{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基本方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statistics import mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_opt(opt_df):\n",
    "    \n",
    "    gainloss_df = pd.DataFrame()\n",
    "    \n",
    "    gap = 0.0001\n",
    "    \n",
    "    (start, end) = (mean(opt_df.loc['X']) - 0.5, mean(opt_df.loc['X']) + 0.5)\n",
    "    \n",
    "    \n",
    "    for opt_num in opt_df.columns:\n",
    "        \n",
    "        opt_info = opt_df.loc[['direction', 'catagory'], opt_num].tolist()\n",
    "        price = opt_df.loc['price', opt_num]\n",
    "        X = opt_df.loc['X', opt_num]\n",
    "        count = opt_df.loc['count', opt_num]\n",
    "\n",
    "        if opt_info == ['buy', 'call']:  # 买入看涨期权\n",
    "            \n",
    "            v = [(-price * count) for i in np.arange(start, X - gap * 0.1, gap)]\n",
    "            for i in np.arange(X, end - gap * 0.1, gap):\n",
    "                v.append((i - X - price) * count)\n",
    "                \n",
    "        elif opt_info == ['buy', 'put']:  # 买入看跌期权\n",
    "            \n",
    "            v = []\n",
    "            for i in np.arange(start, X - gap * 0.1, gap):\n",
    "                v.append((X - i - price) * count)\n",
    "            v += [(-price * count) for i in np.arange(X, end - gap * 0.1, gap)]\n",
    "            \n",
    "        elif opt_info == ['sell', 'call']:  # 卖出看涨期权\n",
    "            \n",
    "            v = [(price * count) for i in np.arange(start, X - gap * 0.1, gap)]\n",
    "            for i in np.arange(X, end - gap * 0.1, gap):\n",
    "                v.append(-(i - X - price) * count)\n",
    "                \n",
    "        elif opt_info == ['sell', 'put']:  # 卖出看跌期权\n",
    "            \n",
    "            v = []\n",
    "            \n",
    "            for i in np.arange(start, X - gap * 0.1, gap):\n",
    "                v.append(-(X - i - price) * count)\n",
    "                \n",
    "            v += [(price * count) for i in np.arange(X, end - gap * 0.1, gap)]\n",
    "\n",
    "        gainloss_df[opt_num] = v\n",
    "\n",
    "    gainloss_df.index = np.arange(start, end - gap * 0.1, gap)\n",
    "    gainloss_df['sum'] = gainloss_df['opt1'] + gainloss_df['opt2']\n",
    "\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    \n",
    "    # 正常显示负号\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.plot(gainloss_df['opt1'], '--', label='opt1')\n",
    "    plt.plot(gainloss_df['opt2'], '--', label='opt2')\n",
    "    plt.plot(gainloss_df['sum'], c='r', label='sum')\n",
    "    \n",
    "    plt.axhline(0, c='gray')\n",
    "    \n",
    "    # 2.3 找到各条线与Y轴(图表左边界)的交点Y值并标注\n",
    "    y_axis_x = start  # Y轴的X坐标就是图表的起始点\n",
    "    \n",
    "    # 获取各条线在Y轴位置的Y值\n",
    "    opt1_y_value = gainloss_df['opt1'].iloc[0]  # 第一个点的Y值\n",
    "    opt2_y_value = gainloss_df['opt2'].iloc[0]  # 第一个点的Y值\n",
    "    sum_y_value = gainloss_df['sum'].iloc[0]    # 第一个点的Y值\n",
    "    \n",
    "    # 在Y轴上标注这些点\n",
    "    plt.plot(y_axis_x, opt1_y_value, 'bo', markersize=8)\n",
    "    plt.annotate(f'opt1: {opt1_y_value:.3f}', \n",
    "                xy=(y_axis_x, opt1_y_value), xytext=(y_axis_x + 0.1, opt1_y_value),\n",
    "                arrowprops=dict(arrowstyle='->', color='blue'),\n",
    "                fontsize=10, color='blue')\n",
    "    \n",
    "    plt.plot(y_axis_x, opt2_y_value, 'go', markersize=8)\n",
    "    plt.annotate(f'opt2: {opt2_y_value:.3f}', \n",
    "                xy=(y_axis_x, opt2_y_value), xytext=(y_axis_x + 0.1, opt2_y_value + 0.02),\n",
    "                arrowprops=dict(arrowstyle='->', color='green'),\n",
    "                fontsize=10, color='green')\n",
    "    \n",
    "    plt.plot(y_axis_x, sum_y_value, 'ro', markersize=8)\n",
    "    plt.annotate(f'sum: {sum_y_value:.3f}', \n",
    "                xy=(y_axis_x, sum_y_value), xytext=(y_axis_x + 0.1, sum_y_value - 0.02),\n",
    "                arrowprops=dict(arrowstyle='->', color='red'),\n",
    "                fontsize=10, color='red')\n",
    "    \n",
    "    # 找到sum曲线与X轴的交点（盈亏平衡点）\n",
    "    def find_zero_crossings(series, x_values):\n",
    "        \"\"\"找到曲线与y=0的交点\"\"\"\n",
    "        crossings = []\n",
    "        for i in range(len(series) - 1):\n",
    "            if series.iloc[i] * series.iloc[i + 1] <= 0:  # 符号改变或为0\n",
    "                # 线性插值找到精确交点\n",
    "                x1, y1 = x_values[i], series.iloc[i]\n",
    "                x2, y2 = x_values[i + 1], series.iloc[i + 1]\n",
    "                if y2 != y1:  # 避免除零\n",
    "                    x_cross = x1 - y1 * (x2 - x1) / (y2 - y1)\n",
    "                    crossings.append(x_cross)\n",
    "        return crossings\n",
    "    \n",
    "    x_values = gainloss_df.index.values\n",
    "    \n",
    "    # 标注sum的盈亏平衡点\n",
    "    sum_crossings = find_zero_crossings(gainloss_df['sum'], x_values)\n",
    "    for x_cross in sum_crossings:\n",
    "        plt.plot(x_cross, 0, 'ro', markersize=8)\n",
    "        plt.annotate(f'平衡点: ({x_cross:.3f}, 0)', \n",
    "                    xy=(x_cross, 0), xytext=(x_cross + 0.05, 0.05),\n",
    "                    arrowprops=dict(arrowstyle='->', color='red'),\n",
    "                    fontsize=10, color='red')\n",
    "    \n",
    "    # 2.4 找到sum的最小值并标注\n",
    "    min_sum = gainloss_df['sum'].min()\n",
    "    min_index = gainloss_df['sum'].idxmin()\n",
    "    \n",
    "    # 标注最小值点\n",
    "    plt.plot(min_index, min_sum, 'ro', markersize=10)\n",
    "    plt.annotate(f'最小值: ({min_index:.3f}, {min_sum:.3f})', \n",
    "                xy=(min_index, min_sum), xytext=(min_index + 0.1, min_sum + 0.1),\n",
    "                arrowprops=dict(arrowstyle='->', color='red', lw=2),\n",
    "                fontsize=12, color='red', weight='bold')\n",
    "    \n",
    "    # 画虚线到Y轴\n",
    "    plt.axhline(min_sum, color='red', linestyle='--', alpha=0.7)\n",
    "    plt.text(start + 0.02, min_sum + 0.02, f'最小值: {min_sum:.3f}', \n",
    "             fontsize=11, color='red', weight='bold')\n",
    "    \n",
    "    plt.ylim(min(gainloss_df['sum']) - 0.2, max(gainloss_df['sum']) + 0.2)\n",
    "    plt.xlim(start, end)\n",
    "    plt.xticks(np.arange(start, end, 0.1))\n",
    "    \n",
    "    plt.xlabel('标的资产价格')\n",
    "    plt.ylabel('损益')\n",
    "    plt.title('期权组合损益分析')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    # 打印关键信息\n",
    "    print(\"=== 关键信息汇总 ===\")\n",
    "    print(f\"Y轴处各曲线的Y值:\")\n",
    "    print(f\"  opt1在Y轴的值: {opt1_y_value:.4f}\")\n",
    "    print(f\"  opt2在Y轴的值: {opt2_y_value:.4f}\")\n",
    "    print(f\"  sum在Y轴的值: {sum_y_value:.4f}\")\n",
    "    print(f\"sum曲线最小值: {min_sum:.4f}，一单最大亏损为: {min_sum*10000:.2f}\")\n",
    "    print(f\"最小值对应的标的价格: {min_index:.4f}\")\n",
    "    print(f\"sum在大于{sum_crossings[0]:.4f}或者小于{sum_crossings[1]:.4f}才会盈利\")\n",
    "    print(f\"当价格小于{sum_crossings[0]:.4f}，收益为: {sum_y_value*10000:.2f}\")\n",
    "    print(f\"当价格大于{sum_crossings[1]:.4f}收益没有上限\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 版本b，考虑行权\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def analyze_single_option(direction, option_type, strike_price, premium, quantity=1, \n",
    "                         price_range=None, current_price=None, expiry_month=None, \n",
    "                         intend_exercise=False):\n",
    "    \"\"\"\n",
    "    分析单个期权的收益情况\n",
    "    \n",
    "    参数:\n",
    "    direction: 'buy' 或 'sell' - 买入或卖出\n",
    "    option_type: 'call' 或 'put' - 认购或认沽\n",
    "    strike_price: 行权价\n",
    "    premium: 权利金\n",
    "    quantity: 期权数量（默认1）\n",
    "    price_range: 价格分析区间，格式为(min_price, max_price)，默认为行权价±50%\n",
    "    current_price: 当前标的价格，用于标注当前损益（可选）\n",
    "    expiry_month: 到期月份，如\"2024-03\"（可选）\n",
    "    intend_exercise: 是否打算行权（默认False）\n",
    "    \"\"\"\n",
    "    \n",
    "    # 设置价格分析区间\n",
    "    if price_range is None:\n",
    "        price_min = strike_price * 0.5\n",
    "        price_max = strike_price * 1.5\n",
    "    else:\n",
    "        price_min, price_max = price_range\n",
    "    \n",
    "    # 生成价格序列\n",
    "    gap = (price_max - price_min) / 1000  # 生成1000个点\n",
    "    prices = np.arange(price_min, price_max + gap, gap)\n",
    "    \n",
    "    # 计算收益\n",
    "    profits = []\n",
    "    \n",
    "    for price in prices:\n",
    "        if direction == 'buy' and option_type == 'call':\n",
    "            # 买入认购期权\n",
    "            if price > strike_price:\n",
    "                profit = (price - strike_price - premium) * quantity\n",
    "            else:\n",
    "                profit = -premium * quantity\n",
    "                \n",
    "        elif direction == 'sell' and option_type == 'call':\n",
    "            # 卖出认购期权\n",
    "            if price > strike_price:\n",
    "                profit = -(price - strike_price - premium) * quantity\n",
    "            else:\n",
    "                profit = premium * quantity\n",
    "                \n",
    "        elif direction == 'buy' and option_type == 'put':\n",
    "            # 买入认沽期权\n",
    "            if price < strike_price:\n",
    "                profit = (strike_price - price - premium) * quantity\n",
    "            else:\n",
    "                profit = -premium * quantity\n",
    "                \n",
    "        elif direction == 'sell' and option_type == 'put':\n",
    "            # 卖出认沽期权\n",
    "            if price < strike_price:\n",
    "                profit = -(strike_price - price - premium) * quantity\n",
    "            else:\n",
    "                profit = premium * quantity\n",
    "        \n",
    "        profits.append(profit)\n",
    "    \n",
    "    # 创建DataFrame\n",
    "    df = pd.DataFrame({\n",
    "        'price': prices,\n",
    "        'profit': profits\n",
    "    })\n",
    "    \n",
    "    # 绘图\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    \n",
    "    # 绘制收益曲线\n",
    "    color_map = {\n",
    "        ('buy', 'call'): 'blue',\n",
    "        ('sell', 'call'): 'red',\n",
    "        ('buy', 'put'): 'green',\n",
    "        ('sell', 'put'): 'orange'\n",
    "    }\n",
    "    \n",
    "    strategy_name = f\"{direction.upper()} {option_type.upper()}\"\n",
    "    if intend_exercise:\n",
    "        strategy_name += \" (打算行权)\"\n",
    "    color = color_map.get((direction, option_type), 'black')\n",
    "    \n",
    "    plt.plot(df['price'], df['profit'], color=color, linewidth=2, label=strategy_name)\n",
    "    \n",
    "    # 添加零线\n",
    "    plt.axhline(0, color='gray', linestyle='-', alpha=0.5)\n",
    "    plt.axvline(strike_price, color='gray', linestyle='--', alpha=0.5, label=f'行权价: {strike_price}')\n",
    "    \n",
    "    # 找到盈亏平衡点\n",
    "    breakeven_points = []\n",
    "    for i in range(len(profits) - 1):\n",
    "        if profits[i] * profits[i + 1] <= 0:  # 符号改变\n",
    "            # 线性插值找到精确平衡点\n",
    "            p1, profit1 = prices[i], profits[i]\n",
    "            p2, profit2 = prices[i + 1], profits[i + 1]\n",
    "            if profit2 != profit1:\n",
    "                breakeven_price = p1 - profit1 * (p2 - p1) / (profit2 - profit1)\n",
    "                breakeven_points.append(breakeven_price)\n",
    "    \n",
    "    # 标注盈亏平衡点\n",
    "    for bp in breakeven_points:\n",
    "        plt.plot(bp, 0, 'ro', markersize=10)\n",
    "        plt.annotate(f'盈亏平衡点: {bp:.2f}', \n",
    "                    xy=(bp, 0), xytext=(bp + (price_max - price_min) * 0.05, max(profits) * 0.1),\n",
    "                    arrowprops=dict(arrowstyle='->', color='red'),\n",
    "                    fontsize=11, color='red', weight='bold')\n",
    "    \n",
    "    # 标注Y轴处的收益\n",
    "    y_axis_profit = profits[0]\n",
    "    plt.plot(price_min, y_axis_profit, 'ko', markersize=8)\n",
    "    plt.annotate(f'起始收益: {y_axis_profit:.0f}', \n",
    "                xy=(price_min, y_axis_profit), \n",
    "                xytext=(price_min + (price_max - price_min) * 0.05, y_axis_profit),\n",
    "                arrowprops=dict(arrowstyle='->', color='black'),\n",
    "                fontsize=10, color='black')\n",
    "    \n",
    "    # 如果提供了当前价格，标注当前损益\n",
    "    if current_price is not None and price_min <= current_price <= price_max:\n",
    "        # 找到当前价格对应的收益\n",
    "        current_idx = np.argmin(np.abs(prices - current_price))\n",
    "        current_profit = profits[current_idx]\n",
    "        \n",
    "        plt.plot(current_price, current_profit, 'mo', markersize=12)\n",
    "        plt.annotate(f'当前损益: {current_profit:.0f}', \n",
    "                    xy=(current_price, current_profit), \n",
    "                    xytext=(current_price + (price_max - price_min) * 0.05, current_profit + max(profits) * 0.05),\n",
    "                    arrowprops=dict(arrowstyle='->', color='magenta', lw=2),\n",
    "                    fontsize=12, color='magenta', weight='bold')\n",
    "        \n",
    "        # 添加当前价格的垂直线\n",
    "        plt.axvline(current_price, color='magenta', linestyle=':', alpha=0.7, \n",
    "                   label=f'当前价格: {current_price}')\n",
    "    \n",
    "    # 找到最大收益和最大损失\n",
    "    max_profit = max(profits)\n",
    "    min_profit = min(profits)\n",
    "    max_profit_price = prices[profits.index(max_profit)]\n",
    "    min_profit_price = prices[profits.index(min_profit)]\n",
    "    \n",
    "    # 设置图表属性\n",
    "    plt.xlabel('标的资产价格', fontsize=12)\n",
    "    plt.ylabel('收益/损失 (元)', fontsize=12)\n",
    "    \n",
    "    title = f'{strategy_name} 收益分析\\n行权价: {strike_price}, 权利金: {premium}, 数量: {quantity}手'\n",
    "    if expiry_month:\n",
    "        title += f', 到期月份: {expiry_month}'\n",
    "    \n",
    "    plt.title(title, fontsize=14, weight='bold')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    plt.legend()\n",
    "    \n",
    "    # 调整Y轴范围\n",
    "    y_range = max_profit - min_profit\n",
    "    plt.ylim(min_profit - y_range * 0.1, max_profit + y_range * 0.1)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 打印分析结果\n",
    "    print(\"=\" * 60)\n",
    "    print(f\"期权策略分析: {strategy_name}\")\n",
    "    print(\"=\" * 60)\n",
    "    print(f\"行权价: {strike_price}\")\n",
    "    print(f\"权利金: {premium}\")\n",
    "    print(f\"合约数量: {quantity}手 (每手10000个)\")\n",
    "    if expiry_month:\n",
    "        print(f\"到期月份: {expiry_month}\")\n",
    "    print(f\"是否打算行权: {'是' if intend_exercise else '否'}\")\n",
    "    print(f\"分析价格区间: {price_min:.2f} - {price_max:.2f}\")\n",
    "    print(\"-\" * 40)\n",
    "    print(f\"最大收益: {max_profit:.0f}元 (标的价格: {max_profit_price:.2f})\")\n",
    "    print(f\"最大损失: {min_profit:.0f}元 (标的价格: {min_profit_price:.2f})\")\n",
    "    print(f\"起始位置收益: {y_axis_profit:.0f}元\")\n",
    "    \n",
    "    if breakeven_points:\n",
    "        print(f\"盈亏平衡点: {[f'{bp:.2f}' for bp in breakeven_points]}\")\n",
    "    else:\n",
    "        print(\"无盈亏平衡点\")\n",
    "    \n",
    "    if current_price is not None:\n",
    "        current_idx = np.argmin(np.abs(prices - current_price))\n",
    "        current_profit = profits[current_idx]\n",
    "        print(f\"当前标的价格 {current_price} 下的收益: {current_profit:.0f}元\")\n",
    "    \n",
    "    # 行权相关提示\n",
    "    if intend_exercise:\n",
    "        if direction == 'sell' and option_type == 'put':\n",
    "            print(\"\\n*** 行权提示 ***\")\n",
    "            print(\"卖沽且打算行权：如果被行权，将以行权价买入标的\")\n",
    "            print(\"如果看好标的长期表现，这种'被迫买入'可能是有利的\")\n",
    "        elif direction == 'sell' and option_type == 'call':\n",
    "            print(\"\\n*** 行权提示 ***\")\n",
    "            print(\"卖购且打算行权：如果被行权，将以行权价卖出标的\")\n",
    "            print(\"需要确保持有足够的标的资产用于交割\")\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 看涨策略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 买入看涨期权 (Long Call)\n",
    "1. 策略构成：仅买入一份看涨期权（Call Option）。\n",
    "2. 适用情景：*强烈看涨*。您预期股价在短期内会大幅上涨，超过“行权价 + 您支付的权利金”。\n",
    "3. 目标：以小博大，利用杠杆获取股价上涨的收益。\n",
    "4. 优点：风险有限（最大亏损是您支付的全部权利金），潜在收益理论上无限。\n",
    "5. 缺点：权利金是纯粹的成本，如果股价不上涨或上涨幅度不够，您将损失全部或部分权利金。时间对您不利（时间价值衰减，即$\\theta$损耗）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1. 买入认购期权示例:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "期权策略分析: BUY CALL\n",
      "============================================================\n",
      "行权价: 2.75\n",
      "权利金: 0.0456\n",
      "合约数量: 1手 (每手10000个)\n",
      "是否打算行权: 否\n",
      "分析价格区间: 1.38 - 4.12\n",
      "----------------------------------------\n",
      "最大收益: 1元 (标的价格: 4.13)\n",
      "最大损失: -0元 (标的价格: 1.38)\n",
      "起始位置收益: -0元\n",
      "盈亏平衡点: ['2.80']\n",
      "当前标的价格 2.746 下的收益: -0元\n"
     ]
    }
   ],
   "source": [
    "# 示例1: 买入认购期权\n",
    "print(\"\\n1. 买入认购期权示例:\")\n",
    "df = analyze_single_option(\n",
    "    direction='buy',\n",
    "    option_type='call', \n",
    "    strike_price=2.75,\n",
    "    premium=0.0456,\n",
    "    quantity=1,\n",
    "    current_price=2.746\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 牛市价差策略 (Bull Call Spread)\n",
    "1. 策略构成：买入一份较低行权价的看涨期权，同时卖出一份同一到期日、但行权价较高的看涨期权。这是一个“垂直价差”策略。\n",
    "2. 适用情景：温和看涨。您预期股价会上涨，但涨幅可能有限，不会超过您卖出的看涨期权的行权价。\n",
    "3. 目标：降低买入期权的成本，牺牲无限的上涨空间来换取更高的获胜概率。\n",
    "4. 优点：净支出（权利金成本）低于单独买入看涨期权。最大亏损也有限且已知。\n",
    "5. 缺点：潜在利润有上限。如果股价出现超预期的暴涨，您的收益将被锁定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32452 (\\N{CJK UNIFIED IDEOGRAPH-7EC4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21512 (\\N{CJK UNIFIED IDEOGRAPH-5408}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26368 (\\N{CJK UNIFIED IDEOGRAPH-6700}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 23567 (\\N{CJK UNIFIED IDEOGRAPH-5C0F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 关键信息汇总 ===\n",
      "Y轴处各曲线的Y值:\n",
      "  opt1在Y轴的值: -0.0456\n",
      "  opt2在Y轴的值: 0.0264\n",
      "  sum在Y轴的值: -0.0192\n",
      "sum曲线最小值: -0.0192，一单最大亏损为: -192.00\n",
      "最小值对应的标的价格: 2.2750\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m opt_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopt1\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuy\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcall\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0.0456\u001b[39m, \u001b[38;5;241m2.75\u001b[39m, \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      9\u001b[0m opt_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopt2\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msell\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcall\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0.0264\u001b[39m, \u001b[38;5;241m2.8\u001b[39m, \u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 10\u001b[0m \u001b[43mdraw_opt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopt_df\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[2], line 147\u001b[0m, in \u001b[0;36mdraw_opt\u001b[0;34m(opt_df)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum曲线最小值: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmin_sum\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m，一单最大亏损为: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmin_sum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m10000\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m最小值对应的标的价格: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmin_index\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum在大于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m或者小于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m才会盈利\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m当价格小于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m，收益为: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_y_value\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m10000\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m当价格大于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m收益没有上限\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "#上证50ETF，20250615测试\n",
