Double DQN Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Double DQN Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most popular value-based RL agent is the Double DQN Learning, made famous by its recent success in beating Atari games. Here, I've developed a similar model to take on the financial market. For more in-depth explanation of DQN, this site is a good place to start as Jaromiru made it easier to digest: https://jaromiru.com/2016/10/03/lets-make-a-dqn-implementation/. \n",
    "\n",
    "\n",
    "\n",
    "To begin, we create the custom OpenAI gym environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-08-27 18:36:49,670] Making new env: trading-v0\n",
      "C:\\Users\\Henry\\Anaconda2\\envs\\tensorflow\\lib\\site-packages\\matplotlib\\cbook.py:136: MatplotlibDeprecationWarning: The finance module has been deprecated in mpl 2.0 and will be removed in mpl 2.2. Please use the module mpl_finance instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please invoke .initialise_simulator() method next to complete initialization\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import gym_trading\n",
    "env = gym.make('trading-v0')\n",
    "\n",
    "instrument = \"GBPUSD\"\n",
    "accountID=\"\"\n",
    "access_token=\"\"\n",
    "additional_pairs = []\n",
    "input_planets_data = {}\n",
    "env.initialise_simulator(instrument, additional_pairs, accountID, access_token, input_planets_data, time_frame=\"1440\",\n",
    "                         oanda=False, size_of_history=4500, ATR=True, SMA=True, RSI=True, BB=False,\n",
    "                         trade_period=1, train_split=0.6, dummy_period=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Learning Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from gym_trading.envs.Q_learning import Q_Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters:\n",
    "* env: The OpenAI gym environment\n",
    "* hidden_layers: list, each element in the list represents the number of neurons per layer \n",
    "    Default: [256, 128, 64, 32]\n",
    "\n",
    "    states -> 256 -> 128 -> 64 -> 32 -> Q values\n",
    "\n",
    "\n",
    "* train_episodes: integer\n",
    "* update_frequency: Update target network with online network every t steps Default:500 (see below)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Naive Q network tends to result in instability - oscillation and divergence of reward function. DQN prevents/ minimizes this problem through:\n",
    "\n",
    "1. Use of Experience Replay: Store every experience <s, a, r, s'> in memory, then perform random sampling of batch to train the network off-policy . This breaks up correlation between adjacent data.\n",
    "\n",
    "2. Use of Separate Network (aka Target Network): Generate Target Q-values to compute loss function of every action during training. Update this network every t steps (update_frequency)\n",
    "\n",
    "Fret not, these are taken care of behind the scene. \n",
    "\n",
    "Make sure to install both Tensorflow and Baselines\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Q_learning = Q_Network(env, hidden_layers=[256, 128, 64, 32], train_episodes=1500, update_frequency=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Phase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call train_model method to start training network\n",
    "\n",
    "Params:\n",
    "- batch_size: number of experiences to be randomly chosen from memory for training\n",
    "\n",
    "- episodes_to_explore: Number of episodes to be used for random actions (Select this carefully to ensure sufficient exploration)\n",
    "\n",
    "- convergence_threshold: Average reward(pips) over the last 50 episodes to be exceeded for the network to reach convergence. Otherwise, training only terminates by the end of 1500 episodes.\n",
    "\n",
    "As you can see, in the first 100 episodes, the agent randomly picks action (Exploration). Exploration percentage stalls at 2% and the agent selectively picks action that maximises reward (Exploitation).\n",
    "\n",
    "Episode with the highest reward collected will be saved and reused for testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of Episode 1, Total Reward is -4807.2, Average Reward is -5.696\n",
      "Percentage of time spent on exploring (Random Action): 98 %\n",
      "{0: 406, 1: 438, 2: 396}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Henry\\Anaconda2\\envs\\tensorflow\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2889: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "C:\\Users\\Henry\\Anaconda2\\envs\\tensorflow\\lib\\site-packages\\numpy\\core\\_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of Episode 2, Total Reward is -345.1, Average Reward is -0.407\n",
      "Percentage of time spent on exploring (Random Action): 97 %\n",
      "{0: 427, 1: 420, 2: 393}\n",
      "\n",
      "End of Episode 3, Total Reward is -2514.5, Average Reward is -3.074\n",
      "Percentage of time spent on exploring (Random Action): 96 %\n",
      "{0: 384, 1: 434, 2: 422}\n",
      "\n",
      "End of Episode 4, Total Reward is -3830.3, Average Reward is -4.565\n",
      "Percentage of time spent on exploring (Random Action): 95 %\n",
      "{0: 404, 1: 435, 2: 401}\n",
      "\n",
      "End of Episode 5, Total Reward is -6578.9, Average Reward is -8.004\n",
      "Percentage of time spent on exploring (Random Action): 94 %\n",
      "{0: 402, 1: 420, 2: 418}\n",
      "\n",
      "End of Episode 6, Total Reward is -2763.8, Average Reward is -3.275\n",
      "Percentage of time spent on exploring (Random Action): 93 %\n",
      "{0: 444, 1: 400, 2: 396}\n",
      "\n",
      "End of Episode 7, Total Reward is -3950.6, Average Reward is -4.599\n",
      "Percentage of time spent on exploring (Random Action): 91 %\n",
      "{0: 437, 1: 422, 2: 381}\n",
      "\n",
      "End of Episode 8, Total Reward is 770.6, Average Reward is 0.990\n",
      "Percentage of time spent on exploring (Random Action): 90 %\n",
      "{0: 380, 1: 398, 2: 462}\n",
      "\n",
      "End of Episode 9, Total Reward is -2563.8, Average Reward is -3.150\n",
      "Percentage of time spent on exploring (Random Action): 89 %\n",
      "{0: 388, 1: 426, 2: 426}\n",
      "\n",
      "End of Episode 10, Total Reward is -4729.1, Average Reward is -6.032\n",
      "Percentage of time spent on exploring (Random Action): 88 %\n",
      "{0: 371, 1: 413, 2: 456}\n",
      "\n",
      "End of Episode 11, Total Reward is -5390.0, Average Reward is -6.349\n",
      "Percentage of time spent on exploring (Random Action): 87 %\n",
      "{0: 426, 1: 423, 2: 391}\n",
      "\n",
      "End of Episode 12, Total Reward is 431.6, Average Reward is 0.510\n",
      "Percentage of time spent on exploring (Random Action): 86 %\n",
      "{0: 407, 1: 439, 2: 394}\n",
      "\n",
      "End of Episode 13, Total Reward is -2652.7, Average Reward is -3.354\n",
      "Percentage of time spent on exploring (Random Action): 84 %\n",
      "{0: 399, 1: 392, 2: 449}\n",
      "\n",
      "End of Episode 14, Total Reward is -2832.5, Average Reward is -3.328\n",
      "Percentage of time spent on exploring (Random Action): 83 %\n",
      "{0: 412, 1: 439, 2: 389}\n",
      "\n",
      "End of Episode 15, Total Reward is -512.2, Average Reward is -0.635\n",
      "Percentage of time spent on exploring (Random Action): 82 %\n",
      "{0: 358, 1: 448, 2: 434}\n",
      "\n",
      "End of Episode 16, Total Reward is -6168.5, Average Reward is -7.672\n",
      "Percentage of time spent on exploring (Random Action): 81 %\n",
      "{0: 359, 1: 445, 2: 436}\n",
      "\n",
      "End of Episode 17, Total Reward is -315.2, Average Reward is -0.409\n",
      "Percentage of time spent on exploring (Random Action): 80 %\n",
      "{0: 370, 1: 401, 2: 469}\n",
      "\n",
      "End of Episode 18, Total Reward is 1038.8, Average Reward is 1.441\n",
      "Percentage of time spent on exploring (Random Action): 79 %\n",
      "{0: 354, 1: 367, 2: 519}\n",
      "\n",
      "End of Episode 19, Total Reward is -3270.0, Average Reward is -4.479\n",
      "Percentage of time spent on exploring (Random Action): 78 %\n",
      "{0: 340, 1: 390, 2: 510}\n",
      "\n",
      "End of Episode 20, Total Reward is -1320.0, Average Reward is -1.801\n",
      "Percentage of time spent on exploring (Random Action): 76 %\n",
      "{0: 353, 1: 380, 2: 507}\n",
      "\n",
      "End of Episode 21, Total Reward is 1217.7, Average Reward is 1.659\n",
      "Percentage of time spent on exploring (Random Action): 75 %\n",
      "{0: 357, 1: 377, 2: 506}\n",
      "\n",
      "End of Episode 22, Total Reward is -3796.4, Average Reward is -5.355\n",
      "Percentage of time spent on exploring (Random Action): 74 %\n",
      "{0: 336, 1: 373, 2: 531}\n",
      "\n",
      "End of Episode 23, Total Reward is -3751.3, Average Reward is -5.247\n",
      "Percentage of time spent on exploring (Random Action): 73 %\n",
      "{0: 323, 1: 392, 2: 525}\n",
      "\n",
      "End of Episode 24, Total Reward is -3655.0, Average Reward is -4.919\n",
      "Percentage of time spent on exploring (Random Action): 72 %\n",
      "{0: 369, 1: 374, 2: 497}\n",
      "\n",
      "End of Episode 25, Total Reward is -4461.0, Average Reward is -6.196\n",
      "Percentage of time spent on exploring (Random Action): 71 %\n",
      "{0: 321, 1: 399, 2: 520}\n",
      "\n",
      "End of Episode 26, Total Reward is 1135.5, Average Reward is 1.425\n",
      "Percentage of time spent on exploring (Random Action): 69 %\n",
      "{0: 444, 1: 353, 2: 443}\n",
      "\n",
      "End of Episode 27, Total Reward is -124.9, Average Reward is -0.165\n",
      "Percentage of time spent on exploring (Random Action): 68 %\n",
      "{0: 435, 1: 321, 2: 484}\n",
      "\n",
      "End of Episode 28, Total Reward is -2507.4, Average Reward is -3.174\n",
      "Percentage of time spent on exploring (Random Action): 67 %\n",
      "{0: 455, 1: 335, 2: 450}\n",
      "\n",
      "End of Episode 29, Total Reward is -2810.3, Average Reward is -3.777\n",
      "Percentage of time spent on exploring (Random Action): 66 %\n",
      "{0: 412, 1: 332, 2: 496}\n",
      "\n",
      "End of Episode 30, Total Reward is -3578.4, Average Reward is -4.588\n",
      "Percentage of time spent on exploring (Random Action): 65 %\n",
      "{0: 413, 1: 367, 2: 460}\n",
      "\n",
      "End of Episode 31, Total Reward is -2470.8, Average Reward is -3.140\n",
      "Percentage of time spent on exploring (Random Action): 64 %\n",
      "{0: 407, 1: 380, 2: 453}\n",
      "\n",
      "End of Episode 32, Total Reward is 680.3, Average Reward is 0.992\n",
      "Percentage of time spent on exploring (Random Action): 63 %\n",
      "{0: 379, 1: 307, 2: 554}\n",
      "\n",
      "End of Episode 33, Total Reward is -3037.6, Average Reward is -4.105\n",
      "Percentage of time spent on exploring (Random Action): 61 %\n",
      "{0: 416, 1: 324, 2: 500}\n",
      "\n",
      "End of Episode 34, Total Reward is -5497.3, Average Reward is -8.205\n",
      "Percentage of time spent on exploring (Random Action): 60 %\n",
      "{0: 364, 1: 306, 2: 570}\n",
      "\n",
      "End of Episode 35, Total Reward is -3070.3, Average Reward is -3.838\n",
      "Percentage of time spent on exploring (Random Action): 59 %\n",
      "{0: 367, 1: 433, 2: 440}\n",
      "\n",
      "End of Episode 36, Total Reward is 1201.3, Average Reward is 1.593\n",
      "Percentage of time spent on exploring (Random Action): 58 %\n",
      "{0: 451, 1: 303, 2: 486}\n",
      "\n",
      "End of Episode 37, Total Reward is 2014.7, Average Reward is 3.148\n",
      "Percentage of time spent on exploring (Random Action): 57 %\n",
      "{0: 366, 1: 274, 2: 600}\n",
      "\n",
      "End of Episode 38, Total Reward is -1533.2, Average Reward is -2.445\n",
      "Percentage of time spent on exploring (Random Action): 56 %\n",
      "{0: 335, 1: 292, 2: 613}\n",
      "\n",
      "End of Episode 39, Total Reward is -1893.4, Average Reward is -3.059\n",
      "Percentage of time spent on exploring (Random Action): 54 %\n",
      "{0: 347, 1: 272, 2: 621}\n",
      "\n",
      "End of Episode 40, Total Reward is 2528.4, Average Reward is 4.344\n",
      "Percentage of time spent on exploring (Random Action): 53 %\n",
      "{0: 332, 1: 250, 2: 658}\n",
      "\n",
      "End of Episode 41, Total Reward is -2066.0, Average Reward is -3.847\n",
      "Percentage of time spent on exploring (Random Action): 52 %\n",
      "{0: 277, 1: 260, 2: 703}\n",
      "\n",
      "End of Episode 42, Total Reward is -637.9, Average Reward is -1.061\n",
      "Percentage of time spent on exploring (Random Action): 51 %\n",
      "{0: 354, 1: 247, 2: 639}\n",
      "\n",
      "End of Episode 43, Total Reward is -149.8, Average Reward is -0.244\n",
      "Percentage of time spent on exploring (Random Action): 50 %\n",
      "{0: 379, 1: 235, 2: 626}\n",
      "\n",
      "End of Episode 44, Total Reward is 2353.2, Average Reward is 4.128\n",
      "Percentage of time spent on exploring (Random Action): 49 %\n",
      "{0: 325, 1: 245, 2: 670}\n",
      "\n",
      "End of Episode 45, Total Reward is 2049.4, Average Reward is 3.781\n",
      "Percentage of time spent on exploring (Random Action): 48 %\n",
      "{0: 306, 1: 236, 2: 698}\n",
      "\n",
      "End of Episode 46, Total Reward is -927.9, Average Reward is -1.747\n",
      "Percentage of time spent on exploring (Random Action): 46 %\n",
      "{0: 296, 1: 235, 2: 709}\n",
      "\n",
      "End of Episode 47, Total Reward is -1465.7, Average Reward is -2.750\n",
      "Percentage of time spent on exploring (Random Action): 45 %\n",
      "{0: 322, 1: 211, 2: 707}\n",
      "\n",
      "End of Episode 48, Total Reward is 1692.9, Average Reward is 3.231\n",
      "Percentage of time spent on exploring (Random Action): 44 %\n",
      "{0: 298, 1: 226, 2: 716}\n",
      "\n",
      "End of Episode 49, Total Reward is -1572.5, Average Reward is -3.332\n",
      "Percentage of time spent on exploring (Random Action): 43 %\n",
      "{0: 259, 1: 213, 2: 768}\n",
      "\n",
      "End of Episode 50, Total Reward is -2932.5, Average Reward is -5.523\n",
      "Percentage of time spent on exploring (Random Action): 42 %\n",
      "{0: 309, 1: 222, 2: 709}\n",
      "\n",
      "End of Episode 51, Total Reward is -2638.9, Average Reward is -4.488\n",
      "Percentage of time spent on exploring (Random Action): 41 %\n",
      "{0: 360, 1: 228, 2: 652}\n",
      "\n",
      "End of Episode 52, Total Reward is -1357.0, Average Reward is -2.328\n",
      "Percentage of time spent on exploring (Random Action): 39 %\n",
      "{0: 388, 1: 195, 2: 657}\n",
      "\n",
      "End of Episode 53, Total Reward is -233.4, Average Reward is -0.436\n",
      "Percentage of time spent on exploring (Random Action): 38 %\n",
      "{0: 320, 1: 215, 2: 705}\n",
      "\n",
      "End of Episode 54, Total Reward is 684.6, Average Reward is 1.366\n",
      "Percentage of time spent on exploring (Random Action): 37 %\n",
      "{0: 303, 1: 198, 2: 739}\n",
      "\n",
      "End of Episode 55, Total Reward is 3485.9, Average Reward is 6.602\n",
      "Percentage of time spent on exploring (Random Action): 36 %\n",
      "{0: 341, 1: 187, 2: 712}\n",
      "\n",
      "End of Episode 56, Total Reward is -775.3, Average Reward is -1.405\n",
      "Percentage of time spent on exploring (Random Action): 35 %\n",
      "{0: 351, 1: 201, 2: 688}\n",
      "\n",
      "End of Episode 57, Total Reward is -304.1, Average Reward is -0.575\n",
      "Percentage of time spent on exploring (Random Action): 34 %\n",
      "{0: 340, 1: 189, 2: 711}\n",
      "\n",
      "End of Episode 58, Total Reward is 2410.4, Average Reward is 4.514\n",
      "Percentage of time spent on exploring (Random Action): 33 %\n",
      "{0: 353, 1: 181, 2: 706}\n",
      "\n",
      "End of Episode 59, Total Reward is -374.0, Average Reward is -0.733\n",
      "Percentage of time spent on exploring (Random Action): 31 %\n",
      "{0: 338, 1: 172, 2: 730}\n",
      "\n",
      "End of Episode 60, Total Reward is -5.50000000001, Average Reward is -0.011\n",
      "Percentage of time spent on exploring (Random Action): 30 %\n",
      "{0: 300, 1: 197, 2: 743}\n",
      "\n",
      "End of Episode 61, Total Reward is 2015.8, Average Reward is 4.040\n",
      "Percentage of time spent on exploring (Random Action): 29 %\n",
      "{0: 349, 1: 150, 2: 741}\n",
      "\n",
      "End of Episode 62, Total Reward is 448.0, Average Reward is 0.911\n",
      "Percentage of time spent on exploring (Random Action): 28 %\n",
      "{0: 339, 1: 153, 2: 748}\n",
      "\n",
      "End of Episode 63, Total Reward is 2494.3, Average Reward is 5.186\n",
      "Percentage of time spent on exploring (Random Action): 27 %\n",
      "{0: 337, 1: 144, 2: 759}\n",
      "\n",
      "End of Episode 64, Total Reward is 313.3, Average Reward is 0.677\n",
      "Percentage of time spent on exploring (Random Action): 26 %\n",
      "{0: 324, 1: 139, 2: 777}\n",
      "\n",
      "End of Episode 65, Total Reward is -1547.9, Average Reward is -3.053\n",
      "Percentage of time spent on exploring (Random Action): 24 %\n",
      "{0: 360, 1: 147, 2: 733}\n",
      "\n",
      "End of Episode 66, Total Reward is -179.8, Average Reward is -0.369\n",
      "Percentage of time spent on exploring (Random Action): 23 %\n",
      "{0: 337, 1: 150, 2: 753}\n",
      "\n",
      "End of Episode 67, Total Reward is -1702.8, Average Reward is -3.399\n",
      "Percentage of time spent on exploring (Random Action): 22 %\n",
      "{0: 358, 1: 143, 2: 739}\n",
      "\n",
