VuManchuCipherB_Indicator.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CryptoRobotFr/General-code-trading-bot/blob/main/VuManchuCipherB_Indicator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Z9JUAJF23Jl",
        "outputId": "a5660bff-9e1c-43be-f7db-060cfd07bd19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: python-binance in /usr/local/lib/python3.7/dist-packages (1.0.16)\n",
            "Requirement already satisfied: ta in /usr/local/lib/python3.7/dist-packages (0.9.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from python-binance) (3.8.1)\n",
            "Requirement already satisfied: websockets in /usr/local/lib/python3.7/dist-packages (from python-binance) (10.2)\n",
            "Requirement already satisfied: dateparser in /usr/local/lib/python3.7/dist-packages (from python-binance) (1.1.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from python-binance) (1.15.0)\n",
            "Requirement already satisfied: ujson in /usr/local/lib/python3.7/dist-packages (from python-binance) (5.2.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from python-binance) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ta) (1.21.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from ta) (1.3.5)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (21.4.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (1.7.2)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (6.0.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (1.3.0)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (2.0.12)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (4.1.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (1.2.0)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (0.13.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (4.0.2)\n",
            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl<2.0,>=1.0->aiohttp->python-binance) (2.10)\n",
            "Requirement already satisfied: regex!=2019.02.19,!=2021.8.27,<2022.3.15 in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (2019.12.20)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (2.8.2)\n",
            "Requirement already satisfied: tzlocal in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (1.5.1)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (2018.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (2021.10.8)\n"
          ]
        }
      ],
      "source": [
        "!pip install python-binance ta"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAPn9B1b5ZGc"
      },
      "source": [
        "<h1> Récupération des données à partir de Binance <h1>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "Lq9Ots8U3GSh",
        "outputId": "5ceff4d2-8ef6-41b3-8f29-a641ee2e2743"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "                        open     high      low    close       volume\n",
              "timestamp                                                           \n",
              "2021-01-01 00:00:00   736.42   739.00   729.33   734.07  27932.69884\n",
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              "2021-01-01 02:00:00   748.27   749.00   742.27   744.06  33019.50100\n",
              "2021-01-01 03:00:00   744.06   747.23   743.10   744.82  17604.80859\n",
              "2021-01-01 04:00:00   744.87   747.09   739.30   742.29  18794.15424\n",
              "...                      ...      ...      ...      ...          ...\n",
              "2022-04-15 06:00:00  3038.77  3042.80  3019.13  3023.17   8383.74300\n",
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              "2022-04-15 08:00:00  3034.62  3040.54  3017.08  3025.00  11739.49650\n",
              "2022-04-15 09:00:00  3025.00  3034.34  3017.39  3024.05   8042.57060\n",
              "2022-04-15 10:00:00  3024.06  3032.90  3023.27  3031.61   5026.42110\n",
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              "      <th>2021-01-01 00:00:00</th>\n",
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          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Importation des librairies\n",
        "import pandas as pd\n",
        "from binance.client import Client\n",
        "import ta\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime\n",
        "\n",
        "# Variables pour les données\n",
        "client = Client()\n",
        "pair_symbol = \"ETHUSDT\"\n",
        "time_interval = Client.KLINE_INTERVAL_1HOUR\n",
        "start_date = \"01 january 2021\"\n",
        "\n",
        "# Récupération des données\n",
        "klinesT = client.get_historical_klines(pair_symbol, time_interval, start_date)\n",
        "\n",
        "# Créer un tableau grâce aux données\n",
        "df = pd.DataFrame(klinesT, columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore' ])\n",
        "\n",
        "# Supprime les colonnes inutiles\n",
        "df.drop(columns = df.columns.difference(['timestamp','open','high','low','close','volume']), inplace=True)\n",
        "\n",
        "# Convertit les colonnes en numéric\n",
        "for col in df.columns:\n",
        "    df[col] = pd.to_numeric(df[col])\n",
        "\n",
        "# Convertit les dates dans un format lisible\n",
        "df = df.set_index(df['timestamp'])\n",
        "df.index = pd.to_datetime(df.index, unit='ms')\n",
        "del df['timestamp']\n",
        "\n",
        "# Affiche le tableau\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUL8TfpZ5tAq"
      },
      "source": [
        "<h1> Définition des indicateurs <h1>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Partie VMC\n",
        "\n",
        "\n",
        "#df.drop(df.columns.difference(['open','high','low','close','volume']), 1, inplace=True) \n",
        "\n",
        "class VMC():\n",
        "    \"\"\" VuManChu Cipher B + Divergences \n",
        "        Args:\n",
        "            high(pandas.Series): dataset 'High' column.\n",
        "            low(pandas.Series): dataset 'Low' column.\n",
        "            close(pandas.Series): dataset 'Close' column.\n",
        "            wtChannelLen(int): n period.\n",
        "            wtAverageLen(int): n period.\n",
        "            wtMALen(int): n period.\n",
        "            rsiMFIperiod(int): n period.\n",
        "            rsiMFIMultiplier(int): n period.\n",
        "            rsiMFIPosY(int): n period.\n",
        "    \"\"\"\n",
        "    def __init__(\n",
        "        self: pd.Series,\n",
        "        high: pd.Series,\n",
        "        low: pd.Series,\n",
        "        close: pd.Series,\n",
        "        open: pd.Series,\n",
        "        wtChannelLen: int = 9,\n",
        "        wtAverageLen: int = 12,\n",
        "        wtMALen: int = 3,\n",
        "        rsiMFIperiod: int = 60,\n",
        "        rsiMFIMultiplier: int = 150,\n",
        "        rsiMFIPosY: int = 2.5\n",
        "    ) -> None:\n",
        "        self._high = high\n",
        "        self._low = low\n",
        "        self._close = close\n",
        "        self._open = open\n",
        "        self._wtChannelLen = wtChannelLen\n",
        "        self._wtAverageLen = wtAverageLen\n",
        "        self._wtMALen = wtMALen\n",
        "        self._rsiMFIperiod = rsiMFIperiod\n",
        "        self._rsiMFIMultiplier = rsiMFIMultiplier\n",
        "        self._rsiMFIPosY = rsiMFIPosY\n",
        "        self._run()\n",
        "        self.wave_1()\n",
        "\n",
        "    def _run(self) -> None:\n",
        "        try:\n",
        "            self._esa = ta.trend.ema_indicator(\n",
        "                close=self._close, window=self._wtChannelLen)\n",
        "        except Exception as e:\n",
        "            print(e)\n",
        "            raise\n",
        "\n",
        "        self._esa = ta.trend.ema_indicator(\n",
        "            close=self._close, window=self._wtChannelLen)\n",
        "        self._de = ta.trend.ema_indicator(\n",
        "            close=abs(self._close - self._esa), window=self._wtChannelLen)\n",
        "        self._rsi = ta.trend.sma_indicator(self._close, self._rsiMFIperiod)\n",
        "        self._ci = (self._close - self._esa) / (0.015 * self._de)\n",
        "\n",
        "    def wave_1(self) -> pd.Series:\n",
        "        \"\"\"VMC Wave 1 \n",
        "        Returns:\n",
        "            pandas.Series: New feature generated.\n",
        "        \"\"\"\n",
        "        wt1 = ta.trend.ema_indicator(self._ci, self._wtAverageLen)\n",
        "        return pd.Series(wt1, name=\"wt1\")\n",
        "\n",
        "    def wave_2(self) -> pd.Series:\n",
        "        \"\"\"VMC Wave 2\n",
        "        Returns:\n",
        "            pandas.Series: New feature generated.\n",
        "        \"\"\"\n",
        "        wt2 = ta.trend.sma_indicator(self.wave_1(), self._wtMALen)\n",
        "        return pd.Series(wt2, name=\"wt2\")\n",
        "\n",
        "    def money_flrow(self) -> pd.Series:\n",
        "        \"\"\"VMC Money Flow\n",
        "            Returns:\n",
        "            pandas.Series: New feature generated.\n",
        "        \"\"\"\n",
        "        mfi = ((self._close - self._open) /\n",
        "                (self._high - self._low)) * self._rsiMFIMultiplier\n",
        "        rsi = ta.trend.sma_indicator(mfi, self._rsiMFIperiod)\n",
        "        money_flow = rsi - self._rsiMFIPosY\n",
        "        return pd.Series(money_flow, name=\"money_flow\")\n",
        "\n"
      ],
      "metadata": {
        "id": "RLUOdf1LGNwp"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "fEtAOcaQ4jQH",
        "colab": {
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              "                        open     high      low    close       volume  \\\n",
              "timestamp                                                              \n",
              "2021-01-01 00:00:00   736.42   739.00   729.33   734.07  27932.69884   \n",
              "2021-01-01 01:00:00   734.08   749.00   733.37   748.28  52336.18779   \n",
              "2021-01-01 02:00:00   748.27   749.00   742.27   744.06  33019.50100   \n",
              "2021-01-01 03:00:00   744.06   747.23   743.10   744.82  17604.80859   \n",
              "2021-01-01 04:00:00   744.87   747.09   739.30   742.29  18794.15424   \n",
              "...                      ...      ...      ...      ...          ...   \n",
              "2022-04-15 06:00:00  3038.77  3042.80  3019.13  3023.17   8383.74300   \n",
              "2022-04-15 07:00:00  3023.18  3046.41  3010.85  3034.62  15958.94770   \n",
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              "2022-04-15 10:00:00  3024.06  3032.90  3023.27  3031.61   5026.42110   \n",
              "\n",
              "                            HLC3  VMC_WAVE1  VMC_WAVE2  MONEY_FLOW  \\\n",
              "timestamp                                                            \n",
              "2021-01-01 00:00:00   734.133333        NaN        NaN         NaN   \n",
              "2021-01-01 01:00:00   743.550000        NaN        NaN         NaN   \n",
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              "2021-01-01 03:00:00   745.050000        NaN        NaN         NaN   \n",
              "2021-01-01 04:00:00   742.893333        NaN        NaN         NaN   \n",
              "...                          ...        ...        ...         ...   \n",
              "2022-04-15 06:00:00  3028.366667   6.553563   5.175912    0.325796   \n",
              "2022-04-15 07:00:00  3030.626667   7.483695   7.659646    3.004584   \n",
              "2022-04-15 08:00:00  3027.540000   3.467782   5.835013    0.687440   \n",
              "2022-04-15 09:00:00  3025.260000  -3.378751   2.524242   -1.465572   \n",
              "2022-04-15 10:00:00  3029.260000  -0.912994  -0.274655    0.121328   \n",
              "\n",
              "                           EMA50       EMA200  \n",
              "timestamp                                      \n",
              "2021-01-01 00:00:00          NaN          NaN  \n",
              "2021-01-01 01:00:00          NaN          NaN  \n",
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              "2021-01-01 03:00:00          NaN          NaN  \n",
              "2021-01-01 04:00:00          NaN          NaN  \n",
              "...                          ...          ...  \n",
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              "2022-04-15 09:00:00  3049.387231  3135.419644  \n",
              "2022-04-15 10:00:00  3048.690084  3134.386712  \n",
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      ],
      "source": [
        "# Ajout de nouvelle colonne dans le tableau représentant nos indicateurs\n",
        "\n",
        "#---------VMC-----------\n",
        "df['HLC3'] = (df['high'] +df['close'] + df['low'])/3\n",
        "vmc = VMC(high =df['high'],low = df['low'],close=df['HLC3'],open=df['open'])\n",
        "df['VMC_WAVE1'] = vmc.wave_1()\n",
        "df['VMC_WAVE2'] = vmc.wave_2()\n",
        "vmc = VMC(high =df['high'],low = df['low'],close=df['close'],open=df['open'])\n",
        "df['MONEY_FLOW'] = vmc.money_flow()\n",
        "\n",
        "\n",
        "#---------EMA--------------\n",
        "df['EMA50'] = ta.trend.ema_indicator(close = df['close'], window = 50)\n",
        "df['EMA200'] = ta.trend.ema_indicator(close = df['close'], window = 200)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Affiche notre nouveau tableau\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "WTT18Xwp9J09"
      },
      "outputs": [],
      "source": [
        "dfTest = df.copy()\n",
        "initial_wallet = 1000 # Somme d'usd au début de la stratégie\n",
        "taker_fee = 0.0007 # ~ 0.07% de frais sur binance et ftx \n",
        "\n",
        "sl_price = 0\n",
        "tp_price = 1000000\n",
        "\n",
        "# Initalisation de notre portefeuille\n",
        "wallet = initial_wallet\n",
        "usd = initial_wallet\n",
        "coin = 0\n",
        "\n",
        "# Initialisation de nos tableaux qui vont contenir nos données d'analyse\n",
        "trades_hitory = []\n",
        "days_history = []\n",
        "previous_day = 0\n",
        "buy_ready = True\n",
        "\n",
        "previous_row = df.iloc[0].copy()\n",
        "#previous_row['close'] < max(row['SSB25'],row['SSA25']) and previous_row['close'] > min(row['SSB25'],row['SSA25']) and\n",
        "\n",
        "def buy_condition(row, previous_row=None):\n",
        "    if row['EMA50'] > row ['EMA200'] and row ['close'] > row['EMA50'] and row['MONEY_FLOW'] >  0 and row['VMC_WAVE1'] < 0 and row['VMC_WAVE2'] < 0 and row['VMC_WAVE1'] > row['VMC_WAVE2'] and previous_row['VMC_WAVE1'] < previous_row['VMC_WAVE2'] :\n",
        "        return True\n",
        "    else:\n",
        "        return False    \n",
        "\n",
        "def sell_condition(row, previous_row=None):\n",
        "    if row['close'] >10000000 :\n",
        "        return True\n",
        "    else:\n",
        "        return False  \n",
        "\n",
        "\n",
        "# Simulation de notre stratégie en parcourant notre tableau ligne par ligne\n",
        "for index, row in df.iterrows():\n",
        "\n",
        "    # Garde en mémoire l'évolution de notre portefeuille chaque jour pour pouvoir analyser plus tard\n",
        "    current_day = index.day\n",
        "    if previous_day != current_day:\n",
        "        temp_wallet = wallet\n",
        "        if coin > 0:\n",
        "            temp_wallet = coin*row['close']\n",
        "        days_history.append({\n",
        "            \"day\":str(index.year)+\"-\"+str(index.month)+\"-\"+str(index.day),\n",
        "            \"wallet\":temp_wallet,\n",
        "            \"price\":row['close']\n",
        "        })\n",
        "    previous_day = current_day\n",
        "\n",
        "    # Vérifie les conditions si l'on doit acheter\n",
        "    if buy_condition(row, previous_row) == True and usd > 0 and buy_ready == True:\n",
        "        # Condition vérifiée, on achète à la clotûre\n",
        "        coin = usd / row['close']           \n",
        "        fee = taker_fee * coin             \n",
        "        coin = coin - fee                   \n",
        "        usd = 0                            \n",
        "        wallet = coin * row['close']       \n",
        "        # Ajout de données pour analyse future\n",
        "        buy_data = {\n",
        "            'date': index,\n",
        "            'position': 'buy',\n",
        "            'price': row['close'],\n",
        "            'fee': fee * row['close'],\n",
        "            'usd': usd,\n",
        "            'coin': coin,\n",
        "            'wallet': wallet,\n",
        "            'reason': \"market\"\n",
        "        }\n",
        "        trades_hitory.append(buy_data)\n",
        "        sl_price = row['EMA200'] # A editer si l'on souhaite ajouter un sl\n",
        "        tp_price = row['close'] + 2*(row['close']-row['EMA200'])# A editer si l'on souhaite ajouter un tp\n",
        "\n",
        "\n",
        "    elif row['low'] < sl_price and coin > 0:\n",
        "        # On vend au prix du stop loss\n",
        "        usd = coin * sl_price          \n",
        "        fee = taker_fee * usd               \n",
        "        usd = usd - fee                     \n",
        "        coin = 0                           \n",
        "        wallet = usd                      \n",
        "        # Ajout de données pour analyse future\n",
        "        sell_data = {\n",
        "            'date': index,\n",
        "            'position': 'sell',\n",
        "            'price': sl_price,\n",
        "            'fee': fee,\n",
        "            'usd': usd,\n",
        "            'coin': coin,\n",
        "            'wallet': wallet,\n",
        "            'reason': \"stop loss\"           \n",
        "        }\n",
        "        trades_hitory.append(sell_data) \n",
        "        # buy_ready = False # A commenter si l'on ne veut pas bloquer les prochains achats\n",
        "\n",
        "    elif row['high'] > tp_price and coin > 0:\n",
        "        # On vend au prix du stop loss\n",
        "        usd = coin * tp_price          \n",
        "        fee = taker_fee * usd               \n",
        "        usd = usd - fee                     \n",
