dOnUJ2djY+//xzfP7557XRx9iB0yv+oGlxbqTtgY4dodbdChSasgG2lky+jxIvm299SAVywm38841hokwkw4aUMDgYzwz3WNkxAPLu6e4NU8jbA2bd1c5q6YTNHs3tfD4rL9hk4eTzTrop2ff1EfZU2/KKau+3Fze+KH+QBMWWYoxa9azscX+e2w4n41RgllSpuZHStsUFaT5SbTIJvEkTEhmMrpf2BOVYYW0+2cBvGznODjvGA+WdJIWbXGkoJ+NUvuYG6bJR0UDR0x08eDAWLlyIv/76Czt37sTChQsxePDgaPetThk8mDDwczpFpT4YlqFmKBFpVRDaxUNd7wq8nuxL5HE7l8q+n1eZp7oPHDigtCuw/UXgLDn56zff8FpqSYkOVcrDBiODZ4a7pWCLssNP9QfcYgeNwPt2vvq8rObGsAyqLJGYtdLfP8mqAAA4UkC8xHVk2fQOzJUqEmzrOPK6pgre2PoGXE75vr3+5xv+GoIRdCgham5xKpNYnO0HQCLOTa49Jk7QDzfLSAs3iiaPizcCn+2HqZT+DGlmUp9DiRqzJGSqfUSBKAR6NAzGjycICG8iUhloGhHDkTW3QK0rVLNk4MciL9z4oE4pzY0khOWwljUD5u0BfqMXpnz99SS8+WYCOndOw1VXpeH33/2CIeq1DAOEkItxKQssLhCvpwU+I6vLSvVQ5C/UCG7WjRprBGatDL10jc8sSXBEAfwehIpQOOgX7VSQFcbfqIpjCWdzHBYdXyR9r72wRqw7sw77ivdJCjdXaTuMHJmEsjJlv5doERGZexU6lJDW3JRkKHGmCIUbKWYyELtEcmNXCjZ914feR9q75hFubtYdtpNdNJG8k61atVIUfJmfny97TH0j63IHMHRokBecDhaXBY0TCbFYAVDrhXEMcfYXqLmpEm6N/aXuXawLieBNUna3jGCyyWtuatfbAKDgt6FUb71AZs7k+1lTo8PQocmorKyCTgdcuBDl8koBM9waZ42ywOIztwPthJre9J3TcW8276nKZ5WQMIm6k+Dm3DDbI6C5sUaq2ZrjOMBJr+YQjYidymPKwwugC68DvhI7SsxgnmM+2v0RwEk7vSxbFo/GjTm8/778ZI444ROZJcPwllSSW9KVgiqbBZmNPEsLTpn7ykpPBs4fbUXdJ6hOsfoTfq3+ptlgs6sC+qtcc3O6a1dzkxRuM2bMqK1+xBxmpxlIPyfcyOlDrl4L8Gp+kVnsXhuouW3NV7HW1cgfgxg46AkEJOnd3zcK6D1b0qEilGwCVSdlsvJT+OmnOAwe7MKZM1E2JAQsvIvie2hjBCFA1rtmB3gX9KU0N37NzRIR4UbX3BiWAU7cRz810mZJAPFNLwBQVoA2XM3tUMkhTzPKHYdqnDUAJ58fdv78BGXCzU74DYbg5xq6cGM4RlEC8LJKFzp5ltJcYS+l0p+Lr4/bJgBHHub//dOXsN7+FAAF6c2CsLtiyFvy4Ycfrq1+xBxmlxnQiUvaWwLc9vMq8xBviEfrtNaCw6Q0t/wasZbrNSlZXBaM3zReeScDPizvi6iomGPplZL9BCjrC3LdSQzN3XvUqKTaEW4BJp9qR7XwXtFm3LTMIx7crFtauLn5NTeLPTL11LzmrNOm0/jr4l/ofXlvtM9ozw80EiEJ0TBLGhqRF035MkBB5rAwNTezy/PdKRlMAwWgwsF34sREvPuuHUaJEdFiIeUEDRK2YWhuDCcfxA0ApZX+77a4Wl3pIRESz8U3Sd4WWB1Gj3Ob7gAe9VicVKy5OdwxlFtSjosXL+KFF16IVF8AAEVFRRgzZgw6duyIrKws9OrVC1u3+rUZjuMwdepUdO3aFS1atMCgQYNw/PjxiPYB8GhuwQ8+QHN7Y+sb6PF1D1z35XX49ui3gsN4LyIdsGMcsGiFL5MBwzEoqCkAzvcBvv4dWLIEqGrpG2Tf2/EevUOkwSnAA86rhYm0MQkbvpTmZnOpF26hxlK53fxvy8+PtnATmiWFGi7l2rQ1rMB1BxnNbWvBVlhsERBuTBxcjAsnK07i5u9vxjO/PYO+3/dFXmUeyo9dJx1zxUb+3jKk0j0AlpxYQtgannDzWSYUmSUD3nmFwu1//0vAtGnSHrfnThFM7rueA0yBk9swhJtCT+nySv+5F6uUFwEmIRW3S3NuE2SAUWGWtDtiKLckAJw4cQLz58/HV199BZPHj7uyshKTJk1C9+7dsX07ObN8KJhMJtx5553gOA5LlizBzp07MWPGDGRmZvqOmT17NubMmYPp06dj48aNyMzMxODBg1FTI194Tw0Wl0WsuXnW3MxOM2btmQWAF1jP/f6c+NzjQ4B1M4Gc+/hMBhd6gGEZVFiqgR+WA2fuAI4NBdZ95Ftzm7NvDr1DpHWdgA/X+7GIgjIlXj6pNbdQgm71CeGVP1ISwxQWQWbJ4+WBkyLKoERxrfdqtko0t6U5S2GxR2DWyhrBcAze2PqGb5JlcVnw9va3ceKLCZKnqgtYVjZAsy7y7z5ffZ7QZHjCzScMFJkl+WN6ZPVQZTb74ANKGIWH334MzkYCPrzj06O+JOZha24KGPfgLSgq4q/jdIV3X3V6BWbJIPQJvAlXrVnS4Yqhkjdr167FLbfcgpdffhkvvPAC+vfvj23btqFnz544cuQIvvrqK+zcuTNinfn444/RokUL/O9//0OPHj3Qrl073HrrrejShY8P4jgOc+fOxbhx43DvvfeiW7dumDt3Lsxmc8SrE/CDB1lzIyUUDZx1WVwW4Peg9crfPgTDMTh7vLmwHMyxB5VlPXGRcl36P3TvhyHySCJpbhn84ryUt2RImltieMKNYaLtUOK/FxW2IHMO7SOlBMhanPxvlV9zawRwgNUWgQ+bNcLNurH69GrB5pW5P8NRIc5FKThVzeUVDtA04UZ+n8PU3Dj1mptRb1RlNpPjTA7F89CZ6q8GoCRxsj2VKMjUhAE99RRvLg93zY2angt0YWuI54UbH2qhwizpjCGz5Icffognn3wS+fn5ePvtt3HmzBmMGzcOX331FVatWoW77qIksg2RX3/9FT169MCTTz6JTp06oW/fvpg3b57vYzl37hyKi4vRv78/hiopKQl9+vSJqJAFPKZF0ZobL9xIa1WB0fxWlxUwtRcecPEGsBwLm138Aku9YD5IGkTAh+7VwkSzLdLL5zkv0pobNTu5QqIfCuAfeEQxVzThRjHrejUnN+smFwj1tWsAmLgICTeyQwlnak04OOgYVQ4lCrtDEW7EQbE2NTdPhQgX61KWc1Qh3W++QN95/H7+/zL12AAAPy7C6LWjyRVBaPUKg9ixwwCOA1yu8J6rTuK5UDW3eH5s4NfclGtu61+Zjn/8IxlHj9ZOBJrkk8/JycHcuXORkpKCMWPGYMqUKXjvvffwt7/JL3qGwtmzZ/H555/jmWeewbhx43D48GFMnDgRADB69GgUFxcDgMBM6f27sLCQ2m5ubq7qvuRfyCesuelw9uJZpBCS0J44dQKmJN5sm1tEuB5rhMPlQN4e8UB05swZ2JKCNCUOwIWegN4FXL6fXC+swB+jknc2DygHCq1B94H0cXsGCCmHklJTqei+yd1Hl01ZuQ8Subm5KC9vBUBaAwmLgIHneJHHJFneEdg2kZ5lhDITP5Z3DJ3TOsNV0h5Y81/JyzZdtQ6lHU6G1GVhX4w4fdZTY4vV8WEXcRag5ErZU11OVsV3cJWio9wOI7HNSlMl4Wj+WwrlWwSA4tJiTzMKBsbj9wM3zEeNrQZgJeK8CBw+nIfERPJExFQlIaCtHvdFJcI0dxBKrCVYuWclrszwPzu73a0s0w74APRjx06hoiy8nJ1ut4v6TCxWC3Gt1sFWIzc3FzWWGlVrbrbyTGzfDjz7LDBvXujvAgBfKkgpJJ9ETU0N0tP5l8NoNCIpKQmdOnUKuUNysCyL66+/HlOm8GmPrr32Wpw+fRoLFizA6NGjQ25XyY0IJDc3F81bNCdqbo0yGqFJC3FqqTZt26BtOm+T3+feR25YD+StEpfJaN2mLdpltBFuXPehsH7b/YTyGgBw9AHgymVo2bolsjOzYTQFPVLSy1fTEoC0Q4khwSC4b7m5ubL3kbWqD/z2kp2djaTkyJmQiLBG/mPdOwpnrH8Dus4AFq0ETBIFIimDVZMWTcBZOLBrPpK9bPmBfqg4dEuovfbDxOHyVpcDtnRg4Sogvy/QdjNwJcmBQ4hBb1T9HQSTnOKGxey/HzomUdRmbm4uUtIIFQg8E0UlfWBYBqvyViFOH4eBHQZCp9OhsckTW6pkMK3ivyW9Ua9MkwogI6MTWrcmC7E4QzH9RG95H4X13ABgt2037rsxIHzjV3U171q16oSEhDRV5wRjjNNT7Ow/JgAAIABJREFUn0nc3jjAKX6W8XEJ/Pd6OEnV7/Wyf38qOA7o3Dm891EO2WnG0aNHkZHBZ7TgOA4nTpxAVVDepOuuuy4incnKyvKtr3np3LkzCgoKfPsBoLS0FK1b+zWg0tJSNG8uH8+iBpZjiQ4lVpdVWL7EQ6BpkRYLV24rJ263WXVAYCV4Z5JQsAHA8u/JHV26FLCOBTOcNwWJTEK0mWThteAuO0jeh9DMkm4zoZy9CmzRjoNh44Cd/wbWzUQ5AGxSUNGCtubmssBtdwOn7lZ06eBSJSHBGvkJyd7RvGADgHO3EssLia6vxqEkaM2ta1c32rfncM3f8jH9P/6JAEsxx5LXjjjfPrkEAU+vexrLcvg19Geufwbv3fqe/71W5FDCP7NQzJLl5TqqcHPYFdxDBc/CS5xe+G4xTmVam+9SdhYuR5jvlVwogEMsPL3ekhw41ZMHL+vXN0bnziGdqhjZp3X//fejX79+6NevH6xWKx5++GHf3/369ROsf4XLTTfdhFOnTgm2nTp1yifI2rZti6ysLGzatMm33263Y8eOHejVS6LsSAjwDiJihxKzy+yPuQkg8IOmBnpT3LGdwQoULdkpjV/n+rQwRWtuALBhqvBvRwqw/wngNP88Q4lz40J0N+/Zk+9z1IM8Tw3kPVjVILHm9vqB18Pu0o39CZ6F1L7E8cVt1wc5K5XIB8+Hk1ty/UYTFi2yolUb4XpQKGtucskBTHaTT7ABwKf7PxWep2SS4Blw+ZAcdYNvRQX9HXbLrW9tfBOobiN9TABGvfDdYhzS3prB1NhccLnCW7+SWu93s27AIQ6F8a6NK8qoQuGVVzqitDS6DmSS05qDB+kz+2jwzDPPYMCAAfjggw9w//3349ChQ5g3bx5ee40vva7T6TB27Fh89NFHyM7ORqdOnfDBBx8gOTkZDzzwQET74ubcxCBuq8vq85QLRJFwI2TKBwAmeCyQclCg4P34FWtuZQFFZi/0AObv8f9992jYBihLLhyI2vpYXrxZsOyu2k3PowjKx2t2mrG/Yn/YzbdorcLDlDXiaNnRkK6jLkOJEO/zSQwaezkXJUmzhLekm3ULSjwFQ0pyAATkYlTyjnk1NyY0zY0G45a5h3+qmOxwYuHGSqRPI2GxuVFx/nJV5wSjE1mn/NA0N+8kluVYquaWlMQhsZELleX0Zz1vXjxefTV6+SYln3ybNspnIZGge/fu+P777/HWW2/h/fffR6tWrfDKK69g5MiRvmOef/552Gw2TJgwASaTCT169MDy5cuRmkoOtg0VhmWIQdxu1k0UXoHCjVgAkEmgvghM8GRW4aJyIF7hJpoZ02a6zgD7fqBgA4BV82DtT4jpkSNEhzhvfJtS4Tb4zXlYMUV6Dfa992xYsHM5Tq+krFUqhTXyRSbz/g602glkHQEgMYFRSVq6G4lp1bBXK1g7CdEEBIRnlvTm4IwLunxwbTEvRLOk51uSi+WqcpCznrAsy79fOf9H3C88mB/WXKxL9T2rqaELMLeccFMDayBobuLvvlUrFgUF5GdnsblxZFl41Vl0BvrzYFiGmMTAZ5bkOOLkwWjk8MYbdsz+L1BJXokBAJw+HV2vScnWrVYrXnrpJVxxxRXo1KkTRo4cifJyid5GgDvvvBPbtm1DcXEx9u7dizFjxggS3Op0OkyePBk5OTkoLi7G6tWr0a1beAUQSRA1N+jAsAzRLBn40dbYKetVlPIToqw0tJpcEniFmmhgoc1cZRIcS1XopaG2hIivKx5zj0OBcOvQ2YoPRjwke9wzzzjR/LrdIfVHQFUb4LMDwC8LgP/tAwp6AgBx3TUU4uKA1BYKK5+H4dbOMaEHces9319wairaOiI50wYnsc8PSTBynKeO3c5/B6WCosCEvuYmFY4S0ThMzgCjLqAiBsfCUSbUwnr3ceHIkRo88i55vd1sdcNlVeeEIkJOcyPEcHpN3CzEZsk//6xBSUk1nn7aCYNe+n6VltahcJs6dSoWLlyIAQMG4P7778emTZvw4ovhFRysL/C1jMSaG8MxxIEt8KOsMlM+4F3PETeLylbQajBJoHrNTUa4hZI4OVSzpMvz+x0yKUoaN2YxZzaL+HhlKqJBIvuCYkztffXvwMYB360F4MkQH4HYMb3eb/aTJQzhxjpV1F4Lum3e/ikVbr7yO4JGPM9YpuwJSbgxnKc+4trZkuf6Gwl9zU20RBCA2x3BwZjTw6D33z+rywrsfkZwyI7tfN/bXU2Or7NGIuuNRBFbvqQNoeq7571nXHpfnlovSUn8Ow0AepnlUaWlhkJF8mv55Zdf8Mknn2DIkCEAgGHDhuHOO+8EwzAwGKLstl3HuFnSmhsv3OTMkkV5WaL9AIBDZBOZmwkaTYqvVtVXwFO0ECrW3ACgoj3wx5vEXSHVlQtRlhwvycXeokI4XMI1yZSmJrgt6bjiCgbTp9vRtSuDtDTAqdDvRG+MQkYEe2PAnsqv51goz1kFcUYD9AaFAd4hLt4DAGtTY7anmCWNwu0cQ363pDQ3OXMusUI1y6ibbHlMaRHX3MIMmBZeyCDwljS7zNSkyckpDDDwWVE8pcUW/vvN6ekfE/8sCMLNo8EWrhdbUOLi/IOAjOKGkpLoCjfJqciFCxfQu3dv3989evSA0WiUDJhuKDAcac1NBzfrJprsAoVb/vbeov18oxTvsuB3tES9cHO6AhL5CjomMQlZ/BNwiOwOrzSJayCham5gjfjPn/8RCfk7Rv+GoqJqbNpkQc+evGADxGs/1P5IfLhhsfdpXgOp6Bh2U/EGQ+1obnYVwo1TZpakef9KpZESlRoKws25gbLOwOrZwI7nAVYPN+dWP9kyteb7QUpbJ4Gk5hZps2TAmhvJSc2LUW8Ees0BOq0RbH/20a6UM5TDgL4UQMsd6X28piM3ifYFviN6meHgiSei6x0teXmGYRAfLxyQjUYj3FHPcFv3SGlupOBngUNJKWVGH1zY0Hut4A+qrAvxOCm8CVRVaW40F/L4mshpbtm/Av1fkT6PNWLHxR04ZyoQbDYayIOJUmHA6qL08Rx4gn8HFBRmlYPX3BSqvEqdI678AROnCCegNLd9Jej1Xs1NuD0Us6TcWqXVxgILdgC7/g2smwVseQVu1g2HjT5Ude9OGI/2Ps3/36EuQ4lUmlcmkmZJ1gCDzn//alw1QIffBYc88AD//vqOM4aeJIHaDQnhRjVLegQeKZdsUsBcQk64DRsWXe9oyakgx3EYPXq0QMDZ7XY8//zzSAr4FYsXL45eD+sIXnMTO5SwLAu3jlJF1/tvWrxXh/XAifvF1wpuzq48ENSL1xeDDbarhJI4Nqk8pDW34Fley+wiXBj4HHD0QenzPIN2cJxbuGtmCfFRWrAuvZJfz5Gp9aaEtIQU6HUmZQcr1Nx0N8xHUtKtwo0Uz8ZgeDd+8n2LiwvaTumP6B3kWwbgKSAqwdY1rQF7gHl609tYd2Y+LDVkJ6v2Nx7Fxt9bISMjSIhteRW4/T+AXbg9rc0ZVJ8PyvsagJTmFlHhxhmE4UNOC5AkvDcDB/LfoE/DU5h3UlU3jHTHMX6CKxZunFeD1YtvVtOmAWZJmdslVTsvEkhefvjw4WjRogWaNGni++/BBx9Ey5YtBdsaInzeRXIogC/mJgCBcKN5plHMdkzwWECILZHDW27ezgTN7kIxZXlKq6gl2Fty9LsbgSZnJD2yvNfjGxDeH6lEFt7Abynap0mk1QoTB+OgCrenn1Y+CGUkpSnX3BSuucUZCc4fCic5pKBen0NJcF1OmuYmEQpA09wKzYUYtWYUPv/zd9G+t7a9BVsV+V4nxcvckyDN7fLu0rGJUvGAEfWWZA2Cb8zsMos0c+/6ld4rJRIiW9YLANyiwSdgH+smjlms5zsPTtowcZqwrqaccDMofe9DRHLk+/TTT6N68VimoKaAGMTNcAxxPUoo3CgDCcXF32flZQzAjhcB82Wq+3usJBc1zhpxqZoQhRugLFWSgCDhlpbgMdvJCjfPRx00WEoN+u++a8fYsUk4dYo+aD/YdRi+kL5yyPCaG3li16ePG0Wt52Llf8bJtiPpLdlquyA5ttJnaYwjCCKFmpsUcXFBHWWM4Djg6FE97HYdevSgODXxPQBA19wm/jERP5/6GWDfEe0rqClAJ4a8dtYmvTUACfNzkOaW0aISSDtPzSRSe5qbXnCfLE4LwAgTwnvXln0hA+3+APaNkmz2sjZmFJ4n5PakICXc+DU3uuZWfeg2wfb0dOHN08t4lChdOw+V2qk9UA8x2U3UIG6iR1fAi0qNKaIIN98HtetZcWolhczf9yX6fNsHORU5wh35FOcWKTyDqFrTZLDmlprgcWIIUXOTipO58UYGe/aYJcvs9LqyOdq2jU4dHbuLbpY0GoF2154F2m2UbUenkxDiHdYL/1Yo3OLilLvtB8OyEppbcJuuJDRunI6+fVNxxx0pGDaMvx+haG4/n/qZ/wfF6apgdw/i9oTghcBggjS35BQ3MOgZILkISClEy87C2oxS3pJsJDW3/L+h9ILfycfsMos0c+9qkC9k4IofZZvVqzTlSzrQUDU3PeyE5b9gTU3OWzLaaMKNAjGI29ocRdvuREWeOJu1Ms2NPPv0vWDrZoXSVc9FjcivyceMnUHCcZN4JqykLSCEcACR5uaZQcoJN0c6UNlWtD6oJNpEF0fPganTAV9/HZlMIsHYLEaqcDMYgDhDHJAov5am10sMSIYgjUShQ0mcQSd2xlEo3KRyDcYZhMMF5xQ61Pz2Wxzy8xPI782R4QBjlPWW9JWOCeLU2n8Qt8uatoI0t+RUN9DlV2DCZcBLlyO7l3AyWGua29KlmP3EGPzyC/+t1ThrRILdaOR/m09zi1Ni7lYnUUTObAFQ19xYHUwm8XZD0DsnZZZMTGTQpEl0zZKacKNATJwMIO+r13Dg3c+AVZ8KdgcKN2oCYTnNLRw8sz7B4OQI0ZsvRM0teJaXplRzA4DZZ/nExgHI2ewBoNGAaZL7r7uOxbXXRj7ezVqTSBVuKSkcnz9RQvB6kRRuRgVV1UmnxXMwKkyVFYyUt2BwmyQOH06mhwL89bysQwlOKquy4EV2AhSkuaWmCt8FJsikKRnnFknhBoBxG/DPf/LvkMVlIay58f83yEVDB6A2SxDjpuUCpXtLcqweJId5keZG8XYGgKeeKkSC+iyDqtCEGwWyt2QAe8YCJ+4DSrsCnPAFoQ4klJibiOQLJs3q//xPaG15BlGpYqYkgj+s5Pgkfs1OiXAjoERzS+j5newxak01SpASbhkZHB+gq8B1W6+XcJzRB70YCh1K4o16ccC1QocSkVky4NkFt0mirCwe9gpKVYvfPxCYJTmOwyd7P8GQFXySCNRkAVZx6aokIz1WTXYCFKS5paYFVb8OcoUPFG5VVcCIEY3QpUsqJk9OhNsV+cQVTid/T81Osyi0pFEjvq+BIQNyJCaqtbYYiFljOI7jx8BycV0altURhVvwMoKB8mzWrjXjySeLyDsjiCbcKBATJwfzwwpgznFg7SyBlyJ1fYOiuX35SQQSVHsHPsYIbJkILF2kLA8fsS1euK0+vVrdeUHCzWgwICUuJXThpuDt1CWYgYcHSbcThWQ69upGfK5DAhkZHG+WTCAnAQ7EYJAYoINTIyldczMYRA4likMBRNYK/9/xwaEABMrK4lC8jWxCBIQOJStzV+K1La9hw7kN/AYzoQp7UhlxHdCLpHBjDCLNLT0oYsAtobktXBiPVaviUFysx9y5CTBXiAPhW7dm8fHHFHf6PsrXzy0uizCZOYBkz59qNLfH/nVW8bEAANZATErhYBx8HtU/3hLt41h/PthAgh1ISM/mhRfsuOmmKGQOIiD5tr711luwWv0//LfffoPN5je1VFdX4+mnn45e7+oQcuJkCjufx4rDG3x/UjU3iofWwd0Z4edZ82pue0cBG6YBR+WTC1PhjAAHbMlXW/YmeOamQ0p86MJN8TedViC5OxrCzVrWnKpJpadziNfHAzfKexvr9UDBUcrkxhCkuTFxivJZGnUJYrOkQsEoZZaUS4QL8MLtws9jKA3YBZrbE6ufCLo4YThi4yRd8CWf7bqZohRp6UFRNsEZOgKvNXmydHaTxz+biv37a3xeoiK6/AzcNgVotUOyHcAj9IOqXns1t8AEy3J07mbFlCkUi0GjUvE2zkBMiWZ324Hf3ic2wzA6OAjLf3pdsHATP7fMzOiuswmuL7Vz1qxZsFj8P/yf//wniov9pdbtdjuWLl0avd7VIbzmpnxQXrzb7xnHhlB1edWqMCMavQPt6giFb7AGpMWrjLcLWmvU63X8RxuicKNlKBHR4hBw2V7fn+PGCT9uJWt3ajHtoxfpTUnxVFlumifbjl7Poaaccp+DNbcTg4Hv1sm2GaeLF2tuoXpLBlgv5Fy7AaCkRMJ02mqn9JobyXTKxINx0/vuFW5Er9igROUGI4uUZGFbbk44SqtZ/05raoXR6PdqFGG0A7e9BYzsA7TeJtmWxWURmSWTkz1mSRWam16vwwsvOBD/t/+Kd95NmHSwBmJhYgfjAM7fQrwGy+rIde848eQ2mNpMSSz52QcvNNIWHhsifKC28t/bNqO179+hxBSFU0ySbyDC4f6sEdc0l6/wHAgXpLkZ9fqwhJsqofTY7cCA8cC9T+D114UD1lVXRd4MwlCSBgO8l2acwTPINzkp2Y5kKrHgNTdrJnD677J9S7u8EEZRkuPwzZI6BZ54xcXxMCRRPCJZg7S3JElzY+LAuen32ustSTUNBtAoxY2EoEKpXFAoiZRDiejaRv5gapWKwMmJTHYRi80pMKHqdBy8JSrVCLcmnuRGOiNhUazbcny+JFe4jSObJUkCzwvLAkuXiiW6Iag+Hen7jcb6Nw1tzY0Cy7KqBuVbWvXz/VuUTksBpRVhepWEUciSCGtEcpxKb8vgmZtX84qiQ4mPpCqgz0fA9V+LPqrx46NQ7dcg7QnpqzYt4wSi1wMJjSj9kyhHQiX9HBLi4pAYFP/Fsgbf5NTislA9YaXmr0oC+m02A/SNaMLNiEJzoec6hAuRhBtnlEx+7H3Wt97KwCBTBSI5xSWqAh7sUGI2K59kel31qZpb4PML9nwNwOkEHGahSTIl3el7//UqhmlvcvFGvYJqwF3zLQAgzhicQo1slpQqTcQyOqKlKTVeuCZJFG4SxVEjjSbcKPCJk5XPMlxufgA/WXEyJC1q6jvqU24JYOIBl7RvrSFOxWAZSgoukVnC83pF0aFECS1acLjxbwrzNyrE7pS+N75yJjKTDr0e6HXPAfLO4DU3JXRag3hDvDi4mdXByTgxdt1YtJzTEj2/6YlTladEp7PBQifgG9ApyFhtt+vgKm9F3snxmpvdbSebJ2mmU4kE1YEDaPaN0mbg5FS3uPp1UCq9775TnmDam7FFmXCje86OH58ER1Al9vTGfkcXqdhDAbdN8XlWJmQWAP/f3nmHR1F1f/w7O1vTewcCSSCEXgNISwBFikGqCIKIiCCWV0GKioAoohAEBX8qorzCqygiggpYQEWaHVQQoxAE1AAhgYTULb8/NrvZmblTdnc22cT7eZ48sLOzs3fu3rnnnnNPyXrM7tSU8DWQtdDeZr7Hq0VP1NIqzOLttZp1KC4WPpwmlus9TNLSmDrU3GRn4Q0bNiCwxm3HbDbjjTfeQHi4XfctLVWnGrE/IhsKwMMh3D478xlgvctXzZJoQABwVWRSqcFotOKa0vnSqpUte1NhrsCO33cg2hSNrGZZgpW3lvVOuLlhjZElvU05vjkQpt4FZaqlO4WbzEJHowEi4kT2oTzR3HTl0Gl0wv0xmwafnfkMb554EwBwqvgUnjnyDF4e/DL3NAnVTYnmVlkp8aO5hJhcLCM5N4hcv0L8d3PV7vUm6f4KCrYINDebVj4WkQhjdi7exM2SLs+PXnyufOMNPdo8xNV67MLN3l+KhRtjcZowWYYF+i21/7kQHsYbFxXhuFZ93PnyWvU1LD+8HB/88QEA8mLBWik29vl1AAlnaKyC83yF5JOXlJSEzZtr1duYmBiBA0lSkvSE2lCxay3KVxlV1faBrNfo1d//UtSAINnimWER1bhWonBlqkBzu/m9m3HovN0T7Ik+TwCYxXnf6T1luiz8sLZcNGOL8xQVhZsgL6K3mKW1ZHfMktZqkfHiiXDTOoQb/w0G637gOhu9/evbAuFmEWw6ubfnJknNc1FtrUZBWYHwfTHhJhHY7SrcHGZCMYJDzPbn04WQ1oclPyP+xdVOc6EizS3wguTlqkp4IQsR1XBOz0qnIY3FuQARW4hE8tOhlkVytLSF+xfi1WOvSn6NWFV3/rqIlPXfXpi3brxKJGfhn376qU4a4Y+46y1ZXZOAtKSqpJ6EW7DAlZhPRisdzp9ReD2rVrLo5LELx5yCDQAe2/8YYOXGfTm9pSKE5i+E/wFcbCvZBCXeedM7TsdTh55yvr6j3R3E81Qvr2GRFm61ZknpxQTDABYxhwm+Q4kSdGVgNaxQuNk0uFIpH3cnWNC4eku6k0SbhItwu1pJ2JcTE25fPC56SXeEW1xiZa2jTw2aiLOC8ywWBfu9GrNTS1Im3AjC3IXyS9ykyWHh1QDsiz/Oc9j3CeDLx8gXYaxOsyTf/OogIrwmqYKjryvDUOliYpcTbABgq1JWADY2lmSWrDvh5vWuxrlz0jFGDZUIUwSiA6PlT6zBbLb/kEUVRfWnuVWJ702kpFigd0d7kTFLnio+JTwoSHwsUaqDJPB4KAkFmNZhGlpF2Iu7Ng1pige7P0g8T0l2DbcQMUsOHmwXSM5JVGYs2GxAbFMRBwxPNDe2GhpGI9zvsGnIAoWHZMC0SsKtylJFtgp4UHvQNbekVict3BKaVgg0N7PFDIOB+7lff1Vwn5pqZ38wDMCQ9kddf78gaeFWcSmB8zosovazHLNk5hqg5Q6ysGSstWZJEZt+oMEIfQDXO7Lkqpvzlcwi2gEpPMOuudUNHo/WgoICzJ49G127dlWzPX7DB6M/wG/Tf1V8fnVNJezLFZfrR7iZDZIb7xERNve8Dy16Sc2NVI1cEMTtEE6k/YboE8JjPJS0N9wYjs9v/RwHJh7AgYkHkBRMNpMPzVF5f5gg3EJCbFi61G7iUepQYrMB7fvkk9/0xKHkXA+yELJp5JMWw8dmyctpwBeP2CtrEIWb+9ORa+VnOc0tOqZaoLlVWasEpVeuuy4Yp07JFSOr5jjYaAiu97d3uK32hYzmVnmJW+bKVbhxnsPAS8CtOcB/mkAAY4GBtVsUnOOPh1FrhCGY6x15tdhNT+uz1xEP882SJOFm19zqBslfsLi4GNOmTUNKSgrS09Pxf//3f7DZbHj66afRsWNHfPvtt3jhBUKwYCNBgXOYk6qa6M/CsiLUixOqRS+puUVE2MQf/uifYYj8m3vsbC/JPTeiKzm/2Kij/3SEzPxR8gsHJRkxAHvuwTZRbQSuyK6kpKjsgswTbgxbjUOHSpCaWhP75Nhzs0kvdLRa2DXq+3iFVUPzPdPcyiNqNDfecZvGblWQwczX1vlmybjv3W+TK/uW4tw5Rlit+6/OigLU+RiNte3TyQg3nY4RTPrVlmpnUVBXliyRyeqrMXNc9DVa4UJkXJtR6JPUBwDQOUXaN6GqMJHzOjyy9rc3aglWAkIVbDBW5/2RPrOwl91j0sgTbiVX1Akj4gu35GQ/Fm5PPPEEDh48iPHjxyM8PBwLFizALbfcggMHDuCdd97B559/jtGjR9dVW+sFRmE4gMMseblMfnXsEywGSXNBaKiE5nZ3R1QW8gqkmk3Swo1QjZwfCuCcYFkLEP1z7RsReUDYadFrO1Azm4HYHoTH8BxK4rseQmKiy0QrsnLmExBgswuNiNPADQ8AjBkwFgHD7/Jszy3mJ7AMac9N2UKBL9wYfihA27fcbxOPjz8KEVoFPlnu0bUCXHwb5KoWaLWMwFuSpLkBwPbtMo5XmmqO6Y+kuUUGhGLHqB04eONBvDFupeTlqop4acJCa/unZ0JPxATwEkqT/AEYm1NzM7JC4eZ4BozBPLNksfLwB3eIiSEIN3+Jc9uzZw/Wrl2LpUuX4s0334TNZkPz5s2xc+dO9O7du67aWK8ozZLh3HMrU78UvCIsekmzZEiIiHDTVAGsBW36H+UeL42TFm6kSHUxzQ0Ahk8HYo8CMT8BN91J3ofjN01F4cYyrL0CM4nIk+TjUvA0N42W+yA7zV8xxyQvExBgq8363nM1sCAImB0LpH7imebW9SVUWiqJmpsSLBbxODcAwHUrgPHDgdZbgTZbgBYfu93E6mqbcGydHuj2dQCu5ibnNMRqhMLNrrl58MVsNcf8SxJuOi0DhrFriwEB0osLG88D0aCvPZ/VsNiSs4WbVEHkco5xR9LcHGZUYyA3hq2s1DeaWyBhOmI8DAvyBMkR/88//6BVK/tmfXJyMoxGIyZPnlwnDfMXlJomHd6Shd4mQOYRH69wMMiYJU0mkYe/ZrB1zeSVtymJF5qOXFCy58aZYJseBGZ0BGa2B5K/lIz7caBVsZSvhtEAN02rzdTfbS1GzjoEjBkDXOdB9XMLd/LQ6rn94XRcGMLNb8gnJsbG3SPTVQIOE5cne26mQlwquyQctwqFm9AsyR0DGg0DtPoAGDcGGHMLMOkGIPoXt5pYVW11P0GACK6am5yQ0rIEs6S12jNPWp5ZkpQgwTUFmmgsnAj80JVOsZ3wzgjpPL4a6JxjiSTcHO9pddzflFS+hp8nVgndunF/U0fiZ1f8xixptVqhcxkxLMvCZFLmBtpYUKq5VVczsNlsuLxzrqrf37mzRVmxTRmHEp1ORHOzseiT1Ad903l1m+Q0N3f23Ejo5TU3fmVfb2A1LJD6MfBgIvBwJDB0FoZPOgm02Qow3k+0OhM3XZFTc0v+Ehg3Auj2AnDrECBztfOc7OxqJCSqcWH5AAAgAElEQVTYxOt1eaK5MVZcKr/kueYm8JbkviY6q7jZziqzVTZBgFJMJuV7blpWIxBuVZYqtwUPAIHJmJRWyrVEkLuFOfUE716HyVEM1+oBJLOk47fjVy+vNhPuXyaOk0/08FxBZW2SqLCi7oSb5JrFZrPhrrvugr4mkKOiogL333+/QMC99Zb3dnh/Ranm9sdrj+F/ba2w/jxWxe+24dFHKzB3roIFhUUPVIsPHK1WxJvMqsOOUTtw5AhvgnVXuFkZgSu35J6ZTj7RreKqAApwChDDNQD2DXXnPhxpc95N9Hzh5jqJtn7f/gcAqbsx7caOyAjtjltusWvLommtPBFuGgtKqkqIQdxK4HtL2tMl1X6W6DFZ0MGtJhLNkh7iKtxk99xYCPfcLFUICvJAuLHV2H16N6Z2mAoA0AUKx7PO5QHQau1u8FaLskUGKemA3D6ua+JikubmeAZY3qxP1NxkMvBwaHIAMTdsADCVc5g0rMvL6iY7CSAj3MaPH895PXasehN3Q0HxnltpOO5xt7RdwjfAX91E337//Wto3dqK4GAFD5/FIJkLs2lTK4qKyNKGYRhhwGVpvOTqWmCW5Acrs1XSCwNWvsq3rx1KnALPEyHCQ2fgmSVZkU16jQ29h55BTlpHYTsE53pglmSssMHmseZmNpNCAVxc3r2NdQNgtlglw0zcwXWdLWeWDNCboOMtmKqt1QhSFrbFRWNGaniq82VM619R+Fsr7im8vtLprKj0QrgZtDK5Y12mc1L1cqdZkrfIvVpI6AB3hJvumqjDVkDbT1H2c+1+ass2JQBEKrWrjKRwW7dOpdpgDRh3wgHcxlQo+lb4sKfRt+8MAFAo3PSiaYpCQ20YNaoav/wiLi3Cw3mTTUUYykr0eP55Pd58U4/k5GS8+GJtJWM54cZoZYSXgv0kvYppRUgCxOntRjBLRiafR2F+ouC4GIERXC9ZqVU2fyIQFRgemiWtNitZuFkZQCZxrZS3JKCOcKuqtqlmlnR1KAk0SHv9hZtCoNdwNewqS5Wy54uPphrDUmqft4TWp3Hife4pfI1cp7eiUjwfMQc9P3s/IAhA58MytWOOJAjFhNvhNwfh26ml6NrV5TdxR7hpzKILtOjhK3HmnwSguDlw3XKYIvsCSFZ+bS/waqTu378fd91VD0mC6xC+HVk1jEWS5jC9oVbYhIQoaENFqCAlVFKbU7jzzkrs2VMq7lBSQwA/XVxVMFaPWYTHHjPh+HEWH30UhQ0baq8vMEue6cd5KSvcFGwsm0TzGrkPyfSngQatI1sTf4fFjzKcifOWu6U9KiOSuLkDibFJNfAnAtF6XZ44lDAW2Gw2YQhLUQrwVCmwfYOks4Bgz413HTU0rqpKhmuW9OIRcx234cZwyXNZloFWo+Vo1RabBcZAD/pZY+a457foyPPEjTwp1Nzc2NsjaW6i1oAaXMeRlObGN0sCwH//y7u2THo57oUtosLtjGE3MLMt8EgAkLUYo7ePrrO6oG4Lt4KCAuTm5qJz584YMWIEzp8/74t2+Q2Zmd6brESRcGTQu6QEUiTcCFVzH7rHiBUrKpCebp+Q+BvJruh0ACOz97R4ce2EzRFu39wNvLmTcy4pqNVdAvRurB49JDc7l1iGI6NFCD744BomTarC8uXleGJOlOR1IpK4We6l4uoEmpvYY+ih5maDjWxxMAcAP04BvhNfkEoFcQPSdb6UUl7OcIWkB5lJHLguQMquSu9Ns6x9kRNt4qbVYw3y+7/Ci3Hj3MJCtcDwafaE4NpyYMB8gXAruqRcYBgIWcPlhJvWRXOT9JYkzAMC4eaO5lZtklzMQWNzWraLKoqQfyVf+bW9QNGoslqt+Oijj3DLLbegTZs2WLp0KcaNG4fjx4/jww8/9FnjcnNzERYWhjlz5jiP2Ww2LFu2DOnp6YiLi8PQoUNx4oR8KidPycz0UdChjZHW3FxWeR6ZTQBUVXEnUSnNjWEAnVH5ZOqc5Cxa4MMXhdfzROvgYdL5JrjUlZ6JPfF4n0cFx3U6G7p2tWDNmnJMn16FAKNE5wX+A2OQcNIXE3CKNTdP9tw0FrJZ0pUPXxTVlq5VcUvAKEpi0OwL5e0DUF7GcjU3L9LVcSwOMrkpHX0SE8gLiDZ4kHhBU835HQO1gUCX9cCcaGB+CJDxnvheqgKCjEJhIetQ4vJ9Ut6Siqz97gi30wMRZlReTkpJGjg1kBRup06dwqJFi5CRkYEFCxagQ4cOOHLkCDQaDXJychAbK11ixRu++eYbvP7662jTpg3n+OrVq7F27VosX74ce/fuRXR0NG6++WaUlPgmeFqR1iRCxxtEilA6kNDcXLcPPN16Muq5y3c5Bw29QflkWmmumczLyaYgjc574RZYB5obAGREtxIc41tEJX+DsHyiIAvQkkuD8IWZW3tuBpnM/owVNhvBoYTPtjeI5sk5ex8WXE+WwffLn+NCRTnL1dy8qCLv6i0pWuy0Bkcy6YQgbpLiai2hJJMcLlUBACBAV/NbG64BrP1382Z/MsQkHPtyDiWuTk3ScW7Cz2Zk8OYid4QbgFBDqPxJNfT7Xz/MOjILzxzxIL7UDSR7PzMzE4WFhXjttdfw448/Yv78+UhJSfFpgwDgypUrmDZtGl544QWEhdWuCGw2G1588UU88MADyMnJQUZGBl588UWUlpZi69atPmmLR9kLaoiMktKEpDU317iYixc982q5YTB330tuwtMa5DU3hyNAhaVmZ1zENs/q7d/9wiDPc48avOl8NyAJLv5XSzYlqIC4SudklHCBf65bwu3+5kDO7eJtYeyam6wj1E8Tgf9+itOnud/9Vwk3xyi/UjWR+KPy57hQdk3DdSjxQnNzVXBsMoHHjvGfGMx1FKrUEgqnysHyNDfCb+2NcNN64FBiCKi1HkibJYWDQ+Bb4KZwC9GHyJ/kwpFLR/B9gZd5SmWQ7P2WLVvi008/xUcffYSff/5Z6lRVcQivvn25+0hnzpxBQUEBsrOzncdMJhN69eqFI0eO+KQtcpnGpWClPNNsjKTmVmqt9aT01CU+NIQ3ico8a0o0t6MX7BOZU3P7dBnxPF2wXcOYkDEBI1uOJF8s7QPJ7zL6WLg5nExIe5H8ZLqSfaetIAo3k468B8TXHETNTSTTrrEY6LQRSBFJMsxYYYWMWdJBfhb69g3ChQv2fjBbzYIclGp6rDoovFqJRQcW1R5QKtwIz4urELdYpCW64zlqEszNqF+m+UfZ97vCN0uqLNxIAdCshpU0dYYk1ZYfkxJuNouwvwWB7D7U3Bzwfwe1kez9AwcOYPPmzSgtLcWQIUPQq1cvrFmzBoBE4KmXbNy4EadOncKjjwr3QQoK7GUjoqO5G8LR0dG4cEG60q2neDO/SudGlNbc8kpqcxKOG+eBiS/jbU7GAoCcpdsVvYI9N0cFZafm9tNE4nnG0GIA9nEyqtUo8sUG/wdI3if6XTo1S3FLQBIEbjlqspXEfTOSxxog1BxE9ysYoGs3F+076WCtK79WxKdcY5Pfc3OhpITBunX2m620VAqcO8QEtDdcucpPAKDwITNcAVI/cr50BME7kPNsdmpuQdz+v4q/lH0/52Jmjima1E/eCLfAQPK9cJxKrn+o9v9N9yMqNd/5UiqI22oW9rcg0547GUr6LRYVbs1Dm4t+jP87qI3skqlz587o3LkznnzySbz33nvYtGkTLBYL7r33XowZMwbDhw9HXFycKo3Jy8vDkiVLsHv3bk7aLzWu6+lnLlwIAdBS+mQRKipk8idKpX1iK51tsHvOulk3z1iM/FP5nEMtW2oAdBac6uwfDb8GvZDj+ceRaknF5avS+xSs6bLzuo5FiYDI34Hbs4Gtm4Gfb+W9acWf+WdQpPNgP0Qh58+fR151Hv75JwhAOue9s2f/wNWr3N9Ho+1AnBhQ2BJXir4SjDONmTy55f+Rz1kcSlXInrfgGNa/1ByXKy/j6w4uSRVYgtdizXgym83Izz8FoKPwHAKff16NvLw8XKy4CH4mkwDWgLy8nxRdRzH86hUkzS3+O+DvLtxjjA0YOxphPz+Mu9KnYMSIS8jLqxUCffuygKatqLA8e/YMtNoKVBVxheJVs7AatyxsNfJP56NQZ7ewXL4kHKeu4yEvLw/hEe1QdFmZ0DhzhjxnaV2n7F659kTk12KAjK2oKu/l/M7LF4TtKfinAHmaPJRdEy6W8y+dQ16eS9ytRCo/AWkfoqJ4KHGevTvlbsz9npySkL3GejQ3A0BaWprsOYptDgEBAZgwYQImTJiAvLw8vPHGG3j22Wcxb948FBaKByO7w9dff43CwkL06NHDecxiseDgwYPYsGEDDh8+DAC4ePEimjSpVWkvXryImJgYwfUcKOkIV/Ly8pyfOX/ec+0hNFQi9YGMt2TPZl0k2/3aa2WYMoXssAAA/Vt2In6+SRMrzp7lTrqO84JC5Ot9hUaGIi0tDYafpB/SkOhy53XztfnAtxInd3lFKNw0FqSlpknWaPOWxMREpCWn4do1oRBKT28hyFwREX8Fl84SQgLO90B01E5Bf0cciwCKhae3bMldLCVbkoFPheexDIsB/VMxMIvB/rMnMfxdl1gqkuZW4/zBaBikpLQQvi9CaKgJZYEWDLn7DPDrIc57Op3O7edHFjnhFnbaXlpHINysgL4ciddvxYKJ/wEg1HjDpo5H8a6HgHM9Be8lJDRDWpoVhYGFwOHa4/pgD0IuzEbO+CwOKgZ4OyOOfnPMJ7PnluGRucqEm1ifG/cZUWJ2cZ5L/cT538jQyNp5y3Ae+Ib72cSERKSlpSEo4Bz4XK4qQlpaGlimxpNVxFGMSPwPaNn0P0hLEbY5LS0Nub/mOi0+rrRr0Q5pTVUeWy54pDenpaVhyZIl+OWXX/D666+r1pihQ4fi4MGD2L9/v/OvU6dOGDVqFPbv34/U1FTExsZi375aU1ZFRQUOHTqEzMxM1drhijdbDlJxZQAkNbfsFuRqtw5IBRZdiQgn/7RLl5YTjwOA3iAf9uBw4ZbLMBEQWqu1irq6O4gllIVhrF65UrsDaX+DZJaMCBNvjzt7bnx0rI64IR9uDHdqeIKEs0ThZv9NBjQboNgsCdjd/W8aFgZ8dzdwjWuF0chkNPGI8ijgze3A3zU5KfnekhozOT1bmT1tk5i5FwCC0o8Ad/YCFgm3TSoq7Mf45nqrRmHaEM6HtByzpGScVw1pqbKnyCKVPNl175ZkEj1VfAoA0HtIvuA9RwiI1cICBx4CPlmhrEHp2wDWLLnnlhGVQTyueo1F/vW9+bBOp8Pw4cPVagvCwsI43pGAXWMMDw9HRoa9g2bMmIHc3FykpaUhNTUVK1asQGBgoM+KpnpjHZVO/MuAYW2iyRlubC80H7oil2W85DJZqxs+3IzwcCuKiuyDf8KE2klEkXCrEWrEYqUuBIa7CDc5IUXyCmTEsx6ohWP/wjUQ2AHpd0+IDMJvItdyJxSARKQpUhD/45pxQ1B+SERzC9AG4NFej4JxI+3H119rUVYmDIcAfJh+7mQOcDEDmNYN+HUE9z2NWSTGzz5mpYSbVKCzYx+L/1vZNB4EptsYzviUapOD2Gjvx7OUQHAVfCThdvaq3fyakl4KRPwGXK61IFjOdQFQCdsnTwGHHhJ8NjqhFBf/IliisuzVvUMM4t6SYs+xr59vSeEWHh6uyHHk8mXf7Yvwuf/++1FeXo45c+aguLgYXbp0wbZt2xAc7BvzlZhw0/RcA/w+GNaL4vtxcl6OWg1AdBVhzIiNlu53uTIdYit3jQb4+ONrWLXKgMhIGx5+uHaS1BvlhZtDg5Cq9QYAxoDaa8mOIZIGy1hVX9mNajkK7/72LgC7MOmV2AsAWTsnNVkqwa47DiUkTl8RViZ3FW6CLPoE4abTsfji1i/QPKw5rroRJ1smkaldsQaYvBfIz5Y/z5XLacBygt1WUy0ZkC0p3Fzd5TNXA0fsMXgZGZbaTD283+o3djsQeMG+d6UUm0bWW5JPlAr5gqWeCWeZJZAFR2l1ae01bp4MvOpigjYbYbVWEAUbALTPvIjP3iM8AEb7frGU5iZmualX4cY3Od59991YsmSJ5P6W2vAzoDAMg/nz52P+/Pl18v3ioQBWBA5+CiVvvC762X/+lHCPtTHi3pRsFXSkBHAuyMasSYybtDQr1q0TmicNBvmA3eoaRU+uZInBpZaV7CDWEUylNo0qSXpdebLfkwCAwopCzOsxzzlRhIYq03JES6PckoMAbX/BYdfcg57A0dz4OR21Qm0jyKRDWoR9D0MtjUvxdYbfBbzyDVAh3KsJDLQS9zVF0ZiBInEvOykToOsEj+tnAxG/Y1LKf/D4f6Kc9yIQEKwZGDYd2PKe8ia6FAYFgLjAOKSFpyGvyO4g0Tupt+AzauSp5ZtUXXHVWknP3PjWdockrUZLDM4//5d4+0LDRTy2a8zHksJN5PlX+/nmIzmD5uTkcF7fc889GDRoEJKTk33ZJr9CTHOzwYaA1G8hlRfl5A8SGVw0Fmi1DIgGEV72A0/wZK/QoEBzq6q0D0g54ea6ZyV7LxorwJgBm0ujzQFgGO+znLgSFxiHV4e8KjgeFASMG1eFLVvsjb73XrKZipgGrdX7QMsPEKgbKnhreqfpWPXtKkVtiw2IFWy6u4YIKNHcii7XThaCRNgeolhzi/wDeKAZsP8R4ADXO+7ll8swYYIbdWU0ZiAsX/Rtqb1MjubGmoHMFzBl/ChERtaqTcTJtvV2IPZHoECZh6mBNXIsEgzDYP2N67Hoq0XQs3o81fcpYbsVKvKdO4ub/KWeJVdhQTqvc5x9q4NlWKJwM0tYY8IjpYVbkE789xXV3Lyc4+TwrehsBIgKN8Yi69QxZMJJcacSw1VxsyUvhoZEUBDQSqLsfIvW7udvc03WLEZlpf2BlnMo0euVaW7OfalAD7JEqMi6deXYtOka3nnnGpYsITsYEIXb+BGAxlqbfsmFuEDlITKT200WHAs3uJgl+f0tFudWg1q18NzSAI0lQNJhweGhQy1AoEg4CAm2Gmi7RfRtE+venhtfQxB9ttwoWmvQCNvQIaYD3hv5HrbkbEFKuOeZnJ5+Wvy3lZoXXLPtE0s81Ryza27Ce7VC/P6jYkSqfNQINylBVVZNTkzta82NCjcZpMySOhntqFv/v0UnSiQeEXc40VgE5ofx42sHV9OmVnTsaMHmVS1gaP8+/9MAgF4D3M+6YNLLq3vVlfZBLOdQYtTVDnYp4eZchQd7EEirIiwLDBtmxqBBZtEJXWpbV2zPJS1cmasz6fOuZkm+matJeDT/dAH9+nlf0cIdr0sA4rkoCWZU8S81A0EXgAhyDJSU5sYxSzq+micQRMejVNwpD73GR3lPEw+je3fxdkiZJV1N1yTB4egH+7/CQV5VLT5e2nS6CugIcbtsley2g1iZJF/vuVHhJoOotyRjg55Qc8mV4BAzbrutCj16EAbNoLkS+2I2wUpo+fJy3HtvJSZOrML27dfAMEBqeCoWTepFvEKQyY0MAzW0iEySPaeyqsY13WqVrAumd0narJGYIZ3OAaa6c0ryFKkML2LCTWmJGNJk5CrcwoxhWNp3KViGRXxgPEa3uUn2mitXiod9+AwR4ca4K9wAhKeRa+hJOZSQUpnxJ1E1NDdNtXu5FBUjs8iT0pBcfa9J53E0N0KS6SpBFfZaYuPMQDSh+orG7LEG5muzpORS/eGHuRnCq6qq8PTTTyMkhPvDPvOMb7M71yeiws2ih06nAYL+AkoThO9ry2E0aBASAnz44TX0fGUwfjuSCpztCbR5G4j8nZgcFQBQFgMNw81aERICPPGEUAsMCyRMqqZCj5wZAo3yOaeqKlmcvHwSv17+FbCKn++quUmV6nCakYwErzk/Y9iwakRFW3DpYs299XnS+R7JLAnYKz0rgeRRyi++OavzLNzT6R4AwNtvy/9WqalWjM59ElsffERRG0ioprmZlXuONglLwOIbN2DtkUR8R3hfsUNJDYqFmxuaW1iEuvvBTkjxfS4UlosnzJA1S9YIEw2jIXqjlleIa24GvRbQXRO+wchrYAxBSxRro5pICrfjx49zXnfv3h3nznGj232VY9JfEBVup7OgZzcBAx4B3n9N+L7ham3lWxbQBZUAHTbZ/2qoupDsdfuIORDZKrfqKzlQEtN3rdyC67dcb39x5D6JdtXOiilh4vsPTsHnzp5MPaHTAbt3l6LrzNeB0D/truY1eK25EYwopMrSjueNFJtHIj6tAEj4Gviru6Lzhd/n7gfIws12Rd4q4CAjJhUjWyXgJd0fxPcVhwLUwNcQRIWbXiZdngt9xn4NQPk9OWm1HTg5Qvx9GeHGKRLMwzXQn1h5vmY+sgs34XirNJOFe2Ki1V4fTkcWrHIamJiMqFfh9sEH0lnb/w2I7bkFlqfbJ+ZOrwOhZ4D/7uWeYLjC9V4i/JAhSX/i0jeCw25RUEAYOJpq2cKGJJQkC95/+htciboCVIQAnzwrep6Wqb2Y1Erb+WBkPg98M8t5PKDnawBEqgnUI6kpQPjNi1FUwU1VJibclGpucmZJPqT54pVXhBv3Oo0OuGkasGu1PefiWenMN4J28ZrVr0k/fHFWojipkvpvMjicYbQ68rXcDeLm963osyFlGtdUA/2WAGf6Aq3fRVwrD2tZDnoYONsLKBOxrMgINyllwlVzc/2/4BpgiGbJikqycOvXz2x/htN2Ab8PqX0j2K7oeCqk/NJb0mw2o7RU+SqnISOmzVy7aqz9cSJ+F57gorkB5MkrsYv39Ywc3osc2GqPAqCVlPe5eq0KqDIBO9ZLnhfM8wwWm/ydWRWifgOyHgWMl4Gkg0i/aaeiNtcHJKEjZpasMCtL7UQaH2EGce2b5LU9ZozQVKbVaIG4Y8CULGBqb6DdJuEHJeBX4p7dfbb0B+J+4L42XXLr+4DaMBad3n3hpmTPLcQQgg4xHYQfDpDIkau7BvRbCky6Huj2kudxhFF5wKx04M7uQAwh7ZzM3qSYiQ+QFmicazAM0SxZWUX+PMvW9Hnn9dyq6x03AvDc67FevSW/+OILvPceN7Bx1apVSExMRLNmzTBq1CgUF/v/Xok3iAm3jAyX9FB6gi3acJXzUJFWN9EtvPcQNJOsFB6mrlKiuZlskcCrB4HjY8RPSt+G0DDuxCTWHo5W1+9JYF4kcOd1GJ3ZTUmT6wVSwKpYqi1+fJrY5ER60KVSGpnNymZXwWSfuUbR55zt4jWrT5M+dhOVGEEXa/cidaVof1euW98H1OZNVUtzI2kIb930lvDDCRLZva+fw3k5spUXVoWAIiDpG7Iw9UJzE/NKFFwDDNFxpbSM/Hmt1mbvc10FMDkbGHczMPEGINtelkzWLFlPe26Swm3VqlU4f/688/V3332HJUuWYNy4cVi8eDF+/vlnrFy50qcNrG9YllfKvoaHH66o/VFJMUcKNDcDawDivNPeRo8mbGzbNB5pbkr23Kp+Hi4d6HrzRGDMOMGkKvYAiMUszeg0Q74x9QSxVpbI/WVEcpPGto9pTzyPND6kTMvERQ0BwWSf9A3QSVrrdoU0lwqSOPMZ8CgwOxZ4MAl9Brgfb+kwS+pEhJtBK+4JTBJuJPf5+KB44YebfkW+aMoeoN1m58s5mXOQHJos2gbFkOYNOeEmpbm5eEuKZ62tEZBRwiypV66KCTeXBYXGag94T/3YGU3QIM2Sx48fR+/etfE127dvR2ZmJtasWYNZs2Zh+fLl2LVrl08b6A+EhwsHSp8+llonAFJtLV05R8CQfkgDawBuulNw/MYblXtiNW9OGJA21iOVnxikzOPaSXLoAQB7qZIOmwHWLJhkFGluNfRJ6uPXjkpyeTVdWd5/Oed1bjZZkyH9XlK/IT+8pEkTkYmJtMjJmSZ6XT6kn0HRPmLQBcB0xa38mg4cZkmtSJIEqcmUaJZUOokGEEyoPVcCEwcD+nLsGbsHxQ8U45GennufcvBEuKmluQFAIjfg/moJ+fMajd0L1dMEyPXlUCI5A165cgVRUbX1q44cOYIBAwY4X3fq1Al///2371rnJ0RFCR8ylnWJRWMJG7E2hvNgk35gg9YAJPwA3OVStyrgEh54wIMs5Zzv1ngkHDp1UpA4uUQieNglk7vSwFnS5OdrW7y3VFmVOYkAdjPexqEbcUe7O/DmTW+iS1wX4nmke5aalJOTbbj9dvs4CQy04bnnyDFtnjgWuWL0MlbZsXgJ7arcOc1hChUzS0pNikSzpNJJ1EDQMgMvODUU1WsLEoRbTIh0HTUpzc1VuEnlenRmzjFwkweWlJIXE47FhthCxVOzpFT5HjWQnEViY2Nx+rQ9W3llZSWOHTuG7t1rXYpLS0uhV7JR08Bp00Y46Ws0MpOwVcdxMiA9YE1CagquJnxvrz/1cASC5rZGZqbyeBsi7hQadIEkxBHjRhVmlyBYgXATeQBImpu/Czd3yUnLQe6AXNzY4kbRc0iLEblJ+bnnKnD06FUcO1aCAQPIdkpR4XbjvZLXduAoE+Mpjgkxqpdy4eZQjKMTyE5rUpMpKc5N8XgizcEuz1J6ZDrhBC/QChckA1oIEy67otQsmRCUgD5JfZyvp3Wo1dbDjGH217xSUyUiiXJLSmrCT0S8nj15XjXQSO4pq4FkqwYNGoTHH38cX3zxBR5//HEEBASgZ8/aCre//PILWrRQXvG3oUIyS2o0QME1idgsi47jIUgUbsFNuAcCisDq3Q8OZXS8FWCV54PmnodcNHFjEdDhv8o/bBPfY2xMmptSrzR3IMW5KdE4mjWzITJSvD1aseoSyfvIx3mIVkJQiOP3DYhRbuFxCLeOfc7aw2wcZNvNgZKaGyHOjSTwFFNT5WBS20nqj0uC5hYik11ZaSgAALyV8xaW9VuGNQPX4Ol+T3Pee6b/M4KaeYc+I+xDAsjPt9+3mHDzxLy4rsc6tz/jLpJeB4Zhzl0AACAASURBVAsWLMDEiRMxYsQIBAUFYd26dRxNbdOmTcjKyvJ5I+sb0uqVZaWzBcCi50zcpAeDlEnbIy/HmHxUnldnVTnl7ktY+8syoKgF0OVl4FxP+Q85cBFufJODO5nBpQJVGyvumiWVIqq5KUw1JVXDTgmOCTEwWnlibMccrdUywJ09gO+mAaFngQ7yruek50fSu1OOGs1tQc8Fnl9DDIJwM+iltxOk7p2/5xaoCxR1zGIYBjodw6kn+dtR8paDQ7h5bJYkCOQukWTzvJpICrfIyEjs2rULV65cQVBQEFheqvGNGzcikJT+qZFBcrTQaKQ9kpSYJQP1wr7zxMsxuM1+rnALOw0gwu3rAIDBwAA9nq898JcbLvkugaFRpijOW+5sRvu7cJP83VVEDU1BVLgpTDWllllSy7KA4QpQKVHjsAaHcGMZFgj+B+j/BOd9qWeEVIpJTNsJ0YcIqp8LG2P/DfjjWRUIdQzjQ6Urmio1SyqBIfkKEBg82C4Cxaqc+9oxxFMUPT2hoaECwQbYK3X/G/bcSKtXlpXJAmA1yIYCkAKbPRkosYM2AgaXeMM+wlpSShG0U+OGoLHaJ53YgFjB/oTYhETqF7