      "End of Episode 68, Total Reward is -441.4, Average Reward is -0.894\n",
      "Percentage of time spent on exploring (Random Action): 21 %\n",
      "{0: 369, 1: 125, 2: 746}\n",
      "\n",
      "End of Episode 69, Total Reward is 327.1, Average Reward is 0.694\n",
      "Percentage of time spent on exploring (Random Action): 20 %\n",
      "{0: 342, 1: 129, 2: 769}\n",
      "\n",
      "End of Episode 70, Total Reward is -275.9, Average Reward is -0.621\n",
      "Percentage of time spent on exploring (Random Action): 19 %\n",
      "{0: 337, 1: 107, 2: 796}\n",
      "\n",
      "End of Episode 71, Total Reward is 466.9, Average Reward is 1.045\n",
      "Percentage of time spent on exploring (Random Action): 18 %\n",
      "{0: 347, 1: 100, 2: 793}\n",
      "\n",
      "End of Episode 72, Total Reward is -863.2, Average Reward is -1.744\n",
      "Percentage of time spent on exploring (Random Action): 16 %\n",
      "{0: 378, 1: 117, 2: 745}\n",
      "\n",
      "End of Episode 73, Total Reward is 489.4, Average Reward is 1.030\n",
      "Percentage of time spent on exploring (Random Action): 15 %\n",
      "{0: 381, 1: 94, 2: 765}\n",
      "\n",
      "End of Episode 74, Total Reward is -2137.3, Average Reward is -4.471\n",
      "Percentage of time spent on exploring (Random Action): 14 %\n",
      "{0: 361, 1: 117, 2: 762}\n",
      "\n",
      "End of Episode 75, Total Reward is -1439.9, Average Reward is -2.951\n",
      "Percentage of time spent on exploring (Random Action): 13 %\n",
      "{0: 383, 1: 105, 2: 752}\n",
      "\n",
      "End of Episode 76, Total Reward is 207.1, Average Reward is 0.429\n",
      "Percentage of time spent on exploring (Random Action): 12 %\n",
      "{0: 376, 1: 107, 2: 757}\n",
      "\n",
      "End of Episode 77, Total Reward is 1684.0, Average Reward is 3.523\n",
      "Percentage of time spent on exploring (Random Action): 11 %\n",
      "{0: 377, 1: 101, 2: 762}\n",
      "\n",
      "End of Episode 78, Total Reward is 1309.5, Average Reward is 2.859\n",
      "Percentage of time spent on exploring (Random Action): 9 %\n",
      "{0: 356, 1: 102, 2: 782}\n",
      "New Maximum Score found! Saving this model. Score: 3744.08351528\n",
      "\n",
      "End of Episode 79, Total Reward is -494.6, Average Reward is -1.186\n",
      "Percentage of time spent on exploring (Random Action): 8 %\n",
      "{0: 311, 1: 106, 2: 823}\n",
      "\n",
      "End of Episode 80, Total Reward is 676.3, Average Reward is 1.606\n",
      "Percentage of time spent on exploring (Random Action): 7 %\n",
      "{0: 325, 1: 96, 2: 819}\n",
      "\n",
      "End of Episode 81, Total Reward is 193.5, Average Reward is 0.459\n",
      "Percentage of time spent on exploring (Random Action): 6 %\n",
      "{0: 333, 1: 89, 2: 818}\n",
      "\n",
      "End of Episode 82, Total Reward is 134.7, Average Reward is 0.313\n",
      "Percentage of time spent on exploring (Random Action): 5 %\n",
      "{0: 322, 1: 109, 2: 809}\n",
      "\n",
      "End of Episode 83, Total Reward is 1127.2, Average Reward is 2.585\n",
      "Percentage of time spent on exploring (Random Action): 4 %\n",
      "{0: 331, 1: 105, 2: 804}\n",
      "\n",
      "End of Episode 84, Total Reward is -1147.8, Average Reward is -2.884\n",
      "Percentage of time spent on exploring (Random Action): 3 %\n",
      "{0: 313, 1: 85, 2: 842}\n",
      "\n",
      "End of Episode 85, Total Reward is 259.9, Average Reward is 0.661\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 302, 1: 91, 2: 847}\n",
      "\n",
      "End of Episode 86, Total Reward is 279.6, Average Reward is 0.719\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 306, 1: 83, 2: 851}\n",
      "\n",
      "End of Episode 87, Total Reward is -87.4, Average Reward is -0.217\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 334, 1: 69, 2: 837}\n",
      "\n",
      "End of Episode 88, Total Reward is 48.3999999999, Average Reward is 0.104\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 387, 1: 78, 2: 775}\n",
      "\n",
      "End of Episode 89, Total Reward is -55.6, Average Reward is -0.122\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 376, 1: 78, 2: 786}\n",
      "\n",
      "End of Episode 90, Total Reward is 1093.0, Average Reward is 2.440\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 368, 1: 80, 2: 792}\n",
      "\n",
      "End of Episode 91, Total Reward is 1167.9, Average Reward is 2.624\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 358, 1: 87, 2: 795}\n",
      "\n",
      "End of Episode 92, Total Reward is 1028.9, Average Reward is 2.360\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 354, 1: 82, 2: 804}\n",
      "\n",
      "End of Episode 93, Total Reward is 625.3, Average Reward is 1.434\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 350, 1: 86, 2: 804}\n",
      "\n",
      "End of Episode 94, Total Reward is -725.5, Average Reward is -1.560\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 392, 1: 73, 2: 775}\n",
      "\n",
      "End of Episode 95, Total Reward is 301.8, Average Reward is 0.629\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 366, 1: 114, 2: 760}\n",
      "\n",
      "End of Episode 96, Total Reward is 152.6, Average Reward is 0.338\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 346, 1: 106, 2: 788}\n",
      "\n",
      "End of Episode 97, Total Reward is -410.5, Average Reward is -0.961\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 333, 1: 94, 2: 813}\n",
      "\n",
      "End of Episode 98, Total Reward is -382.8, Average Reward is -0.914\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 328, 1: 91, 2: 821}\n",
      "\n",
      "End of Episode 99, Total Reward is -500.4, Average Reward is -1.145\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 344, 1: 93, 2: 803}\n",
      "\n",
      "End of Episode 100, Total Reward is 1052.6, Average Reward is 2.483\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 335, 1: 89, 2: 816}\n",
      "\n",
      "End of Episode 101, Total Reward is -1212.6, Average Reward is -2.840\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 348, 1: 79, 2: 813}\n",
      "\n",
      "End of Episode 102, Total Reward is -213.2, Average Reward is -0.461\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 373, 1: 89, 2: 778}\n",
      "\n",
      "End of Episode 103, Total Reward is -832.1, Average Reward is -1.908\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 338, 1: 98, 2: 804}\n",
      "\n",
      "End of Episode 104, Total Reward is -1462.2, Average Reward is -3.151\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 398, 1: 66, 2: 776}\n",
      "\n",
      "End of Episode 105, Total Reward is -1690.9, Average Reward is -3.852\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 352, 1: 87, 2: 801}\n",
      "\n",
      "End of Episode 106, Total Reward is -1431.2, Average Reward is -3.587\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 305, 1: 94, 2: 841}\n",
      "\n",
      "End of Episode 107, Total Reward is -1783.2, Average Reward is -4.360\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 325, 1: 84, 2: 831}\n",
      "\n",
      "End of Episode 108, Total Reward is -967.3, Average Reward is -2.214\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 338, 1: 99, 2: 803}\n",
      "\n",
      "End of Episode 109, Total Reward is -1549.8, Average Reward is -3.630\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 343, 1: 84, 2: 813}\n",
      "\n",
      "End of Episode 110, Total Reward is -1067.1, Average Reward is -2.615\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 331, 1: 77, 2: 832}\n",
      "\n",
      "End of Episode 111, Total Reward is 396.1, Average Reward is 1.024\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 285, 1: 102, 2: 853}\n",
      "\n",
      "End of Episode 112, Total Reward is 315.9, Average Reward is 0.798\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 312, 1: 84, 2: 844}\n",
      "\n",
      "End of Episode 113, Total Reward is 185.0, Average Reward is 0.455\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 312, 1: 95, 2: 833}\n",
      "\n",
      "End of Episode 114, Total Reward is -513.1, Average Reward is -1.270\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 299, 1: 105, 2: 836}\n",
      "\n",
      "End of Episode 115, Total Reward is 579.8, Average Reward is 1.502\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 277, 1: 109, 2: 854}\n",
      "\n",
      "End of Episode 116, Total Reward is 395.4, Average Reward is 1.011\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 288, 1: 103, 2: 849}\n",
      "\n",
      "End of Episode 117, Total Reward is -298.6, Average Reward is -0.760\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 295, 1: 98, 2: 847}\n",
      "\n",
      "End of Episode 118, Total Reward is 102.3, Average Reward is 0.185\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 461, 1: 92, 2: 687}\n",
      "\n",
      "End of Episode 119, Total Reward is -1524.7, Average Reward is -2.818\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 446, 1: 95, 2: 699}\n",
      "\n",
      "End of Episode 120, Total Reward is -230.4, Average Reward is -0.567\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 306, 1: 100, 2: 834}\n",
      "\n",
      "End of Episode 121, Total Reward is -779.9, Average Reward is -1.398\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 443, 1: 115, 2: 682}\n",
      "\n",
      "End of Episode 122, Total Reward is -417.2, Average Reward is -0.763\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 451, 1: 96, 2: 693}\n",
      "\n",
      "End of Episode 123, Total Reward is -924.3, Average Reward is -1.715\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 448, 1: 91, 2: 701}\n",
      "\n",
      "End of Episode 124, Total Reward is 217.6, Average Reward is 0.408\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 437, 1: 96, 2: 707}\n",
      "\n",
      "End of Episode 125, Total Reward is -968.3, Average Reward is -1.767\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 466, 1: 82, 2: 692}\n",
      "\n",
      "End of Episode 126, Total Reward is -1125.5, Average Reward is -2.077\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 458, 1: 84, 2: 698}\n",
      "\n",
      "End of Episode 127, Total Reward is 263.9, Average Reward is 0.493\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 439, 1: 96, 2: 705}\n",
      "\n",
      "End of Episode 128, Total Reward is -482.0, Average Reward is -0.898\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 423, 1: 114, 2: 703}\n",
      "\n",
      "End of Episode 129, Total Reward is 635.0, Average Reward is 1.185\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 428, 1: 108, 2: 704}\n",
      "\n",
      "End of Episode 130, Total Reward is -809.8, Average Reward is -1.491\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 446, 1: 97, 2: 697}\n",
      "\n",
      "End of Episode 131, Total Reward is 623.8, Average Reward is 1.126\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 456, 1: 98, 2: 686}\n",
      "\n",
      "End of Episode 132, Total Reward is -1307.3, Average Reward is -2.239\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 479, 1: 105, 2: 656}\n",
      "\n",
      "End of Episode 133, Total Reward is -532.8, Average Reward is -0.922\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 464, 1: 114, 2: 662}\n",
      "\n",
      "End of Episode 134, Total Reward is 788.1, Average Reward is 1.368\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 473, 1: 103, 2: 664}\n",
      "\n",
      "End of Episode 135, Total Reward is -316.0, Average Reward is -0.582\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 443, 1: 100, 2: 697}\n",
      "\n",
      "End of Episode 136, Total Reward is -1203.2, Average Reward is -2.156\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 454, 1: 104, 2: 682}\n",
      "\n",
      "End of Episode 137, Total Reward is -98.2000000001, Average Reward is -0.180\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 452, 1: 93, 2: 695}\n",
      "\n",
      "End of Episode 138, Total Reward is -1010.2, Average Reward is -1.754\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 478, 1: 98, 2: 664}\n",
      "\n",
      "End of Episode 139, Total Reward is -18.1000000001, Average Reward is -0.031\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 473, 1: 108, 2: 659}\n",
      "\n",
      "End of Episode 140, Total Reward is 271.8, Average Reward is 0.489\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 452, 1: 104, 2: 684}\n",
      "\n",
      "End of Episode 141, Total Reward is -504.0, Average Reward is -0.897\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 452, 1: 110, 2: 678}\n",
      "\n",
      "End of Episode 142, Total Reward is -349.7, Average Reward is -0.622\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 464, 1: 98, 2: 678}\n",
      "\n",
      "End of Episode 143, Total Reward is -985.2, Average Reward is -1.696\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 473, 1: 108, 2: 659}\n",
      "\n",
      "End of Episode 144, Total Reward is 372.0, Average Reward is 0.670\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 455, 1: 100, 2: 685}\n",
      "\n",
      "End of Episode 145, Total Reward is -1231.9, Average Reward is -2.106\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 474, 1: 111, 2: 655}\n",
      "\n",
      "End of Episode 146, Total Reward is 97.0999999999, Average Reward is 0.172\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 467, 1: 97, 2: 676}\n",
      "\n",
      "End of Episode 147, Total Reward is -503.5, Average Reward is -0.899\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 474, 1: 86, 2: 680}\n",
      "\n",
      "End of Episode 148, Total Reward is 185.7, Average Reward is 0.326\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 472, 1: 97, 2: 671}\n",
      "\n",
      "End of Episode 149, Total Reward is -865.2, Average Reward is -1.447\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 495, 1: 103, 2: 642}\n",
      "\n",
      "End of Episode 150, Total Reward is -242.3, Average Reward is -0.417\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 470, 1: 111, 2: 659}\n",
      "\n",
      "End of Episode 151, Total Reward is -537.3, Average Reward is -0.928\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 478, 1: 101, 2: 661}\n",
      "\n",
      "End of Episode 152, Total Reward is -834.1, Average Reward is -1.474\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 465, 1: 101, 2: 674}\n",
      "\n",
      "End of Episode 153, Total Reward is 136.3, Average Reward is 0.244\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 462, 1: 97, 2: 681}\n",
      "\n",
      "End of Episode 154, Total Reward is -209.2, Average Reward is -0.359\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 488, 1: 94, 2: 658}\n",
      "\n",
      "End of Episode 155, Total Reward is 185.2, Average Reward is 0.316\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 493, 1: 94, 2: 653}\n",
      "\n",
      "End of Episode 156, Total Reward is 21.9999999999, Average Reward is 0.037\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 501, 1: 93, 2: 646}\n",
      "\n",
      "End of Episode 157, Total Reward is -112.0, Average Reward is -0.191\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 487, 1: 99, 2: 654}\n",
      "\n",
      "End of Episode 158, Total Reward is -234.1, Average Reward is -0.399\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 492, 1: 94, 2: 654}\n",
      "\n",
      "End of Episode 159, Total Reward is 1380.4, Average Reward is 2.456\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 463, 1: 99, 2: 678}\n",
      "\n",
      "End of Episode 160, Total Reward is 1345.8, Average Reward is 2.365\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 466, 1: 103, 2: 671}\n",
      "\n",
      "End of Episode 161, Total Reward is 511.9, Average Reward is 0.931\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 456, 1: 94, 2: 690}\n",
      "\n",
      "End of Episode 162, Total Reward is 1972.1, Average Reward is 3.509\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 466, 1: 96, 2: 678}\n",
      "New Maximum Score found! Saving this model. Score: 6920.24628114\n",
      "\n",
      "End of Episode 163, Total Reward is 105.6, Average Reward is 0.189\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 459, 1: 99, 2: 682}\n",
      "\n",
      "End of Episode 164, Total Reward is -130.1, Average Reward is -0.235\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 455, 1: 98, 2: 687}\n",
      "\n",
      "End of Episode 165, Total Reward is 514.2, Average Reward is 0.923\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 463, 1: 94, 2: 683}\n",
      "\n",
      "End of Episode 166, Total Reward is -211.4, Average Reward is -0.368\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 470, 1: 105, 2: 665}\n",
      "\n",
      "End of Episode 167, Total Reward is 292.0, Average Reward is 0.506\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 479, 1: 98, 2: 663}\n",
      "\n",
      "End of Episode 168, Total Reward is -200.1, Average Reward is -0.344\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 483, 1: 98, 2: 659}\n",
      "\n",
      "End of Episode 169, Total Reward is -464.5, Average Reward is -0.808\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 472, 1: 103, 2: 665}\n",
      "\n",
      "End of Episode 170, Total Reward is 812.3, Average Reward is 1.425\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 465, 1: 105, 2: 670}\n",
      "\n",