        "        coin = 0                           \n",
        "        wallet = usd                      \n",
        "        # Ajout de données pour analyse future\n",
        "        sell_data = {\n",
        "            'date': index,\n",
        "            'position': 'sell',\n",
        "            'price': tp_price,\n",
        "            'fee': fee,\n",
        "            'usd': usd,\n",
        "            'coin': coin,\n",
        "            'wallet': wallet,\n",
        "            'reason': \"take profit\"\n",
        "                       \n",
        "        }\n",
        "        trades_hitory.append(sell_data) \n",
        "        # buy_ready = False # A commenter si l'on ne veut pas bloquer les prochains achats\n",
        "\n",
        "    # Vérifie les conditions si l'on doit vendre\n",
        "    elif sell_condition(row, previous_row) == True:\n",
        "        buy_ready = True\n",
        "        if coin > 0:\n",
        "            # Condition vérifiée, on vend à la clotûre\n",
        "            usd = coin * row['close']          \n",
        "            fee = taker_fee * usd               \n",
        "            usd = usd - fee                     \n",
        "            coin = 0                           \n",
        "            wallet = usd                      \n",
        "            # Ajout de données pour analyse future\n",
        "            sell_data = {\n",
        "                'date': index,\n",
        "                'position': 'sell',\n",
        "                'price': row['close'],\n",
        "                'fee': fee,\n",
        "                'usd': usd,\n",
        "                'coin': coin,\n",
        "                'wallet': wallet,\n",
        "                'reason': \"market\"           \n",
        "            }\n",
        "            trades_hitory.append(sell_data) \n",
        "\n",
        "    previous_row = row\n",
        "\n",
        "# Convertit en tableau exploitable les données récupérer lors de la simulation\n",
        "df_days = pd.DataFrame(days_history)\n",
        "df_days['day'] = pd.to_datetime(df_days['day'])\n",
        "df_days = df_days.set_index(df_days['day'])\n",
        "\n",
        "df_trades = pd.DataFrame(trades_hitory)\n",
        "df_trades['date'] = pd.to_datetime(df_trades['date'])\n",
        "df_trades = df_trades.set_index(df_trades['date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "QUhBzbReEDPo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ec3935e8-ff2b-43f3-b2d8-82130e5b5de6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Period: [2021-01-01 00:00:00] -> [2022-04-15 00:00:00]\n",
            "Initial wallet: 1000.0 $\n",
            "\n",
            "--- General Information ---\n",
            "Final wallet: 1995.98 $\n",
            "Performance vs US dollar: 99.6 %\n",
            "Worst Drawdown : -33.71%\n",
            "Buy and hold performance: 310.79 %\n",
            "Performance vs buy and hold: -51.41 %\n",
            "Total trades on the period: 37\n",
            "Global Win rate: 40.54 %\n",
            "Average Profit: 2.46 %\n",
            "Total fee: 90.11 $\n",
            "\n",
            "Best trades: +26.37 % the 2021-05-12 02:00:00\n",
            "Worst trades: -13.21 % the 2021-01-11 03:00:00\n",
            "\n",
            "--- Plot wallet evolution vs asset ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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9ZRNEtq7PueM9LvhDMRw6OTIlwKvEEk2NSonmOgPO2r3FHgoR5QBLNImIiArMH4qhtd6Af/72Vfjnb1+Fnbd/DIIo4CevnsWSRiNuu24VTp134/X3B4o91EWpZ9AHADjWO5beFolVbgYPAJY1mXDO7oUky8UeChEtEAM8IiKiAguEY9BrLhTR2Cx63Pv/bcLlaxvw9c+uwyc3NWN5kwmvMMDLi57BZKbqnN2HUCQ59y41B09boQHe8iYTgpE4hsaCxR4KES0QAzwiIqICC4Tj0GurJm2zWfT40xvXwmrWQhAE2Or0CIbjRRrh4hWLSxgYCWBZkwmSLONknxtAskRTFAQoFZX502hZkwlAch7ebPpH/Dg/7C/EkIhonirzU4yIiKiIAqEYDB8J8D5Kq1Kms0uUO/0jfiQkGddc0gKVUsSxnmSZZjiagEalgFChcx5tdXpo1YpZA7xDJ4ex8wcH8S/PHS3gyIgoW2yyQkREVECyLI+XaM4R4KkVCEcTkCQZoliZQUc+pObfrWyuxspWM471ugAkFzrXqCuzPBMAREFAe6MJb3YN4uR595T9sixj0BmEKAoYHAsiIUlQiMwTEJUiBnhEREQFFI1JiCdk6LWzfwVr1cn94WgCOg2/rnOld9ALvUYJS7UGq9vMePr1bgTDMYSj8XRX00p1w2VteOOwfcb965dZUF+jxX+9dAqj7jCX8SAqUfzGICIiKqBAOAYAGWTwUgFenAFeDvz0tbPoH/FjYCSA9kYjBEFArUkDAPAEogjHEhW5yPlE65ZasG6pZdbHnB1ILobucAYZ4BGVKObWiYiICsgfyizAS63Hxnl4ufHbIw4cOeuE0xvGsqZqAIBJpwIAeAPR9Bw8ml2jJRnUOcYCRR4JEc2ksm9VERERFVhgPMAzzFGiqRvP4IUiibyPaTE6eGIYr39gx9c/uw6yLMMTiOILVy3Diubq9OLmJv14gBeMIRxJwGiePeim5I0Jk64Kg04up0BUqhjgERERFVBgfOmDOTN4qQAvygwekMxkunyRjB57qt+N/zxwErIMdDu86cxcU50eHW016celA7xAFJFYvGIXOc9Wo0UPB9fLIypZDPCIiIgKyJ+agzfnMgks0Zxo1xPvoS+L9ddWtlTjdL8H54f8MOqS73XjR+aMGbRKCJhYosmfRZmwWXQ4dHKk2MMgohnwk4yIiKiAAuk5eJl30ax08YSE/pEAOlfXo7PDOufjqxQi1i2z4K//5S30DftgMWmgEAVYzdpJj1OIIgy6KniD0eQyCczgZaSxVgd/KAZfMArj+DxGIiodcwZ4LpcL9957L/r6+qBSqbBkyRLs3LkTHo8H27dvx8jICJRKJdavX48dO3ZAo9Ggv78f1113HVauXJk+zg9+8APU1CTLIn784x/jX//1XyHLMq688krcf//9ELmWChERVYBAOI4qpQjVHC35U9kkZvCAYVcIkixj04o6fGxNQ8bPa6s34vyQH9GYhPoaLZSKqb81THoV3L4IonEJmgpfJiFTtvFGK4NjQQZ4RCVozqhKEATccccdOHDgAPbv34/W1lbs2bMHVVVV+Ou//mu8+OKLeO655xAKhfD9738//Tyj0Yh9+/al/5cK7s6fP49//Md/xI9+9CO89NJL6O3txXPPPZe/V0hERFRCAqHYnNk7AOlFtxngJQMJ4EIHx0y11hvgcAbRN+SbUp6ZYtKpMOIJAwAzeBlKvZdstEJUmub8hjGbzdiyZUv63xs3bsSTTz6JlpaW9DZRFLFhwwacPXt2zhMeOHAA1157LWprawEAN998M5555hl89rOfnc/4iYiIykogHJ9z/h0AiIIAjUrBLpqYEOBlue5aW4MBkixj1BPGpWvqp32MSa/CmfG13dhkJTN11cls6A9/eRpPv9Gd02M31mhx75c2QxSEnB6XqJJkNQdPkiQ8+eSTuPrqqydtD4fDePrpp3HXXXeltwUCAXz+858HAHz605/G7bffDkEQ4HA40NTUlH5cU1MTHA5H1gO3WAxZP4coV6xWY7GHQASA12I5iiYkmI2ajP52em0VIApl83fO1zhdgShqjGq0tdTM/eAJNooi8LMuAMCqJZZpx9dg0SMWHwIA1NcZyua9Lrb//vn1ONPvyekxB0cDOHx6BJKoQEOdfsHH49+Siq1Y12BWAd4DDzwAnU6HW2+9Nb0tHo/jW9/6Fi677DJcc801AID6+nq8/vrrsFgscDqd+NrXvobq6mrcfPPNORu40+mHJMk5Ox5RpqxWI0ZGfMUeBhGvxTLl9oZhNWsz+tuplCJcnlBZ/J3zeT322D2oz/A9m0iQZWjVySyoXiVO+/yqCZNVIuFYWbzXpWDzcgs2L7fk9JjnHF4cPj2Cw8cHoVw9fcY1U/x8pGLL9zUoisKMCa+MO5vs2rULvb29eOSRR9INURKJBO6++25UV1fj/vvvTz9WpVLBYkn+R2+xWHDjjTfivffeAwDYbDbY7fb0Y+12O2w2W/avioiIqAxlWqIJJBc75xy85FwvW5bz74BkmWurNfkDaLY5eClalmgWVXOdHqIg4HwWy2EQ0VQZBXgPP/wwurq6sHfvXqhUyQ9CSZLwV3/1V1AoFHjwwQchTKiVdjqdiMWSbaBDoRBeeeUVrF69GgDwqU99Cr/85S8xNjYGSZLwk5/8BDfccEOuXxcREVFJ8odiMMyxyHmKRq1EqMKXSfAFowiE41nPv0tZvaQGjbU6GGYIqo36CwEe18ErLlWVAjaLjgEe0QLN+Ul2+vRpPP7442hvb8e2bdsAAC0tLbj55pvx3HPPYdWqVem5dps3b8aOHTtw6NAhPProoxBFEfF4HJ/85CfTZZ2tra34+te/jj/6oz8CAFxxxRW46aab8vX6iIiISkY0lkAsLkGvzSyQ0KoUGPOG8zyq0jbfDpopN12xFJ+5vH3G/dUTAjw2WSm+1gYDTp13F3sYRGVtzm+YlStX4uTJk9Pum2n7ddddh+uuu27GY27bti0dLBIREVWKQDhZbqnPIoNX6Qudp1rxzzeDJ4oCRHHmjowTSzS5TELxtdUb8fbRoWSmO8NSZqJSNewOod6sLfh5ubo4ERFRgQRCyekLmf5wXaxz8EKROH51qB+x+NzB6+BYEEqFgLrq/PxIMukv/C3UXOi86FobknMmzw/50Dvog3/8vxmicnOqz4W/euwtHD4zmt6WkCQ8+tMP8frhgbyemwEeERFRgQTCyR+rmSx0DiQzSuFoYtF1jf7VoX488fIp/POzR5GQpFkf2zfkg82inzULtxBVSgW044vKs0Sz+FrrkwHeC7/rw87/+y4e/emHkOTFdf1TZXCMBgAAL73Tl9722vt2HD4ziq7usbyemwEeERFRgfhD4yWaGWbwtOpkILjYyjQPnxmFTq3E4TOj+LefH0csPn2QF09IOD3gwapWc17HY9KpoK5ScHHtEmDSqVBjVKPr3Biqxxehf/2wHe8cH8IjP/kAQ+NzMolKndsfAQCc6HPj/LAf3kAUz7zRPWlfvrBdFBERUYH4Q1EAmc/BuxDgxaHLMOtX6jz+CM7ZvfjDrUuhEAU8/Xo3hl1BfP2z62Gp1kx6bM+gD9GYhNVt+Q3wjHrVoguiy1lHqxnnh/2495ZNeGzfUfzw5VNISDIEAN12L/7y5ouxrMlU7GESzcrti0AhClCIAn78ymkEwnFEYwksbzbB5ctvgMcMHhERUYF4/MkAzzShc+NsUgHeYpqH98FZJ2QAG1fU4TOXt+Mbn1sPhzOI/3ppauO2k30uAMh7Bq/WqM44q0r5d8cfXIQdX7kURp0KX76+A3XVGtx0RTt23rEFGpUCD//ocMV3l6XS5/KFYdKrcPm6RhztccETiOL2z6zB6rYaePzRvJYeL47bgURERGXA7Y/AoK1ClTKz+6uphbdDkcWTXfrgzChqTer0XKtLOqx475QVJ8aDuYlO9rnRXKeHUZdZQDxfX7xqOYKLKIgud6IoQESyXLahRoeHvnp5et/d2zZix7+/i+//4ji+vW0jy2qpZLl9EZh0Ktz8yRW4eHkd1i2rhVIh4leH+pGQZPgCUVQb1Hk5NzN4REREBeL2R2HO4gtdk8rgRRdH8OH2R3D03Bg2rqiDMOGHeVOdDi5fZFKmMp6QcLrfg448l2cCQJ1Zi7YGY97PQwtXX6PDtmtW4HivC7861F/s4ZQdSZLx3qkRyGxck3dufwTVBhV0GiU2rqyDUpEMu2qMye8AVx7n4THAIyIiKhCXP5L+cs/EhQxe+Qd4p8678T//z7uAAGzd0DRpn82iB3BhUXMA6B3yIRJLoKOtpqDjpNJ35cVNWL/Mgmfe6M57s4rF5vCZUfzjM0dwut9T7KEsei5vZNI6mymp7wC3L5q3czPAIyIiKhC3PwKzIfNyw8XSRVOWZfzTz45Ao1Lgb7/ciSWNk7NlNktyEXOHM9lWPBZP4GdvdEMhCujI8/w7Kj+CIOCW31+JRELCT187W+zhlJWeQS8AwMk5jHklyTI84xm8j0pVceQzg8c5eERERAWQkCR4A9mVaC6WJitufxTeYAw3XrEUzVbDlP1WsxYKUYDDGYQkyXhs31Ec63Hh9s+sybghDVWWhhodrru0Dc+/3QulQkSVYnLOQqutQmiRL5LeUq/HVRubs3pOz6APwIWGT5QfwXAcCUmeNoNn0ldBEJDXTpoM8IiIqGLJsjxpLlg+eQMxyDJgzqJEU71ISjTt4wv+NtXpp92vVIior9HCPhrAB2dH8f7pUWy7egWuWG8r5DCpzPzBx5fgVL8bh04OT9knigIkafHOM4vFJcQTMrZuaIIoZvYZJssyescDPJa25p4nEMVPXzuDlS1mLB9fxmO6G1QKUUS1XgU3AzwiIqKFC0XicHrCaKk3IBZP4Ds/OIgrL27CdZe2ZnWcp18/i8GxIL7xufUZPyf1gyqbEk1REKBRKdA35Mf7p0eyGiMAtNYbUFetzfp5uTYwHuA1zxDgAcl5eA5nAAdPDEOvUeLqS1oKNTwqUxqVEn9z6yXT7rNajRgZ8RV4RIXzynv9+K+XTsETiGY8r9fli8AXTGY1PQFm8HJhzBvGL97qRTwh4f3To/CHYjg/7Ie1egUAoHqGCoQaozqvQTYDPCIiqhi/PNSPZ3/djR1/cinODnhgHw2g2+4BkHmAJ8kyfv2BHaFoApIkZ3z3/EKAl11b7BqjGofPjOLwmdGsngcAWrUCD/3Z5UUvc7SP+mHQVs06DptFhw/OjMLtj+CSjvp0xzkimqpm/HPEnUXjpt6hZMCrqhIzyh4NjgVxoteFy9c1Ql2lmP9gF7G3jg7i1fcHYDaokku66FV4/9QIxsbf35k+88wGNYbdobyNiwEeERFVDJcvAlkG/vPAyXTAlW2ZzDmHF97xu+Cj3jDqzdNnyCRZhiTJ6UDFPT7nJdsA76++tBlj3uzv9PqCUTzykw+x77fncNt1HVk/P5cGRgOzZu8AoMmiR0KSEYok0NlRX6CREZWnVKm3yxfB0gwrmXsHfRAEYHVbDYZccwcXLx88j1ffG8D+N3twxx9chDVL2NH2o3oHfbCaNdj13z8OAHjn+BAOnhjGsZ7kup4zBnhGNU6dd+dtXAzwiIioYviDUQgCcNae7CRXY1Rn3cnsgwmZNMdoYNoA70y/B/+y/yha6w248wsbACQDSUFITrDPhlGnmvdC35/c1ITX3rfjms0tM85/yzdZlmEfDeKyixpmfVzjeCdNnVqJi9r5Q5JoNulW+1l8fvUO+tBk0aPerM0ouPAFYzDpkp9X+397jgHeNHqHfFgyYQ3NVIfgo+ecUCoE6DXTh1o1BjUC4TiisQRUeciOsv6BiIgqhj8Uw/LmaqxbWovVbWZsWdMAly+a1aK/H5xxomW8E6R9vK3/RIdPj+KhJw5h1BPGsR5XutGD2x+BSa+CQizcV+9Nn1gKtUrEz9/qKdg5P8rtjyIUic8ZYNosOggANq2qY3km0RxMOhVEQcioE+OZfg9+dagf3Q4v2hqMMBvVCEcTCEdnb94UCMVQX6PDkgYj/KHybvSUD8FwDCPu8KRlX6xmLTQqBbzBGMwG9YxNvOYToGeDn6BERFQx/KEYjNoq/OUfXYxvb9sIs1GNeII+PisAACAASURBVEJCIJzZjxenJ4zzw35cvq4B1XoVHKPBKY85fGYUOrUSX/r9VYjEEum13Vz+SNblmQtl0qmwcUUdjp0byyqIzaWBUT+A2RusAMmGGXd+YQM+f+XyQgyLqKyJooBqQ2adGP9l/1E88fIp+IIxdLSZ040/5loqwR+KwaCtgl6rRDCyuJecmI++oeRnW9uEDJ4oCOl/z9YxeWKJbT4wwCMioorhC8Vg1FVBFAQoRDF9F3W2L9mTfS688HYv/nX/Ufz/TxwCAGxcUQebRQeHM4BYPIF/ee4o+sYbGDi9YdSZtelyptS6U25fNN0YoZBWt9XAG4zB7pwajBaCfWR8iQTr3CWiG1fWZdwwgqjSZVJiHo0l4PSEccOWNvz9N67A1g229I2mubJH/lAMeq0Sek0VAszgTZFqWjMxwAOQLtk0GzUzPrcmz4udcw4eERFVBFmW4Q/GYNBemM+W/pL1RdBaP3kBbpcvgv966STeP52cc1dtUGF5UzW+cJUVNosetjo93j46hPdPj+LtY0NoqNWhrcEIpyeM5jo9Gi06qFUKnHN4ccV6G9z+CFY0mwr3gsd1jAeaJ3pdc2bR8mFgNJDsoDnPeYRENL0ag3raMvGJhl0hyEjODUvdPEkt1eKeI4MXGM/g6dRKRGIJxBMSy6cn6BvyocaonrIUwpLG5HfJbDerak3JffNpoJUJBnhERFQRwtEEEpIMg/ZCkxOzMfVDZ+qX7L/9/BjODHjwxU8ux1Ubm6DXTG6O0mTRIxSJ4/m3ewGM/5CSZYx5w9iw3AJRENDeYMQ5hw+xuAR/KFbwEk0AsFZrYDGpcbLPhWsKvLZcKBLH+6dHsbKluqDnJaoEZqMax3rHZn3M4Fgyc99Qo0tvqx7/HPLMkj2KxhKIxiUYtFXQqpPhQiAcn3Fdt0rUO+RH20duDAITM3gzf95rVEoYtFUYzdNSCQzDiYioIvhDyTkkkwI8w8wlmnZnAJdd1IBPX7ZkSnAHAE3jXR9T8zCG3UH4QjFE4xIspmRpzlKbCeeH/Rh2JX9kzfaFny+CIKCjrQYn+tyQCjwP75X3+uEPxfCZy9sLel6iSlBjVCMUmb1ZSjrAq73Q7VevUUKpEOGeZbHz1OelXlsF3XgnyGCY8/BSUvOrJzZYSbFZ9Ni0sg6b5ljupa5ag1FPOC/jYwaPiIgqQjrA010I1pQKESZd1ZQALyFJ8Aai6Tvd07FNKHdc2VKNwbEgnONf1pbqZIDXbjMinpDwj88cQZVSxKpWc85eTzZWt9Xgza5BnB/y5225hFg8gVhcSv87EkvgwDvnsX6ZBcuaCl+aSrTYTSwxt1mm/0k/NBZEjVENjerCfkEQYDaoZs3gpT8vNVXQqJJt/DkP7wL7aACyjCml/UCyAc6dX9gAq9WIkRHfjMeoq9agf2T2Etv5YoBHRESLmiTJEEUBvvHFyY3aydk4s1E9pUTTG4hBloEaw8zlSNV6FXRqJeprtNi4og4/ee0s+keS2byJGTwAGHKFcPtn1qCxVjfj8fJpdVsysPzOD94t+Llv+kR7wc9JVAlSFQFuXwQ2y/Q3bgZdQTTUTF2rs9qgmnUO3sSKh9Q6bQFm8NJGxksrJ5a+ZqvOrMXhM05IsgxxhuUU5osBHhERLVon+1z4h6eP4E9uWI1oPAFgcgYPSJZpfjSD5wlE0vtmIggCvvLp1ag1aTDmTWbujve6AFzI4NVVa9BUp8fa9lpcsd6Wmxc1D3VmLf70xovS48wHvV6NQGDy+2ip1mB5E+ffEeVDqlnKbJ0YB51BXLp6aqmgWT97g5aJAV5VVXJGFwO8C4ZdyQCvzjxzp8y51FVrEE8kq0VyPT+bAR4RES1Kx3td+N8//QDRmIQzAx7UjmfVPprBqzGq0W33Ttrm9iXvbM81Z+6S8TkWqc5yx3tdUKsU0I/PWREEAQ/c/rEZF7stpMvXNub1+HOVIxFRbs21zIs/FEMgHEfDNJUDZoM6fUNqOoEJJe2pzzeWaF4w4g7BpFdNKn3NVt34jcBRTzjnAR6brBAR0aLTP+LHo09/CGu1FvVmLQbHgvCHohAFId0RLqXGoIY/FJs0fyxVspnpl269OVkC5fFHUWfSTAroSiG4I6LFR6NSQqtWpG9IfdTg+NqX05WG15jUCEbiCEWmD9omZvB06S6azOCljLhD6c/9+aqrTj4/H500mcEjIqKyFwjHEIkmSzCjcQn/8PSH0KgUuOuPN+KpX51G76APtUY1DFrllIArPY/FH4F1/Avb7Y9AAGDST+2eOR21SoFqgwoefzRdnklElG9mgxofnB1F6OeTAzW9pipdjj5dgGcb32Z3BqYto/aH4lCrFOnsnU6tRCDMDF7KsDuEjgU2zbJMyODlGgM8IiIqa0NjQfzNv76NiSsAKBUC/sctm1FjVKOhVoeDJ4fh9uthmGax7YllThcCvCiMehUUYuaFLg1mbTLAMzHAI6LC6Oyox1tHB3HqvHvSdrc/gnhChkIUpr3plOqmax+dKcCLwTBheRidRsllEsbF4hJc3gvfF/OlrlLApKvCqKcIGTyXy4V7770XfX19UKlUWLJkCXbu3Ina2locPnwY27dvRyQSQXNzM773ve/BYrEAwLz3ERERZcMxFoQsA3/w8fb0nIYlDcb0+kS2Wh1kGeh2eKcvVZpmHovbH0k3MMiUtUaLU/0eZvCIqGA+d+UyfO7KZVO2D7uCeOpXZ6BUCOks3ERWsxZKhQjHaHDa4wbCsUlrhuq1VczgjRv1hCADqJ+mO2m26szavGTw5rw1KQgC7rjjDhw4cAD79+9Ha2sr9uzZA0mScM8992D79u04cOAAOjs7sWfPHgCY9z4iIqJseccX673yYhuuvLgJV17cNGnx2VSDAW8gOqXBCpCcPycAcEzoKJcM8LKb9F4/3i6bGTwiKrb6Gh3+4osb8PXPrZ92vygKsFl0GBidvpOmPxSDQXshD6TXKDkHb9yIOxmQLTSDB+RvsfM5Azyz2YwtW7ak/71x40bY7XZ0dXVBrVajs7MTALBt2za8+OKLADDvfURERNnyjAd41frpM24Ts3YfXSIBAFRVCljNWtidF+5ku/3Zt61OzWnJxZc+EVG+NdfpYZ8lwNNPzOBpqthFc1xqDbyFNlkBkvPwnJ4wJEme+8FZyGoOniRJePLJJ3H11VfD4XCgqakpva+2thaSJMHtds97n9mc+WRFi2XqyvFEhWK1Gud+EFEB8FoEYpIMnUaJJtvM3yFmoxpuXwT1Fv2071l7UzUGxwKwWo1IJCT4glE0Nxizen+vq9XDbNbiY+tsFds5k9cjlRJej7NbsaQGbx8bgsGkndJdOBiOw1p74fOyrkaHU/1uvqcAfJFkA5rl7ZY5P+vner+WttQg8XYf9j7bBaUyu8UNLNUafOOLG6fdl1WA98ADD0Cn0+HWW2/Fyy+/nNUgcs3p9Oc82iXKBNd6olLBazFpaDQAo7Zq1veivloDty8CUZanfVydSY1DJ4bgGPTAF4xBloEqAVm/vysajRgd9Wf9GhYDXo9USng9zs08nqE7cnIIS22m9PaEJCEQikGBC5+XImT4gzEMD3sr9gZWSp/dmyytnOOzPpNrsLVWixXN1RibT5nmLHFQxgHerl270Nvbi8ceewyiKMJms8Fut6f3j42NQRRFmM3mee8jIiLKljcQhWmG8syURosOp/o9k5oGTNRUp0NCkjHiDiE8vtxCrheeJSIqJRM7aU4M8ALhOGRgSolmQpIRjiamZPsqzYg7lJMGK0Byjvjf3HbJvJ4rijMH2hnlAh9++GF0dXVh7969UKmSX6Lr1q1DOBzGwYMHAQBPPfUUrr/++gXtIyIiypY3OHeAl2q0YpxmDh5w4YfOwEgA7vFummZjdl00iYjKidWsgVIhTJmHFxhf5HxiUyqdJhnUBSu8k6Y3GMWQK5j+TilVc4bgp0+fxuOPP4729nZs27YNANDS0oK9e/di9+7d2LFjx6TlDgBAFMV57SMiIsqWNxDF6iU1sz6mrT45D6J2hg6XNoseApKL/qZ+1DCDR0SLmUIU0Virx68/dODtY0O4fksbfr+zFf7xAM/wkQwekFw+oZKXgnn53fNIJGRs3WAr9lBmNWeAt3LlSpw8eXLafZs3b8b+/ftzuo+IiChT8YSEQDiO6mkWMJ/oovYaPHDHFjSPZ+o+Sl2lgKVaA/toAPU1OggCYJrjmERE5W7LRfV45/gwfMEofvOhY1KAN7lEMxkyVPJaeP5QDL861I/O1fWwWab/LikVlV1ES0REZS21Bt5cJZqCIMwY3KU01enRbffi1Hk3musMs85vICJaDD5zeTs+c3k7nn+7Fz997Sxcvgi67V6IgoC6CZm6VLCXKt+sJA5nAK++PwDHaADhaAI3fry92EOaU3b9OImIiEqIN5hZgJeJpjo9Rj1hhCIJ/NmNFy34eERE5WLd0loAQFe3E28fHcRFS2tgnFDFcCGDV1kBXiyewKNPH8Fr7w+gZ9CHqzY2oaW+9JdqYwaPiIjKVqYZvEy0Nybn6d3+mTVl8QVORJQrrfUGVBtU+PlbPXB6I/j8Vcsn7U/Nwau0Jiv73+zF0FgQ3/7jjVg7HgSXAwZ4RERUtjw5DPA6V9djT3P1jI1YiIgWK0EQsH6pBb854oC6SoHNK62T9quqRKhVCrjGuwxXgsGxIF54uxeXr20sq+AOYIkmERGVsVQGb64mK5kQBYHBHRFVrHXLkkHMplV1UKsUk/YJggBbrQ4OZ2C6py5K754YRkKScfPvLZ/7wSWGAR4REZUtbyAGtUox5ccIERFlZ/0yC1a3mfH7na3T7rdZ9LA7gwUeVfEc7xlDa72hLJfMYYBHRERlyxuM5iR7R0RU6bRqJe69ZTOW2kzT7m+q08HliyAUWfzz8KKxBM4MeLFmjjVWSxUDPCIiKlveQDQn8++IiGh2jbXJpWYGxxZ/Fu/0gAfxhISL2hngERERFZSHAR4RUUE01ekAAPbRxT8P73iPCwpRwKpWc7GHMi/soklERGXLG4iW7RcwEVE5sZq1UIgCHAuch9d1zolA6EKZZ121Bsubqxc6vJw61jOGZU0maFTlGSqV56iJiKjixeIS/KEYqpnBIyLKO6VCRH2NdkGdNI/1jOHhH30wZfulq+txy7UrUV3AhiayLEMQhCnbA+EYegd9uPGK9oKNJdcY4BERUVlyesMAknd/iYgo/5osegwsoETzxXf6YNKrcM+2jRBFAbIMHDo5jP1v9kKSZXzjc+tzONqZDY0F8d3/OIg/u2kt1i+zTNp3otcNGcBF7eW19t1EnINHRDklSTLeOT4ESZaLPRRa5EbcIQDJsiEiIso/W50Ow64Q4gkp6+f2j/jR1T2GazY3o9lqgM2iR1OdHjdesRTrl9UuuPQzG71DPgTCcXz/58fS66mmHO8dg6pKxLKm6buJlgNm8Igop473ufDYvqP4H3oVOtrKs/sUlQcGeEREhWWz6CHJMg680wdjlkvUHDo5ApVSxO9tbpmyz2LS4Fiva8ayyVwb80YAAMFIHP/+/HF884sb0uc93utCR2sNlIryzYMxwCOinPL6k3fCPB+5I0aUayPuEKqUIqoNnINHRFQIS20mCALw9Ovd83r+tZ0tMGirpmyvNWkQiSYQisSh00zdn2tj3jA0KgU+c/kSPP16NwbHgrBZ9HD5InA4g9i6oSnvY8gnBnhElFO+YDKw+2jJA1GujbrDqKvWQCzA3V4iIgIaa3X433+xFdFYYl7PNxunb6JSa0puH/NGChLgOb1hWEwaXLqmAU+/3o2j58Zgs+hxrGcMAMp2/bsUBnhElFO+UAwA4A3GijwSWuxG3CGWZxIRFZhBWwVMk4VbiFpTslmW0xtGS70hp8eezpg3glqTBvVmLerNWhw9N4ZrO1txvNcFg7aqIGPIp/ItLiWikuQbD+xSmTyifJBlGSMeBnhERIuBZTzAG/NFCnK+ZAYvmTVcu6wWJ/rc8Idi+PCsE2uW1JR9ZQgDPCLKKX8qg8cSTcqjQDiOUCTBAI+IaBGo1qugEAWMjS9/k0+RWAL+UCydNVzXXotILIG9zxyBPxTDpz7Wlvcx5BsDPCLKKf945s7HEk3KowsdNLkGHhFRuRNFAWaDuiABXuocqazh6vGM3cnzbly2tqGsl0dIYYBHRDl1YQ4eM3iUP1wigYhocak1qeH05r9EM7VEQqqxi1atxPJmE6qUIr541fK8n78Q2GSFiHLqwhw8ZvAof9IBXjUDPCKixcBi0uDMgCfv53F+JIMHALd9qgO+4IWyzXLHAI+IckaSZARCMSgVAkKROGJxCVVKFgpQ7o24QzDpqqBWKYo9FCIiyoEakxquExFIspzXJidj3jAETF6yocVa3l0zP4q/vIgoZwLhGGQADbU6AOykSfkz7GIHTSKixcRi0iAhyXlv0ub0hmE2qqFULN4waPG+MiIquFQHzeY6PQCWaVJ+JCQJPYM+tDYYiz0UIiLKkVrjhbXw8im5Bt70C64vFgzwiChnUgFdkyUZ4JVqoxVZlnHo5DASklTsodA89Az6EI4msLrNXOyhEBFRjqSCLleeG604veF0MLlYcQ4eEeVMOsAbz+CV6lp4R3vGsPdnXfjLmzdgw/K6Yg+HsnSi1wUAWN1WU+SREBFRrqQanPzktTN46eD5vJ1n1B3G5pXWvB2/FDDAI6Kc8YWSAV1TiZdonulPduly+0szAKXZneh1odmqh0mvKvZQiIgoR/QaJX5vUzMGx4J5Pc+a9hp0rq7P6zmKLaMAb9euXThw4AAGBgawf/9+rFq1Cv39/fjGN76RfozP54Pf78c777wDALj66quhUqmgVifTrXfffTe2bt0KADh8+DC2b9+OSCSC5uZmfO9734PFYsn1ayOiAvOPB3RWswZVSrFkSzRTbZg9JZphpJnF4hJO93uw9eKmYg+FiIhySBAE3PapjmIPY1HIKMC75ppr8OUvfxlf+tKX0ttaWlqwb9++9L8ffPBBJBKJSc979NFHsWrVqknbJEnCPffcg4ceegidnZ34p3/6J+zZswcPPfTQQl4HEZUAfygGtUqBKqUCRl0VfCUYQEmSjLN2L4DSLSGlmZ1zeBGNS1izhOWZRERE08moyUpnZydsNtuM+6PRKPbv348vfOELcx6rq6sLarUanZ2dAIBt27bhxRdfzHC4RFTKfMEojNoqAIBRp4IvVHolmv0jfkSiyZtRXMah/Jzoc0EA0MEGK0RERNPKyRy8V155BQ0NDVi7du2k7XfffTdkWcYll1yCu+66CyaTCQ6HA01NF0pramtrIUkS3G43zObMv7AtlsW1ICGVF6uV7dkneufoIIw6FSIJGWaTBlarEXVmLTyBaMm9V++eHgUA1FVrEIpKJTe+bJX7+LPlCcZQV6NFe2ttsYdC06i065FKG69HKrZiXYM5CfCefvrpKdm7J554AjabDdFoFA8++CB27tyJPXv25OJ0AACn0w9JknN2PKJMWa1GjIz4ij2MkhGJJrD7Pw9Cp1FCp1Gi1qjByIgPGqWIHk+o5N6rwyeGUK1XYUmjEfbRQMmNLxuVeC0OOQMwaasq7nWXg0q8Hql08XqkYsv3NSiKwowJrwUHeENDQ3j33Xexe/fuSdtTJZ0qlQq33HILvva1r6W32+329OPGxsYgimJW2TsiKh3vnR5BJJZAJJaAyxdBW33ybpVRr4LbH8UPXz5V5BFO1nVuDKtazDDpVTjZ5y72cChLbn8UjbW6Yg+DiIioZC04wPvZz36Gq666CjU1Fya8B4NBJBIJGI1GyLKM559/HmvWrAEArFu3DuFwGAcPHkRnZyeeeuopXH/99QsdBhEVyVtdg7CY1LCYNDjV74FRl5yDt7KlGr/50IE3uwaLPMLJBAHoXF2PwbEg/KEY4gkJSkVG05GpBHj8Ec6/IyIimkVGAd53v/tdvPTSSxgdHcVXvvIVmM1m/OIXvwCQDPDuu+++SY93Op248847kUgkIEkSli9fjh07dgAARFHE7t27sWPHjknLJBBR+XH7IzjaM4ZPX7YE65bWYtcP30f1+Npkm1ZasembpbuQ6KvvDwBIrtVXY1QXeTSUiVg8gUA4DrOBfy8iIqKZZBTg3X///bj//vun3XfgwIEp21pbW/Hss8/OeLzNmzdj//79GQ6RiErV744NQZaBj69rhM2ix51fWI8VzdXFHlZGTLpkIOoLRhnglYnUwvRmLnBOREQ0o5w0WSGiynTO4UVdtQY2ix5AMmtXLkz6ZCkp18IrH57xAK+aGTwiIqIZMcAjonnzBqIwl2n2yzSeBfIwwCs5vz3iwNtHBwFBwMYVdfi9zc0QBQFufwQAYDYwg0dERDQTdhYgonnzBWPpUsdyc6FEs/QWY690B97pQ++QH25fBE+8fAp7nnwfgXBsQoBXnjcViIiICoEBHhHNmzcYhWm8a2a50agUqFKKLNEsMZFYAvbRID65qRk7b/8Y/tv1HTjR58abRwbhCUShEAUYyvSaIyIiKgQGeEQ0L5Ikwx+MwVimGTxBEGDSqViiWWLOD/shyTLaG40QBAFXbWyG2aDCuUEv3P4ITHoVREEo9jCJiIhKFgM8IpoXfygGGRfmspUjk74KviADvFLS4/ACANobjeltS20m9Dh8cPujnH9HREQ0BwZ4RDQv3vHAyFjG5XImnYolmiWmd9AHk65q0tIV7Y1GDI4FMegMcv4dERHRHBjgEdG8pAKjcm2yAiSzjx5m8EpKz6AP7TYThAllmEttJgCA0xvmEglERERz4DIJRDQvqQxeeZdoquALxHD03BiwwGld1moN6mt0uRlYhYpEE7A7A7ikY/J6iksmlGtykXMiIqLZMcAjonnxBZLLC5RzgGc1ayHJMv7+R4cXfCyTrgr/685PTMo8UXb6hn2Q5ckBHQAYdSrUVWsw6gmX7bqLREREhcIAj4jmxRuMQhQE6DTl+zFyxfpGNFv1SCTkBR3ngzOjeOF3fXD5Iqg1aXI0uplJsox//8Vx/OEnV8C6iJqO9Dh8AID2RtOUfe02E0Y9YVSX8Q0FIiKiQijfX2ZEVFS+YBRGXVVZt6xXiCKWN1Xn5Fgv/K4PfcP+ggR4g84g3uwaRF2NDp+9oj3v5yuUYz1jqKvWTGqwkrLUZsTBE8NsskJERDQHNlkhonnxBsp3Dbxca7EaAAD9w/6CnK93MJnpGhgpzPkKIRaXcLzPhfXLLdPuv+yiRlx3aSuarfoCj4yIiKi8MINHRPPiC0Zh0pfvEgm5pNMoUVetQX+BAq5zg8m14uwjgYKcrxBO9bsRjUlYv2z6AK/GqMa2a1YWeFRERETlhxk8IpoXbzBa1g1Wcq213oDzBc7gDY0FEItLBTlnvh0564RSIWBNW02xh0JERFTWGOAR0bx4A7GyXgMv11rrDRgcCyIaS+T1PJIko2/ID6OuCpIMjLhDeT1foXSdG8OqVjPUKkWxh0JERFTWGOARUdYi0QQisQSMOpZoprRYDZBlYGA0v2WTjrEgIrEELl1dDwAYGgvm9XyF4PSEYR8NzFieSURERJljgEdEWfOlFjlnBi+ttaEwjVZ6x+ffbbmoAQAw6Cr/AO90vxsAsGYJyzOJiIgWigEeEWXNG0wucm7kHLw0q1kLdZUCx3tdCEfjeTtPj8MHVZWIZU0mmA1qDDrLP8A7P+yHUiGgqY4dMomIiBaKXTSJKGteZvCmEAUBq1rNePvYEA6eHIZOnZ+P12AkjvZGExSiiCarflGUaPYN+9FUp4dSwXuOREREC8UAj4iydnbAAwBcJuEj7vzCepzu96DrnBOhSP6arVzaYQUANFsN+N3Rwbydp1DOD/uxflltsYdBRES0KDDAI6Ks/PpDO37xVi86O6ywmDTFHk5JUSpErFlSU7C5ZM1WA7yBKILhOHSa0v447x/x48A7ffjyp1ajSnkhU+fxR+ANRNFWbyzi6IiIiBYP1sMQUUbiCQnPvNGNH7xwAhe11+BPb1wLQRCKPayK1mRNNnYZmqXRij8Uw7O/7kYsnt/lG+by7vFh/PbIID44Mzppe994U5rWekMxhkVERLToMMAjojnJsozvPfk+fv5mDy5f24g///z6SVkYKo5U5077R5ZmeOHtXnx41gkA+N2xITz32x4cPDFS8PFNZHcmx/jbI45J21OLw6deCxERES0Mf6ER0ZwGx4I43e/BF65ahjv+4CJoVKVdDlgpbBY9FKIAx4ROmvGEhJ/9uhsvvN0LADhrT86XfLPLMe0xCiU1xiPdY/AEountfUM+WExq6DWcz0lERJQLDPCIaE7HelwAgEvXNBR5JDSRQiGisVY3KYM36AwinpBx1u5FLJ5A94AXApJ/Q5cvUpRxxhMShsaC2LSyDpIsT2oMc37Yj1bOvyMiIsoZ3oYnojmd6HXBYtLAWs2mKqXGVqdH35Av/e9UyWM8IeGDM04Mu0O48uImvPGBHW8fHcQNly0p+BhH3CEkJBmbV1nh9kfwo1fP4Jk3ugEA0biEzo76go+JiIhosWKAR0SzkiQZJ/pc2LTKyqYqJajJosOhk8OIxhJQVSlwftgPhShAkmS88Ls+AMDH1zViYNSPN4sU4NlHk+WZTXV63HpdB949MZzepxAFXLWxqeBjIiIiWqwyCvB27dqFAwcOYGBgAPv378eqVasAAFdffTVUKhXUajUA4O6778bWrVsBAIcPH8b27dsRiUTQ3NyM733ve7BYLHPuI6LScn7Yj0A4XrDW/5Sdpjo9ZDk5T7KtwYjzwz40W5Pbzjm8EAUBSxqNWL/Ugmd/cw6xeAJVSkVBx5hqsGKz6KBRKbHUZiro+YmIiCpJRnPwrrnmGjzxxBNobm6esu/RRx/Fvn37sG/fvnRwJ0kS7rnnHmzfvh0HDhxAZ2cn9uzZM+c+IiotsizjWO8YADDAK1FNdXoAF4Ko5Jw2uQUzXgAAIABJREFUAzpazQCSyw+oqxSoMSVvxLn80ekPlEcOZwAWk5rNeYiIiAogowCvs7MTNpst44N2dXVBrVajs7MTALBt2za8+OKLc+4jotLxq0P9uH3Xq/jJq2dhs+hgNqiLPSSaRkONDqIgwD4ahCcQhTcYQ2u9ER1tyQBvWXMyW1ZrTM6fdHnDBR+jfTQA23ggSkRERPm14Nupd999N2RZxiWXXIK77roLJpMJDocDTU0X5lTU1tZCkiS43e5Z95nN5oUOh4hy5M2uQTTUaHHZ2kZc1M7sXamqUoqor9HCMRrA+eFks5XWegNa6w2oMaqxaUUdAKDGOJ7BK3AnTUmWMegMYnUbryEiIqJCWFCA98QTT8BmsyEajeLBBx/Ezp07C1ZuabFwUVwqHqt1cbd19/gj6Bn04pZPrca23+8o9nBoFlarEUubq3F+yAdXIAYA2LimESa9Cv/xP69PP04/nsGLyYW9fgedAUTjEla1Wxb9fze0+D8bqbzweqRiK9Y1uKAAL1W2qVKpcMstt+BrX/taervdbk8/bmxsDKIowmw2z7ovG06nH5IkL2T4RPNitRoxMuKb+4Fl7K2uQcgysKzBsOhfazlLXYsWowpvHfHjB784hhqjGpFgBCPBqZk6jUqB8w5vQf+mL77VAwCwmdW8lha5SvhspPLB65GKLd/XoCgKMya85h3gBYNBJBIJGI1GyLKM559/HmvWrAEArFu3DuFwGAcPHkRnZyeeeuopXH/99XPuI6LS8GG3EyZdFZY08u5nOfjkxmQDrIQkY1XLzDfLaozqgpZoSpKM1963Y82SGtgsnINHRERUCBkFeN/97nfx0ksvYXR0FF/5yldgNpvx2GOP4c4770QikYAkSVi+fDl27NgBABBFEbt378aOHTsmLYUw1z4iKj5JktHV7cTFK+ogct27slBr0uDzVy6f+3FGNcYKGOB9eNYJpzeMP756RcHOSUREVOkyCvDuv/9+3H///VO2P/vsszM+Z/Pmzdi/f3/W+4iouLrtXgTCcWxYzrUpF5saowYDo86Cne+V9/thNqiwcWVdwc5JRERU6TJaJoGIKsebXQ5UKUWsW1pb7KFQjtUY1fD4/x97dx5eZX3n//9532fNvgcStrAGFBQVFatYBa3gqGg36HS3/qp2U2e0tdMW/aKdFtvpMlVHZ8Y606nVLmipWndcqlYUFARU9hDIvq9nv+/fHyc5EBMg+zk5eT2uy0tyr+8T7oTzOp8tSDhijfi9Ovwhdu5v5LxTinA69E+NiIjIaNG/uiIS4w+G+ft7NZw1t5BUryve5cgwy8n0YAOtHSO/2Pmu8mZsYP50tQSLiIiMJgU8EYl58/1aAsEIH+2atEOSS27XWnijMQ7vg/Im3E6T6UWZI34vEREROUIBT0RiXt5aSXF+GjMn6U15MsrpWgtvNGbS/OBgM7MmZ+Fy6p8ZERGR0aR/eUUEgMZWPweqWjlvQRGGZs9MSjldLXhNrf4RvU9bZ5DDde2UTs0Z0fuIiIhIbwp4IgJAZUMHANOLtPZdskrzOnE7TZraR7YFb1d5MwDzFPBERERG3aAXOheR5FLd0AnAxNzUOFciI8UwDHIyPOw93MIr2ypH7D5bdtXhdpmU6MMCERGRUaeAJyIAVDd2kuJxkJnmjncpMoImF6SzZXcd+ypbR/Q+p88p0PIIIiIicaCAJyIA1DR2MjE3VePvkty1K08elWUS9EGBiIhIfCjgiQgQbcGbPSU73mXICHM6THIzvfEuQ0REREaI+s+ICIFQhIbWgMbfiYiIiIxxCngiQm2TD9AEKyIiIiJjnQKeiFDdqBk0RURERJKBAp6IUN21Bt6EHAU8ERERkbFMAU9EqG70kZvpweN2xLsUERERERkCBTwRobpriQQRERERGdsU8ETGufcPNlFW1cr0osx4lyIiIiIiQ6SAJzKOtXYE+c/HdzIhN5V/OGdavMsRERERkSHSQuci41AobPHClsO8sOUwHb4w//TphXjd+nUgIiIiMtbpHZ3IOPT0poM89rcDzJmcxZcvncuUwvR4lyQiIiIiw0ABT2Qc2rq3gZnFmdz6uTPiXYqIiIiIDCONwRMZZ9o6g5RVtbJgRl68SxERERGRYaaAJzLO7CxrxAbmK+CJiIiIJB0FPJFxZsf+RtJTXJRMzIh3KSIiIiIyzBTwRMYRy7bZcaCRk0pyME0j3uWIiIiIyDDTJCsiCWrDqwd49q1Dw3xVG18govF3IiIiIklKAU8kAdm2zUtbK8jL9DB3Ws6wXtvjcrCotHBYrykiIiIiiUEBTyQBlde009Ie5JMfncm5C4riXY6IiIiIjBH9Cnjr1q3jmWeeoaKigscff5w5c+bQ1NTEt7/9bcrLy3G73UybNo21a9eSm5sLQGlpKXPmzME0o8P87rrrLkpLSwHYuHEjd911F5FIhJNPPpkf/ehHpKSkjNBLFBl7tu2txwB1pRQRERGRAenXJCvLli3joYceYtKkSbFthmFwzTXX8Mwzz/D4448zZcoUfvrTn/Y475FHHmHDhg1s2LAhFu46Ojr4wQ9+wH333cdzzz1HWloaDzzwwDC+JJGxb9u+BmYUZ5KZ5o53KSIiIiIyhvQr4C1atIiiop7dxLKzszn77LNjXy9cuJDKysoTXuuVV15h/vz5lJSUALB69WqeeuqpAZQsktxaOqILkZ8yU613IiIiIjIwwzIGz7IsHn74YZYuXdpj++c//3kikQjnn38+3/zmN3G73VRVVVFcXBw7pri4mKqqquEoQyQp7NjfgA2cOis/3qWIiIiIyBgzLAHvjjvuIDU1lc997nOxbS+99BJFRUW0t7dzyy23cM8993DTTTcNx+0AyMtLH7ZriQxUQcHILRJe0bifNK+T008uwjC0Vp0c30g+iyIDpedREomeR4m3eD2DQw5469at4+DBg9x3332xCVWAWJfO9PR0PvWpT/Hggw/Gtm/atCl2XGVlZa/un/3R0NCOZdlDrF5k4AoKMqiraxux6+8qa2BKYTr19e0jdg9JDiP9LIoMhJ5HSSR6HiXeRvoZNE3jmA1e/RqDdyw/+9nP2LFjB/fccw9u95HJIFpaWvD7/QCEw2GeeeYZ5s2bB8CSJUvYvn07ZWVlQHQilhUrVgylDJGkEY5YHKrtoGRiZrxLEREREZExqF8teHfeeSfPPvss9fX1fPnLXyY7O5tf/OIX3H///ZSUlLB69WoAJk+ezD333MP+/ftZs2YNhmEQDoc57bTTuOGGG4Boi97atWu59tprsSyLefPm8b3vfW/kXqHIGFJZ30E4YjFtorqViIiIiMjAGbZtj8l+juqiKfEykk3uf9tWyYNPfcC/fnUxE3NTR+QekjzUBUkSiZ5HSSR6HiXexmwXTREZXmU1bXjdDgpzUuJdioiIiIiMQQp4IgnkYHUb0yZkYGr2TBEREREZBAU8kQQRsSwO1bZr/J2IiIiIDJoCnkiCqKrvJBTWBCsiIiIiMngKeCIJ4nBddN27KQV9D5gVERERETkRBTyRBFFR34FpGEzQ7JkiIiIiMkgKeCIJorK+gwm5Kbic+rEUERERkcHRO0kZU3YfauYvrx6IdxkjorK+g+L8tHiXISIiIiJjmAKejCl/e7eSP796gL2HW+JdyrAKhSPUNvuYpIAnIiIiIkOggCdjSkOLH4CnNh2McyXDq6qhE9tGLXgiIiIiMiQKeDKm1Lf4MQ2DrXvqqWroiHc5w6aiPvpa1IInIiIiIkOhgCdjhmXZNLUFOHfBRJxOk+c2H453ScOmsr4Dh6kZNEVERERkaJzxLkCkv5rbA0Qsm+lFmbR0BJNqHF5FXQcTclNxOvSZi4iIiIgMnt5NyphR3zX+Li/Ly4ScVGqbO7FtO85VDY/KBs2gKSIiIiJDp4AnY0ZDa1fAy/QyITeFYMiiuT0Y56qG7pVtldQ2+ZhamB7vUkRERERkjFMXTRkzjm7BK8xJAaC2qZOcDE/sGMu2MQ0jLvV12/xBLS++U8FNnz71mF0uK+raeW1HNXXNPrbsqmP+jFwuWjR5lCsVERERkWSjgCfH5AuE8QXCcbl3Zpobp8Ok3Rfi/g07+OQFs2ho8ZOR6sLjclCYE52MpKbJR+nUHCDaEvbHF/fyr19dTEaqmz+9tI9gKMLl55aQkeoetdpfebeS9w828fed1Sw5pbjPY37/4l7eO9BEVrqbZWdMZtXSWRp/JyIiIiJDpoAnffIHw9xy7+t0xingTSlM518+fwaPv1bGzrIm8rdW0NDiIz/LC0BepgeHaVDb5AOg3Rfijy/upcMfZsuuOk6ZmcdTbxzEBl7bUc0NnzyFOVOyR7zuUDjC7vJmAJ56o5xzFxT1alFs94V4v6yJS86awqcunDXiNYmIiIjI+KGAJ33aVd5MZyDMP5wzjYLslFG9d1tnkPUv7+e/Hn+PbXvrMQ2Dd/bU43U7YuPUHKZJfnYKtU2dAPzltQN0BsJkpbl58/0afMEwNvDNTyzgt8/uZsOrB7jlM6cNW437K1t5/2AjAAtnF8TWr9tzuIVg2OKckyfy953VvLO7njNKCwAIBCN43A7e2V1HxLI5c17hsNUjIiIiIgIKeHIM75U14XKaXHFuCS6nY9Tv3+EL8/Sb5bhdJlctmcHvN+6ltQNOn10QO2ZCTgo1TT5qm328+HYFHz21mMw0N4+/VkZ9i58ZxZmcNruAyvoO1r+8n8r6oc9UGY5Y/M8TO3n0xb10z9+561Az//TphQDsPNCIwzT47MVz2FfRwgtbDnFGaQH1LT6+e/8bXP6REvZWtpCf5WXahIwh1SIiIiIi8mHjbtCPZdlJM7X+SHrvYCNzJmfFJdwBXHX+DE6fU8DqpbNZckoxDjPazTGvq4smQGFOCrVNPv62rRLLtrnsIyWcNW8CNtEJWc45eSIAS04txukweeHtoS+M/sq2Sta/uJclpxbzqxuXcMHCYvYcbiFiWUA04M2enEWq18nC2fnsq2wlHLHYVd5MxLL586sH2Lm/kTPnFWLEeTIYEREREUk+4yrgRSyLW/7jdV7YMvQ3+smsuT1ARV0HJ5Xkxq0Gl9PkGx9fwAWnTSLV62TetOhEKkcHvAk5qQRCEV58u4IFM/LIzfRSnJ/G5IJ0HKYR6wKZmerm7HmFvL69mk7/0MYU7qtoJTfTy5dWzCXN62LutBwCwQjlNe20dAQpr23n5OnR79uM4kxCYYvDde3sr2zF63Zwysw8bOCsuROGVIeIiIiISF/GVRfN2iYfTW0Bdh5o5KJFU+JdTsJ6v6wJIK4B78MWzS1kx4FGJuQcGQ/YvVRCZyDMeQuKYttXLZtFbZOPzKNmzlxyajGv7ajm/YNNsTFxg3Goto0Zk7JiX3dP3LKrvBm3K/p5yfzpeQDMLI4et6+ilX2VLcwozuTrVy2gqqGDqeqeKSIiIiIjYFwFvEO17QAcqG7Dtm11kfsQ27ZpaPWz6f0a0lNcTJmQOAtvn3dKEVMK0ynKOzKGrjvspae4WDg7P7b95JJcTi7pef70okycDoN9FS2DDnihcISqhk7OOWrpg+x0DxNyUvigvIm6Zh/TizKY2vV9y830kJ3u5r2yRg7XdnDpOdNwOU2FOxEREREZMeMq4B2uiwa81o4gTW0BcjO9JzhjfHni9TIe+9sBIBqo4r1g+NFMw2B6UWaPbXlZXlI8Ts47peiEa8i5nCYlEzPZW9Ey6Boq6zuJWHaPFjyA0qnZ/O3dKmwbrrlsXuyDA8MwmFmcxdt76rBtmFmc2ddlRURERESGzfgKeLUdOEyDiGVTVt2mgHeUUNji2bcOMW9aDp++cBZTChOn9e5YHKbJndecTUaqq1/Hz5qUxfNbDhEKW7icAx9+Wl7TBsCM4izgyEQ9pVNyeGVbFZmpLs780Ni6GZMy2bK7rus8BTwRERERGVnjapKVw3XtnDIzD4dpcKCqNd7lJJRte+vp8IdZfvZUpk3MwDQTp/XueHIyPCdsves2c1IW4YjNwa6gNlDlte14XA4m5vVcaqF0ajamYXDBaZN6BcfucXiFOSlkHDUmUERERERkJIybgNfpD8fWRpuUn0ZZ9eDe5CerV7dXkZ3u5uQEmlhluM2aFG1B23t4cN00D9W0MaUwvVf4zc30cvvVZ3LZR0p6nTNtYgYO01D3TBEREREZFScMeOvWrWPp0qWUlpaye/fu2PYDBw6watUqLrnkElatWkVZWdmQ942kivro+LsphemUFGVQVtWq9fC6NLcH2LG/kY/MLxozLXeDkZXuIT/Ly75BjMOzbZtDde3HnHhmckF6ny2JHpeDr105nyvOmz7ge4qIiIiIDNQJx+AtW7aML3zhC3z2s5/tsf22227jH//xH1m5ciUbNmxgzZo1/OY3vxnSvpF0uGsGzckF6TS2BXhlWxV1LX4Ks1NOcObY19QWICfDc8z97+yuw7Jtzpk/cRSrio9Zk7N4Z089v/jjtgGdF4lY+AIRpg5ibOJpcwa/LIOIiIiIyECcMOAtWrSo17aGhgbee+89HnzwQQAuu+wy7rjjDhobG7Fte1D7cnMH1jWwprGTYCjS7+N3H24hzeskJ8PD1MLoNPUVte1JH/C27Krlnsd28JmLZnPxMdb+21fZSmaqi+K81FGubvQtWVBEbZOP1o7ggM+dMzkrtsadiIiIiEgiGtQsmlVVVUyYMAGHwwGAw+GgsLCQqqoqbNse1L6BBrx/+/1Wapt8Azpn3rQcDMMgOz062UVr58Df5I81r22vBuCR5/eQk+5h0dzCXsccqGplRnHWuFgXcF5JLt9P4nGGIiIiIjK+jdllEq77+CkEBtCCBzBnag4FOalk50RbqiKGQUFB8i463e4LseNAAyvOKaGsqpX/euI9phRnMX/mkUXBO3whqho6WXbm1KT+Xgw3fa8kUehZlESi51ESiZ5Hibd4PYODCnhFRUXU1NQQiURwOBxEIhFqa2spKirCtu1B7RuoGRPSsawBTpISjlBXF509M8XjpLq2Pfb1QFXUd9DhC/Xr2Mw0NxNzR7/746vvVhGO2JwxO58VZ03hX/9vC3c8sInvfu50JhVEx5LtLGsEYEKWd9Dfi/GmoCBD3ytJCHoWJZHoeZREoudR4m2kn0HTNMjL63tuiEEFvLy8PObNm8cTTzzBypUreeKJJ5g3b16sm+Vg942mjFQXbf0MaB/W0OLnB/+9qd/HO0yDn3/zPNJT+rcg93B58/0a8rO8TC/KwDAM/unTp/LD/9vCr9Zv5/arz8TrdnKgMroe4PQifcolIiIiIjLWnTDg3XnnnTz77LPU19fz5S9/mezsbJ588kluv/12br31Vu69914yMzNZt25d7JzB7htNmanuQU20AVDd2AnAZ5bNprgg7bjHVtZ38PDzeyiramX+jJGdoGP7/gbaOoN8ZH4R7b4Q75U1sfzsqbGxdfnZKVy38mTu+t07/H7jXr64fC77K1uZmJtKqnd0w6eIiIiIiAy/Ewa873//+3z/+9/vtX3mzJn88Y9/7POcwe4bTRmpLuqaBzZJS7eGVj8Ap83OJ/8Es3DOKMrk4ef3cKC6bUQDXkOLn//48w4s2+aMOYW8u68++ufSnlP0l07NYfnZU3lqUzn5WV72V7Um9eLmIiIiIiLjyZidZGWoMtPc7OvqnjhQ9S1+TMMgJ/PYa8t1S/E4mZibSlnV4O7VH7Zt8z9Pf0AgFMG2oy15W/fUk53uZtrE3l0vr1wyg/Ladta/vB+AGcWZI1abiIiIiIiMnnEb8DJS3bR1BrFsG3OAywM0tPjJyXDjMM1+HV9SlMGu8ubBlNkvW3bVsfNAI5+5aDaPv1bGpvdq2FHWyDknTejztbmcJv+8aiEHqlp5Z089Z580YcRqExERERGR0TNuA15mqgvbji4TkJHqHtC5Da1+8jK9/T6+ZEIGb+ysoaU9QFb6iVv9BmpvRQtup8myMyZTUdfOK9uqADh1Vv5xz5telMn0IrXeiYiIiIgki/41QSWh7lDX2jnwmTQbWvzkZQ0g4HWFqLLqoU+V+scX9/L462U9ttU2+SjMScE0DM4ojS5k7naZzJuWM+T7iYiIiIjI2DFuA15manTWyLYBzqQZsSya2gIDCnhTJ6RjMPSAZ9k2L22t4C+vHqChxR/bXtPUyYSuxdvnTcshzetkwfQ83C7HkO4nIiIiIiJjy7jtopmR1t2CN7CA19QWwLLtAXXR9LqdFOWn8c7uOlI8Pb/llmXT4Q+Rne5h2RmTj3udumYfvkAEgCffOMgXLinFsmzqmn0s7OqO6XSY/MvnzyBNyx6IiIiIiIw74zbgZXZ10WwbYBfN7pazgbTgAZxckstzmw9R/sKeYx4zf0ZurCWuLwe7WgBnTcrib9sq+YfF07CxCUdsCnOOLNdQlHf8tflERERERCQ5jduAl57iwgDaBtiC170G3kBa8ABWL5vFyvNKem03DIMOf4hv/8ff2fxBLf9wTu9juh2sacNhGlxz2Tz+5T838fK2SuZOzQY4bjAUEREREZHxYdyOwTNNg/RU14AnWYm14A0w4BmGQarX1eu/FI+T/KwUZk7K5K33a497jYPVbUwuSKcwJ5XpRRm8f7CRmqboYu1Ht+CJiIiIiMj4NG4DHnSthTfASVYaWv1kprmHfQKTM+dOoLy2nerGzj7327bNweo2pk1MB2DutBzKqtoor2nD5TTJzhj+5RdERERERGRsGdcBLzPVNeBJVhpaBrYGXn8tKi0A4Nm3DrGzrLHHfwer22ho8dPhDzNtYnTJhbnTcohYNm++X0thdsqAF2sXEREREZHkM27H4EG0Ba+8tn1A59S3+JkyIWPYa8nN9FI6JZuX3qngpXcqeu2fOSka7KZ13XvWpCycDgNfIExh1zg8EREREREZ38Z1wMscYBfNpzeVU9Pk46MLJ41IPV//+AIq6zt6bd+yq47nNh/CNAwmF0RnyPS4HMwszmLXoWZNsCIiIiIiIsA4D3gZaS46A2HWv7wPiC4kjg3R/9nR/9vR8W/+YIRXt1exaG4hHztzyojUk57iYs6U3q1xc6ZkM6M4k5aOYI+xf3On5bDrULMmWBEREREREWCcB7ySiZk4HQZPvVGOYUB0GJuBaQBGdOZLgyP/P6O0gP/vspMwzdEf73b2SRN6bTt1Vh5/ee0A0yYOf5dREREREREZewzbtu14FzEYDQ3tWNaYLH1YdfpDpHpd8S5jXCkoyKCuri3eZYjoWZSEoudREomeR4m3kX4GTdMgLy+9730jdlcZFQp3IiIiIiLSTQFPREREREQkSSjgiYiIiIiIJAkFPBERERERkSShgCciIiIiIpIkFPBERERERESShAKeiIiIiIhIklDAExERERERSRIKeCIiIiIiIklCAU9ERERERCRJKOCJiIiIiIgkCWe8Cxgs0zTiXYKMY3r+JFHoWZREoudREomeR4m3kXwGj3dtw7Zte8TuLCIiIiIiIqNGXTRFRERERESShAKeiIiIiIhIklDAExERERERSRIKeCIiIiIiIklCAU9ERERERCRJKOCJiIiIiIgkCQU8ERERERGRJKGAJyIiIiIikiQU8ERERERERJJEwga8pUuXsnv37niXIeNUS0sLp5xyCnfeeWe8SxEB+vc7Ub83ZSQFg0F+/OMfc9FFF7F8+XKuvPJKnn/++eOec/jwYX7/+9+PUoUy3uh3nsRTIr9XTNiAJxJPTzzxBKeeeipPPvkkwWBwQOdaloVt2yNUmYhIfNx+++1UV1fz5JNP8vTTT3PXXXexdu1a3nrrrWOeU1FRoYAnIkkpkd8rJnzA+/Wvf80nPvEJrrzySlatWsX7778f21daWsp9993HJz7xCZYtW8YzzzwTx0olmaxfv56vfe1rlJaW8sILLwDwq1/9ihtuuIEvfOELLF++nG9+85u0tbXF9n3rW9/i6quv5tJLL6W1tTWe5UsS+/An1voEW0ZDRUUFTz31FLfffjsejweAOXPmcN1113H33XcDcP/993P55ZdzxRVXsHr1aizLYu3atezbt4+VK1fyrW99K54vQZKY3itKPCTye0XniF15mFx55ZVcffXVALz++uvcdttt/OEPf4jtT09PZ/369WzZsoUbb7yRSy65JF6lSpL44IMPaG5uZvHixdTV1bF+/XpWrFgBwJYtW/jzn/9Mfn4+3/3ud7n33nv5zne+A8C7777Lo48+Sm5ubjzLFxEZdrt372bq1KlkZ2f32L5w4UJ++ctf8thjj7Fx40Yefvhh0tPTaWpqwjRN1qxZw7p163j00UfjVLmMB3qvKKMt0d8rJnzA27FjB/fffz8tLS0YhkFZWVmP/ZdeeikQ/UemtraWQCAQ+3RRZDD+9Kc/sXLlSgzD4GMf+xh33nknNTU1AFxwwQXk5+cD8MlPfrJHv+vzzz9f4U5EktKJuhK9+OKLfOYznyE9PR2AnJyc0ShLBNB7RRl9if5eMaEDnmVZ3HDDDfz2t7/l5JNPpqamhvPPP7/HMd0/oA6HA4BwOKwfWhm0YDDIE088gdvtZsOGDQCEQqF+ffqclpY20uWJ4HA4sCwr9nUgEIhjNTJezJkzh/Lycpqbm3u04m3dupXS0tI4Vibjnd4rymgbC+8VE34MXjgcpqioCIDf/e53ca5Gkt0LL7zA9OnTeeWVV9i4cSMbN27k17/+NY899hgAL730Eo2NjQA8+uijLF68OJ7lyjg0depUtm/fDsDf//536uvr41yRjAeTJ09m+fLl3H777bEPFXbv3s19993HN77xDS688EIefvhh2tvbAWhqagKiXeO6t4mMFL1XlNE0Ft4rJmwLXjgcJiUlhW9961t88pOfJDs7W32mZcStX7+eyy+/vMe20047DcuyePPNN1m0aBE33XQTNTU1zJo1i1tvvTVOlcp40/2J8w033MCtt97Kb3/7WxYvXkxxcXG8S5Nx4raC8NSmAAAgAElEQVTbbuNnP/sZl156KS6XC4/Hw/e+9z3OOussbNumpqaGVatW4XQ6SU1N5aGHHqK0tJTp06dz2WWXMWPGDP793/893i9DkojeK0o8jIX3ioadgPO519bWsmLFCl577TW8Xm+8yxEBorMfdXZ2xgbKiowW/U4UEelJvxclESXKe8WEa8H7zW9+w+9+9zu+853v6AdWRMY9/U4UEelJvxdFji8hW/BERERERERk4BJ+khURERERERHpn7h30WxqauLb3/425eXluN1upk2bxtq1a8nNzWXr1q2sWbOGQCDApEmT+MlPfkJeXh4HDhxgzZo11NXV4XQ6WbBgAbfddhter5dgMMj111/Pjh07ANi0aVOcX6GIiIiIDNZwv1d8++23ueuuu2htbQXgox/9KN/+9rcxDCPOr1RkeMS9i2ZzczO7du3i7LPPBmDdunW0tLRw5513cskll/CjH/2IRYsWce+993Lo0CF+9KMfcfjwYVpbWznppJOwLIt/+qd/Yvbs2Xz9618nHA7z5ptvkpOTw5e+9CUFPBEREZExbLjfK+7evRu3201JSQnBYJAvfvGLrFq1iiuvvDLOr1RkeMS9i2Z2dnbsBxZg4cKFVFZWsmPHDjweD4sWLQJg9erVPP3000B0PZ6TTjoJANM0OeWUU6isrATA6XTykY98hIyMjFF+JSIiIiIy3Ib7veKcOXMoKSkBwO12c9JJJ8X2iSSDuAe8o1mWxcMPP8zSpUupqqrqsb5Tbm4ulmXR3Nzc4xy/38/69etZunTpaJcrIiIiIqNouN8rNjQ08Mwzz3DBBReMdOkioyahAt4dd9xBamoqn/vc5/p1fDgc5qabbmLx4sUsW7ZshKsTERERkXgazveK7e3tXH/99Vx99dWx1j6RZBD3SVa6rVu3joMHD3LfffdhmiZFRUU9mssbGxsxTZPs7GwAIpEIN998M1lZWXz/+9+PV9kiIiIiMgqG872iz+fjuuuu49xzz+Xqq68e1dchMtISogXvZz/7GTt27OCee+7B7XYDMH/+fPx+P5s3bwbgkUceYfny5UC0ef7WW2/F4XDwwx/+ULMeiYiIiCSx4XyvGAgEuO666zj11FO54YYbRv/FiIywuM+iuWfPHi677DJKSkrwer1AdGDsPffcw9tvv81tt93WY+rb/Px8XnrpJa699lrmzJmDaUYz6umnn85tt90GwCc+8QlqampoaGigoKCAJUuW8MMf/jBur1FEREREBme43ys+9NBD3HnnncyZMyd2j+XLl3P99dfH5fWJDLe4BzwREREREREZHgnRRVNERERERESGTgFPREREREQkSSjgiYiIiIiIJAkFPBERERERkSShgCciIiIiIpIkFPBERESO49Zbb+XnP/95vMsQERHpFwU8ERERERGRJKGAJyIiIiIikiQU8ERERI7y3nvvcdVVV3Haaadx4403EggEAGhpaeHaa69l8eLFnHnmmVx77bVUV1cD8NRTT/Hxj3+8x3UefPBBrr/++lGvX0RExjcFPBERkS7BYJCvf/3rrFy5kjfffJPly5fz7LPPAmBZFh//+Md58cUXefHFF/F4PKxduxaAZcuWcfjwYfbt2xe71oYNG7jyyivj8jpERGT8UsATERHpsm3bNkKhEF/84hdxuVwsX76cBQsWAJCTk8Mll1xCSkoK6enpXH/99bz11lsAuN1uVqxYwV/+8hcA9uzZQ0VFBRdeeGHcXouIiIxPCngiIiJdamtrmTBhAoZhxLYVFxcD4PP5WLNmDRdeeCGnn346n/3sZ2ltbSUSiQBw1VVX8fjjj2PbNhs2bGDFihW43e64vA4RERm/FPBERES6FBQUUFNTg23bsW2VlZUA/PrXv+bAgQP84Q9/4O233+ahhx4CiB27cOFCXC4Xmzdv5oknnuCKK64Y/RcgIiLjngKeiIhIl4ULF+J0OvnNb35DKBTi2WefZfv27QB0dHTg8XjIzMykubmZu+++u9f5V155JWvXrsXpdLJo0aLRLl9EREQBT0REpJvb7eZXv/oVjz32GGeddRZ//etfufjiiwH44he/SCAQYPHixaxatYolS5b0On/lypXs2bNHrXciIhI3hn10PxQREREZNL/fzznnnMNjjz1GSUlJvMsREZFxSC14IiIiw+Thhx9mwYIFCnciIhI3zngXICIikgyWLl2Kbdvcc8898S5FRETGMXXRFBERERERSRLqoikiIiIiIpIkFPBERERERESShAKeiIiIiIhIklDAExERERERSRIKeCIiIiIiIklCAU9ERERERCRJKOCJiIiIiIgkCQU8ERERERGRJKGAJyIiIiIikiQU8ERERERERJKEM94FDFZTUweWZce7DBmH8vLSaWhoj3cZInoWJaHoeZREoudR4m2kn0HTNMjJSetz35gNeJZlK+BJ3OjZk0ShZ1ESiZ5HSSR6HiXe4vUMqoumiIiIiIhIklDAExERERERSRIKeCIiIiIiIklCAU9ERERERCRJKOCJiIiIiIgkCQU8ERERERGRJKGAJyIiIiIikiQU8ERERERERJKEAp6IiIiIiEiSUMATERERGQl2JN4ViMg4pIAnIiIiMswcbdvJ31iEs3lTvEsRkXFGAU9ERERkmHlqn8Cw/KSU/0e8SxGRcUYBT0RERGSYuRs2AuCpfRwjWBfnakRkPFHAExERERlGRqgFZ+tmAoVXYNghvBW/jXdJIjKOKOCJiIiIDCNX098w7Ai+KdcRzFlCSsWDYFsAZG77HKn7fhjnCiHj3S/jrnsq3mWIyAhQwBMREREZRu6GjdiONELZZ+Ev/iwOXxnOtncx/ZV4av9C2v51uOuejlt9RrgVb816PNV/jFsNIjJyFPBEREREhpGrYSPBnPPAdBPMvxgAd/2zuBpfBCDimUTGzutxtG2PS32mvxIAZ5zuLyIjSwFPREREZJgYgVqcvv2Ecs4HwHYXEMo8DXf9c7gbXsByF9Jy+p/BjpD7xrlkbbmc9J1fw3voP0etRtN/GABHxx6I+EbtviIyOhTwRERERIaJs+1dAMKZC2PbgnkX42x5C3f98wTzlhJJL6XxvK10zPweZqAWT+0TpH9wC1jBUanR4a8AwMDC2f7+qNxTREaPAp6IiIjIMOnu9hjOmB/bFsy/GAMLM9xMMG8pALYrl84Z36HpI5vomHMnBjZmoGpUajQDFUfqbd8xKvcUkdGjgCciIiLST+kf3Iy79q/H3O9se5eIdyq2Kye2LZy1CKvr62Du0l7nRLyTAHB0dZ0caaa/AstdiOVIj9s4QBEZOc54FyAiIiIyJtgRvIf+C3f9szQWXAKGo9chzvbthDMW9NxoOAhM+DiOjl3YnsJe51jeKcCRsXEjzRGoIOKdDIZLE62IJCG14ImIiIj0gxFqwsDG4SvDXftk7wMiHTg69vQOeED73J/RckYf53CkBc/0V/S5f7iZ/gos72TCGfNxtu8E2x6V+4rI6FDAExEREekHM1gX+3PqwV/12u9sfw8Dm3DGKb1PNozof31xpGE5s0eni6ZtY/oriHiKCWcswAy3YPrLR/6+IjJqFPBERERE+sEM1gMQKLgMV8smnC2be+w/MsFK7xa8E7G8k0eli6YRbsWMtHe14EWDqKvptRG/r4iMHgU8ERERkX7obsHrLPkWtuHA86Fums7Wd7GcWVjeqQO+dsQ7KbZ8wUjqnkHT8k4inHk64dRZpBz6rxG/r4iMHgU8ERERkX4wQtEWvEjKDMJZZ+Jq3Hhkp23han492np3rK6Yx2F5J2MGRr4Fr3ucX8Q7CQwT35RrcbVuwdn85ojfW0RGx4AC3t13301paSm7d+8GoLS0lMsvv5yVK1eycuVKdu3aFTt248aNLF++nIsvvpgbb7wRn8/Xr30iIiIiiai7i6btyiWYuxRn61aMYAMAnqqHcXZ8gL/4c4O6dsQ7GTPUBJGOYau3L92thJYnOrGLv/izWM4sUsr/Y0TvKyKjp98Bb+fOnWzdupVJkyb12P7II4+wYcMGNmzYQGlpKQAdHR384Ac/4L777uO5554jLS2NBx544IT7RERERBKVGazDcuWC6SSYdyEGNu7GlzHCraTvuY1Q1pkEilYP6tpWbC28ke2mafoPY2NgeSZGNzjT8Rd/Dk/tBoxQy4jeW0RGR78CXjAYZO3atdx+++39uugrr7zC/PnzKSkpAWD16tU89dRTJ9wnIiIikqjMYAOWOx+AcOYZWM4sXA3Pk/7BP2MGa2kvvQuMwY1+sTyTo/cYholWMrZfg6fit33uMwOV0XBnumLbwpmnY9hhzGDNkO8tIvHXr4XOf/nLX3LFFVcwefLkXvs+//nPE4lEOP/88/nmN7+J2+2mqqqK4uLi2DHFxcVUVVUBHHffQOTlpQ/4HJHhUlCQEe8SRAA9i5JYkv95bIS0iUde58SlpBzuClIL1pIz64LBXzplLmyBbFcDDOX7GAlCzZ/wel1QcH3v/VY1pE/p+XcVji60npvqG9q9E0zyP4+S6OL1DJ4w4L3zzjvs2LGDm2++ude+l156iaKiItrb27nlllu45557uOmmm0ak0A9raGjHsrQwp4y+goIM6ura4l2GiJ5FSSjj4XnM6aghkjaX1q7X6cn6GJmHH6N99lp8E2+Eobx+K5N8DDrr9tKZOfjrODp2k2tbBNrrY3UeLbdxO6G8ZbQdtc/Z6SUHaKk7RNBIjr/D8fA8SmIb6WfQNI1jNnidsB/BW2+9xb59+1i2bBlLly6lurqar3zlK7z66qsUFRUBkJ6ezqc+9SnefvttAIqKiqisrIxdo7KyMnbs8faJiIiIJCozWBfrogkQKPpHGs7bga/kxmG4uBvLXTjkLpqOzn3Ry4V7j6czA9U4gjWEM3suxG658qL7Q41DureIJIYTBryvfvWrvPrqq2zcuJGNGzcyceJEHnjgARYsWIDf7wcgHA7zzDPPMG/ePACWLFnC9u3bKSsrA6ITsaxYseKE+0REREQSkhXGCDX1CHgYBlbKwNe8O+YtvJNwDFPA62vCFGfrNgDCGQt73tcdDXjdM4Iej6vxZdy1fx1SjSIysvo1Bq8v+/fvZ82aNRiGQTgc5rTTTuOGG24Aoi16a9eu5dprr8WyLObNm8f3vve9E+4TERERSURGqBEDG8tdMGL3sLyTcHTsGdI1YgGvjxY8Z1t3wJv/oZNSsc0UzNCJA176ru+CHSZYeOmQ6hSRkTPggLdx45FFPR9//PFjHnfRRRdx0UUXDXifiIiISKIxQ91r4OWf4MjBszxFuBpfGdI1HJ37gb67aDrb3iWcOhPbmdn73q5cjBN00TSC9Tjbd8S6dIpIYhrcXL4iIiIiScwIt+JseTv2dfci5yPZghfxFEeD2XEWO0/deyfeit8cc3+sBS/SAVaoxz5n6zbCGaf2eZ7lzsM8QRdNV9PfotcONYIVPu6xIhI/CngiIiIiXUx/FZlb/5G8l2aQ8+YFuBqiPZfMYB1AzzF4w8zyRCedc/gr+z4g3E5q2c/xVvxv3/sjfkz/oehi7ERDajcj1ITDf/CYAc925Z2wi6a7q3XRwD5ha5+IxI8CnoiIiAjgbH6T7E3n4258Ed/Ua7Fc+aQcfgCIdk+EkW3Bs7zRdYLNQN/rA7sbX8awQzg6doH9oaWibAuHrwwDm3Dm6dGaw82x3c627QC9ZtCM3duVg3GCgOdqfAXbiC6Q3h14RSTxKOCJiIiIABnvfQ1MD01nvUDHnB/in/R53HV/xfRXYgbrsDGwXTkjdn/L0x3w+m7Bc9c/G90fbsUMVMS2eyofJu+V2bgbo62Noa6AZ4aOCnixGTSP0YLnzut7mYSIj5QD/4ar8WWcnXsI5kXnUFDAE0lcCngiIiIiRNeJCxRcSiT9JAB8k76EYUfwVvwvZqgB25ULhmPE7h/p6qJp+qMteKav/EhLnW3jrn82doyj/f3Yea7WzZjBOtJ2fx+AcNYZQM+ZNB2d+7BcedjH6GJqufIwQs29xta5G54nfe//I3vL5QAEJn4iWlvXpDMikngU8ERERERsCyPc1mOGSSt1OsG8ZaQcvBt37ZMj2j0TAGc6ljMTM1CJo30Xua8uwNXVKudofw9HoALf1Oujh3bsip1m+sqxzRQMO4zlyiWSMg3oGfDMQFWshbAvlis3OrbuqG6dAA7fQQB8k68hULiSYN7S6PXUgieSsBTwREREZNwzwq0Y2Niu7B7b20vvIpS3FAyTUPZHRrwOy1OEI1CFq2UTBjbOrpa67u6ZgYmrsNwFPVrwHL5ygnlL6Zz6NQKFV2I7swAwQ0cHvGoinonHvK/dtfTBh7tpOnxlWM5M2uf+G62n/h+2KxfbcGAo4IkkrEEvdC4iIiKSLLpnnOwOR90iabNpPfX/Rq0Oy1OMGaiMLUpudrWguVrfJpw6E8tbRDhtHs6OroBn25j+QwTzLqCj9MfRbeF2oHcLXjhjwbHv644GPCPYAGlHtpu+g1jeaWAY0Q2GieXKjy0bISKJRy14IiIiMu51hyGrj0XAR5PlKcL0V8UmRXH4D0X/37mfSOpMACLpc3G0R2fSNEKNmJF2LO/UIxdxpEVb2boDnhXGDNZiHbcFL7q0woeXSnD4yomkTO15rLtAXTRFEpgCnoiIiIx73d0ZP9yCN9oi3mLMYDXOth0AOHyHoq10vgNEUqYDEE6bixlpwwxU4PCXR8/rGncHgGFgO7Nis2iawToMrNg6e32xurtoHr3YuW3j8B3seW2iS0Uo4IkkLgU8ERERGVeMYAMZ734J86gFxWNdNF3xDXiWpwjDjmBYnViuHEx/OUaoPtpKlxoNeJH0eUB0Jk3T1x3wPtTK5syKteB1r6t33IDX3UXzqDF4Rqg+WkevgJevgCeSwBTwREREZFxx1z2Ft+ZRvJW/jW3rnj0y/l00J8X+HMxfgRluwdm6FaBHCx6As21HrAtnjy6agOXKPirgVXdd+9hdNHGkYpspPbpoOnxl0ft6e7fgGRqDJ5KwFPBERERkXHE3vQKAp+bPsW1muLuLZnaf54wWyxttZbNNL8G8ZQC4m/4GQCRlRnSfO49w2lzcjS9FJ0FxZvaa/dN2Zse6nfanBQ+iSyX0DHh9dP+kq4tmpA0ivkG9RhEZWQp4IjKsjEANWW9fpU93RSRunM2bwI70vdO2cTW9im24cbbvwNGxBzh6Fs34tuBFutaqC2fMJ9LVJdPV8DI2Ro+gFcxbhqv5dZwdu3q13kHvLpo25gnX8bPceRjBI100u2fwtPqYZAXQTJoiCUoBT0SGlavlLdwNL+Bq2RzvUkRkHDI7D5Dz1sWkHLq/7/2+Mhz+w/imfR0AT+0GAIxQC7aZCqZr1Grti+0uwDY9hDMWEukKbs62rdFFyh3e2HHBvGUYVgBX4yu9xt8BWK6sHl00LXchmMdfHct25X2oBe8glisX25nR89qxgKdxeCKJSAFPRIZV9wxs+odfROLB4a8AwFvxW7DtXvvdTa8C4C9aTSjrTNw1XQEv3IIV5wlWooWYtJz2KB0zvtMV9rwY2LHWvG6hnHO79lmxIHi06CyaR1rwTtQ9E7q6aAaqYt83h7/3DJoQnWQF9HteJFEp4InIsDJC0S47RrA2zpWIyHjU/TvI2b4jtlh4dHsTRrAOV9PfsFz5RNLmEij4B1xt26JryYVb475EQrdQ7hJszwQwDCLeKcCR8XcxjhRCOecCvbtQQlcXTasTrCCOQPXxJ1jpvm/ehTj8h3A3PAdEu2hGUkp6Hdfdgtf9vRaRxKKAJyLD6kgLngKeiIy+7lYlG7PHLJlZ73yavJdn4aleTzB3STQ8dS0cbvorMMItCRPwjmaldAW8D7XgAbFJWPpsZetqjTTCrf1uwfMXrSbinULq/h9DuB2H71Cf4/vURVMksSngiciw6h6/YQYU8ERk9HWHjmDhFXiq/whWAKwgzta3CWedQTjzdALFnwXA8kYnNHF0Bbx4L5HQl+4lCqyU3gEvMPGTBPJXEMo+p9e+7rBqBuswQ/X9asHDdNM5/Z9xtWwm97XTwQ4RzP1o7+Mcadhmqn7PiyQoBTwRGVbds2eqBU9E4sEM1mG5cvEXrcIMNeFq2Yyz/X0MO4Rv6jdoPus5gvkfA46sOWf6K6KTrCTCGLwPOV4LnuWZSOtpv8fuGhN3tO6A5+jY3XXsiVvwAPzFn412yzQctJzxJKH8i/o8LuKdFFuDb6RkvX0VKWW/GNF7iCSj40+nJCIyQLEWPAU8EYkDM9iA5S4glL0YAFfz36MzSALhjFN6HGt5JmAbDsxABWaCdtEM5l2Mq+n12OLm/WV1rYvn7Pgg+nV/WvAATA9NZ7+MbXrAkXrMwyJps3F07hlQTQNhhFqiMzI3vkQo90LC6XNxtr9HOGMhGMaI3VckGSjgiciwMkPRNZQU8EQkHoxgHZYrP7YYuLP5DayUaViODCKpH5qoxHBguSd2ddFMnElWjhbOPJWW0x8d8HndryWl/F4g2uLW73NdOSc8JpI6C3fDC9H1Bg0H2BGcrVuxTReRDwXpwXB07OoqxiZjx1fACuP07cc/8dO0zfsFONOHfA+RZKUumiIyrGJdNENNYIXiXI2IjDdmqC62EHco+xxczZtwtm4lnLEAjN5veyzvJByd+zDsEFYCBrzBsryTiXgnE0krpW3uT4mknzSs14+kzsawApj+wzhb3yHv5RnkvHkh2Zsv63N5ioHq7lraMfsOnB27wXDQOeU6PNV/IuetizBCzUO+h0iyUsATkeET8WNG2ol4JwOaYU1ERp8ZrIut0xbKXowZbsHV8lav7pndIt5JONrfB0jIMXiDZTszaFzyHs1nPot/ylf7DLdDEUmbBYCjYw/u2icwwq34J1yFGW7GCLfGjjPCbWTsvB7TXzmg6zs7PsA2PfimfY3Gc96k6Zy/0zH3LlpO+xOOjt1kbv8yWOFhfU0iyWJAP+133303paWl7N4d/VRl69atXHHFFVxyySVcffXVNDQ0xI4d7D4RGbu6u2dGusaKHN1NM/uNj5K6/ydxqUtExgkrjBlqik3jH8o5MrtkOPPUvk/xFGNG2gCwE3AWzUQVTp0NgKNzbzRAp88nWHg5AGbgSJhzNr+Bt/IhUg7ePaDrOzp2EUmdDYaDSPpcMN0AhPIvon3uz3E3vEDqgXXD9GpEkku/A97OnTvZunUrkyZF+3BblsUtt9zCmjVreOaZZ1i0aBE//elPh7RPRMa27kVvw11dgWIBL+LH1fYOzrat8SpNRMYBo2uSp+4WPMs7jYg7OrlIOOMYAe+osWnJ1EVzpNnuAixnJs6OXThbNhPOOhPLE1124uiA5+jcDxBdkzDS2e/rOzt2EU6b0+c+/+QvEsw+F3f980N4BSLJq18BLxgMsnbtWm6//fbYth07duDxeFi0aBEAq1ev5umnnx7SPhEZ27oXOQ+nzwOikx0Asam0zUBVfAoTkXGhu1t4dwsehkEo5xxs00MkrbTPc7pDCZCQk6wkLMOITrRS+yRmpJ1Q9tlEupZiMP1Hftc7fNGAZ4ab8VQff7IYR8cevIcegIgP03cw1hukL5G02Th8B4fhhYgkn37NovnLX/6SK664gsmTJ8e2VVVVUVx85Jdibm4ulmXR3Nw86H3Z2dn9LjwvT7MnSfwUFGTEu4TE1BH9dDZz8iLYCZmuFijIgFD0TZcrVKPv3TDT91MSSdyfx0gHAFmF06K/ewDO/DG0XU3BhNxjnDQbtkf/lFNYDFn6meq33HlQ9jYAmTOWQkr0/V2mo/7I93/nIcg+BewImdW/hoXXH/t6794MNS+SkWYCNmnFC0k71jOVPxsq6inIMcGZ1uchcX8eZdyL1zN4woD3zjvvsGPHDm6++ebRqKffGhrasayhz9IkMlAFBRnU1bXFu4yE5G04RAZQ788jz5GGr/EQHXVteKvfJwOwfVXU17YM+2D/8UrPoiSSRHgePbXlZAKNHalEYrUUgbsIjlGb6csmr+vPDW1OrKB+pvor1ZxGGtEW04bOfPCFyHPlEWgso73r+53TsptI2lyCOeeRsevbNJa9TSRtdq9ruZpeJ7vmRQDsd76LATRGph3199iTJzIx+nd9aCeRrl4jR0uE51HGt5F+Bk3TOGaD1wnfZb311lvs27ePZcuWsXTpUqqrq/nKV77CwYMHqaw80se6sbER0zTJzs6mqKhoUPtEZGwzQw3YGNiuHCx3QWwMnsMf7UZj2OHYGBkROYoViHcFScEMdXfRzO/3OZZnIjbRhbM1Bm9guoNaKOvs2OLjlqf4yBg8O4Kjs4xI6gyCBcsBcDW8EN1nhXG0vYun8mGczZtI3f9jLHchHTP+BcPqxDYcRFJnHvveKdMAcPjKRubFiYxhJwx4X/3qV3n11VfZuHEjGzduZOLEiTzwwANcc801+P1+Nm/eDMAjjzzC8uXRH9758+cPap+IjG1msCG6QK7hwHIXxsbDmEeNk9A4PJEPsYLkvVJK6t610a8jfhztu+Jb0xhlBOuxDUe/FuqOMV3RkGe4wPSOXHFJKNwd8LLPim2LeI8EPNNfiWEHiaRMx0opIZwyA3fDRrBtsrdcSu4b55G581py3roYd+NLdE67gc5p38By5RFJmR6bObMvkZSS6D185SP3AkXGqH6NweuLaZrcdddd3HbbbQQCASZNmsRPfvKTIe0TkbHNCDVguaKdnSx3YWxwvcN3EMuZhRluwRGoJnKM9ahExiOHrxwz1EjqgX8jnHU2qWX/hqv5DVpO+V+CE66Kd3ljihmsx3LlD7gbuOUpxrBCsVYo6Z9I+nza5/wr/qLPxLZZnmJcLVuAIxOsRFJnABDKW4q36mHc9c/ian6Djuk3E5j4KRwdu3F07sU35RpwpNC64EEMO3jce9vuAmwzRROtiPRhwAFv48aNsT+ffvrpPP74430eN9h9IjL2pL/3LSzvlGgLXnCaJ+oAACAASURBVPf05O5CXM1/B6IBL5x1Ju6G5zED1fEsVSThmP6uFgjTS9bWT2EbTsJppWTu+CrNniLC2YvjW+AYYgbrYr+DBiKSUoJh+UagoiRnmPimfaPHJstThBmqByuAo/MAAJGUaMAL5i0j5fB/k/7BTVjuAjpnfAdMT68xdKG8C/pxb4NIytTYEAAROUIzHYjIkJi+clIq/ofU/etwdOw60oLnKcQINWIEajFDDYSyol141EVTpKfuFoi2k+8lkjKd1gX/Q/Oip7E8RaTv+k6cqxtbzGDdkSUSBqBjzr/Sesr/jUBF40/3uoJmoAqHbz+24cbyRmfXDOUuwTYcOPyH8U36MpieId0rkjKtxxAAEYlSwBORIfFW/6HrTzaOYA2WOxrwQrkfxcAmtfxeIDoY33LlqwVP5EMcvnJsw0lgwpU0nreN4IQrsN15BHPOwwzUxru8hOZs+jtpe/5f7OtowBt4C57lLepzZkcZuKPXwnN07ieSWgKGAwDbmUk460xsw4l/yjVDvpflnYpDY/BEehn0GDwREWwbT9UjBLM/QiT9JFIO/zd2VwteKPscIt6peA/9JxD9pNXyTFQLnsiHmL4yLO/k2JvgbrYzCyPcEqeqEp/pryBr22cwQ434plyD5Z6AGajGck+Id2njWndrnSNQiaPzQKx7Zrf22XdEx2V7Jg75XpGUEsxwM0aoGdul2dhFuqkFT0QGzdn6Ds6O3QSKVtM5/WYsZxbhtDnRnYaJv+jTmJF2QAFP5Fgc/vLYlO9Hs52Z0Z8fKxyHqhKUbZG670ek7l9H5rtfwAg1A+Bs2YyzfSeG5SOceXqcixzfLE9XwGt/D0fnPiKp03vsD2efTaDo08Nyr/+fvTsPkLuuDz7+/l1z7uzMXkk2N5CDSAjhPgQURMGKQKkHptr68GC1VKSt1fp4gBe1oOXxsaX1wNZbi1VBFEUROUTCFSBADnJfu9lk77nndz1//GZmd7Ozu7OzM7Ob3c/rn2zmd31nM5mZz+/7+X4+Q60SJE1TiOEkwBNCVMzf9VNcxUd2/tU4gYX0XLyD7LBqaoWfXTWEa7Ri+9slRVOIY2jpfdiBEgGe4fVkU+zBeg9pxjL6nyS8+wuEd92GMfAM8df8G67iwxh4Fn3gKWBkyX5Rf64exVVDhPbeCa5JdkF1grlSnOBSYFihIiEEICmaQogp0FK7scMrhnpOaSN7SNnhlZjRs70mzoqSn8HrAtcelY4mxJxkp1FzR4pfVIcrNN1WzAFco7neI5uR/Id/gqsG6bnoZRQ7jRNcSvDgN9EHnkXNdmL7FuAERv8uRR0pCnagHT21i/jJX8SKnlmzS5U9g2envR6HigJ2Ci29H7vh5JqNS4jpJjN4QoiKlVOxbnDdt4mf+l+AVz5bwSk2QBdirisUiCidoukFeKolM3gAOBb+rnvJtl2B62srBsVm9CyMwecx+jdixc6RXnYzQHb+taSWfYjM4v9d0+u4ehOO0YyWfHXMfbTkDlofPZFAh1cltWH7x2l6+hLvRqMQs5QEeEKIiim5o15T4XE4gcXF6nROobqarMMTc52dQskeKfbwGi/Ak0IrHqPvcVSzm+z8a0c8bkXPQnFSaJn9mNFzp2l0YrjUik+RXPX52gfbioIVXoOe2Fp6u+vSsP2jKHaS4L5/RbEGCRz+bxQ7iZLrru3YhJhGEuAJISqmmt2T6jlVqJom6/DEXBfedRvNT56DPvgCAE6JNXiOIQHecP6un+JoDeRa3zTicTN61tDPsv5uzrEb1qAlt4LrjtrmO/oAvp7fkYtdgJ7cTsOWD6HYSQA0+RwSs5gEeEKIytgZVGsQVwI8ISZNSx9ANXsJ7bkTV/Xj+EeX9nf1RgAUSdEEwNf9Wy+404IjHneCJ+IYzbiKD6tx/TSNTkwXq2ENqjWImu0YuSE3QMP2f8QKr2Hg9P/BMZoJdP0UVwsDkkkiZjcJ8IQQFVFNL71lUjN4+X3VnDRvFnObYvV5fzop7MASUEZ/HBfX4OVbAcxlitmPlu3AipxWYqNCruVSzOaLQPXXf3BiWtnhNYDXlmGE525GzRwk/pqvgN5AZtF7AUgveT+AV/BLiFlKqmgKISpSKJQymQAP1YejxyTAE3OeYvZhNp6BlnwVJ7i85D4ygzdES24HGLPyYfyUrwGjU/TE7Gc1eAGentiG2fpGAPydP4Y93yZ1wkexYt66zNSym7w/l99MaO+dMoMnZjUJ8IQQFakowAMc/zzUrFTRFHObavZiNl1MYvU/F2fqRlE0HC0ia/DwvrwDWOExSturRh1HI2YS19eC45tXXIcX3H8X4Vc/Ca0XkDrxH0fsl1z5aQAcoxk1JzN4YvaSAE8IURGl0gDPNw9FZvDEHKeafThGE1bsvHH3c40oqgR4aMltuGoQp0S1USGshjXoiS0E999Fw6sfJzvvKvyv/wH0OSX3d/ztshZczGqyBk8IUZGKZ/B8bZKiKeY2J4tiJ3GNpgl3dfVGSdEE9ORWrPDqkmsVhfBaJWwhvPOzZNv+hMF13wE9POb+jn++pGiKWU3eKYUQFVFzR3HVIGhjf4iW4vraUKX/kJjDlHzRFKesAC+KYsoMnpbYjt2werqHIWYou2ENipPBVf0kTv6/E94I8GbwJEVTzF4S4AkhKqLmjnqzd5NsZOv45qFa/eBkazQyIWY21fQqaLpG84T7Onp0zq/BU6xBtOwhrHy1RCGOZTWeAUBy1W04gfYJ93d8C7w1eG7pFE4hjncS4AkhKuIFeK2TPs7xzSseL8RcpOQDvPJm8Brn/Bo8LV9gxR6rwIqY86zG0+i5aCuZRX9R1v62fwGKa6GYPTUemRDTQwI8IURFlFz3pNffwbAALyvr8MTcNDSDV0aAZ8gMnp5vkWCN0SJBCAAnsKj8ff0LAGl2LmYvCfCEEBUppmhOkuOXZudibis0OS9nBs9L0RwEd3b1eFMznYRf/WRZlQy1xFZcNSAVNEXVFAI8TSppillKAjwhxOS5rldkZSozeJKiKeao4gyeXmaRFdcGOzn5C7kuOLnJH1cHob1fIrTvK8Q2Xozev3HcfY2+x7Aip4Gi1Wl0YrYbmsGTAE/MThLgCSEmTbEGUFyzwhRN7xjphSfmKsXsxVU0XL1xwn0LTdArWYcX3vkZmv54DtipSR9bS4oVx9/xI3LNl4AWJPbM5TS8ciNKibRtLbkDI76Z7PxrpmGkYraSAE/MdhLgCSEmrdIeeABoIRytQVI0xZylmn24eqysCrSFILCSdXj6wNPo6d2E9n550sfWkr/zHlQ7TnLFJ+k79zHSy24i0PnfRLb8zeh9u34KIAGeqC7Vj2M0SYAnZi0J8IQQk6bk+9hVFOBR6IUnKZpiblLMvrLW3wE4hjeDV0mzcy25A4DQ3i+jpvdP+viacG2CB+/GjKzHajwL14iSXPV5Mov+AqPvj+DaI3b3d/0MM3b+pApoCFEOx7dAAjwxa5UV4N14441cddVVXHPNNWzYsIGtW7cCcOmll3LFFVdw9dVXc/XVV/P4448Xj3nhhRe46qqruPzyy7n++uvp6ekpa5sQYuab0gwe+V54EuCJOUo1+8rqgQfDUjTzzdHLpViDaLku0otvABRCe7442WFWnd73JE0bL0JPvEJ66QdGzGCa0bNR7Tha8tXiY1piG3piC5n5107HcMUs5wQWomYOTfcwhKiJsgK822+/nZ///Ofce++9XH/99Xz84x8vbvvKV77Cfffdx3333cdFF10EgOM4fOQjH+GWW27hwQcf5KyzzuJLX/rShNuEEMcBxyLQ8T1cVBz/wspO4Z8nbRLEnDWZGbxigJftIvLSDWjxl8s6TkvuBCDXcgm51ssweh+f4IgacyyiL7wTxRpgYN23yba/a8RmK3o2AMbAMwBo8ZdpfOE6XNUv6ZmiJqzQCrTUjllXoVbMIHaOwIFvgp0e8bCa3oti1rb9TVkBXiQSKf6cSCRQJlg38PLLL+P3+znrrLMAuO666/j1r3894TYhxAznWES2fBB/969JnPxFXF9LZafxzZM1eGLO8mbwykzRzAd4wf13ETh8D+Hd/1zWcVrKS8+0QysxG89CT+9GyU1ftoyW3IJq9ZNccSu5+X86av2hHToJR4+hDzyDltxB09OXodgp+s/4Oa5//jSNWsxmdngVqp2QXniidg4/RGTb3xHe/YXiQ/rg8zT/8VzCOz9d00vr5e74iU98gieeeALXdbn77ruLj//DP/wDruty5pln8vd///c0NjbS2dnJwoVDd/abm5txHIf+/v5xt8VisbIH3tLSUPa+QlRbW1tk4p1mm8Ht8ORfQs9TcOpniJz691T8W+hcDAd7aWsJglr225AoYU6+Fo93dh9a43wC5fzb2QYAenIbKCr+o7+kLdQP4SXjH9e5HxSV5qXrIJSEndCqbIW2N1fhCYxtzNdj34sANJ50GYTH2KftPILJTQS7/h0UB+1PnqEptLhGIxVzwbjvj/Z62AYtxkFoW12/QYm5Y/tuAEL7/o3QKe8DoxH+8C5w0gSzrxKs4ed32d+sbrvtNgDuvfde7rjjDr7xjW/w/e9/n/b2dnK5HLfddhuf/exn65Zu2dOTwHFkWl3UX1tbhKNH49M9jPpyHZofex2KkyWx9ptk578NpvA7CFiNRHDp6dhTLFctJm9OvhaPd45JmzlI0gyTKvPfrlX1ozhZ4qv/hYZtf09q81dIrbhl3GMiR1/BCCyltzcHzmpaUUntf4yUcWE1nkVJ470eIwcfwfC305tsglTpfULB9YQ6H4SB7WQWvYdEMgpJeX2Lykz0/qiai2kB4h0vkNHOqd/AxJzRltyDq/px1SDuQ5ehmr24ioEVuwB9YDs9U/z8VlVlzAmvSVfRvOaaa3jqqafo6+ujvb0dAJ/Px4YNG9i0aRMA7e3tdHR0FI/p7e1FVVVisdi424QQM5Oa2Y+W6yK58jNk299eVnn38TiBpcDQOiEh5opCu4Ny1+B5+zZjRtaTWXw9udYrCB76NjjZcY/RUjuxQiu9v+gN2A1rMAaerXjcU2X0P40ZO2/c9w4zejYKLrgm6aWjWyYIUU2OfwGOFkEfVthHiKpK7MEOLvOWtKhB0ov+F/3nPESu9XLU3JGKqiOXa8IAL5lM0tk5lJ/88MMPE41G8fv9xONe5Om6Lg888ABr1qwBYO3atWQyGZ591vsw+dGPfsQVV1wx4TYhxMykJ7YBYDWsqcr5zNg5uCgY/U9U5XxCHC9Usw+g7DV4APFTvkb81G+CopBZuAE1dxR98IWxD3Ad9ORO7PCK4kNm9Cz0weempaCEmulEy+zDio4/S2JFz8RFITfvyhFjF6ImFAU7vLLYTkSIqkvuxQ4sI9v+TvoufIHkyXdgN7ym+P6mpXbV7NITpmim02luvvlm0uk0qqoSjUb56le/Sk9PDzfddBO2beM4DieddBK33norAKqqcscdd3DrrbeSzWZZtGgRX/ziFyfcJoSYmQqly+3wqqqczzWasBvWen2vhJhDFLMXmNwMntny+uLPhf+DWnofVuzckvur2Q4UJ4VdmMEDrMazCB76NlpqV92DJ33gKQBvBm8crtHM4Gk/wGo8vR7DEgI7vGr6K8yK2SuxB2f+maMetkP5AC+5s2bvdxMGeK2trdxzzz0lt917771jHnfGGWdw//33T3qbEGLm0ZLbcHzzyu7dVY5c0wUED30XHBNUo2rnFWImU/MBXqX/l+xgPr05vW/MfQqpz3Z4KMAzo17l6vCr/wez5VKvP16d/t8Z/Rtx1SBWZN2E++bmvaUOIxLCY4dXEej8EYoVx9WlYJWoAtcBXBQrDmY/dnD5qF3s4Am4KGip2i1TmfQaPCHE3KMnt2GFT67qOc2mC1GcFPrg81U9rxAz2dAMXoXrzrUwjq8N9ZgAL7ztIxg9vwNAj28GwA4PVQa0G04m2/YWjIFnaNj+j/i6f1vZ9SdJTe0hcOi75JpfLzdyxIxjhfIz4jX8oi3mDjW9n6Y/nknD1g+jZvYDYAeXjd5RC+AEltQ0RVMCPCHE+FwXLfkqdkN1y0ibTRcAYPTJOjwxd2jpfbgoOIHKy//bwWVomaEAT8kdJXTga4R2e8sdfEcfwGpYO7JCraIxuP6H9Fy0FVfRMfJpkzXlWDS+fAMoKomT61NhW4jJKKY8S6EVMUVaYiux565ET+3C3/VTtJTXIsHJZ10cyw6dJDN4Qojpo2Y7Ua1BrHB1AzzX14YVXoWv92HU9H5w7aqeX4iZSEvt9KrIqv6Kz2EHlo1I0dQHvR5zRv+TaPHNGP0byY6V6qgFsSKnofc/XfH1J6JmDhF95s20PHoCxsAzJNZ8GSc4Qd8+IaaBHToRV9HQktvLO2AaihSJmU1L7iS28WKanzwXxewntexDqFY//i5vGVvJGTwKAd6umr2mJMATQoxLS3oVNO0qp2gCmE0X4+t9lJY/rCXyyl9X/fxCzDRekZOTpnQOJ7gcNXMQHAsAPe4FeAoukVc+iIJDrm3stWxm7ByMwU3e+tcaCB74OsbARrLt72Dw1P8ku+DPanIdIaZM9WGHVxfTmsejJbbR+nC7LCsQIwT3/l/01A4Sq/6JvgueJrX873BR8B+5H4xGXL10QS07vALVGkAxe2oyLgnwhBDj0vN3Nqu9Bg8gueJTDKz7NlZ4lfeFVYjZzHW9AC80tQDPDi5DcS3UrNdT1hh8ETu4HDOyDiP+AnZgMVbktDGPt6LnoDhp9MTLUxpHSY6Fv+MH5FovJ3Hyv5Bd8LbqX0OIKrIaT8cY2DThTIqv5yEUJ4W/87+HHrTTBPd+mdCu2zC6H5JMlLnGTuLv+hmZ+deSXvZBHP8CXF8LVmQ9imtC+IQxe38WK2nWKE1TAjwhxLi0xHYcPYbra6v6uV2jidz8P8XxL0JxclU/vxATUVO7Kmo2q5j9qJnOiXcccUw3qjVY/GCvVCHlp5CmqcdfwIqsJzv/WgBybW+eoKG414+uFmmavp7fouW6yCx8T9XPLUQtmI1noJrdqJkD4+6nDzwDgP/IL8B10Qc20bTxAhp23EJo9xeJPX8tgYP/VY8hixnCf+R+VDtBduGfj3jcbLnU+6HhhDGPtYa1SqgFCfCEEGNzbYz+J7AaXjPuF8YpX0b11SxdTIjhjN5Hibx0PVgJtMRWmp88j9CuL0z6PI0vvZfoc1dO6phi+4IqzOABqOl9KGYfWnovZuNpZNvfgR08gUz7n497vBNYjO1fiDFQ/QAv0PE9HF8budY3Vf3cQtSC1XgGwISpl0b/UzhaA1pmP/rAUzRufjeKnaH/jPvoueQAdnA5vu7f1GPIYoYIdPzAy56InT/i8VwhwAuPHeA5gaW4ig89VZsCPxLgCSHG5O+8Bz25ncySv6rthVQ/iiszeKL2/EfuJ3D4f2h86b00vnQDipNFT7wyqXNoia34eh5GT+1AzR4ecz81exgl1138u55PxbGmOIPnBJbgoqKl96LHX/LOGTkNJ7CY3gtfxIqeMf4JFAUreg5GfkaiaqwkvqO/IrPgHdISQRw3rMhaXMXw1qWOQc0cRMt2kF56Iy4qjZv/Ei1zkPipd2O2XIKrR8i1vAFf3+Mg2ShzgprpxOh9lEz7hlE3wM3YueRiF0D7G8c5gY4dXlGzCq4S4AkhSnOyhHfdhhk5nez8a2p6KVcx5ENR1IWa6cBVA/i7f4OeeAk7eMKk10AE93+t+LPev3HUdsXso2HLzTQ//hqiL7yj+LiW2oWr6F4VzalQDZzAIrTMvmJxCKtx7DV3pZiNp6Gl94KdnNpYhksdRHGtshqaCzFjqH6shrXjzuAZ+XTmXNufYMbOR8t2kpn/Z5hNry3uk2u5DMVOYpR4TxCzjz7wDAouudbLRm9UfQyc/WtY+OZxz2GHVpVfwXWSJMATQpQUOPQ9tMx+kitvBaXGbxWqT9bgibpQs4cwm15LfPUdJFbeRmbhBrTMwbIDHSXXQ6Dzh2Ta34WrBjH6nxy1T2jPnQQOfRur4RSMgWdRM14xFC21Ezt4Aqj6lJ+HHVyGltyBr+d32P6Fk14j6xotAKhm35THUpT21iQ6/vbqnVOIOrCiZ3gBnuuU3K4PPI2rBrEip5JduAHHaCK56nMj9jGbL8JVdHw9v6vHkMU00+Mv4ioaVsMpFZ/DCq/0brQ52VHbFCs+pRYKEuAJIUrS4y/g+OYNLRauIVf1gaRoijrQMoewA4vJLP0A6eU3YYdWeo+ndpXeP7Gd8PaPEX3urTQ/fgotj56I4qRJLfsgZvRMjP6NKLlump68AKPnEQD0+GasyGnE13ozfYV1OdWooFngBJdhDG7C1/M7su3vmPiAY483YoBXLKZqigHeggl2FGJmsRpPR7UGxnwfMPqfwmw8HVSDzKL30HPxLpzA4hH7uHojZvRcDAnw5gQ9vhk7tAq0YMXnsMOrUVy72BS9QEtsofnxNTRs/0jF55YATwhRkprtxPYvrM/FFENm8ETtOTnU3BGcYa9rK+wFeHpyx6jdQzs/S/OTZxM8+E0UO4UZO5/UiR+l/4yfY0dOxYydhx7fTHjnZ9ETL+M7+gDgfTjbkddgh9dgB5bi6/41uE4+wJva+ruC7Lyryba8kf4z7ye58rOTPt7VvQBPrUmAN7965xSiDsxioZXnRm90ct5Nm9g5Q4+NMQtvtr4BI74ZJdtVi2FWTSGrQFROj7+E1Ti1dHQ7vArAW4fn2vl12z1EX3gXqjVI4MA30AdfqOjcEuAJIUpSs4frdifem8GTKpqitgp944bfeS/MqJVahxfo+D655tfRc9FW+s/5HfFT7yZ10icwW14PgBU7D8W1CR76FgDG4PMouR60XBdW2Ks8m2u7HF/PIxh9T6A4marN4OXarmDwjJ9gNr+uouPdwgyeVcUAL9OJq/qLwaMQxwu7YQ2O3ojR/9SobVpqD4pretWkJ5Bt9dZc+bvurfoYq8XoeYSWx09GK6O5uyhNyXWjZTvG7TdajqEbjNsJ7/wMLY+tovXRE1AzBxk4/X9wfa00bPuHMVOHxyMBnhCiJC3bWb+1NLIGT9SBlr9rbQeGzUxrIezAErRjZ/DsJFq2E7PpYlxfa8nzmdFzcFFwVT/ZeVejxzcPFT2JeF8Gs61XoDgpopuuwvYv8nrUzQC1StF0fAtq2lJFiJpQNKzoWSULpGgp772hkM49HjtyClbDKQQO31P1IVaL/8h9AOiDEuBVSo+/CIAVOXVqJ9LC2IEl6PEXCRz8FrnYBSRP+CgDp/+EXOubSK74NMbA0xi9j0761BLgCSFGc0zU3NH6zeApPqmiKWrC13UfTU+cgdHzMGrmIACOf+TaGTu0ovglrqCwJmK8GTfXiJFd8GckT/wY2XlvQXHSxTv3dtgL8Mymi3CMJuzwyfSf8xBOoE5pzxNw9SYA1GrO4KU7JT1THLfM2PloiS2jbnoUZvfLTa/OLHgHxsAzqMesq5oRXBdf94PAUNsWMXmF4HjKAR5gh1fiO/ILVKuf1IpbSK34ZDFLJJvvJ3rs51M5JMATQoyi5rz1A3WdwcMB167P9cSso8U3j6o41rDtw0Q3vwc9tRN/173DUjRHBlleL6KdI44vFFuYKKUyfup/kj7hw8Vmyf6un+AYTUM3R7QAfec/Rd85v8cJLJrSc6wmV4/goqJUtYpmhxRYEcctM3YeCi7GwNMjHteSO3B883CNaFnnyS54GwCBwz+u+hinSku84lUNpnRa+rEUK44Wf2lK1RxnIz2+GTuwFNdonvK5rPBqFFys8JpRDdNdXxuu4kPLHJr0eSXAE0KMombrWw3PVX3eDzKLJyqgD75I88YLR6SxGN2/JXjgG6SX/BW55tdhDDyLljmIo0dx9ciI463QSlQ7jpI7UnysEOBZZa6Zs0MrcLQGVGvQK5s9LE3R8S8ALTCVp1h9ioprRGswgycBnjg+mdGzcBVtVG9LPbUTaxLFkZzgEnJNF+Lv/BE4VrWHOSX+7l8DXlGZcgK80J4v0rzxtTQ/sR7fDF5XWG9epeTq9Pu0Q16hlfSSG0antysqTmARaubApM8rAZ4QYhQ1exio4wyeYnh/lOgFI8RECsFYMY3FydKw/aNYoZNIrLoNM3YeWmKLdye+RGVYu0QlTT21E9u3APSG8gahqMUF93bDmik8m/px9dik1uCFdv0z+kCJKoMAdhrMAQnwxPFLC2NF1mH0PUng4LcI7v2K93ByR/E9olzpJR9AT+0iuO9fazHSSVNyR9EHnsV35H7MyOmYTRd675sTFO/QkjtxfPPAyRHad1edRjuzafGX0VM7MGPnVeV82XlXklryATILN5TcbgcWF2ddJ0MCPCHEKIUZPLtOAV5xBk8qaYoKFFIvtfxdzuD+r6OndpFY/UVQ/ViNZ6HgYPQ9XjJNsrBeTk8MFR2opGed1Xi69+cUGt/Wk2PESjY6N3ofp+X3S4s3egCwk4R3/xOBju+VPFdhX9snAZ44fpmx8/D1P0Fk64cI7/gUWmIbqtldVoGV4XLz3kp23tWEd92GlthWo9GWL/bslTQ9fSnG4PPk2t6MHVqB4mSL65LHomYOYUXWYTa9duT7wRwW2vdvuGqIzKJ3V+V8rn8+yZPvAC1ccrsTWIx6TIqm0fuolzo7DgnwhBCjqNnDuIo2ZvXAqlO8AE8qaYpKFL6kqGkvwPP1PIQZWYfZehngpV4BKK6FXSLAcwLt2IFlGH1PFh+rKMDLX6caC+/rwdVjJdskBDq+h2r1j+i/VKhAqqX3ljzX0LpdKbIijl/Z+ddihU4ieeLHUXAJ7b0TKL/ASpGiEF9zJ64eoeHV/1ODkU5iKLlu9ORW0ovey8D6/ya1/EPF5zNR8Q4texA7sBgnsNC78TtH1+Kp6f34O36AmtqD//CPySx6d1XW35XDDizybmIW0n0di8YX30PDtvGboJfu1CiEmNO0bKHceX3uAbmqM0LtCgAAIABJREFUl6Ipa/BEJYrBR6F4QHpPMagDcH0tWMET0dO7cfylC52YTefj6/kduC6KHUfNHZ10gJedfzX9xr1YjWdNvPMM4BhN6Meu7XBMfEd/BQylvsLwIHpfyXMNpXXLDJ44flmxc+l77fMA+Lt+ij9fKGWyKZrgFcjILHw3wf3/Dlai/HTvKjP6vaIx2fbrMJsuALzCUuClYJotbyh9oJPNV9NeiGs0org5FLMX19dSl3HPJJEtN+Hr/T2uooNrk1p6Y92u7QSWoLg2au4wTmAx+sAzqFY/xsAz4KSB0q8rmcETQoxSzybn3gXzM3iSoikqoGa99BU1cwCcHGp6P3bwxBH7WLGzAcasZGnGLkDNHUVL7Sy7guYoiobZculx0wfO1WOox6zBM/r+UCy8MrwIQzENNr2/5Lodrc6FmYSotez8q1FcG1fRsIPLKzpHrvUyFNfE1/d4dQc3CcbA07iKjplPIQdwfPNxtIZxC62oxb6hi7Hza5cLyzfmEr3/aXy9vye96C8xm19PeumNOKETJz6wSuyA19ankKbp7/4N4H1fMgY2jXmcBHhCiFHqHeC5qt/7QWbwRAUKH3xqthMttRMFZ1RwZuZn1UqlaALFO9tG/5NDAV54kmlZxxnXyKdoDku78h/5Oa4awmo4ZcQMXmF2VHFzJb/kqdkuUHRcY+7d3RezU3beNQBecFfIMpkkM3YerhbG1/NQFUc2OXr/U17FRy049KCiYIdWjNsLr1Ca3wksKhZcm4sBXmjPHThGM4lVX2DgjJ+SXP2Ful7fyQd4hTXmvu4HMRvPwFUMjP6nxjxOAjwhxChqts7lzotVNCXAE5PkWKjZw9j+dq+HVa93p9w+5g5rdv41ZNrfiRU9u+Rp7NBKHKMFo++PaMnt3mPBE2o79mnmGE0orgV20nvAdfAd+QW51jeNCvCGL/IvtQ5PzR6GYP3SuoWoNbvhNVgNrylWx62I6ifXdBG+7mkK8BwTY3ATZuzcUZvs0Ekj/o8fq5AZ4QV43vcBbY4FeFp8M/7u35Be9sFpS7EtZJ2omYOomUPoiZfJzv9TzOg56FMN8G688UauuuoqrrnmGjZs2MDWrVsB2LNnD+985zu5/PLLeec738nevXuLx1S6TQgxzZwsqtlbvxYJDK+iKQGemBw114WCUyxZ7cv3wju2KILrn0987TdG9cArUhTM2Pn4jv6S0J7/ixk9Z+Qd71nI1WMAw1Iyd6Plusi1XoYdWuHdMbbT3j6ZQziF/UsFeLnDEKjfe4YQNaco9J/5CxJr/t+UTpNrvQwtvQd1nGCq6uw0Rs/D6PEXUZw0VrREgBde6a2ptRIlT1G4qWP7FxYDvLk2gxc8+C1cNUB68Q3TNgZXb8TRY2iZg/i6fwtArvVNmM0Xoye3jHlcWQHe7bffzs9//nPuvfderr/+ej7+8Y8DcOutt7JhwwYefPBBNmzYwC233FI8ptJtQojpVfceeCBVNEXFCsU/rPwdaqPvD14z8woqnJlNF6BaA5ixcxk4/cdVHedM5BhewFbohVdsdRBYOlSEIb3H+zN7CDN6Ni5K6Rm8TAcEJcATs4vra8U1olM6R67Fq+Zbz1m8hu0fJbbpGqKbrgXAjJ0zah8zdq6X9TBQehZIyxz0buroDaD6cYwW1MwcCvCcLP7D/0N23pW4+ffKaRtKYBFq+gCBzh9gB5djh08m1/x6FMaualpWgBeJDN3xTCQSKIpCT08PW7Zs4corrwTgyiuvZMuWLfT29la8TQgx/Ypf8uq6Bk+qaIrKFIp/mFHvC4xq9XvpmRUUOkkvvp7BtV9n4Iyf4RpNVR3nTFT40lLohacOK5RSWMOoJb01OmrmEE5oOU5gMdoxlTTVTCd6chu0jp4lEGKuc0In4ujRcQuaVJM+8CyBQ98h13IZrurHCq0oruMazoyei6toGH1PlDyPmu0YUZTK8bfPqRk839FfoVr9ZNpLNyCvJzuwGF/fYxj9G0kt+yAoClb0TLKtl495TNltEj7xiU/wxBNP4Loud999N52dncyfPx9N0wDQNI158+bR2dmJ67oVbWtuLv+Oa0vL9OTCCgHQ1jZGmtds0O+lkcTaV0OsTs9T875MxyI6zObfbQ3M6tdiOXp6AGhauh42z4PMEYym1RX+XiKw4H3VHd9Mpnlf3mKhrPf/rtubyWtetNJbS/cURNWDEAOsAYItJ0H2RDTrAIHhv98d3/f+XHQ1bfV6zxCiDDPm/dHfREhLEar1eBwbnvsoBBfgu/QnoOjgZGnzlbpuBJrPJhz/I+FS47I6oXHZ0O8wsgQ9e2Tm/E5r7ZV7ILiQ2OqrQNUqPk1Vfl+xE6D7QQi2Ezntb4hoAe/x+V8b85CyA7zbbrsNgHvvvZc77riDm2++eWqDnaKengSOMzcbLorp1dYW4ejR+HQPozZcl6at/woNa+nLLYE6PU8tbtIMDPQPkPPP0t9tDczq12KZwt27CKohuvs1Yr5FGJkjJNWlpOb476UcatqgBYj3dJIJxAn37iWoBunuV0FRaPbNJ3fkFdLB7TQDg1Yrhr4EX8/v6B32+23c/VP04Alo0dfM+dejmDlm0vtjTG3ESRxlsMbj0Qefp6n3WeJr/pVMvwLYeF/1S183HLmA4L6v0H34MGjhEdtaEvvJhtaTyI+5QWnDl9g04v9+NanpfV4qaL2yh1wHX89D5JpfX2zVVKBkj9DS+SvSyz5EsidV8SWq9RoMMp8GILHkJtK9JuC1lFJVZcwJr0mXu7rmmmt46qmnWLBgAV1dXdi2DYBt2xw5coT29nba29sr2iaEmF5G3+PoiVdIL/3r+vbyUmUNnqiMmu3ADiwERcEJLAVGV9AUpRWKrAytwctXz83/3y9U2RtRTS+43Kukly++olhxfL2Pkm17y3HT/0+IenP1GKo1UPPrFJZYWJG1Ze2fa3otimsVm6EX2RlUs+eYFM0FqLkj4FjVGazremv6nByBQ9+l+YkzadhSv8kjo++PRJ9/G42b/wKc7IhtgcP3oLj2jEjPBMi1XkFmwdtJL76+7GMmDPCSySSdnUM5tw8//DDRaJSWlhbWrFnDL37xCwB+8YtfsGbNGpqbmyveJoSYXsH9/4FjtJBZ8Pa6XleqaIpKaZlDxfUlhYawk25QPke5egQXFcUqrMHrGnH33A6tQE9uKxZVsQOLsIPLgKGeTEbPwyhujlzbn9R38EIcR1wjilKXAK8LAMc/v6z9rdh5uIqGr/vX6IPPFytqFm7qDO8b6gQWouB6QV4V+LofpOXx1bT+bh6RLX8D2HVbpwigpV4FwH/0ARo3/+VQP1DXJdDxfczGM7EbVtdtPOOxI6cQP/WboIXKPmbCFM10Os3NN99MOp1GVVWi0Shf/epXURSFT3/603zsYx/j3//932lsbOT2228vHlfpNiHE9FByR/EdfYDUCR+GQn533S4uM3iiMmrmEGbz6wCwwyfjKr5RLRLEGBQV14iiDpvBsyLriptz868i2PEdQnvuBMDxLyz2BtSSr2KHV+E/+ksco6nYpkIIMZqjR9HNOgR4+eDL8bWVtb+rR7AaTye0/z8I7f8PMgveQfzUu4eanPtHFlmBQvGVhVMeq5beDUB6+Ye82cHUHoKHvusFWnXIBtBSu3HVAMkTP0bDzk9j9D2B2XwhenwzeuIV4iffWfMx1NKEAV5rayv33HNPyW0nnXQSP/5x6VLSlW4TQkwPPf4yCm7xy3I9FWfw8gGemtqFE6ysEqKYQ5wsau5w8S5zZuEGcs0X4/papnlgxw9Hb0KxhtokOK1vKm7LtbyRXMsb8PX8Dsc3D1QfVuNpOFoEX/dvyLVega/7QXKtV4Ba9pJ+IeYcV6/TDF6uy2ttoPrLPiZx8p0Y/X/E6NuIv+unJFd9HqP3MWBkuvtQL7zDVRmrkuvBVTSSKz4Nikpw310oTgrF7K3Le7iW2oUdPIH00g8Q2nsngY5vYzZfiL/zB7iKj+yCa2s+hlqa9Bo8IcTspCe3AWCFT67/xfNtEhTXREvuoOWJ08cs3SxEgZ7YiuLa2A2neA+oBk7ohOkd1HHGNWKoZj+KFUe1EyP7XyoKiVX/hKtoQ6laqp9c6xvxH30Ao/+PqGYfWUnPFGJcrh5FtRPgmDW9jpo9iuOfN6ljrMb1pJfeSHLFLSiuRWj37YT230Vm/p/iBJcW97P9C/PXqE6rBDXX4/UrVbxQxA4sAbz+e/WgpXZ7AawWIrvg7fi77kPvf5rAoe/me98d30vHJMATQgCgJbbjGE24vsl9OFSDqwzN4BVSTNT8Gh8hxqLHNwOMSCsUk+MaMRSzt3hX/ti1O3bDGhKrbye95P3Fx3Jtb0HNHSG883O4qp9cyxvqOmYhjjdOvuekYg3W9DpqrgvHV976u2PZ4RXkWi4jePCb4GRInfSpEdtdXyuuGkRPbK3GUL0iLsbQTF1hLbVajwDPddDSe4ozlJlFf4HiZIg99xbQgiRXfb72Y6gxCfCEEABoyW3Y4ZOnJy2yMIPn5FDspPdzjT8IxfFPj7+Io0WkauYUWKGV6IltqJn9ACNn8PIyS/6K7MKhanK51jfiKjrGwFPkml8HuvSlFWI8rh4FKKZD14qSO1L2+rtSCjdyMoveix0+Zi2zopFrfRO+I/eDa09lmN7pjgnw7GKAV/ubu2q2E8XJYAe9zw4rsh4zsg5cm8F13ynZGP54IwGeEAJcFz25dXrSMwEUDVfRvCqa+fLrqgR4YgL64GbsyNpiio+YPLPpIhQnhf/oA0DpAO9YrhHDbLoIQKpnClEGNz+Dp9a40IqaPeKtl61QrvVNDK79BsmVnym5PbPgWrRcV1WWUKi5nhFr7bwZQn9dUjS1lFfgpXhzUFGIn/pf9J/1AGbTa2t+/XqQT0UhBEruKKrZN70lgRU/imOi2F5TUZnBE+NybfTEy95dV1GxwpcZ/+GfAZSd3pVZ8HZcNSgBnhBlGJrBq2GAZ6dQ7XjZLRJKUhSy7e/E1RtLbs61Xo6rhfEf/umkTqsPPo/e9+SIx45N0URRsf2L6pKiOSrAA+zwSqxZVA1YAjwhxPQWWMlzVR84WRSn0EC5jADPsVDM3soumO/3I45PWmo3ip3Eipw23UM5rrm+FqyGtahmN64aGvOL3bGyC/+cnou3j+ibJ4QozalDimZh/XpN19FrIbJtb8Z/5N5JFYwJ77gl32vO8R5wHS9F85hqmU5wSX1m8NK7cRXfrEjFHIsEeEIItHyAZ4fXTN8gVAPFHT6DN/GdzuD+/6D5D+shf0y5tPgrtD6yBP/h/6loqGL66YMvAGA1ygzeVOWaLgTA9i8ofw2uohTTzoQQ46tHiqaaLfTAq22htOz8P0M1ezF6Hy37GDV9AC13GH3gGcD7fFdcG9c4JsALLK7TDN4u7NByULSaX2u6SIAnhEBPbMXRo9N6N95VfODkigFeOWvwjL7HUK1+jP6nJ3UtX/dvUFybhm0fQcn1VDReMb30+GZcxfAKA4kpMZu99XTlrL8TQkyeU4cUzWKT80m2SZisXOtlOHoj/q4y0zRdFy3bAYD/yC8Ab/0dMGoGzw4s9tow1LidhJbaXSywMltJgCeEQEtuxw6vnt7G4qqB4uZQ7EKK5gQfhK6LMbgJAKPv8Uldytf3GLZvPoo1QMOrH6touGJ66YPPYzW8BlTfdA/luGc2vRYXZWprd4QQY9PCuIpW1QBPS2zH3/HD4t+LAV6FbRLKpvrJtb3FC9ac3IS7K1YfipMByFfgdFHMfIA3agZvCQouaj4grDY100lg/9fQUjtnffVlCfCEmOP0wRcxBp6Z9l5i3ho8E5zyiqyo2UOouaMA+Pr+UP6FHBOjfyO5+VeRXvZBAp3/jZrxGrc2vHIjwf3/UdkTEHWjZI9g9D+B2XLpdA9lVnCNZtLLPkR2wduneyhCzE6K4jU7N/uqdsrwzs/Q+Mr7UTOHAFCzXQBTapNQruz8a1Gtfnw9v5twXzXjBWu55kvQ07vRkltR82vnj03RLLRKqNU6vMYXryOy/SM4/gXk5r21JteYKSTAE2IOU3LdNL64AcdoJXniNM9kKb58H7zyiqzoA97sXa7pIvSBZ8teh6cPbkKxk+SaLibXeoX3WPxFsDMEOn+Er+vnU3gSoh4Ch+9BcW0y7e+a7qHMGslVnyM37y3TPQwhZi1Xj1ZvBs9K4Ot5CKBY0VLNHcExmot9ZWsp13IJjh4rK01Tyweg6SXvw0XBf+R+1Fw3MDpFs6bNzh0TPf4KqaV/Q++Fm2dNO4SxSIAnxByk5HoI77iV5ifPQ80dZXD9D3BrnLc/EVc1wB1qdD7RGjxjcBOuopNe+gEU1xxzHZ7R/RDhHbcU/+7Lp3OaTRdiRdYC3nouPfEyimuhp3ZU4+mIGgp0/BCz8XTsBll/J4Q4PjhGDKVKRVZ8PQ+hOBkcPYq/yysWpuam1gNvUlQf2XlX4TvyywlvrhbSLa3G07Eb1qAPPDNmiqYdWASAlt5f9SFrqd0obg6rcW5UXpYAT4i5xk4Tff5tBPd9BTN6Nv1n/hyr8fTpHhWohT54+Rk8OwmONWo3f+ePUDMd6IObsBrWYja/DlfRMMZI0/R33Utw7/9DseIAGL2PYzWsxfW14OqNWMETvQBv8HlvGLkjKFVMoxHVpcVfQk+8RKZ9w3QPRQghyubqMdQqzeD5u+7DMVpJnfBhjMHnUVO7vCbndVxHm134LlQ7QWCCatRqtgMXFcc3HyuyDj3+ktfkXPWDFh65sxbGDixDS7wy8QBcFy2xtezxasktAN7a7TlAAjwh5hLXIfLKB9AHNzG47rsMrv/hjGns6VXRzKI4Q3cDFXvkLJ5i9tH48l8Rfe5Kr8hG4xlekBY5DaPviZLnVc1eFFy0xBZwct76u3xZeACr8bR8gPdC8TEtKbN4M1Xg8E9xFZ3sgrdN91CEEKJsVUvRtDP4uh8kO+8txffB4IGvo2Y76rL+rsCMXYDVcAqBA18H1x1zPzXT4QWeqo4VWYeW7URLbvNm70oUdrMa13nLJibg6/41zU+e6y3RKIOe2IKLih1aVdb+xzsJ8ISYQ4z+Jwl0/YzkilvIzbtyuocz0jFVNGH0OrzCLJye2olqDWBFzwC8O3JaalfJ0xZm4/T4S+jxl1CcNGbTBcXtVmQdWnovvr7HsYPLAdAkTXPG0hJbsMOrcI9ZuyGEEDOZY0RRzKk3Ojf6Hke1E+TmvRUnsJhs65sI7f8PtMyB2lfQHE5RSC95P0Z8M3r/xjF307KHcPwLAYrF3Hx9fxhVYKXAiqxDT+0qft6PpZC1Y/Q+VtZw9cRW7NBJoAXK2v94JwGeEHNIIZ0h237dNI9kNG8GzxyRzz8qwMuvz8u0vxM7sJhc88UAOMFlaLnDMCw4LFCtoQDPGHgKACt6TnG7HTkVAC29l+y8q3EVA11m8GYsLb3b+5AWQojjiKtHq5KiaeSzTczY+QAMrvse/WfcR2LFrWQW/+8pn38yMu1vx9FjBA98bcx91EwHTn5tnZX/vFXsJI6vteT+hSBQi7+M78gDND15XsnPdqP/qfyfT5Y1Vi2xBXuOpGeCBHhCzClaaheuGpyZDY3VQhXNVHHhtWoeG+AlAMjOfxu9F23Byc+42YGlQOnSykrOK8esJ15C738aO7AEJ7CwuN2MDC24NqNnY4dOkBm8mcq10VJ7JMATQhx3XCPm9YOzM1M6j5Z4BTu4HFeP5B8IYLZcQvqED2OHV1RhpJMZTJjMwg34j9yPkm9efiw124Gdn8FzjSbswDIAr+JnCVZkPQBG/AWCB76GntiCHt88cicniz74Ai6KF+i5zvjjtNNoqd1YDWsm8eSObxLgCTGHaKldXnNPZeb91y9W0XTSxQB0dIqmN4Pn6g0jHi+kVqrpvcec1B02g7cFo38jZvTskbv452Pn01qsxtOxQ6vQkq9W4ymJKlMzB1HcHHZQAjwhxPHF0aPAxC2AJqInXsZqWFuNIVVFZuF7UFyTwOF7Rm1TrDiqNTjipmphFm+sNHvHvwDH14bR8zBG76MAGIPPjdhHH3wBxc2Ra3sLqtU/YbEVPfkqCs6cKbACEuAJMad4Ad4M/XKs+PJVNFPFSmDHLkgvpGi6x1TecoL5GbxjSys7aRQnixVeheKk0LIdWLFzOJbVeBqO0YoTWIwdXoWW2l2ygqeYXlpqN4B3k0IIIY4jbj7Am1Kapp1GS+7EipxSpVFNnR05BTNyOoFD30PNdBDddC1G3x8BULOdAMU1eDCUgnlsi4QiRcGKrMPf/SAKDq4aKFa5LjAGngEgtfxm7+8TpGlqCa+CpqRoCiFmH8dCS++dsQGeq/qgkKLpXwBMIsDzt+MqBlpm34jH1XyBFbPp4uJjZvTcUddOrvw8g+u+5X2whFeiuCbasbOBYno4JqFd/4SS7SoW0pmpr2EhhBhLIcCbShsePbktPxM1c2bwADKL/hw98RKxZ96Ir+chQrv+CQA13+S8sAYPKPahO7bJ+XBWfumEFT6ZXMul6AObRmw3+p/GDi7Hip6D7W/H6P/j2INzHXzdv8FVfNjBuXNzUAI8IeYINXMAxTVn7pdj1YfiZLzmrT4vwDu22flQgDcyRRNFxQ4sQT1mBk8xvfV3Zux8XEXHVQPF9JDh7IaTMfMFW+zQSmBylTQD+79K9Lmrxy0VLSpj9D5CePc/EzzwjZm9hlQIIcbhGDFg9I3LydDiXn84u2HmzOABZBe8DVfxoWa7vAbofY+hJbYXm5zbw2bwzOg5XnCWX2tXipkPArPzr8VqPAM9tWOoSbzrog885S23UBTM2Pn4eh5BzxddGcHJEnnpfxPo+gnppR8AVa/ek57hJMATYo6Y6bMfruIrrk1wjCiuGhiziqarh0cd7wSXjZp1K8zgOf4FXlP06Nmg+sYdhx32euQUcvr1/qcIv/qJcYO3QMf38fX+Hj3+wpj7iElwssVqqv6jDwDg634QLbU7v4Z0dO8kIYSYyQpLD0oVA5uQ63iBTeJlXDWEHTqhyqObGtdoZvDUuxk442fE13wZV/ER3H8X/q77vCbnw27Kub4Wei/cXHK5RIHZfAmZBe8gs/i9mI1eO6TC56uW3IqW7cTM9/DNLL4BsGl65o3Enn4DgQN3Fz8/gvu/TqDrJyRWfIbkys/V6NnPTHMnlBVijpvpAR6qDwWvEparhXD1xhJFVhL57aMDPDu4DP+RX4zcvxDgGU0Mrvs2qMaEw3CNGHZwOcbgC6SB4MH/JND5Q9LLbiqmjo64Ru4oRr4pq//wT7AaT5/4uYpxRV5+P3pyB33nPorv6K9xFR0j/iJO5iBm02une3hCCDFpTmApjt6IHn+p7GOM3scI7vsKvr4nyLZegZrr8ipBKloNR1qZ3Pxrij9n519D8NC3AEis/Pyke8+5Roz4qXd7Pyve57Y++Dxm8+sIHPoOrmKQnf+nAJjNF9Jz4SsEO75D4NB3iGz7e4z+jcTXfoPAoW9hRs8lfcLfTfn5HW9kBk+IOUJL7cLRGurbCHUS3GHBl6uGcEoFeHYSVw2W/HCzg8tQzW7IB4EAaj5F0zWacEIn4AQWlzUWK7K+eLdQH3jW+3PwxZL7+np+710/sAx/188mLtcsxuc6+HoeRk+8RHjHLWjZQ6SX3giAavbM3BsUQggxHkXFalg7qQAvvPNzGAPPkGt+HYGun+Dr+wPWDEvPLCW1/ENYoZUMrPsO6eUfmtK5XF+Ld9N1YBPYGQKdPyQ770rc4X309AbSS2+k77wnSS27Gf/hHxM4+J/oqR2kF793ak/mODVhgNfX18f73vc+Lr/8ct761rfywQ9+kN5e70vT6tWreetb38rVV1/N1Vdfzfbt24vHPfzww1xxxRW88Y1v5G//9m9Jp9NlbRNCeNTMwWLlp2oYapEwQ9PblGGpk1oQV28cVW1MsRMlZ+/AuzsKoGUODO1fnMEr3W9nLGbjerT0XtT0XvT8Wjw9UfpD2dfzMI7RTPKkj6FlDqDnq3uJymiJrahWP67iI7T/33BRSC2/udg7aS4tkhdCzC5W5FT0xMvl3Qh0LPT4ZjLt72Jw/Y9ILbu5eI6Zzo6so++1z42Y1ZsKM3Y+vqMPENl6M6rZR2bRX5beUVFILf9bXK2Bhm0fxtEbyVZpDMebCQM8RVG44YYbePDBB7n//vtZsmQJX/rSl4rbf/SjH3Hfffdx3333sXr1agCSySSf+tSn+OpXv8pvf/tbwuEw3/zmNyfcJoQY0rD174g9+xZvPVIVzOgWCeSraBZ+1kK4erT0DN4xPfAK7KAXAAxfh6eafbhqALTgpMZiNXqLv4MH/6v4mD64efSOrovR8zC55teTm/dWXNWP//BPJnUtMVKhGlpitVeFzYqeg+trI9t2BTCDU4yFEGICdmQdip0stnwZj5bciuKksfJr0JIrP8vAad8nu3BDrYc54yRW/RNm9CwCnT/EDizDbH79mPu6vhbSS/8aBYfsgrfDGDeFZ7sJA7xYLMa55w6VFV+/fj0dHR3jHvPYY4+xdu1ali9fDsB1113Hr371qwm3CSHyXBujfyOq2YP/yC+nfj4ni5bZN7O/HA9P0RwvwBvjzboQ4A2vpKmYvThG06SHUijRHDj0HVwUcs2XoMVHB3hacita7jBmyxtw9UbMpovw9T026euJIUb/k9j+djKL30dq6V+TWu6tncgs+gtysddiNa6b5hEKIURlCrNv2hgZIcMZ+dYAhQAPRfFuJOqRmo1vpnJ9LQyceR+p5R8msfqfQRk/fEkvu4lM+3Wklt1UpxHOPJMqsuI4Dj/84Q+59NJLi4+95z3vwbZtLr74Ym666SZ8Ph+dnZ0sXDhUEnXhwoV0dnrNDsfbNhktLaXv4gtRD21tNX6D7dsM+fTExqPfg1PHSEcoV+dGcG3CS19PuNZjr1R/Y/HHWEsr9LdAPD7yd61mINBY+vfvNoBsEWdNAAAgAElEQVQWJKJ0EilsV+MQbK3g3ysC4WWoyX3QuAbf4jfA5k/SFnPBGBonPRu9vVdeSSQcgYUXwUufpi3qgC86yWtWpuavxXpyXRjcCPMvpm1eI8z796FtbRfAiX+gdeyjxQwwq16P4rg3416PzWfD0zpRezv0fw96n4dzvz603c7B/v+Gpe+EPS+BEaN52fqZu7SiriIw/0sT71bYd+EPmVxpl9qYrtfgpAK8z33uc4RCId797ncD8Mgjj9De3k4ikeAjH/kId911F3/3d/WpVNPTk8BxpOeUqL+2tghHj8Zreo3AgYeIAOmF7yHY8V169m3GmUJZ5PCunxFUA3RrZ0GNx16pQNKl8DbYO+gSMIMEcwN0DxtvLD2Aq4UZGOM5NPnasfr2Es9vjyaOghIdc//xNIZPw5/cR6bhDLLqKqJA356NWE3nD+1z8GH04HJ6U02QimMYpxHDpX/3I5gtl4598iqpx2uxntT0flpSB4kHzyIzi57XXDHbXo/i+DZTX49N4dWw92doqV0obo7uxf+nWDAkcODrRLb9A4meg/i7nsKNnM5Ad2KCM4qZqtavQVVVxpzwKruK5u23386+ffv48pe/jKp6h7W3e30tGhoaePvb386mTZuKjw9P4+zo6CjuO942IYTHS1NbSOqkj+OiEuj4/pTO5+v+Dbmmi0ALVWmE1Teiima+yIpiJ8Gxio+Pl6IJ4PjbUbOHi39XzV7cClI0YWgdntl4FlbESwvU48MqabouRt8TmLELhh1zJi4KhhRaqUhh/Z3ZdMEEewohxPHJipyKntxa/Luv91HvB9chuM/LWgjtuRM98cpQeqYQk1RWgHfnnXfy8ssvc9ddd+HzeYUQBgYGyGQyAFiWxYMPPsiaNWsAuOiii3jppZfYu3cv4BViefOb3zzhNiGEx+jfiBk7DyewCCuyDmPwuYrPpSV3oqd2kWt9UxVHWAMjqmiGcQ0vxVExe4Z2sZMlm5wXOP4FaNmhlG/F7Jt0Bc2CXPPrcFU/ZvPFOP52HKN1RHlrLbkd1ewZ0ZfNNaLY4ZPR+5+u6Jpznf/w/2D75mM3vGa6hyKEEDVRuGGYOPlfcPQoRj7A8x39NXp6N6mlN6Ka3SiuhRmVAE9UZsIUzR07dvC1r32N5cuXc9111wGwePFibrjhBm655RYURcGyLE4//XRuvtkr4drQ0MBnP/tZ3v/+9+M4DmvWrOETn/jEhNuEEKCmD6BlDhZLItvhFRj9lc8I+Xp+AzDjA7wRVTTVIGb0bMBrQ5Bd+C7Aa3TuamOvv3UCC1GPPuCt5QJUq6/yGbzo2XRf0gmq9zZpNZ6Gke+JB2D0/QGA3DGNt83o2fiP/Nwbg6ybKJua2oWv+7ekTvzHGdnEVwghqiGz8D3YgUXk5l2Dr/vX+HofASC4/9+wA0tIrvw8WnIH/p7fygyeqNiEAd7KlStH9Lcb7v777x/zuMsuu4zLLrts0tuEmOuM/icBsGLnAWCHVnql9+106XL/rkt00zVk2/6EzNL3j9rs6/4tVnjVlNbw1YUysoqm1XgGtr8d/9FfDgV4ZaRoKk4axRrAVX0oThZHryzAA4rBHUCu+RIadnwSNXMIJ7AIo+8JbH87TnDk79WKnUOw4ztoqZ3Y4ZWVX3uOCR74JigamUX/a7qHIoQQNeMaUXLz/xTwMkX8R39JaNcX8PX9gcSqL4Cqk1jzZXK9j+AEFk3zaMXxquw1eEKI+jD6n8TRIlgNpwBgh1ei4KKldpXcX80ewtf7exp2fBItuWPUdi3+Mmb0nJqOuRoKM3iuonstExSVXNtb8HU/5AW3ro3ipCcI8BYAoGYPo5q93vkqnME7Vq7lDYA3o4jrYvT/0Vt/d8wsXeF3bfRvrMp15wQ7SaDje2TnXY0TkDXZQoi5wWy+hP/f3p3HZ1Xe+f9/neXecme5kxAg7CC7oqiouOCCbcUdqx2sdcbWXztqF62tVjttwYfiWJdpf7XV4kyrHaeItaOOFResxaXWBQEXkFUE2UII2Zd7Ped8/7hDMLJlI/edm/fzH5P7us45n6Mn8XxyXdfnAgh/cheJ4tOIDk3/kdYNDSU2+J8zGZr0cUrwRLKMr+5tUpET2kaPnLz0KJDVsnfyBmC37pWD55C/+gbw3D2NTjNWonKvUaastDvB+0wCF+9/AYbbgr/6FQynpbX9AFM0A+nkwIxXYCRr0591cQ3e5zn5E3EC5fiqX8ZueA8rXkGy+LS9+4XH4oRGENr8IHhOj1w71/l3vYyZqiM25OpMhyIi0muc8FicwCBcfxmNkx5uN2tEpDuU4IlkESNZh9W0imRkTyn+VHg0APY+RucAfA3L8QybpnE/x1/7Bv5di9rarJZNADjZPj2Tz4zgmXumoSaLp+HaRQSqnk1X1IQDFllx2kbwKjBbE7yeGsHDMEiUfgF/9SuE1/0E11dKfOCl++hn0jTmduymjwhue7Rnrp3jfHVvpdddRk7KdCgiIr3HMGg4Zj51xz/fNgNFpCcowRPJIr76JRh47RI8rDBOYPD+R/AalpPKP4rY4G/gmXn4qhfvOTS6CQCnL4zg7a6i+dl1hqaPROnZ+Gpex0il9wLq6BRNo3WKpttTCR6QLD0bM1WHv+4fNB/xb3i+yD77JfpfTCJyCuGP78BI1vfY9XOVr+5tkkXHt43iiogcLlJFx+Pkj8t0GJJjlOCJZBG77m08w0q/7H6GEx6zz/V1eC52w3vpSlumj2TkJPy1/2hrtqIb08eHRhzKsHtE2wje5/bqc/JGY8a2YaTqW9v3P0UTK4xrF2HGK7Cb1uBh4IaG91iMidIz8TBJhccfuBiIYdA85jbM5C78Vc/32PVzktOC3fghydaiQiIiItI9SvBEsoiv9i1SBcfA50apnLzRWC0ft5X/381q2YCZqifVuldOsvhUrKaP2kavrJZPcO0ivB5ah3ZItVbR/HyC5waHYuC2jWAeaAQPdu+FtwO7YSlOeDyeXdBjIXq+EhqPeoiGSY8cdK1EqugEXDuC7zMJd0Y4UQIVf87a9YC++mUYXopUkaZnioiI9AQleCLZwo3ja1jWfnpmKyc8BjPVgJHY2e5zuyFdYCVZuDvBOw0DD19teqsFK7opPT2zD+zHtt8RvNAQAOym9HYtB1qDB+lCK2Z8O776pSSLpvR4nPHyWTgFRx68o2GRjJzctl9eb8pfdQPBbf8DQGjbHyhc+f+1fZ9tdlcbTUayv9KriIhIX6AETyRL2A0fYLixfU5VS7VW0vx8oRW7YTmeGcIJjwcgWXQ8nhlsSyrM6EbcPjA9E9hTRdNsv9efGxwKgNW8Jt1+oCmapEfw7MYPMZM1pD431bW3JUumYUc/wYxV9No1jUQVoW2PkPfJz8Fz8Vc+A0DehjuhdR1jNrHr3iYVntBzxXBEREQOc0rwRLKE3fgBQHo93ec4+ekELlDxWLtpmnbDh6QKJu2ZLmgGSBadkJ4W6DlY0c19ooImgLefKZpO60avexK8g4/gGW4c4JCM4HVGsvhUgF4dxfPXvAaAFdtCYMeT+OreIt5vBlaikrxP7++1OA7I8yhY+a9E3j4Nf+3ftf5ORESkBynBE8kSdsMHuL4S3OCQvdrc4BBaRtxIaPsfyV97U1uSZ0U/wQmPadc3WXwqduOH2A3vY3jJvlFBEz6zD177BA8rjOsrwWrZ2Np+8DV4QOvI5sSej7MTUgVH49qFvZrg+Wpew7ULca0C8tf8EAOP5jF3EO9/UevefN7BT3IIGIldBDfPAzeBv+oFghWPg+EjFR5PfOBXMhKTiIhILtKOiiJZwm78MF1gZT/r5ZpH3wZOlLwt84gNupJUeDxWvILY5xK4+MDLyNt4L/mrvw/0jQqaAJ4ZSH9hhfZqc4LD8LUWjvHsA0/RdAKDAEgVTs78prGGRTIytX2C5zSDEehebJ5HaNMvwT8LGNyuyV/zWnr/QF8poe2PkgqPx8kfR6LkdAI7/4IZ34EbLO/6tbsotPlBwhvvw1f3NnbjClJ5Y6g74SUwfb0ei4iISC7TCJ5INnAT2E0fkSo8Zv99DIPY0G8BYDWt+swedyPadXPCY4kNugpf65TPvjJFs62Kppm3V9PuUU3PsPfsl7cfu0fwkoWZXX+3W7L4dOyW9ZjRTeA5FL89jfDHc7p1Trt+Cfkf3wbr2k+5NKObsKKbSJScQXzQFQDEB1wM7NkL0Wx9bnqbv+bvuFYBwcqnsFvW0zx2rpI7ERGRQ0AJnkgWsJrXYHjJ9AjeATihkXiGD7t5bduUxX0lcM1H/BjPCuMZ9j6nfGYlwyA66J9JlE7fq8nZneBZ+QetCOrkjcb1lZIom3FIwuys+IBL8DAIbl+Ar3oxdsvH+97TsBOCFY+nv9j5ervP/dXp9XfJkjNJRk6mYdLDRId9F6Ct2I4V/aRb1+4Spxm7YRmxod+iaey/Ex36ryT6Zcd/HxERkVyjKZoiWcDX0FpgpeDoA3c07fSeeE1rcf39AXBCo/bq5gUG0DT2LuyG98CwejzeQ6XpyAf2+fnuSpoHW38H4PlLqT5zY4/G1R1uaCjJkjMIVizAbloFgJmo6voJnRiBHU/imQGM+o8wEtV4/lIA/NUv4/gH4oTHgWEQH3jZnsNCw/Ew20Z+e5Ov7m0ML0Wi+DSS/b7Q69cXERE5nGgETyQL2I0f4Fr5OHlHHLSvEx6H1bwWK7oR1y7c7ybmsSFfp2nir3o61IzYvRfewfbAy1axQVdgRTcR2JnessBMVnf5XP5dL2Cm6mgZcSMAvro3ATAS1firnic+8JJ9j3KaftzgkLaRX5xm8Nwux9GpmGvewDNsVcsUERHpBUrwRLKA3fBBevTOOPiPZCo8Fiu6CatpbZ/ZxLy72tbgmX0zwYv3vxDXKgAgUXIWRmJX107keYS2PowTGJRO8Kw9ex4GKxZgeElig6/a7+FOaARWdCO4SUremEz449u7Fkcn+Wr/nt7+4yAFckRERKT7lOCJZJjV+FFrBc2DTM9s5YTHYeDiq3sLt69sgdBNTnAY0HdH8LDCRIddQ7zsAhIlp2M6TeBEO32a0Ke/wV/zGtER16erjfY7GV/tm+B5BLf9gWTRCTj5+98awskbiRXdhN34PlaiktCWhzCStd24sQ5INWE3LCdZPO3QXkdEREQAJXgiPc5X+w/y1/zwgNPfzNhWgpvnEV4/h+Il03HtImJDvtGh86fyxwGk97jrKxUyu8nzl+EZ/g6twctWLaNn0zD5MTxfPwDMTo7i2bVvEf54NvH+FxMdel36w/5nYDd+SN6GO7Cb1xEb/PUDnsMJjcRMVOGvegEAw2kmuPXhdKPndCqejgpteSi9/k5r70RERHqFEjyRbrAaV2LGtrX7LLTp/ye05b8IVPxpv8flr/4BBWt/RN6mX5IsPpnaqW/g5E/o0DWdvDF4pKdl9plNzLvLMHHCo9sKy/Rlrr81wUt2LsELbflPPF8pjUc+sGda7sAvYeAR3ngfqfB4YgMuOfC1WytpBrcvIBUeT6L0bPI+fYDIu1+i9NXhGN0p/rIPVvN6wp/8nHj/mSSLT+3Rc4uIiMi+qYqmSDcUvf9PpAqPpeGY+QAYqUb81a8AEN4wl/iAS8AKtjvGjFXg3/USLcOvT29e3tkNr60Qbmg4VnTTYTOCB1A/+c941t575PU1uxO8zq7Ds5tXkyw8Ds8u3PNh2clUn7YCz8pPF9s56BYS6efFim8jWvZN4gMuIbLsfGgxMFMN+KsXEy+f1bkbOoD81d/HM0M0jr+3x84pIiIiB6YRPJEuMpK1WLGt+GrfAs8DwL/rrxheguZRt2LFthDa8l/pzp6Hf+ezGMlaAhULMHDT0+k6m9y1SoXT0zQPmxE80tsN7N4OoC/zfOl76NQUTTeJ1bx+n6O8bmh4+t9LB4rtfPZ5SZScTrJkGjWnLKN62ipcX3HbHyd6gtW0Gn/t32kZ9SO8wIAeO6+IiIgcmBI8kS6ymtcC6al2Zuvm0f6qhbi+frSMuoVE8TSC29Lrm+y6tyn64GtElp5HaNsfSEROxQmP7vK1UwXH4NpFuMHB3b4P6V2uvwzoXIJntXyC4SVJdXAa7/54vgiuHQFomzLphMeAGSBZfAa+mlfb/ljRXYHK/8PDIDbwKz1yPhEREekYJXgiXWQ3rWn72le3BNwE/l0vES87DwyLRNm52C0bMGPb8de8hoeB1bIRK7qJ2OAru3XtlpE/oHbqG31qE3NJ8+xCPMPXqTV4VnN6g3Qn3L0ED8DJO4JUeAJea6K5W6L0LKz4dqzmdd2+BqQTvGTkFI3eiYiI9DKtwRPpIqt5DZ6Zh2fa+OqX4NkFmKkGEv3PB2grC++rfR1f7RukCibRNP4/CFY8nl6b162L5+GGhnf3FiQTDAPX369Ta/DsptV4mKTCY7t9+aYJvwD2ns6ZKD0LAH/NYqKtlVq7ympai928msZx93TrPCIiItJ5Bx3Bq62t5Vvf+hbnnHMOF154Id/97nepqakB4P333+eiiy7inHPO4eqrr6a6urrtuK62ifQVdvMaUuFxpAqPx1f3Dnmf3IsTGkGiNF0OPlUwCdeO4N/1Mr76d0gWTyMVOYmmCb+EHCgWIl3n+fp1aoqm3bQGJzQivfddN6UKjyVVOHmvz93QCJzQSHw9sA4vsDM9PTPR/+Jun0tEREQ656AJnmEYfPOb32TRokU8++yzDB06lPvuuw/Xdbn55puZPXs2ixYtYsqUKdx3330AXW4T6UuspjU4+eNIRk7CblqJr/E9WkbeBKYv3cEwSRafRqDyKQw3TrLk9MwGLFnD9ffDTHZ8SwKreXWHt9HojkTJmfjq3jzgHo4HY6QaCG79A8nIybjB8h6MTkRERDrioAleJBLhpJNOavt+8uTJbN++nZUrVxIIBJgyZQoAl19+OS+++CJAl9tE+gojWY8V304qPIFkJP3z4QSHESv/art+yZLTMLwUHibJyMmZCFWykOsv7fgInhvHavm42wVWOiJZdAJmqgGr5WPwPEKf/gazZWOnzhFePwczXkHz2DsOUZQiIiJyIJ1ag+e6LgsWLGD69OlUVFQwaNCgtraSkhJc16Wurq7LbZFIpMOxlJbmdyZ0kR7Vz78VgPxBx8KAM2H1YKxj76JsQEn7jva5sPZWjJJj6TdoaO8HKtmpaDDsWkRZWUH7zxO16SqWgc88R3WbwHMIlx9H+PP9Ye9zdIdvGqyCEm8V+IOw7t/I97bCCQ907PjK12Dr72H8DygePb3n4pI+o0efR5Fu0vMomZapZ7BTCd4dd9xBXl4eV155JX/9618PVUwdUl3dhOv2TDlvkc4oKyugcesyCoBqZxhuHXDa6nRjVWP7zt5wikOjiJecT8vn2+SwlZcqJJxqpGpHFVjB9IduguK3T8MNDKD++Gfb+gZ2LKUQqHFH4HzuGSorK6CqJ58rbwj9rDCxrW/gVG8jH3C2Pk/NiD3XMKObMJzoPqeM5q/5bwJ2EdWDfrT3z4LkvB5/HkW6Qc+jZNqhfgZN09jvgFeHE7y7776bTz/9lHnz5mGaJuXl5Wzfvr2tvaamBtM0iUQiXW4T6SusxhV4ZvDglSwNk9pTl7OvqoVy+HL9/YD0HoquNQSA0OaHsJvX4MW2pNfAGekZ9HbjSjzDh9MDFTQPyrBIFh6LXb8MM/opAFZ0E2bLBty8IwAo/PAqzEQVNad9tNfm6lbsU5y8USoiJCIikkEd2gfvF7/4BStXruSBBx7A7/cDcNRRRxGLxVi6dCkAjz/+ODNmzOhWm0ifsOkxQlt/R6LkrI7tQ2eYe70Iy+GtLcFrXYdnNX9M3id34VoFGE4zZnTPuje78UOc8Hgw/b0SW6rweOzGFfhr3yBRmp5m6a/+WzrOxpX4Gt7Dim3Fbli+17FmdAtuUNt3iIiIZNJBR/DWr1/PQw89xIgRI7j88ssBGDJkCA888AD33HMPc+bMIR6PM3jwYO69914ATNPsUptkll3/Lnmf3Ifd9BFOeDRNY+biFByV6bCyim/XX+G9K0kWn0bDpN9nOhzpo1xfOsELbn0Yf/VirNhmPDNA45G/pXDF1diNK0m0jphZjStIlp7da7Eli44jz0uAkyA65JtYLZ/g3/UysaH/SnD7H/EMH+AS2LmQVNHxew70PKzYFhJl+oOdiIhIJh00wRszZgxr167dZ9txxx3Hs88+26NtkiGeS8HKazGTNSRKTsdf8yrFb59G0/h7iQ39Vqaja+OreQNfzWJajvhZRkbFQpvnQd5g6o/93x7Zk0wOT17rCF5o2x9IFp1AdPi3SZR+ESc4BA8Tu3EFiQEXY8QrsRKVRAsm9VpsqcJ00uZhkiyeRqL0CwQrFqS3P6j4E4my8zFStfirFtI8Zk7bcUaiCsON4QSH9VqsIiIisrdOFVmR3OWvehG7ZT0NR/2eePlXMJK1FKy8lvw1N+EGBpHof36mQwQguO0Rgjv+jBMeS7z88l69thHfib9mMUy4WcmddIsTGka83zkki6cRHf6ddlN9nfAY7KaVANiNKwBIFRzda7G5waG4/jKc0Ag8XxHxsnMJbf0dpa+NwXCjxAZ9DTO6iYK1N2M1r8cJjwHS6+8A3JASPBERkUzq0Bo8yX15n/4KJziM+IBLAPB8xTQc/QipwuMoXHE1ZnRTZgNsZTetASB/7b9hJGt69drByv/F8BwY+c+9el3JQWaAhmP/THTE9Xut40zlH9WW2O1J8HpxqrRh0HDkPJrG3Q1Ast8XqT/mcWIDLyM24MskSs9u+4OPf+df2g6zolsANIInIiKSYUrwBLv+XXx1bxEd/m0wPzOoa+XReNR/YrhR/NWv9Fo8gYo/E9yyj/VtnoPVso5E6dkYqVrCG/6912JKx/UnkgWToWhir15XDi+pgklYsS0Yydp0gZXgMDxfca/GkOz3RVJFU9q+T/Q/j6YjH6Dx6D+AaeMGh5AsOolgxePpffsAM7YZADek/R5FREQySQlejrOa11G0fCZ2w/v77RPYuRDP8BEbtPfIlJM3GtdXgl2/7FCG2cZI1pK/5kbC638Gbrxdm9WyEcONExtwKYl+5+Lb9XKvxATpQhe+hveIl8/qtWvK4Wl3YSO78SPsphWkenH9XWfEBv8LdvNa7Pp3ALCim3HtCJ5dmOHIREREDm9K8HJc3qZf4q9eTNHS8/HVvLHPPr6aV0kWnYBnF+zdaBgkC4/H17D0EEeaFtr8IGaqAdNpwlfbPl6rOV3sx8kfR6rwWOzoJxiphkMflOeRv/5nuHaE2KCvHvrryWEtlZ9O6EKbH8Bq/jh7E7wBl+Ba+YS2/TeQHsFztP5OREQk45Tg5TAjUU1gx5PE+1+MGxxE0Xtfxl/1Qvs+yVrshg9Ilpy+3/Okio7HalqNkWo8tPEm6whtnke89It4Zh6B3bGmmgCwm1cD4ITHtxWdsBtX7jmBm8BqWr3ne6cF3ES34/JX/xV/9WJaRt2C5yvp9vlEDsQNDCRZdCL+qucxcEkWn5bpkPbNzic+8DICO57GSNZjRbfgav2diIhIxinBy2HB7Y9huDGaR91K3ZQXSeVPoPCDKwhUPNHWx1f7ZvolsuSM/Z4nVXg8Bt4Bp3n2SLxbH8FM1dMyejaJ0jPxV72Ir/pV+r06gsD2+VhNa3CCQ/HsAlKFxwBgN7bG5HkUrriakrdOSsfppih+53RKXx9LeP2cro/0eR7hdT8llXcE0SzaLkJymGFQd+LL7PpCDVXTdxzwjy+ZFhv8LxhuC8Ftf8CKbcbR+jsREZGMU4KXS9wERrIu/bXnEtr6O5KRk3EKjsTzl1J//EKSkZMp+OhafDWvA+CreQ3PDJH8TEGFz9vdZjccwnV4nkdw+/+QiJxCqvAYEv3OxYptpvDDKzG8BHmb7sduWo0THpe+1cBAXH9/7IYPgfR0tsDOv+BhENr0KwI7nsBuXocTHkdo06/IX3Nzl8Iyo5uwm9cQHfZtMP09drsiB2VYYOVlOooDShVNId