lacY0RXwlMb5MEeyvcHJqbTqMDcu5Q9BmHWVJMuEs9I+6MnYe6E6pOZ/M0tF4rwTKs6lXhARA1t9AAGe9SCcVOqbek81IKxoDRaMMdd9gdqNx5hv0B/2yVn0Had2BZ6cGksXJNIaTM+CTvK09MUfqgMrv5pvdTwMhb7ZkEPETw/e4It5pV7nMDn1PsLUl6MKotPkpK68f4Yh8P8F5zU2vPTavRAhnbgDt6AcOF4S+uyAo3iWfEHa2/RRjBX6Dr/6Frv7/tCdF7PQMkHpFMH+cVBOEmpyuokaHEgUaB5rZ3bynS0+0/iFi5HTnhVl9hPVS4KSAgQDhoGEZGuNm4o5TkMEBKr+SJ5sYyLBB9Ehj4CND+TcnVnRyCdrol3FhEB0QTvQLdWYXL1YprjPhKc/N2z4206zC5rbCwqhgc4QYATQ8BXYTV0F3xRnMTK4xJgnj9gCLMeGYXMDsRuH4uoLH5zmWdINzkCiDXpVkyIfMrZGTUznFimluDNkv+2wkgZ1aSzgJg4T4QJHMJySzpiYqv5uDyyixp04iu7sRihIiam/VfqLn5SriJeQpqlPUxSXN7tNejir/fofW4Y9azWu0TuNizIPWMuCOIxJ6bk5e5deR8J9yEe26ympsb3pJyyGluWp7ntthvKDd2s5tmc14PbjFYQeu8hwo3BZDSbwHSmhvD89UhmdpIE48nE7uaaWy823NjRduS1YzsVUt6WC1W/95z84UJ0VdmSVGhElAIyKXRAnnPLTogWnFwuENzcyeY3KG5iTohSYz3iW0mcl4/1usx0XPFhOQzR7j1KaXSfXkFIWhcTrj1bdJX9D1399zkhRv3fbGxJJex5ra2t6FbnN0xjWVYTMyYKHm+WlDhpgCSWRKQ0dxs3IGgNKuFJ/tN/qO5saKTWFZTsnAjtf3fqLn5ClLcFwCANctWfQbEQwGUajMOzc2dMeqQ856YJVPCU7Bm4Bp0i+uG29vejrs63iV6rtI2eRVKwINjVowSVhqXM0u6BmbzcVf7Z43SJlytnvvsiwk3ucoXJq0J20Zuw9YRW7F/wn4MSx3mVjs9xQcuQI0PMbOkpOZm5Xat0o1ud1I7OfAnzU3sARCrTUYdSuz4zKFEKoA59E+gRLrgprfeko40UCSHKjFkNTcZoTSp7SRMajtJ9nuUbgGoqbkxDFP7W0cKkxfLVXsYmDwQs7vPxsenP8axi9ySOe4KN12YdDkivULNTUlB3mB9MAYmD1TeOBWgmpsCYmNFqgtLFgTkdq3SCVtpcUtXxPa5PMHrPTeRByBIT1YBGmIogC/2x1TPW1gD6fdoHtrc/p/QP2U/L+YtSfQ0JBCit2fLITlUieEQbqIJd1VazCkVuD7bc+NnFoI9Z60UWo0Wj/Z6FF9O+FLwnrtmSX2otHDTGdQTbvUBFW4KCAkBpk6t/QEXLrQPSukgbu4DqNTU5olw8xvNzcaKrqrFJm9Sfrl/o1myf9P+nNeOPQpvIZmJnVq0AuEmprk93f9pSc89AGgb1dYpoDxxlBJ7FtSKq1IqcH2a4LdXbTX7sDArevb0fGHntnCT0dz4e25iz3almQq3Bs2KFRXYtasU+/aV4sEH7T/mfV3uE/+Ajdu1Ss2SnkzsagZRCgawQq86AJLVCAJ1geidxE0KGx0QjSEthgjO9XezZHqEsgB1d4gwReCpvk9Bp9EhRB+C+T3ne31NgCzcnJO1Is2NfLxXYi9sHLYROWk5WHTdIuI5S/sudf7fnTHqMIiIPQtqxZ0pXRT6VLj1XwT0W4TbbqvChx9eU1QwWAx3TduGcIn0gQD0BmXC0l81N7rnphCGgWBVNbPzTHx25jMcPH9QcL7NyjNLijyoLMN6bYbzqUNJ+GnlHzYW46eL4lUENg3bhHU/rMOp4lMY0GwArm9+PSJNwnRD/m6WvDXjVrxz8h3n68W9F6ty3ZmdZ2Jyu8kwskbVtHGS4HWmUQqSSPxdg9Se202pN+Gm1JsAAIsOLOK8d3vb2znaqDtjdO5c+2QptiCMCYhRfC0plApcUpo81dCXAVmL8fwD/3H7o+2j23P23dx1sTeEFkm+r9VwF6omHTl7Sl2lo3MXqrl5gUlrws5RO/H8oOcF7ykVbmqsCtVMDSR44AMvAXqRWhh8+i2RfDvMGIYFPRdg/Y3rMT5jvFOw8b/T33NL9m/aHyuyVqBPTB881usxTO84XbVrB+oCVTUzkxxKnMKNpJUH1Wbv797d7HE9N74Gr0SQhIbaMHNmJTp3ti9uSM9M17iuqlkqlApcUoJzT1HTcSg3O9fZttaRrTEufZxbn5fzzGR5e5LDU4YTz3PXHFpXUM3NS1gNi4zIDMFxXUAFgNqVjtigVkMw+VRzA4DAAqBKxuFBXwJ0ks48IcbMTjPxwvcvOF/f1UHcfdsfYBgGd3a4E/0C+iEtLa2+myMJySxZK9wIi4hJA9DxzHp0jO7i1KA8gT/e5QTSgw9WYOFC7veR9tz4pVu8QamQFHOGqm+6xnfFkUlHcKr4FLrGd3Xbq1O+yCj3/aEpQ5HVNAv7/lRWLqm+oZqbCpi0JmDUeM6xnne+wXktJsSahjTlvHZ4l7mDmit94p6ZAvMVpnUHdJ5NhuNaj3Ped3JoMmZnzvboOhQhkntuJOEWdgaD792J556rQHy851oG31QlGFfDahcwOp0NM2cKBRlJg+8a39XjNvFRmizAV56sapAYnIg+TfqIlqORQm5RzHe4YRgGm4Zvcvt76gsq3FTApDUBrd8Feq4A4r4H+i1CYntuDItYcPO9XbgVkddd734RP7Vzu60dtJZ7IFCBcGPd9/J00C66Hb6e/DW2j9yOQ7cd8uvJpKEhuedGEm5slSrjiS/cBNfsvB4d73gJU6dWYs+ea8Qq8PxsHGnh6mrJFRbp4GMHao7H+koiTEJuS0RDCDEK1AWKlr7xN6hZUgUMWgOgrQZumOM8ptVO45wTYSLXVxuRNgLfFXyHfWf2YUiLIRiSIvQelGNg8kC8eeJN5+tWEa3cvoYrE9pMgMVmwft576PgWgF+DpB2GQagOMu8GHGBcYgLjPPqGhQh0mZJwm+mMfskEa4wxMSGltfvxcrBt4h+pkVYC0xtPxWvHnsVYYYwrBqwStU2yWXWcKDmnhtpf8on5XQUoNdKCymNiCB2J2axPmkYrfRzSJMB/2Hme9Q9m2WPbzFoDXim/zP4ZvI3WNxnsUeb5TlpOegQ0wGAXYtcmb3S7WvwmdR2Et69+V17+qJqBd5ijH9uKv/bITqUOFJy2QiTF6OumdsBsdK4AiG6MnslTtx5AsfuOCYIJakr1Nxz6xTbSXBs9YDVql3fHYysEci5XfT9pJbkRa2/1m/j41etzM3NRVZWFpo0aYKUlBSMGzcOx48f55xjs9mwbNkypKenIy4uDkOHDsWJEyfqqcV2SA8p/1jXuK5Y2GshMiIzMLntZNyacatq36/VaPHx2I+xY9QOHL7tsKqTQHazbMBYLH+iN3V2KD6DNBE5M3PYyI+/LyqAk64pmRrMhfigeGKwv7f0bdJXkVampnCb3Z27n/xgtwdVnQvcQc/qgQ5vANc/CLR5C2i3Gc5k2omH0arTP8TPlZuF1Qz8Eb8Sbl999RWmTp2KPXv2YMeOHdBqtRgxYgSKimrjMVavXo21a9di+fLl2Lt3L6Kjo3HzzTejpEShu7oPIK10+Q8zwzB4sPuDOHjbQaweuJpY7sYbDFoD+jbpi2ahzVS9blJwEsAqcBSxqr/ap/iGAG1NslQR4eaJmWxQ8iDO65EtR3JekxaAaqaN8wSj1ohXh7yKTrGd7ONcBDWF29CUoXj1xldxZ/s7sWPUDiy8bmG97cMZtUZAYwV6rQLGjAdGTQSmdwFuuQmY0g9NQsl9MiZ9DOc1KRGDP+BXwm3btm2YOHEiMjIy0KZNG7z00ku4dOkSDh8+DMCutb344ot44IEHkJOTg4yMDLz44osoLS3F1q1b663dnppcGgqhJplVc/gf7gV7U+oU1+Kiw1KG1U7WIsLNk7E7veN0Z/b8nLQcXJd0Hed94jPiA/Onu9zQ/AbsG78Pn4//XPScYJ26Dk6jWo3CiuwVkuVr6gKiQ0n8j0D6TgSZ9Oge3534uantpzrHiJ7VY1m/Zb5spsf4tUNJaWkprFYrwsLsFavPnDmDgoICZGfXFr8zmUzo1asXjhw5gilTptRLO5XsuTVktBKTkLH9DlT0mUutkn5MbnYueiT0QLW1Gre0vgVrv3d4w4okJvZAuA1MHoijdxzF979+j8GdhZkySNpJfTlSkJCKEfPXODdvEQsf6BzbGQt6LhCt5JGZkIk9Y/fg0F+HMLj5YNWtRWrhP6OLwLx589CuXTt0725fQRQU2F3So6OjOedFR0fj77//FnzeQV5entvf7c5nSLnVrhRf8eh7/RKRDASdxryDPzvPQEUlN0ddo7lvBTSUe+2qs8eHnTl1BkWFNWb+wAvEcy9duuTxfaWGpOL3338XHC8uEu7bll4p9Zv+k8qKU3CuAEyhd6s3f7lPVyquCr1Fe0X3wuquq4Fq6TaHIhSDQwYDhUBeoWf35k2fKEme4LfCbcGCBTh8+DB2794NlvXOfOFuFom8vDy3PlNtqQZ2c49FRUb5ffYKpUSm7kDhXuHxoMAAaFnhEGos9y2Hu+PEX4i+Gg38CiD+eyD2KFBg97RF76cAAGww6/F9ifVJdGE0wJvLoiOj/ab/bDYbNLs1RFf99NR0RAdEEz6lDH8dJ02uNhH8JiHBIXXS1rroE7+0nc2fPx/vvvsuduzYgeTkZOfx2NhYAMDFixc551+8eBExMeokU/UEJQ4lDZmk7keIx/1gy4TiAU6zIwPg9v7AwIeBnClA9qMAgKMXjqr+naQ8kf6w5+aAYRjRits+rQpQjzgdi1yobycfNfG7GXju3LlOwdayZUvOe82aNUNsbCz27avNbVZRUYFDhw4hMzOzrpvqhOhu3YiEW4BBDwSfExzXahiB59SwlLopIU/xHM7YNBUDvZ8FOr0OaOxZQm5rc5vq33ng/AHBMX9zuhJLtNBohZuOINz8aB/UW/xqBp49ezb+97//4ZVXXkFYWBgKCgpQUFCA0tJSAPbV1YwZM7B69Wrs2LEDx48fx8yZMxEYGIjRo0fXc+u5XCy7KH9SA8GkNQGELCUaRoP7utyH+MB4AECYIQyPX/d4XTeP4iZyJVz4hVPV4M8rwtpxUaYo1b/HG8Qy5DSUdFPuQhoHYmkCGyJ+JabXr18PAMjJyeEcnzt3LubPtxdvvP/++1FeXo45c+aguLgYXbp0wbZt2xAc7F/5CI9fOi5/UgPBqDUCrNCsVFkaiJjAGByYeADfF3yPNlFtEB8UXw8tpLhD70TxIP/0iHSfTOazuszC/C+4BVh9IUS9gXTfBtbgV/kg1SRQKxRu/mQq9ha/Em7FxfKZMBiGwfz5853Czl9pH9O+vpugGvZgT5I3mV3xjzBFYGDywLptFMVjmoc1F33vpcEv+eQ7x6aPxXPfPIeCMrvH831d7kNqeKpPvstTSGZSphHHuDR2s2TjuRM/w1+j9j1BTLjJFTukNCwyIjOcOUrVJtIUie9u/w55RXlICUvxSTotbyFpLf5aZVoNSMLtXIlwb72h4ld7bo0J0sBpqBi1ZC8yjabxrmobOxuHbuS87tukr+CY2gTpg9AptpNfCjaArLn5wrnGXyClAKR7bhRZxARCQ8TEmogZ5BtK6QuKkJy0HLw74l38fOlnDE8djhZhLeq7SfUOycO5sTqTAOQFeGZC/Xmdqw0VbipxXeJ1TnfnmIAYr2uq+RM6VgcwQvNMYwp3+DcyIHkABiQPqO9m+A3+mv/SV5Di3CZkTKiHlvgGOjupxLJ+y9AxpiPSwtPwwqAXGtfGrEYLRAhT5RhN3hUopVD8CSWlqxoTJLOkWD7JhkjjmYHrmfYx7fH5rZ/XdzN8glajBVJ3Az/ewTneZ3gegDb10ygKRWVIWlpjFm46VofWka1xotBeD7NVRKtGFbBONTeKLCzDAunvA8baunowXEFoONXcKI2Hf5tZErBXAe8Q0wHto9tj9cDVjSqmj2puFFm0Gi2grQKm9gIOPgSYLgP9lkCrWVffTaNQVOPfZpYEgO4J3fHFrV/UdzN8AhVuFFmcD3j0r0DONOdx6lBCaUwQhVsj19waM3R2osgi5hzTmJxmKBSSSa6xa26NGSrcKLKIPeCNqTwGhfJvNEs2Zqhwo8gipqGRanRRKA0VYl1GDZ0iGyr0l6PIIrbv8OdVYRkTCqWhQtLSqHWi4UKFG0UWMc0tq1lWHbeEQvEd/8ZQgMYMFW4UWcT2HZqGNK3jllAovoPuuTUuqHCjyCKmudHEyZTGBClJMhVuDRc6O1FkETPNUJMNpTHRObaz4JiObTwlYP5tUOFGkUVs9UpXtZTGxPDU4UgLT3O+ZsCgd1LvemwRxRuoKxBFFjGzZGPKQ0ehGLQG7B67Gy//+DL+vvY3xrQaQ/eVGzBUuFFkIblDU62N0hiJNEVifs/59d0MigpQsyRFFpLmRvfbKBSKP0OFG0UWUvwP9ZSkUCj+DJ2hKLJQzY1CoTQ0qHCjyEISbrTcDYVC8WfoDEWRhQo3CoXS0KAzFEUWYs496i1JoVD8GCrcKLJQzY1CoTQ06AxFkYXoUEI1NwqF4sdQ4UaRhda0olAoDQ0q3CiyhBnDBMcKygrqoSUUCoWiDCrcKLJoNVr0iO7BOdY8tHk9tYZCoVDkocKNooj5becj3BjufE2zpVMoFH+GbqZQFJEQkIB94/fhhe9eQLgpHP/p+p/6bhKFQqGI0mA1t/Xr16N9+/aIjY1Fv379cPDgwfpuUqMnOTQZK7JX4JGejyBAF1DfzaFQKBRRGqRw27ZtG+bNm4eHHnoIX375Jbp3744xY8bg7Nmz9d00CoVCofgBDVK4rV27FrfeeismT56MVq1a4dlnn0VsbCw2bNhQ302jUCgUih/Q4IRbVVUVfvzxR2RnZ3OOZ2dn48iRI/XUKgqFQqH4Ew3OoaSwsBAWiwXR0dGc49HR0bhw4QLxM3l5eW5/jyefaezQPhFC+0QI7RMhtE+EeNMnaWlpsuc0OOHmCUo6wpW8vDy3P9PYoX0ihPaJENonQmifCKmLPmlwZsnIyEiwLIuLFy9yjl+8eBExMTH11CoKhUKh+BMNTrjp9Xp07NgR+/bt4xzft28fMjMzVfkOusoSQvtECO0TIbRPhNA+EVIXfdIgzZL33HMPpk+fji5duiAzMxMbNmzAP//8gylTptR30ygUCoXiBzRI4TZy5EhcvnwZzz77LAoKCtC6dWu8/fbbaNq0aX03jUKhUCh+AFNcXGyr70ZQKBQKhaImDW7PjUKhUCgUOahwo1AoFEqjo9EJt9zcXGRlZaFJkyZISUnBuHHjcPz4cc45NpsNy5YtQ3p6OuLi4jB06FCcOHGCc86KFStwww03ICEhAWFhwmKdABAWFib488cUYHXZJwCwZcsW9O7dG7GxsWjRogWmT5/uk/vyhrrqk82bNxPHSVhYGL7//nuf3qO71OU4+f7775GTk4OmTZuiadOmuOmmm/Ddd9/57N48pS775IsvvsD111+PpKQktGzZEo8//jjMZrPP7s0b1OiXM2fOYNasWejQoQPi4uLQoUMHLF68GOXl5ZzrnD17FuPGjUNCQgJatGiBhx9+GFVVVbJtbHTC7auvvsLUqVOxZ88e7NixA1qtFiNGjEBRUZHznNWrV2Pt2rVYvnw59u7di+joaNx8880oKSlxnlNZWYlhw4ZhxowZkt+3Zs0anDx50vk3fvx4n92bp9Rln/zf//0fFi5ciHvvvReHDh3Czp07MWTIEJ/enyfUVZ+MHDmSMz5OnjyJsWPHIjk5GZ06dfL5fbpDXfVJaWkpRo0ahbi4OHz66af45JNPEBcXh5EjR3Ku4w/UVZ/89NNPGDNmDPr3748vv/wSGzZswK5du7Bo0SJf36JHqNEveXl5sFgsyM3NxeHDh/HMM8/grbfewrx585zXsFgsGDduHEpLS/HRRx/h1VdfxY4dO/DII4/ItrHRO5SUlpaiadOm2Lx5M2688UbYbDakp6dj2rRpmD17NgCgvLwcaWlpeOKJJwThBO+//z4mT56M4uJiwbXDwsKwceNG5OTk1Mm9qIWv+qS4uBgZGRnYvHkzsrKy6ux+1MCX48SVsrIypKen4/7778dDDz3ks/tRA1/1yQ8//ICsrCz8+OOPSE5OBgDk5+c741f9Tei74qs+WbJkCT755BPs37/feWzXrl2YMmUK8vLyEBwc7Pub8wJv+8XB+vXr8eSTT+L06dMAgE8++QRjx47FTz/9hKSkJAB2y9B9992HvLw8hISEiLap0WlufEpLS2G1Wp2mgDNnzqCgoICTeNlkMqFXr14eJV6eN28eWrRogaysLGzYsAFWq1W1tvsKX/XJvn37YLFYcOHCBWRmZqJ169aYMGEC8vPz1b4F1fH1OHHw3nvvoaysDBMnTvS6zb7GV32SmpqKqKgobNq0CZWVlaisrMR///tfJCUlIT09XfX7UBNf9UllZSWMRiPnmMlkQkVFBX788Ud1Gu9D1OqXkpISjtn266+/RqtWrZyCDQAGDBiAyspK2X5p9MJt3rx5aNeuHbp37w4AKCgoAAC3Ei+LsWDBAmzYsAHbt2/HyJEj8eijj2LlypXqNNyH+KpP8vPzYbVasWLFCjz55JPYtGkTzGYzhg0bhrKyMvVuwAf4cpy4snHjRtxwww2IjY31vLF1hK/6JDg4GB988AHee+89xMfHIz4+Htu2bcP27dthMpnUuwEf4Ks+GTBgAL799lts2bIFZrMZf/31F5YvX875Dn9GjX75888/8fzzz2Pq1KnOYxcuXBBcw5GCUa5/G2QQt1IWLFiAw4cPY/fu3WBZVvXrP/zww87/t2/fHlarFStXrsScOXNU/y618GWfWK1WVFdXY/ny5c4V28svv4xWrVph9+7dGDlypKrfpxa+HicOTpw4ga+//hpvv/22z75DLXzZJ+Xl5Zg1axa6du2KV155BRaLBc8//zxuvfVW7Nu3D4GBgap+n1r4sk+ys7PxxBNPYM6cOZg5cyYMBgPmzJmDQ4cOQaPxbx1EjX65cOECRo8ejaysLNxzzz2qtMu/e80L5s+fj3fffRc7duxw2vUBOFfMvki83KVLF1y9etWrlb0v8XWfOK7TqlUr57HQ0FDExcXh3LlzXrTcd9TlOHn99deRlJSEgQMHetzeusDXffLOO+/g9OnTWLduHTp37oxu3bph/fr1OHfuHD744ANV7kFt6mKczJo1C2fOnMHPP/+MP/74w+mI5fp9/oYa/VJQUIDhw4ejdevWeOmll8AwjPO9mJgYwTUcZc/k+rdRCre5c+c6O7xly5ac95o1a4bY2FhO4uWKigocOnTI68TLP/30E4xGI0JDQ726ji+oiz7p0aMHAOD33393HistLUVBQQGaNGni5R2oT12Ok4qKCmzZsgUTJkzw65V4XfRJeXk5GIbh9INGowHDMH65Z12X44RhGMTHx8NkMmHr1q1ISkpChw4dvL4HX6BGv/zzzz8YNmwYWrZsiVdffRVaLdeY2L17d5w8eRLnz593Htu3bx8MBgM6duwo2b5GZ5acPXs2tmzZgk2bNiEsLMxp+w0MDERQUBAYhsGMGTOQm5uLtLQ0pKamYsWKFQgMDMTo0aOd1zl79iyKiorw559/AgCOHTsGAGjRogWCgoKwa9cuXLhwAd26dYPJZML+/fuxbNkyTJ48GQaDoe5vXIK66pPU1FQMGTIE8+bNw6pVqxAWFoZly5YhKioKN9xwQ93fuAR11ScO3n//fVy9etWvHUnqqk+ysrKwcOFCPPTQQ5g+fTqsVitWrVoFlmXRt2/fur9xCepynKxZswYDBgyARqPBzp078dxzz+G1117zqancU9Tol7///hvDhg1DXFwcli1bhsLCQuf1o6KiwLIssrOz0bp1a9x9991YunQpioqKsHDhQkyaNEnSUxJohKEAYgGSc+fOxfz58wHYgwuffvppvP766yguLkaXLl2wYsUKZGRkOM+fMWMG3nzzTcF1du7ciT59+uDTTz/F4sWLcfr0aVitViQnJ+O2227DtGnTBKuP+qau+gSwezs98sgj2LFjB2w2G3r06IGnn34azZs398GdeU5d9gkADBkyBIGBgXjnnXdUvhP1qMs+2bdvH5YvX47jx4+DYRi0a9cOjz32mGplq9SiLvtk+PDhOHr0KKqqqtC2bVvMnTsXgwYN8sFdeY8a/bJ582bR/bWjR4+iWbNmAOwLg9mzZ+PL6eCRqQAAArRJREFUL7+E0WjEmDFj8MQTT8gqEY1OuFEoFAqF4r/GfwqFQqFQPIQKNwqFQqE0Oqhwo1AoFEqjgwo3CoVCoTQ6qHCjUCgUSqODCjcKhUKhNDqocKNQGgibN29GYmJifTeDQmkQ0Dg3CsUPkKpsDgDjx49Hbm4uSktLBVnSKRSKECrcKBQ/wLWsyZ49e3Dffffh5MmTzmP+mrOUQvFXqFmSQvEDYmNjnX8OIcY/xjdLLlu2DD179sT//vc/tGvXDgkJCZg5cyaqqqqwfv16tGnTBs2bN8eCBQs4CYmrqqrw+OOPIyMjA/Hx8cjKysJnn31W5/dMofgS/0qCSKFQ3OLPP//ERx99hC1btuDvv//GpEmTUFBQgNjYWGzbtg2//fYbpkyZgszMTOTk5AAA7rnnHpw+fRqvvPIKEhMT8fHHH+OWW27B3r170a5du3q+IwpFHahwo1AaMBaLBWvXrkVoaCgyMjIwYMAAHDhwACdOnIBer0erVq2QmZmJr776Cjk5OTh9+jS2bt2KY8eOOcsQ3XXXXfj888/x+uuvN4hK8hSKEqhwo1AaMElJSZy9uJiYGKSmpkKv13OOOQo+Hj161FmtwZXKykq/KzdDoXgDFW4USgNGp9NxXjMMIyi5xDAMLBYLAMBqtYJhGOzdu1fwWaPR6NvGUih1CBVuFMq/iPbt28Nms6GgoIBqapRGDfWWpFD+RaSmpmLs2LGYOXMm3n//feTn5+OHH37A888/jx07dtR38ygU1aCaG4XyL2Pt2rVYsWIFFi5ciL/++gvh4eHo3Lkzp3I4hdLQoUHcFAqFQml0ULMkhUKhUBodVLhRKBQKpdFBhRuFQqFQGh1UuFEoFAql0UGFG4VCoVAaHVS4USgUCqXRQYUbhUKhUBodVLhRKBQKpdFBhRuFQqFQGh3/D8IR0p3zaa23AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def buy_sell_function(data):\n",
        "  buysig = []\n",
        "  buyprice = []\n",
        "  flag_long = False\n",
        "  flag_short = False\n",
        "  for i in range(0,len(data)):\n",
        "    if data['Middle'][i] < data['Long'][i] and data['Middle'][i] > data['Short'][i] and flag_long == False and flag_short == False:\n",
        "        buysig.append(1)\n",
        "        buyprice.append(data['Close'][i])\n",
        "        flag_short = True\n",
        "    elif data['Middle'][i] > data['Long'][i] and data['Middle'][i] < data['Short'][i] and flag_short == False and flag_long == False:\n",
        "        buysig.append(1)\n",
        "        buyprice.append(data['Close'][i])\n",
        "        flag_long = True\n",
        "    elif flag_short == True and data['Short'][i] > data['Middle'][i]:\n",
        "        buyprice.append(data['Close'][i])\n",
        "        buysig.append(-1)\n",
        "        flag_short = False\n",
        "    elif flag_long == True and data['Short'][i] < data['Middle'][i]:\n",
        "        buyprice.append(data['Close'][i])\n",
        "        buysig.append(-1)\n",
        "        flag_long = False\n",
        "    else:\n",
        "        buysig.append(0)\n",
        "        buyprice.append(data['Close'][i])\n",
        "  return (buysig, buyprice)\n",
        "\n",
        "df['buysignal'] = buy_sell_function(df)[0]\n",
        "df['buyprice'] =buy_sell_function(df)[1]"
      ],
      "metadata": {
        "id": "lGaKlgh7v2Oy"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfi=df['MFI_14']\n",
        "rsi=df['RSI_14']\n",
        "mfisig=[]\n",
        "for i in range(len(mfi)):\n",
        "  if mfi[i]>80:\n",
        "    mfisig.append(-1)\n",
        "  elif mfi[i]<20:\n",
        "    mfisig.append(1)\n",
        "  else:\n",
        "    mfisig.append(0)"
      ],
      "metadata": {
        "id": "W96cxFXILhAI"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rsi=df['RSI_14']\n",
        "rsisig=[]\n",
        "for i in range(len(rsi)):\n",
        "  if rsi[i]>70:\n",
        "    rsisig.append(-1)\n",
