trix.ipynb

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        "<a href=\"https://colab.research.google.com/github/CryptoRobotFr/easy_backtest/blob/main/trix.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "!pip install python-binance ta"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Récupération des données à partir de Binance <h1>"
      ],
      "metadata": {
        "id": "MAPn9B1b5ZGc"
      }
    },
    {
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      "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 2017\"\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"
      ],
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      "execution_count": 10,
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              "                        open     high      low    close       volume\n",
              "timestamp                                                           \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Définition des indicateurs <h1>"
      ],
      "metadata": {
        "id": "RUL8TfpZ5tAq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ajout de nouvelle colonne dans le tableau représentant nos indicateurs\n",
        "df['sma_long'] = ta.trend.sma_indicator(close = df['close'], window = 500) # Moyenne simple longue\n",
        "df['stoch_rsi'] = ta.momentum.stochrsi(close = df['close'], window = 14) # Stochastic RSI non moyenné (K=1 sur Trading View)\n",
        "\n",
        "trix_window = 9\n",
        "trix_signal = 21\n",
        "df['trix_line'] = ta.trend.ema_indicator(ta.trend.ema_indicator(ta.trend.ema_indicator(\n",
        "                    close = df['close'], window=trix_window),\n",
        "                    window=trix_window), window=trix_window).pct_change()*100 # Ligne trix principale\n",
        "df['trix_signal'] = ta.trend.sma_indicator(close = df['trix_line'], window = trix_signal) # Ligne signale\n",
        "                \n",
        "# Affiche notre nouveau tableau\n",
        "df"
      ],
      "metadata": {
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          "height": 455
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        "id": "fEtAOcaQ4jQH",
        "outputId": "20b64ff1-96af-4a26-a4f8-4147546c1cc0"
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      "execution_count": 11,
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              "                        open     high      low    close       volume  \\\n",
              "timestamp                                                              \n",
              "2017-08-17 04:00:00   301.13   302.57   298.00   301.61    125.66877   \n",
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              "2017-08-17 08:00:00   307.95   309.97   307.00   308.62    150.75029   \n",
              "...                      ...      ...      ...      ...          ...   \n",
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              "\n",
              "                       sma_long  stoch_rsi  trix_line  trix_signal  \n",
              "timestamp                                                           \n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-17 12:00:00</th>\n",
              "      <td>3048.05</td>\n",
              "      <td>3056.63</td>\n",
              "      <td>3042.80</td>\n",
              "      <td>3048.00</td>\n",
              "      <td>6347.39540</td>\n",
              "      <td>3260.13636</td>\n",
              "      <td>0.608632</td>\n",
              "      <td>0.008024</td>\n",
              "      <td>0.006563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-17 13:00:00</th>\n",
              "      <td>3047.99</td>\n",
              "      <td>3083.00</td>\n",
              "      <td>3046.03</td>\n",
              "      <td>3061.27</td>\n",
              "      <td>31516.29500</td>\n",
              "      <td>3259.89890</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013467</td>\n",
              "      <td>0.007086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-17 14:00:00</th>\n",
              "      <td>3061.27</td>\n",
              "      <td>3074.86</td>\n",