7vS4Q33IXhNGsET0REJAsowcsVTjORJdOJLJkOnouv9g2s6EaiQ65u6+LZBTQcswAnbzSFH1yJr+YN/DWvkYxMBTOw31N7/lKc0Ah8h7DQil3/LnbLx8QHfQ2ARNk5ABhukpbh38NuXp0uOJE/oe2YZMEx2I0fYDWuJLz+Z8T7X0R0+PUEKp8mvOFOUvmTqJvyItER3ydYsQC7vvPrCH2txyQjJ/XAXYrknuaxd4GXngqtNXgiIiKZpwQvF3geBR99B1/jh9gtH+OrXkywYgGuVUC8/4Xtu/qKqD/2CTwzQGTZedjNq0kcYHrmbsnC47uUIHVUcPtjeGYe8QEzgfQIXfPIm2k46r9oPuKnuK1r35zw+LZjUoXHYDWvIX/9z/CsfBon3p8eaTNsrNgWmkfdBIZBy8gf4vgHkL/2lraS7h1lNyzFM/NwwtoaQWRfnPAYokOvSX8dGpHZYEREREQJXi4I7PgzwcqnaB71Y1xfKXmbH8Rf+Ux60/J9TPFyQyOoPXUZDUfOI1Z+BfHyyw96jVThsVjxbRiJ6p6/ASdKoPJJ4gMualfJs2X0z0gMuAisELHBV6XjyP9MgldwNIbn4K/+G9Hh1+P5SnCD5USHfpNk4fEk+l8MpEcum0fPwVf/Lv5dL3YqNF/9UpKFk9vvDygi7TSPnk39MY/h5PfihuwiIiKyT3pr7WPM2FY8M4DnL2v7zFf3Fq4doWXUrRipRvI2/waA+KAr9nsezy4kPuiKA/b5LCdvNABWdCMpf2k37mBvgarnMFP1xFqnZ+5Ly4gf4ASHkio8vu2z3ZU0XV8p0WHXtn3ePO7nex0fL5+F8/HtBLf8jkTZuR0LzE1gN37YNjohIvthhUj0vyDTUYiIiAgawetzit77CpGl54ObbPvMblqFkz8BDKNtpMsJjUivreshTt4oAKyWT3rsnLsFt8/HCQ4lWTxtv6esIegAAB4xSURBVH08XxGxod8EY88j64ZGkig+jaaxc/e9h99nmT5iQ76Ov/plzIPdg+eCE8VuXIHhxkkWHX/g/iIiIiIiWUIJXh9iJHZhN32E3byG0OYH0x96HlbTalL56TViTv44WoZfT/MRP22XDHWXExqBh9HjCZ4Z246v+hVi5Zd3Pl7DoH7K822FWQ4mNvjrYJiEtj6y3z6+XX+l+K2plP59AsGtvwcgVbj/CqMiIiIiItlECV4f4qt7C4BU3hGEN9yFGduKGa/ATNW1qy7ZPHYu8fJ/6tmLW0Hc4GCsaM8meIGKP2HgEuvgVNHucIODSJRdSHDbf2Mkdu3V7q/8C5H3LsVw43hWAaHtf8TxD8ANDjnksYmIiIiI9AQleFnOV/smJW8cjRmrwFf7Jp4ZoGHyE+AlCW3+LVbTKgCc/CMPeSxOaFTPjuB5HsGK+SQjJ+PmHdFz5z2A5lG3YDgtFKy6fq+KmqFtj+AEh1FzyhLqTvxra6GWC8EweiU2EREREZHuUoKX5XzVi7Gimwhu/T2+ujdJFk7BCY8hWXIGgZ0LsVsTvNRntg84VJy8PQmeGf10z6bqXWTGPsVuXkdswJd7IrwOcQqOpHn0HAJVCwlu/589scR3tE4V/ScwA7iBgdSd9ApN4/+j12ITEREREekuJXhZzm5eDUBo6++xGz8kWXwKAPH+F2BFNxKofArHPxCvhytb7ouTNwozuQsjUU3xO2cRXn9bt863e+P0VC9vIh4d/h0SkVPT8btxAAIVf8bAJV7+1fadNXonIiIiIn2IErwsZzWtwgkMwkxWY3hOW4KXKDsPAF/D8nQFzV7ghEYCENz2KGZyF3bTim6dz25YjmcGSPXC9NJ2DJOWUT/CTO4isONJAIIVj5MsPA4nPKZ3YxERERER6UFK8LKZE8Vq2Uhs8D+TCo/DwyRVdCIAbmAgyaJ0dcfdFTQPeTitWyWENv8WAKt5/V7r2DrDrl+W3svO9PVIfJ2RLDmTVHgcoc0PEdz2KHbTCmKfH70TEREREeljlOBlMbt5HQYuqfyJNI2/j+Yxd7Tb7y1elt5Y2OmtBK91BM9K7MAzbMxUHUaiqoMHx/DVvLHnezeFr+F9koXHHYJIO8AwiA69Fl/je+Sv+h6J0unEhnwjM7GIiIiIiPQQJXhZrK1CZngiyZIziI74Xrv2+MCvkCw6kUTJGb0TkJ2P4x8A0DbaZTev69Choc2/JbLsPKzmjwGwmtdiuC2kMriJeGzQ5bj+MlJFJ1B/zHww/RmLRURERESkJyjBy2J28xo8w9c2NfLz3NBQ6k58GTc0rNdicvNG4Rl+osO/C6QTtf3x73weM7oFgMCuFwDw1b2Z/mfDcgBShZlL8LDC1Jy8hLopL4IVzlwcIiIiIiI9RAleFrOaVuGEx2Zkjdr+RIdcTfMRP8EJj8Ozwlgt+x7BM1KNFH5wBflrb8ZIVGPXLQH2bNZuNyzHtQtxemn/u/3x/KVg2hmNQURERESkp3Tozfbuu+9m0aJFbNu2jWeffZaxY8cCMH36dPx+P4FAAICbbrqJadOmAfD+++8ze/Zs4vE4gwcP5t5776W0tPSgbbKH3bSaZOTETIfRTrx8VtvXqbwx+52iaTcsx8DFX/UCoW2PYODiBIfhq30TPA9f3VukCo8FQ39jEBERERHpKR16uz777LOZP38+gwcP3qvt/vvv55lnnuGZZ55pS+5c1+Xmm29m9uzZLFq0iClTpnDfffcdtE32MFKNWLHNOOHe2QKhK5zwmHQlzX3w1b+b/sIwydvw77i+fkSHfgsruhH/zmexm1YR739xL0YrIiIiIpL7OpTgTZkyhfLy8g6fdOXKlQQCAaZMSZfxv/zyy3nxxRcP2iZ77C6w0ut7xHWCEx6HFdsMTstebXbdu6TCY0mUXYjhpUj0+2LbHn4Fa27EtYuIlV/e2yGLiIiIiOS0bs+Pu+mmm7jwwgu57bbbaGhoAKCiooJBgwa19SkpKcF1Xerq6g7YJnvYjR8AkCo8OsOR7F8qnJ6qG9r6MHkb7gKnOd3gefjq3yVVdAItw64DIN7/QlIFk/HMEGaiitigK8HOz1ToIiIiIiI5qVvVJebPn095eTmJRII777yT22+/vdemW5aW5nhysGEVBPpROmQ8GEamo9k337HwIeSv+zcAwtH34YxnoGULJHcRHDyN4JgvwaCNFIWHp++jbCpUvkreMTeSV1BwkAtkr7Kyvhu75BY9i5JN9DxKNtHzKJmWqWewWwne7mmbfr+fK664guuuu67t8+3bt7f1q6mpwTRNIpHIAds6o7q6Cdf1uhP+oeV5+GpfJ1k8rUuFRCJVy/Dyj6Z+V9MhCK6HeMMIjf13nNBIzHgFBWt+QOzVr5EoOYtCoMaahFPVCJRCNH0fvsE3YkfOIRrrD7HGjIbfVWVlBVRV9c3YJbfoWZRsoudRsomeR8m0Q/0Mmqax3wGvLk/RbGlpobExHbTneTz//PNMmJAuCHLUUUcRi8VYunQpAI8//jgzZsw4aFsuseuXEFl2IYHKpzp/sBvHblpFquCYng+sJxkG0eHfJdH/fGJDv0nT6DkEd/yZgtXX45l5+ywQkyw9k+iwb2cgWBERERGR3NehEby5c+fy0ksvsWvXLr7xjW8QiUSYN28e3/ve93AcB9d1OeKII5gzZw4Apmlyzz33MGfOnHZbIRysLZdYLZ8A4N/5HPGBl3XqWLtpNYaXzP4E73OiI3+Ikz+Rgo+uI1l0gvaXExERERHpZYbneVk8z3H/sn2KZt4n9xDeMBfXLqL6jE86tVl5cNujFKz6LtWnvoeb4Y3Au8RpBs/L2SIqmvYh2ULPomQTPY+STfQ8Sqb1ySmahzMjWUvxW6fgq/7bfvuYsa3pf6bq8dW92anz2w3v49qFuKGR3YozY6xwziZ3IiIiIiLZTAleF/jq38VuWknBR9/BSDXss48V20wqPA7PDOCveqFT57cbPySVP6lLxVlEREREROTwpQyiC6zGFQCY8R2E18/ZZx8zthUnPI5EyRkEqp4Hz2nXHqh4guDmeQS3/oHglt/h3/VSusGNpxO8wsmH9B5ERERERCT3qApGF9iNK3BCI4iXnUfe5gdpGXkTbnDwng6ehxXdQqL0i6QKJxNY+S2K3ruMhkm/x/OVYDWvp3DlN9ud08Og+vT12M1rMNwYyZLTe/muRERERESkr9MIXhfYjR+SKphEovQLAJixLe3ajWQ1hhvFDQ0lXj6Lxgn346v5O0Xv/RMAvtq/A1B74itUT1tN3fHPYeAR2PUi/upX8AyLZPFpvXtTIiIiIiLS52kEr7NSTVgtG4gP/CfcQH8AzPjOdl2s1oTPCQ4FIDbk6xipBvLX/xQzuglfzd9x/ANJFR4HhoEbGIQTHIZ/53OYiUpSRSfg2QW9e18iIiIiItLnaQSvk+ymjzDwSBUcjedvTfAS7RM8M5pO8NzWBA8g3v8CAAJVz+Or/QfJktPAMNKNhkG87Dz8NYuxG94jUXJWL9yJiIiIiIjkmsMuwbOa1qb3aesiu7XASqpgEq6vHx4GZqKy/TU+N4IH4OaNIhWeQGjzPKzEjr2mYCb6n4/hxjHwSJQqwRMRERERkc47rBI8I1lP8TvTyN9P5cuOsBtX4NoR3OAQMG08XwlmvKpdHzO2Bc/Mw/OVtPs8UXYeVnQTwF4JXjJySvq8VgGpwuO7HJ+IiIiIiBy+DqsEz1ezGMONEah4ApxY50/gediN75MqOLpteqUbGLDXFE0rtgUnNHTPFMxW8f7npY/x98fJG9P+3KaPlhE3EB3+bTB9nY9NREREREQOe4dVgheoWoSHiZmqI1D1XKeONRJVFKz4Or6G99qNvrn+/vtYg7e13fq73VKFx+MEh5AoOXOv5A8gOvKHtBzxk07FJSIiIiIistvhk+B5Lv7qvxIfMBMnOITg9j/uu5+bpODDq7Br3wLArnuHyNtnUPraaAI7n6Vp9G20jLp5T3d/WVuCZyRr8Fe9iBXd2G79XRvDpO6El2ka/x89fXciIiIiIiKHzzYJdsNyzEQVibJzcfJGk7fxXszYtvYblAN2/VKClU9jN62m9qTXKPjoWgwnSsuoW4kPuAQnf3y7/q6/f9s2CUXvfxVfXToxTBVN2WccbnDQIbg7ERERERGRXB7B81zsunfAcwHw70pPz0yUfoH4wC9j4OGreW2vw/w1rwJgN68hsuwi7JYNNE28n5YjfrxXcgfpBM9wWzBSjdgNHxArv5xdZ2wkNvifD+ntiYiIiIiIfF7OjuAFKv5E4UfXkCw6gWTx6YQ2/5Zk5GQ8fymOXYRn+LGb1xD/3HG+mtdIFhwLVgBf3dskSr9Aot+X9nud3Zud2/XLMNyWtmuIiIiIiIj0tpwdwfPV/gPPCmO1fELepv8gXnYOjUf9Z7rRtHHC47CaVrU/yGnGV/8uydIzaBp3D8miKTSN+/kBr+O2bnbuq30dgFT+hB6/FxERERERkY7I2RE8X/07JIpPo/Go/8JI1uDmjWrXnsqf0LZeru2Y2jcxvCSJkjNIFU6m7sTFB72O15rg+VunezrhcT10ByIiIiIiIp2TkyN4RrIGu3ktqchJeL7IXskdpBM8K7YFI9XQ9pm/5nU8w0cycnKHr7V7BM9uWI7jH4jnK+7+DYiIiIiIiHRBTiZ4vrp3AUgWnbTfPk7+RACsptV7jqt5nWTRiWDldfharr8fAIbn4Gh6poiIiIiIZFBOJnh2/Tt4hkWy6Lj99tm9Vs7eneC5CeymlaQiJ3buYqYP11faes69q2yKiIiIiIj0lpxag2c3fIBn5eGre4dU/tFghffb1w0OSxdhaS20YjWvwfCSpAqO7vR1XX9/zGQ1TlgJnoiIiIiIZE7uJHhuiqJl52OkmsAwiA755oH7Gyap8Hjs5vQInt3wIUDXErxAf2herQqaIiIiIiKSUTkzRdNu+ggz1UCy5HQ8q4BE2fkHPSaVPxG7dQTPbvwQzwrj7KMgy8G4/jJAFTRFRERERCSzcmYEz65fAkDjxN/ghoZ16BgnfyLm9v/BjG7CblxBKv9IMKxOXzsZORkzXqkKmiIiIiIiklE5M4Lnq3sHxz8QNzi0w8fE+18AQHD7Y9hNK7o0PRMgNvRb1E95rkvHioiIiIiI9JSDJnh3330306dPZ9y4caxbt67t840bNzJr1izOOeccZs2axaZNm7rd1h2++iWkIieBYXT4GDc0nETJmYQ2z8NMNXQ5wRMREREREckGB03wzj77bObPn8/gwYPbfT5nzhyuuOIKFi1axBVXXMHs2bO73dZVRrwSK7qJZGT/+97tT2zQlZipOgBSBZO6HYuIiIiIiEimHDTBmzJlCuXl5e0+q66uZtWqVVxwQXqK4wUXXMCqVauoqanpclt3+FrX3yWLOrmHHRDvfyGuXYRnWKRaNz8XERERERHpi7pUZKWiooIBAwZgWemCJJZl0b9/fyoqKvA8r0ttJSUlnYqhtDR/zzdb3wPTT/Go08AKdPJuCmD8DVD3AWUD+3fyWDlclZUVZDoEEUDPomQXPY+STfQ8SqZl6hnss1U0q6ubcF0PgKLKpRj5R1FXkwASnT9Z+U1QDlQ19miMkpvKygqo0rMiWUDPomQTPY+STfQ8SqYd6mfQNI32A16f0aUEr7y8nMrKShzHwbIsHMdh586dlJeX43lel9q6w2rZSDLS+emZIiIiIiIiuaRL2ySUlpYyYcIEFi5cCMDChQuZMGECJSUlXW7rMjeJGduCExrZ9XOIiIiIiIjkAMPzPO9AHebOnctLL73Erl27KC4uJhKJ8Nxzz7FhwwZuvfVWGhoaKCws5O6772bUqFEAXW7rjN1TNM2WDZT+41gajvwt8UFf68K/ApHO0bQPyRZ6FiWb6HmUbKLnUTItk1M0D5rgZavdCZ6v+m9Ell9C3ZQXSRafkumw5DCg/2lIttCzKNlEz6NkEz2PkmmZTPC6NEUzm1gtGwFwQiMyG4iIiIiIiEiG9f0EL7oJzwziBgZmOhQREREREZGMyoEEbyNOaDgYff5WREREREREuqXPZ0VWyyZV0BQREREREaGvJ3iehxndqPV3IiIiIiIi9PEEz0hWYzpNuHkawRMREREREenTCZ7V8gmgCpoiIiIiIiLQ1xO86CYAnFDnN0oXERERERHJNX06wbPr38UzbJzQsEyHIiIiIiIiknF9NsEzkrWEtj1KfOBXwAplOhwREREREZGM67MJXmjbo+DGaBn5w0yHIiIiIiIikhX6bIIX3P4o8QEzccJjMx2KiIiIiIhIVuizCZ7r60fLqFsyHYaIiIiIiEjWsDMdQFfVnbAI1/UyHYaIiIiIiEjW6LMjeCIiIiIiItKeEjwREREREZEcoQRPREREREQkRyjBExERERERyRFK8ERERERERHKEEjwREREREZEcoQRPREREREQkRyjBExERERERyRFK8ERERERERHKEEjwREREREZEcoQRPREREREQkR9iZDqCrTNPIdAhyGNPzJ9lCz6JkEz2Pkk30PEqmHcpn8EDnNjzP8w7ZlUVERERERKTXaIqmiIiIiIhIjlCCJyIiIiIikiOU4ImIiIiIiOQIJXgiIiIiIiI5QgmeiIiIiIhIjlCCJyIiIiIikiOU4ImIiIiIiOQIJXgiIiIiIiI5QgmeiIiIiIhIjsjaBG/69OmsW7cu02HIYaq+vp6jjz6auXPnZjoUEaBjvxP1e1MOpUQiwc9//nO+8IUvMGPGDGbOnMnLL798wGO2bt3Kn/70p16KUA43+p0nmZTN74pZm+CJZNLChQs55phjeO6550gkEp061nVdPM87RJGJiGTGbbfdxo4dO3juued48cUXueeee7j99tt5991393vMtm3blOCJSE7K5nfFrE/wHn74YS699FJmzpzJrFmzWL16dVvbuHHjmDdvHpdeeilnn302ixYtymCkkkuefPJJvv3tbzNu3Dj+9re/AfDrX/+aG264gX/5l39hxowZfO9736OxsbGt7frrr+fqq6/mvPPOo6GhIZPhSw77/F+s9Rds6Q3btm3jhRde4LbbbiMQCAAwduxYrr32Wn7zm98A8NBDD3HhhRdy0UUXcfnll+O6LrfffjsbNmzg4osv5vrrr8/kLUgO07uiZEI2vyvah+zMPWTmzJlcffXVALz55pvMmTOHJ554oq09Pz+fJ598kmXLlvH973+fc845J1OhSo5Ys2YNdXV1TJ06laqqKp588knOPfdcAJYtW8b//d//0a9fP3784x/z4IMPcssttwDw4Ycf8tRTT1FSUpLJ8EVEety6desYNmwYkUik3eeTJ0/mV7/6FU8//TSLFy9mwYIF5OfnU1tbi2mazJ49m7vvvpunnnoqQ5HL4UDvitLbsv1dMesTvJUrV/LQQw9RX1+PYRhs2rSpXft5550HpP8ns3PnTuLxeNtfF0W64n//93+5+OKLMQyDL33pS8ydO5fKykoAzjzzTPr16wfAZZdd1m7e9emnn67kTkRy0sGmEr3yyit89atfJT8/H4Di4uLeCEsE0Lui9L5sf1fM6gTPdV1uuOEG/vjHP3LkkUdSWVnJ6aef3q7P7h9Qy7IASKVS+qGVLkskEixcuBC/388zzzwDQDKZ7NBfn8Ph8KEOTwTLsnBdt+37eDyewWjkcDF27Fg2b95MXV1du1G8999/n3HjxmUwMjnc6V1ReltfeFfM+jV4qVSK8vJyAB577LEMRyO57m9/+xsjR47k9ddfZ/HixSxevJiHH36Yp59+GoBXX32VmpoaAJ566immTp2ayXDlMDRs2DBWrFgBwFtvvcWuXbsyHJEcDoYMGcKMGTO47bbb2v6osG7dOubNm8d3v/tdzjrrLBYsWEBTUxMAtbW1QHpq3O7PRA4VvStKb+oL74pZO4KXSqUIhUJcf/31XHbZZUQiEc2ZlkPuySef5MILL2z32bHHHovruixZsoQpU6Zw4403UllZyejRo7n11lszFKkcbnb/xfmGG27g1ltv5Y9//CNTp05l0KBBmQ5NDhNz5szhF7/4Beeddx4+n49AIMBPfvITTjzxRDzPo7KyklmzZmHbNnl5ecyfP59x48YxcuRILrjgAkaNGsX999+f6duQHKJ3RcmEvvCuaHhZWM99586dnHvuufzjH/8gGAxmOhwRIF39qKWlpW2hrEhv0e9EEZH29HtRslG2vCtm3Qjeo48+ymOPPcYtt9yiH1gROezpd6KISHv6vShyYFk5giciIiIiIiKdl/VFVkRERERERKRjMj5Fs7a2lh/96Eds3rwZv9/P8OHDuf322ykpKeH9999n9uzZxONxBg8ezL333ktpaSkbN25k9uzZVFVVYds2kyZNYs6cOQSDQRKJBNdddx0rV64E4J133snwHYqIiIhIV/X0u+Ly5cu55557aGhoAOCMM87gRz/6EYZhZPhORXpGxqdo1tXVsXbtWk466SQA7r77burr65k7dy7nnHMOd911F1OmTOHBBx9ky5Yt3HXXXWzdupWGhgYmTpyI67r84Ac/YMyYMXznO98hlUqxZMkSiouL+frXv64ET0RERKQP6+l3xXXr1uH3+xkxYgSJRIKrrrqKWbNmMXPmzAzfqUjPyPgUzUgk0vYDCzB58mS2b9/OypUrCQQCTJkyBYDLL7+cF198EUjvxzNx4kQATNPk6KOPZvv27QDYts0pp5xCQUFBL9+JiIiIiPS0nn5XHDt2LCNGjADA7/czceLEtjaRXJDxBO+zXNdlwYIFTJ8+nYqKinb7O5WUlOC6LnV1de2OicViPPnkk0yfPr23wxURERGRXtTT74rV1dUsWrSIM88881CHLtJrsirBu+OOO8jLy+PKK6/sUP9UKsWNN97I1KlTOfvssw9xdCIiIiKSST35rtjU1MR1113H1Vdf3TbaJ5ILMl5kZbe7776bTz/9lHnz5mGaJuXl5e2Gy2tqajBNk0gkAoDjONx0000UFRXx05/+NFNhi4iIiEgv6Ml3xWg0yrXXXsupp57K1Vdf3av3IXKoZcUI3i9+8QtWrlzJAw88gN/vB+Coo44iFouxdOlSAB5//HFmzJgBpIfnb731VizL4s4771TVIxEREZEc1pPvivF4nGuvvZZjjjmGG264ofdvRuQQy3gVzfXr13PBBRcwYsQIgsEgkF4Y+8ADD7B8+XLmzJnTrvRtv379ePXVV7nmmmsYO3YsppnOUY877jjmzJkDwKWXXkplZSXV1dWUlZUxbdo07rzzzozdo4iIiIh0TU+/K86fP5+5c+cyduzYtmvMmDGD6667LiP3J9LTMp7giYiIiIiISM/IiimaIiIiIiIi0n1K8ERERERERHKEEjwREREREZEcoQRPREREREQkRyjBExERERERyRFK8ERERA7g1ltv5Ze//GWmwxAREekQJXgiIiIiIiI5QgmeiIiIiIhIjlCCJyIi8hmrVq3ikksu4dhjj+X73/8+8XgcgPr6eq655hqmTp3KCSecwDXXXMOOHTsAeOGFF/jyl7/c7jyPPPII1113Xa/HLyIihzcleCIiIq0SiQTf+c53uPjii1myZAkzZszgpZdeAsB1Xb785S/zyiuv8MorrxAIBLj99tsBOPvss9m6dSsbNmxoO9czzzzDzJkzM3IfIiJy+FKCJyIi0uqDDz4gmUxy1VVX4fP5mDFjBpMmTQKguLiYc845h1AoRH5+Ptdddx3vvvsuAH6/n3PPPZe//OUvAKxfv55t27Zx1llnZexeRETk8KQET0REpNXOnTsZMGAAhmG0fTZo0CAAotEos2fP5qyzzuK4447ja1/7Gg0NDTiOA8All1zCs88+i+d5PPPMM5x77rn4/f6M3IeIiBy+lOCJiIi0Kisro7KyEs/z2j7bvn07AA8//DAbN27kiSeeYPny5cyfPx+gre/kyZPx+XwsXbqUhQsXctFFF/X+DYiIyGFPCZ6IiEiryZMnY9s2jz76KMlkkpdeeokVK1YA0NzcTCAQoLCwkLq6On7zm9/sdfzMmTO5/fbbsW2bKVOm9Hb4IiIiSvBERER28/v9/PrXv+bpp5/mxBNP5Pnnn+eLX/wiAFdddRXxeJypU6cya9Yspk2bttfxF198MevXr9fonYiIZIzhfXYeioiIiHRZLBbj5JNP5umnn2bEiBGZDkdERA5DGsETERHpIQsWLGDSpElK7kREJGPsTAcgIiKSC6ZPn47neTzwwAOZDkVERA5jmqIpIiIiIiKSIzRFU0REREREJEcowRMREREREckRSvBERERERERyhBI8ERERERGRHKEET0REREREJEf8P13EgVj4HIBfAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def plot_bar_by_month(df_days):\n",
        "    sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "    custom_palette = {}\n",
        "    \n",
        "    last_month = int(df_days.iloc[-1]['day'].month)\n",
        "    last_year = int(df_days.iloc[-1]['day'].year)\n",
        "    \n",
        "    current_month = int(df_days.iloc[0]['day'].month)\n",
        "    current_year = int(df_days.iloc[0]['day'].year)\n",
        "    current_year_array = []\n",
        "    while current_year != last_year or current_month-1 != last_month:\n",
        "        date_string = str(current_year) + \"-\" + str(current_month)\n",
        "        \n",
        "        monthly_perf = (df_days.loc[date_string]['wallet'].iloc[-1] - df_days.loc[date_string]['wallet'].iloc[0]) / df_days.loc[date_string]['wallet'].iloc[0]\n",
        "        monthly_row = {\n",
        "            'date': str(datetime.date(1900, current_month, 1).strftime('%B')),\n",
        "            'result': round(monthly_perf*100)\n",
        "        }\n",
        "        if monthly_row[\"result\"] >= 0:\n",
        "            custom_palette[str(datetime.date(1900, current_month, 1).strftime('%B'))] = 'g'\n",
        "        else:\n",
        "            custom_palette[str(datetime.date(1900, current_month, 1).strftime('%B'))] = 'r'\n",
        "        current_year_array.append(monthly_row)\n",
        "        # print(monthly_perf*100) \n",
        "        if ((current_month == 12) or (current_month == last_month and current_year == last_year)):\n",
        "            current_df = pd.DataFrame(current_year_array)\n",
        "            # print(current_df)\n",
        "            g = sns.barplot(data=current_df,x='date',y='result', palette=custom_palette)\n",
        "            for index, row in current_df.iterrows():\n",
        "                if row.result >= 0:\n",
        "                    g.text(row.name,row.result, '+'+str(round(row.result))+'%', color='black', ha=\"center\", va=\"bottom\")\n",
        "                else:\n",
        "                    g.text(row.name,row.result, '-'+str(round(row.result))+'%', color='black', ha=\"center\", va=\"top\")\n",
        "            g.set_title(str(current_year) + ' performance in %')\n",
        "            g.set(xlabel=current_year, ylabel='performance %')\n",
        "            \n",
        "            year_result = (df_days.loc[str(current_year)]['wallet'].iloc[-1] - df_days.loc[str(current_year)]['wallet'].iloc[0]) / df_days.loc[str(current_year)]['wallet'].iloc[0]\n",
        "            print(\"----- \" + str(current_year) +\" Cumulative Performances: \" + str(round(year_result*100,2)) + \"% -----\")\n",
        "            plt.show()\n",
        "\n",
        "            current_year_array = []\n",
        "        \n",
        "        current_month += 1\n",
        "        if current_month > 12:\n",
        "            current_month = 1\n",
        "            current_year += 1\n",
        "\n",
        "def plot_wallet_vs_asset(df_days):\n",
        "    fig, axes = plt.subplots(figsize=(15, 12), nrows=2, ncols=1)\n",
        "    df_days['wallet'].plot(ax=axes[0])\n",
        "    df_days['price'].plot(ax=axes[1], color='orange')\n",
        "\n",
        "def show_analys(days, trades):\n",
        "    df_trades = trades.copy()\n",
        "    df_days = days.copy()\n",
        "\n",
        "    df_days['evolution'] = df_days['wallet'].diff()\n",
        "    df_trades['trade_result'] = df_trades['wallet'].diff()\n",
        "    df_trades['trade_result_pct'] = df_trades['wallet'].pct_change()\n",
        "\n",
        "    df_days['wallet_ath'] = df_days['wallet'].cummax()\n",
        "    df_days['drawdown'] = df_days['wallet_ath'] - df_days['wallet']\n",
        "    df_days['drawdown_pct'] = df_days['drawdown'] / df_days['wallet_ath']\n",
        "\n",
        "    initial_wallet = df_days.iloc[0][\"wallet\"]\n",
        "\n",
        "    close_trades = df_trades.loc[df_trades['position'] == 'sell']\n",
        "    good_trades = close_trades.loc[close_trades['trade_result_pct'] > 0]\n",
        "    total_trades = len(close_trades)\n",
        "    total_good_trades = len(good_trades)\n",
        "    avg_profit = close_trades['trade_result_pct'].mean() \n",
        "    global_win_rate = total_good_trades / total_trades\n",
        "    max_days_drawdown = df_days['drawdown_pct'].max()\n",
        "    final_wallet = df_days.iloc[-1]['wallet']\n",
        "    buy_and_hold_pct = (df_days.iloc[-1]['price'] - df_days.iloc[0]['price']) / df_days.iloc[0]['price']\n",
        "    buy_and_hold_wallet = initial_wallet + initial_wallet * buy_and_hold_pct\n",
        "    vs_hold_pct = (final_wallet - buy_and_hold_wallet)/buy_and_hold_wallet\n",
        "    vs_usd_pct = (final_wallet - initial_wallet)/initial_wallet\n",
        "    total_fee = df_trades['fee'].sum()\n",
        "\n",
        "    best_trade = df_trades['trade_result_pct'].max()\n",
        "    best_trade_date =  str(df_trades.loc[df_trades['trade_result_pct'] == best_trade].iloc[0]['date'])\n",
        "    worst_trade = df_trades['trade_result_pct'].min()\n",
        "    worst_trade_date =  str(df_trades.loc[df_trades['trade_result_pct'] == worst_trade].iloc[0]['date'])\n",
        "\n",
        "    print(\"Period: [{}] -> [{}]\".format(df_days.iloc[0][\"day\"], df_days.iloc[-1][\"day\"]))\n",
        "    print(\"Initial wallet: {} $\".format(round(initial_wallet,2)))\n",
        "\n",
        "    print(\"\\n--- General Information ---\")\n",
        "    print(\"Final wallet: {} $\".format(round(final_wallet,2)))\n",
        "    print(\"Performance vs US dollar: {} %\".format(round(vs_usd_pct*100,2)))\n",
        "    print(\"Worst Drawdown : -{}%\".format(round(max_days_drawdown*100, 2)))\n",
        "    print(\"Buy and hold performance: {} %\".format(round(buy_and_hold_pct*100,2)))\n",
        "    print(\"Performance vs buy and hold: {} %\".format(round(vs_hold_pct*100,2)))\n",
        "    print(\"Total trades on the period: {}\".format(total_trades))\n",
        "    print(\"Global Win rate: {} %\".format(round(global_win_rate*100, 2)))\n",
        "    print(\"Average Profit: {} %\".format(round(avg_profit*100, 2)))\n",
        "    print(\"Total fee: {} $\".format(round(total_fee, 2)))\n",
        "\n",
        "    print(\"\\nBest trades: +{} % the {}\".format(round(best_trade*100, 2), best_trade_date))\n",
        "    print(\"Worst trades: {} % the {}\".format(round(worst_trade*100, 2), worst_trade_date))\n",
        "\n",
        "# Fonction pour afficher une analyse complète de la stratégie sur le passé\n",
        "show_analys(df_days.loc[:], df_trades.loc[:])\n",
        "\n",
        "print(\"\\n--- Plot wallet evolution vs asset ---\")\n",
        "# Fonction pour afficher une analyse complète de la stratégie sur le passé\n",
        "plot_wallet_vs_asset(df_days.loc[:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Q55Q_rUyBGOp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "outputId": "fbe6f433-00bb-4c87-e4c6-b0f13d56b5b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   date position        price       fee  \\\n",