        "  elif rsi[i]<30:\n",
        "    rsisig.append(1)\n",
        "  else:\n",
        "    rsisig.append(0)"
      ],
      "metadata": {
        "id": "ohVoOCeEMuYM"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "obv=df['OBV']\n",
        "obvema = df['OBV_EMA']\n",
        "obvsig=[]\n",
        "for i in range(len(rsi)):\n",
        "  if obv[i]<obvema[i]:\n",
        "    obvsig.append(-1)\n",
        "  elif obv[i]>obvema[i]:\n",
        "    obvsig.append(1)\n",
        "  else:\n",
        "    obvsig.append(0)"
      ],
      "metadata": {
        "id": "j68RRISv20GF"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bs=df['buysignal']\n",
        "ss=df['buyprice']\n",
        "ct=0\n",
        "for i in range(len(bs)):\n",
        "  if(bs[i]==1 and mfisig[i]==1):\n",
        "    ct+=1\n",
        "print(ct)"
      ],
      "metadata": {
        "id": "gKKKLh16HNnR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f39e325d-f4c5-4e73-9d6b-3f33632a9b17"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "jYZ3Pg142gbZ",
        "outputId": "c31bf1bc-394a-4269-a79d-782d24c6b23c"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2015-01-01  312.450012  315.000000  310.700012  314.000000  295.929230   \n",
              "2015-01-02  314.350006  318.299988  314.350006  315.250000  297.107269   \n",
              "2015-01-05  316.250000  316.799988  312.100006  312.750000  294.751129   \n",
              "2015-01-06  310.000000  311.100006  298.700012  299.899994  282.640656   \n",
              "2015-01-07  300.000000  302.549988  295.149994  300.149994  282.876251   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2019-12-23  335.700012  338.850006  330.350006  332.399994  324.298157   \n",
              "2019-12-24  333.200012  334.200012  329.200012  331.450012  323.371307   \n",
              "2019-12-26  333.000000  333.450012  328.100006  329.850006  321.810303   \n",
              "2019-12-27  333.399994  338.350006  333.000000  337.250000  329.029938   \n",
              "2019-12-30  337.750000  337.850006  332.500000  334.399994  326.249390   \n",
              "\n",
              "              Volume     MFI_14     RSI_14       Short      Middle  \\\n",
              "Date                                                                 \n",
              "2015-01-01   6138488        NaN        NaN         NaN         NaN   \n",
              "2015-01-02   9935094        NaN        NaN         NaN         NaN   \n",
              "2015-01-05   9136716        NaN        NaN         NaN         NaN   \n",
              "2015-01-06  15329257        NaN        NaN         NaN         NaN   \n",
              "2015-01-07  15046745        NaN        NaN         NaN         NaN   \n",
              "...              ...        ...        ...         ...         ...   \n",
              "2019-12-23  30150335  36.291109  55.111574  329.482358  317.397523   \n",
              "2019-12-24  21747782  31.458883  54.357410  329.744712  317.948601   \n",
              "2019-12-26  24616942  31.856480  53.040899  329.758751  318.415323   \n",
              "2019-12-27  33477173  39.337531  58.095855  330.757584  319.153938   \n",
              "2019-12-30  22775554  41.604710  55.612881  331.243239  319.751822   \n",
              "\n",
              "                  Long          OBV       OBV_EMA  buysignal    buyprice  \n",
              "Date                                                                      \n",
              "2015-01-01         NaN    6138488.0  6.138488e+06          0  314.000000  \n",
              "2015-01-02         NaN   16073582.0  1.146086e+07          0  315.250000  \n",
              "2015-01-05         NaN    6936866.0  9.732679e+06          0  312.750000  \n",
              "2015-01-06         NaN   -8392391.0  4.187709e+06          0  299.899994  \n",
              "2015-01-07         NaN    6654354.0  4.831253e+06          0  300.149994  \n",
              "...                ...          ...           ...        ...         ...  \n",
              "2019-12-23  311.298579  848034653.0  8.530043e+08          0  332.399994  \n",
              "2019-12-24  311.697617  826286871.0  8.494420e+08          0  331.450012  \n",
              "2019-12-26  312.057070  801669929.0  8.430724e+08          0  329.850006  \n",
              "2019-12-27  312.555940  835147102.0  8.420157e+08          0  337.250000  \n",
              "2019-12-30  312.988495  812371548.0  8.380631e+08          0  334.399994  \n",
              "\n",
              "[1228 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c98a7cc8-677e-46f8-a6c5-afaf1cacadfd\">\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",
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              "\n",
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              "        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>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>MFI_14</th>\n",
              "      <th>RSI_14</th>\n",
              "      <th>Short</th>\n",
              "      <th>Middle</th>\n",
              "      <th>Long</th>\n",
              "      <th>OBV</th>\n",
              "      <th>OBV_EMA</th>\n",
              "      <th>buysignal</th>\n",
              "      <th>buyprice</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-01</th>\n",
              "      <td>312.450012</td>\n",
              "      <td>315.000000</td>\n",
              "      <td>310.700012</td>\n",
              "      <td>314.000000</td>\n",
              "      <td>295.929230</td>\n",
              "      <td>6138488</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6138488.0</td>\n",
              "      <td>6.138488e+06</td>\n",
              "      <td>0</td>\n",
              "      <td>314.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>314.350006</td>\n",
              "      <td>318.299988</td>\n",
              "      <td>314.350006</td>\n",
              "      <td>315.250000</td>\n",
              "      <td>297.107269</td>\n",
              "      <td>9935094</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16073582.0</td>\n",
              "      <td>1.146086e+07</td>\n",
              "      <td>0</td>\n",
              "      <td>315.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>316.250000</td>\n",
              "      <td>316.799988</td>\n",
              "      <td>312.100006</td>\n",
              "      <td>312.750000</td>\n",
              "      <td>294.751129</td>\n",
              "      <td>9136716</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6936866.0</td>\n",
              "      <td>9.732679e+06</td>\n",