      "End of Episode 171, Total Reward is 1039.8, Average Reward is 1.834\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 471, 1: 96, 2: 673}\n",
      "\n",
      "End of Episode 172, Total Reward is 1444.0, Average Reward is 2.574\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 475, 1: 86, 2: 679}\n",
      "\n",
      "End of Episode 173, Total Reward is 1859.6, Average Reward is 3.286\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 484, 1: 82, 2: 674}\n",
      "\n",
      "End of Episode 174, Total Reward is 1425.8, Average Reward is 2.569\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 477, 1: 78, 2: 685}\n",
      "\n",
      "End of Episode 175, Total Reward is 1568.5, Average Reward is 2.776\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 481, 1: 84, 2: 675}\n",
      "\n",
      "End of Episode 176, Total Reward is 107.5, Average Reward is 0.191\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 483, 1: 79, 2: 678}\n",
      "\n",
      "End of Episode 177, Total Reward is 1057.0, Average Reward is 1.854\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 481, 1: 89, 2: 670}\n",
      "\n",
      "End of Episode 178, Total Reward is 545.0, Average Reward is 0.971\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 475, 1: 86, 2: 679}\n",
      "\n",
      "End of Episode 179, Total Reward is 358.4, Average Reward is 0.638\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 476, 1: 86, 2: 678}\n",
      "\n",
      "End of Episode 180, Total Reward is 1360.9, Average Reward is 2.383\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 476, 1: 95, 2: 669}\n",
      "\n",
      "End of Episode 181, Total Reward is 1095.7, Average Reward is 1.922\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 496, 1: 74, 2: 670}\n",
      "\n",
      "End of Episode 182, Total Reward is 1991.5, Average Reward is 3.575\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 475, 1: 82, 2: 683}\n",
      "New Maximum Score found! Saving this model. Score: 7120.41696589\n",
      "\n",
      "End of Episode 183, Total Reward is 745.3, Average Reward is 1.272\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 502, 1: 84, 2: 654}\n",
      "\n",
      "End of Episode 184, Total Reward is 2231.9, Average Reward is 3.978\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 476, 1: 85, 2: 679}\n",
      "New Maximum Score found! Saving this model. Score: 8879.46098039\n",
      "\n",
      "End of Episode 185, Total Reward is 93.9, Average Reward is 0.169\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 497, 1: 59, 2: 684}\n",
      "\n",
      "End of Episode 186, Total Reward is 1385.8, Average Reward is 2.515\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 468, 1: 83, 2: 689}\n",
      "\n",
      "End of Episode 187, Total Reward is 1544.8, Average Reward is 2.710\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 489, 1: 81, 2: 670}\n",
      "\n",
      "End of Episode 188, Total Reward is 1512.3, Average Reward is 2.639\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 486, 1: 87, 2: 667}\n",
      "\n",
      "End of Episode 189, Total Reward is 776.7, Average Reward is 1.436\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 471, 1: 70, 2: 699}\n",
      "\n",
      "End of Episode 190, Total Reward is 585.8, Average Reward is 1.019\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 462, 1: 113, 2: 665}\n",
      "\n",
      "End of Episode 191, Total Reward is 1122.3, Average Reward is 1.972\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 469, 1: 100, 2: 671}\n",
      "\n",
      "End of Episode 192, Total Reward is 1724.5, Average Reward is 2.973\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 480, 1: 100, 2: 660}\n",
      "\n",
      "End of Episode 193, Total Reward is -34.7000000001, Average Reward is -0.060\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 469, 1: 107, 2: 664}\n",
      "\n",
      "End of Episode 194, Total Reward is 1958.1, Average Reward is 3.613\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 444, 1: 98, 2: 698}\n",
      "\n",
      "End of Episode 195, Total Reward is 2030.0, Average Reward is 3.593\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 464, 1: 101, 2: 675}\n",
      "\n",
      "End of Episode 196, Total Reward is 1961.2, Average Reward is 3.553\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 449, 1: 103, 2: 688}\n",
      "\n",
      "End of Episode 197, Total Reward is 2516.8, Average Reward is 4.486\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 454, 1: 107, 2: 679}\n",
      "New Maximum Score found! Saving this model. Score: 11291.0556863\n",
      "\n",
      "End of Episode 198, Total Reward is 2287.2, Average Reward is 4.212\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 432, 1: 111, 2: 697}\n",
      "\n",
      "End of Episode 199, Total Reward is 2956.3, Average Reward is 5.385\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 442, 1: 107, 2: 691}\n",
      "New Maximum Score found! Saving this model. Score: 15919.3254827\n",
      "\n",
      "End of Episode 200, Total Reward is 2524.5, Average Reward is 4.421\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 452, 1: 119, 2: 669}\n",
      "\n",
      "End of Episode 201, Total Reward is 2445.9, Average Reward is 4.246\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 467, 1: 109, 2: 664}\n",
      "\n",
      "End of Episode 202, Total Reward is 2304.8, Average Reward is 3.994\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 472, 1: 105, 2: 663}\n",
      "\n",
      "End of Episode 203, Total Reward is 3046.8, Average Reward is 5.336\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 473, 1: 98, 2: 669}\n",
      "New Maximum Score found! Saving this model. Score: 16257.4259895\n",
      "\n",
      "End of Episode 204, Total Reward is 3414.9, Average Reward is 6.033\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 453, 1: 113, 2: 674}\n",
      "New Maximum Score found! Saving this model. Score: 20603.4311131\n",
      "\n",
      "End of Episode 205, Total Reward is 3819.9, Average Reward is 6.643\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 469, 1: 106, 2: 665}\n",
      "New Maximum Score found! Saving this model. Score: 25376.7582783\n",
      "\n",
      "End of Episode 206, Total Reward is 3698.7, Average Reward is 6.617\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 463, 1: 96, 2: 681}\n",
      "\n",
      "End of Episode 207, Total Reward is 1911.3, Average Reward is 3.377\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 478, 1: 88, 2: 674}\n",
      "\n",
      "End of Episode 208, Total Reward is 3346.6, Average Reward is 5.790\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 480, 1: 98, 2: 662}\n",
      "\n",
      "End of Episode 209, Total Reward is 3383.2, Average Reward is 5.734\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 484, 1: 106, 2: 650}\n",
      "\n",
      "End of Episode 210, Total Reward is 1682.2, Average Reward is 2.785\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 497, 1: 107, 2: 636}\n",
      "\n",
      "End of Episode 211, Total Reward is 2702.0, Average Reward is 4.587\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 490, 1: 99, 2: 651}\n",
      "\n",
      "End of Episode 212, Total Reward is 2956.1, Average Reward is 4.854\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 504, 1: 105, 2: 631}\n",
      "\n",
      "End of Episode 213, Total Reward is 1868.7, Average Reward is 3.157\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 490, 1: 102, 2: 648}\n",
      "\n",
      "End of Episode 214, Total Reward is 1783.7, Average Reward is 2.998\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 488, 1: 107, 2: 645}\n",
      "\n",
      "End of Episode 215, Total Reward is 2321.4, Average Reward is 3.869\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 493, 1: 107, 2: 640}\n",
      "\n",
      "End of Episode 216, Total Reward is 2297.9, Average Reward is 3.700\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 527, 1: 94, 2: 619}\n",
      "\n",
      "End of Episode 217, Total Reward is 703.0, Average Reward is 1.149\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 514, 1: 98, 2: 628}\n",
      "\n",
      "End of Episode 218, Total Reward is 2233.4, Average Reward is 3.626\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 525, 1: 91, 2: 624}\n",
      "\n",
      "End of Episode 219, Total Reward is 3227.2, Average Reward is 5.343\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 524, 1: 80, 2: 636}\n",
      "\n",
      "End of Episode 220, Total Reward is 4477.2, Average Reward is 7.164\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 523, 1: 102, 2: 615}\n",
      "New Maximum Score found! Saving this model. Score: 32072.511744\n",
      "\n",
      "End of Episode 221, Total Reward is 2742.0, Average Reward is 4.345\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 516, 1: 115, 2: 609}\n",
      "\n",
      "End of Episode 222, Total Reward is 2502.3, Average Reward is 3.941\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 526, 1: 109, 2: 605}\n",
      "\n",
      "End of Episode 223, Total Reward is 2733.6, Average Reward is 4.212\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 534, 1: 115, 2: 591}\n",
      "\n",
      "End of Episode 224, Total Reward is 1139.1, Average Reward is 1.752\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 527, 1: 123, 2: 590}\n",
      "\n",
      "End of Episode 225, Total Reward is 1869.1, Average Reward is 2.841\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 519, 1: 139, 2: 582}\n",
      "\n",
      "End of Episode 226, Total Reward is 2309.6, Average Reward is 3.564\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 536, 1: 112, 2: 592}\n",
      "\n",
      "End of Episode 227, Total Reward is 499.1, Average Reward is 0.755\n",
      "Percentage of time spent on exploring (Random Action): 2 %\n",
      "{0: 547, 1: 114, 2: 579}\n",
      "\n",
      "Converged!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Q_learning.train_model(batch_size=50, convergence_threshold = 2000, episodes_to_explore=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xeP/997Fp0yZ89dVXAIDjx49j0aJFuP7668+ud/r0aQBA3759MX/+fGzfvh1jxozBu+++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\nJtTRF56S48+YIc8a995rv12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hOxuoWLGksGhKf5cHEcxuNBqQ81y9emRKV+/f79v1F2ny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C8Ad4HD8pciO0fsbgB9jDFLAFwF4LtFblu7FNQOCbR+JdiGDdlB8/m4oMqvd+/gECxuJViuVkgg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\nIip7DMGIiIiIiIiIiKjs/R+PSA3BTThYhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f430c3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode with highest Reward: 220\n",
      "Episode with highest Average reward per trade: 220\n",
      "Episode with the most optimal policy (average reward * total reward): 219\n"
     ]
    }
   ],
   "source": [
    "Q_learning._overall_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the peak of exploration (episode 1), you can see that the agent is buying and selling in a highly random fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUMMARY OF EPISODE 1 \n",
      "\n",
      "Total Trades Taken: 844\n",
      "406 Buys, 438 Sells\n",
      "Total Reward: -4807.2\n",
      "Reward: -2399.4999999999745 pips(Buy), -2407.6999999999643 pips(Sell) \n",
      "\n",
      "Average Reward: -5.69573459716\n",
      "Win Ratio: 47.1563981043 % \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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9j69yDiaWL3eub2sLTCwVjaqfR/f/W3dHsimpUwAAAAtSd7dTqFEMti4t8R8f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\n3dLkyFlYjGlP1G+5tlT9p/e38pG0TjpduNxFRT2pw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4bvsbmtu4670tTI4P4Lzxkf1QoYiIiIiIiIiIyMClII+IAHDP/BGcPSaCn7+xjvs+\n2MadZyR32R/h60aAZ9eQTmNLG9e9upbtRdWYJizaWaoX4vuYaZq88V0O93+0jeZWJxaL0WuQZ3th\nDUCPIaxzx0eSEuXLV9uL8Xe3c3JS0FGrecIQ//12xzEMg0lx/nybVspNvVzLraclMWGIP+52K5Pj\nA3qdK8zHldOGh7AkvYyvd5QA4O1q474FIzhnbO9fizuKalixq5wp8QEUVjWyOrv9yXzThKZWJzH+\n7pzbj1/LH20qpLnVyeWTY/utBhEZOAzD4JIJ0fz50x24uVg5fURof5ckMmjtXX5ue2E1cUEePLdo\nF1dOiSXK352dxbVAe1B4akIgr6zIJuW+L5g4xJ/7F4zsDBAfjD11zfh72I/GJQwKuRX1NLY4+Wxr\nEZe/tAqXHywTmltRz87iWjbkVujvBxERERERERERkR9QkEdEAPBw2JiSEMjNsxO578NtnPXUsi77\n44M8+Pq2Uzo/bnOa3PLWBlbsKuexC1P48yc7WJhWwrnjInh5eTaZpbU0tzqpa26jvqkVh83KIxek\n4NGPXU4Gm9qmVn7zziY+3lzIjKQgfN1c+GZHCaZp9vhU+I6iagwDkkJ6vsmUEOxJQrDn0S77oNx7\n1ggq65sJ9Oy5g5Onw8bckQe+UW0YBs9fnorTaZKzp57thdW8tCyLW97aSEKQF6Mie14OY29w5+Hz\nUwj3devc3uY0GXXv52zKq+zXIM/O4hq8XW0khQyM/18i0v+uPzmeSyfFYLMYB+w6JiK9G94R5NlW\nUM3CHSW8uDSLt1bn8sDZI9hdXo9hQHyQJ2eODuODjQWcPiKET7cUccYTS7hiciw3z0nE27VrJ582\np0lWWR3bCqtxc7ES6u3KgqeX8sIVqcxKDumPy+x32WV1ACQGe1Jc1YiLrevvrm52Gw6bhfrmtv3O\nY5omeRUNmGbX7cHeDn0vFBERERERERGR45buqItIF1dOiWVEuA81jS2d277aXsK/V+VQUdeMj5sL\nLyzdxaqsCr7aXsxd84ZxzthIVmSW8+mWIv6xKJOHPkvDx80FNxcr7g4rFsMgo6SWi7KjOGVocD9e\n3eDR0NzGVS+vZu3uCn77o2Sumx7HG6ty+GBjAUXVjfi52/l8axG1Ta3sLKphS0E1W/KrGBLggZt9\n4N/UCPR09BriORwWi0FsoAexgR5Mjg8g9Y9f8dGmgl6DPGt2VxDh69YlxANgtRiMjPBhY15Vn9V2\nMBpb2rrcjMour2NIoIeW8RCRLvpzyT+R40WwlytBXg425lWyLKOMyXEBHQH1jXg6bET7u+Nmt5Ia\n68+6u+cAcMfpyfztizT+uTyLZRllvP+Lqbi6WGlzmlz76hqWZ5bR2OIEwDDglKQgnCa8snz3CRvk\nyeoI8rx9/WT8eulM9PTCDP72eRq1Ta29fn+7451N/GdtXrftVovBFZNj+cP84X1XtIiIiIiIiIiI\nyAChuwEi0oVhGN2WRXJ1sfLvVTlsKajCZrHw4Cc7sNss/PLURK6ZHgfAmaPDeXtNHg99lsas5GBe\nvCK1M4RQ1dBCyn1fsLWgWkGeg/TSsixWZe3h8YvGsGBMBND+RDNAenEtGSW13P/RNgA87FZGhPtw\nycRozhwd1m81DxS+7namJgTy8eZCfvuj5G5hGNM0WZO9h0lxPS/ZlRLpwysrdtPc6sRusxz1estr\nm5j612+4bc5Qrp3R/u8pu6ye1Fi/o35uERGRE9HwMG8+2lRIm9Pkb+fFMSMpiH98m8FjX6UzMrx7\nCNjPw86D54xi1tBgrnl1Db97dzNnjg7D27W9W+L8lHBOTgoiwteNK15axcK0UuxWC4vTS3l/Qz4z\nEoN6DbMcr3aV1eHr7rLf644Pav/ddldpLaMjfbvt/3BjAf9Zm8fFE6JIjfn+7xOnafL2mlze25DP\n3WcOU/BZRERERERERESOOwryiMgB7b2hsTm/iprGVmwWg7V3zcZrn2UFZiQF8dFN01iVtYdzx0V2\neUHdx82FmAB3Nh/jLieD2daCKoYEenSGeIDOZbHSS2r5dHMhyaFevHTlSYR6u2Kx6AbGvuaNDuOO\ndzYx74mlQPuSF61OJ21Ok5Y2k+LqJlJjeg7KpET50rwki7Siml47+vSlDbmVNLY4eejzHUxLDGRI\noAcFVQ3EBvTf0l4iIiLHs1+fNhQ/dxea25zMSArCajH4xaxEzkqJwMPRe2fD2cNDuHxyDK+u2M3/\n1uWTEuWLzWLwp3NGdi63ddaYcN5Zm8dvfpTMg59s51dvbiAu0IP3fjG125Jc/a2oqpEHPt5GW5vJ\n5PgA5qeE4/+D4I3TadLqNNlZXMP6nArW5VRimiZ/mD+i27H7yi6rIzbAY7/nTwhu35/ZQ5DHNE0e\n/XInIyO8eWDBSGzWruHqplYnd723hbyKBqL83Q/lskVERERERERERAY8BXlE5IB83F2I9ndnS34V\n2WX1jIvx6xLi2WtkhA8jI3oOPoyM8GFDTuXRLvW4kV5c2xnc2SvAw46fuwvLMspYs7uC2+YkdVsa\nStqdMSqMRTtLqW9qxWqxYLMYWK0GVsPAZjFwd1iZNzq8x7EpHTeSLnp+BS42C7OHhfDw+SlHrdZN\neVVYDPBxs3PLWxt4+PwUTBOGBO7/5peIiIgcnlGRPvz9orHdtkcHHDgQct9ZI7hmWhwXPb+CjbmV\nTIkP6BLQuWVOEgEedi6bFMO0hEDSimu49a0N3PzmBl64PHVAha+fXZTJ51uKCPd147OtRTzw0TZO\nGRpEoKeDjXlVNLa0kbOnnjan2TkmyMtBdUMLm/KrePeGqfi49RxOyiqrY3Iv3Q/3ivb3wGoxyCyp\n67Zvze4KssrqePj8lG4hHvj+97WNeZUK8oiIiIiIiIiIyHFHQR4ROSijInxYnllGRX0Lt58+9LDG\nf7ypkIq65hNuaYFD1dLmJKusjjnDQ7psNwyDxGAvvtlRAsCPRmkZrd54Omw8fcm4wxob6efG785I\nJr+igazyet5Zm8cVk2OPWneezflVJAR78tsfJXPVy2v47f82ARCrII+IiMiAYxgG0QHuXH9yPPd8\nsJVTh3X9fS3C1407zxgGwNBQL4aGelFZ38wf3t/KY1/t5LbTDv336L5mmiZZZXW8tTqXBWMieOSC\nFLYXVvPu+nze35BPTWMrE4b442G3MXdkKG4uVmIDPRgb5UuknxsrdpVzyf99x6vLswnxcSV3T32X\n+V2sFgqrGg/4u4zdZiEmwJ0lGWXdwumfbinEw27ljFGhPY4dGuqF3WZhU14VZ/YSzhYRERERERER\nERmsFOQRkYMyYYg/H28uBGD2D25YHIxRHZ163tuQz0+nDunT2o43u8vraHWa3TryAFx/chzB3g7i\ngjx73C9HzjAMrpsRD0BNYwtT//INf/hgCwtSwgn0chDq7cr4GL8uy8cdLtM02ZxfxYzEIGYlh3Dx\nhCj+vSoXgCEHWI5CRERE+s9FE6Joam3j/NQDL4V52aQYtuRX8eQ3GYwI92buyP4LY5umyc9fX8dn\nW4sAuGZ6++/lw8K8GRbmzW/nJuM0zR674Ow1JT6QWcnB/P3rdNqcJoYB+/5WtLeBT1KI1wHrGRvl\nx3/X5bExt3vnzp9Misbd3vNLFnabheFh3ixNL2Pt7j2Mj/E/4LlEREREREREREQGCwV5ROSgXD45\nhpOTgnB1sRLq43rI41Nj/ZgU5899H24jwNPBWSl6crY36cW1ACQGd7/5ceqwkG5PfsvR4+Xqwu2n\nD+XeD7exfp+l4W6bk8RNpyYe8fzF1U2U1jQxuqPbz13zhrMso5y6plZ83HteqkJERET6n8Nm7Qz+\nHohhGNy/YCRpxbX87PV1WPdZXuvWOUncODPhqNS4s7iGz7cU8fNT4rFZLdQ3t/Lst5l8trWIK6fE\ncsrQIIaFeXcZY7EYWDhwWPnGmQl8m1bCpROj+ePZI7sEnKsaWthRWE1q7IHDNQ+dN5o75vbcpSjI\n07HfsROH+PPc4l2c+48VLPvtLCK05KyIiIiIiIiIiBwnFOQRkYNiGMYRLfXjsFl57eqJjL3/S9bt\nrlCQZz/SS9qDPPHB6sgyEFw2OZZLJsZQWd9MeV0zD3y0jX8uz+aa6XG42a1HNPeKXWUApET5AuDh\nsPHa1RMorm464rpFRERk4HB1sfLiFan8+7scmlqdAHy2tYj3N+QflSBPYVUDl734HcXVTeRVNOBi\nM3h/fQE1Ta38aGQo98wffkTdBcfH+LHyd6cS5OnoNo+PmwsT4wIOah6rxSDE+9AfEgC47bShpET5\ncsMb69iQU6kgj4iIiIiIiIiIHDcU5BGRY8bFaiHY20FpjUIK+5NWXEOEr1uvSwnIsWe1GAR4Ogjw\ndPCLmQlc+PxKxj3wJbOGBfP0JeMOe9731hcQ4evG6I6l5wBiAjyI0bJaIiIix51AT0eXjn6erjb+\n8ukOSqobCT7EMIvTaVLT1AqAm4sVu63rUlhPfJ1BdUMrp48I4a01uThsFuaNCuPiidGk9tESocFe\nhxfA6St2m4XZw0KwWy1syq9k3uj+W7JMRERERERERESkL+kusYgcU8FeDoqrG/u7jAHLNE1WZ+05\n6KeY5dibMMSf88ZHsiyjjCU7SzFNs9ebYaZpUlLThIvVgr+Hvcv27PJ6lmaUcd2MOCyWI7+ZJiIi\nIoPLtIRAAJZnljNrWDBL08tYk11BS5uz85jpiYGcNiK0y7jWNieXv7SK5ZnlQHtA6L0bpxDp5955\nzPbCasZG+/LkxeP4Nq2ECUP88XW3c7yx2ywMC/NiU25Vf5ciIiIiIiIiIiLSZxTkEZFjKsTblfU5\nlf1dxoCVXV5PSU0TE4f493cp0gvDMHj4/BTe+G43v393C3kVDUT5u/d47Ovf5XD3e1sAuOGUeG47\nbShWi8EDH23npWVZAJw9JuKY1S4iIiIDx/Awb3zdXbj/o23c9p+NtDlNXF0snV0ZG5rbeHd9Pkvu\n8Mdvn0Dws4syWZ5ZzrXThxDs5crDX6Txl0938FRHl0DTNMkoqeXccRHYbZZuQaDjzahIH95fX4DT\naSocLSIiIiIiIiIixwXLgQ/pnWEY5xuGsdUwDKdhGKk/2HenYRgZhmGkGYZx+j7bxxuGsblj3xNG\nRxsDwzAchmG81bH9O8MwYo+kNhEZmIK9HJTUNGKaZn+XMuDUNrWyPLMMgEnqyDPgDQ/zBmBrQXWP\n+9ucJs8vzmR4mDfnjovkmW8zufbVNRRUNvDm6hymJQTy7E/GMTTU61iWLSIiIgOExWJw6cRown1d\nuX5GHG9fP5kt957OurvnsO7uObz/i6nUNbfy/JJdnWPqm1t5amEGZ4wK5ffzhnPtjDiuPzmejzYV\nsjp7DwBF1Y3UNrWSEOzZX5d2TI2O9KWmqZVdZbX9XYqIiIiIiIiIiEifOKIgD7AF+DGweN+NhmEM\nBy4CRgBzgWcMw7B27P4HcC2Q2PE2t2P71UCFaZoJwGPAX4+wNhEZgIK9XGlscVLd2NrfpQwo2wur\nmfTg19z7wVYCPR3EB3n0d0lyAMmh3lgM2FbQdSkH0zT5alsx93ywhdw9DfxiVgIPnz+aBxaMYPHO\nUuY8uoj65jbumDuUuSPD+ql6ERERGQhuPz2Zj26azh1zk5kwxB+b9fs/0ZNCvDgrJZznFmXy6Jc7\naW1zsiitlMYWJz+ZFNN53M9OjiPU25X7PtyK09nejQcgIfjECAtPiQ/AZjF4eXl2f5ciIiIiIiIi\nIiLSJ45oaS3TNLdD+zIjP7AAeNM0zSYgyzCMDGCCYRjZgLdpmis7xr0KnA182jHm3o7x7wBPGYZh\nmGrbIXJcCfZ2AFBa04iPm0s/VzMwlNY0cc0ra3C3W7HbLMwZHtzT91UZYNzsVuKDPHlnbR7vbSig\ntc0JQHObk7LaZgCSQjw5bXgIhmFw2eRYEoK9uPFf6xgW5s3oSN/+LF9EREQGgQfPGYXNYuGJr9NZ\nkVmGq4sVP3cXJsR+vwyru93GnWck86s3N/DO2jxqm9oD8ydKR55IP3cunRjN69/l8NOpQ4gPOjGu\nW0REREREREREjl9HFOTZjwhg5T4f53Vsa+l4/4fb947JBTBNs9UwjCogACj74eSGYVwHXAcQHR3d\n17WLyFEU7OUKQHF10wnzlPD+NLa08bPX11Je18Q7P5tCspZZGlRGhHvz3oYCTor1Izbg+y5Kk+IC\nmDc6DLvVgsXyfShrcnwAi24/pR8qFRERkcHIw2HjkQtSmJ4YyO/f3UxdcxsXpEZ26dwDcFZKOK8s\nz+ahz9OYEh+Aj5sLgZ72fqr62Lvp1ET+uy6fhz7bwXOXpR54gIiIiIiIiIiIyAB2wCCPYRhfAaE9\n7Pq9aZrv931JB2aa5vPA8wCpqanq2CMyiIR0dOQpqWns50r6n2ma/O5/m1m7u4JnLh3HyAif/i5J\nDtEvT01kakIg546L7BLY2R8vV3WiEhERkUNz9tgIxkb78vhX6VwzPa7bfsMwuGf+CBY8vYwPNraH\njE+kDo+Bng6unxHHI1/uZE32HlL36VgkIiIiIiIiIiIy2BwwyGOa5uzDmDcfiNrn48iObfkd7/9w\n+75j8gzDsAE+QPlhnFtEBrBg7+878vQH0zRpbHHiZrf2y/n3euzLnbywZBd1zW3cMjuJM0aF9Ws9\ncnjigjyJ0/INIiIicgzEBHjw6IVjet2fEuXLkxePpaCygZOHBh3DygaGq6cP4bWVu3nwk+389+dT\nTqggk4iIiIiIiIiIHF8sBz7ksHwAXGQYhsMwjCFAIrDKNM1CoNowjElG+6tqlwPv7zPmio73zwO+\nMU1T3XZEjjOeDhvuditbC6opr+0a5nE6TT7cWMATX6fz7vo8ahpbaGpto6m1jeZWZ5+c/9MtRQz7\nw2f85dMd9Ne3mKbWNl5enk1ckCf3LxjBL09N6Jc6REREROT4Mj8lnOtPjic51Lu/Sznm3O02bp2T\nxLqcSl5dsbvb3xoiIiIiIiIiIiKDxQE78uyPYRjnAE8CQcDHhmFsME3zdNM0txqG8TawDWgFbjRN\ns61j2A3Ay4Ab8GnHG8CLwGuGYWQAe4CLjqQ2ERm4ov3d+XBjAYt3lvLGNRMZGeFDm9Pk0hdWsnLX\nnl7HnTY8hPsWjCDMx+2wz70xtxKAZxdlMivEE0RHAAAgAElEQVQ5mAlDjn3b/YU7SqlqaOG205I4\nZWjwMT+/iIiIiMjx6Lzxkby8PJt7PtjK84t3sey3s/q7JBERERERERERkUN2REEe0zTfBd7tZd+f\ngD/1sH0NMLKH7Y3A+UdSj4gMDi9deRLbCqq554OtXPrCd7xxzURKa5tYuWsPd/4omaumDWHd7grW\n5lSwt2lOZX0zr6/M4Wevr+Pdn0/BYjm8Vvl5lQ2d7xdWNeznyKPnf+vyCPR0MC0hsF/OLyIiIiJy\nPLJZLbz9s8k8/Hkar67YTVV9Cz7uLv1dloiIiIiIiIiIyCE5oiCPiMjhCPd1I9zXjaGhXlz0/Eou\nfeE74oI88Pew89OpQ3CxWpgYF8DEuIAu45JCvLj9nU18uKmABWMiDuvceRUNjIrwYXN+FeW1zX1x\nOYckd089X20v5toZcdisR2t1QxERERGRE5O3qwvTE4N4dcVudpXVMjbar79LEhEREREREREROSS6\niywi/SbK3503r5uEp8PG+pxKzh4Tgd3W+7elc8dFMizMm398m3nY58yvqGdEuDc2i0FZbdNhz3M4\nymqbeObbDCyGwZVTYo/puUVEREREThRxQR4A7Cqt6+dKREREREREREREDp2CPCLSr/aGec4dF8nV\n04fs91iLxWB+Shg7imooqWk85HPVN7dSVttMlL87/h72o9aRp7K+mYySWhpb2jq3LU0vY9KDX/Pv\nVbnMTwknzMftqJxbREREROREF+3vjtVikFWmII+IiIiIiIiIiAw+CvKISL+L8nfnkQtSiPA9cLhl\nekIQAMsyyg75PPkVDQBE+rkR4OmgvK7vO/I4nSazH13E7EcX8as31wNQWtPEzW9tIDbQgycvHss9\n84f3+XlFRERERKSdi9VCtL87u8pq+7sUERERERERERGRQ6Ygj4gMKiPCvfFzd2FJ+qEHefI6gjxR\n/u4EetopO8KOPE2tbTS3Ortsy69soKy2mQAPOwvTSqlpbOHWtzdQ09jCU5eMZX5KOL7u9iM6r4iI\niIiI7F9coIeW1upDdU2tFFY1HNbYptY2tuRXUdPYQnOrs/NNRERERERERER6ZuvvAkREDoXFYjAl\nIZDlGeWHPDa3oh5o78gT6Okgu/zwX9jP3VPPJS+sxNfNzlvXT8Ld3v7tNK2oBoCrpw/hoc/SuO7V\ntazYVc6fzhlJcqj3YZ9PREREREQO3pBAD5ZlluF0mlgsRn+XM+AtSS+l1Wkyc2hwj/v/8ukO3lyd\nwx/OHM5PJsVgGAf/OX16YSZPfJ3ebfu8UWE8efFY/f8REREREREREfkBBXlEZNAZEe7Nx5sKqW9u\n7QzQHIwt+VU4bBaCPB0EeNgpP4yOPJX1zcx+dBFltc14OWzkVzRw29sbefqScVgsBjtL2oM8F58U\nzfOLd7FiVzlnjArlkgnRh3wuERERERE5PEkhXjS2ONlVVktCsFd/l3PEcvfUE+HrdlRCL8XVjVz/\n2loaWtr4+4VjWDAmotsxG3IrcZpw9/tbWZ9byYPnjMLVxXrAuU3T5L31+aRE+jB3ZBhO0wTaO5n+\n67sckkO9uOnUxG7jCqsaWJNdQbivG+Nj/I78IkVEREREREREBhEFeURk0InwdQMgv6KBxJCDe1H+\n27QS3l6Tx08mRWMYBgGeDuqb2w45DLQis5yy2mYunxzD5ZNj+DatlD9+vJ2/f7WTW08bSnpxLaHe\nrvh52Jk/Opwl6aX8+cejD+mJVREREREROTKT4gIAWJZRPuiDPJvyKlnw9DIuTI3iwXNGYbEYlNU2\nkVVWx0mx/kc0d01jC/e8v5XWNpMxUb7c+vZGHDYLc0eGdR7T5jTZWVzDFZNj8XK18fjX6aQV1XDl\nlFhOGxGKj5tLr/NvyK0kZ089v5g1mgtSozq3m6ZJcVUjr6zY3S3IY5omP3ttLRvzqrBZDFb9fjb+\nHlqeWEREREREREROHJb+LkBE5FBF+rUHefIqGg7q+KqGFu54ZxPJoV7cNW84AAGe7S8EH2pXnpW7\nynFzsXL3mcNJCPbi6mlDuCA1kie+yeDDjQXsLK4hKbT9RsF9Z43gi1tO3u8L2yIiIiIi0veiA9yJ\n8ndjWUZZf5dyxBbuKMU04c3Vudz34VZM0+Tu97Zw0fMrya88uL+JetLQ3Mbpjy3ms61F/PLUBF6/\neiIpkT7c9O/1LNxR0nlcdnkdTa1OhoV5ccucJF66MpXCqkZuf2cTFzy7gj11Pf9Nde8HW7nsxVXY\nbRbmjgztss8wDCYM8aestomq+pYu+5akl7Exr4qLToqi1Wl2qeVoKK1pYmNuJRtzKympbjyq5xIR\nERERERERORjqyCMig06knzsAeT940XpDbiVLdpYCEODp4OIJURiGwUOf7aCstokXrzips/17YEeQ\np6y2iSh/9x7P8/ziTLYVVHd+HObrxrLMclJj/XCxtucgDcPggbNHklVWx6//sxGnaXJFx9O/FouB\n/Si0vhcRERERkQObGh/Ix5sLaW1zYrMO3ueYlmWWMTLCm8lxAfzfkiwaWtr4YlsxbU6Tfy7N4q4z\nhx/WvGnFNRRUNfLQuaO54KT2bjkvXzWBS//vO65/fS3/vnYS42P8SCtqXz44OdQbgFnJIaz+/WwW\n7yzlZ6+v5a73NvPMpeO7zN3mNHlnbR4xAe5cNXUI3q7dH25ICPYEILOslnHR3y+f9dQ3GYT5uHLf\nghEsTCvhy23FnDs+8rCu8UBM0+Tsp5d1BqIcNgt/v3AMPxoVdoCRIiIiIiIiIiJHz+B9JUtETlhB\nng7sVgv5P+jIc/d7W3jky5088uVOfvfuZtbnVrImew9vfJfDT6cOYVSkT+exAR4OAEpqmsivbGBF\nZjk7ir4P7eypa+bPn+5gSXoZ63MrWZdTyT++zSSjpLazTf9eDpuVZ38ynglD/GlpMxkd5XsUr15E\nRERERA7G1IRAahpbWbGrvL9LOWz1za2sz6lganwgvztjGJdPjuHtNXk4TZMJQ/x5c3UuVQ0tB56o\nB+nF7QGdk4Z8vzyXt6sLr141gVBvV37++lqeXZTJ51uLsBiQGOLZeZzVYjAzOZizx0SwPLMc0zS7\nzJ1ZWkttUytXTR3SawgnPqgjyFNS27ntu13lrMrew89OjsdhszJ7WAiL00upbWrt9TqKqhq56uXV\nLOp4qKM3j36RxplPLuGhz3awOnsPrW1O0ktqya9s4PoZcbx4RSojwr254V/reGHJrm7XJCIiIiIi\nIiJyrKgjj4gMOhaLQZivK3kV9Z3b6ppa2VZYzY0z47l6WhwT/vQVH20sZEl6KRG+btw6J6nLHHuX\n1rr+tbWd29ztVpb9ZhZ+HnaWZZRhmvDCFamM7Xg69JlvM3joszRmJAZ1qynA08GrV00gs7SWuEDP\nbvtFREREROTYmj0shAhfN/740XY++mVAZ1fNwWRNdgUtbSZTEgIxDIN754/Aw2HDAOaNDmPeE0t5\nfnEmt5+evN957v9wG+tyKjo/DvJyEOHrht1mIapj6eK9/DzsPHfZeC594Tv+8ukOAOKCPDq7m+5r\nXIwvb63JJausjrig7/8OWt9xrjHRvT/kEOnnht1qIaP0+yDPUwszCPR0cGFHh6Bzxkbw71U5nP/s\nCmYkBnYZX93YQs6eetKLaympaSJ3Tz3Tb56BpYeuqC1tTl5eno2L1cLzi3fxzLeZBHjYOXVYMAA/\nmRRDlL87UxMCufXtDfzx4+3sLq/nwpOiWLijhKyyOpLDvLh2ehyG0XPXVdM02drR0dXTYcPdYcXT\nYcNhs2IAhkGvY0VERERERERE9qUgj4gMShG+bp3tzwE25lbS5jRJjfXH38PO5PgA/rk8C9OEl65M\nxcNh6zb+FzMTaHWaRPu742a3cMtbG3l5eTa3zEliaXoZ3q42Rkd+/8LzDackcP74KIK8HD3WZBgG\nCcFeR+eCRURERETkkLjZrdwzfzjXvbaW5xfv4saZCf1d0iHblFcJwLiOQIzFYvCbud+HduanhPPS\n0myqGlq4auqQLmGavSrqmvnn8iwSgjwJ83WjrqmVL7cV4+VqIy7Qo8dlx4aFebPqd6eyp76Zx79K\nJymk579z9j70sD6nssu5N+RW4uPmwpAAj16vzWa1EBvoTmZJXcccFSxJL+POHyV3hoZSY/158YqT\n+M1/N/HKiuwu491crMQGejA01Iuzx0bw/OJdvLM2j3PHR2L9QZhnVdYeqhtbee6y8UyOD2Bpehl3\nv7eFt9fkEe3v3rncsquLlacuHsdf/Xbw3OJdvLZyNwBhPq78b30+tU1t3DI7scdAzpL0Mi5/aVWv\n1+tut/LJL6cTG9j750REREREREREBBTkEZFBKtLPjYVp37dOX7O7AsOAcR0vJM8dGcqS9DLmjQpj\nVnJIt/GGYfDr04d22fbxpiJeWpqFl6uNb3eWMCU+sNsLwL2FeEREREREZOCZMzyEeaPDeOzLnUxL\nCCRlkC2Du72whmh/d7xcXXrc/+vTktiaX8V/1uSxctcePrppWrfOOd9llWOa8OcfjyI11p/GljbG\nPfAlNY2tJPYS0IH2oE2wlyt/OmdUr8ckBHni5bCxLqeicwmt5lYnq7MrSIny7bE7zr7igzzZVlhN\nXkU9T3ydjq+7C5dOiulyzMzkYFb9fvZ+52ltc/L19mLu+O8m/vLZDk5JCmL+mHCmJwTyqzc3sKus\nDofNwvTEQNztNs4YFUZpTRP3fLCVqQldl062WAzuPGMYc4aHUFrTxMgIHyL93Lj9nU088XU6a7L3\ncNe84QwP9+4y7rOtRXjYrTxyQQr1zW3UNbdR19RKU4uT+pZWnlu0i+WZ5QryiIiIiIiIiMgBKcgj\nIoNShK87pTVNvLYiG8Mw+HxrEUnBXvi4tb/AfVZKOBkltdxwysE/dfv7ecO4+a32NupAZ5t1ERER\nEREZnAzD4MGzR7F+dwU3v7WBj26axo6iGl5alsVjF4zBbhvYy21tL6xmWFjvYZuYAA+++fUpLN5Z\nyuUvreIvn+7g3rNGdDlmeWY57nZrZ7dRVxcr0xIC+WJbMYnBR7YssMViMCbal+WZ5TS1trE0vYw/\nfrydrLI6rpwSe8DxSSFefLqliGl/XQjArXOS8HQc+ktVNquFd2+cysIdJSzcUcI3aSX8b30+DywY\nwcebCwE4bXgI7vbv5754QjRpxTVcMiG6xzlTY/27fPyXH49idKQPj365k3lPLuHC1ChOHRZCUogn\nUX7ufL29mJOHBjF3ZFi3uUzT5K3VuWzMreSSiT2fT0RERERERERkLwV5RGRQGhnR/vTj3e9v7dx2\n3Yy4zve9XF24Z/6IbuP2Z0igB+/dMIW8igacZvuSWyIiIiIiMrj5uLvw6IVjuPj/VnL/h9vIKq9j\nVdYeLkyNYkZSUH+X16uG5jayyuuYnxJ+wGNnJAVx5ZRYXl6ezazk4M7rMk2T5ZnlnBTr3yW0NHtY\nCF9sKybhCIM8AJdOjOZnr69j5t++paCqkfggD17+6UmcMvTAD0ZcNXUI0f7utJkmdquFuSNDD7sO\nb1cXFoyJYMGYCPbUNZP6xy95+IudGAa8cc3EbsuD2W0WHtxPt6EfslktXD45lgUpETzxTTqvLM/m\nzdW5WC0G0xMDKa5uYvaw7t1goT1QNjrSl40dS6WJiIiIiIiIiOyPgjwiMiidOiyE9XfPocXp7NwW\n5Hnky14ZhkGUAjwiIiIiIseVSXEB3HBKPE8vzOzc9uW24gEd5EkrrsE0YViY94EPBn77o2SWZZTx\n6/9s5PObZ1Dd2MKv3txARkktl/6gC8z8lHBKa5uYlXzkXUjnjgzj9tOH8tLSLP5w5nAumxyDi/Xg\nOh35uLt0LsnVl/w97KTG+LMqew8pUb5MiQ/ss7l93F24+8zhXDcjjsKqRt7fkM8HGwrwctiYuZ/w\n0phIH55aWEp9c2uXzkAiIiIiIiIiIj+kVw5EZNDy87D3dwkiIiIiIjJI3Dw7iaUZ5WSV1jI60pcv\ntxXzuzOG4Wa39ndpPdpeWA3A8IMM8ri6WHnswjGc88wyrvjnKgqrGmlpc/LgOaO4ILVrWMbNbuXG\nmQe/DPGB3DgzgRtOiccwjD6b80jNHh7Mquw9zEjsuxDPvkK8XQnxdmVMlC9/OHM4zW1OHLbev5ZS\nonxxmvDQZ2mEeLsC7Z1mpycO3DCZiIiIiIiIiPQPBXlERERERERE5LjnYrXw72snUlHfwsrMcm77\nz0aG/eEz/n7hGM4eG9Hn58sqq8Pdbu0MbRyqjJJaXF0sRPq5HfSYkRE+3L9gJE99k0GIt4NHLxjT\nbUmpo2UghXgAzhwdzn/W5HHm6AMvTXakDMPYb4gHYFy0H96uNl5ent25zdvVxoY/nIbFMrA+dyIi\nIiIiIiLSvxTkEREREREREZETgrvdhrvdxpkpYdQ1t/LGyhwe/iKNeaPDsBoGpbVNlNY04eGwMSTQ\n47DPU17bxIKnluLpsPHhTdMIOIxlgPMrGojwdTvkkMfFE6K5eEL0gQ88zoX7uvHlrSf3dxmd/Dzs\nrLt7Dq1OE4B31uZx13tb2L2n/oi+1kRERERERETk+KMgj4iIiIiIiIicUBw2K5dPjiXK352f/nM1\nMx5aSHldM82tzs5jPrppGiMjfA5r/oe/SKOuuY3GVie/+e9mXrgi9ZDnKKhqIMLP/bDOLwOTzWph\nb+OecdF+AGzKq1SQR0RERERERES6UJBHRERERERERE5IpyQFceWUWEprmoj0cyPS3x1vVxu/enMD\nKzLLDyvI09jSxttr8rh4QhQBHg4e/zqdgsoGwn0PfoksaO/IMyL88IJEMvAlhnjisFnYnFfFgjF9\nv7SbiIiIiIiIiAxeCvKIiIiIiIiIyAnJMAzuPWtEt+2PfLGTtbsruPYw5txVWkeb02RSXACjInx4\n/Ot0PthYwPUz4sjZU8/m/CqmxAfi72HvdY7GljbK65qJ8HU9jApkMHCxWhge7s0X24opqm7kT2eP\nwsfdpb/LEhEREREREZEBQEEeEREREREREZF9jI/xY0l6GaZpYhjGIY3NKK0FICHYk5gAD8ZG+/Ls\nokxeWJJFWW0TAJdNiuGBs0f2Okd+ZQMAEX6H1sVHBpeUSF9eXp5Nzp56ThsRylkp4f1dkoiIiIiI\niIgMAJb+LkBEREREREREZCAZF+NHWW0T2wtrME3zkMZmFNdgMWBIoAcA106PI9DTwfTEQP549kim\nJwby+dYinM7e5y3oCPKE+yjIczy7ZGI0l02KwdXFwvqciv4uR0REREREREQGCHXkERERERERERHZ\nx0mxfgCc8cQSfn1aEr+YlXjQYzNKa4kJ8MBhs7bPMSqMM0aFde73dNi4+a0NrM+tYFy0X48df/Ir\n1JHnRJAU4sUDZ48kraiGDbmV/V2OiIiIiIiIiAwQ6sgjIiIiIiIiIrKPoSFePHnxWIaFefPehoLO\n7QWVDby+cjdt++mmk15cS3yQZ6/7ZyYH42I1uPC5lZzxxFJqGlu6HVNQ2YDFgBBv1yO7EBkUxkb7\nsjW/mqbWtj6f2zRN/vVdDkvSS/t8bhERERERERE5OtSRR0RERERERERkH4ZhMD8lnLLaJu77cBsZ\nJbUsSS/l4c/TqGtuI9zXlVnJId3GtbQ5yS6vY/bw7vv28nFz4ebZSewoquHTzYWc/+wK4oI8uhyz\nJb+aUG9XXKx6/upEMCbKl+Y2J9sKqhkb7dencz/+dTp//yodh83Cf38+hZERPn06v4iIiIiIiIj0\nPb0iJCIiIiIiIiLSgzkdgZzznl3OfR9uY3ysP3arhRWZ5V2Oq2po4ZXl2cx/ciktbSYjwr33O++N\nMxN48uKxPHJBCtDexWffN4fNwjnjIo7ORcmAMz7WD8OAhTtK+nTezXlV/P2rdM4cHYa/h50b3lhH\nXVNrn55DRERERERERPqeYZq9t4MeDFJTU801a9b0dxkiIiIiIiIichz68TPL2F1ezx/mD+eslHAu\nfH4lNY2tDAv1YkdRDSaQVVZLY4uTURE+XDIxmotOisIwjP4uXQaRK15axc7iGpb+ZhYb8yp5c1UO\nOXvqefyisQe9xNpLS7P4YGMBI8K9GR3pwwcbC9iSX82S38xkR2ENFz6/gh+PjeSuecPw87Af5SsS\nERERERERkR8yDGOtaZqpBzxOQR4RERERERERkZ7VNrViNQzc7FYAHvtyJ49/nQ7A9MRAHDYrEb6u\nnJ8apWWL5LB9urmQn7+xjghfN/IrG3Dv+Hrzc7czLMwLpwmmaeI0wdnxWp7TNHGxWrjvrBFE+bkz\n8c9fY5omTS1Oajo679x++lBunJkAwJ8/2c5zi3dhtRhMivNn7ohQzk+NwtXF2j8XLSIicpg25FaS\nu6cegKQQL4aGevVzRSIiIiIH52CDPLZjUYyIiIiIiIiIyGDk6ej60snk+AAe/zqdU5ODeeGKVHXe\nkT4xe3gISSGeuLlY+cWsBOanhLOjsJoHP9lOQWUjFgtYDAMDMAwDi9H+3+WZ5by7Pp+pCYGU1jTx\nxMVjOXNUGLv31JNdVse0xMDOc/z2R8nMGx3G51uL+HRLEXe/v5WtBdX85dzR/XfhIiIiB9DY0kar\n0+z8naymsYULnltBc6sTgEBPOyvvPBWb1dKfZYqIiIj0qSPqyGMYxt+A+UAzkAn81DTNyo59dwJX\nA23AL03T/Lxj+3jgZcAN+AT4lWmapmEYDuBVYDxQDlxommb2gWpQRx4REREREREROVbanCbPLc7k\nvHGRBB/kkkciR8uCp5biYrUwMsKHf63KYd3dc7qFz3pz34dbeWV5Np/fPIPEEHUyEBGRgeme97fw\n33X5PHnxWGYmB/PZliJ+9vpaHr9oDNUNLdz9/lb+de1EpsQHHngyERERkX52sB15jjSi/CUw0jTN\n0cBO4M6Okw8HLgJGAHOBZwzD2Nun9x/AtUBix9vcju1XAxWmaSYAjwF/PcLaRERERERERET6lNVi\ncMMpCQrxyIAwNSGQ9bmVvLs+n5lDgw46xAPwy1mJeNhtPPlNxlGsUAabtKIaCiobqG9u7fK2t/OF\niMixtmZ3BbVNrVz9ympeWLKLb9NK8HLYOGNUGOeOj8TVxcJnW4r6u0wRERGRPnVES2uZpvnFPh+u\nBM7reH8B8KZpmk1AlmEYGcAEwzCyAW/TNFcCGIbxKnA28GnHmHs7xr8DPGUYhmEeScsgERERERER\nERGR49S0xECe+TaT2qZWbjtt6CGN9fOwc864CN5anUt1Ywveri5HqUoZyLLL6thWWA3AorRS3lqT\n2+Nxbi5WFv76FEJ9FGIUkWOntc1Jekktl06Mpry2mT9+vB2rxeC04SG4WC24WC2ckhTMO2vz2Jhb\nidNs757oNE1Ms/3n5F3zhmkpVBERERl0jijI8wNXAW91vB9Be7Bnr7yObS0d7/9w+94xuQCmabYa\nhlEFBABlPzyRYRjXAdcBREdH990ViIiIiIiIiIiIDBLjY/wI8LBzfmoUSYexPNaPx0Xy6ordfLq5\nkAtP+v41tpY2J+nFtdQ0thAX5EmQl6Mvy5Zj7OvtxfzruxwA3B02bpwZT055Pa+t3M2S9K4vvV47\nfQjRAR7UN7V2biutaeKFpVlsyK1krk/oMa1dRE5su/fU09zqZEyUL+eOi+SRL9N4emEmc0d+/73o\nupPjaGxtA8BiGB1vUFnfwotLswjxdpBVVkfTPp3FzhkbwfTEoGN+PSIiIiIH64BBHsMwvgJ6+gvt\n96Zpvt9xzO+BVuCNvi2vZ6ZpPg88D5CamqqOPSIiIiIiIiIicsJx2Kws/c0sXF0shzU+JdKHuCAP\nfvu/zTzyxU6mxAeQVV7P9sLqzqWUkkO9+OzmGX1ZthyG2qZW8isaCPN1PeTuSX//Kp3d5XVEB7iT\nu7uBDzcWABDm48ptc5KYNSwYm8WCh8NKpJ97t/H1za28sDSLncU1XW6ei4gcbTuLagBIDvXGYjG4\n/fRkLpsUS4j39wHTcdF+vPzTCd3GNrc6mfv4Yh78ZAeeDhu+7u3fO2saW/ns/9m77/iq6vuP469z\n7829N7m52XuRhAxI2FtBEFHEiRtsq9W6t7XaqW1/Vlttq9ZaraPuuvceyEaW7BEgQBICGWTvfe/5\n/ZEYTdkkIYDv5+ORB7nf7znf8zkh3CDn7ee7oZjPb5tIQuie73kiIiIiR4MDBnlM0zx1f/OGYVwB\nnA1M+d42WAVA/PcOi+sYK+j4/H/Hv3/OLsMwbEAgUH7gWxAREREREREREflh8rVbD/tcwzD420VD\nmLO5hO0l9SzaVk5KhIufntCPQbGBZBXV8NT8HLYU15Iedegdf6T7Wtq8fLS2kPs/3URFfQvBfj68\nf9N4+oW69nmOaZqd28jsrmlifUE1v5yWzo0np1BS28TLS3aQGRPIqQMjsFkPHALzs9tICPEje3ct\nTa0eHDaLtqkRkSNiy+5aDANSIvw7xw52iz+7zcIjlwzjgzWF3HxKCiEuOwAFVY2c/sgC/vjRRp67\nYnSv1C0iIiLSXd3aWsswjGnAL4FJpmk2fG/qQ+BVwzAeBmKAVGC5aZoewzBqDMMYBywDLgce+945\nPwWWABcBc74XDBIREREREREREZEeNrJfCCP7hex1bnxKGM8syOHDtQXcFTXgCFf2w5ZTWscb3+zk\n7ZW7KK9vYUhcIL89cyB/+jiLGU8tJTGsvYuExTD46YmJnJ7Z3iln5Y4KLn16GaOTgpk+LJaaxlYA\npgyIBCDC7eQXU9MPuZ60SH/W7KzixAfm8KMxCdx5+qGvISJyMEzT5PVvdlJR38KsrN30C/E77NDq\n0PgghsYHdRmLDfJl+rAYPlpb2CX4KCIiInI06VaQB/gX4ABmdfxlZ6lpmtebprnRMIw3gSzat9y6\nyTRNT8c5NwIvAL7AZx0fAM8CLxuGsQ2oAGZ2szYRERERERERERE5TGH+DsanhPHi4h0UVTXx5wsG\n4/Q5/A5A8h2v16S6sZUdFQ3M2VxCfdYDeQoAACAASURBVHNb59zGwmqW5lRgtRicOjCCmWMSmJga\njtVikBDixz9nb6XF0771WWFVI7e8uprXrh3HyH7B/HdpPj5Wg12Vjfzy7XUAxAX7khbpv9c6DlZa\npJuvNpUA8OT87UwfFkNqpLo0iUjPm5ddym/eXd/5+sdjE3r8GklhLmqa2qhsaO3s1CMiIiJyNOlW\nkMc0zZT9zN0P3L+X8RXAoL2MNwEXd6ceERERERERERER6Tl3nJbG43O38e7qAsYkhTBzTM8/UP0h\n+dsXm3lt+U6qGlrwdvQitxjt21d9K8Lt4K7T07l4ZBwRAV23kBmTFMJ/rx7b+bqyvoXznvia615e\nyWvXjOXzDcWcPyKW+88bxJqdVXy0togR/YK63XHi263VUiL8Kalp4qzHFnFSShinZ0YxKT2cAKdP\nt7Z5ExH51nOLcokMcDDnFydjsxrYD2ILwEOV2LE9YW5ZvYI8IiIiclTqbkceEREREREREREROU4N\nTwjmmctHccajC3lhcR4zRsdrG5LDVFbXzDMLchkSF8gJ/RMI9rMT7nYwISWM4MN8kBzssvOfy0dx\n/hOLOfOfC2n1mFw4Ig7DMBieEMzwhOAeqT0zJhCAa05KYnhCMK8v38kXG4uZvbm9S4/FgNeuGcfY\n5NAeuZ6IHL22FNeyOr+yV34ezNm8m4Vby7hzahouR+89vkoMaw/y7CivZ2hcIK9/s5Npg6II83f0\n2jVFREREDoWCPCIiIiIiIiIiIrJPhmFwxYmJ/Prd9Ux4cC7J4S76h/sT7nZgGGBgYDHAMMBiGBiG\ngUF7uCMq0Mm0QdF9fQu9yuM1sVoMCqsaKapuJC3Sjdvps8dxb3yzkxaPlwcuHExKRM9tS5Ua6eal\nq8bw4ZpCIgOcjEgI6rG1v5US4c9Xd0yif7gLwzD4/TkZ3HP2QDYW1rA8t4K/fLaJuVtKj0iQp6Cq\nkcv+swzDgCA/O0G+PgT6+eCy29hXpiDU5eCMwVGkaTswkW4xTZM73lzDxsIaWr0ml43r1yPrerwm\nj87eymNztpIe6eaycYk9su6+JIT4YTEgr6yerzaVcPf7G3hlWT6vXzuOQN89379FREREjjQFeURE\nRERERERERGS/LhoZR32Lh3W7qtheWsebK3bS0OI5qHPn/GISyeH+vVxh7/F6TbJLamlta98Ly+Ww\nEuKyE+D04R9fZfPS0h385fzB3Pb6Glo8XsL8Hfz+nAzOGRLd2a0ip7SO57/O5cT+oT0a4vnWiIRg\nRvRQ9519SYno+ntoGAaDYgMZFBvIJ+uLWJ5bvt/zH/hsMyt3VHDqwEium9T/sOv4JreCnLJ6Tk4P\np9Xjpbimic3FtTS0tO3znOrGVv4xO5ubTk7hjtPSsFjUVUrkYFU1tHS+36/Kr2RjYQ3RgU7u/Wgj\nZw2OPuytqUzT5IHPNxPg9GFpTjkLt5ZxwYhY7j9vcK9v1We3WYgN9iW3vIFdVY342a1sK6nl6he/\n4aWfjcXHapBVVIPHa+KwWRkY7VY3OhERETmiFOQRERERERERERGR/bJZLVw1IanztWmatHi8mCbt\nH5h4zfbxb3/dWdHIOf9axOLt5cdskKesrpnbXl/N19v2DKkYRvu9Wwy48dVV+NttPDJjGE8t2M6t\nr63mnZW7ODUjkifnbae0thm308b/nZvZB3fR+8YkhfDMghwaWzx7fQCfVVjDk/O34/SxkFtWz7UT\nkw/7oXhOaR0WA566bCQO28E97C+ra+bBzzbzr7nbGBwXyOmZUYd1bZEfmk/WFXHr66vxeM3OsagA\nJw9cOISfPrec9QXVTEoLP6y1v9pUwlPzc4D2YM0DFww+ots3Joa62FZSR2FVI9MyozhlYAS3vLaa\nm15dhQGdWwcCPH/FaCYPiDgidYmIiIiAgjwiIiIiIiIiIiJyiAzDOGCIItDXh6gAJ0tyyvlJD22/\ncqQ9Nnsr3+RWcvdZA0kMdWECdc2tVNS3UtXQQoTbgWEY3P3+Bm6cnMJZQ6KZNiiKl5fk8bcvtjA/\nu5ShcYFMGxTFj8Ym0P8YDTQdyJikEP49bztvr9zJgOiAPeafWZCDy27lukn9eXhWNrtrmokKdHbO\nv7tqF60eL2cPicHl2P8/WW8vqyc+xO+gQzwAYf4O/nLBYOZll/L2yl0K8ogchO2lddz51loGxwZy\n6Zj4zvFh8cFEBbT/+c0qrDlgkGdHeT1LtpezvqCaDYU11DS2MjUjks82FJMc7uLhS4bh77Dt0fWr\ntyWHuVi4tQyAqZlRTBsURVVDK3e/vwGA26akMjQ+kOtfXsXS3HIFeUREROSIUpBHRERERERERERE\nepxhGJzQP5SFW0sxTfOY3JZk0bYyTkwJ5eqTkvd5jGmaZMYEMDQuCACrxeCK8UmcPiiKxdvKOXdY\nDD5Wy5EquU+M7BeM3Wbhng827vOYaycmMz4llIdnwfqC6s4gz7aSWu58ay1eEz7fUMzzV47Z77Vy\nSutJDnMdco02q4Xzh8fy3KJcyuuaCfV3HPIaIj8kn60vorHVw79/MoLoQN895mODfMkqqtnvGl6v\nyflPLKaivgW308agmED8HVaeWpBDsJ8Pj106gmHxQb11C/s1Y3QCtU1tuJ02Jg9oDyP9ZFw/LIZB\nZUMLN57cH8MwyIgJYHV+VZ/UKCIiIj9cCvKIiIiIiIiIiIhIrzghOZT3VheQvbuO9Ch3X5dzSHbX\nNLG9tJ4Zo+P3e5xhGAxPCN5jPDrQlwtHxvVWeUeVAKcPn9wygd01zXudtxgwol8wXtPEYsCGgmpO\ny4jE6zV5ZNZWnD5WpmZE8umGYprbPPvstuP1muSW1XFi/9DDqvOCEbE8vSCHWVm7mTkm4bDWEDnW\nNLS0kVVYw7pd1WwoqGZ7WT355fVkxATw4pVjsO0jaLihoIbEUL+9hngAMmMCyCqs3u+1t5bUUVHf\nwr3TM7lsXL/OQGdTqweHzdKnAc+MmAAenjFsj/Efje363jA8IYjXlufT5vHu82slIiIi0tMU5BER\nEREREREREZFecXJ6OBYDPllfdFQFeWqaWrny+W8IcNq4d/og/OztwRHDMPCzW3H6WFm8vX3LlRP7\nh/VlqceM1Eg3qZEH/j3uH+7PN3kV/GdhDq8syye3rJ6bJ6eQGRPA+2sK2VRUu0eHjuW5FXi8JvEh\nvjS1ekkOP/SOPADpkW7cDhsbC/ffRUTkePHmip38+p11eM321xFuB6mR/pzYP4xP1hfx6Oyt/GJq\neufxS3PKiQ3yJT7Ej41F1QyJ3Xe3nIyYAGZt2k1DSxt+dhumafLFxmLmbSkl1N/OHaels3JHJQAT\nU8O7hHacPge/NV5fG5EQzPNf57G5uJZBsYF9XY6IiIj8QCjIIyIiIiIiIiIiIr0iIsDJ+JQw3l9d\nwM9PTT0qttdqavVwzYsrWLuzCovF4KS/zt3jGLvVgmFAoK8PA6MD+qDK49eg2EDeW13A4u3ljOoX\nzG1TUjlnaAylte3dfFbnV3YJ8rS0ebn51VWYwF8vGgJAcpj/YV3bMAwGRgcccDsgkePF+6sLiAv2\n4/dnZzA4LpDIAGfnnOPNNTwxbzszxyQQ6rLz50838dKSHUQHOnn5qrHsrGhk5uh9d67KjAnENGHx\ntnLSo9zc88EG5m0pxe20UdvURmZMIKvyKwl12ekX6nckbrdXDE9ofz96Z9UuMmMCjoqfYyIiInL8\nU5BHREREREREREREes15w2L5xVtrue31NVwwIpaT0yP6rBaP1+T219ewLLeCR2cOY2B0AEtzyjvn\nvV6T+hYPtU1t1Da1MioxGKtFD2170pXjE4lwO5g+LJaMmO9CUlGBTqIDnazOr+LK8d8d/9mGIko6\nQj4Pf5mNYUBq5OEFeaC9i8ibK3bi9ZpYLAb1zW08vSCHd1btwmW3YbdZuP3UVKYMjARg4dZSfvve\nevwdPrjsVm4/NY0JqerSJEdGTmkdvnbrPre32p+mVg8rdlRy+bh+nJoRucf8Hael8cGaQv76+Wa2\nl9axoaCGS0bF8dHaIi59ZinQvn3WvkxICaN/uIs7315LU6sHq2Fwz9kZXDauH1Mfmc+T87dT09jK\n8ITgYzr8EhvkyyWj4nj+6zxaPV7+79xB+rkgIiIivU5BHhEREREREREREek10wZF8fziXOZtKeHD\ntYVcNymZO6em42O19No1TdNk5Y7Kzi4vqZH+9A/35+73N/D5xmL+cE4G04fFApB2ENtBSc8ZEhfE\nkLi9b9czPCGIL7OKmfaPBZ1jRdVNJIW5qGxoYX1BNT8em0CYv+Owr58RHUBDi4f8igaKqpv45Ttr\n2VnRyKS0cBw2C8vzKvjv0h1MGRhJUXUjt72+BrfTRnSgk8Xby/hwbYGCPILHa+4zzLGtpJZ5W0qZ\nOSYBf8fhPYJp9Xi5/uWVzN5cgtViMH1oDNdN6n9IWxSuyKukpc3L+H18v8YF+3HW4Gg+WFNIoK8P\nz1w+itMyIjllQATX/3cV0N51Z1987Vb+/ZORXPzkEialhfPHczM7A0fXTEzmd+9tAGDmmH139TkW\nGIbBgxcOIcTl4Mn52ymtbeYfM4bT5vXidvr0dXkiIiJynFKQR0RERERERERERHqNy2Hj41tOoqnV\nw70fZ/HU/BxW7ajksUtHEBXoPPACh2hVfiUPfLaZ5bkVnWNBfj7cekoqry3P56bJ/blyfFKPX1e6\n74oTk/B4TUzzu7GEED9+NDaBxdvLeW91AXednt6ta3y7Vdqdb61lxY5KEkP9ePO6ExiTFALA3e+v\n571VBTS1erj1tdU0tXp487oTSInw55Inl5BX1tCt68uxo6GljdmbSvB4v/uGTI9y88isbLKKanjt\nmnHEh/hRUtvEqh1VrM6vpKS2mS82FtPQ4uHxudsIcdnxmu3BH4/XxGt+96vNYuEP52RwxuDoPa79\nn4W5zN5cwq1TUqlvbuPVZfm8u7qAqRmRPDxj2EEFhOZtKcHHajAmMWSfx9w5NR2308YNJ/cnLrh9\n+6tpg6K5+6yBLNleTrh7/6G5tEg3q+85Dcv/hJpmjIrH18dKm8fkjMFRB6z1aGcYBr8+YwARbgd/\n+iSLofd+CcCCuyb3ys8xEREREcP8/n8VHYNGjRplrlixoq/LEBERERERERERkYPwwZoCfvPuelwO\nG3N+ManHOhqU1TXz23fX82XWbsL87dxySipjk0PIL2/g2pdXAu0dX9694cRjepuXHyqP16S5zYOf\nvXv/b2pTq4dBf/gCj2lyxYmJ/PL0AfjarZ3zn60v4oZXVjExLZwF2aX8Y8Ywzhve3r3pV2+vY/bm\nElbcfWq3apBjw2Ozt/LQrOy9zrnsVqwWA3+HjcLqJgDsVgvhbgcpEf5cNq4fH60rpNXjxWqxYDXA\nYjGwGgZWS/vH6vwqtpXWceGIWCwd70mltc2s3VVFaW0zp2VE8tRlowCorG/h+cV5PDZnK5eN68e9\n0wcB8NryfN5euatLbQYQE+TLx+sKmZoRxZOXjeylr9AP05cbi/lgbSGfrCvi7xcP5aKRcX1dkoiI\niBxDDMNYaZrmqAMdp448IiIiIiIiIiIicsRMHxZLuNvBj55Zxpcbd3NhDz0EfWZhDnM2l/CL09L4\n2YQkXB0dKwZEBXDqwEi+2rSb3505UCGeY5TVYnQ7xAPg9LHyrx8NJ9ztYGS/PTuVjEsOxTBgQXYp\nl46J7wzxACSGuSira6amqZWAjgCaaZoUVDXS6jHpF+K3R2cSOXZ9vK6IEQlBPHTJMADaPF4WbC0j\nJtBJfIgf/1mYA7RvPzWiXzCDYgNw2L4LhZ2aEbnf9SvrW7ju5ZXMytrdOeZy2BjfP4yoQCdXn5Tc\nOR7ssnPHaWnUNrXy/Nd5TMuMIjMmkPs/2USIy05CiF/nsa0eL7M37WZiWjgPzxjaI18L+c7UzChO\nHRjJ4m1lLM0pV5BHREREeoWCPCIiIiIiIiIiInJEnZAcSlywL++vKWBscgh1zW1AexeJgMPs0LNq\nRyWD4wK5ZUrqHnN/Pn8Ql4yKY9R+tpiRH45pg/bcyuhbwS47w+ODaGjx8IdzMrvMJYW5ANhcVMvu\nmiYWbi1l0dayzo4sv5o2gBtO7k+bx8umolrqmttoaGmjocVDY4uH8alhxAb57re24uom3l65k6tP\nSsbpY93vsdJ7tpXUsmV3LX88J6Pz9x0gNdLd+fk/Zg7v1jWCXXbevP6EQzrnrtPTWZBdyq2vr2Zi\nWjh1zW28ed0JZMQEdDnONE2FFnuRxWIwLjmUJdvL+7oUEREROU4pyCMiIiIiIiIiIiJHlGEYTB8W\nw+NztzPhwbmd43arhVMGRHDe8FgmDwjv0t1iX5paPXhNk3W7qvnJuH57PSYiwMnUzKgeq1+Oby/8\nbAw2i7FHkCY5vD3QcfWL31DT1EaA08b4lDBuODmUpxfm8E1eBTfQn6cW5PC3L7bssW5MoJNPbj2J\nYJe9c6zN46WmqQ2304bNYvDLd9axILuUmqY2fnvmwN69UemipLaJF77O4+N1RRRXN2EYcMbgfYe+\n+oKf3cZTl43k/CcW8+6qAqZmRO4R4gEU4jkCxiWH8tmGYv70cRZXjk8kLtjvwCeJiIiIHCQFeURE\nREREREREROSImzk6gRV5lUxKDycp1IXXhJU7KvlwbSGfbywmwu3g41smEBHg3Oca1Q2tnPv4IkwT\nmtu8DE8IOoJ3IMerfXWFSgjxwzCgpqmNW6ekctuUVKwdW2mtzq9i0bYyAL7eVkZqhD/3Th+En92K\nn91KSW0zVz7/Dac9Mp8Apw9tXpOqhhZqmtq7UQX5+ZAe6WZZbgVJYa7OreJCXHZCXXZOz4zqss3X\n8cY0TUrrmvGz2/B3HLnHFg0tbXy1qYQP1xQwP7sUj9dkYlo4k9PDiQ/xI3I/7z99JSXCzeJfn0J5\nXQsxB+jwJL3nlAERPPJVNs8uysXjNfnjuZkHPklERETkICnIIyIiIiIiIiIiIkdcfIgfb1zXdVuZ\ns4ZE89szB/DxuiJuf2MNi7eX7xFeME2TlTsq+XhdEavyK9lR3tA5Nzwh+IjULj9MTh8rsUG+NLZ4\nuH5ScmeIB2BQbCDvri6gsKqR1flVzBgdzwn9QzvnUyPdPP7jEXy4thAAqwGBvj4E+dkJ8PVh/a4q\ncsvqmTEqnt+dPZDH525jR1kDFfUtfJNXycKtZUzNjMTPfvz9k36rx8tVL65gQXYphgHD4oN46rKR\nRLh7N0Tz+YZi7nhzDQ0tHqICnPz0hER+PK5fl620jlZupw/uw9yGUHpGfIgfa34/late+IbZm3fz\nh3My1AlJREREeszx97d+EREREREREREROWbZrBbOGhLNL99Zx6aiGs4bHktFfQu5ZfWs21XFa8vz\nyd5dh6+PlVB/O38+fzCvLt9BRV0LMYFHX/cMOb7cfdZAXA7bHoGaQbGBALzxzU4aWz2MTgzZ49zT\nMiI5LSPyoK7zmzO+21ZreW4Flzy1hE/XF3PRyLhuVH90uv+TTSzILuWGk/vjsFn497zt/O69DTx9\n2cheDUY8tyiXcLeDBy8cwpjEECwWhTDk0J0yMILZm0vYXlpHSoS7r8sRERGR44SCPCIiIiIiIiIi\nInJU8bFaSI90k1VUA8Dlzy1jQ0H750Pjg3jwwsGcPSQGV8cWPKdlRFLd2KpuCNLrpg2K3ut4RkwA\nAP9dugOAUYk91x1qdGIwyWEu3vxm51Ed5PlkXRG+dgunDDi4sBLAmyt28sLiPK6akMSvpg0AwM9u\n5c+fbuYfX23lpskpfP+PtcUwunRCOlw1Ta2syq/kuknJjEsOPfAJIvswOT0CgLvf30ByuD8AIX52\nrj+5/xHdJk5ERESOL/pbhIiIiIiIiIiIiBx1Bka7mb2phJzSOjYU1HDl+ERmjk4gPWrPjgfhbgfh\nbkcfVCnSzt9hIz3SzZbdtSSHuYgM6LnuUIZhcP7wWB6alU1pbXOffq9vLq4hq7Bmj/Hcsnoem7MN\ngAtGxBLka8fHZpARHcCIhGDign33CNqtyq/k7vc2MCEljN+cMaBz/KoJyWwuruXR2Vt5dPbWLueE\nuuy8du040iK71/lk8bZy2rwmE1PDu7WOSEyQL2cOjmJ5biXbSuoBqKhv5qtNu3n2itHEBvn2cYUi\nIiJyLFKQR0RERERERERERI46GdEBvLliFy8tae9wcs1JycTogagcxV742Wi2l9STFO7q8bVPTo/g\noVnZLNpWyvnDj2xXnvK6ZhZuLWPNzipeXroDj9fc63GnDowg2M/O5xuKAWhu89Li8QLtYbuYIF++\nH+XJLasnKtDJY5cOx2a1dI5bLQYPXTyUCSlhFFY1do6bJjy/OI+73lrLPWdnMDwhuEt3nm0ltZz/\n+GIaWz08dMlQpg+L3Wud/1mYw2vL8/F32BjRr+c6J8kP1xM/Htnl9YLsUm56ZRXT//U1b1w3jv4d\nnXpEREREDpaCPCIiIiIiIiIiInLUGRjdvlXRq8vyGRYfpBCPHPWiA32JDuyd79PMmABCXXbmbyll\nfP8wInqw4w+Ax2timu0BHZvVQk1TK2+t2MWXG4v5Jq8CrwmGAecNi+XmU1Kw/c/2VhbD6Oy687eL\nhwLQ5vGyubiWVfmVrM6vory+pcs545JDuHNqOsEu+x71GIbBBSP2DCwlhbu4+dXVXPTkEi4ZFcdf\nLxraOffE3O14TJPIACevLsvfa5BnQXYp932yiYQQP64+KQmf7wWIRHrKxLRw3r3xRKY9upD3VhVw\n5+npfV2SiIiIHGMU5BEREREREREREZGjzpC4IMYkhlBW38zVJyX1dTkifcpiMTgpNYwP1xby/ppC\nHrt0OOcMjTns9bxek6yiGuZtKWHullJW51fybaOdu05PZ+WOSuZsLmFAlJubJ6dwWkYUaVH+OGzW\ng76GzWphUGwgg2IDufyEwy61i7OHxJAW6ealJXn8d2k+EW4nvnYrza0ePlhbyBUnJuLvsPHPOVsp\nqW0iwv1d4KmlzcsfP9pIYqgfX/x84iHdi8ihSo100y/Uj+zdtX1dyn41tXp4ZVk+5w+PJWQvobpj\nXUV9Cw99uYXfnDkQf4ceiYqIyLFDP7VERERERERERETkqONrt/Lm9T309F/kOHDusBg+31hMgNOH\nv3+5hWmDog67o8wdb67h/TWFAAyODeSaicn4220szS3nkVnZtHlNfnFaGrdMSe3JW+gRaZFu7j4r\ng9X5Vfxr7rbO8VCXnatPSqK2qY1HZ2/lvVUFXDepf+f881/nklNaz/NXjlaIR46ItAg3W47iIE9L\nm5f7Psniv0vzySqs4aFLhh74pEOwdXctpbXNAKRE+B9yJ7Gi6kY2FNQAsL6gmo/XFTIpLZyfjU8i\nPsTvoNZ4e+VOXlmWzykDIpgyMPLQbkBERKQPKcgjIiIiIiIiIiIiInKUO2VAJJvuncaczSVc9eIK\nZj69lHOHxnB6ZhRRgQf/gLyoupEP1hZyyag47jp9AOFuR+fcRdVxTP77PPzsVq4Yn9gLd9EznD5W\nPrx5Ag0tbfhYLditFiwd231FB8KofsH85bPNfLahGHtH2GldQRWnDoxkcnpEX5YuPyBpkf58mVVM\nU6sHp8/RFR57ZdkOfvfeBgBig3x5d/UuUiL88bO31xkX7Nut4EtOaR1nPLqQto5WXwOi3Hx++8RD\nWuOmV1axKr+q8/XwhCBeXrKDFxfnccagaCYPiODbXf4CfX04OT0C6/9s+/flxt0A5JU3HPa9iIiI\n9AUFeUREREREREREREREjgGGYXDKgAh+d+ZA3lyxkz98uJE/fLiRk9PDeXTmcCwG3PraavIrGmj1\nmDS1emhq9eBy2Pj5aWlcPDKOd1cVYJpw0+SULiEegOhAX/4xYzg+VgO306eP7vLgWC37rvHlq8by\n+NxtrNxR2Tk2KS2c35+TeaTKEyE10o3XhO2ldWTGBPZ1OZ1M0+TFxXn0D3dx9UnJnJYRyfR/fc2D\nn2/uctwnt0447Lr/8dVWfKwWnrtiJF9vK+OpBTnkltWTFOba6/HztpRw51vr+NP0TM4YHE15XTOr\nd1ZxxYmJXDQyjgCnDwmhfhRVN/LC4jxeXZbPJ+uLuqxx+6mp3H5qWufr0tpmVua3vwfkldUf1n2I\niIj0FQV5RERERERERERERESOEYZhcM3EZK6ZmMy2kjo+XlfI43O3cenTS0kM82N+dimnZ0bhsFlw\n+lhx+lhZt6uKX769ju0ldXy8rogxSSH0C937A/Vpg6KO8B31PF+7lTtPT+/rMuQHLi3SDcDW3V2D\nPE2tHsrqmqmob+nogtPzj+qyd9eSVVjDtEFRe3QD2lBQQ/buOu4/fxCXjkkAYM6dk6hv9gDQ0NLG\nmY8u5JFZ2fznp6MBqG1qJauwhl2VjRRUNVJS24TDZuVnE5IwTZP88gaKqptYu6uKtTurWLurmhtO\n7s/EtHCSw108tSCHWVnFXDvxu+3uXl66g6gAJ6dlRPLuqgLK6pq54ZVV3HpKCknhLkwTzh8ey6DY\n77520YG+/OaMgdw+JY2S2qbO8YdnZfPP2VtZsr0co6MpT3F1E6YJAU4beeUK8oiIyLFFQR4RERER\nERERERERkWNQSoQ/t5+axtD4IH7+xhqyimq4ekISd5+d0eU4r9fktjfW8NSCHPzsVh6dOayPKhb5\n4UgKc2GzGDw2ZyuvLc+ntK6ZstpmapraOo+5YHgsD8/o2T+Ppmly2+tr2FRUQ+wXvnx2+0kEdHSv\nKqlp4s+fbsJus3D2kJjOcxw2Kw5be+AnxGXnmpOSeWhWNrOydjM0PpDz/vU1hdXfBWeC/Xyob/bw\n/uoCappaafW0b6HlslsZHBfILaekcMPJ7aGduGA/MqID+GRdET8bn4TNaqG8rpk/fLABE3jwwiHM\nzy7l3KExOH0s/HPONvzsVkJcdgbH7r0jkK/d2iWMeN95gwAo6gjvQPv747UT+7N4exnrdlXv92u2\ndXctuWX1BPnZSQz1I9ztwDCMSWEhQgAAIABJREFU/Z5zMKobWrHbLPjaj66t1URE5OinII+IiIiI\niIiIiIiIyDFscnoE8+48mVlZuzlnaMwe8xaLwV8vHEKE28HZQ6IZnhDcB1WK/LDYbRZ+Mq4fK3ZU\nYJowMCqA8FQHYf52wt0O5meX8vG6Iu45O4Ngl73Hrrt4ezmbimqYMSqeN1bs5IWv87jllBTeWrGL\n+z7JoqnNy+/PziDQd9/b510zMZkvs3Zz++uriQhwUtHQwr9/PIL0KDcxQb44faxkFdZw4ysrmZQe\nzkUj4wjzd9A/3B+rZc8AzCWj4vjjR1lc+OQSHrp4KN/kVeA1YWB0AL98ex0Ap2dGcebgKDJjArn3\n4yzOHByNZS9r7Y3b6cOjM4fvda6oupFP1xfR0ubFbrPsMb8ir4KZTy+lzWt2jvn6WIkN9sVlt/LX\ni4aSHuXe7/X/9HEW20rqeP6K0Z01N7d5OOuxhYxNCuWhS4Ye1H2IiIh8S0EeEREREREREREREZFj\nXJCfnYtHxe9z3tdu5Z7/6dQjIr3rj+dm7nNuaHwQn64v5p1Vu7j6pOTO8YaWNnx9rHy4thB/h40p\nAyMP+npbimu575NNhPnb+b/pmZTXt/DMghzmbC5hzc4qxiSG8MCFg0kO99/vOk4fK09dNpJfvbOO\nmqY2fnvmQE7L6FpHRkwA8+6afFB1XTE+iVB/B/d8sIGz/rmQMH8HiaF+vHbNWM765yKKa5qYkBqG\nYRj89MREJqdHEOzad9DoUCSGuvCasKuyYY/7Lqpu5MZXVhEb7MujM4dT09jKjvJ68sobKKhs5PON\nxczevHu/QZ51u6p4dlEuAB+tK2T6sFgA3lyxi12VjXi9ZT1yHyIi8sOiII+IiIiIiIiIiIiIiIjI\nETQgKoCR/YJ5eekOrhyfhMdr8vjcbTwxbxupEW6yimoI9PVh6W+mHHBrppqmVv4xaysvLsnD32Hj\nrxcNwelj5a7T07n9jTU0tXr40/RMfjy230F3uYkJ8uXlq8b2wJ22O2doDGOTQvj1u+uZs7mEmyb3\nJ8jPzos/G8PW3bVdOgQlhPr12HUTw9rXenT2VmKDfLvMfbGxmMYWDy9dNYYBUQEdo+Gd8yf+ZTbZ\nxbV7rPnJuiL+9sVmapvaaGjxEOpq77L063fW87cvtgBQVteM1WJQWN1EcXUTUYHOHrsnERE5/nUr\nyGMYxp+A6YAXKAGuME2zsGPuN8BVgAe41TTNLzrGRwIvAL7Ap8BtpmmahmE4gJeAkUA5MMM0zbzu\n1CciIiIiIiIiIiIiIiJyNLp6QhI3vLKKR7/K5vONxWTvrmNyejir8qsYnhDE6vwq3l29ix+P7bfX\n85fmlHPvR1kUVDVS09TKpWMSuHNqOiEdW3WlR7n57LaTjuQt7VdEgJNnfzqKb/IqGRIXCEBKhD8p\nEfvvENQdaZFuogOdfLq+aI+5AKcPT18+6nshnv85N8rN5u8FeSrrW7jngw18vK6IQbEBTEgNA+D8\n4bEEOH14ZmFO5xZdFsNgREIwv31vPavzKzljcHQv3J2IiByvutuR52+mad4DYBjGrcDvgesNw8gA\nZgKZQAzwlWEYaaZpeoB/A9cAy2gP8kwDPqM99FNpmmaKYRgzgQeBGd2sT0REREREREREREREROSo\nMzUziqQwF/+cs42oACfPXTGKUwZE0urxYrMYnPOvRfzl080syC7lhpNTGBYf1OX815bnk1/RwKkD\nI7hqQjKDO8IxRzPDMBiTFHLErud2+rDkN1MO69z0KDeLt5XT6vGyZmcVN76yiqqGFu6cmsb1k/pj\ns1q6HP/Xi4Z2ed3S5uWPH21klYI8IiJyiLoV5DFNs+Z7L12A2fH5dOB10zSbgVzDMLYBYwzDyAMC\nTNNcCmAYxkvAebQHeaYDf+w4/23gX4ZhGKZpmoiIiIiIiIiIiIiIiIgcR6wWgz+fP5ivt5Vx7aRk\nApzt20v5dAREHrp4GE8vyGHulhK+ePxrpmZE8oup6aRHufF4TRZklzI1I5KHZwzry9s4bg2IctPi\n8ZJXVs99H2dht1p4/6bxZMYcXGDKbrMwODaQ1flVvVypiIgcb7rbkQfDMO4HLgeqgckdw7HA0u8d\ntqtjrLXj8/8d//acnQCmabYZhlENhAJle7nmtcC1AAkJCd29BREREREREREREREREZEj7oT+oZzQ\nP3Svc+lRbh66ZCh1zW08tyiXZxbkMO3RBYT7O0iPclPZ0Mqk9PAjXPEPR1qkG4A5m0tYu6uaX00b\ncNAhnu+v8eXG4t4oT0REjmOWAx1gGMZXhmFs2MvHdADTNH9nmmY88Apwc28X3HHNp03THGWa5qjw\ncP0FRURERERERERERERERI5P/g4bt05JZcEvJ3P7lDRGJ4awcGsZhgEnpeo5WW9JifDHbrXw+Nxt\nAJwxKOqQ10gI8aO8voW65raeLk9ERI5jB+zIY5rmqQe51ivAp8AfgAIg/ntzcR1jBR2f/+843ztn\nl2EYNiAQKD/Ia4uIiIiIiIiIiIiIiIgct4Jddm47NRWAsUvyKK5uIsRl79uijmMOm5U/npvJb99b\nT0Z0AIlhrkNeo1+oHwD55Q1kxAT0dImyH9tKapm3pZSZYxLwd3R7kxoRkSOqW+9ahmGkmqa5tePl\ndGBzx+cfAq8ahvEwEAOkAstN0/QYhlFjGMY4YBntW3I99r1zfgosAS4C5pimaXanPhERERERERER\nEREREZHjzeUnJPZ1CT8IPxqbQEKIH6H+hxeYSgjpCPJUKMjTm1o9XprbvJimyeLt5by0JI+vt7X3\ni8jeXctfLxratwWKiByi7sYPHzAMIx3wAjuA6wFM09xoGMabQBbQBtxkmqan45wbgRcAX+Czjg+A\nZ4GXDcPYBlQAM7tZm4iIiIiIiIiIiIiIiIjIYZuQGnbY58Z3BHl2VjT0VDnyP5rbPIx/YC5ldc2d\nYzGBTu46PZ3S2mZeWJzHxsIaLIYBQHSgk1unpDIoNrCvShYROaBuBXlM07xwP3P3A/fvZXwFMGgv\n403Axd2pR0RERERERERERERERETkaBDo60Ogrw/5CvL0msKqJsrqmjlvWAyZMYH0C/XjlAER2KwW\nmts8mKbJzsrGzuO/yavg7McWce7QGO46PR0fq4VwtwOrxejDuxAR6UobAoqIiIiIiIiIiIiIiIiI\n9IKEED8FeXpRYVV7SGfG6ARO6B/aZc5hs/J/07v2l6hpauWp+dt5dlEuH64tBGBkv2BevmoMfnY9\nOheRo4PejUREREREREREREREREREekFCiB+bimr6uozjVkFHkCc2yPegjg9w+nDX6QO4bFwiry7b\nQavX5Kn52xl131eMSQrhhSvH9Ga5IiIHRUEeEREREREREREREREREZFeEB/ix5dZxbR5vNislr4u\n57hTUNmIYUBUoPOQzosKdHLH1HQARvUL5ukFOczbUkp1QyuBfj69UaqIyEHTTwsRERERERERERER\nERERkV6QHOai1WNSWNXU16UclwqrGolwO7DbDv+x95SBkdw4OQWAjUXVPVWaiMhhU0ceERERERER\nEREREREREZFekBTuAiCnrI6EUL8+rqZ3ZO+u5dmFuVgsBv3DXQyMDmBAlJtQf0e3166ob+GG/66k\nsdXDr6cN4MSUsC7zhdWNxBzktlr7kxkTAEBWYQ0n9g87wNEiIr1LQR4RERERERERERERERERkV6Q\nFNYe5Mktq+fk9D4u5jDllzcwd0sJAb42pgyMJMDZdeuph77cwtzNpfg7bVTUt3SOGwZkRAfws/FJ\nfLK+CI/XJCXCn6HxQQyNC6S6sZWaxjYmpO47OPPe6gKW5VYQ4LTx7/nb9wzyVDV1hnC6I8zfQWSA\ng6zCmm6vJSLSXQryiIiIiIiIiIiIiIiIiIj0glCXHbfTRm5ZfefY5xuKugRGgvzsXHFiIhaL0Rcl\nHtADn2/i0/XFADh9LJwxKJqLR8WRGuGm1ePlq00lXD0hid+cOZCyuma2FNeyqaiGivoW/rt0B794\nay2xQb4Eu3xYurScZxfldq5tGLDgrsnEh+y9W9GHawrIjAlgysBIHpuzlcKq7zrweL0mBVWNTM2I\n7JH7zIwJZKOCPCJyFFCQR0RERERERERERERERESkFxiGQXKYi9yyekzT5G9fbOGJeds75sA0249L\njfTnpNTwzvPW7qzi9W/yMU3wd9i4aXIKwS57X9wCa/KrOGNQFNdOTObtlbv4cE0h760uAMDHauDx\nmswYHQ+0d7YJS3EwvqNzzkUj41ieW8H5I2Jx2Ky0erxk765l7c5qmlo93PdJFg99uYXy+hZ+NW0A\ng2IDO6+bU1rH2l3V/O7MgUzNjOSfs7dywyuriA1yAmC1WGhp8/bI1lrQ3j1ofnYpOysa9hksEhE5\nEhTkERERERERERERERERERHpJUlhLpblVvCLt9by7qoCfjQ2gXvPzcRmtdDU6mH0fV/x/urCziCP\naZr89r31bCupI8jPh7K6FgqrG3nixyOPeO2ltc0UVjfxswnBDE9o/7jn7AzmbC6hrK6ZFXmVhPk7\nSA733+v5yeH+XeZ8rBYyYwLJjGkP7CzcWsr7awo7rrWWD2+egI/VoM1r8rv3NuCwWThnaAxRgU5+\nMi6BZTkVbN1d1358XXPHNVw9cq/Th8Xw0pI8LnpyMS9fNZa0SHePrCsicqgU5BERERERERERERER\nERER6SVJYf68v6aQd1cVcMdpadxySgqG0b6NltPHyrRBUXy2oZiZefFYDIOsoho2Ftbwt4uGcPGo\neB6fu42/fbGFiX+dy7e7b0W4nZw3PJZwt4MpAyJ6bVuudbuqABgSF9Q55vSxcubgaAAuPyGxW+tf\nfVIyGwtruHhUHI/P3U7a3Z91mX/o4qFEBbZ34LnvvMFd5lravGwurmHw97r4dEdqpJs3rz+By59d\nzsVPLuG5K0Yzsl9wj6wtInIoFOQREREREREREREREREREeklo5OCsdss/Gl6JjNGJ+wxf8GION5a\nuYuLn1zSORYf4st5w2MBuG5iMo0tHnZWNnTOr99VzW/fWw/AY5cO55yhMb1S+9pd1Vg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f3e0b04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Entry Price Entry Time  Exit Price  Exit Time  Profit  Trade Duration  \\\n",
      "0        1.64433 2009-06-02     1.65778 2009-06-02  -137.5               1   \n",
      "1        1.65775 2009-06-03     1.63160 2009-06-03   258.5               1   \n",
      "2        1.63161 2009-06-04     1.61733 2009-06-04  -145.8               1   \n",
      "3        1.63052 2009-06-10     1.63576 2009-06-10   -55.4               1   \n",
      "4        1.63575 2009-06-11     1.65887 2009-06-11   228.2               1   \n",
      "5        1.64345 2009-06-15     1.63202 2009-06-15  -117.3               1   \n",
      "6        1.63203 2009-06-16     1.64108 2009-06-16   -93.5               1   \n",
      "7        1.64099 2009-06-17     1.63990 2009-06-17   -13.9               1   \n",
      "8        1.63988 2009-06-18     1.63311 2009-06-18    64.7               1   \n",
      "9        1.63309 2009-06-19     1.64924 2009-06-19  -164.5               1   \n",
      "10       1.64509 2009-06-24     1.64063 2009-06-24    41.6               1   \n",
      "11       1.64064 2009-06-25     1.63678 2009-06-25    35.6               1   \n",
      "12       1.65245 2009-06-29     1.65660 2009-06-29    38.5               1   \n",
      "13       1.65658 2009-06-30     1.64605 2009-06-30   102.3               1   \n",
      "14       1.64607 2009-07-01     1.64775 2009-07-01   -19.8               1   \n",
      "15       1.64776 2009-07-02     1.63926 2009-07-02    82.0               1   \n",
      "16       1.63930 2009-07-03     1.63315 2009-07-03    58.5               1   \n",
      "17       1.63209 2009-07-06     1.62855 2009-07-06    32.4               1   \n",
      "18       1.62843 2009-07-07     1.61382 2009-07-07  -149.1               1   \n",
      "19       1.61380 2009-07-08     1.60706 2009-07-08    64.4               1   \n",
      "20       1.60696 2009-07-09     1.63340 2009-07-09  -267.4               1   \n",
      "21       1.62023 2009-07-13     1.62263 2009-07-13    21.0               1   \n",
      "22       1.62256 2009-07-14     1.63085 2009-07-14   -85.9               1   \n",
      "23       1.63088 2009-07-15     1.64219 2009-07-15  -116.1               1   \n",
      "24       1.64373 2009-07-17     1.63333 2009-07-17   101.0               1   \n",
      "25       1.63225 2009-07-20     1.65460 2009-07-20   220.5               1   \n",
      "26       1.65477 2009-07-21     1.64578 2009-07-21    86.9               1   \n",
      "27       1.64573 2009-07-22     1.64895 2009-07-22   -35.2               1   \n",
      "28       1.64892 2009-07-23     1.64768 2009-07-23   -15.4               1   \n",
      "29       1.64772 2009-07-24     1.64169 2009-07-24    57.3               1   \n",
      "..           ...        ...         ...        ...     ...             ...   \n",
      "814      1.64812 2014-01-13     1.63821 2014-01-13  -102.1               1   \n",
      "815      1.63689 2014-01-16     1.63522 2014-01-16    13.7               1   \n",
      "816      1.63522 2014-01-17     1.64220 2014-01-17   -72.8               1   \n",
      "817      1.64155 2014-01-20     1.64250 2014-01-20   -12.5               1   \n",
      "818      1.64250 2014-01-21     1.64749 2014-01-21    46.9               1   \n",
      "819      1.65729 2014-01-23     1.66356 2014-01-23    59.7               1   \n",
      "820      1.66357 2014-01-24     1.64827 2014-01-24  -156.0               1   \n",
      "821      1.65023 2014-01-27     1.65824 2014-01-27    77.1               1   \n",
      "822      1.65821 2014-01-28     1.65779 2014-01-28    -7.2               1   \n",
      "823      1.65771 2014-01-29     1.65611 2014-01-29   -19.0               1   \n",
      "824      1.65615 2014-01-30     1.64824 2014-01-30   -82.1               1   \n",
      "825      1.64832 2014-01-31     1.64363 2014-01-31    43.9               1   \n",
      "826      1.63026 2014-02-04     1.63231 2014-02-04   -23.5               1   \n",
      "827      1.63230 2014-02-05     1.63073 2014-02-05   -18.7               1   \n",
      "828      1.63078 2014-02-06     1.63194 2014-02-06     8.6               1   \n",
      "829      1.63991 2014-02-11     1.64481 2014-02-11    46.0               1   \n",
      "830      1.64488 2014-02-12     1.65947 2014-02-12  -148.9               1   \n",
      "831      1.67653 2014-02-17     1.67116 2014-02-17   -56.7               1   \n",
      "832      1.67114 2014-02-18     1.66813 2014-02-18   -33.1               1   \n",
      "833      1.66820 2014-02-19     1.66773 2014-02-19    -7.7               1   \n",
      "834      1.66771 2014-02-20     1.66492 2014-02-20   -30.9               1   \n",
      "835      1.66491 2014-02-21     1.66144 2014-02-21    31.7               1   \n",
      "836      1.66258 2014-02-24     1.66545 2014-02-24   -31.7               1   \n",
      "837      1.66546 2014-02-25     1.66787 2014-02-25   -27.1               1   \n",
      "838      1.66786 2014-02-26     1.66679 2014-02-26   -13.7               1   \n",
      "839      1.66682 2014-02-27     1.66870 2014-02-27    15.8               1   \n",
      "840      1.66873 2014-02-28     1.67414 2014-02-28    51.1               1   \n",
      "841      1.67096 2014-03-03     1.66639 2014-03-03   -48.7               1   \n",
      "842      1.66641 2014-03-04     1.66631 2014-03-04    -4.0               1   \n",
      "843      1.67385 2014-03-07     1.67121 2014-03-07    23.4               1   \n",
      "\n",
      "     Type  \n",
      "0    SELL  \n",
      "1    SELL  \n",
      "2     BUY  \n",
      "3    SELL  \n",
      "4     BUY  \n",
      "5     BUY  \n",
      "6    SELL  \n",
      "7     BUY  \n",
      "8    SELL  \n",
      "9    SELL  \n",
      "10   SELL  \n",
      "11   SELL  \n",
      "12    BUY  \n",
      "13   SELL  \n",
      "14   SELL  \n",
      "15   SELL  \n",
      "16   SELL  \n",
      "17   SELL  \n",
      "18    BUY  \n",
      "19   SELL  \n",
      "20   SELL  \n",
      "21    BUY  \n",
      "22   SELL  \n",
      "23   SELL  \n",
      "24   SELL  \n",
      "25    BUY  \n",
      "26   SELL  \n",
      "27   SELL  \n",
      "28    BUY  \n",
      "29   SELL  \n",
      "..    ...  \n",
      "814   BUY  \n",
      "815  SELL  \n",
      "816  SELL  \n",
      "817  SELL  \n",
      "818   BUY  \n",
      "819   BUY  \n",
      "820   BUY  \n",
      "821   BUY  \n",