              "      <td>3052.94</td>\n",
              "      <td>3063.73</td>\n",
              "      <td>12020.97400</td>\n",
              "      <td>3259.66800</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.020742</td>\n",
              "      <td>0.008339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-17 15:00:00</th>\n",
              "      <td>3063.74</td>\n",
              "      <td>3080.60</td>\n",
              "      <td>3058.06</td>\n",
              "      <td>3073.00</td>\n",
              "      <td>13877.29280</td>\n",
              "      <td>3259.46236</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030468</td>\n",
              "      <td>0.010364</td>\n",
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              "    <tr>\n",
              "      <th>2022-04-17 16:00:00</th>\n",
              "      <td>3073.01</td>\n",
              "      <td>3078.48</td>\n",
              "      <td>3068.07</td>\n",
              "      <td>3073.64</td>\n",
              "      <td>3497.77300</td>\n",
              "      <td>3259.13294</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.040356</td>\n",
              "      <td>0.013107</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>Backtest</h1>"
      ],
      "metadata": {
        "id": "FNunSvlip6NB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "# Condition pour rentrer en achat (row = période actuelle et previous_row = période précédente)\n",
        "def buy_condition(row, previous_row = None):\n",
        "    if row['trix_line'] > row['trix_signal'] and row['stoch_rsi'] < 0.8 and row['close'] > row['sma_long']:\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "\n",
        "# Condition pour vendre (row = période actuelle et previous_row = période précédente)\n",
        "def sell_condition(row, previous_row = None):\n",
        "    if row['trix_signal'] > row['trix_line'] and row['stoch_rsi'] > 0.2:\n",
        "        return True\n",
        "    else:\n",
        "        return False\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",
        "\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) and usd > 0:\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",
        "        }\n",
        "        trades_hitory.append(buy_data) \n",
        "\n",
        "    # Vérifie les conditions si l'on doit vendre\n",
        "    elif sell_condition(row) and  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",
        "        buy_data = {\n",
        "            'date': index,\n",
        "            'position': 'sell',\n",
        "            'price': row['close'],\n",
        "            'fee': fee,\n",
        "            'usd': usd,\n",
        "            'coin': coin,\n",
        "            'wallet': wallet           \n",
        "        }\n",
        "        trades_hitory.append(buy_data) \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'])\n",
        "\n",
        "print(\"Backtest finished, ready for analysis...\")"
      ],
      "metadata": {
        "id": "WTT18Xwp9J09",
        "outputId": "95c9e87d-b913-4d41-87b2-a2aa79583f80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Backtest finished, ready for analysis...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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[:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QUhBzbReEDPo",
        "outputId": "d78fc51a-456f-40fe-eeec-4706d159af0b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Period: [2017-08-17 00:00:00] -> [2022-04-17 00:00:00]\n",
            "Initial wallet: 1000.0 $\n",
            "\n",
            "--- General Information ---\n",
            "Final wallet: 143667.67 $\n",
            "Performance vs US dollar: 14266.77 %\n",
            "Worst Drawdown : -38.58%\n",
            "Buy and hold performance: 913.41 %\n",
            "Performance vs buy and hold: 1317.66 %\n",
            "Total trades on the period: 611\n",
            "Global Win rate: 50.9 %\n",
            "Average Profit: 0.98 %\n",
            "Total fee: 46308.64 $\n",
            "\n",
            "Best trades: +40.44 % the 2021-01-04 12:00:00\n",