    "opt_df = pd.DataFrame(index=['direction',  # 买卖方向\n",
    "                             'catagory',   # 期权类型（call、put）\n",
    "                             'price',      # 期权价格\n",
    "                             'X',          # 执行价\n",
    "                             'count'])     # 期权份数\n",
    "\n",
    "opt_df['opt1'] = ['buy', 'call', 0.0456, 2.75, 1]\n",
    "opt_df['opt2'] = ['sell', 'call', 0.0264, 2.8, 1]\n",
    "draw_opt(opt_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 卖出备兑看跌期权 (Cash-Secured Put)\n",
    "1. 策略构成：卖出一份看跌期权（Put Option），并确保账户中有足够的现金，以便在期权被行权时能够按行权价买入相应数量的股票。\n",
    "2. 适用情景：温和看涨或中性偏多。您不介意以行权价买入这只股票，甚至认为这是一个不错的买入点。\n",
    "3. 目标：赚取卖出期权的权利金收入。如果股价下跌，则能以低于当前市价的行权价买入心仪的股票。\n",
    "4. 优点：立即获得权利金收入。可以作为一种折价买入股票的策略。\n",
    "5. 缺点：如果股价大幅下跌，您需要以高于市价的行权价买入股票，会产生浮亏（但亏损小于直接在当前高位买入股票）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2. 卖出认沽期权示例（打算被行权买入标的）:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25171 (\\N{CJK UNIFIED IDEOGRAPH-6253}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 31639 (\\N{CJK UNIFIED IDEOGRAPH-7B97}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21040 (\\N{CJK UNIFIED IDEOGRAPH-5230}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26376 (\\N{CJK UNIFIED IDEOGRAPH-6708}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20221 (\\N{CJK UNIFIED IDEOGRAPH-4EFD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25171 (\\N{CJK UNIFIED IDEOGRAPH-6253}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 31639 (\\N{CJK UNIFIED IDEOGRAPH-7B97}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21040 (\\N{CJK UNIFIED IDEOGRAPH-5230}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26376 (\\N{CJK UNIFIED IDEOGRAPH-6708}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20221 (\\N{CJK UNIFIED IDEOGRAPH-4EFD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "期权策略分析: SELL PUT (打算行权)\n",
      "============================================================\n",
      "行权价: 2.75\n",
      "权利金: 0.0536\n",
      "合约数量: 1手 (每手10000个)\n",
      "到期月份: 2025-07\n",
      "是否打算行权: 是\n",
      "分析价格区间: 1.38 - 4.12\n",
      "----------------------------------------\n",
      "最大收益: 0元 (标的价格: 2.75)\n",
      "最大损失: -1元 (标的价格: 1.38)\n",
      "起始位置收益: -1元\n",
      "盈亏平衡点: ['2.70']\n",
      "当前标的价格 2.746 下的收益: 0元\n",
      "\n",
      "*** 行权提示 ***\n",
      "卖沽且打算行权：如果被行权，将以行权价买入标的\n",
      "如果看好标的长期表现，这种'被迫买入'可能是有利的\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.37500</td>\n",
       "      <td>-1.32140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.37775</td>\n",
       "      <td>-1.31865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.38050</td>\n",
       "      <td>-1.31590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.38325</td>\n",
       "      <td>-1.31315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.38600</td>\n",
       "      <td>-1.31040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>4.11400</td>\n",
       "      <td>0.05360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4.11675</td>\n",
       "      <td>0.05360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>4.11950</td>\n",
       "      <td>0.05360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>4.12225</td>\n",
       "      <td>0.05360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>4.12500</td>\n",
       "      <td>0.05360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   profit\n",
       "0     1.37500 -1.32140\n",
       "1     1.37775 -1.31865\n",
       "2     1.38050 -1.31590\n",
       "3     1.38325 -1.31315\n",
       "4     1.38600 -1.31040\n",
       "...       ...      ...\n",
       "996   4.11400  0.05360\n",
       "997   4.11675  0.05360\n",
       "998   4.11950  0.05360\n",
       "999   4.12225  0.05360\n",
       "1000  4.12500  0.05360\n",
       "\n",
       "[1001 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 示例3: 卖出认沽期权（打算行权）\n",
    "print(\"\\n2. 卖出认沽期权示例（打算被行权买入标的）:\")\n",
    "analyze_single_option(\n",
    "    direction='sell',\n",
    "    option_type='put',\n",
    "    strike_price=2.75,\n",
    "    premium=0.0536,\n",
    "    quantity=1,\n",
    "    current_price=2.746,\n",
    "    expiry_month=\"2025-07\",\n",
    "    intend_exercise=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 看跌策略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 买入认沽期权 (Long Put)\n",
    "1. 策略构成：仅买入一份认沽期权（Put Option）。\n",
    "2. 适用情景：强烈看跌。您预期股价在短期内会大幅下跌，跌破“行权价 - 您支付的权利金”。\n",
    "3. 目标：从股价下跌中获利，或对冲您持有的股票多头头寸。\n",
    "4. 优点：风险有限（最大亏损是支付的权利金），潜在收益巨大（股价最多跌至0）。\n",
    "5. 缺点：与Long Call类似，权利金是纯成本，时间对您不利。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1. 买入认沽期权示例:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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PP/9kGktq0KBBWV4N9MQTT2jnzp0qXbq0Pvvss3zbPwAAKHhcKQUAQBEzfvx4WZYly7JyPJ5UVrZt26brr79eO3bskCTVrVvXMz5UTv33Vr8lS5bolVde0dixY/XII494lq9evdqzH0nq37+/Dhw4oDlz5uiNN97QSy+9pG+//VZPP/20p09ycrJmzpyZh3fm3YYNG+b5/PI6aPd/18uPwb8LYpvn895773nGyCpWrNg5DwCQpGXLlumTTz6RJL3//vuqVKlSgWYqaPnx2QMA4EsoSgEAgHPEx8eradOm+uuvvyRJjRs3Vnx8vMqXL5+r7ZQuXdozXbZsWV111VWe+bPHJJLk2Zd0eoD2rK6KeeihhzLN//bbb7nKU1SVK1cu03xSUtJ553PyOeZmmw6HQ2XKlMlR1pw4fvx4pque+vfvn2Xm+++/X5ZlqWPHjurTp0++7R8AABQOilIAACCT8ePH65ZbbtHRo0clSR06dFB8fLzCw8Nzva3zPVL+v1eChISE5Hr7hmHkep2iqEaNGipevLhnfvv27ZleP7vgJylTcTA79evXzzR/vm1eccUVCg4OznHeC/n44489RS+n06nBgwdn2e/MgO3z58+XYRiedubpk5L0999/yzAMVatWLd/yAQCA/EFRCgCAIqZfv36e/5zn5j/ilmXpmWeeUf/+/ZWeni5JevDBBzV37txMBY/z7e+/Vz+1a9fOM33kyBFt2rTJM79ixQrPdGBgoKcIkpSUpMceeyzLJ8R98MEHmeb/W1w5uzAxfvz4879hLzVs2LBM7yMnnE5npp/1woULPbdOWpalWbNmeV5r0qRJpnG4WrVq5dlXv379PMsbN26c6Xa42bNne6YPHTrkGd9Lkjp16pTj93chaWlpmT7nLl26qGbNmvm2fen0lYBn/4wv5jbX/JSXzx4AAF/GQOcAAEDS6VvjRo4c6ZmvV6+eLr30Ur3zzjuZ+l155ZWKiYnJ0TbvuusuvfXWW54CU2xsrPr27auEhASNGzfO069fv36egbRdLpfeeecdjRw5Um3atFHjxo0lKdNA59Lp28u6deuWp/daFA0ZMkRz5sxRRkaGduzYoVatWqldu3b67rvv9P3333v6PffccznantPp1NChQ/Xggw9KkqZMmSLTNFW3bl1NnTrV87S/UqVKnXNb5UcffeS5kursJzBK0uOPP+6ZHjhw4DkFpylTpmjv3r2e+SeeeCLbjJ06dcr01MEz1q9fr7///luSFBoaqtjYWEVERFzwPfuyV1991TM4/fr16z3Ljx49muln/swzz+TrrZYAAFwMilIAAECS9Ouvv2aa37x5c5YFgb59++a4KFW6dGnNnDlT7dq1U1JSknbv3q1XXnklU5+oqKhzCl/S6StmlixZoiVLlmS53Tlz5vCf67M0atRIo0eP1n333SfTNLVmzRqtWbMmU5/nnnsu0xVVFzJo0CCtX7/eczvctGnTMr0eEhKiKVOmnDPA+PTp07V8+fIst/n22297ptu3b5+pKGVZlkaMGOGZb9Wqla699tps8519m97Z+vXr53ktPDw805ViRdWnn37qKcSdLTExMdPP/IEHHuD3BgDgNbh9DwAAFKgWLVro119/1cCBA1WjRg0FBwerRIkSuu666/Tee+8pPj5eJUqU8PQvVaqU4uPj9dhjj6lx48aqXLmygoKCVLx4cTVo0EBPPfWUNm/erJYtW2baz4EDBzzTTqdT11xzTaG9R29xzz33aO3aterWrZsiIyMVGBio8uXLq127dvryyy/10ksv5Wp7Z26DnDZtmm688UaVKVNGQUFBqlKlivr376+ff/5Zbdu2zbf8S5Ys0ebNmz3z57tK6mKcfaxUrVpVkZGRBbIfAABwflwpBQBAETN+/Pg8jad09q1x+b2/qlWravTo0TnanmEYuuGGG3TDDTfkKsfZ+QcNGpSjwby90bBhwzRs2LA8r3/ttddq+vTpOe6fk8+9e/fu6t69e75uMytt27Y9ZwD8vLjQMXl2vnfffTdPg+wXhIv57L1lXCwAAHKDK6UAAECRcOZ2sQoVKuT6iiD4lzPHSkxMjG699Vab0wAA4L8oSgEAkA8qVqyY6alZZ7fbb789U9/ff/89276GYWjUqFGZ+n/yySfn7X/27U7+7Eyh4a233lKpUqVytW5UVFS2P9//jmmUmpp63s/jv7ecLV269Lz9Fy1adHFvHLly8OBBbdmyRcHBwfrwww9ztW5R++z5uwUAYDfDyo9rpAEA8GPHjh27YJ+AgACVKFFCGRkZSk5OvmD/kJAQhYSEKCUlRSkpKRfsX6JECQUEcFd+XiQmJso0zfP2cTgcCgsLk2maSkxMvOA2g4KCFBoaqrS0NJ08efKC/UNDQxUUFJTjzCh8Re2z5+8WAIA3oCgFAAAAAACAQsftewAAAAAAACh0FKUAAAAAAABQ6Pz+BnHTNLV3716VLFlShmHYHQcAAAAAAMCnWZalpKQkVapUSQ5H9tdD+X1Rau/evapSpYrdMQAAAAAAAIqUf/75R5dcckm2r/t9UapkyZKSTv+gwsLCbE6D/GKapg4ePKjw8PDzVmWBooZjH/7KH459l8ul1atXS5KaNWsmp9NpcyJ4A3849oHscPzDX/nCsZ+YmKgqVap4ai7Z8fui1Jlb9sLCwihKFSGmaSolJUVhYWFe+0sKFASOffgrfzj2XS6XihcvLun0eYvtRal0SaPc04MkBdqYxY/5w7EPZIfjH/7Kl479Cw2T5N3pAQAA4J1ckpa5m8vmLAAAwCf5/ZVSAAAAvsAwDNWoUcMzbbsASf3PmgYAAMglTiEAAAB8gMPh0KWXXmp3jH8FSOpidwgAAODLKEoBAAAAAIBccblcSk9PtzuGXzJNU+np6UpJSbFtTKnAwMB8Gd+SohQAAIAPsCxLSUlJkk4/Pdj2W/gsSUfc02UlecEdhQCAgmdZlhISEnTs2DG7o/gty7JkmqaSkpJsPR8oXbq0IiMjLyoDRSkAAAAfYJqmfvzxR0lSixYt7H/6Xqqkfu7pmZJC7IsCACg8ZwpSERERCg0Ntf9LEj9kWZYyMjIUEBBgy8/fsiydPHlSBw4ckCRVrFgxz9uiKAUAAIC8sbkuBgAoXC6Xy1OQKleunN1x/JbdRSlJKlasmCTpwIEDioiIyPOXZRSlAAAAkHshkubaHQIAUJjOjCEVGhpqcxJ4gzPHQXp6ep6LUvaMiAUAAAAAAHwSt+xByp/jgKIUAAAAAAAACp1XFaVWrFihDh06qFKlSjIMQ3Pnzs3xuqtWrVJAQIAaNmxYYPkAAADgli7pI3fjieAAACAPvKoodeLECTVo0ECjRo3K1XrHjh1Tnz591KZNmwJKBgAAgExckha7m8vmLAAAXEC/fv1kGIanlStXTjExMfrll188fXbu3CnDMLRx48Zz1m/VqpUeeeQRHTp0SJGRkXr11VfP6dOtWzc1bdpULte5/zCe2fbZ+7/lllv0008/efpUq1ZN77333jnrDhs2zHMBjmEYcjgcCgoKksPhyLRNwzA0bNiwXP9s7ORVA53HxsYqNjY21+vdd9996tmzp5xOZ66urgIAAPAVhmGoWrVqnmnbBUi646xpAAC8XExMjMaNGydJSkhI0LPPPqv27dtr165dOd5G+fLl9emnn+r2229Xhw4ddNVVV0mSZs6cqYULF+qnn34676DfX3/9terVq6fdu3froYceUmxsrLZu3arSpUvnaP/79u3zPH1v9uzZeuGFF/T77797Xi9RokSO34s38PlTiHHjxmn79u2aNGmSXn755Qv2T01NVWpqqmc+MTFRkmSapkzTLLCcKFymacqyLD5T+B2Offgrfzn2L730Us+07e/VIanHWfNF+0fvtfzl2AeywvFf+M78zM80XxMcHKwKFSpIkipUqKCnnnpKLVu21IEDBxQeHu55T9m9vzPLO3TooJ49e6pv375au3atjh07pkGDBum1117TFVdcke26klS2bFlVqFBBFSpU0FtvvaXrr79ea9euVXR0dLb7PjvXmfzp6ekqVaqUDMPwLPtv/4J2JmtW9ZSc/l76dFHqjz/+0JAhQ7Ry5UoFBOTsrbz22mt68cUXz1l+8OBBpaSk5HdE2MQ0TR0/flyWZcnh8Kq7VIECxbEPf8WxD3/FsQ9/xvFf+NLT02WapjIyMpSRkWF3nFw5Uzg5kzs5OVkTJ07UZZddplKlSmV6T1m9vzMFmDPLR4wYoauvvlovvviitm7dqnr16mngwIHZ/lyy2nZgYKAk6dSpU55lZ2c8O/vZ+7YsSy6Xy3OboF2fRUZGhkzT1OHDhz3v5YykpKQcbcNni1Iul0s9e/bUiy++qCuuuCLH6w0dOlSDBw/2zCcmJqpKlSoKDw9XWFhYQUSFDUzTlGEYCg8P5x8o+BWOffgrfzj2LcvSyZMnJUmhoaH238JnSTrpng6V5AV3FPojfzj2gexw/Be+lJQUJSUlKSAgINOFIdd+dq0SkhMKNUtkiUj9cM8POe7vcDi0ePFilSlTRtLpMa0rVqyoBQsWKCgoSJI87+m/70+SZ8ymM8vLli2rsWPHKjo6WsWLF9fPP/98TmHmbP/d9rFjx/T666+rRIkSioqK8rzucDjO2feZsaP+u/zMbYI5vUgnvwUEBMjhcKhcuXIKCQnJ9Np/57PdRkEEKwxJSUlav369fvrpJz3wwAOS/q0eBgQE6Msvv9SNN954znrBwcEKDg4+Z7nD4eAvsiLmzABwfK7wNxz78FdF/dh3uVzasGGDJKlFixb2v88UST3d0zMl5ezcEwWgqB/7wPlw/Beu/w6sfUZCcoL2JO0p9Dy5/YKmdevW+uijjyRJR48e1ejRo9W2bVutW7dOVatW9Wzvv+/v7P2dvbxNmzZq2rSpGjZs6Bn38UJZmzdvLofDoRMnTqhGjRqaPn26IiMjs93H2eue+dOyrEx97Pqi6kzWrH4Hc/o76bNFqbCwMP3666+Zlo0ePVrffPONZs2aperVq9uUDAAAAAAA/xFZIvLCnbxgn8WLF9dll13mmR8zZoxKlSqlzz77TC+//LLn7qnjx4+fs+6xY8dUqlSpc5ZndVXV+UyfPl1169ZVuXLlzhncPCwsLFf7Lgq8qiiVnJysP//80zO/Y8cObdy4UWXLltWll16qoUOHas+ePfrf//4nh8OhK6+8MtP6ERERCgkJOWc5AAAA8lmwpC/c09k/ZAgA4AfW/996uyPkyZmrfE6dOiXp9C155cuX14YNG3TDDTd4+iUmJurPP//M1dBB2alSpYpq1qyZ5Wu1atXyXBV9th9//FG1atW66H17I68qSq1fv16tW7f2zJ8Z+6lv374aP3689u3bl6tHNQIAAKCAGPKyM0kAAM4vNTVVCQmnx746evSoRo4cqeTkZHXo0MHTZ/DgwXr11VdVoUIFNW3aVIcPH9bw4cMVHh6uLl26FGi+Rx99VC1atNArr7yiLl26yOVyaerUqVqzZo1Gjx5doPu2i1edSrRq1eq8jy4cP378edcfNmyYhg0blr+hAAAAAACAz4uLi1PFihUlSSVLllTt2rU1c+ZMtWrVytPnySefVIkSJfTGG2/or7/+UtmyZdW8eXN9++23KlasWIHma9asmZYsWaKXXnpJb7/9thwOh6666iotW7asyN4RZljnqwL5gcTERJUqVUrHjx/n6XtFiGmaOnDggCIiIhj0EH6FYx/+yh+OfZfLpZUrV0o6PdD5mSfu2CZD0v/c033kZV91+g9/OPaB7HD8F76UlBTt2LFD1atXz/HT1ZD/LMtSRkaGAgICbH0a7/mOh5zWWvjNBQAAQO5l6PSYUl+4pwEAAHKJ77QAAAB8gGEYqlKlimfadgGSbj1rGgAAIJc4hQAAAPABDocj26f12CJA0gC7QwAAAF/G7XsAAAAAAAAodFwpBQAA4AMsy1JqaqokKTg42P5b+CxJLve0U5IX3FEIAAB8C1dKAQAA+ADTNLV27VqtXbtWpmnaHUdK1ekxpW51TwMAAOQSRSkAAAAAAAAUOm7fAwAAQO4FS5p21jQAAEAuUZQCAABA7hmSitsdAgAA+DJu3wMAAAAAAECh40opAAAA5F6GpBnu6W7irBIAAOQaV0oBAAAg9zIkTXW3DJuzAABwHsuXL1ft2rXVsGHDTK1+/fp68MEHJUlNmjQ55/WGDRvqsssuU2pqqt544w1deeWV57xet25dTZ48OcdZPvvsM7Vo0UJlypRRmTJldNNNN2ndunUXXC81NVXPPPOMqlatqpCQEF1++eUaO3as5/Xx48fLMIxMLSQkJPc/rELGd1oAAAA+wDAMVapUyTNtO6ektmdNAwDgpU6dOqUePXpo2LBhmZbv3LlTQ4YMkXT639aNGzees26rVq1kWZaOHj2qkSNHqlWrVpleHz9+vJKSknKcJT4+XnfccYeaNWumkJAQvfHGG7rlllu0efNmVa5cOdv1unXrpv379+vzzz9XzZo1tXv37nPOB8LCwvT777975r3ifOECKEoBAAD4AIfDoSuuuMLuGP8KlDTQ7hAAAG/hcrmyfc0wDDkcjhz1lSSn899vO7Lqe/brvua/V1WNGTNGs2fP1rJly9SnT58s14mLi9Py5cu1fft2lS1bVpZl6ZJLLlFAQOaSjmEYioyMLLDsBYGiFAAAAAAAuCgrV67M9rWyZcuqfv36nvlVq1bJNM0s+5YuXVoNGzb0zK9du1bp6emZ+vz3aiVvER8fr9atW2vHjh2qVq1ajtY5efKk0tPTVbZs2Wz7zJ8/X40bN9abb76piRMnqnjx4mrfvr1efvllhYaGevolJyeratWqMk1TV199tV599VXVq1fvYt9WgaIoBQAA4AMsy/KclAcGBvrEJfkAAPiT0NBQ1apVS4GBgTle56mnnlKlSpV00003Zdtn+/bt+u677xQSEqIvvvhCBw8e1KBBg3T06FGNGzdOklSrVi2NHTtW9evX1/HjxzVixAg1a9ZMmzdv1iWXXHLR762gUJQCAADwAaZpavXq1ZKkFi1a2H/rQoqkHu7paZK8fyxVAEABatGiRbav/feLlObNm+d4u02bNs1zpsJ23XXXaevWrTnu//rrr2vatGmKj48/76DkpmnKMAxNnjxZpUqVkmVZevPNN9WjRw+NHj1axYoVU1RUlKKiojzrNGvWTHXq1NEnn3yi4cOHX9T7KkgUpQAAAJA35x8SBADgR3LzZUlB9fUlI0aM0Ouvv66vv/46062NWalYsaIqV66sUqVKeZbVrl1blmVp9+7duvzyy89ZJzAwUI0aNdKff/6Z79nzk+PCXQAAAID/CJY03t2CbU0CAIBPefPNNzV8+HDFxcWpcePGF+zfvHlz7d27V8nJyZ5lf/zxhxwOR7a35rlcLv3666+qWLFivuUuCBSlAAAAkHuGpHLuxvBWAABo3bp1ql27tvbs2ZNtnzfeeEPPPfecxo4dq2rVqikhIUEJCQmZCk5Dhw7N9CS+nj17qly5curfv7+2bNmiFStWaMiQIerfv7+KFSsmSXrppZf05Zdfavv27frxxx/Vu3dv/f3337r77rsL7g3nA4pSAAAAAAAAF+nkyZP6/fffz3la4Nk++ugjpaWlqWvXrqpYsaKnjRgxwtNn37592rVrl2e+RIkS+uqrr3Ts2DE1btxYvXv3Vvv27fXBBx94+hw9elT33HOP6tSpo7Zt2yoxMVGrV69W3bp1C+bN5hPGlAIAAEDuZUia757uKM4qAQB+r1WrVrIs67x9du7cecHtjB8//pxltWvX1ldffSXp9BN5MzIyFBDw7z++7777rt59991c5fUGnD4AAAAg9zIkjXNPtxVnlQAAINc4fYDXWr58ue69995zHo1pmqZuuOEGffjhh2rSpIlSU1PPWTc5OVnLli3Tm2++qUmTJmWqIEtSWlqannnmGTVt2lSxsbEKDQ09ZxvVq1fXF198keO8Dz30kFatWqVNmzapTp062rhxY47XBQDgQgzDUGRkpGfadk5Jbc6aBgDAS5UqVUoLFy7UwoULz3ktOjpaklS6dOlsBx0/M6D4448/nuXrTz/9dP6F9TMUpeC1Tp06pR49emjYsGGZlu/cuVNDhgyRdPqkPKviz5nLJo8ePaqRI0eqVatWmV4fP368kpKSlJ6ermbNmmV5eWTTpk1znXnAgAH6/vvv9csvv+R6XQAAzsfhcKh27dp2x/hXoKRH7A4BAMCFRUVFaf369eftExcXd97XH3jgAT3wwAP5GQuiKAXkmzODzB08eJCiFAAAAAAAF8DT94qAlJQUTZw4UbfddptatWql2267TRMnTlRKSord0YqMatWqnXPFFgAAhcmyLLlcLrlcrgsOogoAAOALuFLKx82fP1/9+vXT0aNH5XA4ZJqmHA6H5syZo4cfflgTJkxQhw4dMq2zf/9+hYeHy+GgJplTNWvWVPny5e2OAQDwY6ZpauXKlZKkFi1ayOm0eSCnFEn93NPjJYVk2xMAACBLVCV82Pz589W5c2cdO3ZM0umT1bP/PHbsmDp16qT5808/r3nXrl3q1auXIiMj9f333xd6XsuydPLkSSUkJOiPP/7Qjz/+qPj4eB04cKDQs+TWsmXLuH8YAID/OuFuAAAAecCVUj4qJSVF/fr1k6RsL+G3LEuGYahv376677779N5776lUqVIaM2aMmjRpkqP9WJalEydOKCkpSYmJiZn+zG76fK+fKZidrV+/fho3blwWewcAAF4rWNInZ00DAADkEkUpHzVz5kwdPXr0gv0sy9KxY8f01ltvqXPnzrrllluUkpKiN998M8tC0n//TE5OzrKQdEZAQIDCwsJUsmRJlSxZ0jNdqlQpValS5ZzlWfWtWrVqfv5oAABAYTAkVbI7BAAA8GUUpXzU3LlzPWNI5YTL5dLs2bM1e/ZsBQYGZlkoKlu2rKpWrZpl4Si7ZcHBwTIMo4Dfrf3atGmjW2+99by38P35559KTk5WQkKCTp06pY0bN0qS6tatq6CgoEJKCgAAAACAb6Ao5aMOHz6c44LUGQ0bNtRDDz2kXr16USTJpb/++kuHDh06b5+7775by5cv98w3atRIkrRjxw5Vq1atIOMBAFD4MiQtdU9Hi7NKAIDXWr58ue69916FhGR+Kodpmrrhhhv04YcfqkmTJkpNTT1n3eTkZG3evFnvvfeeJk6cqICAzP/gpaWl6ZlnnlHTpk0VGxur0NDQc7ZRvXp1ffHFFznK+vPPP+v111/Xd999p0OHDqlatWq677779PDDD2e7Tnx8vFq3bp3la+vWrdO1116badmff/6pRo0ayel0esaoPuPYsWN65plnNGfOHB05ckRVq1bVe++9p7Zt2+Yof25x+uCjypUrl+MrpQzDUFRUlEqUKKEBAwbo6aef1ueff15gB1VRtHPnzgv2iY+PL/AcAAB4jQxJH7un24izSgCA1zp16pR69OihYcOGZVq+c+dODRkyRNLp/zefudvlbK1atZJlWTp69KhGjhypVq1aZXp9/PjxSkpKUnp6upo1a6bx48efs42mTZvmOOuGDRsUERGhSZMmqUqVKlq9erX+7//+T06nM9s7d5o1a6Z9+/ZlWvbcc89p2bJlaty4cabl6enpuuOOO9SiRQutXr0602tpaWm6+eabFRERoVmzZqly5cr6+++/Vbp06Rznzy1OH3xU586dNWfOnBz1tSxLAwcOVO/evbVlyxZ98sknSk9PL+CEAAAgPxmGofDwcM+07RySmp81DQDwbynuP4N1etxB6fQXGBmSnJICc9jXISnoAn2LsAEDBmSar1GjhtasWaM5c+ZkW5QKCgpSZGSkZz49PV3z5s3Tgw8+eM45w7PPPqvatWurTZs25xSlxo4dqyNHjmj16tUKDDz9gRX0XT8UpXzU7bffrocffljHjh3L9ul7Z5QsVVJdu3aVdHp8o/fff78wIl60UqVKaeHChVq4cOE5r0VHR0uSSpcufU7l9wyHw6FLLrlEjz/+eJavP/300ypWrJg2bdqU5Tauuuqqi0gPAED+cjgcqlevnt0x/hUkaYjdIQAAXuN295+TJJVyT8+RNFHSLZIePKtvb0mpkj6XFOFetkjSGEk3SDr7v3B3SUqUNErSpQURvHBVq1ZN/fr1O+eqrfM5fvy4ypYtm+P+8+fP1+HDh9W/f/9My7/55hvNnDlTGzduzPIil/nz5ysqKkqDBg3SvHnzFB4erp49e+qpp56S0+nM8f5zg6KUjwoJCdGECRPUqVMnGYZx3sKUcauh3Sd367KQywox4cWLiorS+vXrz9snLi4uy+WmaerAgQMaNGiQHnzwwSz7nHGhfQAAAAAAkB9q1qyp8uXL57j/6tWrNX36dC1atCjH63z++eeKjo7WJZdc4ll2+PBh9evXT5MmTVJYWFiW623fvl3ffPONevXqpcWLF+vPP//U/fffr/T0dL3wwgs53n9uUJTyYR06dNDcuXPVr18/HT161DPG1Jk/A0IDlNEpQ4nVEhUzKUarBqxShRIV7I4NAAAAAChqZrr/DD5rWRdJHXX69r2zTcqibzudfnDGf28J/zyLvj5s2bJlOe67adMmderUSS+88IJuueWWHK2ze/duLV26VDNmzMi0/J577lHPnj3VsmXLbNc1TVMRERH69NNP5XQ6dc0112jPnj166623CqwoxQgAPq5jx47au3evJk6cqM6dO6tVq1bq3LmzJk6cqF27d6ne9acv8//r6F9qN6WdklKTbE4MAADywuVyKT4+XvHx8XK5XHbHOX3bRV93O/dhRQAAfxPibmcPYRTgXhaYi77/fVB8Vn39wJYtW9SmTRv93//9n5599tkcrzdu3DiVK1dOHTt2zLT8m2++0YgRIxQQEKCAgADdddddOn78uAICAjR27FhJUsWKFXXFFVdkulWvTp06SkhIUFpaWv68sf/gSqkiICQkRL1791bv3r3PeS2ud5yiPo/S7sTd2rBvg7rO7KoFdyxQkPO/v+kAAAC5YEk6ctY0AADIF5s3b9aNN96ovn376pVXXsnxepZlady4cerTp49noPIz1qxZk+lLrXnz5umNN97Q6tWrVblyZUlS8+bNNWXKFM8dWJK0bds2VaxYUUFBBVND4EqpIu6SsEsU1ytOpUNKS5K+/OtL3TX/LpmWaW8wAADg24Ikve9ufNcFAECOtGnTRiNHjsz29U2bNql169a65ZZbNHjwYCUkJCghIUEHDx709Fm3bp2uvPJK7dmzJ9O633zzjXbs2KG77777nO3WqVNHV155padVrlxZDodDV155pcqUKSNJGjhwoI4cOaKHH35Y27Zt06JFi/Tqq69q0KBB+fTuz0VRyg/Ui6inBXcsUEhAiCRp0i+TNPTroTanAgAAPs0hqYa7cUYJAECO/PXXXzp06FC2r8+aNUsHDx7UpEmTVLFiRU+79tprPX1Onjypbdu2KT09PdO6n3/+uZo1a6batWvnKVuVKlW0dOlS/fDDD6pfv74eeughPfzwwxoypOAet8vte37i+kuv19Tbpuq2GbfJtEy9ufpNVSxZUY80fcTuaAAAAAAA+IWdO3ee9/Vhw4Zp2LBh5+3TqlUrpaWlKSAgc0lnypQpOc7Rr18/9evX75zlUVFRWrt2bY63c7H4XsuPdK7dWaPajvLMP7r0UU3bNM3GRAAAwGdlSFrmbhk2ZwEAAD6JK6X8zH2N79PepL0avmK4JKnPF30UUTxCN1a/0eZkAADAp2RIes893VycVQIAvFapUqW0cOFCLVy48JzXoqOjJUmlS5dW48aNs1zf4XDokksu0eOPP57l608//bSKFSumTZs2ZbmNq6666iLSF22cPvihF1u9qL1Je/X5T58r3UxX52mdtaL/CjWMbGh3NAAAkA3DMFS2bFnPtO0ckhqfNQ0AgJeKiorS+vXrz9snLi7uvK8/8MADeuCBB87b50L7wLkoSvkhwzD0cfuPtf/Efi3ctlBJaUmKnRyrNXetUbXS1eyOBwAAsuBwOFS/fn27Y/wrSNILdocAAAC+jO+1/FSAI0DTu05X00uaSpISkhMUPSlah05m/xQAAAAAAABM07Q7ArxAfhwHXCnlx0IDQ7XwjoVqPra5fj/8u7Yd3qb2U9prWZ9lKh5U3O54AAAAAAAvEhQUJIfDob179yo8PFxBQUHecUu5n7EsSxkZGQoICLDl529ZltLS0nTw4EE5HA4FBQXleVsUpfxcudByiusdp2afN9O+5H36fs/36jarm+Z2n6tAZ6Dd8QAAgJvL5dKqVaskSc2bN5fT6bQ3UKqkB93TH0oKtjELAKBQOBwOVa9eXfv27dPevXvtjuO3LMuSaZpyOBy2FgVDQ0N16aWXyuHI+014FKWgaqWraUmvJWo5vqUSUxO1+I/Fum/hfRrTcQxVbwAAvIhX3S5hSdp31jQAwC8EBQXp0ksvVUZGhlwul91x/JJpmjp8+LDKlSt3UQWhi+F0OvPlSi2KUpAkNYhsoLnd5ypmcozSXGkau3GsKpWspOE3Drc7GgAA8EZBkt48axoA4DcMw1BgYKACA7m7xg6maSowMFAhISG2FaXyi2+nR75qXb21Jt46UYZOVzpfXvmyRv8w2uZUAADAKzkk1XE3zigBAEAecAqBTLrV66b3Yt7zzD+w+AHN+W2OfYEAAAAAAECRRFEK53ioyUN6qvlTkiRLlnrO7qmVf6+0ORUAAPAqLknfuRtDigAAgDygKIUsvdbmNfVp0EeSlOpKVcdpHbXpwCabUwEAAK+RLukNd0u3OQsAAPBJFKWQJcMwNKbDGEXXjJYkHUs5pphJMfrn+D82JwMAwH+VLl1apUuXtjvGaQ5JV7obZ5QAACAPOIVAtgKdgZrVbZYaV2osSdqTtEfRk6J15NQRm5MBAOB/nE6nGjZsqIYNG8rpdNod5/QT915zN56+BwAA8oCiFM6rRFAJLeq5SJeVvUyS9Nuh39RxakedSj9lczIAAAAAAODLKErhgiKKRyiuV5wiikdIklb9s0o95/SUy2RUUwAAAAAAkDcUpZAjNcvW1OKei1UiqIQkae7WuRq0eJAsy7I5GQAA/sHlcmnVqlVatWqVXC4v+GIoTdJD7pZmcxYAAOCTKEohx66pdI3mdJujAEeAJOmTDZ/o5RUv25wKAAD/kZ6ervR0L3nUnSlph7uZNmcBAAA+iaIUcuXmmjdrfKfxnvnn45/XmB/H2BcIAADYI0jScHdjoHMAAJAHFKWQa73q99JbN7/lmb934b2a//t8GxMBAIBC55DU0N04owQAAHnAKQTy5LGox/Ro00clSaZlqsesHlrzzxqbUwEAAAAAAF9BUQp5YhiGRtwyQj2u7CFJOpVxSu2nttfWQ1ttTgYAAAqFS9IP7uYF464DAADfQ1EKeeYwHBrfabzaVG8jSTpy6oiiJ0Vrb9Jem5MBAIACly7pJXfzkrHXAQCAb6EohYsSHBCsOd3nqGFkQ0nSruO7FDMpRsdSjtmaCwCAoqhkyZIqWbKk3TFOc0i63N04owQAAHnAKQQuWlhwmBb3XKxqpatJkn498KtunX6rUjNS7Q0GAEAR4nQ6dc011+iaa66R0+m0O87pJ+694248fQ8AAOQBRSnki4olK2pp76UqH1pekhS/M153fnGnTMu0ORkAAAAAAPBGFKWQb64od4UW3rFQoYGhkqSZW2bqkbhHZFmWzckAAAAAAIC3oSiFfNXkkiaaeftMOY3TtxV8uO5DvbnqTZtTAQDg+1wul9auXau1a9fK5fKCx92lSXrS3dJszgIAAHwSRSnku7aXt9WYjmM880OWDdGEjRNsTAQAQNGQkpKilJQUu2OcZkr6zd24Wx8AAOSBVxWlVqxYoQ4dOqhSpUoyDENz5849b/85c+bo5ptvVnh4uMLCwhQVFaWlS5cWTlicV7+G/fTKja945u+af5fi/oyzMREAAMhXgZKecbdAm7MAAACf5FVFqRMnTqhBgwYaNWpUjvqvWLFCN998sxYvXqwNGzaodevW6tChg3766acCToqcGHr9UA26dpAkyWW51HVGV/2w5webUwEAgHzhlNTU3bzgYYAAAMD3BNgd4GyxsbGKjY3Ncf/33nsv0/yrr76qefPmacGCBWrUqFE+p0NuGYah92PeV0Jygmb/Nlsn0k+o3ZR2WjVglS4vd7nd8QAAAAAAgI286kqpi2WappKSklS2bFm7o8DN6XBqUpdJalm1pSTp4MmDip4UrYTkBJuTAQCAi2JK+tXdGFMKAADkgVddKXWxRowYoeTkZHXr1i3bPqmpqUpNTfXMJyYmSjpd0DJNzqgKQpAjSF90+0I3jL9Bmw5u0o5jO9R2clt92+dblQwuWSD7NE1TlmXxmcLvcOzDX/nDsX/mPZ6ZNgzD3kApkjH0dAZrhiWF2BvHX/nDsQ9kh+Mf/soXjv2cZisyRakpU6boxRdf1Lx58xQREZFtv9dee00vvvjiOcsPHjzoPU+zKaIm3DJBHeZ10N7kvfop4Sd1nNxRE2MnKsgZlO/7Mk1Tx48fl2VZcjiK1AWBwHlx7MNf+cOx73K55HK5JEkHDhyQ02nzQE6pUlj5MElS4sFEKdjeOP7KH459IDsc//BXvnDsJyUl5aifYZ35ys3LGIahL774Qp07d75g32nTpmnAgAGaOXOm2rVrd96+WV0pVaVKFR09elRhYWEXGxsXsOXgFrUc31JHU45Kknpe2VMTOk+Qw8jfXyTTNHXw4EGFh4d77S8pUBA49uGvOPbhrzj24c84/uGvfOHYT0xMVJkyZXT8+PHz1lp8/kqpqVOnasCAAZo2bdoFC1KSFBwcrODgc7/KczgcXvthFiVXVrhSC+5YoJsm3qSUjBRN2TRFlUpW0lu3vJXv+zIMg88VfoljH/6KYx/+imMf/ozjH/7K24/9nObyqvTJycnauHGjNm7cKEnasWOHNm7cqF27dkmShg4dqj59+nj6T5kyRX369NHbb7+tJk2aKCEhQQkJCTp+/Lgd8ZFDzS9trmm3TfNcHTVizQi9s+Ydm1MBAAAAAIDC5FVFqfXr16tRo0Zq1KiRJGnw4MFq1KiRnn/+eUnSvn37PAUqSfr000+VkZGhQYMGqWLFip728MMP25IfOdepdieNbjvaM//Yl49p6q9TbUwEAIB3c7lcWrdundatW+cZW8pWaZKec7c0m7MAAACf5FW377Vq1UrnG+Jq/Pjxmebj4+MLNhAK1L2N79W+5H16cfnpgef7zu2riOIRalOjjc3JAADwTidPnrQ7wr9MSRvPmgYAAMglr7pSCv7nhRte0D1X3yNJSjfTdev0W/XTvp9sTgUAAC4oUNJj7hZocxYAAOCTKErBVoZhaHS70epYq6MkKSktSbGTY7Xj6A6bkwEAgPNySmrlbk5bkwAAAB9FUQq2C3AEaOptUxV1SZQkaf+J/YqeFK2DJw7anAwAAAAAABQUilLwCqGBoVpwxwLVLl9bkvTHkT/Ubko7nUg7YXMyAACQJVPSH+7GmFIAACAPKErBa5QLLae4XnGqVLKSJOmHvT+o26xuSnel25wMAACcI03SYHfj6XsAACAPKErBq1QtXVVxveJUKriUJGnxH4v1fwv/77xPZQQAwF+EhIQoJCTE7hinGZIi3M2wOQsAAPBJFKXgda6qcJXm9ZinIGeQJGn8xvF69ptnbU4FAIC9nE6nmjZtqqZNm8rp9IKRxYMlfe5uwTZnAQAAPomiFLzSDdVu0OQuk2W4v3p99btXNXLdSJtTAQAAAACA/EJRCl6ra92uej/mfc/8Q0se0qwts2xMBAAAAAAA8gtFKXi1B5s8qKHXD5UkWbLUa04vLd+53OZUAAAUPpfLpQ0bNmjDhg1yuVx2xzk9uPnL7sZA5wAAIA8oSsHrvXLjK+rboK8kKc2Vpk7TOunX/b/anAoAgMKXlJSkpKQku2OcZkr63t1Mm7MAAACfRFEKXs8wDH3W4TPFXhYrSTqeelwxk2O06/gum5MBAODHAiQ94G4BNmcBAAA+iaIUfEKgM1Azbp+haytdK0nam7RX0ZOidfjkYZuTAQDgpwIkRbsbRSkAAJAHFKXgM0oEldCinot0WdnLJElbD21Vx2kddSr9lM3JAAAAAABAblGUgk8JLx6upb2XqkLxCpKk1f+sVo/ZPZRhZticDAAAP2NJ2uVuls1ZAACAT6IoBZ9To0wNLem1RCWDSkqS5v8+X4MWDZJlcUYMAEChSZU0yN1Sbc4CAAB8EkUp+KRGFRtpTvc5CnQESpI+/fFTvbT8JZtTAQBQsAIDAxUYGGh3jH+FuRsAAEAeUJSCz7qpxk0a33m8Z37Y8mH6dMOn9gUCAKAAOZ1ONW/eXM2bN5fT6bQ7jhQiabK7hdicBQAA+CSKUvBpPa/qqbdvedszP3DRQM3/fb6NiQAAAAAAQE5QlILPGxw1WI9FPSZJMi1T3Wd11+p/VtucCgAAAAAAnA9FKRQJb978pnpe1VOSlJKRoo7TOmrb0W02pwIAIP+4XC5t3LhRGzdulMvlsjuOlCZphLul2ZwFAAD4JIpSKBIchkPjOo3TTTVukiQdTTmqOxbfoT2Je2xOBgBA/jl27JiOHTtmd4zTTEnL3c20OQsAAPBJFKVQZAQ5gzS722w1imwkSdqbvFdtp7TVsZRj9gYDAKAoCpB0t7sF2JwFAAD4JIpSKFLCgsO0uNdiVS9dXZK06eAmdZ7WWSkZKTYnAwCgiAmQ1MndKEoBAIA8oCiFIieyRKSW9FyisiFlJUnL/16uO7+4Uy7TC8bfAAAAAAAAkihKoYi6vNzlmhw7WaGBoZKkWVtm6eG4h2VZls3JAAAoIixJB9yNf14BAEAeUJRCkdUwoqFmdJ0hp+GUJI36YZRe/+51m1MBAFBEpEq6y91Sbc4CAAB8EkUpFGmxl8Xq846fe+af/uZpjd843r5AAABcBIfDIYfDi07fgt0NAAAgDxiWEkVe34Z9tS95n4YuGypJunv+3apQvIJiL4+1ORkAADnndDrVsmVLu2P8K0TSLLtDAAAAX+ZFX7UBBeep5k/pweselCS5LJe6zuyqdXvW2ZwKAAAAAAD/RVEKfsEwDL0b/a661u0qSTqZflLtprTTtsPbbE4GAAAAAIB/oigFv+F0ODXx1om6oeoNkqRDJw8pelK0EpITbE4GAMCFmaapX375Rb/88otM07Q7jpQu6UN3S7c5CwAA8EkUpeBXQgJCNLfHXF0VcZUkaeexnWo7ua0SUxNtTgYAwPlZlqUjR47oyJEjsizL7jiSS9KX7uayOQsAAPBJFKXgd0qHlNaSXkt0aalLJUk/JfykLtO7KM2VZnMyAAB8SICkO92NR+cAAIA8oCgFv1Q5rLLiesWpbLGykqRlO5ap39x+Mi0vuB0CAABfECCpm7tRlAIAAHlAUQp+q054HS24Y4FCAkIkSVM3TdUTXz5hcyoAAAAAAPwDRSn4tWZVmml61+lyGKd/Fd5Z+47eXv22zakAAPABlqTj7uYFQ1wBAADfQ1EKfq9jrY76uN3HnvnHv3pcU36dYmMiAAB8QKqk3u6WanMWAADgkyhKAZLuueYevdjqRc98v7n99NVfX9mYCAAAAACAoo2iFOD2XMvndO8190qS0s10dZnRRT/u+9HmVAAAnOZ0OtWqVSu1atVKTqfT7jhSiKQF7hZicxYAAOCTKEoBboZhaFTbUepUq5MkKTktWbGTY/XXkb9sTgYAAAAAQNFDUQo4i9Ph1NTbpqpZlWaSpAMnDihmcowOnDhgczIAAAAAAIoWilLAfxQLLKYFdyxQnfJ1JEl/HvlT7ae0V3Jass3JAAD+zDRNbd68WZs3b5ZpmnbHkdIlfeZu6TZnAQAAPomiFJCFssXKKq53nCqXrCxJ+mHvD7p95u1Kd3HWDQCwh2VZOnjwoA4ePCjLsuyOI7kkzXc3l81ZAACAT6IoBWTj0lKXKq53nEoFl5Ikxf0Zp7sX3O0d/xEAAMBuAZK6uVuAzVkAAIBPoigFnMeVEVdq/h3zFewMliT97+f/6ellT9ucCgAALxAg6U53oygFAADygKIUcAEtq7bU5C6TZciQJL2+6nV9+P2HNqcCAAAAAMC3UZQCcuC2urdpZNuRnvmH4x7WzM0zbUwEAIDNLEkp7sad7QAAIA8oSgE5dP+19+uZFs9IkixZ6v1Fb8XvjLc3FAAAdkmVdLu7pdqcBQAA+CSKUkAuDG89XP0b9pckpbnS1GlaJ/2y/xebUwEAAAAA4HsoSgG5YBiGPmn/idpe3laSlJiaqJhJMfr72N82JwMAFHUOh0MtWrRQixYt5HB4wSlcsKSZ7hZscxYAAOCTvOCMBvAtgc5Azeg6Q9dVvk6StC95n2Imx+jwycM2JwMAFGWGYcjpdMrpdMowDLvjSIakEHfzgjgAAMD3UJQC8qB4UHEt6rlIV5S7QpK09dBWdZjaQSfTT9qcDAAAAAAA30BRCsij8qHlFdcrTpElIiVJa3avUY9ZPZRhZticDABQFJmmqa1bt2rr1q0yTdPuOFKGpInuxj99AAAgDyhKARehepnqWtJriUoGlZQkLdi2QAMXDpRl8WxsAED+sixLCQkJSkhI8I5/ZzIkzXA3ilIAACAPKEoBF6lhZEN90f0LBToCJUljfhqjYfHD7A0FAEBBc0rq6G5Om7MAAACfRFEKyAdtarTR/279n2f+pRUv6ZP1n9iYCACAAhYo6R53C7Q5CwAA8EkUpYB80uPKHno3+l3P/P2L79fcrXPtCwQAAAAAgBejKAXko0eaPqInmj0hSTItU3fMvkPf7frO5lQAAAAAAHgfilJAPnv9ptfV66pekqSUjBR1mNpBmw9stjkVAAD5LEVSB3dLsTkLAADwSRSlgHzmMBwa22msbq5xsyTpWMoxxUyO0e7E3TYnAwAAAADAe1CUAgpAkDNIs7vN1tUVr5Yk7U7crdjJsTp66qjNyQAAvsrhcKhZs2Zq1qyZHA4vOIULljTJ3YJtzgIAAHySF5zRAEVTyeCSWtxzsWqUqSFJ2nRgkzpN66SUDO5xAADknmEYCgoKUlBQkAzDsDuOZEgq5W5eEAcAAPgeilJAAapQooKW9l6q8NBwSdLKXSvVa04vuUyXzckAAAAAALAXRSmggF1W9jIt7rVYxQOLS5Lm/DZHDy15SJZl2ZwMAOBLTNPUtm3btG3bNpmmaXccKUPSDHfLsDkLAADwSRSlgELQuFJjzeo2SwGOAEnS6PWj9dp3r9mcCgDgSyzL0t69e7V3717v+GIjQ9JEd6MoBQAA8oCiFFBIYi6L0diOYz3zz3zzjMb9NM7GRAAAXASnpFvczWlzFgAA4JMoSgGF6M4Gd+qNm97wzN+z4B4t2rbIxkQAAORRoKQH3S3Q5iwAAMAnUZQCCtkTzZ7QQ9c9JElyWS7dPvN2fb/7e5tTAQAAAABQuChKAYXMMAy9G/OuutXrJkk6lXFK7aa00++Hfrc5GQAAAAAAhYeiFGADh+HQ/zr/T62qtZIkHT51WDGTY7QvaZ+9wQAAyKkUSV3dLcXmLAAAwCdRlAJsEhwQrLnd56p+hfqSpJ3Hdip2cqyOpxy3ORkAADmU6m4AAAB5QFEKsFGpkFJa0muJqpaqKkn6ef/P6jKji1IzOMMHAGTmcDjUtGlTNW3aVA6HF5zCBUv63N2Cbc4CAAB8khec0QD+rVLJSlrae6nKFisrSfpmxzfqO7evTMu0ORkAwJsYhqGQkBCFhITIMAy740iGpAh384I4AADA93hVUWrFihXq0KGDKlWqJMMwNHfu3AuuEx8fr6uvvlrBwcG67LLLNH78+ALPCeS3WuVraeEdC1UsoJgkafrm6Xps6WOyLMvmZAAAAAAAFAyvKkqdOHFCDRo00KhRo3LUf8eOHWrXrp1at26tjRs36pFHHtHdd9+tpUuXFnBSIP9FVYnSjNtnyGk4JUnvff+e3l7zts2pAADewjRN/fXXX/rrr79kml5wNW2GpHnulmFzFgAA4JMC7A5wttjYWMXGxua4/8cff6zq1avr7bdP/8e9Tp06+u677/Tuu+8qOjq6oGICBab9Fe31SftPdPeCuyVJT3z1hCJLRKp3/d42JwMA2M2yLP3zzz+SpGrVqtkbRjpdiBrjno6Wl51VAgAAX+BVV0rl1po1a3TTTTdlWhYdHa01a9bYlAi4eHddfZeGtx7ume8/r7++/OtLGxMBAJAFh6Qb3M2nzygBAIBdfPo7rYSEBFWoUCHTsgoVKigxMVGnTp1SsWLFzlknNTVVqan/PtksMTFR0ulL4r3iUnjkC9M0ZVmWz36mQ5sP1e7E3fpkwyfKMDPUZXoXfdv3W11T8Rq7o8HL+fqxD+SVPxz7Z97jmWnbBzsPkDT4rPmi+6P3av5w7APZ4fiHv/KFYz+n2Xy6KJUXr732ml588cVzlh88eFApKSk2JEJBME1Tx48fl2VZ3vHY7Dx47urn9PfhvxW3M04n0k+o7eS2WtBpgaqVqmZ3NHixonDsA3nhD8e+y+XSiRMnJEkHDhyQ0+m0ORG8gT8c+0B2OP7hr3zh2E9KSspRP58uSkVGRmr//v2Zlu3fv19hYWFZXiUlSUOHDtXgwf9+rZeYmKgqVaooPDxcYWFhBZoXhefMN8jh4eFe+0uaE7N6zFLM5Bh99893OnTqkHov7a3v+n+niOIRdkeDlyoqxz6QW/5w7LtcLhUvXlySFBERQVEKkvzj2Aeyw/EPf+ULx35ISEiO+vl0USoqKkqLFy/OtOyrr75SVFRUtusEBwcrODj4nOUOh8NrP0zkjWEYPv+5Fg8urvl3zFeLcS20+eBm/XX0L7Wf2l7x/eJVIqiE3fHgpYrCsQ/kRVE/9i3L8tyy5xXvM0XSXe7pzyXl7NwTBaCoH/vA+XD8w195+7Gf01xelT45OVkbN27Uxo0bJUk7duzQxo0btWvXLkmnr3Lq06ePp/99992n7du368knn9TWrVs1evRozZgxQ48++qgd8YECUaZYGS3ptUSXhF0iSdqwb4Num3Gb0lxpNicDAPi9RHcDAADIA68qSq1fv16NGjVSo0aNJEmDBw9Wo0aN9Pzzz0uS9u3b5ylQSVL16tW1aNEiffXVV2rQoIHefvttjRkzRtHR0bbkBwpKlVJVFNcrTqVDSkuSvvzrS909/27PgLcAgKLP4XDo2muv1bXXXusd34oGSxrlbudehA4AAHBBXnX7XqtWrc77n+zx48dnuc5PP/1UgKkA71Avop7m95ivmyferFRXqib+MlGVSlbS6ze9bnc0AEAhMAzDM6aUVzAkXWp3CAAA4Mu84Gs2ADnVomoLTb1tqhzG6V/dN1a9offXvm9zKgAAAAAAco+iFOBjbq1zq0a1HeWZf3Tpo5q+abqNiQAAhcE0Te3cuVM7d+6UaZp2x5EyJC11twybswAAAJ9EUQrwQfc1vk/PtnhWkmTJUp+5ffTNjm9sTgUAKEiWZXmKUl4xpmCGpJHuRlEKAADkAUUpwEe91PolDWg4QJKU5kpT52md9XPCzzanAgD4DYekJu7GGSUAAMgDTiEAH2UYhj7p8InaX9FekpSUlqTYybHaeWynvcEAAP4hSNKz7hZkcxYAAOCTKEoBPizAEaDpXaer6SVNJUn7kvcpZlKMDp08ZHMyAAAAAADOj6IU4ONCA0O14I4FqlWuliTp98O/q/2U9jqRdsLmZAAAAAAAZI+iFFAElA8tr7jecYosESlJ+n7P9+oxu4cyTEaeBQAUkFRJd7lbqs1ZAACAT6IoBRQR1UpXU1yvOIUFh0mSFm5bqPsW3ucdT2gCABQ9lqQD7sY/NQAAIA8oSgFFSIPIBprbfa6CnKdHnP38p8/1QvwLNqcCAOQHh8Ohq6++WldffbUcDi84hQuS9I67MdA5AADIAy84owGQn1pXb62Jt06UIUOSNHzFcH30w0c2pwIAXCzDMBQWFqawsDAZhmF3nNNnkZe7G2eUAAAgDziFAIqgbvW66b2Y9zzzgxYP0pzf5tgXCAAAAACA/6AoBRRRDzV5SE82e1KSZMlSz9k9tfLvlTanAgDklWma2rVrl3bt2iXTNO2OI7kkxbuby9YkAADAR1GUAoqw1256TXfWv1OSlOpKVcdpHbX5wGabUwEA8sKyLG3fvl3bt2/3jodYpEt6293Sbc4CAAB8EkUpoAhzGA593vFzRdeMliQdSzmmmMkx+uf4PzYnAwD4PIekhu7GGSUAAMgDTiGAIi7QGahZ3WapcaXGkqTdibsVMzlGR08dtTkZAMCnBUka7m48fQ8AAOQBRSnAD5QIKqFFPRepZpmakqQtB7eo47SOOpV+yuZkAAAAAAB/RVEK8BMRxSO0tPdSRRSPkCR9t+s79ZzTUy6T0WkBAAAAAIWPohTgR2qWranFPRereGBxSdLcrXP1wOIHvGPAXACAb0mVdL+7pdqcBQAA+CSKUoCfuabSNZrTfY4CHAGSpI83fKxXVr5icyoAgM+xJP3jbny3AQAA8oCiFOCHbql5i8Z1GueZf+7b5zTmxzE2JgIAXIjD4VDDhg3VsGFDORxecAoXJOlVd2OgcwAAkAdecEYDwA696/fWmze96Zm/b+F9WrhtoY2JAADnYxiGSpcurdKlS8swDLvjnD6LvMrdOKMEAAB5wCkE4Mceb/a4HmnyiCTJZbnUbWY3rd291t5QAAAAAAC/QFEK8GOGYejt6LfVvV53SdKpjFNqN6Wdth7aanMyAMB/maapPXv2aM+ePTJN0+44kkvSWnfjQa4AACAPKEoBfs5hODSh8wTdWP1GSdKRU0cUMylGe5P22pwMAHA2y7L0xx9/6I8//vCOp6amS3rF3dJtzgIAAHwSRSkACg4I1hfdv1CDCg0kSX8f/1uxk2N1POW4zckAAF7LIamOu3FGCQAA8oBTCACSpLDgMC3ptUTVSleTJP2y/xd1nt5ZqRmp9gYDAHinIElvuhtP3wMAAHlAUQqAR8WSFRXXK07lipWTJMXvjFefuX1kWl4wdgkAAAAAoEihKAUgk1rla2lRz0UKDQyVJM3YPEOPxj3qHeOXAAAAAACKDIpSAM7R5JImmtF1hpyGU5L0wboP9Nbqt2xOBQDwKmmSBrtbms1ZAACAT6IoBSBL7a5op886fOaZf+rrp/S/n/9nYyIAgFcxJf3hbtzlDQAA8iDA7gAAvFf/Rv21N2mvnv32WUnSXfPvUkTxCMVcFmNzMgDwPw6HQ1dddZVn2naBkp4/axoAACCXvOCMBoA3e7rF07q/8f2SpAwzQ11ndNX6vettTgUA/scwDJUrV07lypWTYRh2x5Gckq51N6fNWQAAgE+iKAXgvAzD0AexH6hLnS6SpBPpJ9R2clv9eeRPm5MBAAAAAHwZRSkAF+R0ODW5y2S1rNpSknTw5EFFT4rW/uT9NicDAP9hmqYSEhKUkJAg0/SCQZxMSRvdzQviAAAA30NRCkCOhASEaG73uaoXXk+StP3odrWd0lZJqUk2JwMA/2BZlrZu3aqtW7fKsiy745x+4t5z7sbT9wAAQB5QlAKQY2WKlVFc7zhdEnaJJOnHfT/qthm3Kc3F/0YAwO84JFV3N84oAQBAHnAKASBXLgm7REt7L1WZkDKSpK+2f6UB8wbItLh3AwD8SpCkD9wtyOYsAADAJ1GUApBrdcPrasEdCxQSECJJmvzrZA35eojNqQAAAAAAvoSiFIA8aX5pc029baocxum/Rt5a/ZbeXfOuzakAAAAAAL6CohSAPOtcu7NGtx3tmR/85WBN2zTNxkQAgEKTJmmouzG0IAAAyAOKUgAuyr2N79XzLZ/3zPf5oo++2fGNjYkAAIXClLTJ3RhWEAAA5AFFKQAXbVirYbq70d2SpHQzXZ2nddbGhI32hgKAIsbhcKhu3bqqW7euHA4vOIULlPSUuwXanAUAAPgkLzijAeDrDMPQR+0/UocrOkiSktKSFDs5VjuO7rA5GQAUHYZhKCIiQhERETIMw+44klPS9e7mtDkLAADwSRSlAOSLAEeApnWdpqhLoiRJCckJip4UrYMnDtqcDAAAAADgjShKAcg3oYGhWnDHAtUuX1uS9MeRP9R+anudSDthczIA8H2WZenAgQM6cOCALMuyO87pcaR+czfGlAIAAHlAUQpAvioXWk5xveJUsURFSdK6PevUbVY3pbvSbU4GAL7NNE1t2bJFW7ZskWl6QRUoTdKT7sbT9wAAQB5QlAKQ76qWrqq43nEKCw6TJC3+Y7HuXXivd3yzDwDIH4akiu7mBUNcAQAA30NRCkCBqF+hvub1mKcgZ5AkadzGcXru2+dsTgUAyDfBkj51t2CbswAAAJ9EUQpAgWlVrZUm3TpJhvsr9FdWvqJR60bZnAoAAAAA4A0oSgEoULfXu13vx7zvmX9wyYOavWW2jYkAAAAAAN6AohSAAvdgkwc1pPkQSZIlS73m9NKKv1fYnAoAcFHSJL3obgx0DgAA8oCiFIBC8WqbV9WnQR9JUqorVR2ndtSv+3+1ORUAIM9MSevdzQseBggAAHwPRSkAhcIwDI3pMEYxl8VIko6nHlfs5FjtOr7L5mQA4BsMw1Dt2rVVu3ZtGYYXPO4uQNIj7hZgaxIAAOCjKEoBKDSBzkDNvH2mGldqLEnak7RHMZNidOTUEZuTAYD3czgcioyMVGRkpBwOLziFC5DUxt0oSgEAgDzwgjMaAP6kRFAJLeq5SJeVvUyS9Nuh39RxakedSj9lczIAAAAAQGGiKAWg0EUUj9DS3ksVUTxCkrTqn1W6Y/YdyjAzbE4GAN7LsiwdPnxYhw8flmVZdsc5PY7UdndjTCkAAJAHFKUA2KJGmRpa0muJSgSVkCTN+32eBi0a5B3/0QIAL2Sapn799Vf9+uuvMk0vqAKlSXrY3Xj6HgAAyAOKUgBsc3XFqzWn2xwFOgIlSZ/++KmGrxhucyoAQI4Yksq6mxeMuw4AAHwPRSkAtrq55s0a33m8Z/6F+Bf02YbP7AsEAMiZYEkT3C3Y5iwAAMAnUZQCYLueV/XUiJtHeObvW3Sf5v8+38ZEAAAAAICCRlEKgFd4rNljGtx0sCTJtEx1n9Vdq/9ZbXMqAAAAAEBBoSgFwGu8dctbuuPKOyRJKRkp6jC1g347+JvNqQAAWUqT9Lq7MdA5AADIA4pSALyGw3BofOfxuqnGTZKkI6eOKGZyjPYk7rE5GQDgHKakVe7mBQ8DBAAAvoeiFACvEuQM0uxus9UwsqEkadfxXYqdHKtjKcdszQUAdjMMQ5dffrkuv/xyGYYXPO4uQNJ97hZgcxYAAOCTKEoB8DphwWFa0muJqpeuLkn69cCv6jyts1IyUmxOBgD2cTgcqly5sipXriyHwwtO4QIktXM3ilIAACAPvOCMBgDOFVkiUnG941Q+tLwkafnfy3XnF3fKZbpsTgYAAAAAyA8UpQB4rSvKXaFFPRcpNDBUkjRryyw9EveILMuyORkAFD7LsnTs2DEdO3bMO/4etCTtdTcviAMAAHwPRSkAXu26ytdp1u2z5DSckqSRP4zUG6vesDkVABQ+0zS1ceNGbdy4UabpBSOLp0q6191Sbc4CAAB8EkUpAF4v9vJYjek4xjM/dNlQTdg4wcZEAABJUnF3AwAAyAOKUgB8Qr+G/fTqja965u+af5eW/LHExkQA4OdCJE1ztxCbswAAAJ9EUQqAzxhy/RA9cO0DkiSX5VLXmV21bs86m1MBAAAAAPKCohQAn2EYht6LeU9d63aVJJ1MP6l2U9rpj8N/2JwMAAAAAJBbFKUA+BSnw6mJt07UDVVvkCQdOnlI0ZOilZCcYHMyAPAz6ZLec7d0W5MAAAAfRVEKgM8JCQjR3B5zdWXElZKkHcd2qO3ktkpKTbI5GQD4EZekZe7msjkLAADwSV5XlBo1apSqVaumkJAQNWnSROvWnX+8mPfee0+1atVSsWLFVKVKFT366KNKSUkppLQA7FI6pLTiesWpSlgVSdJPCT+py4wuSnOl2ZwMAAqGYRiqUaOGatSoIcMw7I4jBUjq724BNmcBAAA+yauKUtOnT9fgwYP1wgsv6Mcff1SDBg0UHR2tAwcOZNl/ypQpGjJkiF544QX99ttv+vzzzzV9+nQ9/fTThZwcgB0qh1XW0t5LVSakjCTp6+1fq/+8/jIt0+ZkAJD/HA6HLr30Ul166aVyOLzgFC5AUhd3oygFAADywAvOaP71zjvv6J577lH//v1Vt25dffzxxwoNDdXYsWOz7L969Wo1b95cPXv2VLVq1XTLLbfojjvuuODVVQCKjjrhdbSw50KFBJx+HvmUX6foya+etDkVAAAAAOBCvOZ7rbS0NG3YsEFDhw71LHM4HLrpppu0Zs2aLNdp1qyZJk2apHXr1um6667T9u3btXjxYt15553Z7ic1NVWpqame+cTEREmSaZoyTa6uKCpM05RlWXymfqJp5aaa2mWqbpt5m0zL1Ntr3lbFEhX1aNNH7Y5W6Dj24a/84di3LEtJSafHzitZsqT9t/BZko64p8tK8oI7Cv2RPxz7QHY4/uGvfOHYz2k2rylKHTp0SC6XSxUqVMi0vEKFCtq6dWuW6/Ts2VOHDh3S9ddfL8uylJGRofvuu++8t++99tprevHFF89ZfvDgQcaiKkJM09Tx48dlWZZ33OKAAte0TFO9fv3renLl6aukHv/qcYWaobr1slttTla4OPbhr/zh2He5XPrpp58kSY0aNZLT6bQ3UIpU+v9KS5KOfXpMCrE1jd/yh2MfyA7HP/yVLxz7Z75IuxCvKUrlRXx8vF599VWNHj1aTZo00Z9//qmHH35Yw4cP13PPPZflOkOHDtXgwYM984mJiapSpYrCw8MVFhZWWNFRwEzTlGEYCg8P99pfUuS/xyIe0wnjhF5ccbrw/PC3D6tmZE3dVOMmm5MVHo59+Ct/OPZdLpeKFy8uSYqIiPCKopRRzPDkoShlD3849oHscPzDX/nCsR8SkrMTA68pSpUvX15Op1P79+/PtHz//v2KjIzMcp3nnntOd955p+6++25J0lVXXaUTJ07o//7v//TMM89k+eEEBwcrODj4nOUOh8NrP0zkjWEYfK5+6IVWL2hf8j59+uOnSjfTddvM27Si3wo1qtjI7miFhmMf/qqoH/uWZXlu2fOK9xkqad7pSYN792xV1I994Hw4/uGvvP3Yz2kur0kfFBSka665RsuWLfMsM01Ty5YtU1RUVJbrnDx58pw3euZbQ8uyCi4sAK9lGIZGtRulTrU6SZKS05IVOzlW249utzkZAAAAAOBsXlOUkqTBgwfrs88+04QJE/Tbb79p4MCBOnHihPr37y9J6tOnT6aB0Dt06KCPPvpI06ZN044dO/TVV1/pueeeU4cOHey/pB2AbQIcAZp621Q1q9JMkrT/xH7FTIrRwRMHbU4GAAAAADjDa27fk6Tu3bvr4MGDev7555WQkKCGDRsqLi7OM/j5rl27Ml0Z9eyzz8owDD377LPas2ePwsPD1aFDB73yyit2vQUAXqJYYDEtuGOBrh97vX479Jv+OPKH2k1pp2/6fqMSQSXsjgcAvi9d0hj39N2SAm3MAgAAfJJh+fl9bomJiSpVqpSOHz/OQOdFiGmaOnDggCIiIrz2HlsUjl3Hdynq8yjtTdorSYq9LFbzesxToLNo/u+JYx/+yh+OfZfLpZUrV0qSWrRoYf9V4SmSbndPzxQDndvEH459IDsc//BXvnDs57TW4p3pASCfXFrqUsX1ilOp4FKSpCV/LtE9C+5h3DkAPscwDFWrVk3VqlXzDHhuqwBJd7ibV117DwAAfAVFKQBF3lUVrtK8HvMU7Dz95M0JP0/QM988Y3MqAMgdh8PhKUp5xbeiAZJ6uhtFKQAAkAdecEYDAAXvhmo3aHKXyZ7Hlr/23WsauW6kzakAAAAAwH9RlALgN26re5s+iP3AM//Qkoc0a8ssGxMBQM5ZlqUTJ07oxIkT3nELsiXphLt5QRwAAOB7KEoB8CsPXPeAnr7+aUmSJUu95vRS/M54e0MBQA6YpqkffvhBP/zwg0zTtDuOlCqph7ul2pwFAAD4JIpSAPzOyze+rH4N+0mS0lxp6jStk37Z/4u9oQAAAADAz1CUAuB3DMPQp+0/VexlsZKkxNRExU6O1a7ju2xOBgA+JFjSF+4WbHMWAADgkyhKAfBLgc5Azbx9pq6tdK0kaW/SXkVPitbhk4dtTgYAPsLQ6afuBbinAQAAcomiFAC/VTyouBb1XKTLy14uSdp6aKs6TO2gk+knbU4GAAAAAEUfRSkAfi28eLiW9l6qyBKRkqQ1u9fojtl3KMPMsDkZAHi5DElj3Y2/MgEAQB5QlALg96qXqa7FPRerZFBJSdL83+fr/kX3e8cj1wHAW2Xo3zGlKEoBAIA8oCgFAJIaVWykL7p/oUBHoCTpsx8/04vLX7Q5FQD8yzAMValSRVWqVJFheMEgTgGSbnW3AJuzAAAAn0RRCgDc2tRoowmdJ3jmX1z+oj7d8KmNiQDgXw6HQzVr1lTNmjXlcHjBKVyApAHuRlEKAADkgRec0QCA97jjqjv0zi3veOYHLhqoeVvn2ZgIAAAAAIomilIA8B+PRj2qx6MelySZlqkes3to1a5VNqcC4O8sy1JKSopSUlK8Y8w7S6fHkspwTwMAAOQSRSkAyMIbN7+hXlf1kiSlZKSow9QO2nJwi82pAPgz0zS1du1arV27VqZp2h1HStW/Y0ql2pwFAAD4JIpSAJAFh+HQ2E5jdXONmyVJR1OOKmZSjPYk7rE5GQAAAAAUDRSlACAbQc4gze42W1dXvFqS9E/iP4qZHKNjKcfsDQYA3iBY0jR3C7Y5CwAA8EkUpQDgPEoGl9TinotVo0wNSdKmA5vUaVonpWSk2JwMAGxmSCrubobNWQAAgE+iKAUAF1ChRAXF9YpTeGi4JGnF3yvUe05vuUyXzckAAAAAwHdRlAKAHLi83OVa1HORQgNDJUmzf5uth+Me9o4nYAGAHTIkTXG3DJuzAAAAn0RRCgBy6NrK12p2t9kKcARIkkb9MEqvf/e6zakAwCYZkqa6G0UpAACQBxSlACAXYi6L0ecdP/fMP/3N0xq/cbx9gQD4DcMwVKlSJVWqVEmG4QWDODkltXU3p81ZAACATwqwOwAA+Jo+DfpoX9I+DVk2RJJ09/y7FVE8Qm0vb2tzMgBFmcPh0BVXXGF3jH8FShpodwgAAODL8lyUSk5O1tatW3Xo0CEZhqHy5cvriiuuUMmSJfMzHwB4pSebP6m9SXv1wboP5LJcun3m7fqmzzdqckkTu6MBAAAAgE/IVVFqx44dmjBhgubNm6dNmzbJNM1MrzscDtWrV0+dO3dWnz59VKNGjXwNCwDewjAMvRvzrvYl79PMLTN1Mv2k2k1pp9V3rdYV5bzoSgYARYZlWUpPT5ckBQYGesctfAAAABchR0WpLVu26Pnnn9cXX3yh0qVLq1WrVrr99ttVo0YNlSlTRpZl6ejRo9qxY4c2bNigkSNHavjw4br11ls1fPhw1alTp6DfBwAUOofh0P9u/Z8Onjyo+J3xOnzqsKInRWvNXWsUWSLS7ngAihjTNLV69WpJUosWLeR02jyQU4qkHu7paZJCbMwCAAB8Uo6KUg0aNFC7du20aNEi3XTTTQoIOP9qGRkZ+vrrr/Xxxx+rQYMGSktLy5ewAOBtQgJCNLf7XLUc31K/7P9FO4/tVOzkWC3vt1xhwWF2xwOAguWyOwAAAPBlOSpK/fLLL7m62ikgIEAxMTGKiYnR1q1b8xwOAHxBqZBSWtJriaI+j9Ku47u0MWGjukzvokU9Fyk4INjueABQMIIljT9rGgAAIJccOel0Mbff1a5dO8/rAoCvqFSykpb2XqqyxcpKkpbtWKZ+8/rJtMwLrAkAPsqQVM7dGN4KAADkQa4GOh8/fvw5g5vn1IABA/K0HgD4itrla2vhHQvV5n9tdCrjlKZtmqZKJSrp7ei37Y4GAAAAAF4nV0WpgQMH6tFHH5VlWbnayfvvv09RCoBfiKoSpeldp6vz9M4yLVPvrH1HlUpW0mPNHrM7GgDkrwxJ893THZXLs0oAAIBcnj4EBATo1VdfzfVOPv7441yvAwC+qkOtDvqk/Se6Z8E9kqTHv3pckSUi1at+L5uTAUA+ypA0zj3dVhSlAABAruXq9MEw8jZgQF7XAwBfdffVd2tf0j49H/+8JKnfvH4KLx6uW2reYnMyAL7KMAxFRkZ6pm3nlNTmrGkAAIBcytFA5wCA3Hu25bO675r7JEkZZoZum3Gbftz3o82pAPgqh8Oh2rVrq3bt2nI4vOAULlDSI+4WaGsSAADgo7zgjAYAiibDMDSy7Uh1rt1ZkpSclqzYybH668hf9gYDAAAAAC9AUQoACpDT4dSULlPUvEpzSdKBEwcUMzlGB04csDkZAF9jWZZcLpdcLleuHzoDAADgjXJdlLIsS6Zp5ri5XK6CyA0APqNYYDHNv2O+6obXlST9eeRPtZvSTslpyTYnA+BLTNPUypUrtXLlSpmmaXccKUVSD3dLsTkLAADwSbka6Dw0NFQVKlTI1Q4sy1JAAI9jAeDfyhYrq7hecYr6PEp7kvZo/d716jqjqxbcsUCBTgZjAeCjTtgdAAAA+LJcVYsSEhIKKgcAFHlVSlVRXO84tRjXQsdSjmnpX0t194K7Nb7TeO94khYA5EawpE/OmgYAAMglxpQCgEJ0ZcSVmtdjnoKdp/8H97+f/6enlz1tcyoAyANDUiV3o64OAADyIEdFqejoaK1YsSLXG//2228VHR2d6/UAoChrWbWlptw2RYb7f3Gvr3pdH3z/gc2pAAAAAKBw5agoVbNmTd18882qU6eOhg0bppUrVyo5+dwBepOSkhQfH69nn31WtWrVUmxsrC677LJ8Dw0Avq5LnS4a1XaUZ/6RuEc0Y/MMGxMBQC5lSFrkbhk2ZwEAAD4pR2NKjR49Wk888YTef/99jR49WsOHD5dhGCpbtqzKlCkjy7J09OhRHT16VJZlqWzZsurVq5cefvhhVa9evaDfAwD4pIHXDtTepL16eeXLsmTpzi/uVHhouFpXb213NAC4sAxJH7un2yiXI5UCAADk4vShevXqeu+99zRixAitXLlSa9as0datW3X48GFJUrly5VS7dm1FRUXp+uuvV2AgT5MCgAt5qfVL2pu0V2M3jlWaK02dp3fWyv4rVb9CfbujAfAyhmEoPDzcM207h6TmZ00DAADkUq6/0woICFDr1q3VujXf5APAxTIMQ590+ET7T+zXoj8WKTE1UTGTYrTmrjWqWrqq3fEAeBGHw6F69erZHeNfQZKG2B0CAAD4Mr7XAgCbBTgCNL3rdDWp3ESStC95n6InRevwycM2JwMAAACAgkNRCgC8QPGg4lrYc6GuKHeFJOn3w7+r/dT2Opl+0uZkAAAAAFAwKEoBgJcoH1peS3svVWSJSEnS2t1r1X1Wd2WYPNYKgORyuRQfH6/4+Hi5XC6740ipkvq6W6rNWQAAgE+iKAUAXqRa6Wpa0muJSgaVlCQt3LZQAxcOlGVZNicDgP+wJB1xN/6KAgAAeUBRCgC8TMPIhprbY64CHaefYjrmpzEaFj/M3lAA8F9Bkt53tyCbswAAAJ9EUQoAvNCN1W/UxFsneuZfWvGSPl7/sY2JAOA/HJJquBtnlAAAIA8CctP5xhtvVEZG7sc2MQxDy5cvz/V6AODPul/ZXQnJCXpk6SOSpEGLB6lC8Qq6tc6t9gYDAAAAgHyQq6LUunXrtGHDhlztwLIsNW3aNFfrAABOe7jpw9qbtFdvrn5TpmXqjtl36Os+X+v6S6+3OxoAf5ch6cx3jjcol2eVAAAAuTx9cDgcqlWrVq534nBwTTcA5NVrN72mvcl7NemXSUp1parD1A76rv93qhdRz+5oAPxZhqT33NPNRVEKAADkGtUiAPByDsOhzzt+rltq3iJJOpZyTDGTY7Q7cbfNyQAUJsMwVLZsWZUtW1aGYdgd5/RZZGN344wSAADkAacQAOADgpxBmnX7LF1T8RpJ0u7E3YqZFKOjp47anAxAYXE4HKpfv77q16/vHVehB0l6wd14+h4AAMgDLzijAQDkRMngklrUc5FqlqkpSdp8cLM6TeukU+mnbE4GAAAAALlHUQoAfEiFEhW0tPdShYeGS5JW7lqpXnN6yWW6bE4GAAAAALmTqyEpMzIyNHbs2FztwLIspaWl5WodAED2apatqcW9FqvV+FY6kX5CX2z9Qg8teUgj2460OxqAAuRyubRq1SpJUvPmzeV0Ou0NlCrpQff0h5KCbcwCAAB8Uq6KUg8++KB+++23XO9k4MCBuV4HAJC9xpUaa3a32Wo/tb0yzAyNXj9alUpW0tDrh9odDUABMk3T7gj/siTtO2saAAAgl3JVlHrjjTcKKgcAIJeiL4vW2I5j1WduH0nSs98+qwolKqh9pfY2JwPgF4IkvXnWNAAAQC7lqih14403KiMjI9c7MQxDy5cvz/V6AIDzu7PBnUpITtCTXz8pSbpv4X0aHz1ePSN62pwMQJHnkFTH7hAAAMCX5aootW7dOm3YsCFXO7AsS02bNs3VOgCAnHu82ePak7RH73//vlyWS/d8fY9qVqqpqCpRdkcDAAAAgGzlqijlcDhUq1atXO/E4eAhfwBQUAzD0DvR72hf8j7N2DxDKRkp6jC1g1YNWKVa5XP/dzYA5IhL0hr3dJQkm8ddBwAAvodqEQAUAQ7Dof91/p9aV2stSTp86rCiJ0Vrb9Jem5MBKLLSJb3hbuk2ZwEAAD6JohQAFBHBAcGaffts1StXT5L09/G/1XZyWx1POW5zMgD5pXTp0ipdurTdMU5zSLrS3TijBAAAecApBAAUIaVCSmly7GRVLVVVkvTz/p916/RblZqRanMyABfL6XSqYcOGatiwoZxOL7hXLkjSa+7G0/cAAEAeUJQCgCKmQvEKWtJzicoVKydJ+nbnt+o7t69My7Q5GQAAAAD8K1cDnWdkZGjs2LG52oFlWUpLS8vVOgCAi1OrfC0t7LlQN064UacyTmn65umKLBGpd6PflWEYdscDAAAAgNwVpR566CH99ttvud7JwIEDc70OAODiNL2kqWbcPkOdp3WWy3Lp/e/fV+WSlfVE8yfsjgYgD1wul9auXStJatq0qf238KVJetw9PULcwgcAAHItx0WpESNGqG/fvqpTp05B5gEA5KP2V7TXpx0+1V3z75IkPfn1k4osEak7G9xpczIAeZGe7kWPuTMl7ThrGgAAIJdyPKbUm2++qSuvvFI1atTQQw89pLi4OKWmMnAuAHi7AY0G6OXWL/87P3+Alv651MZEAIqEIEnD3Y2rpAAAQB7kuCi1f/9+rVq1Sr1799bq1avVrl07lStXTh06dNDHH3+sXbt2FWROAMBFeLrF07q/8f2SpAwzQ7fNuE3r9663ORUAn+aQ1NDdeHQOAADIgxyfQhiGoaZNm+qll17S+vXrtXfvXn3wwQcKCQnRkCFDVL16dV111VUaMmSIvvvuO5lm3q7jHjVqlKpVq6aQkBA1adJE69atO2//Y8eOadCgQapYsaKCg4N1xRVXaPHixXnaNwAUVYZh6IPYD9SlThdJ0on0E2o7ua3+PPKnzckAAAAA+Ks8f69VoUIFDRgwQDNnztShQ4f09ddfKyYmRgsWLFDLli1Vvnx59ejRQ99//32Otzl9+nQNHjxYL7zwgn788Uc1aNBA0dHROnDgQJb909LSdPPNN2vnzp2aNWuWfv/9d3322WeqXLlyXt8WABRZTodTk7tMVotLW0iSDp48qJhJMdqfvN/mZAB8kkvSD+7msjkLAADwSflysXVAQIBat26tt956S5s3b9b27ds1fPhwJSUlaeXKlTnezjvvvKN77rlH/fv3V926dfXxxx8rNDRUY8eOzbL/2LFjdeTIEc2dO1fNmzdXtWrVdMMNN6hBgwb58bYAoMgJCQjRvB7zVC+8niTpr6N/qd2UdkpOS7Y5GQCfky7pJXfzovHXAQCA78jx0/ckaceOHbIsK0d9Y2NjFRsbK0navn27atSocd7+aWlp2rBhg4YOHepZ5nA4dNNNN2nNmjVZrjN//nxFRUVp0KBBmjdvnsLDw9WzZ0899dRT2T4mOTU1NdMA7YmJiZIk0zTzfMshvI9pmrIsi88Uficnx36p4FJa3HOxmo9rrt2Ju7Vh3wZ1md5F83vMV5CT0Yrhm/zh733TNFWiRAnPtGEYNieSjMtOZ7Bk8QQ+m/jDsQ9kh+Mf/soXjv2cZstVUapOnTq6+uqrc1yYMgxDlmVpw4YNSktLO2/fQ4cOyeVyqUKFCpmWV6hQQVu3bs1yne3bt+ubb75Rr169tHjxYv3555+6//77lZ6erhdeeCHLdV577TW9+OKL5yw/ePCgUlJScvS+4P1M09Tx48dlWZYcDkZfhf/I6bEfpCBNip6kTvM66XjacX21/Sv1ntlbH7T+QA6D3xn4Hn/5e/+SSy6RJB0+fNjmJG5Puf88ZmcI/+Yvxz6QFY5/+CtfOPaTkpJy1C9XRanAwECtXr0612HKlCmT63VywjRNRURE6NNPP5XT6dQ111yjPXv26K233sq2KDV06FANHjzYM5+YmKgqVaooPDxcYWFhBZIThe/MN8jh4eFe+0sKFITcHPsRERGaHzpf0ZOjlZKRotl/zFaN8jX0+k2vF1JaIP/w9z78Fcc+/BnHP/yVLxz7ISEhOeqXq6JUXi8Tz8l65cuXl9Pp1P79mQfc3b9/vyIjI7Ncp2LFigoMDMx0q16dOnWUkJCgtLQ0BQWdextKcHCwgoODz1nucDi89sNE3hiGwecKv5SbY79ltZaaettU3TbjNpmWqbfWvKVKYZX0SNNHCj4okM/4ex/+imMf/ozjH/7K24/9nObymvRBQUG65pprtGzZMs8y0zS1bNkyRUVFZblO8+bN9eeff2a6V3Hbtm2qWLFilgUpAMC5OtfurFFtR3nmH136qKZvmm5jIgBZcblcWrt2rdauXSuXywsed5cm6Ul3O/8oDQAAAFnymqKUJA0ePFifffaZJkyYoN9++00DBw7UiRMn1L9/f0lSnz59Mg2EPnDgQB05ckQPP/ywtm3bpkWLFunVV1/VoEGD7HoLAOCT7mt8n55r+Zxnvs/cPvpmxzc2JgKQlZSUFO8ZA9OU9Ju7ee84qwAAwIvl6va9gta9e3cdPHhQzz//vBISEtSwYUPFxcV5Bj/ftWtXpkvAqlSpoqVLl+rRRx9V/fr1VblyZT388MN66qmnstsFACAbL7Z6UXuT9urznz5XmitNnad11or+K9QwsqHd0QB4o0BJz5w1DQAAkEteVZSSpAceeEAPPPBAlq/Fx8efsywqKkpr164t4FQAUPQZhqGP23+s/Sf2a+G2hUpKS1Ls5FituWuNqpWuZnc8AN7GKamp3SEAAIAvy1VRKjU1VS1btszVDizLUnJycq7WAQDYI8ARoOldp6vN/9po7e61SkhOUPSkaK0asErlQ8vbHQ8AAABAEZKrotRPP/0ky7IKKgsAwAuEBoZq4R0L1Xxsc/1++HdtO7xN7ae017I+y1Q8qLjd8QB4C1PSZvd0PXnZSKUAAMAX5Or0oVixYnluAADfUS60nJb2XqqKJSpKkr7f8726z+quDDPD5mQAvEaapKfdjafvAQCAPMjVlVJ16tTR1VdfneurpTZs2KC0NM5WAMCXVC1dVUt6LVHL8S2VmJqoRX8s0r0L7tWYjmNkGIbd8QC/FBoaaneEfxmSqpw1DQAAkEu5KkoFBgZq9erVud5JmTJlcr0OAMB+DSIbaG73uYqZHKM0V5rGbhyrSiUrafiNw+2OBvgdp9Op6667zu4Y/wqWNNruEAAAwJfl6va9vH4zzjfqAOC7WldvrYm3TpThvhTi5ZUv66MfPrI5FQAAAABfx5CUAIAL6lavm96Ped8zP2jxIM35bY6NiQAAAAD4OopSAIAcebDJg3qq+VOSJEuWes7uqZV/r7Q5FeA/XC6X1q1bp3Xr1snlctkd5/Tg5s+5G0OHAgCAPKAoBQDIsdfavKY+DfpIklJdqeo4raM2HdhkcyrAf5w8eVInT560O8ZppqSN7mbamgQAAPgoilIAgBwzDENjOoxRdM1oSdKxlGOKmRSjf47/Y3MyAIUuUNJj7hZocxYAAOCTcvX0vdTUVLVs2TJXO7AsS8nJyblaBwDgvQKdgZrVbZZaT2it9XvXa0/SHsVMjtHK/itVtlhZu+MBKCxOSa3sDgEAAHxZropSP/30kyzLKqgsAAAfUSKohBb1XKTmY5vrzyN/asvBLeo0rZO+7P2ligUWszseAAAAAB+Qq6JU3bp1CyoHAMDHRBSPUFyvODUb20wHThzQd7u+U885PTXr9llyOpx2xwNQ0ExJf7mna4pBIQAAQK5x+gAAyLOaZWtqcc/FKhFUQpI0d+tcDVo8iKtqAX+QJmmwu/H0PQAAkAcUpQAAF+WaStdoTrc5CnCcvvj2kw2f6OUVL9ucCiiaQkJCFBISYneM0wxJEe5m2JwFAAD4JIpSAICLdnPNmzW+03jP/PPxz2vMj2PsCwQUQU6nU02bNlXTpk3ldHrBLbLBkj53t2CbswAAAJ9EUQoAkC961e+lt25+yzN/78J7teD3BTYmAgAAAODNKEoBAPLNY1GP6dGmj0qSTMtU91ndteafNTanAgAAAOCNKEoBAPKNYRgaccsI9biyhyTpVMYptZ/aXlsPbbU5GeD7XC6XNmzYoA0bNsjlctkd5/Tg5i+7GwOdAwCAPKAoBQDIVw7DofGdxqtN9TaSpCOnjih6UrT2Ju21ORng+5KSkpSUlGR3jNNMSd+7m2lzFgAA4JMoSgEA8l1wQLDmdJ+jhpENJUm7ju9S7ORYHU85bm8wAPknQNID7hZgcxYAAOCTKEoBAApEWHCYlvRaomqlq0mSftn/izpP76zUjFR7gwHIHwGSot2NohQAAMgDilIAgAITWSJSS3svVfnQ8pKk+J3xuvOLO2Va3OsDAAAA+DuKUgCAAnVFuSu08I6FCg0MlSTN3DJTj8Q9IsuybE4G4KJYkna5G7/OAAAgDyhKAQAKXJNLmmjm7TPlNJySpA/Xfag3V71pcyoAFyVV0iB3465cAACQBxSlAACFou3lbTWm4xjP/JBlQ/S/n/9nYyLA9wQGBiowMNDuGP8KczcAAIA8YFhKAECh6dewn/Ym7dUz3zwjSbpr/l2KKB6hmMtibE4GeD+n06nmzZvbHeNfIZIm2x0CAAD4Mq6UAgAUqqHXD9WgawdJkjLMDHWd0VU/7PnB5lQAAAAAChtFKQBAoTIMQ+/HvK/b6twmSTqRfkLtprTTH4f/sDkZAAAAgMJEUQoAUOicDqcmdZmkllVbSpIOnjyo6EnR2p+83+ZkgPdyuVzauHGjNm7cKJfLZXccKU3SCHdLszkLAADwSRSlAAC2CAkI0bwe83RlxJWSpB3Hduj/27vvOKmqu4/jnzuzDZAOS1EQC4qogA0Fe0RBsWAvIIqGmERNDKaIscYkmNjzqMHYC4iCioqUKAZsGBFFEUWNDQQWsMBS3F2YmeePGReQuuvu3Jmdz9vXeXnnzrkz34HDcPjtveceM/IYlpcvDzmZlLmWLl3K0qVLw46RFAemplo85CySJCkrWZSSJIWmSVETJvSfQLtG7QB4a+FbnPz4yVTEPO1Cynh5wE9TzVvnSJKkarAoJUkK1XaNtmPigIk0LWoKwPOfPs95T59HPOGpF1JGywNOSDWLUpIkqRosSkmSQte5ZWeePfNZivKKABgxawR/eP4PIaeSJEmSVJssSkmSMsKB7Q9k1MmjiATJv5punHYjt0y7JeRUkjYpASxOtUTIWSRJUlayKCVJyhgndDqBf/b9Z+XjIf8ewqj3RoWYSNImlQPnp1p5yFkkSVJWsiglScooP9vnZ1x96NWVjwc+NZDJn04OMZGUOSKRCJFIBk3fClNNkiSpGjJoViNJUtLVh17N4L0HA7A6vpoTHzuRtxe+HXIqKVzRaJRDDjmEQw45hGg0GnYcKALGpFpRyFkkSVJWsiglSco4QRBwZ987OX7X4wFYXrGco0cczWfffhZyMkmSJEk1xaKUJCkj5UXyePTkR+nZricAi1YuovcjvVmycknIySRJkiTVBItSkqSMVT+/Ps+e+SydWnQC4ONvPubYR49lZcXKkJNJ6RePx3n33Xd59913icfjYceB1cD/pdrqkLNIkqSsZFFKkpTRmtVrxsT+E2nbsC0Ab8x/g9PGnMbqmP8KVm5JJBJ88803fPPNNyQSibDjQAz4d6rFQs4iSZKykkUpSVLG277J9kzsP5HGhY0BGP/xeH427meZ8Q9zKVflAWenWl7IWSRJUlayKCVJygp7ttqTp894moJoAQAPzHyAK168IuRUUg7LA05LNYtSkiSpGixKSZKyxqEdDmXESSMICAD46yt/5Y437gg5lSRJkqTqsCglScoqp3Q+hdv63Fb5+OIJFzPm/TEhJpJyVAJYlmpeSStJkqrBopQkKetcvP/FDD1oKAAJEvR/sj9TP58aciopx5QDA1KtPOQskiQpK1mUkiRlpb/85C+c0/UcACpiFZww6gRmLZoVcipJkiRJW8tlKSVJWSkIAu4+7m4Wr1zMhP9NYFn5MvqM6MO086fRvnH7sONJNS4ajXLYYYeFHWOtIuDZsENIkqRs5plSkqSslR/NZ/Spo9mv7X4ALFi+gD6P9OGb774JOZkkSZKkLbEoJUnKag0KGvDcWc+xc7OdAfjgqw847tHj+G71dyEnkyRJkrQ5FqUkSVmvZYOWTBowiVYNWgHw2rzXOOOJM1gTXxNyMqnmxONxZs+ezezZs4nH42HHgdXA3am2OuQskiQpK1mUkiTVCTs23ZEJ/SfQsKAhAM98+AwXPnchiYT3qlfdkEgkWLJkCUuWLMmMcR0Dnkm1WMhZJElSVrIoJUmqM/ZqsxdPnv4k+ZF8AP711r+47qXrQk4l1VF5wGmp5q1zJElSNViUkiTVKb127MWD/R6sfHz1lKu5e8bdISaS6qg84OxUsyglSZKqwaKUJKnOOXPPM7npqJsqH//8uZ/zzIfPhJhIkiRJ0g9ZlJIk1UlDegzh0h6XAhBPxDl9zOm8Nu+1kFNJdUgCKEu1DFjiSpIkZR+LUpKkOuvvR/6ds/Y8C4CyNWUcO/JYPljyQcippDqiHDg11cpDziJJkrKSRSlJUp0VCSLcf8L99NqxFwDfln1LnxF9mF86P+RkkiRJkixKSZLqtIJoAU+e9iR7td4LgLnL5nL0iKNZWrY03GBSFUUiEQ4++GAOPvhgIpEMmMIVAqNTrTDkLJIkKStlwIxGkqTa1bCwIeP7j2eHJjsAMGvxLPqN6kfZmrKQk0lbLwgCotEo0WiUIAjCjgMBUJRqGRBHkiRlH4tSkqSc0Hqb1kwaMIkW9VsAMPWLqZz91NnE4rGQk0mSJEm5yaKUJClndGzekfFnjad+fn0Axrw/hksmXkIi4a3DlPni8Thz5sxhzpw5xOPxsOPAGuDhVFsTchZJkpSVLEpJknLKftvux5hTxxANogDcPv12/vbq30JOJW1ZIpGgpKSEkpKSzCikrgEeTzWLUpIkqRosSkmScs7RHY/m3uPvrXw8dPJQHpj5QHiBpGwUBY5PtWjIWSRJUlayKCVJyknndDuHYUcMq3z802d+yoSPJ4SYSMoy+cDgVMsPOYskScpKFqUkSTnrDwf+gYu7XwxALBHjlNGn8Mb8N0JOJUmSJOUGi1KSpJwVBAG39L6FUzufCsCq1avoO7IvH3/9ccjJJEmSpLrPopQkKadFI1EeOvEhDt3+UAC+WvUVvR/pTcmKkpCTSRmuDDgu1cpCziJJkrKSRSlJUs4ryiti7Blj2bN4TwA+W/oZx4w4htLy0pCTSZIkSXWXRSlJkoAmRU2Y0H8C7Ru3B+Dtkrc56bGTqIhVhJxMSopEIvTs2ZOePXsSiWTAFK4QeCTVCkPOIkmSslIGzGgkScoM2zbalon9J9KsXjMAJn82mXPHnks8EQ85mZRcA62goICCggKCIAg7DgRA41TLgDiSJCn7WJSSJGkdu7XcjXFnjqNeXj0AHn3vUX7//O9DTiVJkiTVPRalJEn6gR7tejDqlFFEguRfkzdNu4mbXrsp5FTKdfF4nI8++oiPPvqIeDwDzt5bAzyeamtCziJJkrJSRhal7rjjDjp06EBRURH7778/b7zxxlYdN2rUKIIgoF+/frUbUJJU5x2/6/EM7zu88vFvn/8tI2eNDDGRcl0ikWDBggUsWLCARCIRdpxkIerhVLMoJUmSqiHjilKPPfYYQ4YM4eqrr+att96ia9eu9O7dm8WLF2/2uM8//5zf/va3HHzwwWlKKkmq6wbvM5hrD7u28vG5Y8/l+U+eDzGRlEGiwFGpFg05iyRJykoZV5S6+eabGTx4MIMGDaJz584MHz6c+vXrc999923ymFgsRv/+/bn22mvZcccd05hWklTXXXnIlVywzwUArI6v5qTHT+KthW+FnErKAPnAxamWH3IWSZKUlfLCDrCuiooKZsyYwdChQyv3RSIRevXqxbRp0zZ53J/+9CeKi4s5//zzefnllzf7HuXl5ZSXl1c+Li0tBZLrNGTE+gyqEfF4nEQi4e+pco5jv3b8X5//o2RFCU9/+DQrKlZwzIhjeGXQK+zY1B+EZIpcGPvff8bvtzPiDnwKXS6MfWlTHP/KVdkw9rc2W0YVpb766itisRitWrVab3+rVq2YM2fORo955ZVXuPfee5k5c+ZWvcewYcO49tprN9i/ZMkSysrKqpxZmSkej7Ns2TISiQSRSMadECjVGsd+7bnloFtYsGwB00ums2jlIo566Cie6fcMLeq1CDuayI2xH4vFWLlyJQCLFy8mGvWaOeXG2Jc2xfGvXJUNY3/58uVb1S+jilJVtXz5cs4++2zuvvtuWrTYun8UDB06lCFDhlQ+Li0tpV27drRs2ZJGjRrVVlSl2fc/QW7ZsmXG/iGVaoNjv3aNHzCeQx44hA+++oDPSj/jvBfO44WzX2Cbgm3CjpbzcmHsx2IxGjRoAEBxcXH4RakyCM5Onq2VeDgBReHGyVW5MPalTXH8K1dlw9gvKtq6iUFGFaVatGhBNBpl0aJF6+1ftGgRrVu33qD/J598wueff85xxx1Xue/7U8Ty8vL48MMP2WmnndY7prCwkMLCwg1eKxKJZOxvpqonCAJ/X5WTHPu1p0WDFkwcMJGe9/Zk/vL5TF8wndOfOJ1nzniG/KiL6oStro/9RCJRecleRnzOCFCR3AwiQQauVJo76vrYlzbH8a9cleljf2tzZVT6goIC9tlnHyZPnly5Lx6PM3nyZHr06LFB/06dOjFr1ixmzpxZ2Y4//ngOP/xwZs6cSbt27dIZX5KUA9o3bs/EARNpXNgYgIn/m8jgZwdXrvUj1ZZIJMIBBxzAAQcckBkT0ELg3lTb8Od9kiRJW5RRZ0oBDBkyhHPOOYd9992X7t27c+utt7Jy5UoGDRoEwMCBA9l2220ZNmwYRUVF7LHHHusd36RJE4AN9kuSVFP2KN6DZ858hqMePoryWDkPvvMgbRu25a9H/DXsaKrDgiDY6lPh0yIAisMOIUmSslnGFaVOP/10lixZwlVXXUVJSQndunVj4sSJlYufz507NzN+OihJymmHbH8II04awamjTyVBgmGvDKPNNm24eP+Lw44mSZIkZYUgkePXG5SWltK4cWOWLVvmQud1SDweZ/HixRQXF1vEVE5x7KffndPv5MLxFwIQEPDYKY9x6u6nhpwq9+TC2I/H43z22WcA7LDDDuF/zjXAc6ntvmTgjzpzQy6MfWlTHP/KVdkw9re21pKZ6SVJyhK/3O+X/PHgPwKQIMGApwYw5fMp4YZSnZRIJJg3bx7z5s3LjDXM1gD3pNqakLNIkqSsZFFKkqQf6brDr+O8bucBUBGr4IRRJ/DuondDTiXVsghwaKo5o5QkSdXgFEKSpB8pCAKGHzucYzoeA0BpeSlHjziaL5Z+EXIyqRYVAL9NtYKQs0iSpKxkUUqSpBqQH83n8VMep/u23QFYsHwBfUb04etVX4ecTJIkScpMFqUkSaohDQoa8NxZz7FL810AmPPVHI579DhWrV4VcjJJkiQp81iUkiSpBrWo34KJ/SfSepvWAEz7chpnjDmDNXFXglYdUwb0T7WykLNIkqSsZFFKkqQatkPTHZjQfwINCxoC8OxHz/LL536ZGXdMk2pSaapJkiRVg0UpSZJqQbfW3Rh7xljyI/kA3P3W3Vw79dqQUymbRSIR9ttvP/bbbz8ikQyYwhUCd6RaYchZJElSVsqAGY0kSXXTT3b4CQ+d+FDl42unXstdb94VYiJlsyAIaNCgAQ0aNCAIgrDjQAC0T7UMiCNJkrKPRSlJkmrRGXucwS29b6l8/Mvxv2TsnLHhBZIkSZIyhEUpSZJq2SUHXMLvev4OgHgizplPnMmrc18NOZWyTTwe5/PPP+fzzz8nHo+HHQfWAJNSzXX8JUlSNViUkiQpDa7vdT0DugwAoGxNGcc9ehzvL3k/5FTKJolEorIolRGL5q8Bbk81i1KSJKkaLEpJkpQGkSDCvcffy1E7HQXAt2Xf0ueRPnxZ+mXIyaRqigD7p5ozSkmSVA1OISRJSpOCaAFjTh3D3m32BmBe6TyOHnE03373bcjJpGooAK5ItYKQs0iSpKxkUUqSpDRqWNiQ8WeNZ8emOwLw3uL36PdYP8rWlIWcTJIkSUovi1KSJKVZq21aMWnAJFrWbwnAS1+8xIAnBxCLx0JOJkmSJKWPRSlJkkKwc7OdGd9/PA3yGwDwxAdP8OuJv86MBaylrVEOnJ9q5SFnkSRJWcmilCRJIdm37b6MOW0MeZE8AO6YfgfDXhkWcippKyWAxalmLVWSJFWDRSlJkkLUZ+c+3Hf8fZWP//jiH7n/7ftDTKRMFYlE2Hvvvdl7772JRDJgClcA3JxqLnQuSZKqIQNmNJIk5bazu57N33r9rfLx4GcH89xHz4WYSJkoCAIaNWpEo0aNCIIg7DjJWWTHVHNGKUmSqsEphCRJGeB3PX/Hr/f/NQCxRIzTxpzGf7/8b8ipJEmSpNpjUUqSpAwQBAE3976Z03Y/DYBVq1fRd2RfPvr6o5CTKVPE43Hmzp3L3LlzicfjYceBGDAl1bxxpCRJqgaLUpIkZYhIEOGhfg9xWIfDAPj6u6/p/UhvFi5fGG4wZYREIsGnn37Kp59+mhl3aVwN3JRqq0POIkmSspJFKUmSMkhhXiFjTx9Ll1ZdAPh86eccPeJolpUtCzmZ9AMRoFuqOaOUJEnV4BRCkqQM07ioMRP6T2D7xtsD8M6idzjp8ZMoX1MecjJpHQXAdanm3fckSVI1WJSSJCkDtW3YlkkDJtG8XnMAXvzsRc59+lziiQxYS0iSJEmqARalJEnKULu22JVxZ42jXl49AEa9N4pLJ12aGesJSZIkST+SRSlJkjLYAdsdwOOnPk40iAJw639v5aZpN4WcSgLKgV+mmleWSpKkarAoJUlShjt2l2O569i7Kh//7vnf8ci7j4SYSAISwLxU8+Q9SZJUDXlhB5AkSVt2/t7ns3DFQq78z5UADHp6EMUNijlqp6NCTqZ0iUQidOvWrXI7dAXAX9fZliRJqqIMmNFIkqSt8ceD/8jP9/k5AGviazj58ZOZsWBGyKmULkEQ0KRJE5o0aUIQBGHHSc4i90w1Z5SSJKkanEJIkpQlgiDg9mNup1+nfgCsqFjBMSOP4ZNvPgk3mCRJklQNFqUkScoi0UiUkSeN5KD2BwGweOViej/Sm8UrF4ecTLUtHo8zf/585s+fTzweDzsOxIDXUy0WchZJkpSVLEpJkpRl6uXX45kznmH3lrsD8Mm3n9B3ZF9WVKwIOZlqUyKR4OOPP+bjjz8mkciAlcVXA39JtdUhZ5EkSVnJopQkSVmoab2mTOg/ge0abQfAmwve5JTHT2F1zOqA0iQC7JZqziglSVI1OIWQJClLtWvcjon9J9KkqAkAkz6ZxPnPnJ8ZZ9Go7isA/p5q3n1PkiRVg0UpSZKy2O7Fu/PMGc9QGC0E4OF3H2bo5KEhp5IkSZK2zKKUJElZ7uDtD+bRkx8lEiT/Wv/bq3/jttdvCzmVJEmStHkWpSRJqgNO3O1E7jjmjsrHv5n0Gx6f/XiIiVTnVQBDUq0i5CySJCkrWZSSJKmO+Pm+P+fKQ64EIEGCs586m/989p+QU6nOigMfp1o85CySJCkrWZSSJKkOufawazl/r/MBqIhV0O+xfrxT8k7IqVQTIpEIe+65J3vuuSeRSAZM4fKBq1ItP+QskiQpK2XAjEaSJNWUIAgYfuxwjt3lWABKy0s5esTRfL7083CD6UcLgoDmzZvTvHlzgiAIOw5Egf1SLRpyFkmSlJUsSkmSVMfkRfJ47JTHOGC7AwBYuGIhfR7pw9ervg45mSRJkrSWRSlJkuqg+vn1efbMZ9m1+a4AfPj1hxz76LGsWr0q5GSqrng8TklJCSUlJcTjGbCIUxyYmWoZEEeSJGUfi1KSJNVRLeq3YOKAibTZpg0Ar3/5OqePOZ018TUhJ1N1JBIJ5syZw5w5c0gkEmHHSd5x78pU8+57kiSpGixKSZJUh3Vo0oEJ/SfQqLARAOM+GsfPx/08M4oaym4RYIdUc0YpSZKqwSmEJEl1XNfWXRl7+lgKogUA3Pv2vVw95eqQUynrFQD/SLWCkLNIkqSsZFFKkqQccPgOh/PwiQ8TkLxr23UvXcfwN4eHnEqSJEm5zKKUJEk54rTdT+PWPrdWPr5w/IU89cFT4QWSJElSTrMoJUlSDvnV/r/i9z1/D0A8EefMJ87klbmvhJxKWakCGJpqLnQuSZKqwaKUJEk5ZlivYZzd5WwAymPlHPfoccxePDvkVMo6ceC9VIuHnEWSJGUli1KSJOWYSBDh3uPvpfdOvQFYWraUPiP6MG/ZvJCTaXMikQidO3emc+fORCIZMIXLB/6QavkhZ5EkSVkpA2Y0kiQp3fKj+Yw5bQz7tt0XgC9Lv6TPiD58+923ISfTpgRBQHFxMcXFxQRBEHYciAIHpVo05CySJCkrWZSSJClHbVOwDc+d9Rw7Nd0JgPeXvM8Jo07gu9XfhZxMkiRJucCilCRJOay4QTGTBkyiuEExAC/PfZn+T/YnFo+FnEw/lEgkWLx4MYsXLyaRSIQdJ7mO1Aep5ppSkiSpGixKSZKU43ZqthPjzxpPg/wGADw15ykuGn9RZhQ+VCkej/P+++/z/vvvE49nQBWoAvh9qnn3PUmSVA0WpSRJEvu03YcnT3+SvEgeAMNnDOcvL/8l5FTKaAHQJtUyYIkrSZKUfSxKSZIkAI7a6SjuP+H+ysdX/udK7n3r3hATKaMVAv9KtcKQs0iSpKxkUUqSJFUa0GUANxx5Q+XjC8ZdwLiPxoWYSJIkSXWVRSlJkrSeS3tcyiX7XwJALBHjtNGn8fqXr4cbSpIkSXWORSlJkrSeIAi4qfdNnL776QB8t+Y7+o7sy5yv5oScTBmlArg21VzoXJIkVYNFKUmStIFIEOHBfg/ykx1+AsA3331Dn0f6sGD5gpCTKWPEgTdTLQNuBihJkrKPRSlJkrRRhXmFPHX6U3Rt1RWAL5Z9wTEjjmFZ2bKQk+WmIAjo1KkTnTp1Iggy4HZ3ecAlqZYXahJJkpSlLEpJkqRNalTYiAn9J9ChSQcA3ln0Dic+diLla8rDDZaDIpEIrVu3pnXr1kQiGTCFywOOSDWLUpIkqRoyYEYjSZIyWZuGbZg0YBLN6zUH4D+f/4eBYwcST3jNliRJkqrPopQkSdqiXZrvwnNnPUf9/PoAPD77cX4z8TckEomQk+WORCLB119/zddff50Zv+5x4NNUsz4pSZKqwaKUJEnaKvtvtz+Pn/I40SAKwD/e+Ac3vHZDyKlyRzweZ9asWcyaNYt4PAOqQBXAr1PNu+9JkqRqsCglSZK2Wt9d+nL3cXdXPv7DC3/g4XceDjGRQhMAzVItA9ZdlyRJ2ceilCRJqpJBew3iz4f/ufLxec+cx6T/TQoxkUJRCDyYaoUhZ5EkSVnJopQkSaqyyw++nF/u+0sA1sTXcPLjJ/PmgjdDTiVJkqRsYlFKkiRVWRAE/OPof3DSbicBsHL1So4ZcQz/++Z/ISeTJElStrAoJUmSqiUaiTLipBEcsv0hACxZtYTej/Rm0YpFISdTWlQA16eaC51LkqRqsCglSZKqrSiviKfPeJo9ivcA4NNvP6XvyL4sL18ecjLVujjwaqplwM0AJUlS9rEoJUmSfpQmRU2Y0H8C2zXaDoAZC2dwyuhTqIh5+kxNCoKAjh070rFjR4IgA253lwf8PNXyQs4iSZKyUkYWpe644w46dOhAUVER+++/P2+88cYm+959990cfPDBNG3alKZNm9KrV6/N9pckSTVvu0bbMWnAJJoWNQXg35/8m/OePo94wlNoakokEmHbbbdl2223JRLJgClcHtA31SxKSZKkasiAGc36HnvsMYYMGcLVV1/NW2+9RdeuXenduzeLFy/eaP8pU6Zw5pln8p///Idp06bRrl07jjrqKObPn5/m5JIk5bbOLTvz7JnPUpRXBMCIWSO47IXLQk4lSZKkTJVxRambb76ZwYMHM2jQIDp37szw4cOpX78+991330b7jxgxgl/+8pd069aNTp06cc899xCPx5k8eXKak0uSpAPbH8ijJz9KJEhOMW547QZuff3WcEPVEYlEgqVLl7J06VISiUTYcSABLEi1DIgjSZKyT0YVpSoqKpgxYwa9evWq3BeJROjVqxfTpk3bqtdYtWoVq1evplmzZrUVU5IkbUa/Tv2485g7Kx//ZtJvGPXeqBAT1Q3xeJyZM2cyc+ZM4vEMuCyyHLgg1cpDziJJkrJSRq0A8NVXXxGLxWjVqtV6+1u1asWcOXO26jX+8Ic/0LZt2/UKW+sqLy+nvHztzKm0tBRITvQyYoKnGhGPx0kkEv6eKuc49pUpBu89mPml87nu5esAGPjUQFrUa8FPdvhJrbxfLoz97z/j99uhL3Yeh6B+MkMinvAOfCHJhbEvbYrjX7kqG8b+1mbLqKLUj3X99dczatQopkyZQlFR0Ub7DBs2jGuvvXaD/UuWLKGsrKy2IypN4vE4y5YtI5FIZMZisFKaOPaVSX6x2y/4ZMknjJwzktXx1Zz42Ik8dfxT7NFijxp/r1wY+7FYjJUrVwKwePFiotFoyImA21L/L001pV0ujH1pUxz/ylXZMPaXL1++Vf0yqijVokULotEoixYtWm//okWLaN269WaPvfHGG7n++ut54YUX6NKlyyb7DR06lCFDhlQ+Li0tpV27drRs2ZJGjRr9uA+gjPH9T5BbtmyZsX9Ipdrg2Femuf/k+yl9vJRxH49jxeoVnD3pbF459xV2aLpDjb5PLoz9WCxGgwYNACguLs6MopRClwtjX9oUx79yVTaM/U2dKPRDGVWUKigoYJ999mHy5Mn069cPoHLR8osuumiTx/3973/nL3/5C5MmTWLffffd7HsUFhZSWFi4wf5IJJKxv5mqniAI/H1VTnLsK5MURAp47NTH6PVQL6Z9OY2SFSUc8+gxvHreq7So36JG36uuj/1EIlF5yV5d/pyquro+9qXNcfwrV2X62N/aXBmXfsiQIdx99908+OCDfPDBB/ziF79g5cqVDBo0CICBAwcydOjQyv5/+9vfuPLKK7nvvvvo0KEDJSUllJSUsGLFirA+giRJWkf9/Po8e+azdGrRCYCPvv6IY0cey8qKlSEn04+yGrg11VaHmkSSJGWpjCtKnX766dx4441cddVVdOvWjZkzZzJx4sTKxc/nzp3LwoULK/v/85//pKKiglNOOYU2bdpUthtvvDGsjyBJkn6gef3mTOw/kbYN2wLw3/n/5bQxp7E6ZjUja8WAyakWCzmLJEnKSkHi+9u45KjS0lIaN27MsmXLXFOqDonH4yxevJji4uKMPZ1Rqg2OfWW6dxe9y8H3H0xpeXJV7EHdBnHv8ff+6DvJ5cLYj8fjfPnllwBst9124X/ONcAzqe3jybBFIXJHLox9aVMc/8pV2TD2t7bWkpnpJUlSndSlVReePuNpCqIFANw/836u/M+VIafKDpFIhPbt29O+ffvMmIDmASelmgUpSZJUDRkwo5EkSbnksA6H8ciJjxCQPDvqLy//hTun3xlyKkmSJKWbRSlJkpR2p+5+Krf1ua3y8UXjL+LJD54MMVHmSyQSlJaWUlpaSkasvpAAvk61DIgjSZKyj0UpSZIUiov3v5jLDrwMgAQJznriLF764qWQU2WueDzOW2+9xVtvvUU8Hg87DpQD56ZaeahJJElSlrIoJUmSQvPXI/7KwK4DASiPlXP8o8fz3uL3Qk6lrRZNNUmSpGqwKCVJkkITBAH3HHcPfXbuA8Cy8mX0eaQP85bNCzmZtqgIGJtqRaEmkSRJWcqilCRJClV+NJ/Rp45mv7b7ATB/+Xx6P9Kbb777JuRkkiRJqk0WpSRJUui2KdiG5856jp2b7QzAB199wPGPHs93q78LOZkkSZJqi0UpSZKUEVo2aMmkAZMoblAMwKvzXuXMJ85kTXxNyMm0UauBf6ba6pCzSJKkrGRRSpIkZYwdm+7IhP4T2KZgGwCe/vBpLhp/EYlEIuRk2kAMGJ9qsZCzSJKkrGRRSpIkZZS92+zNk6c9SX4kH4C7ZtzFn1/6c8ipwhcEAR06dKBDhw4EQRB2HMgDzky1vJCzSJKkrGRRSpIkZZwjdzqSB/o9UPn4qilXcc9b94QXKANEIpHKolQkkgFTuDzgrFSzKCVJkqohA2Y0kiRJGzprz7O48cgbKx9fMO4CnvnwmRATSZvx8stw/vnQuTM0bQoNG0LXrnDTTVBevvWvs3QpXHYZ7LILFBVBs2bQvTs8/PDaPv/7H5x8Muy4I0HDhrRq356gQwc491z47LMa/mCSJNUef64lSZIy1qU9L2XB8gXc/PrNxBNxTh9zOpMHTqZnu55hR0u7RCLBqlWrAKhfv374l/AlgFWp7frApuK88QYMHLjp1xk2LFl0OeKITfe56KJk69Rp031OPDH5WkccAfPnb7xP9+7w0EPJPG+8sfE+224Lkydv+n02ZcQIuO++9fe9+y789rfw4ovw3HNbfo3Fi+GQQ+DDD9fuKy+H6dNh0iQ4++zkvs8/hyefBNb5ZZ83Dx58MNlvzhxo3Ljqn0GSpDSzKCVJkjLaDUfdwMIVC3n0vUcpW1PGcY8ex6vnvUqnFpspUNRB8Xic6dOnA3DwwQcTjUbDDVQOnJHaHg0UbaLfqlVw0EFwz0Yuv7z+eli2DFavhu22gxde2LDPqFHJIgvAmjXJs4R+6PXXYfjw5PYXX2y8T0kJnJEKPHdu8r22227DfpsrfG1ONJo8U+nCC2H33ZOZ+vWD0lIYPx7efBP23Xfzr3HRRcmCVBAkC2znnAMNGsA77yTzf69Nm+Tn7d2beHEx30ybRvMLLiD45JNkvxdfTBbpJEnKcF6+J0mSMlokiPBAvwfotWMvAL757ht6P9KbBcsXhJxMdd655yYLREGQPDtpc66/Hu6/P1l4qlcPDj98/TPEPv5488d/+SU88URye8AA+MMfoHXr5GWABx0Ep5yytu/uu8MFF0CHDlBUxJrdd4fjj1/7fH5+FT6kJEnhsSglSZIyXkG0gCdOe4JurbsBMHfZXPo80oelZUtDzZXTCoGnUq0w5CyZoGHDDfeVla3d3nbbzR//8ssQjye3E4lkcat+/eTZXJddBhUVGz9u9WryZs2CZ1LrrXXsuPlLISVJyiAWpSRJUlZoVNiICf0nsEOTHQCYtXgW/Ub1o2xN2RaOVK0ISC4Ekcem15PKZR9+CCNHJrd33TV5ttPmzJu3dvuRR2DGDPjuu+T6WH/728bX5erUiUhRES2OOip56d6eeyYv3atXr+Y+hyRJtciilCRJyhqtt2nNxAETaVG/BQBTv5jKwKcGEk/EQ06mOumBB5JnLSUSyUvlttann0Lv3sn1tBo1gscfh8gWpt1r1qzdbtQIZs6EhQuTd/ADeOwxeP/9zb/GrFnQp09ynS5JkrKARSlJkpRVdmm+C8+d9Rz18+sDMPr90fxm0m9IJBIhJ8sxa4D7Um3NFvrmkg8/TN5B74svksWl8eOhS5ctH9e8+drtI45IFqNat4azzlq7/9131z9mzhziZWV8NWUKiZ/8JLlv9uyNLyovSVIGsiglSZKyTvdtuzPm1DFEg+Qd6G6ffjt3vHNHyKlyzBrWrillUSpp9mw49NDkJXfNmsHkyXDggVt37F57bbnPxi7Ly89nza67krjoorX7trSouiRJGcKilCRJykpHdzyae45fe0bIX/77Fx5858EQE9WuIAho164d7dq1IwgyYBGnPODEVMsLOUttqcrd92bOhMMOg0WLoFUrmDIluVj5ll73e/vtBzvvnNyePBneeQdKStauS1VQAD16JLf/9CcYPTq5DlVFBdH//Y/gzjvXvtaOO1b1k0qSFIq6OoWQJEk54Nxu57Jw+UIuf/FyAAY/O5jW27Tm6I5Hh5ys5kUiEXbaaaewY6yVB5wXdogMcuut8NVXye1Fiza8ZO/qq+GaazZ9fBDA8OFw9NFQWgrduq3//JVXQnFxcvvFF5OvR/InzC3X7dehA/z0p9X9FJIkpZVnSkmSpKx22UGXceF+FwIQS8Q4ZfQpTJ8/PeRUUjUccQT85z/Qqxdssw0UFcE++8BDD8EVV6ztd9ZZybOyWrcmkZ9PvF49EnvsAb//PUyfnrx0UJKkLOCZUpIkKasFQcAtR93CF19/wbhPx7Fq9Sr6juzLq+e9SsfmHcOOV2MSiQTl5eUAFBYWhn8JXwKIpbajQAZcUVjjHngg2dLZ98AD4fnnN3/8z36WbEAiHmfx4sUUFxcTbOkOf5IkZRj/5pIkSVkvGonyf4f/H4dufygAS1YtofcjvSlZURJyspoTj8d5/fXXef3114nH42HHgXLWrilVHnIWSZKUlTxTSpIk1QlFeUU8edqTHPbgYcxaPIvPln7GMSOOYeq5U2lY2DDseLmrfn145RXo1Gnjzw8bBvn58OWXm+7z/Z3l8vI23efEE5P/3377Tffp3j35//btk5fIbcy22258vyRJqnEWpSRJUp3RpKgJE/pPoMe9PZhXOo+3S97mpMdP4rmznqMgWhB2vLqlEBi1zvamdO8Oc+Zs+fVqqs/kyVvu89BDW+4jSZJqnZfvSZKkOmXbRtsyacAkmhY1BeCFT19g0NODiCcy4JK3uiQAGqRaXVxPSpIk1TqLUpIkqc7ZreVujDtrHEV5RQCMnDWSPzz/h5BTqdZ98QUsWhR2CkmStJUsSkmSpDqpZ7uePHbKY0SC5HTnxmk3cvO0m0NOVYesAUam2pqQs6xcCZddBjvvDLfeGnIYSZK0tSxKSZKkOuv4XY9neN/hlY8v/felPDrr0RAT1SFrgEdTLcyi1DPPwO67J4tRV10F11wTYhhJklQVLnQuSZLqtMH7DGbB8gVcM/UaAM4Zew4tG7Sk146buPtahgqCgLZt21Zuhy4KHLPOdrrNnQu/+hU8/TT06ZNc4HynnUIIIkmSqsszpSRJUp131aFX8bO9fwbA6vhqTnzsRN5e+HbIqaomEomwyy67sMsuuxCJZMAULh/4Rarlp/F9V6+Gv/8ddtsNpk+Hxx+H8eMtSEmSlIUyYEYjSZJUu4Ig4I6+d3DCricAsKJiBUePOJrPvv0s5GSqkpdfhr32gqFD4Wc/gw8+gFNPhUw4c0ySJFWZRSlJkpQT8iJ5PHryo/Rs1xOARSsX0fuR3ixZuSTkZFsnkUhQUVFBRUUFiUQi7DjptWQJDBoEhxwCDRvCjBlwyy3QqFHYySRJ0o9gUUqSJOWMevn1ePbMZ9mtxW4AfPzNx/Qd2ZcVFStCTrZl8Xic1157jddee414PB52HCgD+qVaWS29RzwO99wDnTol14666y549VXo1q2W3lCSJKWTRSlJkpRTmtVrxsQBE2nbMLlo+PQF0zlt9Gmsjq0OOVkWiqVabXjnHTjoIBg8GI47DubMSV6ylwnraUmSpBrh3+qSJCnntG/cnon9J9K4sDEAE/43gZ+N+1nuXRb3YxQCD6RaYQ2+7vLlMGQI7LMPLFsGU6fCAw9AcXENvokkScoEFqUkSVJO2rPVnjx9xtMURpMVlQdmPsAVL14RcqosEgDNU60m1hlPJOCJJ5J31Rs+HP78Z3j77eQ6UpIkqU6yKCVJknLWoR0OZcRJIwhSVZW/vvJXbn/j9pBT5aBPP4W+feGUU2DvveH99+Gyy6CgIOxkkiSpFlmUkiRJOe3kzifzj6P/Ufn4VxN+xZj3x4SYKEusAZ5MtTXVfI3y8uQZUbvvDu+9B2PHwjPPQIcONZVSkiRlMItSkiQp513U/SIuP+hyABIk6P9kf6Z+PjXkVBluDXB/qlWnKPXii9C1K1x7LfzqV/DBB3DCCTWbUZIkZTSLUpIkScCff/Jnzu12LgAVsQpOGHUCsxbNCjfUOoIgoHXr1rRu3ZogqIlFnH6kKHBEqkWrcNyiRTBgABxxRHLx8rffhr/9DRo0qJ2ckiQpY1mUkiRJIln0+dex/+KYjscAsKx8GX1G9GHusrkhJ0uKRCJ06tSJTp06EYlkwBQuH7gk1fK3on8sBnfeCbvuCpMmwf33J++st8cetZlSkiRlsAyY0UiSJGWG/Gg+j5/yON237Q7AguUL6P1Ib75e9XXIybLcjBlwwAFw4YVw6qkwZw6cey5kwhlfgseAA4FtUu1A4PFQE0mScoRFKUmSpHU0KGjAuDPH0bFZRwDmfDWH4x49jlWrV4WaK5FIEIvFiMViJBKJULNstWXL4OKLoXt3qKiAV1+Fu++G5s3DTrZxs4EC1hZnftgKgE9C7FcbrgHOAF4DVqbaa8DpwJ9r6T0lSUqxKCVJkvQDLRu0ZNKASbTepjUA076cxplPnMmaeHVvM/fjxeNxXn75ZV5++WXi8XhoOSqVkSxmnJHaXlciAY8+Cp06JS/Tu+GG5NlSPXumP2dVJIDuwIpNtL1TfcLqV9NmAtelthsC96Vaw9S+a4B3a+F9JUlKsSglSZK0ETs03YHxZ42nYUHyX+jPfPgMFz53YfacpZQO359Zs66PPoKjjoKzzoIDD0xeqjdkCOTlhZGw7vkcCFLtsB/5Wv8Cvq9vXg4MSrXLU/tiwN0/8j0kSdoMi1KSJEmbsFebvXjq9KfIjyRX8v7XW//iT1P/FHKqDFEI3JVqhUBZGVx9Ney5J3zyCTz3HIwZA9ttF25Obdor62z33MT2y2nKIknKSRalJEmSNuOIHY/goRMfqnx8zdRr+NeMf4WYKEMEQNtU+/ek5F30hg2D3/8eZs+GY44JOaC26PN1tluts128zvZn6YkiScpNnkctSZK0BWfscQYLly9kyL+HAPCL535BqwatOKHTCSEnC9n8+fCb38Do0XD44TBuXHIdKdWeDqy/vtSWlhebBSz7wb5dSBae1r30smAT2z+8PFOSpBrkmVKSJElb4Tc9fsNve/wWgHgizhlPnMFr814LOVVI1qyBm26Fjp3g+anw4CMwebIFqUx0MXDwD9r41HMN1ulXvs52xTrb6/aRJKmGWZSSJEnaSn878m/037M/AGVryjh25LF8sOSDkFOl2euvw377we+GQPFA2P9DOK0/BEHYyVRVHdbZXrTOdsk62zukJ4okKTdZlJIkSdpKkSDCfSfcx5E7HgnAt2Xf0vuR3swvnV/r7x0EAS1btqRly5YEYRSAvvkGLrgAevaESARe+S/8/A44rIkzykw2heTlfuu2c1PPHbROv3VP+pu2zvbBtZhNkpTznEJIkiRVQUG0gCdOe4K92+wNwLzSefQZ0YelZUtr9X0jkQi77747u+++O5FIGqdwiQQ89FDy0rxRo+C22+CNN6DnfnAZyVawpRdRjfmc5CLzAXDYj3ytwaz918BfgfuA+1PbANFUH0mSaolFKUmSpCpqWNiQ8WeNZ8emOwLw3uL3OGHUCZStKQs5WQ17//3kAubnnANHHAFz5sDFF0M0GnYy1YS9gCtT2yuA84HzUtsA1wBd0h9LkpQ7LEpJkiRVQ6ttWjGx/0Ra1m8JwEtfvMTZT51NLB4LOVkNWLUKhg6Frl1hwQL497/h0UehTZuwk6mmXQOMAnqQXNS8QWr7MeCK8GJJknKDRSlJkqRq6ti8I8+d9RwN8pO3KBvz/hh+PfHXJBKJGn+vWCzGlClTmDJlCrFYLRa+nn0WOneGW26BK6+Ed9+FI4/csF85cE6qlW/4tGpJB9auDTWlhl7zdJJrSq1ItdeA02rotSVJ2gyLUpIkST/Cftvux5jTxpAXyQPgjul3cP0r14ecqhrmzoV+/eD445PrR733Hlx1FRQVbbx/Avgm1Wq+BidJknJAXtgBJEmSsl2fnftw7/H3cs7YcwC4/MXLadOwDed2OzfcYFtj9erkWVHXXgtNmsDjj8Mpp8CW7vBXANy2znZd8TrQZBPPrciAfpIk1SEWpSRJkmrAwK4DWbh8IZdNvgyAnz7zU4obFHNMx2NCTrYZr7wCv/hFckHziy+GP/0JGjXaumMjwI61mi799gDWbGXfsPpJklSHePmeJElSDfn9gb/nV91/BUAsEePU0afyxvw3Qk61EV99BeedBwcfDA0awJtvwq23bn1BSpIkqQZYlJIkSaohQRBwS59bOG335CrRq1avou/Ivnz09UchJ0uJx+Gee2DXXeGpp2D4cHjtNdhrr6q/1hpgcqp5lo8kSaoGi1KSJEk1KBJEeKjfQxzW4TAAvlr1Fb0f6U3JipJwg737Lhx0EAweDMceCx9+CBdcAJFqTgfXALemWl0qSn0NLA87hCRJucGilCRJUg0rzCtk7Olj6dKqCwCfL/2co0ccTWl5abVfMwgCmjVrRrNmzQi2tAj5upYvh0svhb33hqVL4T//gQcfhOLiamcBkrPIfVMt22eUq4CHgcOBFsBOwJehJpIkKSdk+xRCkiQpIzUuasyE/hNo37g9ADNLZnLSYydREauo1utFIhG6dOlCly5diGzN2U2JBDzxBOy2G/zzn3DddTBzJhx2WLXefwMFwNWplo1330uQvOPdz4DWwEBgSuq5JcCicGJJkpRLLEpJkiTVkrYN2zJpwCSa1WsGwOTPJnPu2HOJJ+K1+8affpq8RO+UU5LrRc2eDUOHQkE2Vo9qWAlwA9AZ6AHczfqX6+0C3Avsk/5okiTlGotSkiRJtahTi06MO3Mc9fLqAfDoe4/yu3//rnberLwc/vIX2H13mDUruZj5M8/ADjvUzvtli9XAWOAEYDvg98CcdZ7fBjgfeDW1/7w055MkKUdZlJIkSaplPdr14LFTHiMSJKdeN79+Mze9dlOVXiMWi/HSSy/x0ksvEYvFNuzw4ovQtStccw1cfDG8/z706wdVWX+qKspJXvr2s9R2JpoN/JZkIepE4Blg3V+6Q4D7gYXAPUBPoJZ+uSRJ0oYsSkmSJKXBcbsex13H3lX5+LfP/5YR746o0ms0feUVdrviivV3LloEAwbAEUdAy5bw9tvw97/DNtvUROxNS5As5ixMbWeKZcBdwP7AHsBNwOJ1nt8WuBz4CJgKnEvyTClJkpR2eWEHkCRJyhU/3funLFy+kKumXAXAoKcHUdygmCN3OjLZIZGAr7+GFSuSRaXmzdee6fTtt+x6440s7do1+TgWg7vugssvh7w8uPdeOPdc2JpF0GtCAfD3dbbDFCe5SPn9wBig7AfPF5C8dO884Eggms5wkiRpUzxTSpIkKY2uOOQKfr7PzwFYHV/NSY+fxDtzpsJtt0HHjsmznXbYIfn/jh2T+5cuJfjjH4mUl/O/Cy+EGTOgRw+48MLkYuYffgjnnZe+ghQkZ5G7pVpYM8ovgD8BOwFHAI+wfkGqG/APYAHwONAHC1KSJGUQz5SSJElKoyAIuP2Y2ylZWcLYOWPp+f4Kdr7mMBKrgw2XM/r0U/jNb+Cyy4iUlfHpBRfQfuRIIk8/nVzM/JVX4MADw/gY4fmO5KLl9wGT2fDSwabAAGAQsFdak0mSpCrKyDOl7rjjDjp06EBRURH7778/b7zxxmb7jx49mk6dOlFUVMSee+7J+PHj05RUkiSp6qKRKCNPGslvlnXmuRFQVAFBIpG8fG9d3+8rKyMBbD9iBG0mTCBx/fXJs6XCLEjFgFdSbSPrrteoBPAmcCHQFjgLeIG1BakA6A08RvKsqH9gQUqSpCyQcUWpxx57jCFDhnD11Vfz1ltv0bVrV3r37s3ixYs32v+1117jzDPP5Pzzz+ftt9+mX79+9OvXj/feey/NySVJkrZevZXl3PSvzwnYuivKAiBv5Uq+7NcPvvoKfv5z+OlPoeyHCyilyWrgb6m2upbeYwlwK9AV2A+4E1i6zvM7AX8meRnfROA0oKiWskiSpBqXcUWpm2++mcGDBzNo0CA6d+7M8OHDqV+/Pvfdd99G+99222306dOH3/3ud+y2225cd9117L333tx+++1pTi5JklQFDz5IsOo7olW4c12QSNDuiScIRo+G2bOhomLDs6vSJULy7nZ7ULMzyjXAc8DJJO+U9xtg1jrP1wcGkrxz3sfAH4F2Nfj+kiQpbTJqTamKigpmzJjB0KFDK/dFIhF69erFtGnTNnrMtGnTGDJkyHr7evfuzdixY6v03rFYjFists89V7rE4/HK39NEWJN1KQSOfeWqrBv7iQSR//s/gA3XkdrcYUDQvj3xOXPW3pUPknfiS6P46jjfjP+G7xp+R2xFjOiNUep1qkezY5oRyd9IheprCK4PYFtIXLKJ35+PIHggIHg4IFi44a9KokeCxLkJEqcmoNH3QWruM2WrrBv7Ug1y/CtXZcPY39r6SkYVpb766itisRitWrVab3+rVq2YM2fORo8pKSnZaP+SkpKN9i8vL6e8vLzycWlpKQCvvvoqDRo0+DHxlUESiQQrV66kQYMGBEFVpvtSdnPsK1dl29jPX7aMgz75pMrHBQCffMJr48axpnHjGs+1RV8B44CnIVgWkIgmkqESEMQCEo0TcAJwLNAieUh0VZRul3aj0YfJStJ/m/2XVR1WVT7XcmpL2kxoQ5P3mmzwduXNyik5qoSSPiWsap88hpm1+xGzTbaNfakmOf6Vq7Jh7K9cuXKr+mVUUSodhg0bxrXXXrvB/lWrVoWQRrUlkUhQllpjI1P/kEq1wbGvXJVtY7/e11//qOMrvv6a7/LSO42LzopS9KciqIAgnvw1DmLr/1oHywISjyTgcSi7qoxEpwT7/Wm/yoLUd82/4+vGX9PkjSa0e74dbV5tQ17Z+p8jHo2zqPsi5vWax5J9liQLXwBbN7fNOdk29qWa5PhXrsqGsb+1NZaMKkq1aNGCaDTKokWL1tu/aNEiWrduvdFjWrduXaX+Q4cOXe9yv9LSUtq1a8cRRxxBo0aNNnqMsk88HmfJkiW0bNmSSCTjlk6Tao1jX7kq68b+V1/9qMMP7NOHaHFxDYXZsmVTlzH7qtkk4onKS+YCAnZiJwA+4RMSqVvhBfEAKqD+lfU54MADKJxZCECiUYKCMwo4+g9HE/xvI5fndU4QPzdO/Kw4LYpb0OL7U620WVk39qUa5PhXrsqGsf/9VWlbklFFqYKCAvbZZx8mT55Mv379gOQv9uTJk7nooos2ekyPHj2YPHkyl1xySeW+559/nh49emy0f2FhIYWFhRvsz8/PJz8//0d/BmWGeDxOXl4e+fn5GfuHVKoNjn3lqqwb+61bw047waefVmmh8kQQ8F2bNhS2akU0TWdKlS8o54MTP1ivIAXJolRR6lZ3AUFlUQqAOOzMzhS+lJpzRSBYHhC94wf3GWwEnAWcB8G+AdEgSnSr7kWo72Xd2JdqkONfuSobxv7W1lcyLv2QIUO4++67efDBB/nggw/4xS9+wcqVKxk0aBAAAwcOXG8h9F//+tdMnDiRm266iTlz5nDNNdfw5ptvbrKIJUmSFLoggIsvrtah8086af1FzmvZgrsWEPsutsGi4nHifJ76L/6DJzvSkW3Zdt3OrFuz4ifAI8BC4J/AflRtxXdJklQnZNSZUgCnn346S5Ys4aqrrqKkpIRu3boxceLEysXM586du14lsGfPnowcOZIrrriCyy+/nI4dOzJ27Fj22GOPsD6CJEnSlp1zDvzxj/DddxDf8m3kYgEkCgtZ1Ls3O6YhHiTvsrfgnwtgEzfQWcGKDfZtx3brF6S+VwjsBHQHDgJOBQpqLqskSco+GVeUArjooos2eabTlClTNth36qmncuqpp9ZyKkmSpBrUpAk88QT07QuRyGYLUzGSJxoN678vu0XKmLtsLtFo7V/mtnLcSlYvWV2lY7Zhm40/UQ68n2oPwNLPllL6u61bb0KbFo/H+Wr5V5QVlmXsJRxSbXH8q64qyiuiuEH61o4MU0YWpSRJknJC797w3HNw8snw/V1q1l1jKghIAGX5cMppAWXt8nhx+h28PP1lYps6fakGnfnymZwXOY+8+ManjA1pCMByllfu+4iPiBChkELixMknH/JgmzXrF6sum3kZd916V+2FlyQpS/XeqTcTB0wMO0ZaWJSSJEkKU+/e8OWX8NBD8I9/wCefrH1uxx0JfvUrvjrhMN4dfTS7rExvtHoV9dZfwHwdESJsz/YAvM/7letKxYnzPu9X9lsdWc2oHqMYechIWq9oTdvlbVlesJxZrWbV/geQJEkZzaKUJElS2Jo0gV/9Krn4+TffwPLl0LAhNGsGQcD2wPNnP8/wccMpj5VT3LQ4LQuDd/yo4ybvhpcgwXd8V7m9KVGi7Nx+Z47vdvx6+3djt5oLmsMSiQTlZeUUFhUSpHEBfCkTOP5VV3Vt1TXsCGljUUqSJClTBAE0b55sP9C5VWf+cf4/0hpnSd4SZo+cvdHnEiT4hE82+ty6IvEIg08dTIsTWtR0PJFcU2fx4sUUFxe7po5yjuNfyn7+yZUkSdJGNe/bnPyW+T/qNfKL82l2TLMaSiRJkuoSi1KSJEnaqEh+hLa/aMsmruDbiheAtr9oSyTfKackSdqQMwRJkqQsEIvFeP3113n99deJxWr/znvfa3tBW6L1ohvMGgMCdkz9F2xsgasIRBtEafuztukJKkmSso5FKUmSpCxRVlZGWVlZWt+zsG0hezy7B0EkWG/mGBBQP/XfBkWpCASRgD2f3ZPCtoVpzStJkrKHRSlJkiRtVtPDmtLl+S5EG0QrL+WLE2du6r848bWdU2dIdX2hK00ObRJKXkmSlB0sSkmSJGmLmh7WlO5zurP9FdtXLn5eGi1lef7yykJVfnE+21+5Pd3ndLcgJUmStigv7ACSJEnKDoVtC9nhmh3Y/o/b8834b1g1ZxVrlq8hr2Ee9TvVp9kxzVzUXJIkbTWLUpIkSaqSSH6EFse1gB1TO3bH8+8lSVKVWZSSJElS1VUAl6e2RwNFIWaRJElZyaKUJElSlqhfv37YEdYKgHbrbEuSJFWRRSlJkqQsEI1G6d69e9gx1ioE7gw7hCRJymZe/S9JkiRJkqS0syglSZIkSZKktPPyPUmSpCwQi8WYMWMGAPvssw/RaDTcQBXAdantK4GCELNIkqSsZFFKkiQpS6xatSrsCGvFgZnrbEuSJFWRRSlJkiRVXT5w6TrbkiRJVWRRSpIkSVUXBQ4LO4QkScpmLnQuSZIkSZKktPNMKUmSJFVdHPgktb0T/qhTkiRVmUUpSZIkVV0FMCS1PRooCjGLJEnKShalJEmSskRRUQZVfgKgeJ1tSZKkKrIoJUmSlAWi0SgHHHBA2DHWKgTuDTuEJEnKZl79L0mSJEmSpLSzKCVJkiRJkqS08/I9SZKkLBCLxZg5cyYA3bp1IxqNhhuoAvh7avv3QEGIWSRJUlayKCVJkpQlli9fHnaEteLAf9fZliRJqiKLUpIkSaq6POCidbYlSZKqyCmEJEmSqi4P6B12CEmSlM1c6FySJEmSJElp55lSkiRJqroEMC+13Q4IQswiSZKykkUpSZIkVV05cGFqezRQFGIWSZKUlSxKSZIkZYn8/PywI6yvUdgBJElSNrMoJUmSlAWi0SgHHnhg2DHWKgJGhB1CkiRlMxc6lyRJkiRJUtpZlJIkSZIkSVLaefmeJElSFojFYsyaNQuAPffck2g0Gm6gCuAfqe1fAQUhZpEkSVnJopQkSVKWWLp0adgR1ooDU1PbF4UZRJIkZSuLUpIkSaq6POCn62xLkiRVkVMISZIkVV0ecELYISRJUjZzoXNJkiRJkiSlnWdKSZIkqeoSwJLUdksgCDGLJEnKShalJEmSVHXlwPmp7dFAUYhZJElSVrIoJUmSlCUikQxbeaEw7ACSJCmbWZSSJEnKAtFolEMOOSTsGGsVAWPCDiFJkrJZhv24TZIkSZIkSbnAopQkSZIkSZLSzsv3JEmSskA8Hue9994DYI899gh/fanVwPDU9s+B/BCzSJKkrGRRSpIkKQskEgm++eabyu3QxYB/p7YHY1FKkiRVmUUpSZIkVV0ecPY625IkSVXkFEKSJElVlwecFnYISZKUzVzoXJIkSZIkSWnnmVKSJEmqugRQmtpuBAQhZpEkSVnJopQkSZKqrhwYkNoeDRSFmEWSJGWlnC9KfX/3mtLS0i30VDaJx+MsX76coqKi8G+ZLaWRY1+5KhfGfiwWY+XKlUBy3hKNRsMNVAasTm2XAhUhZslhuTD2pU1x/CtXZcPY/77GsqU7Bud8UWr58uUAtGvXLuQkkiRJWapV2AEkSVImWr58OY0bN97k80FiS2WrOi4ej7NgwQIaNmxIELgYQl1RWlpKu3btmDdvHo0aNQo7jpQ2jn3lKse+cpVjX7nM8a9clQ1jP5FIsHz5ctq2bbvZs7ly/kypSCTCdtttF3YM1ZJGjRpl7B9SqTY59pWrHPvKVY595TLHv3JVpo/9zZ0h9b3MvPhQkiRJkiRJdZpFKUmSJEmSJKWdRSnVSYWFhVx99dUUFhaGHUVKK8e+cpVjX7nKsa9c5vhXrqpLYz/nFzqXJEmSJElS+nmmlCRJkiRJktLOopQkSZIkSZLSzqKUJEmSJEmS0s6ilLLOSy+9xHHHHUfbtm0JgoCxY8dutv+UKVMIgmCDVlJSkp7AUg0ZNmwY++23Hw0bNqS4uJh+/frx4YcfbvG40aNH06lTJ4qKithzzz0ZP358GtJKNac6Y/+BBx7Y4Hu/qKgoTYmlmvPPf/6TLl260KhRIxo1akSPHj2YMGHCZo/xe191QVXHvt/7qquuv/56giDgkksu2Wy/bP3utyilrLNy5Uq6du3KHXfcUaXjPvzwQxYuXFjZiouLaymhVDumTp3KhRdeyOuvv87zzz/P6tWrOeqoo1i5cuUmj3nttdc488wzOf/883n77bfp168f/fr147333ktjcunHqc7YB2jUqNF63/tffPFFmhJLNWe77bbj+uuvZ8aMGbz55pv85Cc/4YQTTmD27Nkb7e/3vuqKqo598Htfdc/06dO566676NKly2b7ZfN3v3ffU1YLgoCnnnqKfv36bbLPlClTOPzww/n2229p0qRJ2rJJtW3JkiUUFxczdepUDjnkkI32Of3001m5ciXjxo2r3HfAAQfQrVs3hg8fnq6oUo3amrH/wAMPcMkll7B06dL0hpPSoFmzZtxwww2cf/75Gzzn977qss2Nfb/3VdesWLGCvffemzvvvJM///nPdOvWjVtvvXWjfbP5u98zpZQzunXrRps2bTjyyCN59dVXw44j/WjLli0DkhO0TZk2bRq9evVab1/v3r2ZNm1arWaTatPWjH1ITua233572rVrt8WfrkvZIBaLMWrUKFauXEmPHj022sfvfdVFWzP2we991S0XXnghffv23eA7fWOy+bs/L+wAUm1r06YNw4cPZ99996W8vJx77rmHww47jP/+97/svffeYceTqiUej3PJJZdw4IEHsscee2yyX0lJCa1atVpvX6tWrVxTTVlra8f+rrvuyn333UeXLl1YtmwZN954Iz179mT27Nlst912aUws/XizZs2iR48elJWVsc022/DUU0/RuXPnjfb1e191SVXGvt/7qktGjRrFW2+9xfTp07eqfzZ/91uUUp236667suuuu1Y+7tmzJ5988gm33HILDz/8cIjJpOq78MILee+993jllVfCjiKl1daO/R49eqz30/SePXuy2267cdddd3HdddfVdkypRu26667MnDmTZcuWMWbMGM455xymTp26yX+cS3VFVca+3/uqK+bNm8evf/1rnn/++ZxYrN+ilHJS9+7d/ce8stZFF13EuHHjeOmll7b4k7/WrVuzaNGi9fYtWrSI1q1b12ZEqVZUZez/UH5+PnvttRf/+9//aimdVHsKCgrYeeedAdhnn32YPn06t912G3fdddcGff3eV11SlbH/Q37vK1vNmDGDxYsXr3dVTywW46WXXuL222+nvLycaDS63jHZ/N3vmlLKSTNnzqRNmzZhx5CqJJFIcNFFF/HUU0/x4osvssMOO2zxmB49ejB58uT19j3//PObXY9ByjTVGfs/FIvFmDVrlt/9qhPi8Tjl5eUbfc7vfdVlmxv7P+T3vrLVEUccwaxZs5g5c2Zl23fffenfvz8zZ87coCAF2f3d75lSyjorVqxY7ycen332GTNnzqRZs2a0b9+eoUOHMn/+fB566CEAbr31VnbYYQd23313ysrKuOeee3jxxRf597//HdZHkKrlwgsvZOTIkTz99NM0bNiw8hrxxo0bU69ePQAGDhzItttuy7BhwwD49a9/zaGHHspNN91E3759GTVqFG+++Sb/+te/QvscUlVVZ+z/6U9/4oADDmDnnXdm6dKl3HDDDXzxxRf89Kc/De1zSNUxdOhQjj76aNq3b8/y5csZOXIkU6ZMYdKkSYDf+6q7qjr2/d5XXdGwYcMN1s1s0KABzZs3r9xfl777LUop67z55pscfvjhlY+HDBkCwDnnnMMDDzzAwoULmTt3buXzFRUVXHrppcyfP5/69evTpUsXXnjhhfVeQ8oG//znPwE47LDD1tt///33c+655wIwd+5cIpG1J8H27NmTkSNHcsUVV3D55ZfTsWNHxo4du9kFoqVMU52x/+233zJ48GBKSkpo2rQp++yzD6+99ppr8CjrLF68mIEDB7Jw4UIaN25Mly5dmDRpEkceeSTg977qrqqOfb/3lUvq0nd/kEgkEmGHkCRJkiRJUm5xTSlJkiRJkiSlnUUpSZIkSZIkpZ1FKUmSJEmSJKWdRSlJkiRJkiSlnUUpSZIkSZIkpZ1FKUmSJEmSJKWdRSlJkiRJkiSlnUUpSZIkSZIkpZ1FKUmSJEmSJKWdRSlJkiRJkiSlXV7YASRJkrLR7Nmz2WuvvSgoKNjo8xUVFbz99ttb7PPBBx9QVlaW0f122mmnjT4vSZL0Y1iUkiRJqoZEIkH37t155ZVXNvr8AQccsNV9Mr2fJElSbfDyPUmSJEmSJKWdRSlJkiRJkiSlnUUpSZIkSZIkpZ1FKUmSJEmSJKWdRSlJkiRJkiSlnUUpSZIkSZIkpZ1FKUmSJEmSJKWdRSlJkiRJkiSlnUUpSZIkSZIkpZ1FKUmSJEmSJKWdRSlJkiRJkiSlnUUpSZIkSZIkpV1e2AEkSZKy1euvv06TJk02+tyKFSu2uk829JMkSappQSKRSIQdQpIkSZIkSbnFy/ckSZIkSZKUdhalJEmSJEmSlHYWpSRJkiRJkpR2FqUkSZIkSZKUdhalJEmSJEmSlHYWpSRJkiRJkpR2FqUkSZIkSZKUdhalJEmSJEmSlHYWpSRJkiRJkpR2FqUkSZIkSZKUdv8PoFP4k17xoiYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "期权策略分析: BUY PUT\n",
      "============================================================\n",
      "行权价: 2.65\n",
      "权利金: 0.0174\n",
      "合约数量: 1手 (每手10000个)\n",
      "是否打算行权: 否\n",
      "分析价格区间: 1.32 - 3.97\n",
      "----------------------------------------\n",
      "最大收益: 1元 (标的价格: 1.32)\n",
      "最大损失: -0元 (标的价格: 2.65)\n",
      "起始位置收益: 1元\n",
      "盈亏平衡点: ['2.63']\n",
      "当前标的价格 2.746 下的收益: -0元\n"
     ]
    }
   ],
   "source": [
    "# 示例:4 买入认沽期权\n",
    "print(\"\\n1. 买入认沽期权示例:\")\n",
    "df = analyze_single_option(\n",
    "    direction='buy',\n",
    "    option_type='put', \n",
    "    strike_price=2.65,\n",
    "    premium=0.0174,\n",
    "    quantity=1,\n",
    "    current_price=2.746\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 熊市价差策略 (Bear Put Spread)\n",
    "1. 策略构成：买入一份较高行权价的认沽期权，同时卖出一份同一到期日、但行权价较低的认沽期权。\n",
    "2. 适用情景：温和看跌。您预期股价会下跌，但跌幅有限。\n",
    "3. 目标：降低看跌策略的成本。\n",
    "4. 优点：净支出低于单独买入看跌期权，最大亏损有限。\n",
    "5. 缺点：潜在利润有上限。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32452 (\\N{CJK UNIFIED IDEOGRAPH-7EC4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21512 (\\N{CJK UNIFIED IDEOGRAPH-5408}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26368 (\\N{CJK UNIFIED IDEOGRAPH-6700}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 23567 (\\N{CJK UNIFIED IDEOGRAPH-5C0F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 关键信息汇总 ===\n",
      "Y轴处各曲线的Y值:\n",
      "  opt1在Y轴的值: 0.4700\n",
      "  opt2在Y轴的值: -0.4197\n",
      "  sum在Y轴的值: 0.0503\n",
      "sum曲线最小值: -0.0497，一单最大亏损为: -497.00\n",
      "最小值对应的标的价格: 2.0500\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m opt_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopt1\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuy\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mput\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0.0800\u001b[39m, \u001b[38;5;241m2.05\u001b[39m, \u001b[38;5;241m1\u001b[39m] \u001b[38;5;66;03m# 高行权价\u001b[39;00m\n\u001b[1;32m      9\u001b[0m opt_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopt2\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msell\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mput\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0.0303\u001b[39m, \u001b[38;5;241m1.95\u001b[39m, \u001b[38;5;241m1\u001b[39m] \u001b[38;5;66;03m# 低行权价\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[43mdraw_opt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopt_df\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[2], line 147\u001b[0m, in \u001b[0;36mdraw_opt\u001b[0;34m(opt_df)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum曲线最小值: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmin_sum\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m，一单最大亏损为: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmin_sum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m10000\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m最小值对应的标的价格: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmin_index\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum在大于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m或者小于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m才会盈利\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m当价格小于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m，收益为: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_y_value\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m10000\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m当价格大于\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_crossings[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m收益没有上限\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "#上证50ETF，20250615测试\n",
    "opt_df = pd.DataFrame(index=['direction',  # 买卖方向\n",
    "                             'catagory',   # 期权类型（call、put）\n",
    "                             'price',      # 期权价格\n",
    "                             'X',          # 执行价\n",
    "                             'count'])     # 期权份数\n",
    "\n",
    "opt_df['opt1'] = ['buy', 'put', 0.0800, 2.05, 1] # 高行权价\n",
    "opt_df['opt2'] = ['sell', 'put', 0.0303, 1.95, 1] # 低行权价\n",
    "draw_opt(opt_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 中性/区间震荡策略 (Neutral/Range-Bound Strategies)\n",
    "当您预期标的资产价格变化不大，将在一个特定区间内波动时，可以使用以下策略。这类策略主要通过赚取时间价值（$\\theta$）来获利。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 备兑卖出认购期权 (Covered Call)\n",
    "1. 策略构成：持有至少100股标的股票，并卖出一份针对这些股票的认购期权。\n",
    "2. 适用情景：中性或温和看涨。您长期看好持有的股票，但认为它在短期内不会大幅上涨，希望在持股期间创造额外收入。\n",
    "3. 目标：创造现金流收入，降低持仓成本。\n",
    "4. 优点：产生持续的权利金收入，如同“出租”您的股票。在股价小幅下跌、盘整或小幅上涨时均可获利。\n",
    "5. 缺点：放弃了股价大幅上涨带来的潜在收益。如果股价涨破行权价，您的股票可能会被“调走”（以行权价卖出）。此策略并未对冲股价大幅下跌的风险。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1. 卖出认购期权示例:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30410 (\\N{CJK UNIFIED IDEOGRAPH-76CA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 34913 (\\N{CJK UNIFIED IDEOGRAPH-8861}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/jf/xkkdr4_10496xxfqshnj73200000gp/T/ipykernel_65870/862384700.py:165: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25163 (\\N{CJK UNIFIED IDEOGRAPH-624B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30408 (\\N{CJK UNIFIED IDEOGRAPH-76C8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20111 (\\N{CJK UNIFIED IDEOGRAPH-4E8F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36215 (\\N{CJK UNIFIED IDEOGRAPH-8D77}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24403 (\\N{CJK UNIFIED