      "822   BUY  \n",
      "823   BUY  \n",
      "824   BUY  \n",
      "825  SELL  \n",
      "826  SELL  \n",
      "827   BUY  \n",
      "828   BUY  \n",
      "829   BUY  \n",
      "830  SELL  \n",
      "831   BUY  \n",
      "832   BUY  \n",
      "833   BUY  \n",
      "834   BUY  \n",
      "835  SELL  \n",
      "836  SELL  \n",
      "837  SELL  \n",
      "838   BUY  \n",
      "839   BUY  \n",
      "840   BUY  \n",
      "841   BUY  \n",
      "842   BUY  \n",
      "843  SELL  \n",
      "\n",
      "[844 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "Q_learning.episode_review(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eventually, the agent gets smarter and starts buying/selling wherever there's edge. More buys (blue arrows) in uptrend, and it learns to stay out when there's no edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUMMARY OF EPISODE 220 \n",
      "\n",
      "Total Trades Taken: 625\n",
      "523 Buys, 102 Sells\n",
      "Total Reward: 4477.2\n",
      "Reward: 2331.200000000003 pips(Buy), 2145.9999999999977 pips(Sell) \n",
      "\n",
      "Average Reward: 7.16352\n",
      "Win Ratio: 52.96 % \n",
      "\n"
     ]
    },
    {
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p+qd/il89NZHKZ425ZUudd641hNrnJdq6fmaibInrrMFBRYrKS9Itt8hNX1qg\ns9N9DAAAAAAAKKpshltVl9xhZO+IO/aYyd/njavu0vlFS1n93jXPKjvdIcl97OrVBRVaivTzeAE+\nfkWUO+6PnQyWTktp5bYvU1W/0RPPHYhk7AGKqXWgzlZJlxpjFhlj3iTpVyX9zBjzZmPMiZJkjHmz\npN+Q9GSNnxtNMrop4UYfqnTnZtzB2TuoH3llaW6l3DoxgTiS0VNPSVNPHpA+f5Gf8uz6H6/X+EVX\n++tIlQfIxO3j4YNLoyeaH90YClLxfOc74RPIdLZdsuH1ynX+O440NNGrtA3P+pzta5iNwvIBhc9d\nGNEaFJmp7H2GLaxYQ2Hy0KRufmSLNDSsj3T11+bJAsE4keCh/oLbceIy7syD97gcoooBAMBsRWqK\nW+naa6VFHeXbqo4jDe1a5pd8bUibO2YSgiT97u/GrJtyJw74WWM2v6OOO9Y6Qu3zQFu3sBMwUh45\n7jqru1tas0bp9tz3ob1dOuMMqatL+vrXo1l1PvtZd5kUKpsV+lm2rBYvEwAAAACAeclxpPddcFSP\nPmprN+HpcFdu7DEsk5FW677YUlaOI+3Y9k55IQ7ptLRnT0GFlg3S7t3RcahgwgKvTyFuMlg262bn\nkaS0FlfVb+SPmTcwEQPmr6oCdYwx35Q0JulcY8wLxpirjTGfN8Z8XpKstT+T9LCk3ZL+VdLd1ton\nJZ0uabsxZlfu/gettQ/X8oWgORxH+ulDF7jRh1pT2YEqkCEkmZS2P2o1sqmgElogEOda3S7T7h4l\nOzqk5Ei0atpVz38tct9sZscePrhUd11zjR59tOCxP/+Y4oJU4oXXS6elHTuKr51MKpJNx1P0NRTL\nxDLLTCvJpJSdzoTuCz53MhkOSKpoH5vAcaREql86XDq7ky8mW819t55T25NoJcE4VRieHJaGh2u6\nzWaoWck8AAAAuR0r09PR+wvbqsmklDWLSq5Tc0UmIfzHfyhf2le5tuz6Pv1g9Pl81piJZfHXWMdp\nhkWvPR8MzPHKXMWloy6VVSdjchmWjJH+9E/dv7u7o1l1gtl04spmdXZKl1wy59cGAAAAAEDLKtPP\nMDAg/fLAm2Stqd2Ep5HB2LHHdFp6SufFlrIaGJCfJbkYa62uvDI8DjUxId1xh40kLHjoIfnj0d5E\nsMJuAS9jc7lsQqEx8wYlYsD8VlWgjrX2U9babmtth7X2TGvtPdbaO6y1dwTW+Wtr7futtR+w1t6c\nu+/frbXsHTdGAAAgAElEQVQX5n7Os9b+Za1fCJojFH2oNiVXP13+QbkMIX7acms0/tAyrbjnak0d\nWRpa1XGkIa2RzbhHxXRa2rBzuTT1AX+dk6bfp6dePjXyNOUCZOL86K4P68grJ8rafKev40iaXlr2\nsUHnnRc+sI+PF193bExKZxbFLiv6GooFf8wy04q7D+GZpcHnHhsr/fhWOeEkk9L2fWdJI8WzO4Ui\naAPZahxHuvOP/kg7H/hgc0+iiYQSZ8eUu8oZ2TsijYw0cIcAAADqZ7ZZ9hxHoVTC4+NSb2/8usH2\ndNxMqdlcN1TKcSSd8Ko+85loR48kXXll/j3w2rKP3/onftaYYzPtuu66mA3P8wyLfnmvgjTW3nvg\nBebcefcbfpmr666LpqOOBFl5GXHOOENLvNLIMzPhDsevf11661vd9aR8Nh0pvmxWezulsQAAAAAA\nx7fJyaJ9NI4j3bs5Ky9RQS0mPN38yBZp16djl518stTRng09l9eP8HebMyqfWMGt0OJXc5F01VW5\n7MwBca+jWL/RD37gJp4o9brjMvYApdS69BUWkEi5JC3W0Og7Kw5uCKUtn27XYy+cqYvuvCbSUZst\nOOBaGenv/86//fPvXaGO9vDR1Sir/v7SATJxr2f3Ix9Q4YkmmZRUkPGm06R1yinFtpSNS9RSlJe6\n3yvjFQzwKRXk03NST/6G94SzzLSybZu08uxJ6b91xT63v4/Xr3ejSduLZ99pFr8j37ZJE8WzOxXL\n5JJMSlP/dob/WTftNfX1qa+nL3+7IHtSqSAeAACA+SbUNqsiS8zqdU9HsiAG29XF2tOhdWRkr18f\nbXPXMFtNMilp36V68MFoR48kPfWU1V0j3w9MYmiTXv6Av65Vm+69tzWC4mvJL++VS2M9PDkceg/y\ngTn5Dq649zASZBWXEaetLZwRp7tbOvPMfA9dsMTVb/2WtGaNZhblJjF0dkpr1oSDeQAAAAAAOA49\n8dyB2KwxhVlsSmW4PXxwadnMM1KuusXMm7R2bbgPZ/9+6ejRfIKDdFrauFEaXH2Z1q2TZrLh0IYl\nS9zxQa9Ci9qPSXIrtHzztndJcpc/9ZRUSUWUUL/RUCq0T9mYbEJecFOxjD3HW38OaotAHcxaMNDG\nk7GmouCGwrTlsm2SjJwjJ2pgIL/e2JgbABRmpJfP8w9ucdlgrNr0wAPVvBr3RCOb3453EB0dlX9g\n9ZfZTr9vd/9+aVFnMM9+NBVbPfT39udveJlhKsy0kppIhSJjK8lE4ymXfadZgpGqmlkcP/M45/DB\npe4M7Le9T5L7fbznHsk9Sbsn6pY5iRZkTwoF8ZQw29npdZeb5bz+128ID4osW9bsPQMAAM1WYZYY\nx5FG7n9HXbIgrvrWqpplq7n5kS3uxAbbrqNHFZtVp609o7HNl8VeW3kyGZVs285Hf/Xwvdrwjdf9\noJyRJ58OvQczM9LmzQp1cB09mu98szJ+h1koyCouI84JJ0Qz4qxcWbzEVbBsFtl0AAAAAAALxOim\nhB4dtbrova/5fS2OI913n1QY5FJssvvopoRfwrqYUiWi4vpH0mnphafO1He+U3w/HEfaqDWhfoT9\no7+hqSl3uQk8rK1NfoBQyYQPufG5UOKJgtftjcVVmrEHCCJQB7MWm/4rs6iigI3iHdHG7ZA9fLqk\nXOSijLTe6NrlPwn1pXqd1cHoxi9/9yYtbntDkvTaa5V32pc60SQSkta7s239jDfXr/cP3slktB7i\nzMzsDr6hLDlVPbC6x00eyqewC83eLZGJxnueyCzkwEzlRgeHFL6GfEBVbuaxTo+sf/jgUt11zTXS\n3kuV/PkVktzPKm6Gc7NOorP+HgQUyxzUdHGznL1BEQAA0PJufmRLRTOjquU4UiLVX9F265lKeOsz\nW2edpbLQX914QqgjJy4jTGZmkTTxmfAkhpgUzoWTEJzDlc1Qa1Wv/+//5meynJmRtGE89B5MTytS\npz7yWccFVHV3hzLizCxqj8+IU6rEVXe37rkw98GRTQcAAAAAcJxzHOniu6/W+MO9sjJyfrlEv3vN\ns5Lc6/DC63MpfgK/40gTD/cqm3Wz4Kx410ux/RYDA1J2ukNS9Fo/buzZ7VuJL3fl7UcyKU2ro+CB\nbRoYcPclOCbt7V8lfSqFiScKJ/mP7B2pKmMPEESgDmbNC9jQeuMGsORSxxeLPvSzmBxZGnug9WQy\nkn50Y8GDuzQ00Rt4THwK+PEtH1fWtPvbieu0jwsmqehEEwyGCfw9NpbrYA+Ynp7dwTeUJaeqB8Y8\nrkzK/pG9bvadUNCUbSs90BH3PAFPPHcgP8BSw5IBxXjBKLHZnTLSdboxsv6P7vqwjrxyoiQ3UnfX\nLveEHKcpJ9Gentl/D1pJLnNO5GfDhug///Q0M5UBAJgn7rv1nLI1uasxPDksKZ/hMbjduKCgSPnd\nuWRBTCTig3K8LJVzaM86juQ8+puhjpxIRhgrLf/tn0gzbyp6beTxJiF41zLJ0URNP4dGchxJE/lZ\nbtPTkl7rKvseVNw2D2TEse0mvp2ZC+jxdXbq6U+skLq65DhS0vl7pdVBGxUAAAAAcFwqrLrxLy+e\nqUzaG+s02vHgOzU15Y6Bxjn55Gg2Gnes1b0eT6elx5471e+3cBzp3A9OadcuL3FCm79esF+nsKz5\ntde6Zazi/J+feNZPJDA6KmUVHqtVdrG+//1cv0OBdLqyiV/Fxh+Dj511xh4seATqoGFGNyWkfZcq\nObLSP9Bee63UoWPRlXd/Op9WTV3SnTvdjC8BhSngHUcae+C9obqFcZ32cZlGip1oensDB9FgkErg\nb++17N8vP5vPkiXSQw/Fb3POKs2eEzfDtGCwIVKCLLO44ijSOKObEtq+7ywNDEiJ9X0Nm+FbLPDr\nAX08dPvwwaXa/cgH5EW1ZjLS6tXxJ2nvRNrwk2iZYChfAwKh5iQuc05bW3TmsiS9//3MVAYAYB7w\nUhPH1eSeLW/mkZfhccMGafdud1lcUFAlHSQV88rHFjOHEljJpPyMMZ7C/fRTPautaNmrwsemJlLu\n+zXRW9PPoZHi3hvJxL8HXeN+htWKO7hyGXGyxmj8I73F25nBrDrt7fqzS173929q/+X6kv6WNioA\nAAAA4LjjONKfXNGrqalcyaiNkjtulr9Wt1mjgQH3Onz/fklnj2hiIj8O+sor+f4bb5tDQ5LNuONC\nXhYcb8xx9bqn9fPx03TFFRVk0C3YZtwYniQ9tu3dfp/IypVSZ/uMt/eh33H9DdlsZZOBYqvLBCcS\nHe6aU8YeLGwE6qAhbn5kS77e4MQy/+A0NiZNa3H0AbbdD8JJalA6cobSmfbIasEU8JV22h8+GE0T\nH8wONDSe8me4VhOkkUyqbDafmigXyFEqgKNgsKFYrcfZ7Ht+4KZNmzdLj+49q+4zfL0Z2H5DYdHr\noeWv6c2hz/lHd31YsvnvUTrtpqOby0m6aeYwcNQQceUEOjujwTuS9Hd/15h9AgAAc1KrklM3P7JF\niXOn/HZasE1qrXTllYF0yQXBKGU7SKrR0xNpWyfOThTPtFOFsTEFyrK6CvczmOq5s1P6zGfyHV6F\nvMeO7B1x36/cJIb5UO/cceRffzmOtGmTIu+NZ+3acHldff6i6ErLlrm/b7ghnLnRu19SMiHtO/8s\njf5hoviOdXfn6hxLWrNG3zs0ls/YZNs1pDJlgQEAAAAAmIdWr3tav3z2fCWTbp9CfIZb41c2SSYl\n7btUq1fnx0GttbryyvzaceONkrvtL35R+vF33yPZNj3zTPw6cf06xbbp8fpE/KQEmXxGIO/3q68G\nshsX/FQyBhzK8BPor/AfOzI4p4w9WNgI1EHNOOrKlz0qcN+t5+Q79a3xD07j49J5ejJma0YPPJA7\nuLZdLclo8WJpcUF/rpcCXireaf+//lc4KGd0U3yaeC/NW2HZIceRW7KrRCdt4UlgTin44yQS7qBB\nBetVkmnFK0M2Ohpf69HL9l+NwoEbq7a6z/D1ynd5zx+Ztax8KS/HkXY/cr4Ka0R2dORT0F3/4/Vu\nGbcqTtLNFFfGLbxCmeX1lCsnkOnIBUZ1dkqf/ay0Zo3S7YHP4LzzpAsuaM4+AgBwnIorGTXXdkEt\nS07dd+s52v7saRoYkPSNHeEMj5L27JHWrcunSw4GowRncgU7WmbVbuvvjwTq9PX05TPtzCFYZ3xc\nfhaYuE4gR12RVM+bN+c7vDo7pbX/+elQR9D4uNySwEPyJzHU/LqjDpJJaft2+R2Ar79mpeW3Sf+t\nW+2aCa07NCRN6fTSG1yxwp8b5+vslC65xL85daKUuqVfp5zzgdLbcj8Ev8RVMimNHVsmK6PX9GZ1\ndccHAgEAAAAAMB85jvTo1ndKtl0bN+ay4EQusl3ZrDvJyJvQsmdPOBhmz558Vp0H/+lgbMBPNit9\n5zuKyazrWrLE3ae4fp1i1TSCduwoHdCTybivoVa8yWe3/GiLO36869PzMxkAWgKBOqiZpAa1fV80\ni4rjSOMP9eY79TOLQp3JKzUqmYzWLv9X7b/4d/xgnNdec0tbeZ3V6XT0gFzYaW+tZK9fL5vo80tr\nvf6aDQVrFEvX/8RzB/IBOYl8UIwXKVoq8rGmKfjj9PW5gwbl9PcXjbIJBnV4ZcgSiXwJMi8lXGdn\n6OVXpHDgxtPIGb6xs5a12D8RDgxIstFDXjBSt+eknrruY61NHposOug2PDnc/PJYg4Oa8YZR2tvd\nAZDBQS3KBFp9e/Yw+AEAQI147b24klFzzcZXq/ZuYRZGvfirsZ0u3/1uPl1yOu2mDF7xrpd0y4+2\nVNQ+n7O+vtlFr1do4O2bY1M9hwL/x94bDcAZGazvdUeNOY50591v+Gmf3RJnRppYIz10szIKZ03N\nZHIZVVUiKH1wUNMqKFDvtTULFE7CiOjudn93dflBUDu0QsdUkAWyIBAIAADguNDsvkMAQMOtXve0\nPzEqnZbeiE/s6/v+90tntfGy6qy543/KDqWk5bersz0Ts2Z8oE6pPo1gNpuh8VTRrDjlAno2b67d\nBCdv8tnmW9/h7vfMm/xkALPJ2IOFjUAd1MRJ0+/TUNvVytpoFpVkUsrOxHTAJgMZc3Ilsa57Pd/x\nPDPjHjy9zupsNr5MUyQisadH6uvzg0eCQTml0vV7wSvJpPxZtKHU50PSkVeWxr7+mqbgL6KiIJIS\nZbEmD01Kyg+OeK9p1665ZwMqldKuUTN8Y2cty/gnQrdMWrQh0NubP1mW7chvRQWDbqmJlJRKudmG\n6ji4VNKyZW7gzRln6ITp3Bfj9delj35U6u7WnlNVdhY0AACo3uShyZKB6XNRq/ZusNyTG6jSFtuO\nLJzNlU5Ljz13qjbe9J5Q+7wu7cyeHvenjh587bKy68R2Vr2wou7XHbUUvP4KTbywbdIzv63C9nk6\nLe3QJXIc6atXXibdvSOSYcdRt4a0xg+msZ2d0po1blszFwBu10vrf/2G8sHg3jJjZP9mSq8fM1qr\nDepUQd7qIoFAAAAA8xqBOgCwoHjZdLyJUdlssWw67p0dHdKrr5YOgnnqKXec8e61V2vF4GXS3l/z\nswBXotI+jVLjd15AT29v/PJaTXBy1OVPPnv8wQvq3z+F4x6BOqiJn3/vCj/zTSQAZlSyBZlMvANv\nMpnPmDOTNdq8+0L/gD89rcgs085Oae3yfy0dkZgLVgkGj8zMSBdeWDxdf2HwytSRpZFtZDLS+JaP\nx77+UI3COkVLVhNEEjf79PDBpUqk+t0sRYFgpdWr5z47ulS0aqNm+MaWKMulBnIctzHhaV8040e4\nzreI1nIBW16gTlOtWOH+sxaamJCM0fkvx4RMMfgBAEBNBAMjjr1udd11bluoWInaSm3bJi3qdIMX\nlug1v+xUNW0px1Go3JOnrc39KcVtrxrt/sfzQ+3zurQzgyWxYtpVobb2LNpdjiMdPRq+z8RMLIvt\nrPr8RaVro7eQwqyboYkXmcWRbJh+umtdpGRSevFnZ0ovXOxn2PEkk9LX2r4qm/seTWfateWT74lv\ng5YLBl+xInLXMXVqj97vBwKllQsE6uqq/MUDAHCcKFt2Hc011z7AZk3yAwA0RTIp2Wx8Zpswd524\ncdpCHR3uOOOLPztTj71wpnT2qN9Xsf9vt8T2d0huUE2t+zTGx4sH69RiglOye4P//tmZDn9ctJUz\nHaO1EaiDuculCC+WkWXlSkntxyTlAm1yARLbtoUzuUxnFylTpEahJ52Wdrzw9rK75Jz0PnfbgaCf\nl16yRUtnRTLtjKz0O5a9x6TT0tgDMennW5CXPcfjONJd11yj0b1n6d57s6FgpaeemvvsaC9QSV3R\ns2m9Zvje/MiWUGBObAmE3ADLwEC4MZGZWTRvI1zLBWw1NZOOZ3AwOtrW3h4fvCO59xfMgg79UBIL\nAHxeHeT5eA5D/R0+uFQbN8pv61kZ3XuvW042rkRtNZJJ+amRM2qb1bbcNll0qlZc5kxJUsdhOTdv\nccu0es0I2x5qn9e1TZdIxA5+hNraRUqKFbZVg+KyUXZ0KDZVcisG4FSqVI34OH7W1Ys/6X6PZSQZ\nbdRnpcNuVh3vGm1f9u0a0hpl1KYhrdFPXp0u3gYtFQwes2zxknadP3GfFsv9onUqLd1+O21TAPXT\n7Mk2QAmFfYxoEV426zVr6EMDAFRsbMwdHyvkBc2E+l8qlE5Le/ZI3jW8Jj6bH7f7+RXqKKhc7Y0T\n16u/o16JFRxHGnr5Y342Iqnd7/NoZHURHF8I1MHcjQwWzchSOIsyeLCK77iNL010/Y/X5w+mf3xn\n2V1K/vyK2G3Hlc4aGYnZx4llucwz8a+r1Q1PDoduDwxIR145UW5pgfB7XNNBgQbO8L3v1nP8wJzC\nEmX+ybC/PzBzO2y+fJaxEglpeDjyObeM7m5pzRql2wPftauuKj5V3lp3kGQ2s6ABYIHx6iDP23PY\nQlHjATfHcU//pS74vcDs6YKKPZmMW042rkRtNc8/NCS/MyKtxbPa1oMPSsVqknsdNfvVrbMvmNRn\nPiNp5k0aGL8iFDxf+Pi6tun6+mIDoA8fXFo2YC7YVi3UiLK5raBcjfjYslc7pOTMdaHv8RvqkO58\nPHINmdSgtutS/WXboL5527v8NqjfnuysIBNOd7f7U/iYCy+UTj01uj5tUwA1EspSUiToEwCKog8N\nADAL27ZJbR1vSMpntQ2O4RW7jg9mv/F/rl/vB/eEhn5mTtB11xXf3nzt/0gm3b61Yub1mCOahkAd\nzN0LK4oeaOOCcbyDVaUH/NkEeZTsFF5+WyiIZOXKmH20Rg8+2EInkJ6eild11KWR9Tf45bvygSpe\nR3h8h/is5EpLNVphqbJgUFXhydCd/R3dRss0Bqr4bH25QaORvSPS8HB0eSKhxNnN+Wx8g4PKmMCM\n+a9/PTpw4g1+XHihO4ASNwvaC+IBAPjnv7kEXKBBajzglkxK27e7v4uVH1i97mkdeeXE2AwmXlto\ntp0GcW16r6xWpeLKPQX51w8a1N7dZ2nzZkm23Q0yKpGVpdZtunJlRiVpdFOiZMBcpKxuwf9qI8rm\ntgI/6+Z6E+rA6zS5i6z2N/wJA97EjG3bpI3jvaHP3GqRdOQMDQyEr/Om1K0+jej56S7tf+ps985g\ne7LS0qqf+lT8Yz75yei6lGsFUCOhLCVx1/VAE4TKqZcKPA8uIyNUU2z55HuqzySYQzkzAFi4ItVF\nCvo1qu2vcBxp48bCfps23Xuv2xdyPPV/jI1J6Ux70eUtM+aIeYVAHcydl0Xl+vWRLCqloiX9A/T1\n692O2evdn1ocoL1t798vLV5UMK14Yo2mdLp/M3YfM4t05pktdALJlXCqRFKD7uzd8Y9LipZ98gTL\nkM36NfX15f9u4IV5sDExM+POEi9WAmFsLH4bvb3xr7uSwZmaquKz9fX0lA7G6etTX0/fHHaqBrq7\nNdQrZY2RzjgjGojT3i79zu+4f3sBX7lZ0JmOXGOnszMfxAMAKHsxjeNPaiLlZ7PJZt3fT/77gdzC\nlL+e40gj979DxbLVeKpNxet14se1l62MHnigsu1I8cE+wfaoXxq37WpJbaHgorgA/JNPrk/7vFyZ\nUefmLZp4uFdZ26aNG6UV91wdeT9b4X81NNDWCnp68qWFbS5wO3OC/3302uDJpDQd2/FltHmz9NBD\nBbP3ZLR/v6QTXnU/h1x70nqlICppR55/vrRmjdtuDT5mxQr3dy7QPNPRXvk2AaCMUIbcZpevxoIW\nDNqYPDSZDzgvFXgeXEZGqKb4Wccht13SHmg3VdhOiZQzW7WqtjsHAC2oEUGKjqOWLldfqgLKrPT0\nuNfw09FFmYyqmtw1H3jjzkPjKX8yUkuMH2NeI1AHteNlBgl0CAdnUYbKV43HPK4O3FRkBQMWts0N\nZgns47XXSjKZ0EDBfDygOk5ucMO2a2jsvdq1K77sk1TbmomOI1381cuku3fUvRFS2JiYno4GIgUH\nRIIRu0PjqbKfb7nBmZbQ358PxgkGS0mR4J2aN0CrGPBJJqSXlr3bnaUshQdO3vxm6c5cGbu/+Zt8\nLe3RUc0oF4RlbNOyNgFAqyk8/z2WXqbbbjf546f3s2xZc3cUNZWaSIUCXI4dk35012XujcCgSDIp\nZTM2uoEYmYybfacSXif++LgbAK+zRzQxIS1uc9Mkv/Za5W3JStIdJ5NSRtEgjcWL3Z+gap67lpL3\nnaNM7r0+9kZWj71wpt/udBzp4otr3PE1Sy0XqNPfXzLbqtcGHxuTskW6KYoFPSWTCpcZGxyU+bVf\nqzzzTX+/NDiofeefFX5Mf7/7geYCzbNthmw6AGpmZG9BcE4rHbOxoASDNkIBZHGZngq+p2RmaZ7h\nyeFoZuoK2yk37bhJXTflAnoSCWnr1trvIAC0GO98V89r5dXrns5n323Btl2pa/JZ6e93r+GLZEGu\nZnLXfNLf21/XcW0sLATqoHa8zCBFZlIUzVRSbUaRKg6AbiqyReE7M4u1Q/l6vRMT0h132KKp6eeT\nZFLKGndwI5ORVq+Oz6bjmfMM39xnkUxK//LimdILF9d9xnBcY6JQsRRz8yIIZ468TDre/1tklkyV\nIp0uVcyUmjpR6nr8GWnp0vyd3sDJxz4WX0s7kdA9F7of8E8/cmH4sQCwgBWe/8a0QscUcxy95BKh\nuWo5YDHy5DNuBpJcgIu10u5HLtCuXVIi1a+pqUAQV3ZxyW150mlpYsfJSiTKt3uDgzVeMMTq1eH2\nZqVtv1AZpJjZRt7rmM4uijw2nY4G+TQkU03BdYfjSEMTvbKZ3P+ebZNktPGuaU1N5drE/2Lruq+V\nlDe9+ZEtbinckcoCshqlkmCt8XHJDrnB9V/+7k2RAK3C6zVHXbrz7jfC13Ld3W52imoy33R3K3VL\nf/Qxf/zH7ux0SeMf6SWbDoD6SCQqKzcE1FkogGxkJPp9jAvUoXRbU4zsHXHbPJ/7nDshTqq4nXJ0\n+qh+cfQX7o2CCYAAcLwLZY+rIS/TsZ99d/Cy1hprTKUquiavVqi6SYtMsGqI2VTKAGIQqIM5cRxJ\nQ8MVHWzLBklUGoBTxQFwfFz5FGS5slxabzSui/x1rroqN2ig+V1Gwk8lnwtMSqelp54q/Zg510zs\n7/drULqlFow2bqzvyTeuMSG5paxIMZdXq6CkuQb6RHgDJzfeWLSW9sq97s0Pbd0p3XADWSIAQNHz\nX1KDsoVN+dxxFM31xHMH/CCaORsZjAQo22ybrrhC2r7vLCWT5YOY/TaSjK691j39ti/Kavv28u1e\nb7DGDway7dqzJ9zerFWge6nXkc1GlzWk9nfBdUdstk5J6WyHBgZy75FM7fc1kGEw0saLGdC979Zz\n3Awzaq3jQcW16XPv+/iWj5ed7ZfUYH3LjN1yi7Rhg6Rc25R2KYB6CJ5vCNRBEyXOTuT7Z3MBZKEs\nO55ccM7I3pHypdv4TtdcaGKANyFutsgIAGCBGZ4crkmQaeEkrWAJ7MLsu83gONIp73sy31/zhS9o\n2wsXqOOEGUnSkiXuOrUaS6t5th5ggSBQB3MSSTM+Sz0n9ZQMwCmajWe2cp3dExPSnj2SG2TSvNT0\ntRB3IuzokF/O6/ofr5eVKTqLeS7PG6xBmU7X9+Rbbjb2QlL0/6KZF9nLlvkDGHa93L9vuCE6mJEr\ng+Xr7PRraY+eHbNdskQAWOBC5z8ZObZbS65dk89OFjiOorlGNyX8IJo5e2FFTICy0TPPSFnbpqEh\naXS0dBCzn7FGXRoactuLL0+epmy28nZvqSCaWnW8VBSMXVADvNHtv9hsnXLfm82b8+9R26KMtPRF\nv9NrzvsauE6KBOoUzAR0HGni4V43w4zWzMvrGs9/PPvekrP9nMNLNaQ1NSszFtu2XrEiPgvkJZcw\n8Ahg1iLZ93LlrSXFZzEJXGdT8hS1FgzE6e/tz7c7ct/JkxaflF+51Pe0mDpkLVjoUhOpfJZFb0Jc\naIVU5RsjIwCABSDY9qooyLQCwQnOheXq/ey7wUntDb5+TCalg8+8L99fc/Sokgc+r5kZt+Oi1kE0\n9cjWAywEBOqgat5JLTiz1usQddQ1qxnE5bJ/1LxkUe7C8qqroovma5RnM06EXjad4MBNNuveN6XT\n6/fEkFSiFmYTL7Kffvfbig9mFArW0g5kgUjGVXQgSwQARBU5jkpiQKdJHEf66UMX+EE0cwmScBxJ\nJ7yqiYlo+mBPJuPGn1cSxJzs3lB2dtPNj2yJlMTyszbGBNFItWtvVpxtpdEC2WzGxyWb6FP3r9+v\nTh0LrZbJ5N+j7Ey7dOQMDQzUaB+Kte0SCWl4WKu+tcq/a2BAmknnSpOprSHXNbUs9xZU7juRHE0o\nq3CGo7lcy8VecwaPs550Wrr9djc4kuMqgFkombnWK4MVGMyJvc6W3NlnHIcwR8FyV5FzYV+f7v+D\n++MfWEEAzqpvraI0Vh2M7B0p3Vde+NkQXAxggZs8NOkHplZSTrocx5FS6/LjoMUmN4UmtTcwcNVx\nFOeO8WoAACAASURBVCkR7ahLQ1rjl/KuddKClu3TAVocgTqomtcRGzz5eB2iSQ3ObQZxAzOBOE58\naaj5GuXpnwivX+9mzyk4Efac1CNHXRWXKqtEYTYdTzqtlku1f9wqGLipefapKv3gimVFS1pFdHdL\nF+XK0HlZIJYtk/M3MRt+/XV3fTogASAfOBDMTrZihdTV5bbTUqnSWSBQN8FUx4UBAxUHM6RSSk2k\n/MyVq1dLY8eWuWVcC34eSy+rqGPFcaShAx8PBNvEZ5O879ZztP1RGy4tFNPh1NkprdVtC6PjxZs5\nHrhtn/9VpVUkespntHlznTN19vXJGXlGWwe+5Ha8OdJ990neZX5aixuSLTRuwDk4uaOW1x++Zct0\n+84P6Q0tifxP1PRazjvOmmjJM0kcVwFUxTs2Dk8ORweJvP6wvj53lndgMOfPLnk9ep3t4TiEeonr\no/Xuu/zykgE43nd96zNbZ521oF6BwMeLqia11mJwmGAfAPPY8OSwnyGur6dvzttLJqV9T+THQYtl\nCK4mk3EtxfVNJTVY04kuAGqDQB1UbWTvSGRmbTrtZlEZMlfPbQZxHTOBOOpyx7UOu5lekkm3NFRQ\nZ6dbKup4HGzo7+13g2dqUKrMMzYWHymczUo7REdRXRUJaqt59qkqHTn5RGnNGs0scmdyly3FsnKl\n+9sL5FmxQsfai2ycDkgAC5zfWR1sLw0O6g116N8ffUEyRv3L1rjH3Q0bor0EZCerKy/TYLEyPCVn\nz+ekJlLS5KTuHP6+n7nyqaekHVqhYwoHXh1Tp3boEs3MlO9YSSalrImWbJLyHTOn/vkF2rntA8pa\no6Eh6S+2bJWGhmNLa6XTs2vrzXnApZnlPQNu/M4PZRN9Sgz1yQ6l1Nsbv17dO716ekLt+4EB9zkb\nug+SDh9cqsS5U6HrP+/7vnrd0zW9/vCtWOEe04I6O7Vs7SW1v5YbHIw+l8fLrrMQg8kZsAOq5h0b\nR/aORAeJgu27XMY0z/d++Vi4dHQQ7TvMQcm2WVwfrXff/YEsO4mCoLNUqqJ2bzmThyZrc66Zx+er\n4OcTzKAYmw2i8HMo2EZwW2/ueLNOf3PlmdBXfWvVvH4fAWBk70g0Q9wsj2uOI929cUY2MA7qZd/V\n8ttjs+8mk2pYhjnHkYbunon0TY3q1yITjuZr0gLgeEKgDmYlbmZtOi2l5Ua+tGIkZlKD2r5d0sig\n1NNz/NZM7OmJzariONJQ29WhdHdzFUpnd/36cEo7XTT3J0BxlQS1xV2kN6KTY3BQGeNGbJftNDzx\nRPe3F8gzOChbZMIyHZAAFjq/wzt4Duju1pd0s344/Z813VaQQaetLT/7ulzgJOYsmZTS0+EGciSI\npsw59InnDiiR6tdjqU/4be2ODun5zwxq8eLwpVtW7UpqUNPT5duvxWZ3Sfn274GHPi/vJJzJSLcO\nXCztu9QvrRVJXzyLtt4Tzx2YW3aVJpb39PX0uIHRfX3uIGt/v98m3r9fWqzXQ6vXc/ac85v9ofb9\nAw9E12nE9c3opoS2P3ta5Prv1D+/QD/+3tk1vf7wDQ5GZ13Uq63Y3S319rrH07e9Lbp8AQaTO46U\nWN/X8JmhwHyWmkj5JRcuP/fy4tlwe3rc811hFpJAKT7r3Uf7DnOUmkiVLgFSKki6r0+Xn3t5NPtg\nLUt71GJbDSw1Umv+9V8q5WYnyomdpFf4ORRsIxio85VLvqKpr1R+Eo/NjETgDoAmqEW2tZ6TetzM\ncLM8jiWT0syM22ETGgcdGZFeWFE8GGaWGeZms39xZc8Ti8ZkT+9yJxxRmgpoGQTqYFbiOvuzWSlr\nc2nWa1zfcK4cRxrSGvcENbFGUx/pDwWZxJWKmrf6+2Mv2NyZ1O5M0FYMpEIdxF2kV9lB4XUkVrMN\nR92654SPK2tM5Z2Gy5a5JQXOOENLZmKW0wEJALG8Ns5f6M81ky1o2nd25stfEexYd6Ojki34DCJB\nNGXOoaObEnp071nS7k+HMlfe8u1uHb0iP5PednZqkz6tX6hLS5ZIDz1Uet+8du+110qd7e6JtlPH\ntHate/+2bZIm1oRmXL08eVrJAAtHXZEsKiExnV6jmxL1ya5ST4UDVCWChZJJKVtwiV3PdnewfT8z\nI736anj5Er0mx6n++qaazk/HkSYe7o3Nqnrg+1+WZk6QVIf3IVj6T6p/W3HlSunSS6Vvfzu6bIEc\nX1MTqVAZ7DmVvAYWgMJjafD2/X9wf/FsuP397s/ll4fvD5SO9psbC+T4g/oZ2TtSOjNzmSDpSHYC\nKZ8xoNgAaIUDo8OTwxVlH/BL/x6H/D65gtcX+5kF26wx79vI3hoPEM/jACgA81dqIiW99a0VXfcF\n217BoNT+3n43M9wsAme8SiM24/a1hcZBEwklbvyS7PXr3fHGoVR0zLGe56vctsfGpHQmnFE5nZZ2\nnPIJaWqqJqW/ANQOgTqYlVAmFa/Tv2ASdysFgwQ7sWXbWma/GsUvVZY7QdclkKpFShGgtoIX8o4j\nJVL9mjqytORjkkkpeeQ2PXHKByvvNFyxInoQCaIDEgB0+ODSSDYSr40zpW6lzGeUNrkME52d0mc/\nmx/IJtix7laulNTupTh257oHg2gOH1yqRKpfu3YpNsDFC3iwapNsuMxOJiP9D+Vn0k9n2vU1/d/+\nsoEBlc1UE2kParHfHkwmpWIp7Yq16ZMajM2i4pucDHWMTUxIOx/4YH2yq9RTiQGqwmwIY2NSWieE\n7qtXRpvCz3N6OqbslWZ33VNNqYmBAWkmnZ8MMDCQ+34/d7q0+yp5XQ51uf4IZJeoe1vx/PPdjtwP\nf1g677z8/QsomHzykPs/7X33Ki15HQn88gL0C38WYvkwHNeCpX+8kjVVDYwESgv5g0u50tH/uCJX\nsmaBHH9Qe8Fj86xLqJfrB/TaEjGlscpJTaTc/qgKBlEnD03GBo34paKGh1sykMf/DErsm98nV8lg\ncpE2a1zAU9GMXoWC+1b4OTaohAsABI3sHXFnqPziF2XXrcm5rkCxbDXJpKT+fr+t13NST/S4nEjU\n/HwUutbKnQvXDaVkr18vrTfhzDk3/jC/bwBaBoE6qIlWLiPlHF7qdmLbXBBAZvH8GhyogZINiFqp\nohRBJbN0a5HGEDHmcCHtz5wdWVl0ncMH3f+3KZ2pFUd+oimV6TT0OnaCAy05VpJOPtm9QQckAESy\nkfgD9bk2zl/Y65XxAjy8QWtv4Jpgx7ryPgsvI42ULyGVTLrL77rmGj269yz91n/5Dz3689N00UWK\nBF1lMjb0eE86LT28y51Jb02bNmqNntdZ/rLNmyXtLZ2pplR7cGwsuO8e42+/sO3slVQtN1AfHKS8\n6iqFSmsdD4HzhZ1927ZJi9vekOQGaTlO/TJ2xn2ehdJaXP56rFhHYQWzpG9+ZIvuu08KBuNs3iw9\n+vPTtOmrvxUbcFbzrDqJRHVZHGcreK2zeXP+7wUWTD6ydyT03avkM33iuQPh4MS4AP0FWD4Mx79g\ndtpDxw7Nafay/9jzz5cSCe350mr3duD4Qx8KqhFso81asX7Avj53ENALLilcr9Lgmwp03dSlm3bc\nFNvX5ZeKOumklgvUSU2k8oHRwTZXQTCrXS/372oVZtgueM8rGbBe9a1V4fetzDYBoNUEJyBXctxz\nnNykk1u2FF2n5Dhof797/vPKZReKK21aqMpJDaH23/Cwum7q0he2fSF+DCh3Pq5V0BKA2iBQBzXh\nZ9i5fr2sTCilW7M7C5KjifoHqbS4VgukquSCuyadBoiq4kLaceTPzg/OnN2wc7l2786vF/wf/+Zt\n76qq4179/W40uVe+wOu0b2vT66e+VfriF93bC2gABMBxoA4dwY4j/fShC0LZSAoH6r2sOlkFBq27\nu92FBDvWVbGgCS/IZd066cgrJ8qqTfufO0lWbXIc62bCUTR9suSO/3uBHn6wx8qVerbrUn2tLXxe\ndDOplM5UU6o9OD4uafntksnoXP1MRja0XuE5vdKSqt4g5cSEtGePVCr4Z96JmUXeyFKzcZ+nJPX2\nSkPjuRTbMuWDhGICcrzsT+U+nxtveEs0i09GsmrT61NvV1zAWc2vP+67Ty8te3dj24q9vdLy5e7f\nCy2Y/HCXGyAaKM23YYNC1waFRjclwtm3YgL0F1rAExaG4OBQX0/fnGYv+4/t75f6+nTk5BPd24Hj\nTzXZ0ACvjRYsBTJrhW2iwgHKKib2eYYnh3X5uZeXXe8XR3+ho9NHS680y/Im9eT3eRa2w4oFs55x\nRkXvR5yKBmQTCWnVqtBdW5/Z2nLvGwDUUzIp97pl8zuKruONg0ay1eSuu/t7+4uf9yo5H65YoZlF\n4QkvpSY1lCprWJNz/P/P3vuHuVHf977vkdaLHQx1S4mtHGIrIScm4deuw20xl1hK05YkTbumTQvB\n3EQb2iR281ySE+7z2Od2y9K5KTnn+rZwL4FCiKUYu4VD0+yGBMgJp5EWHzZtHFYGTHAS+mhd4lnz\nK06Midld6Xv/+M6M5qc0kkbSSHq/nmef3R2NpJFm5jvf+X7e3/ebENJ2KNQhbafbgovZF98eKZFK\nN3BGlbmyMSPIyVdWI5Xq8QJOVAloM6yqMJ0brAVIAQXXXVdd1zjHi0VAy/++beA+UBHOmJXjiC94\n01PPAX/5l/L/QSqAEEJ6nwBOFI0i22EpnrC6oDj7OH8lbsaTK36zfsGThZxQ8RNNAMDyMvDgg4BT\ntAAo2LfPW3QFyP28a5fjKRdfjGvWFvDvS97XxVriEFd/UBfXz83p4tziOCDiOIKNEDUEFkEiVY24\nzMIzzwHQ3XQa2NaewDHI1pGoWQvWiRKmMEffn0ZBRsO6QP1p58SKmb0p6aK47bmaz3vxyctqPGo/\nhgyHodDvPxIJrPvBkc73FfX4mUESl+RLeSSevMvVVgkhbPcGBpoGXH45MPfoiOm+dftjD7gF+gMU\nH0YGm1ZmLzuf6yX6yZfy7N+RwBiFvVacnkychccGhTleEzwL8wVMXTvlXtkPq9uLQ3ASeayuB35i\n1o9+tLHvwyKeyoxkgFSqdsE2kwGmp4O/vgHbHEJIVGmwffrAXePVeN/iaPdqUhMTEDHH2JHPpAbX\n9TOdxsJNC7jpipuAdDqcazwhpO1QqEPCpV4+cReYmz/HHLjeDjlTeMeOaItU+oGghQE/ZvamcOBx\n0dsFnG7jdz4GKCA//fzLMsJDxLFnD2wzZwEFhw+7Z87KSAv7ZaWhIpw+aC8UBfizP+NgPSGE6Gi3\nPYDsvctmNJEhAHjkEbvoApMKbp37Ni77z1e529Bczm6hOz5e/XvDhs5/qD7DnFW1zt3BXFpyLqkO\nupTLwM6d/kKfhx5yLMhkqgKNbA7H/tNurLQkVjUiDtGwDr/+rmdModBwbJV9+4Zex+e/ttsl8A4S\nqbrtxudw4Oh6oDABTQOefdb9/v0mnO9I1KwXfrbaAFRM4MAB/23QNODye2/AxLb3m8dMsQgcfOg9\n0kXxn99p9vecg4CaBmBpdY0Nsw8u9rwwy8lZDjeLAShUFeYLEP/+mx5tlYJnn3W3O6oK/Mu/CJSX\n5P1BuQzsu0OfneoQ6A+S4In0P7VcpVtx1bHi1e4X5gt0vyCB6LbzuRPnBM+mts86/jU9ja33b420\nk0C+lDfjUWznrT4uZropGGLWiy5q7A2cYql0urZYMIi4ymuMsVQaiD4QIaR32Hq/LtZssG369p7/\nxZIS0OaaVK1tSyTwrf/1XLu7ms+kBpdBgrWdjmCdlhDiDYU6JFw8OvYnX1ltxud0E00DsrEbbJER\npH2omGhaaGPEe1SEwn3VCk3YCwMAcjlbhNXionfx0DpztlqEayLiwNpxnJiA8t73Bhus52AAISSC\nhD3wrWnAyF98CG8s2a1v/YrevgOwpZK/lfqb3xzKthIAn95UFU/dPIljx2QNuhb79knR1X/6x93A\n0C9tj73+eo1+UCbTUsSrigm8cuRd2LnTLcgFAIg4Cnu3uJ5XL1JV04DC1NtQETGgOI5du4AVK+zr\nDw+j74TzXYua9envGfc+lYq/eGvbjc/hX356Hl549jzzmJHCa3kMWF0UnYOAqlpdLwj9Jsxy0QYn\ntShy64Pfrk6Cuez75iVlxQp7u6NpwJ49AKAAQjaCi4tA8VF9dqrhqgPQTYf0HV6u0oZApxVXneqL\nyddCKroiBBJtcsWcKWIJSzzmydhYoLgmI4bLoHSi5BbZ1Bv/cfSHpo9Mh3O+tYnCfME/HmViAmVF\nj6E1xKzNju8Z1BB2mzjaFHMfGN+9cxtSKekGNCB9IEJIBLFcG4yxuOkjujtYA+Jlw2HYTAkQw+2t\nSZVKNccOt2/S7O5qQdx0AHs7ncm09xpPCAkNCnVI25nZmzLjc7qJqgIVRQ4S9t2MzohhFgaaFNp4\nxXuQzqE9/TK0x68yO6eVinuGOAA8+6zAwu0PAJD7yCjCDQ8Dv/UnzwWPWLN2IhMJ2ZEOMljPwQBC\nSAQJO/JTVYEXT62uGUUUmIkJLBuDvgbxOAs9Ou2YXayqsi9TC6Ov89g97weWz/B8zI/ZF85rShxi\nFbDv2+d9nUf5DBx6dJOrH2eL0ILi47ijH2fLK/GNb3RJwNJhrN+LM4qqG8h7HxnD5XUcaRqQ//rb\nIIVZCvbsAR57DDh8GKgKr6suis4i2uwsTJevWlx4YW/E7jbMgM5QNGPVNCBbHLFF3t5z7zI2v+NF\n06XL7SYGlJctEzmMAWe66ZAI0G6HkVAFA8b9s6NoHmX3EBItTJEIQj42nUxN1Y1ryhVzZgyXFdd2\nNTH+03WhjlVcFGSimbFOIoGvXFqRRdqwxKxBhD7W+DDosWiplP+2G+tbo7sIIaSTWK4NrYzFeU1C\nqTkWE8LkYb/tzRVzWDgLwPg4Koq+TR7XAaPv6tX/C1UgTghpOxTqkLZiOKN028VG0/SZwmU5WN1I\nLABpnFZEUdptD8jIJUe8B/dVczSjnFZnUq7OaSzmjslWYmVs+osPYuH5tbaZ+IuLwOxDF3CfEUJI\nkxg33DZHAgCAwMc/3kTRe2xMDqAmEpi76tKqq45hpb66VnzN4NCqyOq27zzgcpGcnQ323CeeAH78\nvXfCeXtWT9Ay96m7q6IZEfzYcPbVvJzzAEARQ7X7cQ6Rl6YB9+5Ztgg4YvjFL+TyRrexl+n2gFj1\n3qfqZOLsT2+78TmI5ard0+Ii8Cd/4v16110Hs4imaUBq4wIeeQTApOLar9u3A8PxZQCyielbHWCr\nM9t7DcdgtKoCFcf9wvJSDN97/lzTpctLACjKw9U2LZGQv+mmQyJA6UQplKJLrpgzhY1tF8442qFu\nX3tIb9H140U/36zuPlbM7RvTHXksYhDjXikUgV1ITs2e26IXkHPFXG2hkdFZsmyLmgJw5ZXdF7Om\n07VdKeo9TgghHSJfygNbt1YXpFKB+2Jek1BqjsW0MnlYdyMzJ8JYtxnymjG2cQyYmMDRi9f7voxx\nf+51Pe/6NZ4Q0hANCXUURdmjKMqLiqI8U2OdtKIoRUVRDiuKUrAs/4CiKEcURfmJoig7W9lo0jtE\nxRlFbod9mXV7aAMXHq2KotT9b286woG4cXXMjGJtDWZfOM/VOfVy1amUh6C9dhb+6f/6Q+4zQgjR\nOfnKaqRymZbEisZAr9uRQMG+fU2IV6emzAHUW983VFVeGlbqxIZzoF3T5FhKve99/x1vt7tIJpOm\ny8r27R6pYziNHTvk4w8/DLzxS9l3WjW0ZBO21BS0NOHq4eyrAcCqVdL5xEld5xvLzFtNA97zrlMo\nL9tXKZeBXbsa3kzSAl73PsvL1b6ZpgEzU+cDqB4DlQrws595v97hwwBOrjVf+8CP3+zZzxvUyRHt\nduLoJuZncwxGz87a2xAAgIjBuE45jz+jvcOk0tciPdI71Cqqt0LpRKlm4aSdsChD/IjkdUqP/SjM\nF6Rzix9TuiOPRQxiiOtzxRzWnrkWZ6440/6cesVZqzgnJKdml+DfUnjNFXO1x+E8xL8bL0oFd5tu\nJ7XuNZLJ6uOMpieEdJIzzwTWrrW1rYX5AjA9XV0nnQ7cN5qbQ3USys2TpnNwW+5bHO5ltm2G/BxT\n104BiQRyt2fqvhz7f4T0Po066uQAfMDvQUVR1gC4E8AfCCEuBPDH+vI4gC8B+CCAdwP4qKIo725m\ng0nvYAzURsEZZXa2tuU+L2jhUU8UZeK8idP/DxLhEMlBhl7BUqz1u5Ge+9TdnjOkrTEXc3MAlAoA\nBS+Vzh2ISAtCCAFQdxByZm8KB46ub0msWJgvmG46XtfUnS1I3r/+8+9JFx0gPCv1PuPp519GKgXc\n/lg13vHAAb0v47P/NQ0oPjpid5G0DLp79kWx0rxW2sTtQgl+/DTh6uHXV0ulYBuYMvoCQQenVBXQ\nfv4mVJzFewAPPdTwZpIW8DrelpaqfbOdO4Hykns/+RGLAShMmPd3FRGT93m6eMcg8H1An/H08y+3\nLNCMIpoGTH48jUOHgFQuY9vfc3N6W6HfG1z2B98H4qcBeLt0Wds7QqKAp4teCPEt+VLeFAhwnIlE\nBdvx7pi53xX0CWTGdjknTwaaTJmTIp+FmxZwx4fusD/mVZy1Wvx5iXNaFJo4I0Jx4oT8vXWrLB47\nXGdsQiKjP28p3tYUL3WI5Jqk3DY/4VMmU912RtMTQjrJHXfIQZc6jl6N9MXMdq6GQDGUmlQyCaTT\nwdt56/WLokhC+pKGhDpCiBkAr9ZY5ToA/ySEOKqv/6K+/DcA/EQI8W9CiEUA9wMYa2J7SQ8RpYFa\nYzazOZg4AJb73aKeKArQ3QYm0/bBbP2mzohwwKRiqped++rp5192RUuQJvC4kQ7a4bz+elTjseKL\npiNAW8+v0VFAUao/t9wif4+OhvxGhBBSgxqDkEbkp1HEPnRIRsQ0c71yu+lUacpVx4rhokM3HU9m\n9qZw4HGBfXe8rSpMqEjh1OaJ93t+96oKlMv+LpJmX9T4yeYgUmnMzVkizgxxe3moreL2IH01DeuC\n9bX0QSxToA8FgHCt9vrr/e+qEiWM4+3YMWBl7A0A0jXpkUfk4//0jdOoRurVp1IBML/Fdn9XLgMo\n2NuQIMdWPxKGQDOKbLvxORx9ej22bQMOHF3v2t8GGtZJoaLFkXPVKj3yziLm4b03iRKuojoQSnxL\nYb5AgQ6JHLbj3TFzvyvoE8gMYZvznAl0DlmKla71k0n3Mqu4PZ+3j33lci0LTQwnLZN0WvaTT5yQ\nxV9HFmi9z9gV53dHcdrcxnquFHqMCwvIhJCOYbTplrbVaGsbih4dq5aozXauxmSo0olS62Id/fWD\ntPPJNUm7Aw9FkYT0JY066tTjnQB+VVGUvKIoP1AU5WP68v8A4N8t672gL/NEUZRPKopyUFGUgy+9\n9FLIm0g6xaAO1A46tkKULrQ5dgw4++xqgabeYLamQRaHsNbz8Zm9KXu0BGkcnxvoupa8AIqbPiEj\nEIwCT3llZ9yyNm925YaI4WHgiiva/MaEDA7WG05Na15kMqg4Iz+3bfOPiHFhESOKSeDOuxSUKwqe\nhFuM2LLwOZGQv+mm46IqtlLw5MOXYNeuqjBhcRH43gvnuZx1DJGKKA+b69W9LmYy5oCLlyirneJ2\na1/t5u9OegpsVUwE62vpg0xSqGQsdAtABsFVJYqoKlBR4gCq+0DTgF+eipvrrBxawue/ttsluN7+\nvucwHJc5ZitWADj16zLWSr+/W1wEUBy3HecuQdoATI6wCTS/vNw310xNAwpTb4MQMRw+LF2UcPDT\neOop97oqJiAq9vOe5zzxouvOuJZrt6uoHiJdFeqkUtFwTCGRop3He9PoxdXAjgKWYmpyTbK2sM6r\nyGpdVihU26N8vj2Fz2TS7O9nRjKuqBPPdsIilOlKO+JXnPYSPlkxPhuFOoSQTmNpW4221ryuBIkJ\nN+IV4dPuerRrQWondanXrvptE0WRhPQlYQt1hgC8B8DvAbgKwISiKO9s9EWEEPcIIS4TQlx27rnn\nhryJpO3oN1tzc1KggQ0F3PadB6IxUBvkAk1CR1WBA48LbLvxORSLwMGH3oOKiOGuu+A92KtCFofg\nnrVpDIbboiVI49QaiKgzk/D6f/9r17K2DsYbAzITE3r2QpWlcpyOEIS0iKbJS/fCgt0WXVUbEJkM\nKNbvzivy0yhuBrpeeYgRMTyMH53jLUZ0CZ+tM4eC9HesszpDiHroF3buBCpLKwAAlbJ0LzKECVKw\no2DPHmDhmZfN57TqIjkz435+p8TtXrO4tDXvQjZ2Q+C+lnHs+zlAARTrdwNjvyzqUWSGgOzGG+2x\nV4vlOOYe+LD7ubMXmM9dWgLw+jrXJAyI2MBfI6wCzdNLMeza1eUNCgnr56oSw3XXudedxWaUl+1R\nauY5n0xW3S91IarpjklXzIHDM26qoxvg//6NuFd0XXBUi0wmGo4pJLqkUhjbGAGTe72YGvjcq1dM\nbQSHuw2A1u6HvIqmFtFLZiQT7P6siUjbjhBku9LpUFzJCCGkITwEjuZ1pcU2NVfMufqOpkNdq+2d\nz7YF6mNarjldcV8jhIRO2EKdFwB8WwhxSgjxMoAZAJcC+CmAt1rWO09fRnoM0+mk1oC9RclqCC5+\n9PVr2rxlAYnqTU8fY0ZGCAUz0+fj2mthRiYJAVx3nV7ozGVkofPkatx97xuyOIRx17HmdCsY9OJA\nU3gNSujUm82kacCzL73ZtbytBThjQCaRAMbHsTwkZ4GfxrA8RkBHCEJaYduNz+HAAXt7Wm27A4pM\nBgzj5llVYX53XmINg0DXKw8xIuJxXPPMRLD4TuvMoaCDqQYcVAUgozn37wfMW6TKSotLTJXFRUAt\nbDEHSFp1kdyyparRGh6GGSfZCXG7V6FD/dE1qAydASDYsWt307ETHyp39POQKl5t0vIy8OCDgNX1\nqCJieOIbF9jaee/2THEvK68caAGWhnU2gaZADPfd19vXzFwx5xKeVlFw+LBlooU+MP4wPoTVw6WQ\nlgAAIABJREFUv/YLAGXsuOxf7depTEYKUZ3QFXNgMQsQHZ4NvPX+rTUL8Y0U/ksnSv4zrLsNx7xI\nPdJpTF07VX+9dpNMIrkmmKNA6Fjvm4x7olbuh4I48vTzuclJsYSQbuHRtoZ1XfFzzgnsBNfke9aM\n7spkbNcrxq0S0h+ELdSZBnCloihDiqK8CcBvAvghgO8D+I+KorxNUZRhANcC+EbI7006gOl0EkAc\nYQ7w0flkoLEO9C8vChw5AliLA4cPy5m9B46ux86dwHvu+aS5fhkxz8Kx1a2Ax1YTGAMRTcwYUlVg\nxbAz0qKCTKZDBbiJCSxV4vq7xvGF2ATFWoS0gKYBj0+fj0pFtqevvboagL3tpijSjbWQaXx3MzNu\nsYZBoOuVQ4yI4WFgfLwaTxVgALTp2TSMSUCumMO+v73QV3BipVIB7jx4GS77z7+DhYVq3A8mlcbi\nfpLJquuJJU6om32bZrZndtbfTae8HB9oIUc38RKQ+e2nxUV7O+/1XIMdO6rxtphUBlqApWLC002r\nl111SidKNYWnAKquOvrA+E7citdePQtAHNniqL29GB0F7rrL/SKLi8Cdd8q/DYcduuwMBN0S6kwf\nmZaFjVwOW+/fWrsIUod8KQ985jOuCNGuOwYR4oFrwmVURBWZTPcKjLpIyPi7FeoK9KLyfbeTTGYw\nPichpLsE6TuG1Rb5THJu93WrMF9wC4Gsn6mfRZ+EDDANCXUURfkHALMANiqK8oKiKDcoivJpRVE+\nDQBCiB8CeBTAUwD+FcC9QohnhBDLAD4D4NuQwp3/JoQ4HOYHIe2nUeENi3xEu/xqW7EHYthzvX/8\nRzmb9777AO21s4CynMW9iJW2Y63VaAnSOt6Fmxgeeqgz768hgZzIoIwYshjHvy+to1iLkBaQThhV\nl7J/+NI7IicciAyWQYHCfMHVz0mlqmKN7dvdKVblxXIgV52yokeNxB3RfvVuyANkXPsOJKfTAx+T\nUDpRwotPXub52DnnAMPxZcdSBT/Q3tJaHySTiVzfppntmZsDRka8HxsZoZNOtzAEZOLmSYhsDkLI\nY9mLSsXuAGU899jfPoCVK+zHfjYLLGBtNJwjuszsmb/jKWjqVL+4HeRL+ZpCLQB49tlqn0DTgP24\nHsZEjLIzDs0r1hEAfvVXPeMe6bLT/xTm9VnA3XDzGxsDcjlMH5luqdBSmC8Ap04Bx4+Ht22EtIFc\nMeeecNkvRT5r7G+jWEVCYUSj1JoE1y/fdz0G5XMSQrpHJ93LDOc1qxM1LEKdVq5BdXBNwGP7Skjf\n05BQRwjxUSFEQgixQghxnhDiK0KIvxNC/J1lnf9bCPFuIcRFQojbLMsfFkK8UwhxvhDiC2F+CNIZ\nAgtvIjg7mHQHdXlXzdmYBkKvScpIK7tbi/VYazVagjjw6OjVc2Mwiz4C+Nv//gBWxt4AALz+emfO\nb1UFvhD7CxzAlVAhC9gUaxHSHMa1WpRloWxxETg287vYtStawoEo4MqmPrmuZj/H83pVDuAskkjg\nK5dWUFEUu5tOEALcvNtmenPWo42Tr6wGllbbFw69Dk0D3vpWYLE85HiGAkDBnj2tXf+i1rdpdnvM\n/oEuCAnsKkTaTzIJZDLQNFlXtrJqlbwW+O0r9UfXoKLYj/1yWTqo/MV17wfufWKg7+/mbtqPY8eA\noWG7VVGn+sXtoDBfwMMPAyv11Kt43K2nWbGi2ifYuRMoI24+tlh2TOrxinUEZAabR9yjTaBKOk+H\nXW46ztSUKRBqSqiTy/WOSLHf9yWpS66Yw935b/Sv03k6HZ6zQZP3RblizhQftqtgSwghRCefj04/\nLMxrkAPGWREyeIQdfUX6lIaENxGcHUy6w+yLb685G9ONM1LJXhx6+GEgtkIKQ+oVFkgNkkmzaOOk\nkc7gj75+DSqKHJjv1Pk9Owv8tPxWpFHAccgCNsVahDSHZ7SFiOFb34qWcKCj+BQ1nn7+ZaRyGRw6\nBKRyGeCxv/bs5+zcCSCbxyOPVIUKRkSMuHky0PVKTQFHL17f/mIlZ+XYeOye9wPLZ9gXLq/Erl26\nCEUXoGzHnQCqDiPOyKBGsQpgoyBwaXl7eFxFD32fNHN/5ifc+iY+jJ/+8DzghcsH+/4umYSqAkLY\nh1V6/b7XOUHHr0+gacD+/UCtiRZGrKOBAIALLwTe/37bclfcI+kOQWYqt0C+lG97MbvlAk4tgUup\nVDPeqhOfLzBt3pck+pROlPC9/b/d107noRUznf1Xox2o0R7kilK4l9qQ6m6MFyGEDAqFQudiRg0B\nZw0hZ2jtviVmKzL9SEJIR6FQhwSi0YHdqM0OJt1hbv4cs8BzIZ5p+PkfR9ZWHJLHYTWipd8GGTpG\nJtNyIc0U7+kOA51yzZqbA27+7iSyczlsx52IKRXs2EGxFiHN4BltUV6J886rFufleSYG5zzzKWrM\n7E3hwNH12LYNeHx+PfD0Ns9+ztemTtut5Ztg4Swgd3umLcXKrheQIjy7+8ffeyfct0aWaEfdkWQP\nxgFUHUYqFbTsqkNIJ2jm/sxLuHXsGPCacjbCcpXqaTIZzM4C5aW4bXFP3/c6HOMAfYLE53e7xHsy\nPtP9Eq7Pb3HVEQqAv/9713K66XSfds9QNpwn0sl0W17bwCjgNPV5UinZV/Horxivly/lMbZxzPPx\ndn2+hkmlakfxkN6h2b5zLifdIovj/et03k53UON7ryF4M9qEdDJNoQ4hhHSIjo1pZTIydaCJNIJm\n3sv8M8i1RBf2UNRDSP9AoQ4JRKMDu9ZB3Zu/O9n12cGk+2zBDKCUsWNH9dgYWafVfM4+/G/mIIIh\nDEFZ+rD33SBDlAiQs9pt16yr1maQjd2AiojxOCCkSebmgMv+4PuAUsaFeBpQKnjLb02Z12pNg36e\nKf1/ntUYBNc04MlHLkFFxHD4MCAQAyor8PGPW8VMFXz848Drryue1vJLF40Ct9wCKIr9Z3RU/liW\niUlg8n23yOUh41tA6kAMlis+LEIUi8Dpk6sAyIJ0sQjPaEdVBZawwvX8Vl11COkEYbk3qSqwJKrn\nwaAf/7bv1XBQ6+X73sKEdx9/7sOuVWdnvV9iZMTx+RMJ4FOfQkVR8KM//i3gkkuqyzdtkn/TTafr\n5Iq50MQdLpGMHhk1tnEs/OKG9f22bjXfr6kZ15mMjMYqlVyfwfh+CvMFTF075bkNqQ2ptny+hkmn\nzYgv0uME6Dt7HqulEv7hS++AIuxC0r6a8Ba2i6PF0cB2/uj3ic7vuTBfoDinHj7fHSGENMWYFEp3\nqu31e5/MSCbcMbRGhTrptPwVBXE4ISQUKNQhgYiaLT/pLYxir7N4Ofepu6VgZ8T7eWXEzEEEddtz\njFPrFOl03c5et12zVBUdj90ipN8wBCgQcRzGRYCI4cX/+Xs2UUJlSEYBnT4N7NrVxY1tNzUGwa1u\nblUU7NsHHDoEUzS4bx98reWfwGacxrD9JYaHgSuuADZvln9bKK+Iy8fCRBdhehaQOhBXZMSHRUXw\nZdjVA8D11wNCyOiWchnYts37GjM7C1QsbjoGlUoPu2cQ0gCaJh2kKpZhBLpKWbAU+XLFHDRNLuqp\n7+aFzd59/FcvcK3qHCMwJuh4jhFMTODoxetxwe377cu3bTMfB1jMaze1vt/CfEEWp5tw8HC+rksk\nk8uZAhevAkSumGvJda8wrxfVp6f1DSj5rltzUkomY7rReAp9aohfDDF0JAr3HRBgk/az9f6tgcRz\nzmO1dKIE5PPQfrgBomy/x+hpx7d2Y70fsop2dOGes50b2ziGzEgmGuK8qKJ/dw0JJyPswEoI6Ty2\ntndK9iMj0deKQOQ3rz+E9A8U6hBC2k49UcXcnJ9YJ2YOIsz+8FcYp9YpAgzsdVO8d/KV1V2J3SKk\n3/ASoIiyAlW1xNvp7a4QwH339ed5Zt74WwYFjWWaBty7Z9l0c7NSLgPXXANUlCHzf5SlsMnaLmmX\nX43MTyakE48VI+bDGv1hPDQ0HH4EiC7C7NaghhEfFhVh5dPPv4zPXjOCxx4DDh8GZIyP3HeHD3tf\nY+bmpGMGJhWXe8bDDwPI5vvyHCHEQFWBpSX38kF31THRZzcCslCqqsCBxwVUtYcEKJ/e1HQfv+Zg\ncSKB/Fcn7a45o6PA5z5nPg5FQWZ0vC2OckQSqFjahPtd6UTJ1o/Kl/L2Feq4u7Qq1LGSL+WBfN61\nDaY4p96kFN2Nxvn8npq1HIHiEWmd6SPT9cVzNR5L3fo5TrhsBOt5Y7meI5/3vIYbzlqRKBhHmEZE\nOlF2YCWEdAdn+8s2twq/C0L6Bwp1CCFtxSz21hFVWIUf2bmcWQCbm5OvcfYZb0DTUC2OcZChfUR8\nYG9mb4ruSoS0iDNO0BAplJeHkM1K9xyv86wfXXVMp5dnXjaXGQOKqgpUyv7PPXIEWCzHPR8z2iV1\neReOiQSyGK+66gwPV2M+EglgfBzLQ/J1TsfRtgiQTs64sQ6oWOPDoiKsnNmbws9/fDH+5E/qr+t7\njbHMtlVVAEev5LWI9DWzs+5rA0BXKSeaBvzNDR9BNgszPvKZf3u5/hM7gFWIGrbbT73BYtfjHo5y\ny0NtcJQjdnJuZ4iGX8Lr+ZbiqulwExDTzafVbTCuy4WCaxusQpsg/SHPz5BKYWzjmO9zOLOZhM7Y\nmK8YxxA1uIRxAJDJ9Ja4LGoYk9eSSbM94fcZgDH/9jEIYUYwEkL6g0b7lAMBnRMJ6Tso1CGEtBXp\n2GBfVk9UYQ7i6gNtqopIzcIn3eXFI2+nuxIhLeLVNhuUy8C3vuWOtwOAhx5q73Z1AqeA5J5PfhKP\nz6+HWtjiWnd2VoqXmmFxUdacssUR6TaBqquOiMXtjjkTEygr0t2ooiB8Nx2djs24ydktzq3uTVEQ\nVmoaUHx0BBBx/Oxn9de3XmM0rKs65+izbU3hmyPik5B+wyqsN2KOKJ63oA+aqipw8t/ebV5Hy2Wg\nsHdLJOIcrELUrrv9eDjKibjStmsg0SmVbNdoz/1f51j1dEjocHHVut2B3XIAIJlsvj+UTpsuGjj7\nbGDtWts2RH1mc5jORaRDTE35itiM89BZxEyuSUZ+8lXkMb4/PQ7PNy6P2Jmasv+vXxfypTywdWvd\npwcRbfaMQyEhhLQLXuMJ6Tso1CGEtJXZWXexN7CoIp02i1/GLHycXFv3aaR1ojwbcP65c6qFIUvs\nCAtEhATHq202WFwEzjsPOHYMWOlIfHr99d4XIViLSzt3Aq+9ehYEYrjr4GV46vibbetmswAUqWhS\nYI8J8+PCC6uF6y1b5PULABYgXXXKiGHmfIdjTiKBr1wq3yc7gra46QSadRNC8ea27zyA1GQar726\nGoDbvSkKcYWqCpTL3vtzeBjYsQMQ2ZynCEHFhMs5xyp8i4IQiRDSJTIZaBqwZw8AxMx2YXERmHtk\nFJsn3h+Ja2j1/qrLbj+6o5zJ8DDmPjACfPCDgKK4fxiJ1TJGLJQVT9GNM3qkTv8guSbZsCOO1ZXO\nWQTfev/WukVdqzjBFOcE6evUK27or+HcJtf98c9/buvMRNFtw1nQplCnT9BdsbzOZ4BRGKGjuxNF\neYws6nz55oPA9HQo1/VGorQIIb0J+yuEkEGDQh1CSFsxZ97ePFmNtAoqqkgmbcWv5WUAd/8gEgPc\n/c5ADO6w008GGKNttsYJOtvoZhzRegHDHl7TgP37ASP2S0DBdV/7I9s6118PQMjHVwwrpoDD+l0Z\nosHt2wEoZWvdSQqiLNFYKiZwAFdKsYeV0VHsOCj//PODaE9RMsisG2dhrkFeuWADPvu716Iw/zbs\n/qP/A1AUJN6i4JXTq6SwUv95/ZcK1iW6U3Q1CtSiPOz5uCkm9vi+NA3Ixm6oOuf86ruqEZ+L1ed3\nW4hECOkeqgosLbmXl5fi+N4L53X9Gpov5e3iwsWydPuBbONMx7BOYXXViccx87GUZyQWhocZiRUC\nXm4Fu5/YjXW710lhitGJcRb/Lf0DUyBgoe69o5cjheU66xS5TB+ZlkXdgJgF9EzG/LtpBwzdQcP5\nmep9xigW8Z0F7WYixkgEscbX1difUTwmexK9XRmIMbKw0d1HT4y8y/2Y5bpOlxxCiBUKdQghgwaF\nOqTt8OaQGLhubOvMeNOuytiKX0tLAF57C3bubMfWkYGjxYI0If2Gs41uyREtwhgzsHfulMKjKgoO\nv/Rm/PM/y3WKReDwYbkcsAgwPpBxfVfa5Vd7Rh9ZY2IEFCxMvgUpzOCxZxyOOZs343TcvqhbRUlN\nk3W6Zgq1z234DzgNe3F1EcN4Hud7Lu/G5/MSoJkuOnXExKoKVBS5o8plQP3RNX0raCOENI7pVOMV\nLSliAJSuC/kKzxyxiwvLcRQfHcXCArDtxudcjmFtJ5EANm0CADz3+5vxzV8c9IzEQjzOSKywSHkL\nWNLJtFlUrVX8L50ouaJ2TPyKKrojhXNZWFj7ZZmRjKfQprEXdPf1ANQcv4hiEd8pqCK9g0244Dxn\nCwUU5gt1j7koHpO9Cr/LJkkmkVyTROFj7ohp63W9pktOrWI9C/mE9CXWfiajBwkhgwCFOqTt8IaG\n+FJncM6r+AUo2LePM9VJa2y9fyuwe3d74mUI6ROsjmj9FjNXddNx85GPyN/XX+9+zE+AoS7vai36\naGLCMO6p0qWipKoCBx4XTRVqVUxAOG4vVqyM4xPD+13Lh1d15/M1K0AznXPKQ+ZzsllgZqY/BW2E\nkMbxuneJYRmA0H+6I+QzCr6aBuDug65tFGUFO3cCj0+f7xKcdoRt2wAA37xmVA7MG5FYhqvO8LD8\nn/32ljAL/4578JuuuAkLN+k7PJmsO5kmX8pjbOOY94N+EyEsTjcdIZ12jUM19P5+4xQhiovajocg\ni8Wu3sEmXHAed2NjSG1oUYxGSCfQRY+/fv7F8n+f67ohKvRsoxzXFZsAkUIdQvoXXTwexWhRQggJ\nGwp1CCGRxauYBnCmOrHTjPvD9JFp4NQp4Pjx9m0YISSyqKrTTcdAwc9+BuD5NJ591v2olwBD04Bs\ncaS16KNEAtkRy/9dKkpqJ1dLNwih4K67gKee8l/XWvhNpYBDh4DHvvvbyGLcdM85jWH884ZxPIVL\nbcu7WXS9MZvDsWMANhSgafVddAz8nHNSKdhj0PpI0EZILeia6sbr3qWCIUhnNoc7WweFMKUTJeSK\nOXn/9NpbXNtYXh7CN78JlMtdEBONjgKf+xwA4KY/+n8gJiGjH2dmbJFY9YSdjM2oj1n49xGbJNck\n5WN1xCiF+QKmrp1yP5BKuSOzLNQSFXSiPRk4UYOHi1GkvgMW2GuSL+WrogXnOTk1Vd23qZS/cI6Q\niJAZyQCXX+6+ruvtgOGeEaQgb3N0Y5QfIYQQQvoACnUIIZ2hzsw8Lww3h2PHgJVDS7bHum0bT6KD\nr/sDB/8IIT7MztZZ4cF/xIoV9kVGPJJTgKGqUthipdEiZ66Yg5pCQ0XJ0EmloM6kTDGKEALXXee/\nulEUNdrgbduAynLc5qpTQRwf+/EEFhftbjsi1r0Ik9KJktw3Dca79GsUHCHNEqmCb0Swxh1m53LY\nvh1Q4FaFWq8RrcQNNsLd+W/IiEYoWLVKvu8xJLBlQwnvueUGnDoFiLIUU3ZUTLR5c3WGvcHwsPxS\nxsfl/wGEnTVjM6y0cH/Q72Kguuf01q11XiDTdNE09PakibGHvsNDcGV+z1G4T+6hGOpunPtBoq0A\nAJmMt3COkKhx1VXA+DgqilK9rpdKtvPLKdp0nnv9fh0mhFjQ+3KcHEIIGQQo1CGEdIYWbKLDKIKS\n/kS7/GrT/cFVUOihwT9CSGcxiqkjIz4rnP61wKKM2dlqHFK9df0onShh4SwAmzZBWAcvO4i2dbvN\nGQhQcPiwv6tOYb5gxkFVhIJnnwWEiGEBCWQxjjJiyGIcxyryc1iXz5zfvQiT//rofbjry79sON7F\nFgVH5xxCSB2uWptBNgsIxF2PLS5KPUNq4wJ27QIOHGj/fc339v+2K6JRxQQOHF2PH9zxWU/HsI7c\na01MVEWqBoZY1RB0NiLsrCdAaOH+ILAYKKLYHDrq4YxNSiaB6enazwkhFiq0ImwvRVR1mlSqq0Kd\nXiy0d+vcD+RCxWOd9ArJJDAxgaMXr69e1/N55Io589rkPOZzxZzNqa3Xr8OEkAbQr2+cHEIIGQQo\n1CGERJ4wiqCkP1GXd7mKDoDHAGAUZg0SQrqOs22YmwO2bweG48uONQUymaorQi1RhtVBIbCAw9Em\nnXxlNZDN48X3fBDKe9/bFbcZ9UfXoCLctwa1XHXccVAyNuW/xHbhmXM34esX2j+HigkcwJVQ0R03\nHQD45f/4PKCLf5sqRHOWPiEkAH4Ri/G4dLPZsgV4/Edvxn33VVCptNfB5tvFpxA7dIMtonHPHiCr\n3CDb/Zfe3T3HsESi6pwDYHkoXhWrJhJyYSPCTgr1AXiLIQrzBTNWpO7s5HTa/n8DYoCt928NLghy\nYGy31/PNz8T7utZowfmoZXK5niy050v56j8hH3/NCpdYuCQ9RyYDJBLIf3Wyel0vFGzXJieF+QLj\nrUhnYN8iMvSioJcQQlqFQh1CSOSZmwNEVhZKb/7uJGexEwCyyJE9NGorOhhFDtcAIAftCXGhaQCy\n+b6LEax1Y+9VHPASgwIxPPSQ/KstA+GONmlmbwo4eiVu+fF1cjCyC24z8ntwOz88+6xP8fjkOmSz\n1TgoIQBACmCOVt6Kza99H/seW2des2/+7iS07LeRwgwee6Y7bjqaBqA4DpRXAmgy3oUzlwkhAZid\nBZaW3MvLZWDnTuhuOzFUKi0IBwPyvf2/DUXY2/fFRWAResZjfAk7dtjvszp6r2V11YnH2i5WbaUA\n0AvFg1wxZ99Oj8gqa9/GU7TjI0oN8vmnj0z7Fl3rUZiXBVmv55t9OEdUCvHHc992uB9j21fOe/Iu\nF0aDHkfGcSmflAtnu/XXyBVz9WPlCOkjKDIj3caz7adQJxIYfdhmBd+EENKrUKhDCOkNWBgjDlQV\nqCj24rpR5MiX8qZFLt11CKliPR9UFcDRK/suRtBvpq7fYLxVDHrs8j/ESqnhwOuvt8/dwIqmAU8+\ncomMYpq9oGvCKdNdaNi+fMUKn+JxYcIVlWLFWXROrkl2/VquqjDddAwYpUkIaQdzc/7xit/8ptVt\nR7ZJ9YSDLQkTXtiM8rK9z1ypoOqiVl6JbBZ47dXVzb9HKyQSQCqFiqJg6IY/a12saunrG9+b9ftr\nxdGjF9xASidKdmFBncgqz6Kpz/U66Oev69jjRSplFmbqPb8X9kMUqFkQ97onDkuEYsG5r3Y/sRvr\ndq/z3wbnJrXxXt587RpCGdf7FwotTwLKFXPmaxTmC65zlEI0MmgEvWY0FOFIiA++bT9Fk8Fp07i6\n0YeloI8QMmhQqEMI6SmaGvQjfcnsLHxt+g2L3HW71+EzD3/GlmtNdx0yyBiD5ZomC4IQ8fqOIsas\nao+b8V4aSC6dKJkRU67Pqxek/OL0QsEoQKVStjZp506gsrSiPe/ZILXaVRcvbHatW+t5URhsmZ2F\n6aZjwChNQki7MKMRs9UIxWPHgFOn/N12nNcA4zrbkjDh05tsbjleosxyGSjs3dL8e7TK/v04evH6\npt108qW8FPjs3m3r6xvfm9lfsd4TNEuvif5TsqiZ2pDq2L10U9f8dNp00vF6vhk/lM+zWBsGXvfE\npVLo98q22Kh8HqeWTuH4qePy/wCRNq57jVIptHPQFLTVELO52l5HP74Z6rXnpRMljG0ca+k9COkl\ngl4zWMAnYWC2/SdOVBem03WFzcRCm8fVeZ4TQgYNCnUIIT0FO2vEwCx+CEBA8bTpP37qOE4tnere\nRhISMYzBclVFcEGKMava42a8dKLUMwWrk6+sxj2f/CQw7+0ipGlAtjjiGacXCkYBKp02CxOaBuzf\nDxhd8tDfs0GCtKsmn95kFp1XDtkrzqtWyc8WtYjKuTkAkwowqXQn3oUQMphY3Ems118nXsLBdjiH\n+IkyXzj81tDfKzCJBPJfnfR20wnQz7A5yHgU0c3HAwgDvKgZ32M8HpH+kEvEkk4DkPfRLd1LWwQ/\nbSGZ9BcS5XK2fchibYsYYhPnMRuGkM2B7dxs4vyzPd+gDc4/gbH044Nug9/EBnN5yn1OTV071fCm\nEdJzWJzUrMsMvARrbPtJK9jaY71/RBqjnZP1KMQmhAwqFOoQQgjpf6w3YM4ByIgMqhPSCQrzBdNN\npxFBioZ1SOUyWFiw35iffGU1UpNp3P7YA+3d8CbRNCC1cQELC8Bj9/w2Xnv1LADeLkKqClTaGYtk\nOBMlk+YA5M6d1viTNrxnB2j790YIIX2El0gGABSlhsCxyb5qzchHQ6x4c9Vp