            "Worst trades: -14.03 % the 2021-12-04 06:00:00\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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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fonction pour afficher les gains et les pertes mois par mois\n",
        "plot_bar_by_month(df_days.loc[:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0Xpj_72uODQW",
        "outputId": "2b723d2e-971c-4ae1-d194-44c46e93f30a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2017 Cumulative Performances: 82.96% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2018 Cumulative Performances: 131.95% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2019 Cumulative Performances: 50.84% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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2m3Xy5AnFxV1QmTJlXTMxAMghKMcAkIFmzozW9OlTbc/Xrl2lkJBQjRo1WomJZg0Y0Efnzp1R3rze6ty5m0JDx6Y5Pj4+Xh98MF9btmyXJHl6eioycpratAlS7ty5NXv2PKfOBwByGn4EBAAAADkCPwICAAAApAPLKgDgIfIX8JKXyeTqGHZLTErS77cSXR0DALI8yjEAPISXyaSeS4e4OobdlvWaLYlyDAD/K5ZVAAAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcA3CJJUsWqnHj+ipZsogGDRqQZtvatatUtqyf7Z8yZXxVrFh+HTr0gyRp3brVqlz5WVWvXlm7dn1jO+7MmV/UvPmrslqtTp0LACD7oBwDcKjo6AhFR0c8MO7r66dhw0aqc+fuD2xr166jzp69ZPsnKmqmypQpq6pVX5TFYlF4eJh27PhWkZHTNHr0SNtxY8aM0uTJkfLw8HDgjAAA2ZmnqwMAyJmCglpKkg4dOqiLF+89dt9Vq/6pDh06y83NTTdu3FDx4n7y9S2uwMCGOneupyRp06YN8vMroerVazo6OgAgG6McA8jULlw4rz17vtN7782VJBUpUkQ3b97QxYtxOnLksCpUCNCdO7c1c+Y0rVv3mYvTAgCyOsoxgExt9eqVql37bypTpqwkyd3dXdHRs9SnT3eZTF6aMWOOoqIi1Ldvfx07dlQzZkQpV65cmjgxQhUrVnJteABAlkM5BpDhunZtr5iYvZKkxESzJGnRovmSpFq1amvFijV2n2v16pUaOjQkzVhgYAMFBjaQJMXGHtGhQz8oLCxc1atX1qZNXyguLk7Dh7+tzz/fmQGzAQDkJJRjABnu/vL755fxRo0ane7zxMTs1ZUrl9WiRfBDt6empurdd0MUETFN8fHxslqtKlWqtIoV89WxY0f/WngAQI5GOQbgEhaLRRaLRVZriqxWq8xmszw9PeXp+Z8/llav/qdef72lvL3zPfQcH3/8oapWfUFVqlSVxWKR2WzWyZMnFBd3wbYMAwCA9KAcA3CJmTOjNX36VNvztWtXKSQk1HaF2Ww2a+PG9fp//++jhx4fHx+vDz6Yry1btkuSPD09FRk5TW3aBCl37tyaPXue4ycBAMh23FJTU1NdHSKjxMffUUpKtpkOABcqWjSfei4d4uoYdlvWa7auXbvt6hgAkKm5u7vJx8f78fs4KQsAAACQ6VGOAQAAAANrjgH8T/IX8JKXyeTqGHZJTErS77cSXR0DAJCJUY4B/E+8TKYsszZ3Wa/ZkijHAIBHY1kFAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcAwAAAAZPZ79gVFSUvvjiC8XFxWnTpk167rnnJEmNGjWSyWSSl5eXJCkkJET16tVzdjwAAADkYE4vx6+88op69Oihrl27PrDt/ffft5VlAAAAwNmcXo5r1Kjh7JcEAAAA7OL0cvw4ISEhSk1NVfXq1TV8+HDlz58/Xcf7+Hg7KBmA7KJo0XyujuAw2XluAOAsmaYcr1ixQn5+fkpKStKUKVM0adIkTZ8+PV3niI+/o5SUVAclBPAwWa2QXbt22679stq8JPvnBgA5lbu72xMvpmaau1X4+flJkkwmk7p06aKDBw+6OBEAAABymkxRjhMSEnT79h9XPFJTU7V161ZVrFjRxakAAACQ0zi9HIeHhyswMFCXL19Wr1699Prrrys+Pl7du3dXixYtFBQUpDNnzmjChAnOjgYAQLaxZMlCNW5cXyVLFtGgQQMeud/06VNVrFh+ff31v21j//jHbAUElFW9ei/r2LGjtvGYmL3q0aOzQ3MDrub0Ncdjx47V2LFjHxjfsGGDs6MAAJDlRUdHSJJGjRqdZtzX10/Dho3Uv/+9Q2bzvYcee+bML9q0aYN8fYvbxq5cuax//nO59u07rNWrV2rKlDCtWLFGFotFYWGjtXDhUsdNBsgEMsWyCgAAkLGCglqqefMgFS5c6JH7hIaO0LhxE2UymWxjv/56QVWqVFW+fPkVGNhQ586dlSQtXDhPTZo0V+nSZRwdHXApyjEAADnQZ5+tl5eXl159tUma8aeffkbHjx/TrVu/6Ztv/q0KFSoqLu5XbdiwTm+9NdhFaQHnyTS3cgMAAM5x585tTZkyUWvWbHxgW+HCPho6NERt2rRQ0aJFNW3aexoz5h2NGzdRW7du0tKli1WgQAFNnTpDJUr4uyA94FiUYwAAspiuXdsrJmavJCkx0SxJWrRoviSpVq3aWrFizWOPj46OVPv2nR65RKJNm/Zq06a9JGn79m3y8jKpSpWqatjw//TttzHatm2rwsLGaNGiZRk0IyDzoBwDAJDF3F9+H/WFvMf59tuvdelSnJYuXSxJio+/rjfffENvvz1MgwcPs+137949TZkySZ98sk6//HJaJUr4K1++/KpWrbpmz56RQbMBMhfKMQAA2ZDFYpHFYpHVmiKr1Sqz2SxPT095enpq3brPlJxsse3bpEkDTZoUoUaNGqc5x6xZ09SpUxcVL+4nNzc3nT59SlevXtWuXd+oTJmyTp4R4ByUYwAAsqGZM6M1ffpU2/O1a1cpJCRUo0aNVuHCPmn29fDwUIECBeXt/Z+f1T116id99dUOff75TkmSr29xDRo0XIGBL6tIkaIsqUC25Zaamprq6hAZJT7+jlJSss10gCyhaNF86rl0iKtj2GVZr9m6du22XftmpXlJ6ZsbAORU7u5u8vHxfvw+TsoCAAAAZHqUYwAAAMDAmmMAADK5/AW85HXfr9hlZolJSfr9VqKrYwB/GeUYAIBMzstkyjJr4Jf1mi2Jcoysi2UVAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHOciSJQvVuHF9lSxZRIMGDUizbf/+79WuXbCee660KlZ8Wn369NCVK5dt29etW63KlZ9V9eqVtWvXN7bxM2d+UfPmr8pqtTptHgAAAI5COc6GoqMjFB0d8cC4r6+fhg0bqc6duz+w7dat39SjR08dOBCrAweOytvbW4MHD5QkWSwWhYeHaceObxUZOU2jR4+0HTdmzChNnhwpDw8PR00HAADAaTxdHQDOExTUUpJ06NBBXbx4L822V155Lc3zPn36KTi4uSTpxo0bKl7cT76+xRUY2FDnzvWUJG3atEF+fiVUvXpNx4cHAABwAsoxHmrPnu8UEBAgSSpSpIhu3ryhixfjdOTIYVWoEKA7d25r5sxpWrfuMxcnBQAAyDiUYzzg6NFYzZgRpeXLP5Ekubu7Kzp6lvr06S6TyUszZsxRVFSE+vbtr2PHjmrGjCjlypVLEydGqGLFSi5ODwAA8NdRjrOJrl3bKyZmryQpMdEsSVq0aL4kqVat2lqxYo1d5/nll9Pq3LmtwsOjVLv232zjgYENFBjYQJIUG3tEhw79oLCwcFWvXlmbNn2huLg4DR/+tj7/fGcGzgoAAMC5KMfZxP3l988v440aNTpd57hw4bzatw/W8OGj1KFD54fuk5qaqnffDVFExDTFx8fLarWqVKnSKlbMV8eOHf3rEwAAAMgEKMc5iMVikcVikdWaIqvVKrPZLE9PT3l6eurSpYtq0yZIvXv3U8+efR55jo8//lBVq76gKlWqymKxyGw26+TJE4qLu6AyZco6bzIAAAAOQDnOQWbOjNb06VNtz9euXaWQkFCNGjVaH3/8oc6dO6tp0yI1bVqkbZ+zZy/ZHsfHx+uDD+Zry5btkiRPT09FRk5TmzZByp07t2bPnue8yQAAADiAW2pqaqqrQ2SU+Pg7SknJNtMBsoSiRfOp59Ihro5hl2W9Zuvatdt27ZuV5iWlb27IerLS+5H3IjIzd3c3+fh4P34fJ2UBAAAAMj2WVWRR+Qt4yctkcnUMuyQmJen3W4mujgEAAPBElOMsystkylL/i02iHAMAgMyPZRUAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGJxejqOiotSoUSNVqFBBP/30k238zJkz6tixo5o0aaKOHTvq7Nmzzo4GAACAHM7p5fiVV17RihUr5O/vn2Z8woQJ6tKli7744gt16dJF48ePd3Y0AAAA5HBOL8c1atSQn59fmrH4+HgdO3ZMQUFBkqSgoCAdO3ZMN27ccHY8AAAA5GCZYs3xpUuX5OvrKw8PD0mSh4eHihUrpkuXLrk4GQAAAHIST1cHyEg+Pt6ujoBHKFo0n6sjAJKy93sxO88NWQvvRWRlmaIc+/n56cqVK7JarfLw8JDVatXVq1cfWH7xJPHxd5SSkuqglJlLVvuD59q1266OAAfJru/FrDYvic9ZdpbV3o+8F5FZubu7PfFiaqZYVuHj46OKFStq8+bNkqTNmzerYsWKKly4sIuTAQAAICdx+pXj8PBw/etf/9L169fVq1cvFSxYUFu2bFFYWJhCQ0M1b9485c+fX1FRUc6OBgAAgBzO6eV47NixGjt27APj5cqV05o1a5wdBwAAALDJFMsqAAAAgMyAcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAIDBMz07WywWrVu3TidPnlSpUqXUoUMH5c2b11HZAAAAAKdK15XjiIgIHTlyRBUqVFBsbKyGDh3qqFwAAACA0z22HC9btkwpKSm25z/99JPCw8PVsWNHTZ06VT/88IPDA2YG58+fU+fObfXss6X1/PPlFRo6QhaLRZL0+++31KFDK5UvX0oDBvSR1Wq1HTdixGBt3vyZq2IDAAAgnR5bjhMTE9WlSxf9+OOPkqTq1aurb9++mjVrlnr37q3AwECnhHSW6OgIRUdHPDD+zjvDVaRIUR058pN27vxOe/Z8p6VLP5AkffjhUlWp8oKOHv1ZFy6c19atmyRJ+/bF6PLlSwoKaunUOQAAAOCve+ya4/79+6t58+aaMmWKChcurJEjR+rw4cP66aef1KVLF7322mvOyulS58+fU58+/ZQ7d27lzp1bDRu+qhMnTti2NWv2ury8vFS79t909uxZWa1WjR//rubPX+Li5AAAAEiPJ645LlWqlBYsWKBGjRqpV69eunz5st588001a9ZMHh4ezsjocv36vaX169cpISFBly5d1M6d29Wo0auSpICAivrmm69079497d27WwEBAfrgg/lq1KixypZ92sXJAQAAkB6PLcd3797V0qVLNWnSJF29elXLli3TuXPn1KVLFx0/ftxZGV2uTp3/08mTJ1SunL9eeCFAL7xQTc2bB0mSunbtodu3f1fTpo1Uu/bf9PzzVbRmzSr17/+WQkKGqmXLpoqMnOTiGQAAAMAej11WMXToUHl7e6tWrVqKiYnRyZMnNXHiRJ08eVLh4eF67rnnNG7cOGdldYiuXdsrJmavJCkx0SxJWrRoviSpVq3a+uijVerUqY26d++pLVu26+7dOxo69O+aNGm8JkyYrNy5c2vGjPdt5+vTp4fGjBmvtWtXKzU1RRs3fq727aRvjdQAACAASURBVFsZV5sbO3+CAAAAsNtjy/GPP/6omJgYubu7q02bNmrXrp0kqUKFCvroo4+0bt06p4R0pBUr1tge//llvFGjRtvG4uPj9euvF9SnTz95eXnJy8tLnTp109SpkzVhwuQ059q5c7tSU1PVqFFjjRw5TC+8UE1ubm568cVqOnr0KOUYAAAgk3vssoq6detq5MiRWr16tUJDQ1W/fv0029u2bevQcJmBj4+PSpcuq2XLlshisejWrd+0atU/VanS82n2M5vNmjw5TOHhUyVJpUuX0e7d3yopKUnff79XZcuWdUF6AAAApMdjy/H06dP16quv6tatWwoODtaIESOclStTWbr0Y+3c+aUqVnxGtWq9qFy5cmnSpKlp9nnvvelq27aDSpTwlyS98UYvxcfHq2LFZ1SiRAk1b97CFdEBAACQDo9dVuHh4aFmzZo5K4vL3b+c4n5VqlTVhg1bH3tsaOjYNM/z5y+gNWs2Zlg2AAAAOF66fj4aAAAAyM4ee+U4q8tfwEteJpOrY9gtMSlJv99KdHUMAACAHCtbl2Mvk0k9lw5xdQy7Les1WxLlGAAAwFXsXlaRnJys/fv3a+vWP9beJiQkKCEhwWHBAAAAAGez68rxyZMnNXDgQJlMJl25ckXNmzfXvn37tH79er333nuOzggAAAA4hV1XjsPCwjR48GBt27ZNnp5/9OmaNWvqwIEDDg0HAAAAOJNd5fjnn39WcHCwJMnNzU2SlCdPHiUmsj4WAAAA2Ydd5djf31+xsbFpxg4fPqzSpUs7JBQAAADgCnatOR4yZIj69++vTp06KTk5WQsXLtQnn3yiyZMnOzofAAAA4DR2XTlu2LChFi9erBs3bqhmzZqKi4vTnDlzVLduXUfnAwAAAJzG7vscV6pUSWFhYQ6MAgAAALiWXVeO3377be3fvz/N2P79+zV48GCHhAIAAABcwa5yvG/fPlWrVi3N2IsvvqiYmBiHhAIAAABcwa5ybDKZdO/evTRjCQkJtnseAwAAANmBXeW4bt26Gj9+vO7cuSNJunPnjiZNmqR69eo5NBwAAADgTHaV49DQUN25c0cvv/yy6tSpo5dffll37tzR6NGjHZ0PAAAAcBq71kUUKFBAixYt0rVr13Tp0iX5+fmpaNGijs4GAAAAOJVdV45tO7u7q1ChQjKbzbpw4YIuXLjgqFwAAACA09l15fibb77RmDFjdO3atTTjbm5uOn78uEOCAQAAAM5mVzmeNGmS3nrrLbVu3Vq5c+d2dCYAAADAJewqx7///rs6deokNzc3R+cBAAAAXMauNcdt27bVunXrHJ0FAAAAcCm7rhwfOnRIH330kT744AMVKVIkzbYVK1Y4JBgAAADgbHaV4/bt26t9+/aOzgIAAAC4lF3luHXr1o7OAQAAALicXeVYkq5fv67Dhw/r5s2bSk1NtY23a9fOIcEAAAAAZ7OrHH/55ZcaOXKkypQpo59//lnly5fXqVOn9NJLL1GOAQAAkG3YVY7fe+89RUREqFmzZqpZs6Y2bNigdevW6eeff3Z0PgAAAMBp7LqV28WLF9WsWbM0Y61bt9aGDRscEgoAAABwBbvKsY+Pj65fvy5J8vf31w8//KDz588rJSXFoeEAAAAAZ7KrHLdv314HDhyQJPXs2VM9evRQcHCwOnfu7NBwAAAAgDPZtea4X79+tsetWrXSyy+/rHv37qlcuXIOCwYAAAA4m923crtfiRIlMjoHAAAA4HJ2leMTJ04oIiJCJ06cUEJCgiQpNTVVbm5uio2NdWhAAAAAwFnsKsfDhw/Xa6+9prFjxyp37tyOzgQAAAC4hF3l+Pr16xoyZIjc3NwcnQcAAABwGbvuVtGqVStt2rTJ0VkAAAAAl7L7bhUdO3bUwoUL5ePjk2bb8uXLHRIMwINatWquAwf2ycPjj4+un5+f9uw5KEmKjT2igQP76Nq1qxoyJEQDB74tSUpOTlaLFq9pyZKP5O9f0mXZAQDICuwqx4MHD1bJkiXVuHFjeXl5OToTkONFR0dIkkaNGv3AtsjI6erW