              "      <td>0</td>\n",
              "      <td>312.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06</th>\n",
              "      <td>310.000000</td>\n",
              "      <td>311.100006</td>\n",
              "      <td>298.700012</td>\n",
              "      <td>299.899994</td>\n",
              "      <td>282.640656</td>\n",
              "      <td>15329257</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-8392391.0</td>\n",
              "      <td>4.187709e+06</td>\n",
              "      <td>0</td>\n",
              "      <td>299.899994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07</th>\n",
              "      <td>300.000000</td>\n",
              "      <td>302.549988</td>\n",
              "      <td>295.149994</td>\n",
              "      <td>300.149994</td>\n",
              "      <td>282.876251</td>\n",
              "      <td>15046745</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6654354.0</td>\n",
              "      <td>4.831253e+06</td>\n",
              "      <td>0</td>\n",
              "      <td>300.149994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-23</th>\n",
              "      <td>335.700012</td>\n",
              "      <td>338.850006</td>\n",
              "      <td>330.350006</td>\n",
              "      <td>332.399994</td>\n",
              "      <td>324.298157</td>\n",
              "      <td>30150335</td>\n",
              "      <td>36.291109</td>\n",
              "      <td>55.111574</td>\n",
              "      <td>329.482358</td>\n",
              "      <td>317.397523</td>\n",
              "      <td>311.298579</td>\n",
              "      <td>848034653.0</td>\n",
              "      <td>8.530043e+08</td>\n",
              "      <td>0</td>\n",
              "      <td>332.399994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-24</th>\n",
              "      <td>333.200012</td>\n",
              "      <td>334.200012</td>\n",
              "      <td>329.200012</td>\n",
              "      <td>331.450012</td>\n",
              "      <td>323.371307</td>\n",
              "      <td>21747782</td>\n",
              "      <td>31.458883</td>\n",
              "      <td>54.357410</td>\n",
              "      <td>329.744712</td>\n",
              "      <td>317.948601</td>\n",
              "      <td>311.697617</td>\n",
              "      <td>826286871.0</td>\n",
              "      <td>8.494420e+08</td>\n",
              "      <td>0</td>\n",
              "      <td>331.450012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-26</th>\n",
              "      <td>333.000000</td>\n",
              "      <td>333.450012</td>\n",
              "      <td>328.100006</td>\n",
              "      <td>329.850006</td>\n",
              "      <td>321.810303</td>\n",
              "      <td>24616942</td>\n",
              "      <td>31.856480</td>\n",
              "      <td>53.040899</td>\n",
              "      <td>329.758751</td>\n",
              "      <td>318.415323</td>\n",
              "      <td>312.057070</td>\n",
              "      <td>801669929.0</td>\n",
              "      <td>8.430724e+08</td>\n",
              "      <td>0</td>\n",
              "      <td>329.850006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-27</th>\n",
              "      <td>333.399994</td>\n",
              "      <td>338.350006</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>337.250000</td>\n",
              "      <td>329.029938</td>\n",
              "      <td>33477173</td>\n",
              "      <td>39.337531</td>\n",
              "      <td>58.095855</td>\n",
              "      <td>330.757584</td>\n",
              "      <td>319.153938</td>\n",
              "      <td>312.555940</td>\n",
              "      <td>835147102.0</td>\n",
              "      <td>8.420157e+08</td>\n",
              "      <td>0</td>\n",
              "      <td>337.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-30</th>\n",
              "      <td>337.750000</td>\n",
              "      <td>337.850006</td>\n",
              "      <td>332.500000</td>\n",
              "      <td>334.399994</td>\n",
              "      <td>326.249390</td>\n",
              "      <td>22775554</td>\n",
              "      <td>41.604710</td>\n",
              "      <td>55.612881</td>\n",
              "      <td>331.243239</td>\n",
              "      <td>319.751822</td>\n",
              "      <td>312.988495</td>\n",
              "      <td>812371548.0</td>\n",
              "      <td>8.380631e+08</td>\n",
              "      <td>0</td>\n",
              "      <td>334.399994</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1228 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c98a7cc8-677e-46f8-a6c5-afaf1cacadfd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-c98a7cc8-677e-46f8-a6c5-afaf1cacadfd');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          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": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tp=0\n",
        "fl=0\n",
        "bp=0\n",
        "initial_buy_price = 0\n",
        "first_buy = False\n",
        "for i in range(len(bs)):\n",
        "  val=bs[i]*50 + mfisig[i]*50 + rsisig[i]*50 + obvsig[i]*50\n",
        "  if val>=100 and fl==0:\n",
        "    tp-=ss[i]\n",
        "    fl=1\n",
        "    bp=ss[i]\n",
        "    if first_buy==False:\n",
        "      initial_buy_price = ss[i]\n",
        "      first_buy = True\n",
        "    print('Buy :'+ str(ss[i]))\n",
        "  elif val<=-50 and fl==1:\n",
        "    tp+=ss[i]\n",
        "    fl=0\n",
        "    print('Sell :'+ str(ss[i]))    \n",
        "\n",
        "if(fl==1):\n",
        "    tp+=bp\n",
        "\n",
        "print(tp)\n",
        "print(\"Profit percentage : \", (tp/initial_buy_price)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5--4BiMwJFH",
        "outputId": "401cc35e-3f1d-460d-bde7-7a8da1a1c959"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Buy :266.45001220703125\n",
            "Sell :277.54998779296875\n",
            "Buy :290.8999938964844\n",
            "Sell :279.6499938964844\n",
            "Buy :291.0\n",
            "Sell :312.5\n",
            "Buy :329.8999938964844\n",
            "Sell :312.1000061035156\n",
            "Buy :285.3999938964844\n",
            "Sell :275.6499938964844\n",
            "Buy :293.6499938964844\n",
            "Sell :284.3999938964844\n",
            "Buy :335.5\n",
            "Sell :320.0\n",
            "-30.95001220703125\n",
            "Profit percentage :  -11.615691795496387\n"
          ]
        }
      ]
    }
  ]
}