IDEOGRAPH-5F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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n8sILL7DPPvvw0EMPFem37jWwatUqrrrqKr7++muWL1/O0qVLefvtt+nduzf/93//V67zS5KkLcvb9yRJ0kbNmTOHK664orB94IEHljiaqqxWrFjBI488Utg+6aSTaN26dYl9GzduzCGHHEKHDh2Ix+O89dZbfPnllwAsXbqUE044gR9//JEaNWpscj3VxaJFi4q01x95tv6cSgsXLmSHHXaosGMuXLiwcDkvL4+BAweSl5fHXnvtxd/+9reNv4HVMjIyOOigg+jUqRP16tXjyy+/LJzTLB6Pc8kll9CzZ0922WUXIJoLbF2xWIwBAwbQsmVLRo4cWXi74BVXXMFhhx1Gp06dylyLJEnachwpJUlSNTVixAjCMCyc72dT/fDDD+y7775Mnz4dgJ122on//Oc/m1Xb8OHDC8OOIAhKnTfouuuu448//uC5557j1ltv5fbbb2fixImcfPLJhX1mzpzJ22+/vVn1VEUV8fNbfy6i8sxNtLnHvOWWW/j222+pWbMmI0aMICUlpUzH79WrF3PnzmXs2LHce++93HDDDYwZM4bHHnussE88Hi+cbwuK30p6ySWXMHz4cG677bbCObUgmgvtueeeK1MdiXTjjTcW/uwr4mcmSVJVZSglSZJKNX78ePbaa6/C27y6devG+PHjadKkySYfs6CggPvuu6+w3adPn1Kf7tWhQwfS0tKKrAuCgAsvvLDIuv/973+bXE910rhx4yLtZcuWbbBdlp9jeY655ni//fYbd9xxBwC33nprmSfEB2jZsiX169cvtv7MM8+kVq1ahe11f+YNGjQo0nfdCfF32WUXGjVqVNguyy2LkiSpchhKSZKkEo0YMYJDDz2UxYsXA3DkkUcyfvx4mjZtulnHHTVqVOGoK4ieCre5giDY7GNUB7vuumuR9prJv9dYN5CpXbt2sfmhNveYa26nW7RoEfn5+QD87W9/IwiCwtevv/5a2H/kyJEEQcDAgQM3Wsf61v2ZlxZqrrHuaCNv85QkqeowlJIkqZoaOHBgYRDQrl27Mu8XhiHXXHMNp59+Onl5eQBceOGFjB49mtq1a5fpfOuOVFnfPffcU7i8xx57lNr3hx9+4NZbb2Xp0qXF6lt/ous1YQjAjBkzioQg48ePL7WWqmxTfn7NmzcvMgH9yy+/XLick5NTOC8TwBFHHEEstvajYLt27QrPd+ONNxauP/TQQ4sEOS+99FLh8tSpU5k6dWph++ijjy5TnRty8cUXFwkt1xg2bBgrV64sbK/7Mz/00EOLPNXv/fffL1yePHlyYbAK0Wi/NcaPH1/kWtmc21wr0o033likLkmSqisnOpckSUVcdNFFDBkypLC98847s+222xZ7clmXLl3o3bt3uY49fvx4vvjii8L2hkZJrVy5kuuuu44777yTXr16seuuu5KTk1NkonOAjh078pe//KVcdVRn1113HUceeSQAH3/8MX369GHffffl9ddf57fffgMgNTWVK6+8skzHa9iwIRdccAH33nsvAHfddRcLFiygZcuWDB8+vHAUUtu2bTn11FOB6Ha6Y489tsTjjRs3rjBcatu2Ld26dWOPPfYo3D5y5EiGDBnC/vvvT48ePahRowZffPFFkUAtPT2dM888s7DdokULzjzzTB599FEA7rvvPpYsWUKLFi2KzD3VpEkTTjzxxDK972R1++23F87Xtu5/a4sXLy4yd9s111xDw4YNK70+SZLWZSglSZKK+O6774q0p0yZUmJ4NGDAgHKHUv/85z8Llzt06FBqcLGuFStW8PLLLxcZ9bNG69ateeWVV4rNO7U1O+KII7juuuu45ZZbAMjMzCQzM7NweywW45FHHqFr165lPuatt97KpEmT+O9//0tBQUGRScchCq5GjRpVOJKuXbt2jBo1qsRjtWvXrvAWvp49ezJixIhifeLxOOPHjy9xlFuNGjUYOXIk22+/fZH199xzD1OmTOGjjz4iHo8zbNiwItvr1q3Liy++WOJ8VdXJY489VuQWyTWysrIKg0WAwYMHG0pJkhLO2/ckSVKlmDp1apEnoV122WVFbh9b384778wbb7zBoEGD6Nq1Ky1atCA1NZV69eqx5557cssttzB58mQ6d+5cZL958+YVLterV6/Y9q3BzTffzFtvvcXhhx9OkyZNSEtLo2XLlhx//PF89tlnnHXWWeU6Xo0aNRg3bhyPPPIIPXr0oF69emRkZNChQwcuvPBCJk+eXOS2uM3x9ttvc91117H33nvTpk0bMjIyqFmzJp07d+aCCy5g0qRJHH/88cX2q1OnDu+++y4PPvgg3bt3p27duqSnp9O+fXvOO+88vv3222K3iq57rbRt25YWLVpUyHuQJEll40gpSZKqqREjRpQ4CmVjNnUOpo2db6eddiIej5f5eGlpafTp04c+ffqUq45167/xxhtp3rx5ufavKjb157fGIYccwiGHHFLm/hubTyklJYXzzjuP8847b5NrKst5unXrRrdu3bj55pvLfey0tDQuvPDCYk9nLM2618p9991XZSZBv/HGG4vM61UeVWVeLEmSysKRUpIkqVpZM8n1LrvsUuZwQlunNddK7969OeaYYxJcjSRJWx9DKUmSKlDLli2LPDVr3Ve/fv2K9P3+++9L7RsEAQ8//HCR/o8++ugG+0+ZMqUy32qVVFBQwEcffQTAww8/XOSJbGXhz2/rMX/+fKZOnUpGRkaxpzluTE5OzgZ/luvPwfbmm29usP/YsWMr8q2Vm9emJClRgnDNI1MkSdJmWbJkyUb7pKamUqdOHfLz81m+fPlG+9eoUYMaNWqQnZ1Ndnb2RvvXqVOn3EGMIv78VBbxeJysrKyN9ktPT6dWrVrk5uYWPm1wQ2rVqkV6enpFlFguXpuSpEQylJIkSZIkSVKl8/Y9SZIkSZIkVTpDKUmSJEmSJFU6bwwvQTweZ/bs2dStW5cgCBJdjiRJkiRJUtIIw5Bly5bRqlUrYrHSx0MZSpVg9uzZtGnTJtFlSJIkSZIkJa3ff/+dbbbZptTthlIlqFu3LhB98+rVq5fgarY+8Xic+fPn07Rp0w0mqlIy8vpWdeW1XXkKCgqYMGECAHvvvTcpKSkJrqj68/pWdeW1rerM6zuxsrKyaNOmTWG+UhpDqRKsuWWvXr16hlIJEI/Hyc7Opl69ev7loWrH61vVldd2JcmD+ENxOv7SkVl9Z1GvXj1DqUrg9a3qymtb1ZnXd9WwsSmR/MlIkiQliwII3g1o8FUDgrjzXkqSpOTmSClJkqRkkQrhwJBwYUi77dv5QBZJkpTUDKUkSZKSRSrEjovRlKaJrkSSJGmzGUpJkiRJkqRyKSgoIC8vL9FllCoej5OXl0d2drZzSm0BaWlpFTKvpaGUJElSsgghXBiyfPlywoYhdevV9RY+SVKlCsOQOXPmsGTJkkSXskFhGBKPx1m2bJn/r9xCGjRoQIsWLTbr+2soJUmSlCxygIGQsyiHqTdOZZ+D9vHpe5KkSrUmkGrWrBm1atWqsoFPGIbk5+eTmppaZWtMVmEYsnLlSubNmwdAy5YtN/lYhlKSJEnJJAWfnyxJSoiCgoLCQKpx48aJLmeDDKW2rJo1awIwb948mjVrtsm/JDOUkiRJShY1IP5ynMkfTk50JZKkrdCaOaRq1aqV4EpUFay5DvLy8jY5lPL3bJIkSZIkqcwceSSomOvAUEqSJEmSJEmVzlBKkiQpWeRBMDSg1autCPL9LbUkSUpuhlKSJEnJogCCcQGNPmtEEDeUkiSprObPn8+gQYPYdtttycjIoEWLFvTq1YuPP/64sE+7du0IgqDY68477wRgxowZBEHAN998U+I5RowYQYMGDcpVVxiGPPbYY3Tv3p06derQoEEDunXrxv3338/KlSuL9J05cybp6el06dKlxGMFQcDo0aNL3DZ+/HiCIGDJkiXlqm9Lc6JzSZKkZJEKYf+QcGHItttt65wekiSV0bHHHktubi4jR45ku+22Y+7cubzzzjssXLiwSL+bb76Zs88+u8i6unXrbrG6Tj31VF5++WWuvfZahgwZQtOmTfn222+5//77adeuHX379i3sO2LECI4//ng++OADPvvsM7p3777F6qoshlKSJEnJIhVip8RouvqPJEnauCVLlvDhhx8yfvx4DjjgAADatm3LnnvuWaxv3bp1adGiRaXU9Z///IdnnnmG0aNHc/TRRxeub9euHUcddRRZWVmF68Iw5IknnuBf//oX22yzDcOGDasWoZS370mSJEmSpGqrTp061KlTh9GjR5OTk5Pocgo988wzdOzYsUggtUYQBNSvX7+w/d5777Fy5UoOPvhgTjnlFJ5//nlWrFhRmeVuEY6UkiRJShYhhCvCaI6JWlCrdi1v4ZMkJV63bjBnTuWes0UL+OKLMnVNTU1lxIgRnH322QwdOpTdd9+dAw44gP79+7PrrrsW6fuPf/yDa6+9tsi6cePGsd9++1VY6Wv8+OOPdOzYsUx9hw0bRv/+/UlJSaFLly5st912vPjiiwwcOLDC66pMhlKSJEnJIgfoD6sWrWLqjVPZ56B9SElJSXRVkqSt3Zw5MGtWoqvYoGOPPZbDDz+cDz/8kE8//ZRx48Zx99138/jjjxcJdv7+978XC3pat269RWoKw7BM/ZYsWcLLL7/MRx99VLjulFNOYdiwYYZSkiRJkiRpK1ZJczBt7jlr1KjBIYccwiGHHMJ1113HWWedxQ033FAk2GnSpAnbb799BRZauh133JFp06ZttN+zzz5LdnZ2kTmkwjAkHo/zww8/sOOOO27JMrcoQylJkqRkkQHxl+JM+WgKYaxsv12VJGmLK+NtdFXNTjvtxOjRoxN2/pNOOon+/fvz6quvFptXKgxDsrKyqF+/PsOGDeNvf/tbsVFR559/PsOHD+fOO++sxKorlqGUJElSsgiAVAhTDKQkSSqrhQsX0q9fP8444wx23XVX6tatyxdffMHdd99dLAxatmwZc9abH6tWrVrUq1evsP39998XO8fOO+8MQEFBAd98802RbRkZGXTu3LnYPscffzyvvPIKJ554Itdeey2HHnooTZs25bvvvuO+++7jwgsvpF27dnz11Vc888wzdOrUqcj+J554IjfffDO33norqalRvDN9+vRi599hhx0Kl7/77jvq1q1b2A6CgN12261YbZXFUEqSJEmSJFVbderUoXv37tx33338/PPP5OXl0aZNG84++2yuvvrqIn2vv/56rr/++iLrzj33XIYOHVrY7t+/f7Fz/P777wAsX76cP/3pT0W2dejQgZ9++qnYPkEQ8Oyzz/LYY48xfPhwbrvtNlJTU9lhhx047bTT6NWrF1dccQU77bRTsUAK4JhjjmHw4MG88cYbHHXUUQBcdtllxfp9+OGHhcv7779/kW0pKSnk5+cX26eyBGFZZ9baiqwZIrd06dIiaagqRzweZ968eTRr1oxYLJbocqQK5fWt6spru5LkQ3xENH/E3F5z2bfnvk50Xgm8vlVdeW2rvLKzs5k+fTrt27enRo0aiS5ng8IwJD8/n9TUVJ9Uu4Vs6Hooa67i3zySJEnJIh+C0QFNPmpCUOAHbEmSlNy8fU+SJClZpELYNyS+IE7rtq39za8kSUpqhlKSJEnJIhViZ8VotvqPJElSMvP2PUmSJEmSJFU6R0pJkiQlixDC/JCcnBxIgYwaGd7CJ0mSkpYjpSRJkpJFDnAMLD90OZ9/+DnxeDzRFUmSJG0yQylJkiRJkiRVOm/fkyRJShYZEH82ztSPpxJPc5SUJElKboZSkiRJySIAakO8poGUJElKft6+J0mSJEmSpEpnKCVJkpQs8iF4LqDZ280ICnzqniRJSm6GUpIkSckiH4LnA5q9ayglSVJZvf/++3Tq1ImuXbsWee26665ceOGFAHTv3r3Y9q5du7L99tuTk5PDXXfdRZcuXYpt32mnnXjmmWfKXMu///1v9ttvPxo2bEjDhg05+OCD+fzzzze4z8CBAwmCoNhr5513Luxz4403FtveqVOnTfuGVSLnlJIkSUoWKRD2CSmYX0CL1i0IAoMpSZI2ZtWqVfTv358bb7yxyPoZM2Zw5ZVXAhAEAd98802xfXv27EkYhixevJghQ4bQs2fPIttHjBjBsmXLylzL+PHjOfHEE9l7772pUaMGd911F4ceeihTpkyhdevWJe7zwAMPcOeddxa28/Pz2W233ejXr1+RfjvvvDNvv/12YTs1tepHPlW/QkmSJEXSIHZBjOar/0iSVFUUFBSUui0IAmKxWJn6AqSkpGyw77rbk836o6oef/xxXnrpJd555x1OO+20EvepX78+9evXL2yPHj2axYsXc/rppxfpl5qaSosWLSq+6C3IUEqSJEmSJG2WDz/8sNRtjRo1Ytdddy1sf/zxx8TjJT9JtkGDBnTt2rWw/emnn5KXl1ekz/qjlaqK8ePHc+CBBzJ9+nTatWtXpn1WrlxJXl4ejRo1KvN5hg0bxsEHH0zbtm2LrP/xxx9p1aoVNWrUoEePHtxxxx1su+225XkLlc45pSRJkpJIGIbk5uaSm5tLGIaJLkeSJK1Wq1YtOnbsSFpaWpn3+cc//kGrVq04+OCDy9R/9uzZjBs3jrPOOqvI+u7duzNixAgyMzN55JFHmD59Ovvtt1+5bi1MBEdKSZIkJYts4ATIWpjF1Ounss9B+yT1LQySpOpjv/32K3Xb+nMg7rPPPmU+7l577bXJNVW2Pffck2nTppW5/5133snzzz/P+PHjqVGjRpn2GTlyJA0aNKBv375F1vfp06dwedddd6V79+60bduW//znP5x55pllrqmyGUpJkiQlkwKg5DseJElKmPL8kmRL9U0m99xzD3feeSdvv/12kVsbNyQMQ4YPH86pp55Kenr6Bvs2aNCAHXfckZ9++qkiyt1ivH1PkiQpWWRAfHicaf+YRjzNZKpKexs4GKgP1AJ2B4ZioChJ4u677+aWW24hMzOTbt26lXm/999/n59++qlMI5+WL1/Ozz//TMuWLTen1C3OUEqSJClZBEBjyK+fHy0nuylAOlCnlFc68HMC+22qJ4BDgXeALGAV8DUwCDhnM44rSarSPv/8czp16sSsWbNK7XPXXXdx3XXXMXz4cNq1a8ecOXOYM2cOy5cvL+xz1VVXlfgkvmHDhtG9e3e6dOlSbNvll1/O+++/z4wZM5gwYQLHHHMMKSkpnHjiiRXz5rYQQylJkiQlRgjsCSwv5bX76j6J6rcp/gAuXL1/KvB/wPNA69XbhwFjN/HYkqQqbeXKlXz//ffFnha4rkceeYTc3FyOO+44WrZsWfi65557Cvv88ccf/Pbbb0X2W7p0KS+99FKpo6RmzpzJiSeeSMeOHTn++ONp3Lgxn376KU2bNq2YN7eFOKeUJElSssgHRkOTqU1YuM/CRFezdZgBsfYxWtCC8IAQxm+k/1PAitXLZwKXrl4OgTW/rB4KHF7RhUqSEq1nz54bfTLujBkzNnqcESNGFFtXv359Vq5cWeo+zz///EaPWxU5UkqSJClZ5ENsRIwWmS0ICqrD/XvV0EfrLO9dyvK6fSRJ2oo5UkpJ4/333+fcc88t9qjMeDzOAQccwEMPPUT37t3Jyckptu/y5cuZMmUK999/P0899RSpqUUv/dzcXK655hr22msv+vTpQ61atYodo3379rzyyitlrve3335j0KBBvPfee9SpU4cBAwZwxx13FDu3JEllEc+Ls/D1haSQwkpWUvuN2ixcvpAmRzYhlubvGauMGessN19nudk6y0uAxUDDSqhHkkT9+vV5/fXXef3114tt69WrFxA9ra60ScdjsRjbbLMNl19+eYnbr7766oordivjv46VNFatWkX//v258cYbi6yfMWMGV155JQBBEPDNN98U23fNMMrFixczZMgQevbsWWT7iBEjWLZsGXl5eey9994lDpfca6+9ylxrQUEBhx9+OC1atGDChAn88ccfnHbaaaSlpXH77beX+TiSJOXMzmH2o7OZ/chs8ubnQWr0/7twYsjUB6aS1jSNVoNa0ercVmS0ykh0udVPO4gXxJk3bx7NmjUj2NgM8yvWWU4vZXlNP0MpSaoUPXr04Isvvthgn8zMzA1uHzx4MIMHD67IsoS370lbxFtvvcXUqVN5+umn6dq1K3369OGWW27h4YcfJjc3N9HlSZKSxOLxi/m84+f8etuvUSAFkA9hXhjNLwXkzc/j19t+5fOOn7N4/OLEFatI7XWW1x28vf7//msjSdJWz1CqmsrOzuapp57i2GOPpWfPnhx77LE89dRTZGdnJ7q0aqFdu3bFRmyt65NPPmGXXXahefO14/Z79epFVlYWU6ZMqYQKJUnJbvH4xUw6ZBIFKwugYCOdC6BgZQGTDplUNJj6EPgrcDFQ+oOAVJHarbM8d53lOessN8BRUpIk4e171dKYMWMYOHAgixcvJhaLEY/HicVivPzyy1x88cWMHDmSI488srB/fn4+ixcvrvKPiqxKOnToQJMmTUrdPmfOnCKBFFDYnjNnTkm7SJJUKGd2DpOPnEwYDyG+dn2MGJ3oBMA0phFfd2McQkImHzWZPcfvSca9GfDsOgc9G+hSQQWGwDJgIbCoDF8BrgKOqqDzV2X7Aq+tXp4ADFi9/Ml6fSRJkqFUdTNmzBj69u1b2I7H40W+LlmyhKOPPprRo0dz1FFH8eabb/K3v/2NZcuWMWPGDIKgcp/kk5+fz7Jly8jKyir8mp6ezjbbbFOpdZTXO++8k+gSJEnV2OxHZ1OwqqBIILVGbAMD3YN4QKvlrUjrkVb0drG/AB1L2CEkmtto3QCpLCHTIgpvHyyzm0nOUGoGxNrHaEELwgNCGL+R/qcSvdcVwDCgE9AKWHdu3PO2RKGSJCUfQ6lqJDs7m4EDBwIQhmGJfcIwJAgCTj31VPbee28yMzPZb7/9ePLJJ8scSK0JktYPkzbl66pVq4odPy0tja+//ppmzZqVcPbk0KJFCz7//PMi6+bOnVu4TZKk0sTz4sx+ZHaJt+zFifMDPxQur6sxjdme7akZ1lwbSNUGDgXaEwUhJQVPlTHVYTPgwko4T1XQEngIOJPoZ3jZetvPBA6v7KIkSaqaDKWqkRdffJHFizc+wWkYhmRlZfHJO+/wt112oXMY8uEFFzA2L49lublk5eWxLC+PrNzc6Ot67VUFG57YolZqKnXT0qiXlhZ9TU+nbloa26xp16pF3fr1o+3p6cX6NatRg9q3305QsyasG5T9/jvMmwezZhU94bJlMHEinH02/PJL9HV9338PgwbBV1/Bd9/BM88U3f7DD1BQAO+8A99+W/IxSjt2CXr8/ju3TZrEvFNOoVnNmgD8d9o06qWlsdNDD0FKSpmOo+onCEPqrVpV/PqWkpzXdsVZOGMb8uYfUOr23BJSpF3YhcY0Lt55BfBKBRZXB2gMNFrna6MS1q37tQGQVoE1JIPTgW2Au4DPiUaVdQLOWf2SJEmAoVS1Mnr06MI5pMpiaV4e9373HQC1gLpAvfW+brNeu6Q+636tA6Tm50N+PpQwCqpCfP11yet/+SX6+vjjJW8fMSL6OmnSxs/x448lr1997IOAY4DSHgh6KLATcOozz3A30dym1wIXABlPPLHx86vaCoj+e5OqG6/tirOSE4F9KM/HtAY0KN9JarHhMGnN8rrtRkB6+U5TLbSDeEGcefPm0axZMwLKGLoesvolSZJKZShVjSxcuLDMgRRE/4A4FrgU2HtLFVVN/Qws2MD2FOB1YBDQg+juiQFEU0xIkrQhBdQkIKSkG/EDAhqufmzbYhYX9prOdNrTnhTWG4mbAuwFHE80r9SacKnGFitfkqQq5/333+fcc8+lRo2i/wOMx+MccMABPPTQQ3Tv3p2cnJxi+y5fvpwpU6Zw//3389RTT5GaWjRGyc3N5ZprrmGvvfaiT58+1KpV/Nd07du355VXyjZ0+dtvv+XOO+/ko48+YsGCBbRr147zzjuPiy++uNR9xo8fz4EHHljits8//5w99tijyLqffvqJP/3pT6SkpLBkyZIi25YsWcI111zDyy+/zKJFi2jbti33338/hx12WJnqLy9DqWqkcePGZR4pFQQBu+ywA1+vWsU+v//O3l278uqDD9KkYeKfTxyPx1m0aBGNGjUiFltnMtePPopGOZ1/ftEdZs2C+++Hf/4TTj65+K15AKefDkOHwr/+BfvuC+v9R8no0ZCbC926wbBhcNttxY+xzrFnlOF9tAXeKEM/bV1Kvb6lJOe1XXFSHl9JOGRliXNKBQS0ohUAS1hSGErNXP2nEY1oRSsa0zga0VMAfLz69SBVc16nT6HUgV7Lq0A/SVLSW7VqFf379+fGG28ssn7GjBlceeWVQPRv5G+++abYvj179iQMQxYvXsyQIUPo2bNnke0jRoxg2bJl5OXlsffeezNizR0669hrr73KXOuXX35Js2bNePrpp2nTpg0TJkzgnHPOISUlhcGDS75XZ++99+aPP/4osu66667jnXfeoVu3bkXW5+XlceKJJ7LffvsxYcKEIttyc3M55JBDaNasGaNGjaJ169b8+uuvNGjQoMz1l5ehVDXSt29fXn755TL1DcOQv193HSeeeCJjx47l5ZdfJrdDB2jVagtXWQbxOPnz5kGzZrDuP2xmzoQ5c6DLes+zrlMH6teP1tesWXw7QO3asPPO0LQptG9fvM8XX0B2Nuy4IzRsWPIxSju2VB6lXd9SsvParjC1es6HB6aUuC0kJIuswuX1LVr9pwY12PXYXan1QS2Yv3rjN1uo4M3RhbI/xS9R/SRJZZO9+msGFN7pnL/6lULR+QU31DdG0dvFS+pbjZ1xxhlF2ttttx2ffPIJL7/8cqmhVHp6epEHauXl5fHqq69y4YUXFnug2bXXXkunTp046KCDioVSw4cPZ9GiRUyYMIG0tOgH1q5duwp4V6UzlKpG+vXrx8UXX8ySJUtKffoeRAlwgwYNOO6440hJSeGoo47iqKOq/jOa69evz+uvv87rr79ebFuvXr0AaNCgQbEkeI1YLMY222zD5ZdfXuL2q6++mpo1azJ58uQSj7HLLrtsRvWSJJVN48Mbk9Y0jbz5ecW2hYT8xm8bPUZBswJqPFcD4kQTnf9EdE+5JElbSr/VX58G6q9efhl4imjS3XVH654C5ADDiJ7QCjAWeBw4AFj3n2xnAlnAw8C2W6LwytWuXTsGDhxYbNTWhixdupRGjRqVuf+YMWNYuHAhp59+epH17777Li+++CLffPNNiQNaxowZQ48ePbjgggt49dVXadq0KSeddBL/+Mc/SNlCD+sylKpGatSowciRIzn66KMJgqDEYGpNSjpy5Mhi99NWdT169OCLL77YYJ/MzMwNbh88eHCp6fIaGzuHJElbUiwtRqtBrfj1tl9LvIVv4weAVoNaEUtbPWKtf4WWJ0mSNkOHDh1o0qRJmftPmDCBF154gbFjx5Z5n2HDhtGrVy+22WabwnULFy5k4MCBPP3009SrV6/E/X755RfeffddTj75ZN544w1++uknzj//fPLy8rjhhhvKfP7ycHx9NXPkkUcyevTowns+18zrseZrgwYNePXVVznyyCMTVaIkSdqIVue2IqVmyiZ8UisgJVxFq0Nyt0RZSrQPP4Qzz4SddoqmG6hbF3bbDe69F0qYnLdEM2bAeefB9ttDrVrRLbf77gsvvFC875IlcNFFsM02kJEBHTrA9ddHUx5I0vpeXP1aN+/46+p1563X9+nV65uus+7w1esuWq/vsNXr21RksYnzzjvvbHSgxBqTJ0/m6KOP5oYbbuDQQw8t0z4zZ87kzTff5Mwzzyyy/uyzz+akk05i//33L3XfeDxOs2bNeOyxx/jzn//MCSecwDXXXMPQoUPLdO5N4Uipauioo45i9uzZjBo1ildeeaVw4tljjjmG4447LulGSEmStLXJaJVBl9e6MOmQSdHcUaufYRIQ0JGOAHzP9+vNKxUnIGSX8CoyBiyHCROiwGFr8PnncNpppW+/4w7Yc0846KDS+wweHL06dSq2KQCa5OcTHHcc3HlndJxZs0o+zp57wpNPRvV8/nnJfVq3hnfeKb2W0jzzDAwfXnTdpElw+eXw7ruwsd+iL14Me+0Fc+euXbdqFcyfDx9/HK2/aPW/BrOz4cADYd1Jf3/5BW65JZqLc+xYCLaCyV0klV1J/8xMpeTUYXP7bgWmTp3KQQcdxDnnnMO1115b5v2eeOIJGjduXGyKnnfffZcxY8Zwzz33ANE80/F4nNTUVB577DHOOOMMWrZsSVpaWpFb9Tp37sycOXPIzc0lPT2dimYoVU3VqFGDU045hVNOOSXRpUiSpE3QsGdDdv3vrkw+ajIFKwugIAqlUld/fAsI1oZSMUiplcouTf6PBjO+hZ+Bww+H996LHghS3a1cGY32efzx4tvuvBOWLoW8vGjEz9tvF+/z/PMwbVq0nJ8PP/1UZHMYj7N03Dga/ec/0Ypffy3WB4geyNJ/9f2Sv/0WnWudWycKlRB8lUlKCgwcCBdcED3A5dNPoW9fyMqCN96IwqJS5tYE4M031wZSffrAc8/BxIlw6KEQhtH3b00o9eCDawOpm26Kznn11fDYYzBuHLz4Ihx//Ka9D0nSBk2ZMoW//OUvDBgwgNtKejJ8KcIw5IknnuC0004rnKh8jU8++YSCgrXzArz66qvcddddTJgwgdatWwOwzz778OyzzxKPxwvvtvrhhx9o2bLlFgmkwNv3JEmSqqyGPRuy57Q9aXttW9KaphEnzk+xn/gp5SfiQTR8Kq1ZGm2va8ue33enwQcPRaNwIAoo+vWLwhhVXQMHRiOOgiC6tW5D7rwTnngiCp5q1oxGMq07QuzHHze8/7qT1B51VPT04oMPjp5ODNGoqTWefjr6WqdOFEY1bgzrTsr7zDMbeWOSpJIcdNBBDBkypNTtkydP5sADD+TQQw/lsssuY86cOcyZM4f58+cX9vn888/p1KkTs9Ybtfvuu+8yffp0zjrrrGLH7dy5M126dCl8tW7dmlgsRpcuXWjYsCEAgwYNYtGiRVx88cX88MMPjB07lttvv50LLriggt59cYZSkiRJVVhGqwza39ieHrN60GV0F1rc2oLsE7PhbNjp5Z3oMbMH7W9sT0arDGjTBjIzo7ABouWzz45GwSj51a1bfN268zutCSRL06cPtG0bLY8ZE40ge/vt6PY9iEZMQTQ/1dSp0XKHDpC6+uaKli1hzeS4X321ae9BkrZyP//8MwsWLCh1+6hRo5g/fz5PP/00LVu2LHztsccehX1WrlzJ999/T956v3gaNmwYe++9N502cURumzZtePPNN5k4cSK77rorF110ERdffDFXXnnlJh2vLLx9T5IkKQnE0mI0OboJDY9oyIwPZwDQeL/GxFLW+x1jly7w6qtRwJCbCyNHRmFFOYb/K0l8/z08+2y03LFjdAvjhtSpE80dddhh0S14qx+MU3hb4Op5Rli0CNbc4rH+E5rq1YtuF5w3r6LehSRtVWZsZFTsjTfeyI3rjkwtQc+ePQlL+IXTs2v+n1AGAwcOZODAgcXW9+jRg08//bTMx9lcjpSSJElKFvnAO9DgywYEBRuYZPqAA6Lbq9ZMRH377fCvf63dHoawYEF0u9iCBY6kSqQRI6LvfxhCu3Zl3++XX6BXr2g+rXr14D//gdhGPtovXw7HHhtNjr6ugoIo4Prtt42fd8214iTnkqQK4EgpSZKkZJEPsQdjbLNoG7J2ydpw3+OOgwceWDtx9eDB0e1fixbBQw/Bzz+v7duhA1x4IQwYsHb0jKqu779f+wTAevWiSc533XXj+z3+OHz2WbR84YVRWPnrr9G8Uh99FM0zNW0aNGoUjZ4qKIhu8VvXsmXR16ZNkaRkUb9+fV5//XVef/31Ytt69eoFQIMGDehWysMiYrEY22yzDZdffnmJ26+++mpq1qzJ5MmTSzzGLrvsshnVV2+GUpIkSckiBmG3kPx5+TRs3JBgY6NVLrwwCi7uuisa4XLaaSWPcPnlF7j0UrjmGnjppWgEjqqmKVOiQGru3Cg8evPNDT9xb11rnjAI0e16depET/Hbb7/oaXo//AAzZ0Zzk+20E3z3XRRe5udH80r98Ud06x7A7rtX+FuTpC2lR48efPHFFxvsk5mZucHtgwcPZvDgwRvss7FzqLikuH3v4Ycfpl27dtSoUYPu3bvz+eefb7D/iy++SKdOnahRowa77LILb7zxRiVVKkmStAWlQ+zGGC3+1YJd/rxL4eOaN+j22+Evf1nbXnOr2LrWrFu1Cg4/PAo6AP73v2j0TKLnowpDWLEiutWwuinP0/e++QZ69owCqebNYfz40gOpdY+7RqtWa5dHjIhu55syBT74IFoXi62dQ+qUU6KvK1ZE19CiRUWfvnfyyWV7f5IkbUCVD6VeeOEFLrvsMm644Qa++uordtttN3r16sW8UiZXnDBhAieeeCJnnnkmX3/9NX379qVv375Mnjy5kiuXJEmqArKy1t6ytTHxeBQA/fWvcP750S1hU6cWDbU2VTwOixdHo7K++greeQdGjYpuKfvnP6NRWuefDyeeGD0lbq+9osm7mzeHjIxoVE/TptF+W6v7718bzM2dG/181gRPQVA0NCrJ6aevvT3zoYei2zm7dImOBVHQtObJjRddBF27Rss33ACNG8Njj0XtPn2gX7+Ke1+Skk48Hk90CaoCKuI6qPK37/3f//0fZ599NqeffjoAQ4cOZezYsQwfPrzExxI+8MAD9O7dm7///e8A3HLLLfz3v/9lyJAhDB06tFJrlyRJSriRI6PJsMsqHo/6DxsGt9wS3daXkRFty82NgqXFi2HJkvItZ2WVPqF6/frQsGEUmDRsGL1aty7abtAgCqV69tz078XWrk0b+PRTuPnmaJTVvHmQlgY77gj9+8Nll63tW6MGvPceXH89vPxy1HebbaIRVFdf7UTn0lYqPT2dWCzG7Nmzadq0Kenp6Ru/lTxBwjAkPz+f1NTUKltjsgrDkNzcXObPn08sFiM9PX2Tj1WlQ6nc3Fy+/PJLrrrqqsJ1sViMgw8+mE8++aTEfT755BMuW/d/qEQTl40ePbrc5y8oKKBgzeNwVWni8Xjh976kx1xKyczrW9WV13YlyQEuhLnz5vLTRT+x1wF7kZKSUnr/MCT20EMAlOfjeAjRrVyvvQZPP10YLAWrVpXcPzV1bXi0OmAKmzWLwo41wVKDBoRrwqV1w6Z69aJJtctj/c9nBQUEYUhYwue2IB6HeJywoIBYGBLfWB8o1mfN9R2u3r+kPmvqWHOOwnOV0K/I/sOGRa/S3tu61u9bkpKOu+4xt98ennxy4/tDNJLqvvui14b6KWn5d7c2xbbbbsvcuXOZNWtWokvZqIKCgg3/f1KbpVatWrRu3ZowDItlJ2XNUqp0KLVgwQIKCgpo3rx5kfXNmzdn2roTNa5jzpw5JfafM2dOqefJyckhJyensJ21egLHjz/+mNq1a29q+dpEYRiyYsUKateubaKtasfrW9WV13bliOXG2GnqTuSvymfG9BnkxfI2OK9U2tKl7LvuU/bKKADIzmZuejq5HTuSX6dO9Kpbl7x1lvPr1iW/Th0KatQo38iZFSui18yZ5a6tJA0mTaL5nDl8v2ZupHVsO306uUuXsqRmTTouWcK3JfRp9r//Ueu335jxwQd0X7WKz9brE4Yhad9/T8HcuUwrpQ9A+qJF7LR0Kd988AFdly7lf599Rs4vvxTrt+fKlXxewv5SZfPvbm2OIAiq9HUThiGrVq2iZs2aVbrOZBWGIWEYMn369BK3r1ixokzHqdKhVGW54447uOmmm4qtX1meoe6qMGEYkp2dDeBfHqp2vL5VXXltV5I4fHfKdyxYsIDseDYrVqzYYChVc+HCzTpdbMkSZv3lLyzo2rX00GnN7X4JlBaG1Pv2W/Y49dQSt08bMIAVubmkz51bap8ZRxzBihUriAdBiX3iYcjcHj1YsWIFK5s0KfU4S3bckRUrVrCiUSN2XW/0/hqrGjcu84d1aUvy725VZ2uu7/z8fK/vBChrnlKlQ6kmTZqQkpLC3DWTL642d+5cWrRoUeI+LVq0KFd/gKuuuqrILX9ZWVm0adOGgw46iHprnkCiShOPx5k/fz5NmzYt21OFpCTi9a3qymu78hQUFPDxxx/TmMbss88+G74tYTOfVtckN5em119P+Kc/kf/hh7AZc0ZsUb16wYUXlrp5tzULJ51Uap+Oq1+U8BvfNdf3tk2b0i4Wi85XiuZArzU1lSJjTR8pwfy7W9WZ13dirbkDbWOqdCiVnp7On//8Z9555x369u0LRBfWO++8w+DBg0vcp0ePHrzzzjtccsklhev++9//0qNHj1LPk5GRQcaaCTzXkZaWRlpa2ma9B5VfPB4nNTWVtLQ0//JQteP1rerKa7vyxGKxwiAqLS1tw6FUixbQoUP0xLvyzBcTBLDddgTffQcff0zw5Zek1aq11U5u7fWt6sprW9WZ13dilTVLqfI/mcsuu4x///vfjBw5kv/9738MGjSIFStWFD6N77TTTisyEfrFF19MZmYm9957L9OmTePGG2/kiy++KDXEkiRJShoFwEdQ/7v60fLGBMEGRxBt0EUXRZOd77cfXHLJVhtISZKkLafKh1InnHAC99xzD9dffz1du3blm2++ITMzs3Ay899++40//vijsP/ee+/Ns88+y2OPPcZuu+3GqFGjGD16NF26dEnUW5AkSaoYeRD7Z4w2z7UhVlDGj3EDBkCtWlHAVFY1asBpp21ajZIkSWVUpW/fW2Pw4MGljnQaP358sXX9+vWjX79+W7gqSZKkShaDcOeQgvkF1G9Yv2z7NGgAL70Ehx8eBVPx+Mb3yciAxYujfSVJkraQKj9SSpIkSaulQ+yuGM2HN2e3PXbb8HxS6+rVC8aOhZo1o9vw1r8Vb826NaOpliyJ9tnMidIlSZI2xFBKkiRpa9CrF8ycCfffD9ttV3TbdttF63/6CTp1itb9+CMccQSU8ZHOkiRJ5WUoJUmStLVo0CCawPzHH6NRUNOnR19//DFa3749ZGZCy5ZR/88+gxNOgPz8hJYtSZKqJ0MpSZKkZJEL8QvjzO43mwnjJ1BQUJZH8JUgCKBxY2jXLvq67u18bdvCG29A3bpR+/XXYdAgCMPNLl+SJGldhlKSJEnJIg7B9ID0Wenk527B0Utdu8Lo0ZCWFrUffxxuumnLnU+SJG2VDKUkSZKSRTrEb4oz44wZxFPL8BS9zfGXv8CTT65t33QTPPbYlj2nJEnaqhhKSZIkJYsY0BWWb7+8cj7F9e8P9967tj1oELz2WiWcWJIkbQ0MpSRJklS6yy6DSy+NluPxaOLzTz9NbE2SJKlaMJSSJElKFgXARKg7rW60XFnuuScaNQWwahUccQR8/30lFiBJkqojQylJkqRkkQexW2O0fbItsYJK/BgXi8GIEXDggVF74ULo1Qv++KPyapAkSdWOoZQkSVKyiEG4fUhB+wLq1KtTuefOyIBXXoFdd43av/4Khx0GWVmVW4ckSao2DKUkSZKSRTrE7o/R/Jnm7L7X7qSkpFTu+evXh3HjYNtto/Y338Bf/wq5uZVbhyRJqhYMpSRJklR2rVpBZiY0bBi133kHTj89mgRdkiSpHAylJEmSVD6dO8Prr0ONGlH72WfhH/9IbE2SJCnpGEpJkiQli1yIXx5n9imz+ezDzygoqMxH8K1n773h+eejSdAhekLf/fcnrh5JkpR0DKUkSZKSRRyCaQHpv6STsyon0dXA0UfDv/61tn3ppfDCC4mrR5IkJRVDKUmSpGSRBvGr4vx2ym/EU6rIHE7nngvXXbe2fdpp8N57iatHkiQlDUMpSZKkZJEC7AVZO2VFy1XFTTfBGWdEy7m50LcvTJqU0JIkSVLVZyglSZKkzRMEMHQoHHZY1M7Kgj594LffEluXJEmq0gylJEmSkkUc+A5q/1I7Wq5K0tLgP/+BPfeM2rNnQ+/esGhRYuuSJElVlqGUJElSssiF2LUx2j/enlh+FfwYV7s2vP46bL991P7f/+Coo2DVqsTWJUmSqqQq+GlGkiRJJQogbBNS0KqAmrVqJrqakjVtCm++Cc2aRe2PP4aTToKCgsTWJUmSqhxDKUmSpGSRAbFHYjQf1Zw99t2DlJSqNNv5OrbbDt54A+rUidqjR8OFF0IYJrQsSZJUtRhKSZIkqeL9+c/w0kuQmhq1H3kEbr89sTVJkqQqxVBKkiRJW8ahh8Lw4Wvb114LI0YkrBxJklS1GEpJkiQli1yIXxNn1hmzmPjxRAqSYZ6mU0+FO+9c2z7rLBg3LnH1SJKkKsNQSpIkKVnEIfg2IGNaBqtWJNET7a64AgYPjpYLCuC442DixMTWJEmSEs5QSpIkKVmkQfzSODOPn0k8JZ7oasouCOD++6MwCmDlSjj8cPjpp4SWJUmSEstQSpIkKVmkAD1hSdcl0XIySUmBp56C/feP2vPnQ69eMHduYuuSJEkJYyglSZKkylGjBoweDTvvHLV/+SUaMbV8eULLkiRJiWEoJUmSlCziwI9Qc2bNaDkZNWwYTXTeunXU/vLL6La+vLzE1iVJkiqdoZQkSVKyyIXY5TE6/KsDsfwk/hjXpg1kZkKDBlH7zTejp/KFYULLkiRJlSuJP81IkiRtZQIIm4XEG8fJqJGR6Go2T5cu8OqrkLH6fTz5JFxzTWJrkiRJlcpQSpIkKVlkQGx4jGavNaP7/t1JSUm22c7Xs//+8Mwz0dP5AO64Ax5+OLE1SZKkSmMoJUmSpMQ59lh48MG17QsvhJdfTlw9kiSp0hhKSZIkKbEGD4Z//CNaDkM46ST48MPE1iRJkrY4QylJkqRkkQvxm+PMGjSLrz79ioKCgkRXVHHuuANOPTVazsmBo46CKVMSW5MkSdqiDKUkSZKSRRyCzwMyvs1gedbyRFdTsYIAhg2DQw+N2kuWQO/eMHNmQsuSJElbjqGUJElSskiF+PlxZvWdRZgSJrqaipeWBqNGwZ//HLVnzoQ+faKASpIkVTuGUpIkSckiFegFi/dcXD1DKYC6dWHsWGjfPmpPngx9+0J2dkLLkiRJFc9QSpIkSVVL8+bw5pvQpEnUfv99OO00iMcTW5ckSapQhlKSJEnJIgR+g4y5GdFydbbDDtGIqVq1ovaLL8Kll0ZP55MkSdWCoZQkSVKyyIHYhTF2eGAHYnlbwce4PfeMwqiUlKj94INwzz2JrUmSJFWYreDTjCRJUvUR1gsJ64SkpqYmupTKcdhh8Nhja9tXXAFPP524eiRJUoXZSj7NSJIkVQM1IPZsjKar/2w1zjgDZs2C66+P2qefHs07dcghia1LkiRtFkdKSZIkqeq79lo499xoOT8f/vpX+OqrxNYkSZI2i6GUJEmSqr4ggIcfhqOPjtrLl0e39v3yS2LrkiRJm8xQSpIkKVnkQvzuOLMum8W3E7+loKAg0RVVrpQUeO452HvvqD13LvTuDQsWJLYuSZK0SQylJEmSkkUcgg8CMj7NYOnipYmuJjFq1oQxY6BTp6j9449wxBGwYkVi65IkSeVmKCVJkpQsUiF+Zpw/DvuDMCVMdDWJ07gxZGZCy5ZR+7PPoH//aK4pSZKUNAylJEmSkkUqcBQs3Hfh1h1KAbRtC+PGQb16Ufv11+G88yDcyr8vkiQlEUMpSZIkJafddoNXXoG0tKg9bBjcdFNia5IkSWVmKCVJkpQsQmAepC1Oi5YFf/kLPPnk2vZNN8FjjyWuHkmSVGaGUpIkSckiB2Jnx+j4z47E8vwYV6h/f/i//1vbHjQomgxdkiRVaX6akSRJSiYZEKaHBEGQ6Eqqlksvhcsui5bj8Sio+uSTxNYkSZI2KDXRBUiSJKmMakDwUkDT1X+0nn/+E/74A557DlatgiOOgI8/hk6dEl2ZJEkqgSOlJEmSVD3EYvDEE9E8UwCLFkHv3lFQJUmSqhxDKUmSJFUfGRnRE/l22y1q//or9OkDWVmJrUuSJBVjKCVJkpQs8iD+YJxZV85i0peTiMfjia6oaqpXD954A9q2jdrffgt//Svk5ia2LkmSVIShlCRJUrIogOCtgIwPMliycAlhGCa6oqqrVSvIzIRGjaL2O+/AwIHRJOiSJKlKMJSSJElKFqkQnhIy95C5hCkGUhvVqRO89hrUqBG1n3sOrrgisTVJkqRChlKSJEnJIhXCfiHzD5xvKFVWe+8NL7wQTYIOcO+9cN99ia1JkiQBhlKSJEmq7o46Cv71r7Xtyy6LgipJkpRQhlKSJEnJIgSWQsrylGhZZXfuuXDddWvbp50G772XuHokSZKhlCRJUtLIgdhpMTrf3plYnh/jyu2mm+DMM6Pl3Fzo2zd6Mp8kSUoIP81IkiRp6xAEMHQoHH541M7Kgj594NdfE1uXJElbKUMpSZKkZFEDgtcDmkxowv6H7k9KSkqiK0o+qanRfFJ77hm1//gjCqYWLUpsXZIkbYUMpSRJkrR1qV0bXn8ddtghav/vf3DkkbBqVWLrkiRpK2MoJUmSpK1P06aQmQnNm0ftCRPgpJOgoCCxdUmStBUxlJIkSUoWeRB/NM7sm2Yz5ZspxOPxRFeU3LbbDt54A+rUidqjR8PgwRD6aENJkiqDoZQkSVKyKIDgtYD0N9NZOG8hoeHJ5tt9d3j55WiuKYgmQr/99sTWJEnSVqJKh1JhGHL99dfTsmVLatasycEHH8yPP/64wX3uuOMO9thjD+rWrUuzZs3o27cv33//fSVVLEmStAWlQtgvZH7P+YQpBlIV5pBDYPjwte1rr4UnnkhcPZIkbSWqdCh199138+CDDzJ06FA+++wzateuTa9evcjOzi51n/fff58LLriATz/9lP/+97/k5eVx6KGHsmLFikqsXJIkaQtIhfCUkLmHzjWUqminngp33rm2ffbZ0a19kiRpi0lNdAGlCcOQ+++/n2uvvZajjz4agCeffJLmzZszevRo+vfvX+J+mZmZRdojRoygWbNmfPnll+y///5bvG5JkiQlqSuugFmz4KGHognP+/WD996Dbt0SXZkkSdVSlR0pNX36dObMmcPBBx9cuK5+/fp0796dTz75pMzHWbp0KQCNGjWq8BolSZIqVQhkQyw3Fi2rYgUB3HcfHHdc1F65Eg4/HH76KbF1SZJUTVXZkVJz5swBoPmax/Su1rx588JtGxOPx7nkkkvYZ5996NKlS6n9cnJyyMnJKWxnZWUV7u9TbSpfPB4nDEO/96qWvL5VXXltV5JsCI4P2GnRTky5MXr6XhAEia6qegkCGDmSYP58gvffhwULCPr0IXjlFeJNmiS6OqlC+Xe3qjOv78Qq6/e9yoRSzzzzDOeee25he+zYsZt9zAsuuIDJkyfz0UcfbbDfHXfcwU033VRs/fz58zc4f5W2jHg8ztKlSwnDkFisyg7mkzaJ17eqK6/tSpIN9XPrU1BQwIoVK5g3bx4pKSmJrqpaCh59lEZHH03a998T/PIL9U46ifmvvEJQt26iS5MqjH93qzrz+k6sZcuWlalfEFaRZwkvW7aMuXPnFrZzcnLo0qULX3/9NV27di1cf8ABB9C1a1ceeOCBDR5v8ODBvPrqq3zwwQe0b99+g31LGinVpk0bFi9eTL169TbtDWmTxeNx5s+fT9OmTf3LQ9WO17eqK6/tShJCmL36t74ZEEuJOVJqS5o5k2CffQhmzgQgfuihMGYMpKUluDCpYvh3t6ozr+/EysrKomHDhixdunSDuUqVGSlVt25d6q7zm6cwDGnRogXvvPNOYSiVlZXFZ599xqBBg0o9ThiGXHjhhbzyyiuMHz9+o4EUQEZGBhkZGcXWx2IxL94ECYLA77+qLa9vVVde25WkNqTg6KhKse22kJlJuO++BEuWEHvrLTjnHBgxIrrNT6oG/Ltb1ZnXd+KU9XteZX8yQRBwySWXcOuttzJmzBi+++47TjvtNFq1akXfvn0L+x100EEMGTKksH3BBRfw9NNP8+yzz1K3bl3mzJnDnDlzWLVqVQLehSRJkpLazjsTjh5NuOYXmE8+Cddck9iaJEmqJqpsKAVwxRVXcOGFF3LOOeewxx57sHz5cjIzM6lRo0Zhn59//pkFCxYUth955BGWLl1Kz549admyZeHrhRdeSMRbkCRJqjj5EB8ZZ/Zds5k2eZqTt1aW/fZjyZAhhGtGR91xB6zzS1FJkrRpqszteyUJgoCbb76Zm2++udQ+M2bMKNKuIlNkSZIkVbx8CF4MSF+Uzrw/zWOHzjskuqKtRs4RRxA+8ADBRRdFKy66CFq2hGOPTWxhkiQlsSo9UkqSJEnrSIHwyJCFey8kjPmLuEp3wQVw5ZXRchjCySfDhx8mtiZJkpKYoZQkSVKySIPwrJA/jviDMNVQKiFuvx1OOy1azsmBo46CKVMSW5MkSUnKUEqSJEkqqyCAxx+HXr2i9pIl0Ls3zJyZ0LIkSUpGhlKSJElSeaSlwahR8Oc/R+2ZM6FPnyigkiRJZWYoJUmSlCyyIXZ0jC5XdyGW68e4hKpTB8aOhe22i9qTJ8PRR0N2dmLrkiQpifhpRpIkSdoUzZtDZiY0aRK1P/gATj0VCgoSW5ckSUnCUEqSJClZZABPQ71X67HXAXsRi/lRLuF22CEaMVWrVtQeNQouvTR6Op8kSdogP8lIkiQliwCCBgHpTdNJz0gnCIJEVySAPfeMwqiUlKj90EPwz38mtiZJkpKAoZQkSZK0ufr0gX//e237H/+Ap55KXD2SJCUBQylJkqRkkQ/x5+PMvn82P0z9gXg8nuiKtK7TT4dbblnbPuMMeOutxNUjSVIVZyglSZKULPIheDog/T/pzJk5h9B5i6qea66B886LlvPz4dhj4auvEluTJElVlKGUJElSskiB8JCQxd0WE8YMpKqkIIAhQ6Bv36i9fHl0a98vvyS0LEmSqiJDKUmSpGSRBuHgkFl/nUWYaihVZaWkwLPPwt57R+1586B3b5g/P7F1SZJUxRhKSZIkSRWtZk147TXo1Clq//gjHHEErFiR2LokSapCDKUkSZKkLaFRI8jMhFatovbnn8MJJ0RzTUmSJEMpSZKkpJENseNj7HzDzsRy/RiXFNq2hXHjoF69qD12bDQRupPUS5JkKCVJkpRUciDICxJdhcpj111h9GhIT4/aw4bBjTcmsiJJkqoEQylJkqRkkQEMgzrP12HP/fYkFvOjXNI48EB48sm17ZtvhkcfTVw9kiRVAX6SkSRJShYBBM0Damxbgxo1axAEjphKKiecAP/3f2vb558Pr76auHokSUowQylJkiSpslx6Kfztb9FyPA79+8OECYmtSZKkBDGUkiRJShb5EH8lzh9D/+Dn738mHo8nuiJtirvvhpNOipazs+HII2HatMTWJElSAhhKSZIkJYt8CIYFpD2ZxqxfZxH6BLfkFIvBE0/AQQdF7UWLoHdvmD07sXVJklTJDKUkSZKSRQzC/UOW7raUMDCQSmrp6fDyy7DbblH711/hsMNg6dLE1iVJUiUylJIkSUoW6RD+LeT3E34nTDOUSnr16sG4cdC2bdT+9lv4618hJyexdUmSVEkMpSRJkqREadkS3nwTGjWK2u++C6efHk2CLklSNWcoJUmSJCVSx47w+utQs2bUfu45uOKKxNYkSVIlMJSSJElKFtkQnBrQ+dbOxHL9GFet9OgBzz8fTYIOcO+9cN99ia1JkqQtzE8zkiRJSSTICkhZmZLoMrQlHHUUPPLI2vZll0VBlSRJ1VRqoguQJElSGWUAD0OtVbX48zZ/Jhbz94vVzjnnwKxZcPPNUfu006BZM/jLXxJblyRJW4CfZCRJkpJFAEHbgFqdalG7Tm2CIEh0RdoSbrwRzjorWs7Lg2OOiZ7MJ0lSNWMoJUmSJFUlQRDdxnfEEVE7Kwv69IFff01sXZIkVTBDKUmSpGSRD/FxceaMnMOMn2YQj8cTXZG2lNTUaD6p7t2j9h9/QO/esHBhYuuSJKkCGUpJkiQli3wIHg5IfTSV3375jTAME12RtqTateG112CHHaL2tGnRZOirViW2LkmSKoihlCRJUrKIQbhnyLLOywgDA6mtQtOm8Oab0Lx51J4wAU48EQoKEluXJEkVwFBKkiQpWaRDeE3Ir6f+SphmKLXVaN8exo2DOnWi9quvwuDB4Eg5SVKSM5SSJEmSqro//Qlefjmaawpg6FC47bbE1iRJ0mYylJIkSZKSwSGHwBNPrG1fdx0MH564eiRJ2kyGUpIkSckiB4KzAzre3ZEgN0h0NUqEU06Bu+5a2z7nHBg7NnH1SJK0GQylJEmSkkUIwbyAtCVpBBhKbbX+/ne46KJouaAAjj8ePv88sTVJkrQJDKUkSZKSRTrwf1Dj4Rp03bMrsZgf5bZKQQD33Qf9+kXtlSvh8MPhxx8TW5ckSeXkJxlJkqRkEYNgx4A6f6pDvQb1CAJHS221YjF48kk44ICovWAB9OoFc+cmti5JksrBUEqSJElKRjVqwOjR0KVL1J4+HQ47DJYtS2hZkiSVlaGUJElSsiiA+Ltx5r4wl9+m/0Y8Hk90RUq0Bg1g3DjYZpuo/dVXcNxxkJub0LIkSSoLQylJkqRkkQfB/wWkPJDCjB9nEIZhoitSVbDNNpCZGQVUAG+9BWedBV4fkqQqzlBKkiQpWcQg3C1k+fbLCQMDB61j551hzBjIyIjaTz0FV1+d2JokSdoIQylJkqRkkQ7hzSEzzphBmGYopfXstx88+2z0dD6AO++EIUMSW5MkSRtgKCVJkiRVF3/9Kzz00Nr2RRfBqFGJq0eSpA0wlJIkSZKqkwsugKuuipbDEE45BT74ILE1SZJUAkMpSZKkZJEDweCAHe7bgSA3SHQ1qspuuw0GDIiWc3Lg6KNhypTE1iRJ0noMpSRJkpJFCMHvARnzMwgwlNIGBAH8+9/Qq1fUXrIEeveG339PaFmSJK3LUEqSJClZpAO3Q/o/09m1267EYn6U0wakpUXzSf35z1F75kzo0wcWL05sXZIkreYnGUmSpGQRg2DXgHr71KNBowYEgaOltBF16sDYsdChQ9SeMgX69oXs7ISWJUkSGEpJkiRJ1Vvz5pCZCU2bRu0PPoBTT4WCgsTWJUna6hlKSZIkJYsCiE+IM2/MPGb9Not4PJ7oipQstt8+GjFVq1bUHjUKLr00ejqfJEkJYiglSZKULPIguD0gdmeMn6f9TGigoPLYY48ojEpJidoPPQR3353YmiRJWzVDKUmSpGQRg7BTyMptVxIGBlLaBH36wOOPr21feSU89VTi6pEkbdUMpSRJkpJFOoR3hfxy3i+EaYZS2kQDB8Ktt65tn3EGvPVWwsqRJG29DKUkSZKkrc3VV8P550fL+flw7LHw1VeJrUmStNUxlJIkSZK2NkEADz4IfftG7eXLo1v7fvkloWVJkrYuhlKSJEnJIheCvwV0eLgDQV6Q6GqU7FJS4NlnYZ99ova8edCrF8yfn9i6JElbDUMpSZKkZBGH4KeAmrNqEoSGUqoANWvCmDHQuXPU/uknOPxwWLEisXVJkrYKhlKSJEnJIg24HlJvSmXnrjsTi/lRThWgUSPIzIRWraL2xIlw/PHRXFOSJG1BfpKRJElKFikQ7BnQ4JAGNG7WmCBwtJQqyLbbRsFUvXpR+4034NxzIfQpj5KkLcdQSpIkSRLssguMHg3p6VF7+HC44YaEliRJqt4MpSRJkpJFHOJfxVnw9gLmzJ5DPB5PdEWqbg48EJ58cm37llvg0UcTV48kqVozlJIkSUoWuRBcH8D18MPkHwi9tUpbwgknwH33rW2ffz68+mri6pEkVVuGUpIkSckiBmH7kOwW2YSBgZS2oEsugcsvj5bjcejfHyZMSGhJkqTqx1BKkiQpWaRDeH/ITxf9RJhmKKUt7K674OSTo+XsbDjySJg2LbE1SZKqFUMpSZIkScXFYtFk5wcdFLUXLYJevWD27MTWJUmqNgylJEmSJJUsPR1efhm6do3av/0GffrA0qUJLUuSVD0YSkmSJCWLXAiuDmj/7/YEeUGiq9HWol49eOMNaNcuak+aBMccAzk5CS1LkpT8qnQoFYYh119/PS1btqRmzZocfPDB/Pjjj2Xe/8477yQIAi655JItV6QkSVJliUMwJaD29NoEoaGUKlHLlpCZCY0bR+333oOBA6NJ0CVJ2kRVOpS6++67efDBBxk6dCifffYZtWvXplevXmRnZ29034kTJ/Loo4+y6667VkKlkiRJlSAN+AekXJVCp106EYtV6Y9yqm46doTXX4eaNaP288/D3/+e2JokSUmtyn6SCcOQ+++/n2uvvZajjz6aXXfdlSeffJLZs2czevToDe67fPlyTj75ZP7973/TsGHDyilYkiRpS0uBYL+Ahkc2pFnLZgSBo6VUyfbaKwqj1gSi//d/0UuSpE1QZUOp6dOnM2fOHA4++ODCdfXr16d79+588sknG9z3ggsu4PDDDy+yryRJkqQKcNRRMHTo2vbf/gbPPZe4eiRJSSs10QWUZs6cOQA0b968yPrmzZsXbivJ888/z1dffcXEiRPLfK6cnBxy1pmoMSsrC4B4PE7c++QrXTweJwxDv/eqlry+VV15bVeSOITTQhYtWkTB9gU0bd7U0VKVwOu7BGeeSTBzJsHNNwMQDhhA2LQp/OUvCS5M5eG1rerM6zuxyvp9rzKh1DPPPMO5555b2B47dmy5j/H7779z8cUX89///pcaNWqUeb877riDm266qdj6+fPnl2n+KlWseDzO0qVLCcPQuTJU7Xh9q7ry2q4k2VD/0vrEl8b56sqv2K37bqSkpCS6qmrP67sU551HvZ9/ptYzzxDk5REecwyLRo8mf+edE12ZyshrW9WZ13diLVu2rEz9qkwoddRRR9G9e/fC9pqRS3PnzqVly5aF6+fOnUvXrl1LPMaXX37JvHnz2H333QvXFRQU8MEHHzBkyBBycnJK/OB21VVXcdlllxW2s7KyaNOmDU2bNqVevXqb+9ZUTvF4nCAIaNq0qX95qNrx+lZ15bVdSXKAtlAwp4BatWvRrFkzQ6lK4PW9AcOHEy5ZQjB2LLHly2l86qmEH30E7dolujKVgde2qjOv78Qq60ChKhNK1a1bl7p16xa2wzCkRYsWvPPOO4UhVFZWFp999hmDBg0q8RgHHXQQ3333XZF1p59+Op06deIf//hHqR/aMjIyyMjIKLY+Fot58SZIEAR+/1VteX2ruvLargQ1oeDRAn748AfAzyqVyeu7FOnp8MILcNBB8NlnBH/8QXDYYfDxx9C4caKrUxl4bas68/pOnLJ+z6vsTyYIAi655BJuvfVWxowZw3fffcdpp51Gq1at6Nu3b2G/gw46iCFDhgBRsNWlS5cir9q1a9O4cWO6dOmSoHciSZIkVWO1a8Prr8OOO0bt77+HI4+EVasSW5ckqcqrsqEUwBVXXMGFF17IOeecwx577MHy5cvJzMwsMgzs559/ZsGCBQmsUpIkSdrKNWkCmZnQokXU/uQTOPFEyM9PbF2SpCqtyty+V5IgCLj55pu5efVTPUoyY8aMDR5j/PjxFVuUJElSouRCcFtA29/a8ttJvyW6Gqmo9u3hjTdg//1h+XJ49VUYPBgeeQR8SqQkqQRVeqSUJEmS1hGH4MuAut/XJQj9R76qoD/9CV5+GVJX/+770Ufh1lsTW5MkqcoylJIkSUoWqRBeHBK7NMaOO+1I4OgTVUWHHAIjRqxtX389DBuWsHIkSVVXlb59T5IkSetIhdghMRrRKNGVSBt28skwezZccUXUPvfcaL6pww9PbF2SpCrFkVKSJEmSKt7ll8PFF0fLBQVw/PHw+eeJrUmSVKUYSkmSJCWLOIQ/hyz+cjEL5y8kDMNEVySVLgjg//4vCqMAVq6MRkr98ENi65IkVRmGUpIkSckiF7gYCi4sYMrXU4jH44muSNqwWAxGjoQDDojaCxZA794wZ05i65IkVQmGUpIkSckigLBRSH7dfEIcJaUkUaMGjB4Nu+wStadPj0ZMLVuW0LIkSYlnKCVJkpQsMiB8ImTaVdMI0w2llEQaNIBx46BNm6j91Vdw3HGQm5vQsiRJiWUoJUmSJGnLa90aMjOhYcOo/dZbcNZZ4NxokrTVMpSSJEmSVDl22gnGjIGMjKj91FNw1VWJrUmSlDCGUpIkSckiF4K7ArZ9dluCvCDR1UibZt994dlno6fzAdx1Fzz0UGJrkiQlhKGUJElSsohDMCGg3uR6BKGhlJLYX/8KQ4asbV98MYwalbh6JEkJYSglSZKULFIhPDeE86BDxw4EgcGUktj558PVV0fLYQinnAIffJDYmiRJlSo10QVIkiSpjFIhdmSMJjRJdCVSxbj1Vpg1C0aOhJwcOOoo+Ogj6NIl0ZVJkiqBI6UkSZIkJUYQwL//Db17R+2lS6Pl339PbF2SpEphKCVJkpQsQghnhSz931KWLF5CGIaJrkjafGlp8OKL0K1b1J41C/r0gcWLE1uXJGmLM5SSJElKFjnAuZB3Zh6TJk4iHo8nuiKpYtSpA2PHQocOUXvKFOjbF7KzE1qWJGnLMpSSJElKImHtkHgNwyhVQ82awZtvQtOmUfuDD6LJzwsKEluXJGmLMZSSJElKFjUgfDZk6vVTiacbTKka6tAB3ngDateO2i+9BJdcEj2dT5JU7RhKSZIkSao6unWL5phKSYnaQ4bAXXcltiZJ0hZhKCVJkiSpaunTBx5/fG37qqvgyScTV48kaYswlJIkSUoWeRA8ELDNqG0I8oNEVyNtWQMHwm23rW2feWY055QkqdowlJIkSUoWBRC8G9DgqwYEcUMpbQWuugrOPz9azs+HY4+FL79MbE2SpApjKCVJkpQsUiEcGBIOCGm3fTuCwGBK1VwQwIMPwjHHRO0VK+Cww+DnnxNblySpQhhKSZIkJYtUiB0Xo+m5Tdl2u22Jxfwop61ASgo88wzss0/UnjcPeveOvkqSkpqfZCRJkiRVbTVrwpgx0Llz1P7pJzjiiGjklCQpaRlKSZIkJYsQwgUhy2YsI2tpFmEYJroiqfI0agSZmdC6ddSeOBGOPx7y8hJblyRpkxlKSZIkJYscYCDknJTDN599QzweT3RFUuXadlsYNw7q1Yvab7wB550HBrSSlJQMpSRJkpJJCn6C09Ztl13g1VchPT1qDx8O11+f2JokSZvEjzSSJEnJogbEX44z+dbJxNMdJaWtWM+e8NRT0dP5AG69FYYOTWhJkqTyM5SSJEmSlHyOPx7uu29t+4ILYPTohJUjSSo/QylJkiRJyenii+Hvf4+W43E48UT4+OPE1iRJKjNDKUmSpGSRB8HQgFavtiLIDxJdjVQ13HknnHxytJydDUceCf/7X2JrkiSViaGUJElSsiiAYFxAo88aEcQNpSQAYrFosvODD47aixdD794we3Zi65IkbZShlCRJUrJIhbB/SHhCyLbbbUsQGExJQPQkvpdegq5do/Zvv0GfPrB0aULLkiRtmKGUJElSskiF2Ckxml7clHbbtyMW86OcVKhePRg3Dtq1i9qTJsExx0BOTkLLkiSVzk8ykiRJkqqHFi0gMxMaN47a770HAwZEk6BLkqocQylJkqRkEUK4PGTFvBWsWL6CMAwTXZFU9XTsCK+/DjVrRu0XXoDLL09sTZKkEhlKSZIkJYscoD+s6ruKLyd8SdzRH1LJ9torCqPW3OJ6333wf/+X2JokScUYSkmSJEmqfo48Eh59dG37b3+D555LXD2SpGIMpSRJkpJFBsRfijPllinE0xwlJW3UWWfBjTeubQ8YAO+8k7ByJElFGUpJkiQliwBIhTAljJYlbdz118PZZ0fLeXnRE/m++SahJUmSIoZSkiRJkqqvIIB//Su6nQ9g2TLo0wdmzEhoWZIkQylJkqTkkQ/BEwEt3mhBUOBQKanMUlPh+eejCdAB5syB3r1h4cLE1iVJWzlDKUmSpGSRD8HogCYfNTGUksqrVi147TXYcceo/f330eiplSsTW5ckbcUMpSRJkpJFKoR9Q+JHxWndtjVBYDAllUuTJvDmm9CiRdT+5BM48UTIz09sXZK0lTKUkiRJShapEDsrRrMrm9GhYwdiMT/KSeXWrh2MGwd160btMWPgggsgDBNaliRtjfwkI0mSJGnr0rUrvPwypKVF7cceg1tvTWhJkrQ1MpSSJElKFiGEeSHZy7PJXpVN6MgOadMdfDCMGLG2ff31MGxYwsqRpK2RoZQkSVKyyAGOgeWHLufzDz8nHo8nuiIpuZ10Evzzn2vb554LY8cmrh5J2soYSkmSJEnaev3tb3DJJdFyQQH06weffZbQkiRpa2EoJUmSlCwyIP5snKnXTSWe5igpqUIEAdx7Lxx/fNRetQoOPxx++CGxdUnSVsBQSpIkKVkEQG2I14xHy5IqRiwGTz4JPXtG7YULoXdvmDMnoWVJUnVnKCVJkiRJGRkwejTsskvUnj49GjG1bFlCy5Kk6sxQSpIkKVnkQ/BcQLO3mxEUOFRKqnD168O4cbDttlH7q6/g2GMhNzexdUlSNWUoJUmSlCzyIXg+oNm7hlLSFtO6NWRmQsOGUfu//4UzzwSfdilJFc5QSpIkKVmkQNgnpODQAlq0bkEQGExJW0TnzjBmDNSoEbWffhquuiqxNUlSNWQoJUmSlCzSIHZBjOY3NmfHnXckFvOjnLTF7LsvPPtsNAk6wN13w4MPJrYmSapmUjd1x+XLlzNt2jQWLFhAEAQ0adKEHXfckbp161ZkfZIkSZKUGMccA0OGwPnnR+1LLoGWLaFfv4SWJUnVRblCqenTpzNy5EheffVVJk+eTHy9+6pjsRg777wzffv25bTTTmO77bar0GIlSZK2dmEYkpeXB0BaWpq38Elb2qBBMGsW3HYbhCGccgo0awYHHJDoyiQp6ZUplJo6dSrXX389r7zyCg0aNKBnz57069eP7bbbjoYNGxKGIYsXL2b69Ol8+eWXDBkyhFtuuYVjjjmGW265hc6dO2/p9yFJklT9ZQMnQNbCLKZeP5V9DtqHlJSURFclVX+33BIFUyNGRE/iO/po+PBD2GWXRFcmSUmtTKHUbrvtxuGHH87YsWM5+OCDSU3d8G75+fm8/fbbDB06lN12241cH6EqSZJUMQoAHwImVa4ggMceg7lzYdw4WLoU+vSBTz6BNm0SXZ0kJa0yhVKTJk0q12in1NRUevfuTe/evZk2bdomFydJkqR1ZEB8eJxpn0wjnmYyJVWqtDT4z3/gwAPhiy+ikVO9e8NHH0HDhomuTpKSUpke2bI5t9916tRpk/eVJEnSOgKgMeTXz4+WJVWuOnVg7FjYfvuoPXVqdCtfdnZi65KkJFWuic5HjBhRbHLzsjrjjDM2aT9JkiRJqjKaNYPMTOjRA+bPj+aWOvnkaBSVc7xJUrmUK5QaNGgQl156KWEYluskDzzwgKGUJEnS5soHRkOTqU1YuM/CRFcjbb06dIA33oCePWHFCnj5Zbj4YnjooWj+KUlSmZQrlEpNTeX2228v90mGDh1a7n0kSZK0nnyIjYjRYlELFu21KNHVSFu3bt1g1Cg48kjIz4eHH4ZttoErr0x0ZZKUNMo0p9QawSam/pu6nyRJktaRAuFBIQUHFNCsZTM/Y0mJ1rs3PP742vZVV8GTTyauHklKMuUaKSVJkqQESoPYpTGar/4jqQoYMABmz4arr47aZ54ZzTvVu3di65KkJFCukVKSJEmSpPVceSVccEG0nJ8Pxx0HX3yR2JokKQkYSkmSJCWRMAwpKCigoKCg3A+fkbSFBAE88AD89a9Re8UKOPxw+PnnxNYlSVVcuUOpMAyJx+NlfhUUFGxycWEYcv3119OyZUtq1qzJwQcfzI8//rjR/WbNmsUpp5xC48aNqVmzJrvssgtf+JsKSZKU7LIh7B+y8JCFfPzOx8Tj8URXJGmNlBR4+mnYd9+oPW9edAvfvHmJrUuSqrByzSlVq1Ytmjcv3/wFYRiSmrppU1fdfffdPPjgg4wcOZL27dtz3XXX0atXL6ZOnUqNGjVK3Gfx4sXss88+HHjggYwbN46mTZvy448/0rBhw02qQZIkqSoJVgTEsh3sLlVJNWvCmDFRMDV1Kvz0ExxxBLz7LtSpk+jqJKnKKVdaNGfOnC1VRzFhGHL//fdz7bXXcvTRRwPw5JNP0rx5c0aPHk3//v1L3O+uu+6iTZs2PPHEE4Xr2rdvXyk1S5IkbVEZEH8kzg+f/UA8zVFSUpXUsCFkZkKPHjBrFkycCMcfD6++Cmlpia5OkqqUKvtrtunTpzNnzhwOPvjgwnX169ene/fufPLJJ6XuN2bMGLp160a/fv1o1qwZf/rTn/j3v/9dGSVLkiRtWQHQCnKb5EbLkqqmNm1g3DioXz9qjxsH55wDzgMnSUWUaaRUr169uOaaa9h///3LdfD33nuPO++8kzfffLPcha0ZlbX+7YLNmzff4IitX375hUceeYTLLruMq6++mokTJ3LRRReRnp7OgAEDStwnJyeHnJycwnZWVhZA4bxYqlzxeLxw7jKpuvH6VnXltV151nyv1ywHgenUlub1rU2y887w8ssEffoQ5ObCiBGErVsT3nxzoisr5LWt6szrO7HK+n0vUyjVoUMHDjnkELbbbjtOOOEEDjroIP70pz9RZ737opctW8aXX37J22+/zYsvvsivv/7KmWeeWaZCnnnmGc4999zC9tixY8u03/ri8TjdunXj9ttvB+BPf/oTkydPZujQoaWGUnfccQc33XRTsfXz588nOzt7k+rQpovH4yxdupQwDInFquxgPmmTeH2ruvLariT5kPpuKrV/rs383eczb948UlJSEl1Vtef1rU22005kDBlCg3PPJQhDgttuI6tuXVaV8u+Syua1rerM6zuxli1bVqZ+ZQql/vWvf/H3v/+dBx54gH/961/ccsstBEFAo0aNaNiwIWEYsnjxYhYvXkwYhjRq1IiTTz6Ziy++uMzzOR111FF07969sL1m5NLcuXNp2bJl4fq5c+fStWvXUo/TsmVLdtpppyLrOnfuzEsvvVTqPldddRWXXXZZYTsrK4s2bdrQtGlT6tWrV6b6VXHW/Na3adOm/uWhasfrW9WV13YlyQZeABbBqr1X0axZM0OpSuD1rc1y5pmEK1cSXHIJAPWuvpq6O+wAffsmtCzw2lb15vWdWKU9nG59ZZ7ovH379tx///3cc889fPjhh3zyySdMmzaNhQsXAtC4cWM6depEjx492HfffUkr5yR+devWpW7duoXtMAxp0aIF77zzTmEIlZWVxWeffcagQYNKPc4+++zD999/X2TdDz/8QNu2bUvdJyMjg4yMjGLrY7GYF2+CBEHg91/Vlte3qiuv7UqQCvF94xTMK6Bp86akpKT4/a4kXt/aLBdfDLNnw913E8TjBCefDG+/Dfvsk+jKvLZVrXl9J05Zv+flevoeQGpqKgceeCAHHnhguYsqjyAIuOSSS7j11lvZYYcdaN++Pddddx2tWrWi7zq/VTjooIM45phjGDx4MACXXnope++9N7fffjvHH388n3/+OY899hiPPfbYFq1XkiRpi0uH2FUxmq/+IymJ3HFH9DS+Z56B7Gw48kj4+GPo3DnRlUlSwpQ7lKpMV1xxBStWrOCcc85hyZIl7LvvvmRmZhYZBvbzzz+zYMGCwvYee+zBK6+8wlVXXcXNN99cOMLr5JNPTsRbkCRJkiSIxWD4cJg3D/77X1i8GHr3hk8+gVatEl2dJCVEEIY+l3R9WVlZ1K9fn6VLlzqnVALE43HmzZtHs2bNHGapasfrW9WV17aqM69vVahly+CAA+Drr6P2rrvCBx9A/fqVXorXtqozr+/EKmuu4k9GkiQpWeRA/LQ4c3rN4f233qegoCDRFUkqr7p14Y03YM0DoSZNiiY9X/2gJ0namhhKSZIkJYsQgkUBqctSCQgSXY2kTdWiBWRmQuPGUXv8eDjtNIjHE1qWJFU2QylJkqRkkQ7x++L8NPgn4qn+41VKajvuCGPHQs2aUfs//4HLL09sTZJUyQylJEmSkkUM2A6yW2X7KU6qDrp3j8KolJSofd99cO+9ia1JkipRuZ6+95e//IX8/PxynyQIAt5///1y7ydJkiRJ1doRR8DQoXD22VH78suhZUs46aTE1iVJlaBcodTnn3/Ol19+Wa4ThGHIXnvtVa59JEmSVIJ84F1o8F0DlnZdmuhqJFWUs86C2bPhhhui9sCB0Lw5HHRQQsuSpC2tXKFULBajY8eO5T6Jj1+UJEmqAPkQezDGNou2IWuXrERXI6kiXXcdzJwJ//435OXBMcfABx9A166JrkySthjTIkmSpGQRg7BbSH7XfBo2bkgQ+AQ+qdoIAvjXv+Coo6L2smXQpw/MmJHQsiRpSzKUkiRJShbpELsxRot/tWCXP+/iaHSpuklNheeegx49ovacOdC7NyxcmNi6JGkL8ZOMJEmSJFUVtWrBa6/BmmlTvv8+mgx95crE1iVJW4ChlCRJkiRVJY0bQ2YmtGgRtT/9FPr3h014ErokVWXlmug8Pz+f4cOHl+sEYRiSm5tbrn0kSZJUghyID44zd85cfrr4J/Y+cG9SUlISXZWkLaFdOxg3DvbfP5pf6rXX4Pzz4dFHo/mnJKkaKFcodeGFF/K///2v3CcZNGhQufeRJEnSekII/ghIW5RGGA8TXY2kLa1rV3jllWjC87y86Ml822wD11+f6MokqUKUK5S66667tlQdkiRJ2ph0iN8Z55cvfyGeGk90NZIqw0EHwciRcNJJUfuGG6BVKzjrrMTWJUkVoFyh1F/+8hfyN+E+5iAIeP/998u9nyRJktYRAzrDygVOeCxtVU48EWbPhssvj9rnnRfNN3XEEYmtS5I2U7lCqc8//5wvv/yyXCcIw5C99tqrXPtIkiRJktZx2WUwcybcfz8UFMDxx8O774L/1pKUxMoVSsViMTqueTRpOfeTJEnSZioAPoL639Vn6U5LE12NpMoUBHDvvfDHH/DCC7BqVTRSasIE2HHHRFcnSZvEtEiSJClZ5EHsnzHaPNeGWIEf46StTiwWzS914IFRe+FC6NUL5sxJbF2StIn8NCNJkpQsYhDuHFLQqYD6DesnuhpJiZCRET2Rb9ddo/aMGXDYYZCVldCyJGlTGEpJkiQli3SI3RWj+fDm7LbHbqSkpCS6IkmJUL8+jBsH224btb/+Go49FnJzE1uXJJWToZQkSZIkJZtWrSAzExo2jNpvvw1nnAHxeGLrkqRyKNdE5/n5+QwfPrxcJwjDkFwTe0mSJEmqWJ07w2uvwcEHQ3Y2PPMMtG4Nd92V6MokqUzKFUpddNFF/O9//yv3SQYNGlTufSRJkrSeXIj/Lc6cOXOYccEMuu/X3Vv4pK3dPvvAc89Ft+/F43D33VEwddFFia5MkjaqzKHUPffcw4ABA+jcufOWrEeSJEmliUMwPSB9UTr5ufmJrkZSVdG3Lzz8MKwZDHDJJdCiBRx/fCKrkqSNKvOcUnfffTddunRhu+2246KLLiIzM5OcnJwtWZskSZLWlQ7xm+LMOGMG8VTnjZG0jvPOg2uvjZbDEE49FcaPT2hJkrQxZQ6l5s6dy8cff8wpp5zChAkTOPzww2ncuDFHHnkkQ4cO5bffftuSdUqSJCkGdIXl2y/3cTWSirv5Zjj99Gg5NzcaQfXddwktSZI2pMwfZ4IgYK+99uLmm2/miy++YPbs2Tz44IPUqFGDK6+8kvbt27PLLrtw5ZVX8tFHHxH3qQ+SJEmSVHmCAB59FPr0idpLl0bLv/+e2LokqRSb/Du25s2bc8YZZ/Diiy+yYMEC3n77bXr37s1rr73G/vvvT5MmTejfvz+fffZZRdYrSZK09SoAJkLdaXWjZUlaX1oavPgi7LFH1J41C3r3hkWLEluXJJWgQgZ+p6amcuCBB/LPf/6TKVOm8Msvv3DLLbewbNkyPvzww4o4hSRJkvIgdmuMtk+2JVbg/XuSSlG7Nrz+Omy/fdSeOhWOPhpWrUpsXZK0njI/fQ9g+vTphGFYpr59+vShz+pho7/88gvbbbdd+auTJEnSWjEItw8pmF9AnXp1El2NpKqsWTPIzIS994Z58+Cjj+CUU+A//4GUlERXJ0lAOUOpzp07s/vuu5c5mAqCgDAM+fLLL8nNzd2kAiVJkrRaOsTuj9F89R9J2qAOHWDsWOjZE1asgJdfhosvhoceiuafkqQEK1colZaWxoQJE8p9koYNG5Z7H0mSJEnSZurWDV56CY44AvLz4eGHoXVruOqqRFcmSeWbUyrYxDR9U/eTJEmSJG2mXr1g2LC17auvhpEjE1ePJK3mDJmSJEnJIhfil8eZfcpsPvvwMwoKfASfpDI67TS444617TPPjOackqQEMpSSJElKFnEIpgWk/5JOzqqcRFcjKdn84x9wwQXRckEBwfHHk/rNNwktSdLWzVBKkiQpWaRB/Ko4v53yG/GUeKKrkZRsggAeeACOPTZqrlhBw1NPhZ9/TnBhkrZWhlKSJEnJIgXYC7J2yoqWJam8UlLg6adhv/2i5oIFBH36wLx5CS5M0taoXE/fy8nJYf/99y/XCcIwZPny5eXaR5IkSZK0hdSoAa++SrjffgRTphD8/DMcfji89x7UqZPo6iRtRcoVSn399deEYbilapEkSdKGxIHvoPYvtVnRbkWiq5GUzBo2JBw7lniPHqT88Qd88QX06wdjxkBaWqKrk7SVKNftezVr1tzklyRJkjZTLsSujdH+8fbE8p2FQdJmatOGxc8+S1i/ftTOzIRzzgEHIkiqJOUaKdW5c2d23333co+W+vLLL8nNzS3XPpIkSVpPAGGbkIIaBdSs5S/9JG2+/E6dCF95haB3b8jNhREjoHVruPXWRJcmaStQrlAqLS2NCRMmlPskDRs2LPc+kiRJWk8GxB6J0Xz1H0mqEAccAM88A8cfH42Suu22KJgaNCjRlUmq5so17jsIgk06yabuJ0mSJEmqBMcdBw88sLZ9wQXwyiuJq0fSVsHJCCRJkiRJcOGFcMUV0XIYwoknwkcfJbYmSdWaoZQkSVKyyIX4NXFmnTGLiR9PpKCgINEVSapu7rgDTjklWs7JgaOOgqlTE1uTpGrLUEqSJClZxCH4NiBjWgarVqxKdDWSqqNYDIYNg0MPjdqLF0Pv3jBrVmLrklQtGUpJkiQlizSIXxpn5vEziafEE12NpOoqPR1GjYLdd4/av/8OffrAkiUJLUtS9VOup+/l5OSw//77l+sEYRiyfPnycu0jSZKkEqQAPWFJypIEFyKp2qtbF8aOhb33hunT4bvv4JhjIDMTMjISXZ2kaqJcodTXX39NGIZbqhZJkiRJUlXRokUUQu2zDyxYAOPHw2mnwXPPRbf5SdJmKlcotdNOO22pOiRJkrQxceBHqDmzJqtaOaeUpEqw447w+uvwl7/AypXwn/9Aq1bwf/8HQZDo6iQlOeNtSZKkZJELsctjdPhXB2L5foyTVEm6d4/CqJSUqH3//XDvvQktSVL14KcZSZKkZBFA2Cwk3jhORg3ndJFUiQ4/HB59dG3773+HZ55JXD2SqoVy3b4nSZKkBMqA2PAYzVb/kaRKdeb/t3fnYVrV9f/Hn+eeFZFVWWRxSQ3cEHKF3FNWQTJNzEBLjVJLpVIptVwS3HJNcMn8oRDlhoiCqYUb7oopbmkiyr7IzAAyzNzn/P44BV+SZVjmPve55/ng4uq8b+57ejF+rnMdXnPuz306zJ4Nv/lNPP/gB9CmDRx9dLK5JKWWd0pJkiRJkurmkkvgRz+Kj2tq4PjjYfr0RCNJSi9LKUmSJElS3QQB/OEPMGBAPFdVQZ8+8MknyeaSlEqWUpIkSWmxCsLLQ2b/ZDZvvPQG2Ww26USSGqLiYvjzn6F793ieNw9694ZFi5LNJSl1LKUkSZLSIoTglYCyt8pYVrks6TSSGrJttoFHH4VOneL5ww+hf39YsSLZXJJSxVJKkiQpLYohPCtk9sDZREVR0mkkNXTbbQdTpkDbtvH80kswaBDU1iabS1JqWEpJkiSlRTHQC7448AtLKUn5YeedYfJkaNIknh99FM46CyLPUZI2zlJKkiRJkrT5unaFCROgpCSe77wTLr88yUSSUsJSSpIkKS0iYBaUzS+LjyUpXxx1FIwZs2b+7W/jckqSNsBSSpIkKS2qIfPTDLvftDuZGi/jJOWZQYPg+uvXzD/+MUyalFweSXnPqxlJkqQUiZpGRNtGFBcXJx1Fkr5q2DA4//z4OAzhu9+NN0CXpHXwakaSJCktyiEzLkOr//ySpLx03XUwdy6MHw9ffgnHHgsvvACdOiWdTFKe8U4pSZIkSdLWk8nAPffAkUfG8+LF0Lt3XFRJ0v9hKSVJkiRJ2rrKyuDhh6FLl3ieORP69oXKykRjScovllKSJElpsQrCa0JmD5vNW6++RTabTTqRJK1fs2YweTLsuGM8T58O3/kOrFqVaCxJ+cNSSpIkKS1CCJ4NKHupjIovKpJOI0kb164dTJkCLVrE81NPwQ9/GG+CLqnBs5SSJElKi2IITw+Z23cuUVGUdBpJqps99oBJk6C8PJ7HjoWLLko2k6S8YCklSZKUFsXAAFh8yGJLKUnp0qNH/Gl8mf/8E/Taa+Gmm5LNJClxllKSJEmSpPp33HFw221r5vPPh7/+Nbk8khKX16VUFEVceuml7LDDDjRq1Iijjz6af/3rXxt8TTab5ZJLLmGXXXahUaNG7LrrrlxxxRVEkT9NlCRJKRcBC6Dki5L4WJLSZuhQuPji+DiKYPBgmDo10UiSkpPXpdQ111zDzTffzOjRo3n55Zdp3LgxvXr1YuXKlet9zdVXX82oUaO49dZbee+997j66qu55ppruOWWW3KYXJIkqR5UQ+bMDJ2u7USmJq8v4yRp/S6/PN7sHOJP4hs4EN5+O9FIkpKRt1czURRx4403cvHFF3PcccfRpUsXxowZw5w5c5gwYcJ6Xzdt2jSOO+44+vXrx84778wJJ5xAz549eeWVV3IXXpIkqb6UQVQaEQRB0kkkafMEAYweDX37xnNFBfTuDbNmJZtLUs7lbSn1ySefMG/ePI4++ujVjzVr1oyDDjqIF198cb2v69GjB08//TQffvghAG+99RbPP/88ffr0qffMkiRJ9aocggcDWk1txaHHHEpRUVHSiSRp85SUxPtJHXBAPM+ZExdTS5Ykm0tSThUnHWB95s2bB0CbNm3WerxNmzar/2xdLrroIiorK+ncuTNFRUVks1l+97vfccopp6z3NdXV1VRXV6+eKysrAQjDkDAMt+Svoc0QhiFRFPm9V0FyfatQubZVyFzfKlSJr+1GjeDRRwkOOYTgo4/gvfeIBgwgeuKJ+M+kLZD4+m7g6vp9z5tSauzYsQwdOnT1/Nhjj23W1/nrX//K2LFjGTduHHvttRfTp0/nvPPOo127dpx66qnrfM2IESO47LLLvvL4woULN7h/lepHGIZUVFQQRRGZTN7ezCdtFte3CpVrW4XM9a1ClS9ru+i++2h57LEULVpE8MILVJ94IkvvvBO8G1RbIF/Wd0NVVVVVp+cFUZ58LF1VVRXz589fPVdXV7P33nvz5ptv0rVr19WPH3744XTt2pWbbrppnV+nY8eOXHTRRZx99tmrH7vyyiu57777eP/999f5mnXdKdWxY0e++OILmjZtuoV/M22qMAxZuHAhrVq18uShguP6VqFybedIDUSjIubNm8eiExaxV9e9/H7ngOtbhSqv1vbrrxMcdRTBsmUARD/5CdEtt8T7T0mbIa/WdwNUWVlJixYtqKio2GCvkjd3SjVp0oQmTZqsnqMoom3btjz99NOrS6nKykpefvllfvKTn6z366xYseIrC66oqGiDt46VlZVRVlb2lcczmYyLNyFBEPj9V8FyfatQubZzIILoqYiyJWVUHFWx+nuu+uf6VqHKm7V9wAHw4IPQrx/U1hKMGkXQoQP86lfJ5lKq5c36boDq+j3P2/8yQRBw3nnnceWVVzJx4kTefvtthgwZQrt27Rg4cODq533rW9/i1ltvXT3379+f3/3udzz22GPMnDmThx9+mN///vd8+9vfTuBvIUmStBUVQ/T9iPnHzCcqyoub3SVp6+nZE+6+e83861/DPfckFkdS/cubO6XW5YILLmD58uX86Ec/YunSpRxyyCFMmTKF8vLy1c/5+OOPWbRo0er5lltu4ZJLLuGss85iwYIFtGvXjqFDh3LppZcm8VeQJEnaeoohOjFi4XMLk04iSfVj8GCYPRuGD4/nM86ANm3AT1OXClLe7CmVTyorK2nWrNlG3/uo+hGGIQsWLKB169beZqmC4/pWoXJt5042m+W5554D4NBDD6XIjYDrnetbhSpv13YUwc9+Bv99R0zjxvCPf8Rv8ZPqKG/XdwNR117F/zKSJElpEQEVULSsKD6WpEIUBHDjjXDCCfG8fHm819RHHyUaS9LWZyklSZKUFtWQGZJhj6v2IFPjZZykAlZUBPfeC4cdFs8LF0Lv3rBgQbK5JG1VXs1IkiRJkvJPeTlMmAB77RXPH38c3zG1bFmisSRtPZZSkiRJaVEOwaSA7adtz2E9D3M/KUmFr0ULmDwZ2reP59degxNPhJqaZHNJ2iospSRJkiRJ+atjR5gyBZo3j+cpU+DMM+MN0SWlmqWUJEmSJCm/7b03PPIIlJXF8//7f3DxxclmkrTFLKUkSZLSogbC20PmXDaHGdNnEIZh0okkKXcOOwzuuy/+dD6Aq66C225LNpOkLWIpJUmSlBZZCB4NKH2ilMULFhP51hVJDc0JJ8BNN62ZzzkHHn44uTyStoillCRJUloUQ3RixMIjFhIVWUhJaqB++lO48ML4OIrg5JPh+eeTzSRps1hKSZIkpUUxRN+PmN9zvqWUpIZtxAgYPDg+rq6G/v3h3XeTzSRpk1lKSZIkSZLSJQjgj3+Enj3jeelS6N0bPv880ViSNo2llCRJUlpEwErIrMrEx5LUkJWUwAMPwDe+Ec+ffQZ9+sQFlaRUsJSSJElKi2rInJRhz9/uSabGyzhJokkTeOwx2GWXeH7nHfj2t+O39EnKe17NSJIkSZLSq21beOIJ2H77eJ46FYYMgTBMNJakjbOUkiRJSosy4H5o8WQLvnnUN8lkvJSTJAB23z2+Y2qbbeL5r3+FYcPiT+eTlLe8kpEkSUqLAIJGAUWNiygqLiIIgqQTSVL+OPBAuP9+KCqK55tuguuuSzaTpA2ylJIkSZIkFYa+feGOO9bMF1wAY8cml0fSBllKSZIkpUUthP8vZM7Vc3j/nfcJ3S9Fkr7qhz+Eyy9fM//gB/DUU8nlkbRellKSJElpUQvB/QGlj5SyYM4CIvdKkaR1u/hiGDo0Pq6piT+R7803k80k6SsspSRJktKiCKL+EYt7LCbKWEhJ0noFAfzhD3DccfG8bBn06QOffJJsLklrsZSSJElKixKIzoiYe+xcomJLKUnaoKIi+POfoUePeJ4/H3r3hkWLks0laTVLKUmSJElSYWrUCCZOhM6d4/nDD6F/f1ixItlckgBLKUmSJElSIdtuO5gyBXbYIZ5feglOOglqa5PNJclSSpIkKTVWQua4DHv/am8yq7yMk6Q622knmDwZmjaN50mT4Cc/AT8wQkqUVzOSJEmSpMK3777w8MNQUhLPd90Fl12WbCapgbOUkiRJSosy4D5o+khTDj78YDIZL+UkaZMcdRSMGbNmvuwyuPPO5PJIDZxXMpIkSWkRQNA8oLRVKaVlpQRBkHQiSUqfQYPg+uvXzD/+MTz6aHJ5pAbMUkqSJEmS1LAMGxb/BgjDeOPzl15KNpPUAFlKSZIkpUUthOND5tw4hw/f/ZAwDJNOJEnpde21cPLJ8fGXX8Kxx8IHHySbSWpgLKUkSZLSohaC+wJK/1rKvM/nEfmpUZK0+TIZ+NOf4n2mABYvhl69YO7cZHNJDYillCRJUloUQXRMxBf7f0GUsZCSpC1WVgYPPQRdusTzp59C375QWZlsLqmBsJSSJElKixKIzomYffxsomJLKUnaKpo1g8mTYaed4nn6dDj+eFi1KtFYUkNgKSVJkiRJatjatYMpU6Bly3h++mn4wQ/iTdAl1RtLKUmSJEmSOneGRx+F8vJ4HjcOLrww2UxSgbOUkiRJSouVkPluhr1+sxeZVV7GSdJW16MHjB8fb4IOcN11cOONiUaSCplXM5IkSWlSDUFNkHQKSSpcxx0Ht922Zh42DP761+TySAWsOOkAkiRJqqMy4I+wbfW2HNjqQDIZf74oSfVi6FCYPRuuuAKiCAYPhlat4Mgjk04mFRSvZCRJktIigKBNQPmO5ZQ3KicIvGNKkurNZZfB6afHx6tWwcCB8M9/JhpJKjSWUpIkSZIk/a8ggNGjoV+/eK6shD59YNasZHNJBcRSSpIkKS1qIXw4ZO7ouXz8wceEflS5JNWv4mL4y1/gwAPjec4c6N0blixJNpdUICylJEmS0qIWgj8GlIwpYfans4miKOlEklT4GjeGSZNg993j+b33YMAA+PLLZHNJBcBSSpIkKS0yEB0WUbFvBVFgISVJOdOqFUyZAm3axPMLL8D3vgfZbLK5pJSzlJIkSUqLUoh+HvHZSZ8RlVhKSVJOfe1r8PjjsO228TxhAvz0p/Gn80naLJZSkiRJkiTVxTe+AQ8+GO81BTBqFIwYkWwmKcUspSRJkiRJqquePeHuu9fMv/413HNPYnGkNLOUkiRJSouVEAwO2OPKPcis8jJOkhIzeDCMHLlmPuMMmDw5uTxSSnk1I0mSlCJBZUDRiqKkY0iSLrgg3lMK4g3PTzgBXn012UxSylhKSZIkpUUZ8AfY5u5t2K/HfmQyXspJUmKCAG64IS6jAFasgH794KOPks0lpYhXMpIkSWkRQLBTwDadt6Hxto0JgiDpRJLUsBUVwb33wmGHxfPChdCrF8yfn2wuKSUspSRJkiRJ2lzl5TBhAuy1Vzz/+99w7LGwbFmisaQ0sJSSJElKi1oIJ4fM+3/zmPnRTMIwTDqRJAmgRQuYMgU6dIjn116L39ZXU5NsLinPWUpJkiSlRS0Efwgovr2YWf+eRRRFSSeSJP1Xhw5xMdW8eTw/8UT8qXyeq6X1spSSJElKiwxEB0ZU7VFFFPiPHEnKO3vtBRMnQllZPI8ZAxdfnGwmKY9ZSkmSJKVFKUS/jvh08KdEJZZSkpSXDj0Uxo6NP50P4Kqr4Lbbks0k5SlLKUmSJEmStqbvfAduvnnNfM458NBDyeWR8pSllCRJkiRJW9s558BFF8XHUQTf+x4891yymaQ8YyklSZKUFtUQnBnQ6ZpOBKuCpNNIkjbmqqtgyJD4uLoaBgyAGTOSzSTlEUspSZKktIggWBBQsrSEAEspScp7QQB33QW9esXz0qXQuzd8/nmisaR8YSklSZKUFqXA76H8D+V0PbArmYyXcpKU90pK4P77Yb/94vnzz6FPn7igkho4r2QkSZLSIgPB1wO27bYtTZs3JQi8W0qSUqFJE3jsMfja1+L5nXdg4EBYuTLRWFLSLKUkSZIkSapvbdrAlCmw/fbx/Mwz8X5TYZhsLilBllKSJElpkYXw7yHz/zKfWZ/MIvQfMpKULrvvHt8xtc028Xz//XD++fGn80kNkKWUJElSWtRA8PuAopuKmPmvmUT+I0aS0ufAA+Myqqgonm++Ga67LtlMUkIspSRJktIiA9G+Ect2W0YUWEhJUmr17Qt33rlmvuACuO++5PJICbGUkiRJSotSiC6PmPnDmUQlllKSlGo/+AFcccXa85NPJpdHSoCllCRJkiRJSfj1r+HHP46Pa2vh+OPhjTeSzSTlkKWUJEmSJElJCAK49VYYODCely2L39r3ySeJxpJyxVJKkiQpLaohOCdg9xt2J1gVJJ1GkrQ1FBXBuHHQo0c8z58PvXrBokXJ5pJywFJKkiQpLSIIPgsoW1hGgKWUJBWMRo3g0Uehc+d4/te/4NhjYfnyZHNJ9cxSSpIkKS1Kgaug9NpSuuzfhUzGSzlJKhgtW8KUKdCuXTy//DIMGhTvNSUVKK9kJEmS0iIDQZeApt9sSvOWzQkC75aSpIKy004weTI0bRrPkybFG6FHfuKqCpOllCRJkiRJ+aJLF5gwAUpK4vmPf4TLLks0klRf8rqUeuihh+jZsyfbbbcdQRAwffr0Or3u/vvvp3PnzpSXl7PPPvvw+OOP129QSZKkXMhCOC1kwcQFzJ41mzAMk04kSaoPRx4JY8asmS+7DO64I7k8Uj3J61Jq+fLlHHLIIVx99dV1fs20adM4+eSTOf3003nzzTcZOHAgAwcO5J133qnHpJIkSTlQA8FVAZmRGT5+/2Mi384hSYVr0CD4/e/XzD/5CUycmFweqR4UJx1gQwYPHgzAzJkz6/yam266id69e/PLX/4SgCuuuIInn3ySW2+9ldGjR9dHTEmSpNzIQNQ5YsXnK4gCCylJKnjnnw+zZ8P110MYxkXV009D9+5JJ5O2irwupTbHiy++yLBhw9Z6rFevXkyYMGG9r6murqa6unr1XFlZCUAYht4Wn4AwDImiyO+9CpLrW4XKtZ0jxZAdkeXj5z8G4u+7m53XP9e3CpVrOyVGjiSYM4fgz3+GL78kOvZYouefh06dkk6W11zfyarr973gSql58+bRpk2btR5r06YN8+bNW+9rRowYwWXr2Dhu4cKFrFy5cqtn1IaFYUhFRQVRFPlR1yo4rm8VKtd27mSzWZYvXw7AggULKCoqSjhR4XN9q1C5tlNk5EhafP45Zc89R7BkCWHPniyeNInwf/7tqzVc38mqqqqq0/PyppQaO3YsQ4cOXT1PnjyZQw89NCf/38OHD1/r7qrKyko6duxIq1ataPrfj+JUzvz3p76tWrXy5KGC4/pWoXJt5042m6Vx48YAtG7d2lIqB1zfKlSu7ZSZOJHoiCMI3nqLos8/p9WppxJNnQr+m3WdXN/JKi8vr9Pz8qaUGjBgAAcddNDquX379pv1ddq2bcv8+fPXemz+/Pm0bdt2va8pKyujrKzsK49nMhkXb0KCIPD7r4Ll+lahcm3nwCrgAtht9m78+0f/9vudQ65vFSrXdoo0bw6TJ8f7SX36KcFbbxGccAI8/jiUliadLi+5vpNT1+953vyXadKkCbvtttvq340aNdqsr9O9e3eefvrptR578skn6e5GcJIkKe1CCD4KaDS7EUHkXlKS1ODssAM88QS0bBnPTz8Np50Wb4IupVDe3Cm1LkuWLGHWrFnMmTMHgA8++ACI74b6751PQ4YMoX379owYMQKAc889l8MPP5zrr7+efv36MX78eF577TXuuOOOZP4SkiRJW0sJcCkUVxSz1757+ZNfSWqIOnWCSZPgqKNg5Ur485+hfXu49tqkk0mbLK+vZCZOnEi3bt3o168fAIMGDaJbt26MHj169XNmzZrF3LlzV889evRg3Lhx3HHHHey777488MADTJgwgb333jvn+SVJkraqIggODGh+THO2a72dn7wnSQ1V9+7wl7/Af384cd11cOONiUaSNkcQRVGUdIh8U1lZSbNmzaioqHCj8wSEYciCBQto3bq1PwFWwXF9q1C5tlXIXN8qVK7tAnDHHfB/PjCM8ePhpJOSy5NHXN/Jqmuv4n8ZSZKktAghfCNk0VOLmDdnHqF7iEhSw/ajH8Gll66ZhwyBf/wjuTzSJrKUkiRJSotVEFwawKXw4Tsf4g3vkiR++1s444z4eNUqGDgQ/vnPJBNJdWYpJUmSlBYZiHaJWNl2JVFgISVJAoIARo2C/+zFTGUl9OkDs2Ylm0uqA0spSZKktCiF6MaIj372EVGJpZQk6T+Ki+ONzw88MJ7nzIHevWHJkmRzSRthKSVJkiRJUto1bgyTJsHuu8fze+9B//7w5ZfJ5pI2wFJKkiRJkqRC0KoVPPEEtGkTz9Omwfe+B9lssrmk9bCUkiRJSotVEPwqYJc7dyGoCZJOI0nKR7vsApMnw7bbxvOECfDTn4IfjqE8ZCklSZKUFiEEMwIaf9KYILKUkiStR7du8NBD8V5TEG+EftVVyWaS1sFSSpIkKS1KgAuhaHgRnffpTCbjpZwkaT2OOQb+9Kc188UXrz1LeaA46QCSJEmqoyIIDg1oQYukk0iS0uD7348/ie/CC+P5zDPj/ab69k02l/Qf/nhNkiRJkqRC9ctfws9+Fh9ns3DiifDKK8lmkv7DUkqSJCktQojejVj8/GIWzFtA5Ka1kqSNCQK44Ya4jAJYsQL69YOPPko2l4SllCRJUnqsAi6A6IKI9//5PmEYJp1IkpQGmQyMGQOHHx7PixZBr14wf36yudTgWUpJkiSlRQDRDhGrWq4iwrukJEmboLwcJkyAvfeO53//O75jatmyRGOpYbOUkiRJSosyiEZHfPiLD4lKLaUkSZuoeXOYPBk6dIjn11+HE06AmppEY6nhspSSJEmSJKmh6NABpkyJCyqAJ56AM84A9ylUAiylJEmSJElqSPbaCyZOhLKyeB4zBn7962QzqUGylJIkSUqLVRBcHrDT/9uJoCZIOo0kKc0OPRTGjYs/nQ9gxAi49dZkM6nBsZSSJElKixCC1wOafNCEILKUkiRtoeOPh1tuWTP/7Gfw4IPJ5VGDYyklSZKUFsUQnRuROT/D1/f8OkFgMSVJ2kJnnw3Dh8fHUQSnnALPPZdsJjUYllKSJElpUQyZYzK0PLElbTu0JZPxUk6StBX87ncwZEh8XF0NAwbAjBnJZlKD4JWMJEmSJEkNWRDAXXdBr17xvHQp9O4Nn3+eaCwVPkspSZKktAgh+jjii9e/YPHCxUR+fLckaWspKYEHHoD99ovnzz+HPn3igkqqJ5ZSkiRJabEKOBeyP80y480ZhGGYdCJJUiHZdlt47DHYddd4fucdOO44WLky2VwqWJZSkiRJaRFA1DKitkktEd4lJUmqB23awJQp0KpVPD/7LAweDP4gRPXAUkqSJCktyiD6U8T7w98nKrWUkiTVk912i++Y2mabeH7gATj//PjT+aStyFJKkiRJkiSt7YAD4jKqqCieb74Zrr022UwqOJZSkiRJkiTpq/r0iT+V778uvBDuvTe5PCo4llKSJElpsQqCqwN2HLcjQU2QdBpJUkNw2mlw5ZVr5h/+EP72t8TiqLBYSkmSJKVFCMG0gKbvNCWILKUkSTnyq1/BWWfFx7W18J3vwBtvJJtJBcFSSpIkKS2KIRoawY9h1067EgQWU5KkHAiCeE+pgQPjedky6NsX/v3vRGMp/SylJEmS0qIYMv0zbD9ke9rv1J5Mxks5SVKOFBXBuHHwzW/G8/z50Ls3LFyYbC6lmlcykiRJkiRp4xo1gokTYY894vlf/4Jjj4Xly5PNpdSylJIkSUqLCKLZERXvVbD0i6VEUZR0IklSQ9OyJUyZAu3axfMrr8BJJ8V7TUmbyFJKkiQpLaqBoVBzeg3/fPWfhGGYdCJJUkO0444weTI0bRrPjz0GP/4x+MMSbSJLKUmSpBSJGkeE5ZZRkqSEdekCEyZAaWk8//GP8NvfJplIKWQpJUmSlBblEI2LePfSdwlLLaYkSQk78kgYM2bNfPnlcPvtyeVR6lhKSZIkSZKkzXPSSXDDDWvms86CRx5JLo9SxVJKkiRJkiRtvvPOg1/8Ij4OQxg0CF58MdFISgdLKUmSpLSogeCmgA4PdCCoDZJOI0nSGldfDd/7Xny8ciUceyy8/36ymZT3LKUkSZLSIgvB3wOav9GcILSUkiTlkUwG/vQn+Na34nnJEujdG+bMSTaX8pqllCRJUloUQ3RaRHRqxM677UwQWExJkvJIaSk89BB07RrPn34KfftCRUWisZS/LKUkSZLSohgyJ2RoNbQVO35tRzIZL+UkSXmmaVN4/HHYeed4fustOP54qK5ONJbyk1cykiRJkiRp69lhB5gyBVq2jOe//x1+8IN4E3Tp/7CUkiRJSosIokURVTOrqKyoJIqipBNJkrRunTrBpEnQqFE8//nPcMEFyWZS3rGUkiRJSotq4DSo/l4101+eTuhPnCVJ+ax7dxg/Pt4EHeD66+GGG5LNpLxiKSVJkpQmRXgFJ0lKjwEDYPToNfOwYXFRJeEljSRJUnqUQ/hQyDtXvkNY6l1SkqSUOPNM+M1v1sxDhsT7TKnBs5SSJEmSJEn16ze/gTPOiI9rauDb344/mU8NmqWUJEmSJEmqX0EAo0bBscfGc2Ul9OkDn36abC4lylJKkiQpLWogGB3Q7pF2BLVB0mkkSdo0xcXxflIHHRTPc+dC796weHGyuZQYSylJkqS0yEIwOaDlyy0JQkspSVIKNW4MkybB178ez++/H2+G/uWXyeZSIiylJEmS0qIYokER0UkRO35tR4LAYkqSlELbbw9TpkDbtvE8bRqcfDJks8nmUs5ZSkmSJKVFMWS+n6HVua3YebedyWS8lJMkpdQuu8Djj8O228bzI4/AOedAFCWbSznllYwkSZIkScq9bt3goYfivaYARo+G3/0u2UzKKUspSZKktIggWhaxfMFyli9bTuRPkyVJaXfMMXDPPWvmSy6Bu+9OLI5yy1JKkiQpLaqBQfDlwC95fdrrhGGYdCJJkrbcKafANdesmX/0o/itfSp4llKSJEmSJClZv/gFnHtufJzNwoknwiuvJJtJ9c5SSpIkKS3KIHwwZMYVMwhLvEtKklRAggB+//u4jAJYsQL69YN//SvZXKpXllKSJElpEQDFEBVF8bEkSYUkk4ExY+Dww+N50SLo1Qvmz082l+qNpZQkSZIkScoP5eUwYQLss088f/IJ9O0LVVWJxlL9sJSSJElKi1oI/hTQ9vG2BFlvlZIkFajmzWHyZOjYMZ7feANOOAFqahKNpa3PUkqSJCktaiGYELD989tbSkmSClv79jBlSlxQAfztb3DGGRBFicbS1mUpJUmSlBbFEA2MCAeEtN+pPUFgMSVJKmB77gmPPgplZfE8Zgz86lfJZtJWZSklSZKUFsWQOSND64tas2unXclkvJSTJBW4Qw6BcePiT+cDGDkSbr012UzaarySkSRJkiRJ+ev449cuon72M3jwweTyaKuxlJIkSUqLCKKaiJXLVrLyy5VE7qshSWoozjprzVv3oghOOQWeey7ZTNpillKSJElpUQ18G5b1XMYrz71CGIZJJ5IkKXeuvBJOPTU+rq6GAQNgxoxkM2mLWEpJkiRJkqT8FwRw553Qu3c8L10aH3/2WaKxtPkspSRJktKiDMJxIe9e8i5hiXdJSZIaoJISuP9+2H//eP78c+jTB774Itlc2iyWUpIkSWkRAI0hbBTGx5IkNUTbbguPPQa77hrPM2bAwIGwcmWisbTp8rqUeuihh+jZsyfbbbcdQRAwffr0jb7mzjvv5NBDD6VFixa0aNGCo48+mldeeaX+w0qSJEmSpNxo3RqmTIFWreL52Wdh8GDIZpPNpU2S16XU8uXLOeSQQ7j66qvr/JqpU6dy8skn849//IMXX3yRjh070rNnT2bPnl2PSSVJknKgFoI/B7R+qjVB1lulJEkN3G67xXdMbbNNPD/wAJx/fvzpfEqF4qQDbMjgwYMBmDlzZp1fM3bs2LXmu+66iwcffJCnn36aIUOGbM14kiRJuVULwfiA1ktas+iwRUmnkSQpeQccEJdR/fvHd0ndcgu0bw+//GXSyVQHeV1KbQ0rVqygpqaGli1brvc51dXVVFdXr54rKysBCMPQj1pOQBiGRFHk914FyfWtQuXazpEAot4R2UVZ2rRr4/c8R1zfKlSubRWMXr3gjjvInH56PF90EVGbNkS9erm+E1LX73vBl1IXXngh7dq14+ijj17vc0aMGMFll132lccXLlzISjdKy7kwDKmoqCCKIjKZvH6HqbTJXN8qVK7tHDoh/p9mNGPRIu+WygXXtwqVa1sFpW9fGl90EU1GjgQgc+aZ1IwaxYK+fV3fCaiqqqrT8/KmlBo7dixDhw5dPU+ePJlDDz10i77myJEjGT9+PFOnTqW8vHy9zxs+fDjDhg1bPVdWVtKxY0datWpF06ZNtyiDNl0YhgRBQKtWrTx5qOC4vlWoXNsqZK5vFSrXtgrOlVcSVVQQjBpFUFtLh/POI+zShcz++yedrMHZUAfzf+VNKTVgwAAOOuig1XP79u236Otdd911jBw5kqeeeoouXbps8LllZWWUlZV95fFMJuPJOSFBEPj9V8FyfatQubZzI4oiampqACgpKSEI3PA8F1zfKlSubRWcW26BefPg4YfJLF9O0L8/wYsvwte+lnSyBqWu55S8OfM0adKE3XbbbfXvRo0abfbXuuaaa7jiiiuYMmUK+9uISpKkQrESGAiVR1Xy0tSX3CdDkqT/VVQEY8cSffObAAQLFsR7Ti1cmHAwrUvelFLrsmTJEqZPn867774LwAcffMD06dOZN2/e6ucMGTKE4cOHr56vvvpqLrnkEu6++2523nln5s2bx7x581i2bFnO80uSJG11WcAuSpKk9WvUiGjCBGp33z2eP/oIjj0Wli9PNpe+Iq9LqYkTJ9KtWzf69esHwKBBg+jWrRujR49e/ZxZs2Yxd+7c1fOoUaNYtWoVJ5xwAjvssMPq39ddd13O80uSJG1VZRDeHfL+he8TlthMSZK0Xi1bsmTcOKJ27eL5lVfgpJOgtjbZXFpL3uwptS6nnXYap5122gafM3Xq1LXmmTNn1lseSZKkRAXAdlDbzAtqSZI2JuzQgejxxwkOOwwqK+Gxx2DoULjrLnBPxryQ13dKSZIkSZIkbbZ99oFHHoHS0ni++274zW+SzaTVLKUkSZLSohZ4GLZ/dnuCrD/hlSSpTo44Au69d83dUVdcAbffnmgkxSylJEmS0qIWMvdkaDulraWUJEmb4rvfhRtuWDOfdVZ8B5USZSklSZKUFkUQfSsie3iW1ju0JnA/DEmS6u7cc+GXv4yPwxAGDYJp05LN1MDl9UbnkiRJ+j9KIHN+hjb/+SVJkjbRyJEwZw6MHQsrV0L//vDCC9C5c9LJGiTvlJIkSZIkSQ1DJhNvdn700fG8ZAn06hUXVco5SylJkqQUiaKIbDZLNpsliqKk40iSlD6lpfDgg9C1azzPmgV9+kBFRaKxGiJLKUmSpLRYCdGgiMXHLOaFp18gDMOkE0mSlE5Nm8LkybDzzvH8z3/C8cdDdXWisRoaSylJkqQUCZYHZFZ6CSdJ0hZr2xamTIHttovnv/8dTjst3gRdOeEVjSRJUlqUQTgq5MNhHxKWeMEsSdIW69QJJk2CRo3iefz4NZ/Qp3pnKSVJkpQWAdAOVm2/Kj6WJElb7uCD4S9/iTdBB/j97+PfqneWUpIkSZIkqWHr3x9uv33N/POfx3dNqV5ZSkmSJKVFLfAYtHyxJUHWW6UkSdqqzjgDfvvbNfOQIfE+U6o3llKSJElpUQuZOzK0e7SdpZQkSfXh0kvhzDPj45oaGDgQ3nor0UiFzFJKkiQpLTIQfTMie1CW7VtvTxBYTEmStFUFAdx2W/x2PoCqKujTB2bOTDRWobKUkiRJSotSyAzP0OaGNuzZdU8yGS/lJEna6oqL4/2kDj44nufOhd69YfHiZHMVIK9kJEmSJEmS/q9ttoFHH4Wvfz2eP/gABgyAL79MNleBsZSSJEmSJEn6X9tvD088AW3bxvO0aXDyyVBbm2yuAmIpJUmSlBbVEA4JmddrHs/87Rmy2WzSiSRJKmw77wyPPw5NmsTzI4/AOedAFCUaq1BYSkmSJKVFBMGSgOKqYgLc5FySpJzo1g0eeghKSuL59tvhyiuTzVQgLKUkSZLSohTCG0I+OucjwuIw6TSSJDUcRx8N99yzZr70Urj77sTiFApLKUmSpLTIAF+Dle1WehUnSVKufe97cO21a+Yf/Qgeeyy5PAXAyxlJkiRJkqS6+PnP4bzz4uNsFr77XXjllUQjpZmllCRJUlrUAk9D89ebE2TdU0qSpJwLArj++riMAlixAvr1gw8/TDZXSllKSZIkpUUtZG7O0OHBDpZSkiQlJZOBMWPgiCPiedEi6N0b5s1LNFYaWUpJkiSlRQai/SNqu9bSYrsWBIHFlCRJiSgrgwkTYJ994vmTT+I7pqqqEo2VNpZSkiRJaVEKmd9maHtbW/bZbx8yGS/lJElKTLNmMHkydOwYz2+8ASecAKtWJZsrRbySkSRJkiRJ2hzt28OUKdCiRTz/7W9wxhkQRcnmSglLKUmSJEmSpM21554wcSKUl8fzvffC8OHJZkoJSylJkqS0qIbwzJC5/efy3FPPkc1mk04kSZIADjkExo2LN0EHuPpquOWWZDOlgKWUJElSWkQQzA0oWVxCFPq2AEmS8sq3vw233rpmPvdceOCB5PKkgKWUJElSWpRCODLk30P/TVgcJp1GkiT9r5/8BH796/g4iuD734dnn002Ux6zlJIkSUqLDLAHrNhphVdxkiTlqyuugNNOi4+rq2HAAHjnnUQj5SsvZyRJkiRJkraWIIA77oA+feK5ogJ694bPPks2Vx6ylJIkSUqLLPA8NHu7WXwsSZLyU0kJ/PWvsP/+8Tx7dlxSffFFsrnyjKWUJElSWtRA5toMHf/ckUzWyzhJkvLattvCY4/BrrvG84wZMHAgrFyZaKx84tWMJElSWmQg2isi2zlLsxbNkk4jSZI2pnVreOIJaNUqnp99Nt78POstz2ApJUmSlB6lkLk6Q5u727DvAftSVFSUdCJJkrQxu+4Kjz8OjRvH84MPwnnnxZ/O18BZSkmSJEmSJNWn/feHBx6A4uJ4vvVWuOaaZDPlAUspSZIkSZKk+ta7N9x115r5oovg3nuTy5MHLKUkSZLSYhWEPw2Zc+Icpk2dRtb9KCRJSpdTT4Wrrloz//CH8Z5TDZSllCRJUlqEEHwSUDq7lNpVtUmnkSRJm+Oii+Css+Lj2lr4znfg9deTzZQQSylJkqS0KIXwspCZP5xJWBwmnUaSJG2OIICbb4bjj4/n5cuhb1/4+ONkcyXAUkqSJCktMkBXWLbbMq/iJElKs6IiuO8+OOSQeF6wIN5zauHCZHPlmJczkiRJkiRJudaoEUycCHvuGc8ffQT9+sV3TjUQllKSJElpkQVehSbvN4mPJUlSurVoAVOmQPv28fzqq/Dd70JNTbK5csRSSpIkKS1qIHNlhp3G7EQm62WcJEkFoWNHmDwZmjWL5ylTYNq0ZDPliFczkiRJaZGBaLeI7C5Ztm26bdJpJEnS1rLPPjBhAjRvHv/v4YcnHCg3ipMOIEmSpDoqhcyNGdr855ckSSogRxwBM2euuWOqAfBOKUmSJEmSpHzQgAopsJSSJEmSJElSAiylJEmS0mIVhL8ImfP9Obz83Mtks34EnyRJSi/3lJIkSUqLEIL3A0qXlFL9ZXXSaSRJkraIpZQkSVJalEA4PGTWW7MIi8Kk00iSJG0RSylJkqS0KAIOhsqayqSTSJIkbTH3lJIkSZIkSVLOWUpJkiSlRQi8DY3/3Tg+liRJSjFLKUmSpLRYBZmLM+xy1y5kar2MkyRJ6eaeUpIkSWkRQNQxIluepdE2jZJOI0mStEUspSRJktKiDDKjMrT5zy9JkqQ0875vSZIkSZIk5ZyllCRJkiRJknLOUkqSJCktVkH465DZP5zNqy+8SjabTTqRJEnSZnNPKUmSpLQIIXgroGxJGV8u/zLpNJIkSVvEUkqSJCktSiA8P+Tztz8nLAqTTiNJkrRFLKUkSZLSogg4ApYWLU04iCRJ0pZzTylJkiRJkiTlnKWUJElSWoTAv6DR543iY0mSpBSzlJIkSUqLVZD5RYZdb9uVTK2XcZIkKd3cU0qSJCktAohaR4RBSFl5WdJpJEmStoillCRJUlqUQebuDK3/80uSJCnN8vq+74ceeoiePXuy3XbbEQQB06dP36TXjx8/niAIGDhwYL3kkyRJkiRJ0ubJ61Jq+fLlHHLIIVx99dWb/NqZM2fyi1/8gkMPPbQekkmSJEmSJGlL5PXb9wYPHgzEBdOmyGaznHLKKVx22WU899xzLF26dOuHkyRJyrVVEI4MmTt3LvNPnc++B+xLUVFR0qkkSZI2S16XUpvr8ssvp3Xr1px++uk899xzG31+dXU11dXVq+fKykoAwjAkDP285VwLw5AoivzeqyC5vlWoXNs5Ugu8DGVLyqgaWEUYhgRBkHSqguf6VqFybauQub6TVdfve8GVUs8//zx//OMfN2n/qREjRnDZZZd95fGFCxeycuXKrZhOdRGGIRUVFURRRCaT1+8wlTaZ61uFyrWdI7VQ/L1iPv74Y5atXMaCBQu8UyoHXN8qVK5tFTLXd7Kqqqrq9Ly8KaXGjh3L0KFDV8+TJ0/e5P2gqqqqGDx4MHfeeSfbb799nV83fPhwhg0btnqurKykY8eOtGrViqZNm25SBm25//7Ut1WrVp48VHBc3ypUru3cyX43y4rnV7AN29C6dWtLqRxwfatQubZVyFzfySovL6/T8/KmlBowYAAHHXTQ6rl9+/ab/DU+/vhjZs6cSf/+/Vc/9t9bxoqLi/nggw/Yddddv/K6srIyysrKvvJ4JpNx8SYkCAK//ypYrm8VKtd2bkRRtPote36/c8f1rULl2lYhc30np67f87wppZo0aUKTJk226Gt07tyZt99+e63HLr74Yqqqqrjpppvo2LHjFn19SZKkREXALCibX0Z16+qNPl2SJCmf5U0ptS5Llixh1qxZzJkzB4APPvgAgLZt29K2bVsAhgwZQvv27RkxYgTl5eXsvffea32N5s2bA3zlcUmSpNSphsxPM+y+ZHfe/e27SaeRJEnaInl9D9vEiRPp1q0b/fr1A2DQoEF069aN0aNHr37OrFmzmDt3blIRJUmScipqGhFtG1FcnNc/W5QkSdqoIIqiKOkQ+aayspJmzZpRUVHhRucJCMOQBQsW0Lp1a9/7q4Lj+lahcm2rkLm+Vahc2ypkru9k1bVX8b+MJEmSJEmScs5SSpIkSZIkSTlnKSVJkpQWqyC8JmT2sNm89epbZLPZpBNJkiRtNkspSZKktAgheDag7KUyKr6oSDqNJEnSFvFjWyRJktKiGMLTQ+bOmEtU5GfVSJKkdLOUkiRJSotiYAAsbrE46SSSJElbzLfvSZIkSZIkKecspSRJktIiAhZAyRcl8bEkSVKKWUpJkiSlRTVkzszQ6dpOZGq8jJMkSenm1YwkSVKalEFUGhEEQdJJJEmStogbnUuSJKVFOQQPBrT6zy9JkqQ0804pSZIkSZIk5ZyllCRJkiRJknLOUkqSJCktaiC8OWT2RbP55+v/JAzDpBNJkiRtNkspSZKktMhC8LeAsmfLWLp4KVEUJZ1IkiRps7nRuSRJUloUQ/T9iPnvzScqspCSJEnpZiklSZKUFsUQnRix8LmFSSeRJEnaYr59T5IkSZIkSTlnKSVJkpQWEVABRcuK4mNJkqQUs5SSJElKi2rIDMmwx1V7kKnxMk6SJKWbe0qtw38/yaaysjLhJA1TGIZUVVVRXl5OJuMFtwqL61uFyrWdIyshqomoqq1i+fLlVFZWUlRUlHSqguf6VqFybauQub6T9d8+ZWOfFGwptQ5VVVUAdOzYMeEkkiRJ63F80gEkSZI2rKqqimbNmq33z4NoY7VVAxSGIXPmzKFJkyYEQZB0nAansrKSjh078tlnn9G0adOk40hbletbhcq1rULm+lahcm2rkLm+kxVFEVVVVbRr126Dd6p5p9Q6ZDIZOnTokHSMBq9p06aePFSwXN8qVK5tFTLXtwqVa1uFzPWdnA3dIfVfvrFSkiRJkiRJOWcpJUmSJEmSpJyzlFLeKSsr4ze/+Q1lZWVJR5G2Ote3CpVrW4XM9a1C5dpWIXN9p4MbnUuSJEmSJCnnvFNKkiRJkiRJOWcpJUmSJEmSpJyzlJIkSZIkSVLOWUop55599ln69+9Pu3btCIKACRMmbPD5U6dOJQiCr/yeN29ebgJLdTRixAgOOOAAmjRpQuvWrRk4cCAffPDBRl93//3307lzZ8rLy9lnn314/PHHc5BWqrvNWdv33HPPV87b5eXlOUos1d2oUaPo0qULTZs2pWnTpnTv3p3Jkydv8DWet5UGm7q2PW8rrUaOHEkQBJx33nkbfJ7n7vxkKaWcW758Ofvuuy9/+MMfNul1H3zwAXPnzl39u3Xr1vWUUNo8zzzzDGeffTYvvfQSTz75JDU1NfTs2ZPly5ev9zXTpk3j5JNP5vTTT+fNN99k4MCBDBw4kHfeeSeHyaUN25y1DdC0adO1ztuffvppjhJLddehQwdGjhzJ66+/zmuvvcZRRx3Fcccdx4wZM9b5fM/bSotNXdvgeVvp8+qrr3L77bfTpUuXDT7Pc3f+8tP3lKggCHj44YcZOHDgep8zdepUjjzySL744guaN2+es2zSllq4cCGtW7fmmWee4bDDDlvnc0466SSWL1/OpEmTVj928MEH07VrV0aPHp2rqNImqcvavueeezjvvPNYunRpbsNJW0HLli259tprOf3007/yZ563lWYbWtuet5U2y5Yt4xvf+Aa33XYbV155JV27duXGG29c53M9d+cv75RSanTt2pUddtiBY445hhdeeCHpONJGVVRUAPEF4Pq8+OKLHH300Ws91qtXL1588cV6zSZtibqsbYgvFnfaaSc6duy40Z/OS/kgm80yfvx4li9fTvfu3df5HM/bSqO6rG3wvK10Ofvss+nXr99Xzsnr4rk7fxUnHUDamB122IHRo0ez//77U11dzV133cURRxzByy+/zDe+8Y2k40nrFIYh5513Ht/85jfZe++91/u8efPm0aZNm7Uea9OmjXumKW/VdW136tSJu+++my5dulBRUcF1111Hjx49mDFjBh06dMhhYmnj3n77bbp3787KlSvZdtttefjhh9lzzz3X+VzP20qTTVnbnreVJuPHj+eNN97g1VdfrdPzPXfnL0sp5b1OnTrRqVOn1XOPHj34+OOPueGGG7j33nsTTCat39lnn80777zD888/n3QUaauq69ru3r37Wj+N79GjB3vssQe33347V1xxRX3HlDZJp06dmD59OhUVFTzwwAOceuqpPPPMM+v9x7uUFpuytj1vKy0+++wzzj33XJ588kk34y8AllJKpQMPPNB/7CtvnXPOOUyaNIlnn312oz9ZbNu2LfPnz1/rsfnz59O2bdv6jChtlk1Z2/+rpKSEbt268dFHH9VTOmnzlZaWsttuuwGw33778eqrr3LTTTdx++23f+W5nreVJpuytv+X523lq9dff50FCxas9a6ZbDbLs88+y6233kp1dTVFRUVrvcZzd/5yTyml0vTp09lhhx2SjiGtJYoizjnnHB5++GH+/ve/s8suu2z0Nd27d+fpp59e67Enn3xyg/s9SLm2OWv7f2WzWd5++23P3UqFMAyprq5e55953laabWht/y/P28pX3/rWt3j77beZPn366t/7778/p5xyCtOnT/9KIQWeu/OZd0op55YtW7bWT1w++eQTpk+fTsuWLdlxxx0ZPnw4s2fPZsyYMQDceOON7LLLLuy1116sXLmSu+66i7///e/87W9/S+qvIK3T2Wefzbhx43jkkUdo0qTJ6veoN2vWjEaNGgEwZMgQ2rdvz4gRIwA499xzOfzww7n++uvp168f48eP57XXXuOOO+5I7O8h/a/NWduXX345Bx98MLvtthtLly7l2muv5dNPP+WMM85I7O8hrcvw4cPp06cPO+64I1VVVYwbN46pU6fyxBNPAJ63lV6burY9bystmjRp8pV9LRs3bsx22223+nHP3elhKaWce+211zjyyCNXz8OGDQPg1FNP5Z577mHu3LnMmjVr9Z+vWrWKn//858yePZttttmGLl268NRTT631NaR8MGrUKACOOOKItR7/05/+xGmnnQbArFmzyGTW3KTao0cPxo0bx8UXX8yvfvUrdt99dyZMmLDBDaSlXNuctf3FF19w5plnMm/ePFq0aMF+++3HtGnT3KNHeWfBggUMGTKEuXPn0qxZM7p06cITTzzBMcccA3jeVnpt6tr2vK1C4rk7PYIoiqKkQ0iSJEmSJKlhcU8pSZIkSZIk5ZyllCRJkiRJknLOUkqSJEmSJEk5ZyklSZIkSZKknLOUkiRJkiRJUs5ZSkmSJEmSJCnnLKUkSZIkSZKUc5ZSkiRJkiRJyjlLKUmSJEmSJOWcpZQkSZIkSZJyrjjpAJIkSWk3Y8YMunXrRmlp6Tr/fNWqVbz55psbfc57773HypUr8/p5u+666zr/XJIkaVNZSkmSJG2hKIo48MADef7559f55wcffHCdn5Pvz5MkSdpafPueJEmSJEmScs5SSpIkSZIkSTlnKSVJkiRJkqScs5SSJEmSJElSzllKSZIkSZIkKecspSRJkiRJkpRzllKSJEmSJEnKOUspSZIkSZIk5ZyllCRJkiRJknLOUkqSJEmSJEk5ZyklSZIkSZKknLOUkiRJkiRJUs4VJx1AkiSpELz00ks0b958nX+2bNmyOj8nDc+TJEnaGoIoiqKkQ0iSJEmSJKlh8e17kiRJkiRJyjlLKUmSJEmSJOWcpZQkSZIkSZJyzlJKkiRJkiRJOWcpJUmSJEmSpJyzlJIkSZIkSVLOWUpJkiRJkiQp5yylJEmSJEmSlHOWUpIkSZIkSco5SylJkiRJkiTl3P8HSLfM7Replv4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "期权策略分析: SELL CALL\n",
      "============================================================\n",
      "行权价: 2.75\n",
      "权利金: 0.0456\n",
      "合约数量: 1手 (每手10000个)\n",
      "是否打算行权: 否\n",
      "分析价格区间: 1.38 - 4.12\n",
      "----------------------------------------\n",
      "最大收益: 0元 (标的价格: 1.38)\n",
      "最大损失: -1元 (标的价格: 4.13)\n",
      "起始位置收益: 0元\n",
      "盈亏平衡点: ['2.80']\n",
      "当前标的价格 2.746 下的收益: 0元\n"
     ]
    }
   ],
   "source": [
    "# 示例:6 备兑卖出认购期权\n",
    "print(\"\\n1. 卖出认购期权示例:\")\n",
    "df = analyze_single_option(\n",
    "    direction='sell',\n",
    "    option_type='call', \n",
    "    strike_price=2.75,\n",
    "    premium=0.0456,\n",
    "    quantity=1,\n",
    "    current_price=2.746\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 铁蝶式策略 (Iron Butterfly) / 铁鹰式策略 (Iron Condor)\n",
    "1. 策略构成：\n",
    "    - 铁鹰 (Iron Condor): 同时构建一个牛市价差（Bull Put Spread）和一个熊市价差（Bear Call Spread）。您卖出一个虚值的看跌期权和看涨期权，同时买入更虚值的期权作为保护。\n",
    "    - 铁蝶 (Iron Butterfly): 与铁鹰类似，但卖出的看跌和看涨期权拥有相同的行权价。\n",
    "2. 适用情景： 低波动性预期。您非常有把握地认为，在到期日之前，股价将在一个明确的、狭窄的区间内运行。\n",
    "3. 目标： 在股价波动性小的时候，赚取时间价值衰减带来的权利金收入。\n",
    "4. 优点： 高获胜概率。只要股价在设定的区间内，就能获得最大利润（收到的净权利金）。风险和回报都预先确定。\n",
    "5. 缺点： 利润空间有限。如果股价突破了您设定的区间（向上或向下），就会开始亏损。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四类：高波动性策略 (High Volatility Strategies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 买入跨式策略 (Long Straddle)\n",
    "1. 策略构成： 同时买入一份平值（At-the-Money）的看涨期权和一份相同行权价、相同到期日的看跌期权。\n",
    "2. 适用情景： 预期有大事件发生，股价将剧烈波动，但方向不明。\n",
    "3. 目标： 无论股价是暴涨还是暴跌，只要波动的幅度足够大，就能获利。\n",
    "4. 优点： 无论方向如何，都有巨大的潜在利润空间。\n",
    "5. 缺点： 非常昂贵，因为您需要支付两份期权的权利金。如果股价波动不大，您将损失全部或大部分投入的权利金。这是对时间价值（$\\theta$）最敏感的策略之一。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 买入宽跨式策略 (Long Strangle)\n",
    "1. 策略构成： 同时买入一份虚值（Out-of-the-Money）的看涨期权和一份虚值的看跌期权，两者到期日相同。\n",
    "2. 适用情景： 与Long Straddle类似，预期剧烈波动但方向不明。\n",
    "3. 目标： 与Long Straddle相同。\n",
    "4. 优点： 比Long Straddle便宜，因为买入的是虚值期权。\n",
    "5. 缺点： 股价需要比Long Straddle有更大的波动幅度才能开始盈利。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "common_3.8",
   "language": "python",
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