7Y0HxqdMCVNYWLgqVaqsBg3qqE2b9vL19dX8+f/Q668HU4wBALCDXeX4+PHjiomJkclkcnQeAH/R+fPnVLdufXl5eemZZ8opLu6CkpIStWXLRm3evN3V8QAAyBLsWnNco0YNnT592tFZANhhypQwBQSU1euvN9Z3331rGw8IqKSvvtqpixfjdP78eZUt+7TGjHlHEyaEK1euXC5MDABA1mHXleOSJUuqd+/eaty48QNrjocMGeKQYAAeNG7cRFWoEKBcuUxav36tunXrqJ07d+npp59RWFi4Ro0apqtXr2ry5Eh9/32MvL29Vbp0GfXo0Um3bt1Snz791LJla1dPAwCATMuucmw2m9WgQQMlJyfr8uXLjs4E5Ehdu7ZXTMxeSVJiolmStGjRfElSrVq1tWLFGlWvXtO2f6dOXbV+/Vrt2PEv9e07QKVKldbKleskSQkJCWre/FWtXr1Bo0ePVHBwGzVu3ESBgbVVr159FSpU2MmzAwAga3hiObZarSpevLgGDhzImmPAgVasWGN7/Lgv5N3Pzc1NqampD4zPmBGl7t3fULFixXT8+FG9++5Y5c9fQH5+JXTmzC+UYwAAHuGJa449PDy0cuVKeXradZEZgIPcuvWbdu78UmazWRaLRWvXrtLevbvVqNGrafY7efKEdu/+Vj179pUklS5dRt9++42uXr2qM2dOy9+/lCviAwCQJdjVeIODg7Vy5Up17drVoWEaNWokk8lku11cSEiI6tWr59DXBLKK5GSLpk6drFOnTsnDw0PPPvusli37p8qVezbNfqGhIxQeHiUPDw9J0pgxYRowoLemTp2sIUNC5Ovr64r4AABkCXaV48OHD+vjjz/WkiVLVLx48TQ/I71ixYoMDfT+++/rueeey9BzAlnNw5ZTFClSRP/619dPPHb9+i1pnleuXEW7du3LsGwAAGRndpXjDh06qEOHDo7OAgAAALiUXeW4dWvn3fopJCREqampql69uoYPH678+fPbfayPj7cDkzlH0aL5XB3BIbLrvOyVZEmWyTPr3Gs4q+VNj+z8XszOc0PWwnsRWZnd37Jbt26dNm7cqCtXrsjX11fBwcFq27ZthoZZsWKF/Pz8lJSUpClTpmjSpEmaPn263cfHx99RSsp/vrmfFT+c167dtmu/rDY3e+eVXRUtmk89l2ade4Iv6zU7x78Xs9q8JD5n2VlWez/yXkRm5e7u9sSLqXaV4/nz52vDhg3q3bu3SpQooYsXL2rx4sW6evWqBg4cmCFhJcnPz0+SZDKZ1KVLlww9NwAAAPAkdpXjNWvW6KOPPpK/v79trG7duurWrVuGFdiEhARZrVbly5dPqamp2rp1qypWrJgh5wYAAADsYVc5vnfvngoXTvujAQULFpTZbM6wIPHx8Ro0aJCsVqtSUlJUrlw5TZgwIcPODwAAADyJXeW4Xr16CgkJ0YgRI1SiRAnFxcXpvffeU926dTMsSKlSpbRhw4YMOx8AAACQXk/8hTxJGj9+vPLmzauWLVuqWrVqatWqlZ566imNGzfO0fkAAAAAp3nkleOPP/5Y3bp1k/THkofo6GhNnTpVN2/eVKFCheTublevBgAAALKMRzbcWbNm2R7/eZ9jd3d3+fj4UIwBAACQLT3yynGpUqU0depUlS9fXhaLRWvXrn3ofu3atXNYOAAAAMCZHlmOZ82apcWLF2vLli2yWCzauHHjA/u4ublRjgEAAJBtPLIcP/3005oyZYokqWfPnlq2bJmzMgEAAAAu8cTFw1arVQcPHlRSUpIz8gAAAAAu88Ry7OHhobJly+rmzZvOyAMAAAC4jF0/AtKiRQsNGDBAPXr0UPHixdNsq1OnjkOCAQAAAM5mVzleuXKlJGnOnDlpxt3c3LRjx46MTwUAAAC4gF3leOfOnY7OAQAAALic3b/mkZycrP3792vr1q2SpISEBCUkJDgsGAAAAOBsdl05PnnypAYOHCiTyaQrV66oefPm2rdvn9avX6/33nvP0RkBAAAAp7DrynFYWJgGDx6sbdu2ydPzjz5ds2ZNHThwwKHhAAAAAGeyqxz//PPPCg4OlvTHl/AkKU+ePEpMTHRcMgAAAMDJ7CrH/v7+io2NTTN2+PBhlS5d2iGhAAAAAFewa83xkCFD1L9/f3Xq1EnJyclauHChPvnkE02ePNnR+QAAAACnsevKccOGDbV48WLduHFDNWvWVFxcnObMmaO6des6Oh8AAADgNHZdOZakSpUqKSwszIFRAAAAANeyqxwnJSVp/vz52rJli65evapixYqpefPmGjhwoLy8vBydEQAAAHAKu8pxWFiYzpw5ozFjxsjf319xcXFauHChrly5osjISEdnBAAAAJzCrnK8Y8cObd++Xfnz55cklS9fXi+88IJee+01h4YDAAAAnMmuL+QVKVJE9+7dSzOWmJiookWLOiQUAAAA4Ap2XTkODg5W37591b17d/n6+ury5ctasWKFgoODtWfPHtt+derUcVhQAAAAwNHsKseffPKJJGnBggUPjP+5zc3NTTt27MjgeAAAAIDz2FWOd+7c6egcAAAAgMvZteYYAAAAyAkoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAcJqbN2/ojTe6qGzZ4nrppee1bt1q27bY2COqV+9lBQSU1fz5/7CNJycnq2nThoqL+9Xh+Twd/goAAADIcaKjIyRJo0aNTjMeGjpCJpNJsbE/Kzb2iLp2ba/nn6+igICKmjIlTGFh4apUqbIaNKijNm3ay9fXV/Pn/0Ovvx4sf/+SDs9NOQYAAIBT3L17V5s3f6Zvvtkrb29v1a5dR02aNNOaNZ9o3LiJOn/+nOrWrS8vLy8980w5xcVdUFJSorZs2ajNm7c7JSPLKgAAAOAUv/zyszw9PVWu3LO2seefr6KTJ49LkgICKumrr3bq4sU4nT9/XmXLPq0xY97RhAnhypUrl1MycuUYAAAATnH37l15e+dLM5Y/f37duXNHkhQWFq5Ro4bp6tWrmjw5Ut9/HyNvb2+VLl1GPXp00q1bt9SnTz+1bNnaYRkpxwAAAMgQXbu2V0zMXklSYqJZkrRo0XxJUq1atRUaOlZ37txOc8zt27fl7e0tSSpVqrRWrlwnSUpISFDz5q9q9eoNGj16pIKD26hx4yYKDKytevXqq1Chwg6ZA+UYAAAAGWLFijW2xw/7Qt7du3dlsVj0yy8/65lnykuSjh49ogoVKj5wrhkzotS9+xsqVqyYjh8/qnffHav8+QvIz6+Ezpz5xWHlmDXHAAAAcIq8efPq9ddbKCpqiu7evauYmL3atm2r2rfvlGa/kydPaPfub9WzZ19JUunSZfTtt9/o6tWrOnPmtPz9SzksY6Yqx2fOnFHHjh3VpEkTdezYUWfPnnV1JAAAAGSgqKiZunfPrOefL6cBA3orOnqmAgLSXjkODR2h8PAoeXh4SJLGjAnT4sULFBj4soYMCZGvr6/D8mWqZRUTJkxQly5dFBwcrI0bN2r8+PFavny5q2MBAAAgnf77/sZ/KlSosJYvX/nYY9ev35LmeeXKVbRr174My/Y4mebKcXx8vI4dO6agoCBJUlBQkI4dO6YbN264OBkAAAByikxTji9duiRfX1/b5XMPDw8VK1ZMly5dcnEyAAAA5BRuqampqa4OIUmxsbF65513tGXLfy6jN2/eXNOmTdPzzz//l86ZZEmWydM5N4zOCOnJm5Xmlp6s1qRkeZiyxrwk+/NmpX9fEu/F9O6bGWTXz1l6smaleUnpy5uV3o+8F7PWvKSsl9fRMs2aYz8/P125ckVWq1UeHh6yWq26evWq/Pz87D5HfPwdpaT8d9c3Z2xQh0tP3qw0N/uyFi2aT1t79HJwlozTfPlSXbt2+8k7Sspa/76knP5eTP++mUH2+5yl5zOWleYlpffPDylrvR95L2aVeUl/5b2Ydbm7u8nHx/vx+zgpyxP5+PioYsWK2rx5syRp8+bNqlixogoXdsw97AAAAID/lmmuHEtSWFiYQkNDNW/ePOXPn19RUVGujgQAAIAcJFOV43LlymnNmjVP3hEAAABwgEyzrAIAAABwNcoxAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGyjEAAABgoBwDAAAABsoxAAAAYKAcAwAAAAbKMQAAAGCgHAMAAAAGT1cHAAAgIySbE9V8+VJXx7BbsjnR1REAPATlGACQLfx2O0m6nfQ/n2fEiMHavXuXfvnltGbPnqdOnbratn3zzVcaPnyQ7t27p/DwqWrdup0k6dat39SmTQtt3LhV3t75/ucMAFyHZRUAANynUqXKioqaqapVX3xg27hxofroo1VatWq93nlnuKxWqyQpPHyiBg8eRjEGsgGuHAMAcJ8+ffpJkry8vB7YdvdugipWrCRJypXLpBs3bujChXM6f/6sgoNnOTUnAMegHAMAYKeiRYsoNvaIJMnd3V0FCxZUjx6d9I9/LHBxMgAZhXIMAICdoqPf09ix78hsvqe5cxdp2bLFql+/gczmRHXo0ErJyckaOfJd/e1vdV0dFcBfRDkGAOQYa9euUkjIUElS7dp1tHfvHtu2Xbu+V8mSpR57fJUqVbVhw1ZJ0pUrlzVhwhht3fqlWrVqpsmTo1S8eHEFBzfTwYNH5ebm5riJAHAYyjEAIMdo166j2rXrmCHnGjcuVO++O1ZPPfWUjh8/phdfrCaTyaTk5GRdv35dRYsWzZDXAeBclGMAAO6TlJSklJQUpaamKjk5WWazWSaTSe7u/7nB01df7ZTZnKjXXmsmSSpduox27fpaJUqUVFJSogoXLuyq+AD+R5RjAADu06FDK+3evUuStG9fjEaMGKz167fo//6vniQpMTFREyeO0/LlK23HRERM07BhbysxMVFRUTPl4eHhkuwA/neUYwAA7vPnmuJH8fLy0r///V2ascDABjpwINaRsQA4CT8CAgAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgIFyDAAAABgoxwAAAICBcgwAAAAYKMcAAACAgXIMAAAAGCjHAAAAgMHT1QGA+yWbE9V8+VJXx7BbsjnR1REAAEAGohwjU/ntdpJ0OynDzrdq1T81aNAAzZw5R926vSFJWrdutSZMGCMvLy/Nnj1PdesGSpLOnPlFf/97P23a9IU8PDwyLAMAAMg6KMfItn777aZmz56hgICKtjGLxaLw8DDt2PGtDh36QaNHj9Q338RIksaMGaXJkyMpxgAA5GCsOUa2FR4+UX37DlDhwj62sRs3bqh4cT/5+hZXYGBDnTt3VpK0adMG+fmVUPXqNV2UFgAAZAaUY2RLBw/u16FDP6hnzz5pxosUKaKbN2/o4sU4ff31v1WhQoDu3LmtmTOnacyYCS5KCwAAMguWVSDbsVqteuedEYqMnCZ397R//3N3d1d09Cz16dNdJpOXZsyYo6ioCPXt21/Hjh3VjBlRypUrlyZOjFDFipVcNAMAAOAqlGNkOWvXrlJIyFBJUu3adbR37x7btl27vte2bVtUqdLzqlHj5YceHxjYQIGBDSRJsbFHdOjQDwoLC1f16pW1adMXiouL0/Dhb+vzz3c6fC4AACBzoRwjy2nXrqPatev4yO3ffPO19uz5Tl9++S9Jf3wx78iRw4qNPaypU2fY9ktNTdW774YoImKa4uPjZbVaVapUaRUr5qtjx446fB4AACDzoRwj25kzZ77M991/uFevrmrRopW6du2eZr+PP/5QVau+oCpVqspischsNuvkyROKi7ugMmXKOjk1AADIDCjHyHYKFCioAgX+89xkMilfvnzKn/8/g/Hx8frgg/nasmW7JMnT01ORkdPUpk2QcufOrdmz5zk7NgAAyAQox8j2NmzY+sCYj4+P7f7Gf3rScg0AAJD9cSs3AAAAwJAprhyHhoZq9+7dKlSokCSpadOmGjhwoItTAQAAIKfJFOVYkvr166du3bq5OgYAAAByMJZVAAAAAIZMc+V46dKlWrVqlUqVKqURI0aoXLly6T6Hj4+3A5IBAFypaNF8ro4ASMre78XsPLf0cko5bt26tS5evPjQbbt379awYcNUtGhRubu7a8OGDerbt6++/PJLeXh4pOt14uPvKCUlNSMiA0C2ldX+I3jt2m1XR4CDZNf3Ylabl5RzPmfu7m5PvJjqlHK8fv36x2739fW1PW7VqpUiIyN1+fJl+fv7OzoaAAAAYJMp1hxfuXLF9vjbb7+Vu7t7msIMAAAAOEOmWHP8zjvvKD4+Xm5ubvL29tb8+fPl6ZkpogEAACAHyRQNdNmyZa6OAAAAAGSOZRUAAABAZkA5BgAAAAyUYwAAAMBAOQYAAAAMlGMAAADAQDkGAAAADJRjAAAAwEA5BgAAAAyUYwAAAMBAOQYAAAAMmeLnowEAzpNsTlTz5UtdHcMuyeZEV0cA0i0rfcYkPmf/jXIMADnMb7eTpNtJro4BZFt8xrI2llUAANLt/7d353FRlfsfwD8Dw6Ji1/W2oYUiJG6YhuQSMCYNJAyrYCp181paoFgqkJpLioq9LAUNK5erRmYComG3m+KSO+qvXIKLoqJQooYiiwwD8/z+AM5lFBABmUE/7788+/c5z3me851zniPl5eVYtGg++vSxgZXVM1AohiI//xYAYP/+vRg4sA969bJGYuJWaZv8/FsYPnwYCgsL9BU2UYvCdqYffHJMREQPLCpqIVJTj2Hnzl2wtOyC9PQ0mJmZAwBmzw7Hxo3foby8HD4+r8PT0xvGxsZYsGAeJk+eCguLtnqOnqhlYDvTDybHRET0QG7duonVq7/Anj0H0aVLVwBAz5520vKiomJp2sTEFHl5ebhyJQuXL1+CSvWZXmImamnYzvSHwyqIiOiBpKX9Drlcjh07ktCrlzUcHftjzZovpeWdO3fCmTOncebMaRgZGaFdu3aYOTMMkZFReoyaqGVhO9MfPjkmIqIH8scfObh9Ox8XLpzH8eOnceFCJvz8PNC9uzWcnRWIivocs2aFoaTkDlau/BLr138NJydnlJSoMWqUFzQaDaZPj8DgwUP1XRQig8V2pj9MjomISMfWrd9h2rRQAICj48s4cuSwtOzAgWMwN28FAPjwwzC0atUKvXr1hpeXL3bv/g+cnRXo06cvtm3bCQDIzb2KOXNmYufOXfDycsMnnyzBU089BZXKDSdPnoVMJmv+AhIZALYzw8XkmIiIdPj5BcDPL6DW5RqNBgB0bri13Xxnzw5HRMQstGrVCmlpv8Pevj9MTU2h0Whw48YNdO7cuWmDJ2oh2M4MF8ccExHRA7Gy6gZHx8H47LNPoVarkZHxX2zbFo8RI5Q66+3dm4KSEjVcXd0AAF27PocDB/YhPT0NpaVqdOjQQR/hE7UIbGf6wyfHRET0wFavXovQ0Pdha/s8OnXqjLCwWXjlFWdpuVqtxrx5s7Fhw7fSvMjIpZg6NRhqtRpLliyDsbGxHiInajnYzvRDJoQQ+g6iqfz1VyG02kemOERERI+0zp3bYmfQP/QdRr24b1iH69f5hzVaOiMjGTp2tKh7nWaKhYiIiIjI4DE5JiIiIiKqxOSYiIiIiKgSk2MiIiIiokr8II+IiIj0ol1bU5iYm+k7jHrRlKhxq6BU32FQI9Xngzz+V25ERESkF7cKSoFGJJxHjhxCYKCvzrzi4iKsWbMRHh4q7N+/Fx98EII7d+5gwYLF8Pb2AwDk59+Cj48HkpJ2wsKibaPKQI8ePjkmIiKiR8LBg79g7NgAnDlzDm3atIGTkyNiY9eivLwcPj6vIy3tIoyNjTF9+lQMHToMKpWPvkOmZsYnx0RERPTY+O67OHh4qNCmTRsAQFFRMXr2tAMAmJiYIi8vD1euZOHy5UtQqT7TZ6hkwJgcExERUYtXVFSEHTuSsGnTd9K8zp074cyZ0wAAIyMjtGvXDkFBgYiJidVXmNQCMDkmIiKiFi85eTs6duyIwYOHSvOioj7HrFlhKCm5g5Urv8T69V/DyckZJSVqjBrlBY1Gg+nTI3S2IeKYYyIiIjJIW7d+h2nTQgEAjo4v48iRw9KyAweOwdKyizTt6+sJB4dBCAubWeO+cnOvIjDQFzt37oKXlxs++WQJnnrqKahUbjh58ixkMtnDLQwZhPqMOWZyTERERC1aTk42Bg7sg0OHTsDKqluN67zzzlvw8wuAq6sbunb9O86fz4apqSl69+6BPXsOoXPnzs0cNekDP8gjIiKiR97332/GSy8NqjUx3rs3BSUlari6ugEAunZ9DgcO7MMzz1iitFSNDh06NGe4ZOCYHBMREVGLtmXLt3j//Sk1LlOr1Zg3bzY2bPhWmhcZuRRTpwZDrVZjyZJlMDY2bq5