              "date                                                                      \n",
              "2021-12-22 15:00:00 2021-12-22 15:00:00      buy  4007.240000  1.577740   \n",
              "2021-12-22 18:00:00 2021-12-22 18:00:00     sell  3965.261438  1.560119   \n",
              "2021-12-22 20:00:00 2021-12-22 20:00:00      buy  4004.690000  1.559027   \n",
              "2021-12-23 00:00:00 2021-12-23 00:00:00     sell  3966.691386  1.543153   \n",
              "2021-12-24 17:00:00 2021-12-24 17:00:00      buy  4098.080000  1.542073   \n",
              "2021-12-28 01:00:00 2021-12-28 01:00:00     sell  3993.334431  1.501606   \n",
              "2022-02-02 09:00:00 2022-02-02 09:00:00      buy  2775.350000  1.500555   \n",
              "2022-02-02 21:00:00 2022-02-02 21:00:00     sell  2630.946493  1.421485   \n",
              "2022-02-06 09:00:00 2022-02-06 09:00:00      buy  3018.980000  1.420490   \n",
              "2022-02-18 16:00:00 2022-02-18 16:00:00     sell  2779.377168  1.306836   \n",
              "2022-02-27 06:00:00 2022-02-27 06:00:00      buy  2732.200000  1.305922   \n",
              "2022-02-27 10:00:00 2022-02-27 10:00:00     sell  2775.201481  1.325547   \n",
              "2022-03-02 21:00:00 2022-03-02 21:00:00      buy  2975.050000  1.324619   \n",
              "2022-03-03 17:00:00 2022-03-03 17:00:00     sell  2803.024617  1.247152   \n",
              "2022-03-18 05:00:00 2022-03-18 05:00:00      buy  2801.820000  1.246279   \n",
              "2022-03-24 00:00:00 2022-03-24 00:00:00     sell  3080.334867  1.369206   \n",
              "2022-03-26 03:00:00 2022-03-26 03:00:00      buy  3108.440000  1.368248   \n",
              "2022-03-28 14:00:00 2022-03-28 14:00:00     sell  3392.644514  1.492301   \n",
              "2022-03-30 03:00:00 2022-03-30 03:00:00      buy  3384.110000  1.491257   \n",
              "2022-04-06 18:00:00 2022-04-06 18:00:00     sell  3175.077929  1.398164   \n",
              "\n",
              "                             usd      coin       wallet       reason  \n",
              "date                                                                  \n",
              "2021-12-22 15:00:00     0.000000  0.562067  2252.336695       market  \n",
              "2021-12-22 18:00:00  2227.181818  0.000000  2227.181818    stop loss  \n",
              "2021-12-22 20:00:00     0.000000  0.555754  2225.622791       market  \n",
              "2021-12-23 00:00:00  2202.961753  0.000000  2202.961753    stop loss  \n",
              "2021-12-24 17:00:00     0.000000  0.537183  2201.419679       market  \n",
              "2021-12-28 01:00:00  2143.650514  0.000000  2143.650514    stop loss  \n",
              "2022-02-02 09:00:00     0.000000  0.771849  2142.149959       market  \n",
              "2022-02-02 21:00:00  2029.270832  0.000000  2029.270832    stop loss  \n",
              "2022-02-06 09:00:00     0.000000  0.671700  2027.850343       market  \n",
              "2022-02-18 16:00:00  1865.602167  0.000000  1865.602167    stop loss  \n",
              "2022-02-27 06:00:00     0.000000  0.682343  1864.296246       market  \n",
              "2022-02-27 10:00:00  1892.312438  0.000000  1892.312438  take profit  \n",
              "2022-03-02 21:00:00     0.000000  0.635615  1890.987819       market  \n",
              "2022-03-03 17:00:00  1780.398672  0.000000  1780.398672    stop loss  \n",
              "2022-03-18 05:00:00     0.000000  0.634999  1779.152393       market  \n",
              "2022-03-24 00:00:00  1954.639798  0.000000  1954.639798  take profit  \n",
              "2022-03-26 03:00:00     0.000000  0.628377  1953.271550       market  \n",
              "2022-03-28 14:00:00  2130.366769  0.000000  2130.366769  take profit  \n",
              "2022-03-30 03:00:00     0.000000  0.629080  2128.875512       market  \n",
              "2022-04-06 18:00:00  1995.979478  0.000000  1995.979478    stop loss  "
            ],
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              "      <th>date</th>\n",
              "      <th>position</th>\n",
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              "    <tr>\n",
              "      <th>2021-12-22 15:00:00</th>\n",
              "      <td>2021-12-22 15:00:00</td>\n",
              "      <td>buy</td>\n",
              "      <td>4007.240000</td>\n",
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              "      <th>2021-12-22 18:00:00</th>\n",
              "      <td>2021-12-22 18:00:00</td>\n",
              "      <td>sell</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-22 20:00:00</th>\n",
              "      <td>2021-12-22 20:00:00</td>\n",
              "      <td>buy</td>\n",
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              "      <td>0.555754</td>\n",
              "      <td>2225.622791</td>\n",
              "      <td>market</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-23 00:00:00</th>\n",
              "      <td>2021-12-23 00:00:00</td>\n",
              "      <td>sell</td>\n",
              "      <td>3966.691386</td>\n",
              "      <td>1.543153</td>\n",
              "      <td>2202.961753</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2202.961753</td>\n",
              "      <td>stop loss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-24 17:00:00</th>\n",
              "      <td>2021-12-24 17:00:00</td>\n",
              "      <td>buy</td>\n",
              "      <td>4098.080000</td>\n",
              "      <td>1.542073</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.537183</td>\n",
              "      <td>2201.419679</td>\n",
              "      <td>market</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-28 01:00:00</th>\n",
              "      <td>2021-12-28 01:00:00</td>\n",
              "      <td>sell</td>\n",
              "      <td>3993.334431</td>\n",
              "      <td>1.501606</td>\n",
              "      <td>2143.650514</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2143.650514</td>\n",
              "      <td>stop loss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-02 09:00:00</th>\n",
              "      <td>2022-02-02 09:00:00</td>\n",
              "      <td>buy</td>\n",
              "      <td>2775.350000</td>\n",
              "      <td>1.500555</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.771849</td>\n",
              "      <td>2142.149959</td>\n",
              "      <td>market</td>\n",
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              "    <tr>\n",
              "      <th>2022-02-02 21:00:00</th>\n",
              "      <td>2022-02-02 21:00:00</td>\n",
              "      <td>sell</td>\n",
              "      <td>2630.946493</td>\n",
              "      <td>1.421485</td>\n",
              "      <td>2029.270832</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2029.270832</td>\n",
              "      <td>stop loss</td>\n",
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              "    <tr>\n",
              "      <th>2022-02-06 09:00:00</th>\n",
              "      <td>2022-02-06 09:00:00</td>\n",
              "      <td>buy</td>\n",
              "      <td>3018.980000</td>\n",
              "      <td>1.420490</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.671700</td>\n",
              "      <td>2027.850343</td>\n",
              "      <td>market</td>\n",
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              "    <tr>\n",
              "      <th>2022-02-18 16:00:00</th>\n",
              "      <td>2022-02-18 16:00:00</td>\n",
              "      <td>sell</td>\n",
              "      <td>2779.377168</td>\n",
              "      <td>1.306836</td>\n",
              "      <td>1865.602167</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1865.602167</td>\n",
              "      <td>stop loss</td>\n",
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              "    <tr>\n",
              "      <th>2022-02-27 06:00:00</th>\n",
              "      <td>2022-02-27 06:00:00</td>\n",
              "      <td>buy</td>\n",
              "      <td>2732.200000</td>\n",
              "      <td>1.305922</td>\n",
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              "      <td>1864.296246</td>\n",
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              "      <th>2022-02-27 10:00:00</th>\n",
              "      <td>2022-02-27 10:00:00</td>\n",
              "      <td>sell</td>\n",
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              "      <th>2022-03-02 21:00:00</th>\n",
              "      <td>2022-03-02 21:00:00</td>\n",
              "      <td>buy</td>\n",
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              "      <td>2022-03-03 17:00:00</td>\n",
              "      <td>sell</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>2022-03-18 05:00:00</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.634999</td>\n",
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              "      <td>market</td>\n",
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              "      <th>2022-03-24 00:00:00</th>\n",
              "      <td>2022-03-24 00:00:00</td>\n",
              "      <td>sell</td>\n",
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              "      <td>1.369206</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>1954.639798</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-26 03:00:00</th>\n",
              "      <td>2022-03-26 03:00:00</td>\n",
              "      <td>buy</td>\n",
              "      <td>3108.440000</td>\n",
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              "      <td>0.628377</td>\n",
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              "      <th>2022-03-28 14:00:00</th>\n",
              "      <td>2022-03-28 14:00:00</td>\n",
              "      <td>sell</td>\n",
              "      <td>3392.644514</td>\n",
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              "      <td>2130.366769</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-30 03:00:00</th>\n",
              "      <td>2022-03-30 03:00:00</td>\n",
              "      <td>buy</td>\n",
              "      <td>3384.110000</td>\n",
              "      <td>1.491257</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.629080</td>\n",
              "      <td>2128.875512</td>\n",
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              "      <th>2022-04-06 18:00:00</th>\n",
              "      <td>2022-04-06 18:00:00</td>\n",
              "      <td>sell</td>\n",
              "      <td>3175.077929</td>\n",
              "      <td>1.398164</td>\n",
              "      <td>1995.979478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1995.979478</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df_trades[-20:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "0Xpj_72uODQW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a9ece5f7-31bf-4fc3-aaf5-0cb3fd15f7a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2021 Cumulative Performances: 114.37% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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iDAAAABhAcQYAAAAMoDgDAAAABlCcAQAAAAMozgAAAIABFGcAAADAAIozAAAAYADFGQAAADCA4gwAAAAYQHEGAAAADKA4AwAAAAZQnAEAAAADKM4AAACAARRnAAAAwACKMwAAAGCAq70D/NGtW7c0efJkRUZGymQyqWLFipo7d65KlSolPz8/1ahRQ87Ov3X9kJAQ+fn52TkxAAAA8guHKs5OTk4aMWKEmjRpIkkKDg7WkiVLtHDhQknS5s2bVaRIEXtGBAAAQD7lUEs1SpQoYS3NklS/fn1FR0fbMREAAADwG4cqzn+UmZmpTZs2qW3bttZtgwYNUmBgoF599VWlpqbaMV3eYzabNW7cC2rQoLYqVy6vNm3+qX37Pr3nsUuWvKIyZYrpwIEvrdvefHO5/P0rqUWLR3X69Cnr9vDwbzV48FM2z5/fcL4AAMh5DrVU44/mzZunwoULa+DAgZKk/fv3y8vLS4mJiZo0aZJWrFih8ePHG34+Dw93W0XNdYKCgrL8V5KSkpxVvXoVLVgwV76+vgoLC9NTTz2lkydPqlKlStbjLl68qLCwj+Xl5aUSJQrL07OoYmJitGXLe7p8+bJCQ0O1ePF87dq1S+np6Zo/f6Y2b94sT8+iOTvIPITzlbPy89gBAPfnkMU5ODhYP/30k1atWmX9MKCXl5ckyd3dXb1799a6dese6jnj4xOVmWnJ9qy5UVKSWZIUF5eQZfsLL7wkSYqPT1KTJq3k41NRX3zxlZ54wsN6zDPPjNS0abM1ZcpL+uWXZMXFJej48TOqVauOzGYnNWjQTG+88abi4hK0YsXrateuo4oU8bjrtWBcbj5fubGEcq0CQP7k7Oz0wIlWh1uqsXTpUkVERGjFihUymUySpNu3byslJUWSlJ6err1796pmzZr2jJnnxcbG6tKlC/L3/++f88cfb5ebm5see6xjlmMrV66iM2dO6/btX3Tw4Jfy86upqKiftWPHNj3//Jicjp4vcb4AALA9h5pxPn/+vFavXq1KlSqpX79+kqQKFSpoxIgRmjVrlpycnJSenq6AgACNHTvWzmnzrrS0NI0aNUJ9+vRX9eo1JEmJiQlasGCOtm796K7jS5Xy0LhxE9WjxxPy9PTU4sWvafr0KZo5c47CwnZq3bq1Kl68uF555VWVL++d08PJ8zhfAADkDIcqztWrV9e5c+fuuW/nzp05nCZvGTCgt8LDv5Ukmc2/zd6vWbNSktSkSVNt3LhV0m8fynzhhWdkMhXQK68ssT4+JGSRevfuJ1/fivd8/h49eqtHj96SpM8+2yM3N5Pq1q2nNm3+qUOHwrVnT5iCgqZrzZr1thpinsL5AgDA8ThUcYbt/F60JCkk5Lf7Yk+ePC3LMRaLRePGvaC4uDj9+98fqECBAtZ9hw4dUExMlNatWytJio+/oWeeeVovvjheY8b890Oad+7c0YIFc7V58zZdunRR5ct7q2jRYgoIaKjly1+15RDzFM4XAACOh+IMq0mTxuv8+XPauvVjFSpUKMu+bds+VlpauvX7jh1ba+7chWrbtn2W45YtW6x+/fqrXDkvOTk56eLF84qNjdVXXx1UxYqVcmIY+QbnCwCAnEVxhiTp6tVIhYb+S25ubqpTp7p1+5Ilr6lXr74qVcojy/EuLi4qXryE3N3/++nT8+d/1P79+/TJJ19IksqWLafRoyeoZctHVbq0Jz/2z0acLwAAcp6TxWLJF/do43Z0QM7z9CyqIetyzwd51w9dzu3oACCfypW3owMAAAAcEUs18qBixd3k9v/3wHZ05tRU/XrbbO8Ydsc5AwDA8VGc8yA3kynX/Hh8/dDlkihhnDMAABwfSzUAAAAAAyjOAAAAgAEUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABhAcQYAAAAMoDgDAAAABlCcAQC4B7PZrHHjXlCDBrVVuXJ5tWnzT+3b96l1/5Ej36lXr0DVqOGrmjUra/jwwbp+/Zp1/7Zt76tOnepq2LCOvvrqoHX75cuX1KXLY8rIyMjR8QD4+yjOAIB8LyRkoUJCFmbZlp6ervLlvbVjR5guXvxZL788QyNGDFFk5E+SpNu3f9HgwUN09GiEjh49JXd3d40ZM8r62Pnzg7Rv3yEtWrRY06ZNsj7v9OmTNW/eIrm4uOTU8ABkE37lNgAA91CkSBFNnjzN+n2HDp3l61tRx4//IF/fimrXrkOW44cPf1aBgV0kSTdv3lS5cl4qW7acWrZso59+GiJJ2rlzh7y8yqthw8Y5Ng4A2YfiDACAAbGxsbp06YL8/Wvec/833/xH/v7+kqTSpUvr1q2bio6O0smTJ+Tn56/ExAQtXbpY27Z9nJOxAWQjijMAAA+QlpamUaNGqE+f/qpevcZd+0+ditCrrwYrNHSzJMnZ2VkhIcs0fPggmUxuevXVNxQcvFAjRozU6dOn9OqrwSpQoIDmzFmomjVr5fRwAPxFFGcAQL40YEBvhYd/K0kym1MkSWvWrJQkNWnSVBs3bpUkZWZm6oUXnpHJVECvvLLkrue5dOminnqqp+bPD1bTpv+wbm/ZsrVatmwtSYqIOKnjx79XUNB8NWxYRzt37lVUVJQmTHhRn3zyhS2HCSAbUZwBAPnS78VYkvWDgX9c0yxJFotF48a9oLi4OP373x+oQIECWfZfvRqp3r0DNWHCZPXp89Q9X8disejllydq4cLFio+PV0ZGhnx8fFWmTFmdPn0qm0cFwJYozgAA/IlJk8br/Plz2rr1YxUqVCjLvpiYaPXo0VXDhj2rIUOG/+lzvPfeu6pX7xHVrVtP6enpSklJ0blzZxUVdVUVK1ay8QgAZCeKMwAA93D1aqRCQ/8lNzc31alT3bp9yZLX1KtXX7333rv66acrWrx4kRYvXmTdf+VKjPXr+Ph4vf32Su3e/ZkkydXVVYsWLVaPHl1VsGBBLV/+Vs4NCMDfRnEGAOR7/7tEQ5J8fHwVG/vrnz5m0qSXNWnSy/d9Xg8PDx08GJ5lW69efdWrV9+/FhSAXfELUAAAAAADmHEGAOR5xYq7yc1ksncMw8ypqfr1ttneMQD8D4ozACDPczOZNGTdWHvHMGz90OWSKM6Ao2GpBgAAAGAAxRkAAAAwgOIMAAAAGEBxBgAAAAygOAMAAAAGUJwBAAAAAyjOAAAAgAEUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABhAcQYAAAAMoDgDAAAABlCcAQAAAAMozgAAAIABrvYO8Ltbt25p8uTJioyMlMlkUsWKFTV37lyVKlVKP/zwg2bNmiWz2Sxvb28tXrxYHh4e9o4MAACAfMRhZpydnJw0YsQI7d27Vzt37pSPj4+WLFmizMxMTZo0SbNmzdLevXvVqFEjLVmyxN5xAQAAkM84THEuUaKEmjRpYv2+fv36io6OVkREhNzc3NSoUSNJUr9+/bRnzx57xQQAAEA+5TDF+Y8yMzO1adMmtW3bVjExMSpfvrx1X6lSpZSZmalffvnFjgkBAIAjeued1WrfvpUqVCit0aOfu2v/e++9q0cffUSVKnmpb9/uunYtxrpv27b3VadOdTVsWEdffXXQuv3y5Uvq0uUxZWRk5MgY4LgcZo3zH82bN0+FCxfWwIED9dlnn2XLc3p4uGfL8yD7eXoWtXcEPKS8fM7y8tiQu3At3l9QUFCW//6uRo0qCgr6bXnnnTt3svw57t+/X4sWzdWXX36p6tWra+zYsXrxxWd04MABpaena+HCOfrhh+919OhRTZ06RREREZKkIUOm6c03X1e5ciVyanhwUA5XnIODg/XTTz9p1apVcnZ2lpeXl6Kjo637b968KWdnZ5Uo8XAXb3x8ojIzLdkd1yHltr9s4+IS7B3B7vLqOctt45K4HvMqrsW8JynJLOnuP6cWLdpLkg4e/I9u3fo1y/733/9QXbsGqkwZX92+bdbzz49XvXp++u674ypSxF1lypSTq6u7HnmkiS5duqS4uATt3LlDHh5lVKVKLc5JHufs7PTAiVaHWqqxdOlSRUREaMWKFTKZTJKkOnXqKCUlRUeOHJEkbd68WZ06dbJnTAAAkEtZLJa7vj579oxKly6tW7duKjo6SgcOfCk/P38lJiZo6dLFmj59tr3iwsE4zIzz+fPntXr1alWqVEn9+vWTJFWoUEErVqxQSEiIZs+eneV2dAAAAA+jbdvHNHLkUD399HBVqVJVS5YEy8nJSXfuJMvZ2VkhIcs0fPggmUxuevXVNxQcvFAjRozU6dOn9OqrwSpQoIDmzFmomjVr2XsosBOHKc7Vq1fXuXPn7rmvQYMG2rlzZw4nAgAAjmTAgN4KD/9WkmQ2p0iS1qxZKUlq0qSpNm7cet/Ht2rVRpMmTdOwYQOVkJCgkSOfl7t7UZUv7y1JatmytVq2bC1Jiog4qePHv1dQ0Hw1bFhHO3fuVVRUlCZMeFGffPKFjUYIR+cwxRkAAOB+/liMQ0IWSpImT572UM8xfPizGj78WUnSxYvntWzZYvn718xyjMVi0csvT9TChYsVHx+vjIwM+fj4qkyZsjp9+tTfHAVyM4ozAADIM9LT05Wenq6MjExlZGQoJSVFrq6ucnV1VUpKii5fviR//5qKivpZL700Vs88M0olSpTM8hzvvfeu6tV7RHXr1lN6erpSUlJ07txZRUVdVcWKlewzMDgEijMAAMgzli4N0ZIlr1i//+CDLZo4caomT54mszlFzz03XD/9dFlFirjrqacGaurUGVkeHx8fr7ffXqndu3+7Ha6rq6sWLVqsHj26qmDBglq+/K0cHQ8cC8UZAADkOn+2RGPy5Gl/uq948RI6cOCb+z6vh4eHDh4Mz7KtV6++6tWr718LijzFoW5HBwAAADgqZpwBAIDDKVbcTW7//zsdcgNzaqp+vW22dwzYGMUZAAA4HDeTSUPWjbV3DMPWD10uieKc17FUAwAAADCA4gwAAAAYQHEGAAAADKA4AwAAAAZQnAEAAAADKM4AAACAARRnAAAAwACKMwAAAGAAxRkAAAAwgOIMAAAAGEBxBgAAAAygOAMAAAAGUJwBAAAAAyjOAAAAgAEUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABhAcQYAAAAMoDgDAAAABlCcAQAAAAMozgAAAIABFGcAAADAAIozAAAAYADFGQAAADCA4gwAAAAYQHEGAAAADKA4AwAAAAZQnAEAAAADKM4AAACAARRnAAAAwACKMwAAAGAAxRkAAAAwgOIMAAAAGEBxBgAAAAygOAMAAAAGUJwBAAAAA1ztHeCPgoODtXfvXkVFRWnnzp2qUaOGJKlt27YymUxyc3OTJE2cOFEtWrSwZ1QAAADkMw5VnNu1a6fBgwdrwIABd+17/fXXrUUaAAAAyGkOVZwbNWpk7wgAAADAPTlUcb6fiRMnymKxqGHDhpowYYKKFSv2UI/38HC3UTL8XZ6eRe0dAQ8pL5+zvDw25C5ci7kP5yzvyxXFeePGjfLy8lJqaqoWLFiguXPnasmSJQ/1HPHxicrMtNgooWPJbW/cuLgEe0ewu7x6znLbuCSux7yKazH34Zwhpzk7Oz1wojVX3FXDy8tLkmQymdS/f38dO3bMzokAAACQ3zh8cU5OTlZCwm//grNYLAoLC1PNmjXtnAoAAAD5jUMt1Zg/f74+/fRT3bhxQ0OHDlWJEiW0atUqjR49WhkZGcrMzFTVqlU1e/Zse0cFAABAPuNQxXnGjBmaMWPGXdt37NhhhzQAAADAfzn8Ug0AAADAEVCcAQAAAAMozgAAAIABFGcAAADAAIozAAAAYADFGQAAADCA4gwAAAAYQHEGAAAADKA4AwAAAAZQnAEAAAADKM4AAACAARRnAAAAwACKMwAAAGAAxRkAAAAwgOIMAAAAGEBxBgAAAAygOAMAAAAGUJwBAAAAA1wf5uD09HRt27ZN586dk4+Pj/r06aMiRYrYKhsAAADgMB5qxnnhwoU6efKk/Pz8FBERoXHjxtkqFwAAAOBQ7jvjvH79eg0ePFjOzr/16x9//FHvvfeeJKlHjx5q1qyZ7RMCAAAADuC+M85ms1n9+/fXDz/8IElq2LChRowYoWXLlmnYsGFq2bJljoQEAAAA7O2+M84jR60rPa4AACAASURBVI5Uly5dtGDBApUqVUqTJk3SiRMn9OOPP6p///7q0KFDTuUEAAAA7OqBHw708fHRqlWr9Pnnn2vo0KF66qmn9Mwzz+RENgAAAMBh3HepRlJSktatW6e5c+cqNjZW69ev108//aT+/fvrzJkzOZURAAAAsLv7zjiPGzdO7u7uatKkicLDw3Xu3DnNmTNH586d0/z581WjRg3NnDkzp7ICAAAAdnPf4vzDDz8oPDxczs7O6tGjh3r16iVJ8vPz04YNG7Rt27YcCQkAAADY232Lc/PmzTVp0iQ1adJE3377rVq1apVlf8+ePW0aDgAAAHAU9y3OS5Ys0aeffqqff/5ZgYGBdxVnAAAAIL+4b3F2cXFR586dcyoLAAAA4LAe6lduAwAAAPkVxRkAAAAwgOIMAAAAGGC4OKelpenIkSMKCwuTJCUnJys5OdlmwQAAAABH8sBfuS1J586d06hRo2QymXT9+nV16dJFhw8f1vbt2/Xaa6/ZOiMAAABgd4ZmnIOCgjRmzBjt2bNHrq6/de3GjRvr6NGjNg0HAAAAOApDxfnChQsKDAyUJDk5OUmSChcuLLPZbLtkAAAAgAMxVJy9vb0VERGRZduJEyfk6+trk1AAAACAozG0xnns2LEaOXKk+vXrp7S0NK1evVqbN2/WvHnzbJ0PAAAAcAiGZpzbtGmjtWvX6ubNm2rcuLGioqL0xhtvqHnz5rbOBwAAADgEQzPOklSrVi0FBQXZMAoAAADguAzNOL/44os6cuRIlm1HjhzRmDFjbBIKAAAAcDSGivPhw4cVEBCQZVv9+vUVHh5uk1AAAACAozFUnE0mk+7cuZNlW3JysvWeztklODhYbdu2lZ+fn3788Ufr9suXL6tv377q2LGj+vbtqytXrmTr6wIAAAAPYqg4N2/eXLNmzVJiYqIkKTExUXPnzlWLFi2yNUy7du20ceNGeXt7Z9k+e/Zs9e/fX3v37lX//v01a9asbH1dAAAA4EEMFeepU6cqMTFRjz76qJo1a6ZHH31UiYmJmjZtWraGadSokby8vLJsi4+P1+nTp9W1a1dJUteuXXX69GndvHkzW18bAAAAuB9Day2KFy+uNWvWKC4uTjExMfLy8pKnp6ets0mSYmJiVLZsWbm4uEiSXFxcVKZMGcXExKhUqVI5kgEAAAB4qEXKzs7OKlmypFJSUnT16lVJko+Pj02CZTcPD3d7R8Cf8PQsau8IeEh5+Zzl5bEhd+FazH04Z3mfoeJ88OBBTZ8+XXFxcVm2Ozk56cyZMzYJ9jsvLy9dv35dGRkZcnFxUUZGhmJjY+9a0vEg8fGJysy02CilY8ltb9y4uAR7R7C7vHrOctu4JK7HvIprMffhnCGnOTs7PXCi1VBxnjt3rp5//nl1795dBQsWzJZwRnl4eKhmzZratWuXAgMDtWvXLtWsWZNlGgAAAMhRhorzr7/+qn79+snJycmmYebPn69PP/1UN27c0NChQ1WiRAnt3r1bQUFBmjp1qt566y0VK1ZMwcHBNs0BAAAA/C9Dxblnz57atm2bevXqZdMwM2bM0IwZM+7aXrVqVW3dutWmrw0AAADcj6HifPz4cW3YsEFvv/22SpcunWXfxo0bbRIMAAAAcCSGinPv3r3Vu3dvW2cBAAAAHJah4ty9e3db5wAAAAAcmuH7ON+4cUMnTpzQrVu3ZLH897Zutl73DAAAADgCQ8X5888/16RJk1SxYkVduHBB1apV0/nz59WgQQOKMwAAAPIFQ8X5tdde08KFC9W5c2c1btxYO3bs0LZt23ThwgVb5wMAAAAcgrORg6Kjo9W5c+cs27p3764dO3bYJBQAAADgaAwVZw8PD924cUOS5O3tre+//16RkZHKzMy0aTgAAADAURgqzr1799bRo0clSUOGDNHgwYMVGBiop556yqbhAAAAAEdhaI3zs88+a/26W7duevTRR3Xnzh1VrVrVZsEAAAAAR2L4dnR/VL58+ezOAQAAADg0Q8X57NmzWrhwoc6ePavk5GRJksVikZOTkyIiImwaEAAAAHAEhorzhAkT1KFDB82YMUMFCxa0dSYAAADA4Rgqzjdu3NDYsWPl5ORk6zwAAACAQzJ0V41u3bpp586dts4CAAAAOCzDd9Xo27evVq9eLQ8Pjyz7QkNDbRIMAAAAcCSGivOYMWNUoUIFtW/fXm5ubrbOBAAAADgcQ8X5zJkzCg8Pl8lksnUeAAAAwCEZWuPcqFEjXbx40dZZAAAAAIdlaMa5QoUKGjZsmNq3b3/XGuexY8faJBgAAADgSAwV55SUFLVu3VppaWm6du2arTMBAAAADueBxTkjI0PlypXTqFGjWOMMAACAfOuBa5xdXFy0adMmuboampwGAAAA8iRDHw4MDAzUpk2bbJ0FAAAAcFiGppFPnDih9957T++8847KlSuX5Vdvb9y40WbhAAAAAEdhqDj36dNHffr0sXUWAAAAwGEZKs7du3e3dQ4AAADAoRn+xN+2bdv00Ucf6fr16ypbtqwCAwPVs2dPW2YDAAAAHIah4rxy5Urt2LFDw4YNU/ny5RUdHa21a9cqNjZWo0aNsnVGAAAAwO4MFeetW7dqw4YN8vb2tm5r3ry5Bg4cSHEGAABAvmDodnR37txRqVKlsmwrUaKEUlJSbBIKAAAAcDSGinOLFi00ceJEXbp0SSkpKbp48aKmTp2q5s2b2zofAAAA4BAMFedZs2apSJEievLJJxUQEKBu3bqpUKFCmjlzpq3zAQAAAA7hT9c4v/feexo4cKAkKT4+XiEhIXrllVd069YtlSxZUs7Ohjo3AAAAkCf8aftdtmyZ9evf7+Ps7OwsDw8PSjMAAADynT+dcfbx8dErr7yiatWqKT09XR988ME9j+vVq5fNwgEAAACO4k+L87Jly7R27Vrt3r1b6enp+uijj+46xsnJieIMAACAfOFPi3PlypW1YMECSdKQIUO0fv36nMoEAAAAOJwHLlbOyMjQsWPHlJqamhN5AAAAAIf0wOLs4uKiSpUq6datWzmRBwAAAHBIhn7l9hNPPKHnnntOgwcPVrly5bLsa9asmU2CAQAAAI7EUHHetGmTJOmNN97Ist3JyUn79u3L/lQAAACAgzFUnL/44gtb5wAAAAAcmuHfZJKWlqYjR44oLCxMkpScnKzk5GSbBQMAAAAciaEZ53PnzmnUqFEymUy6fv26unTposOHD2v79u167bXXbJ1RktS2bVuZTCa5ublJkiZOnKgWLVrkyGsDAAAAhopzUFCQxowZo27duqlx48aSpMaNG2vGjBk2Dfe/Xn/9ddWoUSNHXxMAAACQDC7VuHDhggIDAyX99oFASSpcuLDMZrPtkgEAAAAOxNCMs7e3tyIiIlS3bl3rthMnTsjX19dmwe5l4sSJslgsatiwoSZMmKBixYoZfqyHh7sNk+Hv8PQsau8IeEh5+Zzl5bEhd+FazH04Z3mfoeI8duxYjRw5Uv369VNaWppWr16tzZs3a968ebbOZ7Vx40Z5eXkpNTVVCxYs0Ny5c7VkyRLDj4+PT1RmpsWGCR1HbnvjxsUl2DuC3eXVc5bbxiVxPeZVXIu5D+cMOc3Z2emBE62Glmq0adNGa9eu1c2bN9W4cWNFRUXpjTfeUPPmzbMlqBFeXl6SJJPJpP79++vYsWM59toAAACAoRlnSapVq5aCgoJsGOXPJScnKyMjQ0WLFpXFYlFYWJhq1qxplywAAADInwwV59TUVK1cuVK7d+9WbGysypQpoy5dumjUqFHW28PZUnx8vEaPHq2MjAxlZmaqatWqmj17ts1fFwAAAPid4dvRXb58WdOnT5e3t7eioqK0evVqXb9+XYsWLbJ1Rvn4+GjHjh02fx0AAADgzxgqzvv27dNnn31mvYtFtWrV9Mgjj6hDhw42DQcAAAA4CkMfDixdurTu3LmTZZvZbJanp6dNQgEAAACOxtCMc2BgoEaMGKFBgwapbNmyunbtmjZu3KjAwEB988031uOaNWtms6AAAACAPRkqzps3b5YkrVq16q7tv+9zcnLSvn37sjkeAAAA4BgMFecvvvjC1jkAAAAAh2ZojTMAAACQ31GcAQAAAAMozgAAAIABFGcAAADAAIozAAAAYADFGQAAADCA4gwAAAAYQHEGAAAADKA4AwAAAAZQnAEAAAADKM4AAACAARRnAAAAwACKMwAAAGAAxRkAAAAwgOIMAAAAGEBxBgAAAAygOAMAAAAGUJwBAAAAAyjOAAAAgAEUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABhAcQYAAAAMoDgDAAAABlCcAQAAAAMozgAAAIABFGcAAADAAIozAAAAYADFGQAAADCA4gwAyFa3bt3U00/3V6VK5dSgQW1t2/a+dV9ExEm1aPGo/P0raeXKN63b09LS1KlTG0VF/WyPyECuw/vMPlztHQAAkDuFhCyUJE2ePC3L9qlTX5LJZFJExAVFRJzUgAG9Vbt2Xfn719SCBUEKCpqvWrXqqHXrZurRo7fKli2rlSvf1OOPB8rbu4I9hgI4LN5njoXiDADINklJSdq162MdPPit3N3d1bRpM3Xs2Flbt27WzJlzFBn5k5o3byU3NzdVqVJVUVFXlZpq1u7dH2nXrs/sHR/IFXif2Q9LNQAA2ebSpQtydXVV1arVrdtq166rc+fOSJL8/Wtp//4vFB0dpcjISFWqVFnTp0/R7NnzVaBAAXvFBnIV3mf2w4wzACDbJCUlyd29aJZtxYoVU2JioiQpKGi+Jk8er9jYWM2bt0jffRcud3d3+fpW1ODB/XT79m0NH/6snnyyuz3iA7kC7zP7oTgDAAwbMKC3wsO/lSSZzSmSpDVrVkqSmjRpqqlTZygxMSHLYxISEuTu7i5J8vHx1aZN2yRJycnJ6tLlMb3//g5NmzZJgYE91L59R7Vs2VQtWrRSyZKlcmpYgEPhfea4KM4AAMM2btxq/fpeH1pKSkpSenq6Ll26oCpVqkmSTp06KT+/mnc916uvBmvQoKdVpkwZnTlzSi+/PEPFihWXl1d5Xb58if+hI9/ifea4WOMMAMg2RYoU0eOPP6Hg4AVKSkpSePi32rMnTL1798ty3LlzZ/X114c0ZMgISZKvb0UdOnRQsbGxunz5ory9fewRH8gVeJ/ZT64pzpcvX1bfvn3VsWNH9e3bV1euXLF3JADAPQQHL9WdOymqXbuqnntumEJClsrfP+tM2NSpL2n+/GC5uLhIkqZPD9LatavUsuWjGjt2osqWLWuP6ECuwfvMPnLNUo3Zs2erf//+CgwM1EcffaRZs2YpNDTU3rEAIN/63/vK/q5kyVIKDd1038du3747y/d16tTVV18dzrZsQF7B+8yx5IoZ5/j4eJ0+fVpdu3aVJHXt2lWnT5/WzZs37ZwMAAAA+UWuKM4xMTEqW7as9UcNLi4uKlOmjGJiYuycDAAAAPmFk8Visdg7xINERERoypQp2r37vz9y6NKlixYvXqzatWv/pefMSE2Tiyn33AT8YfKmpqfJ5Jo7xvYwWTlnjuFhsuamcUnG8+blazGvyqvXopS7zm9e/XtRyn15bSGvXot/lCvWOHt5een69evKyMiQi4uLMjIyFBsbKy8vL8PPER+fqMzM//4bwdOzqMIGD7VFXJvoErpOcXEJDz7QKsVmWbKfsaycM0fyMFlz07gkI3nz/rWYV+W9a1HKXddj3v57Ucp9ebNXbr8WnZ2d5OHhft/H5YqlGh4eHqpZs6Z27dolSdq1a5dq1qypUqW49yAAAAByRq6YcZakoKAgTZ06VW+99ZaKFSum4OBge0cCAABAPpJrinPVqlW1devWBx8IAAAA2ECuWKoBAAAA2BvFGQAAADCA4gwAAAAYQHEGAAAADKA4AwAAAAZQnAEAAAADKM4AAACAARRnAAAAwACKMwAAAGAAxRkAAAAwgOIMAAAAGEBxBgAAAAygOAMAAAAGUJwBAAAAAyjOAAAAgAEUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABhAcQYAAAAMoDgDAAAABlCcAQAAAAMozgAAAIABFGcAAADAAIozAAAAYADFGQAAADCA4gwAAAAYQHEGAAAADKA4AwAAAAZQnAEAAAADKM4AAACAARRnAAAAwACKMwAAAGAAxRkAAAAwgOIMAAAAGEBxBgAAAAygOAMAAAAGUJwBAAAAAyjOAAAAgAEUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABjgau8ADzJ16lR9/fXXKlmypCSpU6dOGjVqlJ1TAQAAIL9x+OIsSc8++6wGDhxo7xgAAADIx1iqAQAAABiQK4rzunXr9MQTT+j555/XxYsX7R0HAAAA+ZDdl2p0795d0dHR99z39ddfa/z48fL09JSzs7N27NihESNG6PPPP5eLi8tDvY6Hh3t2xLUrT8+i9o6Ah8Q5g6PgWoSj4FqEo/gr16Ldi/P27dvvu79s2bLWr7t166ZFixbp2rVr8vb2fqjXiY9PVGamxfp9bnzjxsUl2DuCXXHO4Ci4FuFIctv1yLWYd+X2a9HZ2emBE60Ov1Tj+vXr1q8PHTokZ2fnLGUaAAAAyAl2n3F+kClTpig+Pl5OTk5yd3fXypUr5erq8LEBAACQxzh8A12/fr29IwAAAACOv1QDAAAAcAQUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABjg8HfVAAAAfy4txawuoevsHcOQtBSzvSMAfwvFGQCAXOyXhFQpIdXeMYB8gaUaAADkcxkZGVq0aK7q1q2hypXLq23b5rp9+xdJ0sGD+9WoUV3Vrl1N27d/YH3M7du/qF27FkpM5FdoI/9gxhkAgHwuJGSBDh/+TmFhn6tCBR+dPXtGbm4FJUkzZ07Vhg1blJGRoR49HteTT3aXi4uL5s+fozFjxsvdvaid0wM5h+IMAEA+9ssvt7R69Up9+eV/5OPjK0mqWbOWdX9SUrL1+wIFTLp586auXv1JkZFXFBi4zC6ZAXthqQYAAPnYmTOn5erqqp07P1Lt2tXUtGmA3nlnjXW/p2dpRUScVETESTk7O6tEiRKaPn2KFi4MsWNqwD6YcQYAIB+Ljo7Sr7/e1qVLF3TkyEldunRRvXo9oapVq6l167YKCXlNM2ZMUUrKHa1YsUbr169Vq1atlZJiVp8+3ZSWlqZJk17WP/7R3N5DAWyO4gwAQB72wQdbNHHiOElS06bN9O2331j3ffXVdypYsJAk6aWXpqhQoUKqXbuOunXrqX37PlXr1m1Vt2497dgRJkm6fv2aZs+errCwz9WtW2fNmxescuXKKTCws44dOyUnJ6ecHyCQgyjOAADkYb169VWvXn3/dH9aWpokZSm9f1aAZ86cqpdfnqFChQrpzJnTql8/QCaTSWlpabpx44Y8PT2zNzzgYFjjDABAPla5chU1bfoPLVu2RGazWT/+eE47dmxT+/adshy3f/8XSkkxq0OHzpIkX9+K+uqrAzp79oxSU80qVaqUPeIDOYoZZwAA8rnVq/+lceNekJ9fJZUu7akpU2aoZcvW1v1ms1lz5sxUaOgm67aFCxdr/PgXZTabFRy8VC4uLnZIDuQsijMAAPmcl1d5bdmy/U/3u7m56csv/5NlW8uWrXX0aIStowEOhaUaAAAAgAEUZwAAAMAAijMAAABgAMUZAAAAMIDiDAAAABhAcQYAAAAMoDgDAAAABlCcAQAAAAMozgAAAIAB+fY3B6almNUldJ29YxiWlmK2dwS745wBAAB7yrfF+ZeEVCkh1d4x8BA4ZwAAwJ5YqvE3HTp0QO3atVCVKt5q1KieQv8wIxoRcVItWjwqf/9KWrnyTev2tLQ0derURlFRP9sjcr7HOQMAAH8FxflvSEtL05AhAzR48FBdvPiz3n57nWbNmqaIiJOSpAULghQUNF9ffvm1Xnttsa5fvy5JWrnyTT3+eKC8vSvYM36+xDkDAAB/Vb5dqpEdbt26pYSEX9W7dz85OTkpIKChatSooR9/PKs6deoqMvInNW/eSm5ubqpSpaqioq4qNdWs3bs/0q5dn9k7fr7EOQMAAH8VM85/Q5kyZdSjRy9t2vSeMjIydPhwuH7++aqaNGkmSfL3r6X9+79QdHSUIiMjValSZU2fPkWzZ89XgQIF7Jw+f+KcAQCAv4oZ57+pe/feGj/+Rc2YMUWSFBKyzPrj/KCg+Zo8ebxiY2M1b94iffdduNzd3eXrW1GDB/fT7du3NXz4s3ryye72HEK+wzkDAAB/BcX5Pj74YIsmThwnSWratJm+/fYb676vvvpOd+7c0ciRQ/Wvf72n1q3b6tKlixowoLfKlSun9u07ycfHV5s2bZMkJScnq0uXx/T++zs0bdokBQb2UPv2HdWyZVO1aNFKJUuWsssY8xrOGQAAsBWWatxHr159deVKjK5cidHmzR9av75yJUYVKvjo7NnTqlKlmtq2fUzOzs6qVq262rfvqH377l4L++qrwRo06GmVKVNGZ86cUv36ASpWrLi8vMrr8uVLdhhd3sQ5AwAAtkJx/hvq1KmnS5cu6tChA7JYLLp8+ZI+/XSPatWqk+W4c+fO6uuvD2nIkBGSJF/fijp06KBiY2N1+fJFeXv72CN+vsQ5AwAAfxXF+W+oXLmKli9foenTJ6tKFW9169ZFXbsGauDAp7McN3XqS5o/P1guLi6SpOnTg7R27Sq1bPmoxo6dqLJly9ojfr7EOQMAAH+Vk8Visdg7RE6Ij09UZma+GCoAGytR1KQCBd3sHcOwtBTzb795EwBsyNOzqMIGD7V3DEO6hK5TXFxClm3Ozk7y8HC/7+P4cCAAPKTs+vXve/d+ogULghQZGalatWpr2bI35efnL0k6eHC/JkwYrTt37mj+/FfUvXsvSdLt27+oR48n9NFHYXJ3L/q3MwAAjGOpBgDYwaVLFzRq1AiFhLymCxeuqmPHzho0qK/S09MlSTNnTtWGDVu0Zct2TZkyQRkZGZKk+fPnaMyY8ZRmALADijMA2MGXX+5T06bN1LRpM7m6umr06PG6di1GX3/9lSQpKSlZNWvWUp06dVWggEk3b97UsWNHFBl5RYGBPeycHgDyJ5ZqAICd/PEjJhaLRRaLRWfPnlbLlq3l6VlaEREnJUnOzs4qUaKEBg/upzffXGWvuACQ7zHjDPxfe3ceF3W1/w/8NcqAC3WltJtp5IKgaIJaYFcRGRMHAoZhEcylbl4TC1CzFNRc+CaJ9tAEVExRrhaVVzBSsnLfd1KTq1fFRKEAjWQRGQbm/P7gxydHFkfWMV/Pv/js7/M5c86858z5MEQtYNgwFxw9ehiHDx9EWVkZPv30E5SVleHu3bsAgCVLPsXcubPw/vuhWLnyMyQkrIOz83CUlmowerQ31OrXpNFpIiJqHhxxJiJqAg/6FctevawRExOH8PD3kZubAz+/ANjY9Ebnzs8BAF58sT+++eY7AEBubg7mz5+D777bBW9vN/zf/0Xh2WefhUrlhrS0dMhksuYvIBHRY8go/h1dSkoK1q1bh4yMDMyePRvjxo2Ttt29exfh4eFIT09H69atMWvWLLi4uDz0Nfjv6IjImBUU3IadXR/s3LkfvXpZ6217++034ecXAFdXN1haPoMrV7JgamqKfv16Ye/eI+jUqVMLRU1E9KfH4d/RGcVUjT59+mD58uXw8PCoti0+Ph7m5ubYuXMn4uLiMHfuXNy5c6cFoiQialxnz/6EiooK3Lp1CzNmTIVS6VYtad63bw9KSzVwdXUDUPkrlocO7cfFixdQVqbBU0891RKhExE9loxiqoa1deUbRatW1fP4HTt2YPHixQCAbt26oV+/fjhw4ADc3NyaNUYiosY2Z84spKefh1xuAk9PNSIiIvW2azQaLFz4ITZu/FJaFxm5FNOnB0Oj0SAqapn065ZERNT0jCJxrsuvv/6KLl26SMudO3dGTk7OQ5/nQUPvRETN7fjxow/Y4wmcP39Ob42vryd8fT2bLigiosdEp04P///wmyVxVqvV+PXXX2vcduTIkWYZMeEcZyIiIqKmU59EtCUZ7U9ub926td7HPvfcc8jOzpbm8f32229wdHRsrNCIiIiIiAxiFA8H1kWpVOLrr78GAFy7dg0///wznJycWjgqIiIiInrcGEXivH37dgwbNgzff/89VqxYgWHDhuHKlSsAgIkTJ6KwsBAjR47E5MmTERERAXNzzlcmIiIiouZlFP/HuTlwjjMRERFR0+H/cSYiIiIiIgBMnImIiIiIDMLEmYiIiIjIAEyciYiIiIgMwMSZiIiIiMgA/K8aRERERNRgHZ4whbyNWUuHYRBtqQa3i8r01hnNLwcSERER0V/b7aIy4L5k1BAzZoTiyJFDuHo1AytWrEJg4Fhp24UL/8X8+bNx7twZ5OfnIy+vUO/YuXNn4euvv4SVVS/Ex2/Ec891AQAkJW3G6dMnERm5tGGFug+nahARERFRi7G17YeoqGXo39++2ja5XA6VygfLl6+sti0t7RTOnj2D8+cvw9HxFURHLwMAFBYWYOXKaISHf9josXLEmYiIiIhazMSJbwMAzMyqT/OwsuoFK6teuHo1o9q269cz4ej4CszMzDBsmDPWro0DAERGRuDdd0PxxBNPNnqsHHEmIiIiokeOjU0fHDt2BHfv3sWBA/thY9MHZ86k4cqVK/D1Hd0k12TiTERERESPnD59bOHh4QU3txHIzs5CcPA0zJ49E5GRS7B27Wp4eSkRFDQRBQW3G+2aTJyJiIiIqEls2fI1unXrjG7dOiMw0Ef6u1u3zsjKutHg8wcFBWPfviNYuzYBKSnJeOWVIdDpdNi0KQFJSdtgbW2D6OjljVCSSpzjTERERERNws8vAH5+AU1+nby8PGzalIDvvtuFH3/cAVvbvpDL5bC3H4i1a1c32nU44kxERERELaasrAylpaUQISy7WgAAF/pJREFUQkCr1aK0tBQ6nQ4AIIRAaWkptFotAKC0tBQajabaOebNC8cHH4SjXbt2sLR8AT/9lIbi4mIcOXIIL7zQrdFiZeJMRERERC1m9GhvWFo+g5Mnj2PGjFBYWj6Do0cPAwBu3LgOS8tn4OTkAACwtHwG//jHIL3jDx7cj8LCArz2micAYODAlzBy5CgMGGCLw4cPIDT0vUaLlb8cSERERESPPUN+OZAjzkREREREBmDiTERERERkACbOREREREQGYOJMRERERGQAJs5ERERERAZg4kxEREREZAAmzkREREREBmDiTERERERkAJOWDqC5tGola+kQiIiIiMhIGZIrPja/HEhERERE1BCcqkFEREREZAAmzkREREREBmDiTERERERkACbOREREREQGYOJMRERERGQAJs5ERERERAZg4kxEREREZAAmzkREREREBmDiTERERERkACbOABQKBS5dutTSYTSYQqGAUqmESqWCSqVCZGRkrfsmJycjNDS0GaNrOgqFAkOHDkVFRYW0Ljk5GTY2Nvj8888b5RrHjx+Hj49Po5yroQoKCtC/f3989NFH9T7HpEmTcP36dQDA+PHjsXfv3sYKr0Gaoy5bwl+lj6mNIeUzxnvQGG2pIWJiYlBWVvbA/Xbs2AFvb2+oVCoolUrMmDGj3tcsLCzE2rVr6318bbKysuDo6Njo5y0rK8PixYvx6quvQqlUwtvbG7t27XpgLF9//bVB52+quKsoFAp4eHhAp9PprWvpttDQGKryDS8vL4wcORJTpkxBWlpaI0ZY/7ia+t4ycTYS9yYKDREdHY2UlBSkpKRg9uzZjXLOxoqtvLy8Uc5Tk2eeeQaHDh2Slrdu3Yq+ffs+1DmaMr7GtH37dtjZ2SE1NdWgN9176XQ6CCGwdu1aWFpaNlGEDdMYdUlkiIa0pcYQGxsLrVZb5z55eXlYuHAhVq9ejZSUFOzYsQMTJ06s9zULCwuxbt26eh/f1O5/v1mwYAFycnKQmpqK77//HkuWLEFERAROnjxZ6zmys7MNTpwbS13vkyUlJUhJSWnGaJrH8uXL8e2332Lnzp1Qq9V4++23cfbs2ZYOq8EelAswcb7H+vXr4evrC29vbwQEBODChQvSNhsbG8TFxcHX1xcjRozADz/8AKD6p9V7l8vLyzFx4kT4+PjgtddeQ3h4uNQ5Jycn480338S7774LDw8PpKenw8PDQy8eLy+vBn+C27p1K/z9/eHj44MJEybg6tWr0raioiIEBQXB3d0dEyZMQG5ubo2xXbp0qdqnuHuXo6Ki4OvrCy8vL7zxxhvIzs7WuxdRUVFQq9X4z3/+g6FDhyIvL086z0cffYS4uLgGlREA1Go1kpOTAQA3btxASUkJrK2tAQBHjx5FQEAAvL294enpidTUVOm48ePHY9GiRRg9ejSmTJkCAFizZg08PT3h5eWFwMBAaaSgoqIC8+bNk7ZlZGQ0OO76SEpKwjvvvAMbGxvs3r0bQOXI1dSpUzFhwgQolUqEhISgqKhI2hYaGoq33noL7u7uKCwsNIoRj9rUpy7PnTvXJO2nsdXVjhQKBVasWIGAgAAoFAq9EfarV6/iX//6l9TOkpKSmj12Q9RVvirGVFc1taWwsDC9e3/vcm5uLt544w289tprCAoKQlBQkLTt/m9u7l2OjY2Vvg309vZGYWEhFi5cCAAIDAyESqVCYWFhjTHeunULJiYm6NChAwBAJpPB1tYWAHD27FmMHz8ePj4+8PHxwb59+wD82fcuXrwYnp6e8PT0xKlTpwAAERERKCoqgkqlQmBgIIDK5Dw0NBR+fn7w9PTU65MVCgWWL1+OgIAADB8+HNu2bUNCQgL8/PwwcuTIaglsTdcEgP379yMwMBA+Pj4ICAjAmTNnAFR+m+fp6Ynw8HCoVCocOHBAOiY7Oxs7duzAggULYGZmBgCwtrZGUFAQYmNjAdTcX0dERCAjIwMqlUr6ZvXcuXMICAiAp6cnAgICcO7cuSaL+37BwcGIjY2t9uEsMzMTb7zxBjw9PaFWq6VzrFq1Su8b4z/++AOOjo4oKSlBWVkZoqKi4OfnBy8vL3zwwQe4c+cOgMrX6rx58zBhwgS4uLggMjISR48exeuvvw6FQoF///vfetf/9ttv4ePjg5EjRxrc39jY2CAmJgZ5eXlITEyU1ru6uiIwMBDx8fF1xlhUVITw8HCpziIiIgDAaMrl6+srvbZqJUi4uLiI//3vf+L333+X1h0+fFj4+/tLy9bW1mLTpk1CCCFOnTolhg4dKoQQ4saNG8LBwUHa795lnU4n8vPzpb8/+OADkZiYKIQQIikpSdjb24vMzEzpWH9/f3H8+HEhhBAnT54UKpXqocsxatQo4eXlJby8vERMTIyYNGmS0Gg0Qggh9u3bJwICAqTrv/jiiyIjI0MIIURMTIwICQmpNbaqe1TT8r33bfPmzWLatGnSvbC2thapqanS9qVLl4qYmBghhBDFxcVi8ODB4tatWw9VzprKffHiRaFUKsXt27fFihUrxMaNG8WsWbPEpk2bxO3bt0V5ebkQQoibN28KJycncfv2bSGEEOPGjROTJ08WWq1WCCFEcnKyGD16tCgqKhJCCKn+jh07JmxtbUV6eroQQohVq1aJ9957r0Fx18eFCxeEi4uL0Ol0IiUlRUycOFEIIUR0dLQYMmSIuHnzphBCiLCwMLF48WJpm7Ozs1493Vt/48aNE3v27GnmktSsIXXZ0PbTlKrud13tyMXFRaqzGzduCHt7e1FcXCy0Wq1Qq9XiypUrQgghioqKhKurq7RsDAwtX9XfxlBXtbWlqtdalXuXg4ODxcqVK4UQQmRlZYkBAwZI2+5vR1XLf/zxhxg0aJC4e/euEKKy/qr6G2tra1FcXFxnnBUVFWLKlCnCwcFBhISEiA0bNoj8/HxRUFAgVCqVyM3NFUIIkZubK5ycnERBQYHU927dulUIUdl/OTk5CY1GU+09Swgh3nzzTXHixAkhhBAajUaMGTNGHDp0SAih/7o8e/assLOzE59//rkQQojU1FQRGBgohBB1XjMzM1OvX7106ZJwdnaW9uvdu7dIS0urVvY9e/YILy+vauvT09OFg4NDnf21Wq2W9tdoNMLZ2VkcOXJECFH5/u7s7Czdj8aO+15Vr/uQkBCRkJCgt87Pz09s3rxZCCHE5cuXhYODg/j9999Fdna2GDJkiPQ62bhxowgLCxNCCLFy5UrpNSiEEEuWLBHLli0TQlS+VgMDA4VGoxElJSVi8ODBIiwsTFRUVIicnBypT6mKoeqcN2/eFEOGDBEXLlx4YH9jbW0t1qxZU62tCyHEjz/+KNzc3OqMMSwsTERERIiKigohxJ/5g7GUyxAmdafVj5fz589jzZo1KCgogEwmw7Vr1/S2u7u7AwDs7e2Rl5cHjUZT5/l0Oh3Wr1+PAwcOQKfToaCgAG3atJG2Dxw4UO/r8vHjxyMxMREODg744osvMHbs2IcuQ3R0tDQ6t2TJEly8eBH+/v4AACGE3qjGoEGD0KNHDwCAv78/PD09a42tLgcOHEBiYiJKSkqqfcVhZmYGNzc3aXns2LEYO3YsgoKC8O2332LIkCF4+umnH7qc95PJZHBzc0NqaipSU1Px1VdfIT09HQCQn5+P2bNnIzMzE61bt0ZBQQF++eUX2NvbAwA8PT1hYlLZFPbu3YsxY8bA3NwcAGBhYSFdo3v37tJIj729fYvMC96yZQtUKhVkMhlcXV3x0UcfSd8UDB8+HB07dgQA+Pn56c3bHDZsGJ566qlmj7c+6luXjdF+WlpVH9O1a1c8+eSTyMnJgRACGRkZeO+996T9tFotrl69ip49e7ZUqA1iDHVVV1uqzfHjxzF37lwAQJcuXfDKK6888DpPPPEELC0tMXPmTAwdOhTDhw+X+hdDtGrVCqtWrcKlS5dw8uRJ7Nq1C/Hx8Zg5cyaysrIwadIkaV+ZTIbMzExYWFhALpfDy8sLAODo6Ig2bdrg6tWr1a5dUlKCEydOID8/X1p3584dZGRkYMiQIQD+fF327dsXd+/elfr0fv36Sc9KAKj1mqdPn8b169f16rm8vBy3bt0CALzwwgsYMGBAtbILIeq8N3X11/f65ZdfIJfLpfr6xz/+Ablcjl9++QXt27dv9LhrMm3aNEyYMAF+fn5S2S5cuABfX18AgJWVFfr06YMzZ85AoVDAysoK+/fvx4gRI7B161aEh4cDAPbs2YPi4mLpW++ysjL07t1bus6rr74KU1NTAJXvWc7OzmjVqhX+/ve/S31KVb9RFUvHjh0xfPhwnDhxAiYmJg/sb9RqNb766qtqZayqr7pi3Lt3L5KTk9GqVeWEh6r3JWMplyGYOP9/Op0OU6dOxeeff46+ffsiNzcXw4YN09un6qui1q1bA6hsQCYmJnqN+95ketu2bTh9+jS++OILmJubIy4uTi8Zb9++vd75lUolli1bhv/+9784fvx4nQ/3GUIIAV9fX0ydOvWhj70/ttatW+s93FBVzuzsbHz88cfYsmULnn/+eaSlpeH999+X9mvbti1kMpm03LlzZ/Tr1w+7d+9GYmKi9DVNY1Cr1fD398fLL7+s14EuWLAACoUCsbGxkMlkGDVqlF49tWvXzqDzVzVaoPLNrLnnRJeVlWH79u0wNTWV5stptVppWkNd7q9PY1efumzs9tMUamtHVar6mKp9KyoqIJPJYGFh8UjMkXxQ+aq0dF3V1ZYMLcP9ajuudevW2Lx5M9LS0nDs2DH4+Phg3bp1ekmBIaytrWFtbY2xY8fC3d0dQgjY2Njgiy++qLZvVlaWwefV6XSQyWTYsmUL5HJ5jfvc/95Xtfww/aCTkxOWLFlSbX1GRkatfbC1tTWuX7+O27dvS1NVAODMmTOwsbEx6LoNVZ+4a9KjRw84Oztjw4YNBu2vVqvxzTffoGvXrigqKsJLL70EoPJ9ff78+bV+aLu/D6mpT6mLEOKB/U1t5f7555/Rq1cvZGVl1Rljbdc11nLdj3Oc71FeXo7OnTsDgN7cnbp07NgRWq0WmZmZACofNqlSVFQECwsLmJubo6ioSG9bTeRyOXx9fTFlyhR4enqibdu29SxJJYVCgZSUFOTk5AConKN7/vx5aXtaWpqUyCclJWHw4MG1nsvS0hI///wzgMp5plWfuIuLiyGXy9GpUyfodLoaP4Xeb9y4cYiMjISJiYnBn9YN8fzzz2P69Ol455139NYXFRWhS5cukMlkOHz4sFRXNXFxccGXX36J4uJiAJVzy4zF7t270b17dxw4cAB79uzBnj17sH79emzduhUAsG/fPmnUKDk5uc76NHb1qcvGbj9NobZ2VJfu3bujTZs2+Oabb6R1GRkZ0mvUmBhavpauq7ra0gsvvCCVIS8vD8ePH5eOc3BwkNrbb7/9hmPHjknb7i37lStXpGdkiouLkZ+fDwcHB4SGhsLa2hqXL18GUPmB9kH1mJubi59++klazsnJQX5+PqysrJCZmakXw7lz56SBHK1Wi23btgEATp06hdLSUvTo0QPm5uYoLS2VEl5zc3MMGjQIn332mXSe3377DTdv3nyYW1rnNYcMGYKDBw9K5a6K9UG6du0KpVKJBQsWSB9ELl26hLi4OAQHB9faX5ubm+vd1+7du0Or1Ur36ujRoygvL0f37t2bJO7ahISEIDExEXfu3IFMJkOfPn2k11NGRgYuXrwofRPq6uqKkydPYsOGDVCr1dIAlEKhQEJCAkpLSwFUvr7q+7xN1bXz8/Oxf/9+ODo61ru/2bVrF7788ku89dZbdcbo4uKC+Ph46XVa9Z5lrOWqCUecUZkwt23bVno4okOHDhg1apRBx5qYmGDOnDn45z//iaeeegrDhw+Xtnl7e2P37t1QKpV4+umnMWjQoAeOXvj7+yM2NhZjxoxpSJEAAC+//DKmTZuGKVOmoKKiAlqtFkqlEv369QNQOR0jKioKmZmZ6NixI5YuXVrruaZOnSo9JDN48GA899xzACon1CuVSri7u8PCwgLOzs56D1bUxMHBAWZmZnj99dcbXMb7BQQEVFs3Y8YMLFy4EDExMXjxxRfrHKnw9vZGbm4uAgICYGJignbt2tU4otMSkpKS9KbTAMCAAQOg0+lw4sQJvPTSS5g+fTpyc3NhZWWFsLCwFoq0cdSnLhuz/TSm8vJymJmZ1dqO6mJiYoK4uDhERkYiPj4eOp0OTz/9ND799NNmiNww9SlfS9ZVXW3J3t4eBw8ehLu7O7p164b+/ftL+8yZMwczZ87Etm3b0LVrV/Tv31+aJjBp0iRMnToVu3fvhq2trTStq7i4GCEhISgtLYUQAra2tnB1dQUAvPXWW5gwYQLatGmDTZs24cknn6wWa3l5OWJiYpCdnY02bdpAp9Nh2rRpsLW1xapVq7B06VJERkZCq9Xi+eeflx7s69ChAy5evCj9B41ly5bB1NQUpqam0kNwf/vb3/DVV1/hk08+wccffyzdk/bt22PRokXo1KnTQ93X2q7ZrVs3LF26FHPmzEFpaSm0Wi0GDhyod29rM3/+fCxbtgzu7u6Qy+UwMzPDnDlz4ODgACFEjf21jY0NunfvDg8PD/To0QPR0dGIjo7GokWLUFJSgnbt2mHFihXSt4hNEXdNnn32WahUKqxfvx4A8Mknn2DevHlISEiAiYkJlixZIk1daNu2LUaMGIHk5GTpwVUAePvttxEbGws/Pz/IZDLIZDIEBwfXa9qWhYUFfHx8UFRUhMmTJ0v9qaH9TWhoKExNTXH37l307NkTn332Gezs7GBra1trjOHh4YiMjISHhwdat24NBwcHzJ0716jK9SAy8aBJRH9xeXl5cHNzw+HDh/XmH7eUlJQUpKam6n36/6u5ceMGxowZg507dxrlqOCjKCYmBiUlJZg1a1ZLh9KijLH9GFsf09jqWz5jrKsHKS0thYmJCUxMTJCXlwc/Pz8kJCRIz4oYi6ysLPj6+uqNlhNR43isR5w3btyIxMREzJo1yyje0CZOnIjr169j9erVLR1Kk1mxYgWSkpIQFhbGpJkalTG2H2PrYxpbfctnjHVliGvXrmHWrFkQQqC8vBzBwcFGlzQTUdN67EeciYiIiIgMwYcDiYiIiIgMwMSZiIiIiMgATJyJiIiIiAzAxJmIiIiIyABMnImIHmFlZWWYPXs2XFxcMGDAAKhUKuzfv1/afvToUSiVStjZ2WH8+PHIzs6WtkVFRcHV1RUDBgyAUqnU+3EAAPjwww8xatQo9O7d26BfqCQi+qtj4kxE9Air+sXTTZs24fTp05g2bRqmTZuGrKws5OfnIzg4GFOnTsWJEyfQr18/TJ8+XTq2bdu2WL16NU6fPo2oqCgsWrQIaWlp0vbevXtjwYIF0o95EBE97h7r/+NMRPSoa9euHUJCQqRlFxcXdO3aFenp6bh9+zZ69eoFNzc3AJU/+Tt48GBkZGSgZ8+eCA0NlY6zs7PDoEGDcObMGQwcOBAAMHbsWACAmZlZM5aIiMh4ccSZiOgv5NatW7h27RqsrKxw+fJlvZ8lb9euHSwtLXHlypVqx5WWluL8+fOwsrJqznCJiB4pTJyJiP4itFot3n//fajVavTs2RMlJSV44okn9PYxNzfHnTt3qh07f/582NjYwMnJqbnCJSJ65HCqBhHRX4BOp8PMmTMhl8vx4YcfAqgcYS4uLtbb786dO2jfvr3euqioKFy+fBkbN26ETCZrtpiJiB41HHEmInrECSEwZ84c3Lp1CzExMZDL5QCAXr164eLFi9J+JSUluH79ut50jOjoaBw8eBDx8fEwNzdv9tiJiB4lTJyJiB5x8+fPR0ZGBuLi4tCmTRtp/ciRI3H58mX88MMP0Gg0WLlyJWxsbNCzZ08AwJo1a7B9+3Zs2LABFhYW1c5bVlYGjUYDIQTKy8uh0Wig0+marVxERMZGJoQQLR0EERHVT3Z2NhQKBUxNTWFi8ufsu4ULF8LLywtHjhxBREQEfv31V9jZ2eHjjz9G165dAQA2NjaQy+XSCDUATJ48GUFBQQCA8ePH48SJE3rX27hxIxwdHZuhZERExoeJMxERERGRAThVg4iIiIjIAEyciYiIiIgMwMSZiIiIiMgATJyJiIiIiAzAxJmIiIiIyABMnImIiIiIDMDEmYiIiIjIAEyciYiIiIgMwMSZiIiIiMgA/w9si/mTPtIbsQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2022 Cumulative Performances: -6.89% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Fonction pour afficher les gains et les pertes mois par mois\n",
        "plot_bar_by_month(df_days.loc[:])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "VMC Indicator.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOTAQWB5ba0/1Jo6ybNPXad",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}