QqQXgsAoiIiIieizUZ1iFUTPFQkRERERk8JgcExERERFVYnJMRERERFSJyTER6PKYBQAAF2VJREFUERERUSUmx0RERERElZgcExERERFVYnJMRERERFSJyTERERERUaVH6i/kGRnJ9B0CERERERmo+uSKj9RfyCMiIiIiagwOqyAiIiIiqsTkmIiIiIioEpNjIiIiIqJKTI6JiIiIiCoxOSYiIiIiqsTkmIiIiIioEpNjIiIiIqJKTI6JiIiIiCoxOSYiIiIiqvTYJMcKhQIZGRn6DqPRFAoFlEolVCoVVCoVIiMja103ISEBkydPbsboHh6FQoGhQ4eivLxcmpeQkABbW1ts2rSpSY5x9OhR+Pj4NMm+Gis/Px99+/bFggULGryPCRMm4PLlywCAcePGYc+ePU0VXoM1Rz3qy6PSx9SmPuUzxHPQFG2pMaKjo1FaWnrf9X788Ud4eXlBpVJBqVTiww8/bPAxb9++ja+++qrB29cmOzsbgwYNavL9lpaWYvHixXj11VehVCrh5eWFXbt23TeW7777rl77f1hxAxXX/MiRI6HVanXm6bsdNEUMVfmGp6cnRowYgUmTJuHkyZNNFGHj4nrY5/exSY4NQfWEoDFWrFiBpKQkJCUl4aOPPmqSfTZVbGVlZU2yn5r8/e9/x4EDB6TpxMRE9OrV64H28TDja0o//PAD+vXrh+Tk5HrdWKvTarUQQuCrr75C165dH1KEDdcU9UhUX41pS00hJiYGGo2mznWuXbuGefPm4YsvvkBSUhJ+/PFHjB8/vsHHvH37Nr7++usGb/+w3X2/mTt3Lq5evYrk5GT8+9//RlRUFObPn4/U1NRa95GTk1Pv5Lip1HafLC4uRlJSUrPG0hyEEFixYgW2b9+On3/+Gd7e3njnnXfw22+/6Tu0RrtfLvDYJcdr166Fr68vvLy8EBAQgLS0NGmZra0tYmNj4evri+HDh+Onn34CcO+vzurTZWVlGD9+PHx8fPD6668jIiJC6oATEhLw1ltv4f3338fIkSNx9uxZjBw5UiceT0/PRv8SS0xMhL+/P3x8fBAUFIQLFy5IywoKCjBx4kS4u7sjKCgIubm5NcaWkZFxz6+x6tNLliyBr68vPD098eabbyInJ0fnXCxZsgTe3t74/vvvMXToUFy7dk3az4IFCxAbG9uoMgKAt7c3EhISAABXrlxBcXExbGxsAACHDx9GQEAAvLy84OHhgeTkZGm7cePGYeHChRg1ahQmTZoEAFi9ejU8PDzg6emJwMBA6Vd/eXk5Pv74Y2lZZmZmo+NuiPj4eLz33nuwtbXF7t27AVQ8gZoyZQqCgoKgVCoREhKCgoICadnkyZPx9ttvw93dHbdv3zaIpxc1aUg9njp16qG0nYehrnakUCiwfPlyBAQEQKFQ6Dwtv3DhAv75z39K7Sw+Pr7ZY6+PuspXxZDqq6a2FB4ernPuq0/n5ubizTffxOuvv46JEydi4sSJ0rK738BUn46JiZHe6nl5eeH27duYN28eACAwMBAqlQq3b9+uMcYbN25ALpejXbt2AACZTAY7OzsAwG+//YZx48bBx8cHPj4+2Lt3L4D/9b2LFy+Gh4cHPDw8cPz4cQDA/PnzUVBQAJVKhcDAQAAVCfjkyZPh5+cHDw8PnT5ZoVDgs88+Q0BAAJydnbFjxw6sX78efn5+GDFixD1Jak3HBIB9+/YhMDAQPj4+CAgIwK+//gqg4q2ch4cHIiIioFKpsH//fmmbnJwc/Pjjj5g7dy7MzMwAADY2Npg4cSJiYmIA1Nxfz58/H5mZmVCpVNIb0lOnTiEgIAAeHh4ICAjAqVOnHlrc1QUHByMmJuaeH19ZWVl488034eHhAW9vb2n7VatW6bz1vXnzJgYNGoTi4mKUlpZiyZIl8PPzg6enJ6ZPn46ioiIAFdfpxx9/jKCgILi4uCAyMhKHDx/GG2+8AYVCgX/96186x9++fTt8fHwwYsSIevc1tra2iI6Ohq+vr3R/qeLq6orAwECsWbOmzjgLCgoQEREh1dn8+fMBwKDKVnVt1Uo8JlxcXMR///tf8ddff0nzDh48KPz9/aVpGxsbsXHjRiGEEMePHxdDhw4VQghx5coV4eDgIK1XfVqr1Yq8vDzp39OnTxdxcXFCCCHi4+OFvb29yMrKkrb19/cXR48eFUIIkZqaKlQq1QOX47XXXhOenp7C09NTREdHiwkTJgi1Wi2EEGLv3r0iICBAOn6fPn1EZmamEEKI6OhoERISUmtsVeeopunq523Lli0iNDRUOhc2NjYiOTlZWr506VIRHR0thBCisLBQODo6ihs3bjxQOWsqd3p6ulAqleLWrVti+fLlYsOGDSIsLExs3LhR3Lp1S5SVlQkhhLh+/boYNmyYuHXrlhBCiLFjx4p3331XaDQaIYQQCQkJYtSoUaKgoEAIIaT6O3LkiLCzsxNnz54VQgixatUq8cEHHzQq7oZIS0sTLi4uQqvViqSkJDF+/HghhBArVqwQQ4YMEdevXxdCCBEeHi4WL14sLXNyctKpp+r1N3bsWJGSktLMJblXY+qxsW3nYas633W1IxcXF6nOrly5Iuzt7UVhYaHQaDTC29tbnD9/XgghREFBgXB1dZWmDUF9y1f1b0Oor9raUtX1VqX6dHBwsFi5cqUQQojs7GzRv39/adnd7ahq+ubNm2LAgAHizp07QoiK+qvqb2xsbERhYWGdcZaXl4tJkyYJBwcHERISItatWyfy8vJEfn6+UKlUIjc3VwghRG5urhg2bJjIz8+X+t7ExEQhREX/NWzYMKFWq++5ZwkhxFtvvSWOHTsmhBBCrVaL0aNHiwMHDgghdK/L3377TfTr109s2rRJCCFEcnKyCAwMFEKIOo+ZlZWl069mZGQIJycnab0XXnhBnDx58p6yp6SkCE9Pz3vmnz17Vjg4ONTZX3t7e0vrq9Vq4eTkJA4dOiSEqLi/Ozk5SeejqeOuUnXNh4SEiPXr1+vM8/PzE1u2bBFCCHHu3Dnh4OAg/vrrL5GTkyOGDBkiXSMbNmwQ4eHhQgghVq5cKV1/QggRFRUlli1bJoSouE4DAwOFWq0WxcXFwtHRUYSHh4vy8nJx9epVqT+piqFqn9evXxdDhgwRaWlp9+1rbGxsxOrVq3XKUd1//vMf4ebmVmec4eHhYv78+aK8vFwI8b/8wZDKdj/yulPnR8+ZM2ewevVq5OfnQyaT4dKlSzrL3d3dAQD29va4du0a1Gp1nfvTarVYu3Yt9u/fD61Wi/z8fJibm0vLX3zxRZ1X2+PGjUNcXBwcHBzwzTffYMyYMQ9chhUrVkhP2qKiopCeng5/f38AFa9Bqj+dGDBgALp16wYA8Pf3h4eHR62x1WX//v2Ii4tDcXHxPa8jzMzM4ObmJk2PGTMGY8aMwcSJE7F9+3YMGTIEHTt2fOBy3k0mk8HNzQ3JyclITk7G5s2bcfbsWQBAXl4ePvroI2RlZcHY2Bj5+fm4ePEi7O3tAQAeHh6Qyysu9z179mD06NGwsLAAALRv3146hpWVlfTExt7eXi/jdLdu3QqVSgWZTAZXV1csWLBAeuLv7OyMTp06AQD8/Px0xlG+8sor6NChQ7PH+6AaWo9N0XYMQVUfY2lpiSeeeAJXr16FEAKZmZn44IMPpPU0Gg0uXLiA7t276yvURjGE+qqrLdXm6NGjmDVrFgDg2Wefxcsvv3zf47Rt2xZdu3bFjBkzMHToUDg7O0v9S30YGRlh1apVyMjIQGpqKnbt2oU1a9ZgxowZyM7OxoQJE6R1ZTIZsrKy0L59e5iYmMDT0xMAMGjQIJibm+PChQv3HLu4uBjHjh1DXl6eNK+oqAiZmZkYMmQIgP9dl7169cKdO3ekPr13797StwsAaj3miRMncPnyZZ16Lisrw40bNwAAzz33HPr3739P2YUQdZ6buvrr6i5evAgTExOpvgYPHgwTExNcvHgRbdq0afK47xYaGoqgoCD4+flJ5UpLS4Ovry8AwNraGj179sSvv/4KhUIBa2tr7Nu3D8OHD0diYiIiIiIAACkpKSgsLJTeXJeWluKFF16QjvPqq6/C1NQUQMX9ysnJCUZGRnjyySel/qSqz6iKpVOnTnB2dsaxY8cgl8vv29d4e3vXWs6q+qorzj179iAhIQFGRhWDE6ruS4Zetuoeq+RYq9ViypQp2LRpE3r16oXc3Fy88sorOutUvdYxNjYGUNFI5HK5TgOunjDv2LEDJ06cwDfffAMLCwvExsbqJNxt2rTR2b9SqcSyZcvw+++/4+jRo3V+UFcfQgj4+vpiypQpD7zt3bEZGxvrfFRQVc6cnBwsWrQIW7duRZcuXXDy5ElMmzZNWq9Vq1aQyWTS9NNPP43evXtj9+7diIuLk16pNAVvb2/4+/vjpZde0ukk586dC4VCgZiYGMhkMrz22ms69dS6det67b+qYQIVN6zmHqNcWlqKH374AaamptIYNo1GIw1DqMvd9WnIGlKPTd12Hpba2lGVqj6mat3y8nLIZDK0b9++RYxbvF/5qui7vupqS/Utw91q287Y2BhbtmzByZMnceTIEfj4+ODrr7/WufHXh42NDWxsbDBmzBi4u7tDCAFbW1t8880396ybnZ1d7/1qtVrIZDJs3boVJiYmNa5z972vavpB+sFhw4YhKirqnvmZmZm19sE2Nja4fPkybt26JQ0rAYBff/0Vtra29TpuYzUk7rt169YNTk5OWLduXb3W9/b2xrZt22BpaYmCggIMHDgQQMU9fc6cObX+KLu7/6ipP6mLEOK+fU1dZT59+jR69OiB7OzsOuOs7diGXLbqHrsxx2VlZXj66acBAHFxcfXaplOnTtBoNMjKygJQ8YFHlYKCArRv3x4WFhYoKCjQWVYTExMT+Pr6YtKkSfDw8ECrVq0aWJIKCoUCSUlJuHr1KoCKMbNnzpyRlp88eVJK1uPj4+Ho6Fjrvrp27YrTp08DqBj7WfXLubCwECYmJujcuTO0Wi02b95837jGjh2LyMhIyOXyev3qrq8uXbpg6tSpeO+993TmFxQU4Nlnn4VMJsPBgweluqqJi4sLvv32WxQWFgKoGO9lKHbv3g0rKyvs378fKSkpSElJwdq1a5GYmAgA2Lt3r/T0JyEhoc76NGQNqcembjsPS23tqC5WVlYwNzfHtm3bpHmZmZnSNWpI6ls+fddXXW3pueeek8pw7do1HD16VNrOwcFBam9//vknjhw5Ii2rXvbz589L36wUFhYiLy8PDg4OmDx5MmxsbHDu3DkAFT9a71ePubm5+L//+z9p+urVq8jLy4O1tTWysrJ0Yjh16pT0sEaj0WDHjh0AgOPHj6OkpATdunWDhYUFSkpKpKTWwsICAwYMwJdffint588//8T169cf5JTWecwhQ4bgl19+kcpdFev9WFpaQqlUYu7cudKPjYyMDMTGxiI4OLjW/trCwkLnvFpZWUGj0Ujn6vDhwygrK4OVldVDibsmISEhiIuLQ1FREWQyGXr27CldS5mZmUhPT5feZrq6uiI1NRXr1q2Dt7e39IBJoVBg/fr1KCkpAVBxbTX025eqY+fl5WHfvn0YNGhQo/qaXbt24dtvv8Xbb79dZ5wuLi5Ys2aNdJ1W3bMMuWx3e2yeHJeVlaFVq1bSBwnt2rXDa6+9Vq9t5XI5Zs6ciX/84x/o0KEDnJ2dpWVeXl7YvXs3lEolOnbsiAEDBtz3KYS/vz9iYmIwevToxhQJAPDSSy8hNDQUkyZNQnl5OTQaDZRKJXr37g2gYujEkiVLkJWVhU6dOmHp0qW17mvKlCnShymOjo545plnAFQMYlcqlXB3d0f79u3h5OSk8zFDTRwcHGBmZoY33nij0WW8W0BAwD3zPvzwQ8ybNw/R0dHo06dPnU8cvLy8kJubi4CAAMjlcrRu3brGJzP6EB8frzP0BQD69+8PrVaLY8eOYeDAgZg6dSpyc3NhbW2N8PBwPUXaeA2px6ZsO02trKwMZmZmtbajusjlcsTGxiIyMhJr1qyBVqtFx44d8fnnnzdD5PXTkPLps77qakv29vb45Zdf4O7ujueffx59+/aV1pk5cyZmzJiBHTt2wNLSEn379pVe6U+YMAFTpkzB7t27YWdnJw3BKiwsREhICEpKSiCEgJ2dHVxdXQEAb7/9NoKCgmBubo6NGzfiiSeeuCfWsrIyREdHIycnB+bm5tBqtQgNDYWdnR1WrVqFpUuXIjIyEhqNBl26dJE+pmvXrh3S09Ol/5li2bJlMDU1hampqfTh2d/+9jds3rwZn376KRYtWiSdkzZt2mDhwoXo3LnzA53X2o75/PPPY+nSpZg5cyZKSkqg0Wjw4osv6pzb2syZMwfLli2Du7s7TExMYGZmhpkzZ8LBwQFCiBr7a1tbW1hZWWHkyJHo1q0bVqxYgRUrVmDhwoUoLi5G69atsXz5cult4MOI+25PPfUUVCoV1q5dCwD49NNP8fHHH2P9+vWQy+WIioqShhi0atUKw4cPR0JCgvShKAC88847iImJgZ+fH2QyGWQyGYKDgxs0vKp9+/bw8fFBQUEB3n33Xak/fZC+ZvLkyTA1NcWdO3fQvXt3fPnll+jXrx/s7OxqjTMiIgKRkZEYOXIkjI2N4eDggFmzZhlc2eoiE/cb8PMIuHbtGtzc3HDw4EGd8cD6kpSUhOTkZJ1f8Y+aK1euYPTo0fj5558N9glfSxMdHY3i4mKEhYXpOxS9MdS2Y2h9TFNraPkMtb7qUlJSArlcDrlcjmvXrsHPzw/r16+Xvt0wFNnZ2fD19dV56k1ETeORf3K8YcMGxMXFISwszCBuWuPHj8fly5fxxRdf6DuUh2b58uWIj49HeHg4E2NqMobadgytj2lqDS2fodbX/Vy6dAlhYWEQQqCsrAzBwcEGlxgT0cP1WDw5JiIiIiKqj8fugzwiIiIiotowOSYiIiIiqsTkmIiIiIioEpNjIiIiIqJKTI6JiAxcaWkpPvroI7i4uKB///5QqVTYt2+ftPzw4cNQKpXo168fxo0bh5ycHGnZzp07ERgYKC27W0pKCkaOHIn+/fsjMDAQ58+fb5YyEREZKibHREQGruove27cuBEnTpxAaGgoQkNDkZ2djby8PAQHB2PKlCk4duwYevfujalTp0rbtmvXDkFBQZgwYcI9+7106RKmTZuGuXPnIjU1FS4uLpg0aVKz/9l0IiJDwuSYiMjAtW7dGiEhIbC0tISRkRFcXFxgaWmJs2fP4ueff0aPHj3g5uYGMzMzhISEID09XfqzrIMHD4a7uzuefPLJe/Z74MABDBw4EAMHDoRcLseECROQm5uL1NTU5i4iEZHBYHJMRNTC3LhxA5cuXYK1tTXOnTun82e2W7duja5du9Z7eET1/+peCAEhBDIyMpo8ZiKiloLJMRFRC6LRaDBt2jR4e3uje/fuKC4uRtu2bXXWsbCwQFFR0X339fLLLyM1NRVHjx5FaWkpYmNjodFoUFJS8rDCJyIyeEyOiYhaCK1WixkzZsDExASzZ88GUPGkuLCwUGe9oqIitGnT5r776969OxYvXoxPPvkEw4YNw82bN2FtbV3jEAwioseFXN8BEBHR/QkhMHPmTNy4cQNfffUVTExMAAA9evRAYmKitF5xcTEuX74Ma2vreu1XqVRCqVQCAG7fvo34+Hj06dOn6QtARNRC8MkxEVELMGfOHGRmZiI2Nhbm5ubS/BEjRuDcuXP46aefoFarsXLlStja2qJ79+4AgPLycqjVapSVlUGr1UKtVkOj0UjbnzlzBuXl5cjLy8Ps2bOhUCikbYmIHkcyUf1rDCIiMjg5OTlQKBQwNTWFXP6/F37z5s2Dp6cnDh06hPnz5+OPP/5Av379sGjRIlhaWgIAEhISEBERobM/b29vLF68GAAwevRopKenw8TEBEqlEuHh4WjdunXzFY6IyMAwOSYiIiIiqsRhFURERERElZgcExERERFVYnJMRERERFSJyTERERERUSUmx0RERERElZgcExERERFVYnJMRERERFSJyTERERERUSUmx0RERERElf4fcAYlgk5FFeUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2020 Cumulative Performances: 484.67% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2021 Cumulative Performances: 143.06% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----- 2022 Cumulative Performances: 19.53% -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}