59YHv93U+4SFq/g2\nOip/j4/LL8j4MZY7WVgAbrqpaTFOLQzBlDVe1/V4BFw7c0UpaDGcaay0XNzU76e8XjsUMjWERB7f\nLYu1LWCITazf6+ioXHbLLcHOt27Twj18I0XGmkXDGu4+uWLOFPA5389oR3LFnHxtR7GYLtJkYEin\n3W25RdxsFazVPW85rkd0ah0rZrvrpIaLNLET5iQC576iEJsQMqhQqEMIIWSwMAbvzz4bWLuWN2Jk\n4PCazV9PWKFiAgeOroeq2m/MZ/amcODoeuy7420AuhSFNTpqKyhMvu8Ws7CwfPEoCj9KYF1CwaH/\nPgqBGAQUPPHLUdfnnZ0FFstDtmWhxiIZg46ZjDkg/61vuVeLbBST43sWkwAUBf97drS931sNeil6\njRBCACmS8YqeWrGixnXYKKY32GfNFXPA1q0AghV+Izcwvnmze9nwMHDFFY29juN7s0XxNID5PA8h\nkJ+Ax9yEDjnutMOByUrd48jLHaFZrC4j1u82zPcYVAzRuPV73bwZiMft6zVzvtUilcKZK87E2jPX\ntvY6VlebJiidKAXuQ7qKhhbBPfJ5t4hMP89LJ0qmgM/TFWjNGgC68M0oEOtEri0mpF1pL/XlAAAg\nAElEQVQkkzWFOlZqXd9yxVwkxLIkGtQ6VvzEzLVcpInEuG6afd5W+7W5nOe+4jWQEDKIUKhDCCGk\nv6h3s2AM7v7853LmbRtm3RISRYwba7/ICz9hhaYB2dgNqIgYslngtVdXm8uffOQSVEQMxUdHsbDQ\n/gKRJ5s3uyqeYngYpy69Ag+f2IzTsD92GsP4n7jC5apjcxjoQCySpgGnHMl8UY2MAuD5PWN4GBd8\n4gqIbK6h703TZI2j1YivrhxvhBDSIo1eh00aLB4U5gvA9DSAHh30nphwL4vHXcvrFtwd35utaK4L\nmephiHT83GQK84Wa9xSdctzxdP9wiABaITOSqS3WMWI+Q3mzTPVv63cb5nsMKhbRuMnEhFQMWvE4\n31oincZNV9yEhZta7AC2cEznijnkS3mUTpQCC75s7af1u9OPS79oPD8BX2YkA0xNVV/XR5hASN8T\n5Ni3iDZd5+yYjMYqnSi1JbqP9Bec5NMElvF1Y+zF7Ee3KtShKIoQQkwo1CGEENL7WGdc1uvsc3CX\nDChPP/8ykM3jkUcaE6SoKlBR5Azbchko7N1SXV4RAABRVroXdTQxAcTsXdqlchwqJvDXsQkIR3e3\nAvlYN+KZzOJWMtmUs1HHsUYf3HWXd2X5zjuB229v6GVVFTjwuAj9s9YbfOPgHCEkCpjCUCN2Ckoo\nwtC+a+MSCfmj6PGKw8MyBmvdOttqtUSb9ZxuDCFTPQxXDS+RSpDvve52hITndoYlAtDFETVFX8lk\neLE9ftsdovBo4LGKdRKJaswc4Hu+hUaN/bj1/q3+IrpksqVjunSi6nAT1piAXxvkJ+AzzqGeFFAS\n0mks7ZTrnJmqRmNxAh6phxF75dtPcTrNDThOpypDtG5GNrZwzvXdPQshhLQIhTqEEEJ6H+eMwDow\n950MGrliDjN7U8DRKxsSR2gakM1WI6EWF4Hio6M4dAjYswdAeSUAoLw8JB1qnl8bilNKQ+iFheUh\nKSY6jWHswThuf2Adji4lkMW46apzGsPIYhzHsa4rEVPWmbNNOyp0EqeDDgDhtU4DsQga1uHue99A\nRSguV6NWKZ0o1ZzZRQceQkikCCA4sAk86hQPmmrjoi56+OhHq8KBIO4ejs/jKpR7xSYFnBGcGcl4\nFtaDOHOY29GB+Cu/7Wz9hQO8ZqZN723db5k6rj4kGMmk+/yfmKjGX4XtpmO8p4FxPHmcE9NHpv1F\ndC0Kz6yuU2EdRzXj9OpEtfFYJqQOeruRXOMRk0VIAxhiZt/jqEXxSb+RK+Zs9x6GyDUzkqlez5vs\n1xr3LM3G0RJCSL9BoQ4hhJCBgzf4ZJDQNGDXH/8ODj70HkDEGxJHeLm+iLKCbduApSX78qUl4Ct/\n/gk8/rjFFaYDBSkAwMQEliqysFBBHH9VmTBFMCqqrjqGmw4AjIx0N2Kq01FbTeHcyQAU54IGCzkq\nJkwnprY4CNVwVTv5ymogmwdOrg35TQkhpAkCFJwNgUe+lG9P8SDqkStnnQVs2iT/DuLu4fg8rgK5\nl7NmCNb7gZw5Uqm29Yu23i/dR4JG+fQc6bTt/o33ciGQybjP/0RCdpCB9rjpeImsap1/9c6XJs6n\nwnzBPF/N48jqzhskDs9LEOjAFATVcfPlsUxIHfR2iucKaQif64PtOIq6WL3LOK9tRh8zM5Kp9h+a\n7EMbExG8rp+EEDKIUKhDCCFksODNGBkwVBVY+MlbACElFo2II7xcX8rLQ/jJT9wCnuVlYPmNYQiB\nqhioQ7nTGhLIio+jjBiyGIcm1pnbtwDpqlNGDP/4Kx/FglgXPUFMVHn3u+uv00AhR9OALMZNJ6bF\nRYTqqnPyldVI5TKer2d1lUIh5BnihBDSZgZ2IDuZBLbIyM1m3D1cBfJu3ge0aaZ2rpiTDiQIL8on\nciTppNAxtm2Tv8N209FpaD/Wu48I6z7DKlgKEocXUOCYTqY59kAI6Rt6Kq4oyPUh6mL1KGD5joJc\nv4McI7lizjcacmzjWAMbRwgh/QOFOoQQQvoLw5ozlcLaM9fizBVn2h/nzRgZIDQNuOueNyB9UKRQ\npxFxhM31BQq2bwegVPCOd1hTkaxhSLXFQO0a3FFVQFV24QCuNB1zhoeBHTvkdqufvwvxLVfiY899\noS3v35ekUsC+fbXXadRNRwUqDk+eMF11ZvamcODoes/Xmz34S9NVCoc+0dl4NkIIaQJNA5DNYwED\n7AKWyUhXHcBTFJor5mxRNgDs7hj6/+bAfxvuA5Jrkl2NrxmIWEfev3WOz35W/g4gwvbq15vLgrjd\npFK2WI2G7hMcz22JNhxfpmiOxy4hpE8w+xu5nL29trT3URDz2GJjSWs0KNSpF0WeK+Zs/VanE+TU\ntVONbR8hhPQJFOoQQgjpOwyb6YWbFnDTFTdxJhsZWFQVQHmFa3kz4gjt8quRzQIQMRw+bHXacYUh\nVcVAr622LW9XMelb//wKFipvQxoFHMc6cxueeEI+vnAW5IydsC38+5l0WsYfXHih58MCAK6/vqHv\ndHYWWMRK2zJzPzURX2DNNNduewBPPnIJKiJmCtGsA4Vf/8Ifmq5Sw8qq8CO3CCEkZFQVwNGqANUl\nQKnRbvaVyKdGP750omSLsgFgKyok1ySBdNo28G8T1TRT7B+zz/bNjGSCuYSEeD8ShUIYIa5+fc5S\ngGvCzaCh+4R6DlV6++h3rnRSXNdNIR8hhIROqWRvry3tfT2hRifwcmthv6kN+PWha1z/bcdNKtW/\nTpCEENIgFOoQQgjpL4wceOtgeKMz2bp8Y0lIGGgasGcP4NXds4pYgqIu73LFXdWiXAbUwhb3A6Oj\ngKK4f0ZHG9sgCx/5QhbYUMDnv7a76gCkx1tpWCeLlXRQaQyjDfVy1YnFoCQSwBe/2NBLzs0Bx5AA\nNhSgYZ1tP6FUarjttUbBqP/1DFQq0t2pXAbUbc+ZA0HFIvBS6c1oxlWKEEK6gaYBd9/7BiDiyMZu\nwOY1V8vCtJUaA+GmyCe5xzVbtacYHZURi0DwPkOd2b+2Zc3EUU3Vnu3rWwwK0VnD+h4uRyFCukWp\nJEXU69YBu3fXXz+Tcbdr0Cfd1BPR1RO+6e2j83w0zpV2R6lZnbYY20ZIe6EYrv0421LrhBkXHYo/\nb4RcMVe/rzRgEzxbFi95XL8bcjOi4xwhhJhQqEMIIaT3cdxQJdckzU5/UzftEbyxJKRRVBVYWnIv\nj8VkJNTcXPDX0jQge2jU4qJTn8VF4KuHLnWLITZvtuZmSYaHgSuuCP7iDmb2poCjV6Kw1y0MUjEh\ni5V0UGkMY+BkZAS47DL5dzwuf8diwJNPNuVQZO4PeERmNdn2ahqQ1a4CytKtZ3ERyM6cj9delY5O\n11/vfk6YkVuEEBI2qoqq+FBZgaXv7qz7HKNoomnQHfDiyB7/EC47+8Nt3NI2E0afocHCS6uFi05E\nUVmFqgA4I5lEhsJ8ATh+HDh1Clvv39pUYTQzkqkvogtY4PM6V+qOD4QwaSew0xYhpGVc51obJgYN\nOqUTpao4J5+3t635fPQmOzpcKF3ui14MmHCkZfejZNIl1lmzco28dtd43XwpLyNpMxmK7AghRIdC\nHUIIIb2P44bKeqPOATIyqMzOwtMBp1Jpwk1HBSrKUEPPOecc4PWlFdi0ye5c8sDV75RCDyvxODDh\nIdwIgKYBTz5yCSDiKD46ansvTQOysRtksZIOKs2zZQuwYQNw/fUQiiLFO02IdGz7A+P2fXVyNVK5\nTFP7SFWBiiOCrSwU/MOX3gFNA559FnBGtDXjKkUIIaFiFJIAWyFp6aJRKbQxxIflOA49uskVJ+mc\nsWoUTaTIRy4rl4G5B3pYqDMx0XqfocHCi02os3VrQ88F3LPM2x23QJEOiSrTR6abKoxmRjLBBXZe\nxcA6hce64wNNuDyaDJgjAyGRwBDgGH2pYtG9TosTgwYZox9jinO8RJRRE+p4uL0QD3wmSgXqu3pc\nv6eunZIiqVzO85gw+shGJC3H6wkhRNKQUEdRlD2KoryoKMozPo+nFUX5uaIoRf3nLy2PlRRFeVpf\nfrDVDSeEEEI8aXRwLJVqahCekKgzNydjhTCp2OKgzKihBpidhaebzvqNr0AI4ObvTlZ/p9I4dgw4\ndQoQiEHTgJ26EUC+lMcPV5yQMRbGDPnhYfl/HeGH32CB1XVAVGI2lxSrwIgOKi1w8cVyIObWW6G8\n972uGWpBse0PZci+r2ZSOHB0fbB95Bj0mZ0FFrHStmyxPIRjz26Aqlbr4AbDw427ShFCSOj4uMU8\ngStcQltFDEGdcwhuPAolhpuOcc1eXARmH7qgd4WqiUQ1+goI3GcIjPW+IZdDrpizzxKfnm74JZ0O\nHp1w2DFmJHNmMukk+VI+1OKs7fgNKrBzFBlzxVw47rhBXmNszB3xMWCODIREgs2bq86vfrQwMWjQ\nMdx0TIe0VMrulqY7oK3bvQ67n9gdPPoo6kRNfBQSxrharZiqwH1Xr/F3wxHPR/RKgTkhhLhp1FEn\nB+ADddZ5XAgxov/8leOx9+nLL2vwfQkhhJBgNDo4lsnYBuHbPes1itz2nQeQStFtpB+57TsPANl8\ny/vWEP04f+afO8e9cjoNVZXCGIN9++TxZRavrDPkAw6aeQ0WmPEeuutAeanqnGMWK8ty0G5xEXTV\naRajXU0k5KDL6tU1V/fCtT/EsLk/li4axZ0HfwNlEceX7qxtT+5VgJmbAwQUKUpLpeXxefMk8OlN\nmJlxO0vRTYcQEgl83GJUTLjEsYuLwBOvXlD3Ja1uOgY9L1R19hne+U7XKk0LVKz3DaUSSidK9aN6\nGsTpsBN24Se5JmnOSObMZNISPi5fzv6YzV0hxOPZdfw6i8FOUilXkdG8l/eJYglVzDYlHQHCbjMI\nIQ3i577X4MQg4k9hvlBto9Npu9hCF2scP3Ucp5ZO1Y4t7BKpDamG2v/QRJ8RxBhXK8wX7Puqmeu5\n3/i7cX12fIeB4icJIWQAaUioI4SYAfBqm7aFEEII6TyOG4tOzHqNGvvveDsOPC6CFXGY991T7L/j\n7cDRKztWoEuuSUI7uRrZLLC0VF1eLldddQAAH/oQcPq0/PuXv5QCkCaOqVoFyb4sVkaFJmz9a+2P\nJ7AZp+F2lXDZk+dy9dvodFoOwu7eDUCmdhljtPEVZezY0ZyrFCGEhI6PW8xjz6zzdMSr2W7pg+te\nDng9L05MJIBNm+Tf4+PAz35mPmTM8A5LoJIv5dsy09dWyA+58ENxDgkNH5cvZ3/M1hezFvkM0cza\ntcDZZ7cuYEmnax/fxqx9C7ZoFo9zre75YrhGBnWEqLeNhJD2k0gAf/qn1f+Hh4Gzz7bbC955J8et\nWsS3resBJ7F0srG2OlfM9Y8zUFBaiX104hM9ZhWXE0IIqdKoo04QrlAU5SlFUR5RFOVCy3IB4DFF\nUX6gKMona72AoiifVBTloKIoB1966aU2bCIhhBBCAOCW+6dx8KH3oCKUYG4jAQdwSfhoGhpyPtI0\n4MlHLgFEvO1OMsasmMxIBupMyuamY3DffQDufQKvvbra+ziyYhxTloGCk6+sdn3+WgXJvixWRoUm\nBuP89kehAGR+MgHhuC0RMQ+npVKppkWzyfHjMnvt5DpbBIzVcYkQQiKBZRb4siIajmXIFXMywlUv\nSPs54PW8OHHbNuBXfkV+P5ZrQGG+EKqwxhlbZRTtxzaONf2atuKQhwMIoO/HgMWRQXT/JB3Cz5XC\n0S4ZLlEuIY4hmllYAH7+81DOzZoFvVrCcZ9zrS6O4mJdsVGSRUdCIoHTfe8P/sDdnnHcKjDWvoYt\n9gpoatJOt2nUxcXlNtOvjDn6t/r9RL6Ub6nvC8BTrMPrJSGEeBO2UOdJAOuFEJcA+P8ATFkeu1II\nMQLggwD+XFGULX4vIoS4RwhxmRDisnPPPTfkTSSEEELqYAzq9WEmsaYBqY0LZqH6S7suB4S0OA/k\nNhJwAJeEj6oCBw4Ed4SRDiYCQPudZKw33LMvnGdz0zGoVAC8cDkKe7d4H0dWjGPKMhN2Zm/K5fw0\nNwdk53LApIKbvztpK0j2bbGyW7Q4IOfaHzfL/bVlC3BMJJDFuOmqcxrDmDnf257cd9DMKx6hMEFX\nJUJItEkkTDHIkx+41NbuBXGjyBVztgjXvuWznwVuu01+P5ZrQCiRMw4HDVsxRy8yTF07hVawxkV4\nXcMaFeowaoe0BcPly4i/8omLMQRtLiHO2Fi4x2a9vmcmUz1/dVIbUvJ5mUxzRdZkUv7o535dsVEP\nOEkQMhDo7nsVRZHt1q23AitW2NfhuFVdDIGO08XW1hZmMm7hSyqFtWeuxZkrzmzn5gXHcf0ILBDR\nryme17I+GR82xLZjG8fMCEf5QN78szBfaL7va3z3PSjoIoSQbhGqUEcI8QshxGv63w8DWKEoyq/r\n//9U//0igK8D+I0w35sQQghpmpTPbMA+zCRWVeDAj98MVQWKReCl0psByMHYxUV4u01Yb0j1Adzl\nobj8n3nfHUHTgHv3LKNS8dlHHutnswDKKwHU2LdtYO5Td2NkxO9RBcVHR7GgOOI+YjE5qAZpwWjG\nYd1yixmFdc83/lQ6P3152fY5BjGuriu0qRBhOO2oqLrqVBDH50//eWMvlE5XBxDXrgXOPBNnvvg7\ndFUihESf/fsBADMfs/dHg7hRuBxg+hmP61A6mW54lrT7RdLmn64YrUaLDB5FHNv2ebxerphraOZ2\n2C5ChNiYmACGhuTfQYra1vvoqSnbsZlck2zt/AzS93TM2M+MZOTzmu23Op7bcvtCCOkc27bh6MXr\nZbuVSACrV9sfN8YYGH/lizG2Yog5ANmuOttCl/AlncbCTQu440N3tHX7AtPsNcBHpJkr5vpmfNi4\nd3AJccJyEDK+ewpZCSEkMKEKdRRFWacossqiKMpv6K//iqIoZyqKcpa+/EwAvwvgmTDfmxBCCGka\nn/zcfsMQb1REDNkscO217nVMt4nRUVMgYc6sNDK9JyZQVqRTC2cldQZVBZaXpTVIEEcQ6aZjX9Yx\nJ5Fk0u6eAgXbtwNKXComRFmR22F11alUsKRrvyoxxRWLtRQbxhO4HABweimGXbuqj518ZTWQzctI\nLdI76MVK41jRRAKrLrsIAPCmHeP48L3/zb5+gBls5gDiwgJw00246av76apECIk+iQQA4Ju/ONjc\n85uNeOkDkmtCjJ2xCj4NGikypFKe1yrb9nm8Hh1ySKRIJICzz5Z/G0Vt632gE8d9tLWYmxnJtD/m\nolnXhICvy5gOQnqIz34W+a9OVieR/d7vuddh/FUgrELwRtpyc70+cZ8xGIjJYWNj4d9P0FWHEEIC\n0ZBQR1GUfwAwC2CjoigvKIpyg6Ion1YU5dP6Kh8B8IyiKIcA/L8ArhVCCABrARzQl/8rgG8JIR4N\n72MQQggh4aJpQCqX6YgDSaewijeWl4EjRwQMNx0D021i82aXWMIc1Egk8JVL9Reim07bMQRWoiz3\nRy13HMOq2HApsdIxJxFHEUrDOtv2l5eH5PYrCeBTnzLXO2NZir/iFeHa+KVKHCpuAQAIxHDffdXP\nP7M3BRy9EnMPfLhNH4i0Ba/i5xY9GddL/BdgsC8zkqHVMiGkZ2naHUcvlA+i2COUInpY14smo3YK\n84VAn8Po4wF0+SBtpoXidseFLe2asU8nAEJ6Elsb9MUvVqP8DDjRrD1Y+1I+wuWeIEifsFc/Wz0s\nMVih3VPwWkoIIYFoSKgjhPioECIhhFghhDhPCPEVIcTfCSH+Tn/8DiHEhUKIS4UQlwshntCX/5u+\n7FL98S+048MQQgghrWIMfKsqcODo+s44kHQAQ+xh6B+WltzrDA8DO3bobhMTExBLHkqPO+8EFAU7\njEnfd95J6+A204g7jjHTZ24O2L4dgFLGjh3ddRJRMeG//ZZBsjfi3s9fig1jb2wcx1EVhJXLwK5d\n8rh+8pFLABHH7EMX9JWwbuAYHQX+5m/k34kEJt93i332tkfx0zaAZAyq0WqZENIn1BVjjI1V20Ev\nN5h+xRlZ2yphxdy0cN1pRKjjiuciJGy++EX3Mst9oJgEoCiyr3b33Z3euuCkUgMpYCSE6CQSdoEh\nY9vrYkReNdx2WvtA6XR4MUqdxqcvly/lq24zEYjAsoq3Q0X//C3fU3DSFCGENESo0VeEEEJIr5MZ\nyUC7/OpqRNSXl/ui+O8l9vB10wGARAIvbjgXot4L0zq47TTijmMMrBjCLIi4r/tOp5jFZv/t1+M+\nAKDimOxmUBZxTFbcs94eesg4ruVR2rFoL9Iearl4AfZoF71IWy9OhBBCepm6YoypKdtA+sC4rLSx\nvW9ZABNEGOAQGgUthhmOSxTpkLZj9M+NflnMZ+g4FgPOO68z29QM6TTPF0IGnQcfrLZhdNOpSyh9\njT4QaSTXJG39NZvrZQTiZksnSu1x9tH72C3fU3BshhBCGoJCHUIIIcSBurzLFLWUlyt9Ufz3EnsA\nAFacxOe/thsCistx5Z/+z6vrvzAHO9rO3Jx0w8GkYu4nP3ccYwDBKszqtoBlDpsCuftkR1C1ph4e\nBi68EADwL+8ex4t4M6Asm+s+iVG8/IqCO+9SIMqrIKDgjUUFn7xrtC+EdQPJxIS7EGRtX6wz8/SY\nl3oDiANTtCaEEAyQgKNdg/9hFJaCCAP0a5j5r9esZUfxxTpzemD2M+kul19e7Ze5Z3tIhoeB66/v\n3DY1Ac8XQgachCVuewDcdHLFXCgCjpbazi6KNLbevzUUJ7XMSMbVXzP/j4pbkNPZJ0ThDq+dhBDS\nWSjUIYQQQixoGpA9NGqKWhbFcNcdScLgxmzOFEgIKDh2DFj9a78Alt6Ewt4tns85/o4EnjnXssAQ\nT1hdL4zBjn7Nae5BnDFni4vo6jFsOFR5uvtYYtN2HISuSILMZtu/H2+cdQ4+/pMJCMQAUc3GmsVm\nnIbdfeU0hjGrXNEXwrqBJJGQ7YlBCNbkHGAihESe0VFTpGrEydhi/4LQBzOXI0FIhaVGrz2eolJH\n8cWINiWkY1x1VbVf5ueo84lPADfe2LltahS2jYQQoDrxYwAmmJVOlCIRzdQtpo9Mty8KNgLXFEOI\nZYviMgiy3zluSwghkYRCHUIIIcSCqgIVZci2rF2OJG3LFfbAOcC/cyfw2qtnAYjj0KObsIC1ruec\nfGU1ro/fW42/iseB/fvtg7XGYEepVC02OX8aKTaRlvGKOeumq47Nocq5HZs3ez/p3e8GLr0Un9v4\nCDRhCDWq2VgqdPGOhQpkRJZXJBjpEayuOnTrIoQMAvVi/4JAe/newlHooaiURBajH+ZsowDgjDOi\n309j20gIAapxfn3upgPAW8CRy3V07NFFh8UhbXPV7fI1JVfMmUIsWxSXQZ1IrlwxN9AiLkIIiTIU\n6hBCCCEWvCKiFhcRbvFfF7RkRsc7JmjJl/Lm39rlV2P/fsAQPihiCCrsA62aBtzzyU/iqYUMHj/3\nD+XC8XHg0kuB8XEII6IokZDbfsstQLHofuNGi02kZTpyDAfE5VDldPfxGuBXFODv/14+tzhi+yxx\npQIN66CJBFZtHzcLB8tDcbxpxzgWxDrPSDDSIyQSwKZNsn1xuukkk0AqJa2sIzCbjRBCQqFe7B/p\nP2oUerpaSCPEiVHcHh+3iXUEANxww0AUvQkhpJcozBfc0UylkhR4GIKZTruqdEAc0paIUMuYQxhx\nWq1SOlGyC7Gc0Vx1IrnozkgIIdGFQh1CCCHEwtwcqhFRekyUEAi3+B/G7OkGsc642Bn7LyiXq48t\nLgLZ2J/aIomsjjuZV/8Wi+e9zWYZrLz3vdXtrgWLTe3DZ4DFdQzrP90QsNR1qDIKAAbxOLB9O3DJ\nJfK5QrE/V8Swa21W/mMpboq4wuOsX9i2TbYvzv2ZyQDptLSy5gxpQki/0IbYP9JFWhSSehVRKN4h\nXcE4llMpl6BQec972O8mhJAu0Uy/IF/KV8ePOuGqkrIIW+o4vYRBW0QoljGHtsVpNYA5+dIQ5Hj1\nOWuIsDzdlgghhEQCCnUIIYSQTrszdHH2tKYB+//lP7qWl4fOMMUTmgbct68Cw3Hnp/gP+Ny6+6tF\no0SienPo9TkM8Q6LTe2lB2xrA7n7rFpV/TsWM8+D2VlgsWwX+QDAQ6feJ/8wipuxGJ4f28LjrF/4\n7Gdl++K1P5PJ9llZE0JIt2gh9o9tYsRog5CUM6BJVzCO5XTa7HMLRQEuuww4eJD9bkII6RKufoEe\nbWUKeMbG7I/n895OO+3E6vbSqfdto1OQq7/daVciyMmXNsGQV5+zxhihMXkzCu5AhBBC7FCoQwgh\nhHTanUEf7Fweisv/OyhoUVXY3HQMrOKJt//+/RCVqpPJYjmObHHU5rhj4vwc11/fdLGJtEAXBgqC\nEMjd5847ge3b5eD/n/2ZeR7MzQHHjgErV9pf8/XySnt01pVX4oLb93fk85Auk8mEZ2VNCCFRIZEA\nUinv2L86sE3sI2rMPs+X8hjb6Ci8EdJJDEfVFAt8hBDSTayx9gDMaKtcMSdFGFNT9sc7KdDxoxPj\nVW2cyObqb0dx0tzYWH3HnEwmEu5AhBBC7FCoQwghhNSjHTeVExMoK0L+3aKgRdPkmKkhXrjtOw/Y\n/rcyO+v9GiMjUhihacDpJz8Cw03HoCyUalxRrc/xxS9WIxzoptM5ojhQEJRMpjr47zgPVBWoVOyr\nu6Kz/NxXCCGEkF5h/37v2D8yOFhnn1swCnJT1055Pk5IRzD63Bdd1O0tIYSQ5onoBKdGsMbaGxh9\nhcgIuJ2u5ZbxqnZEeg5KrFNNJ82pqfqiLEaIE0JIJKFQhxBCCPFidBRQdLHK+Lj8W1Hk8jBIJPCV\nSyvV129BaKCqwIED8remARPXfhCPPy5sUVbI5rGwUN/hRFUBCHf3YLE8VI0rsn43b3kLzljSP8eb\n3iQ/h1FkYrGpbWgakMplsLDQnoGOjuMjuAkUnUUIIYT0OhSeEmtRyyLaKcwXosmH5sYAACAASURB\nVFN4I4RFPkJIL9PLE5z80KOt0sl03f5Cx8aOalwr2hHp2dFYp1Sqpiiond9xZiQDpFKBP2dfjBUS\nQsgAQKEOIYQQ4sXmzTLKycrwMHDFFa29riFyURTsOKgvu/POpoVAmgbcfe8bqFSAbBa48UbgtVfP\nghAK9uyRH2PXLgBHr/R2xHEgHXfc3YOR9a9U44q8vhsA+P3fl9v/lrfI/xOJ8AVOA8pt33lAiq02\nXw1AF2cdXQ9VBZ5+/mWkchnc/tgDXd7K8DGFZdmcf3QWIYQQQkivU6OoRaEOIYQQQjzRXVRquq3o\neIlktt6/Fdi6NeSNspBKAbt3t1+Mnq4vVAqFTKamc007hEjO9w8aX2UV6nRExEQIIaQpKNQhhBBC\nvJiYAGKOy2SLEVUA/EUuQFNCIBkNJKOnlpeBBx8EjNiqxUXge98T2HtfGRBxZLPecVhWHn4YwNAv\nAQCrVkkhkBDA3Pw51ZW8vhsAuPXW9gmcBpz9d7xdiq1W/TU0TYqyKiKGbBb4H19+Pw4cXY89u99p\nOif1HZy9SwghhJA+x6vINrZxrPMbQgghhPQDVjdoRQFuuaVvJ5L5ilQsDixeEVHTR6aB6en2ua9k\nMsCpU8Dx49Vllggy831DiCXrmFAH8N3efCkfivDJd380MDZmjUkLKu4hhBDSeSjUIYQQQrxIJKqR\nV4AUm7QYUQXAX+QCNCwEMgQbKK8EACwt2R+vVABAgajI9yuXUddVxxp95bu+47s5HYd00TFir9oh\ncBpgNA148pFLpNhq9gLs2mXsWynOeuo7l6IiYnj6O5cA88GckwghhBDSR1hjk0jP4lVgmrp2qvMb\nQogB2xZCSC/DiWRAJmP2LwrzBV83mFwx1x5xsEVYkivmpJDFInIxHWhajSXr9PXKZ3sL8wVgerrp\nlzUEOk6hDh1xCCGkf6FQhxBCCPFjYkKKTIDwxCa6yKW8Im5f3oQQSLrpBFmz6rBTy1WnKvw5o/76\nMzPSagfAyjKAY8ekcOdDH5KfwyAsgdMA43RN2rdP7htAirNERe5fKcgK5pxECCGEkD6CznOEkHbA\ntoUQ0stEfSJZUBeZWutZHHM8sQh1alGYL7RdHFw6UXKJhfKlvP5HvrUX7+T1KpVqfXt9MIRLVjcc\nwC7mrhlz5nOsBIlGI4QQ0h0o1CGEEEL8SCSAkRH5d5hik4kJLEPYlzUxWDA7WxVsBKWWq46X8Md3\n/S1bqm5DBsbMJOtgSJQGQaKOxw21l2tSuexcS7H9DuKcRAghhBBCCCGEENK3GG7QVro8kczmlFLH\nRcZcV1/PMw4poBCnrqCnS5iCFB+nH09CiMlqiXTatr1hx4Z5vZ51H9fc36WS5/fTkVgwQgghTUGh\nDiGEEFKLLVuADRvCFZskEvjKpRVUFAU491y5rInBgrk53dRmUsEI5gI9Z3EReOIJ78e8hD++609M\nuC2EDVFOIgFs2iSX0U3HhnHDrWlyEs7tjz1QfdBjkCa4a1KVes5JhBBCCCEkwjBuiBBCCAkHp6tO\nlyeS2UQYdVxZzHX19XLFnFtsE1Sok04jnUzLgSgLqQ0pIJVqT+yVg3wp37JYaOv9W7sv1HFgxneF\ntF3m6zWK4fTTaowYIYSQjkKhDiGEEFKLs86SFqotiE28ZkOoKeDoxeuBz3xGLmhxsGAOmyCEFO6s\n3/iK5zqKIgUicz6aHkP4IwRw83cnzb89108kgE98ovq/M+Jqyxb5m246NowbblUFDhwA9t3xNuSK\nOSncyWVc4ho/16SREeCcVad834euOoQQQgghPQrjhgghhJBw0CeSVQxHaOfYni6uCNsVxQ9bpJGH\ni4x1O5xuM4X5ghTbNEMyKeOPHH2MdDINpNNtj70CWtx+nekj042573QAM74roFCn3rHWtKApk4nc\nd0MIIaQ+FOoQQggh9bj7bqlycf6MjgZ6uusmLJfDwllA/quTwF/+pVzWhBDIEHggm8fC5qvN5fPP\nnYPtl33fZXizYkXI4o1aEVcXXyx/003HRNOA2z61FYcOScebSgUoPjqKZ/7tZSncOboe6rbnbM+Z\nmwOOHQOwoQBNqwqpHn4YOLV8hu971XJOIoQQQgghhBBCCBkItmyRE+W80N1HmnYxaQBPgYZD3NG2\n7TCcdxxCneSaZHvez8rZZwNr17YteqvjkV7JpMuZCEBgkUytfWyIfpoSNGUyVVcdHzqyvwkhhDQE\nhTqEEEJILZJJ4Lzz3DFPw8PAFVd4P8dxo22dMaNpQGoyDZxc23JGcOlESQpvjl4JdWmn7bHZF84L\nHmPlQaCbN+vMJGfEFWcC28gVc1BV4Oc//v/Zu/P4tso73+PfR15ik53sK8oCgSSAA2EJk6lNKVtL\nCbTcsqRzMZehQLrdaXvb5PampFU7nbZMh7asBSIXkgLtUJIGAhQo2RonELCzEJZsznqclayOI1t6\n7h9aLFnyLmtxPu/Xy69Izzl6znMkd8w589Xvd76mT29oZ2X9Rm/8/qpgcMe69Njfz9G6dfGv046p\nMSErj0cKmNxGRwmotFTNV0ICAAAAAAA4XZx/vsp+Uxo3HA7ONFfhJJmVdhIGNKLaFN30/E0xmxIF\nUJIdtCgtKu38lptHjkjV1Spxl8Stv10hm0YhmY7eW22z0lKppCTytF2hmiaq74SrDrX7cy4paTYw\nlPL3CgDQIoI6AAA0p7Q02MbJ1ehPZuMKMtGa6QccrpyipR1vCfV65Tp5vZJsjrxrL4ppm1Rx7xPB\nwMYDc1puY5VAqy/ewt9MosVVs9ZvORD5rD74oKGdlb8+V+teL5LfH3xuZXTHHQ2ve2LJXxs+Y68i\nn3HillguLVrUuecBAAAAAACQNZr4Ilk4OBMJ0CQIT7S1wk2iYE+rwj5lZVr48cKYgEai+3KdErRI\n4RftGq+/XSGXqJBMojlTra2hmiVVS1psk5XucwIApA5BHQAAWtKzZ7BiTLiqTn5+TAWZ1n7DxnEU\nqZyiyrtigjXtsWr+5yKVWfwmN7ltrVqrZ8/gN5MStLhyNFjF46o7fJ5dwXOPjI18VonU1YUfGX3w\ngSJVdWI+Y39D67KKiobKOd/57wdVoJOSpJoa8X4DAAAAAAC00pKqJc1+6a61EgV7wmNLqpZo2rhp\nDRui2xSFjh0d0OhqYY1IoCWqIk5MyKWF8EpGiapC1NbPaen2pQmr3iSzehMAIHsQ1AEAoCVud7Bi\nTLiqTqNqOtEX4o4jFZeVJgxLeDwNLY9kXfHBmkmTJGPifyZNipvLcSRV3hWpquLzKabiSibwaLZW\nbBqYngBRBnEcyVl+bYIKOGEmbuSOO1r/GVe8cIMCJkdSbJgHAAAAAAAAkqZNi3m6pGqJtGRJ8F+p\nITTTWJICJEu3L9WC2xY0DERXhmnq2OEldIEQRyTQElXBJzJWXNz0+5yJAZ5EVYiKi9vXyiukrLJM\nxWcVJ729GQAgsxHUAQCgJaWl0pAhwSo6Ukw1ncbCra0ahyXC1XQiYQ1/QXzoYsqUhqo9Yfn50hVX\nJDyObGzAI2FIw+3u9Iu8RPM7juR13a2Adcn7ZH1GBYhSLdFnlZcnSU2X2Nm4UZo1K/51jT9jx5HK\nF50rnw3+3mRiYAsAAAAAACCtFiyIeRqubLJ0+9JgYCRBlRNJ7a+0U1bWfMjE7W4I6zR17PAS2tiC\nK6MlCrmUlETeg7hQU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0+vP8qT644S6uPOrQsS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/rZAkSTqClda2AFBS3UI0Gu4zzLC1qpl54zKHJaQzGBfMGc1fV+7kpsc3ceVJ4/rGY+0pDENu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\nfVkTAIuLcgAOOmg0WPUtnbxcUsu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f368d84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Entry Price Entry Time  Exit Price  Exit Time  Profit  Trade Duration  \\\n",
      "0        1.59427 2009-05-29     1.61869 2009-05-29   241.2               1   \n",
      "1        1.61935 2009-06-01     1.64428 2009-06-01   246.3               1   \n",
      "2        1.64433 2009-06-02     1.65778 2009-06-02   131.5               1   \n",
      "3        1.65775 2009-06-03     1.63160 2009-06-03  -264.5               1   \n",
      "4        1.63161 2009-06-04     1.61733 2009-06-04  -145.8               1   \n",
      "5        1.61735 2009-06-05     1.59800 2009-06-05  -196.5               1   \n",
      "6        1.59565 2009-06-08     1.60490 2009-06-08    89.5               1   \n",
      "7        1.60486 2009-06-09     1.63051 2009-06-09   253.5               1   \n",
      "8        1.63052 2009-06-10     1.63576 2009-06-10    49.4               1   \n",
      "9        1.63575 2009-06-11     1.65887 2009-06-11   228.2               1   \n",
      "10       1.65885 2009-06-12     1.64421 2009-06-12  -149.4               1   \n",
      "11       1.64345 2009-06-15     1.63202 2009-06-15  -117.3               1   \n",
      "12       1.63203 2009-06-16     1.64108 2009-06-16    87.5               1   \n",
      "13       1.64099 2009-06-17     1.63990 2009-06-17   -13.9               1   \n",
      "14       1.63988 2009-06-18     1.63311 2009-06-18   -70.7               1   \n",
      "15       1.63309 2009-06-19     1.64924 2009-06-19   158.5               1   \n",
      "16       1.65069 2009-06-22     1.63456 2009-06-22  -164.3               1   \n",
      "17       1.63454 2009-06-23     1.64512 2009-06-23   102.8               1   \n",
      "18       1.64509 2009-06-24     1.64063 2009-06-24   -47.6               1   \n",
      "19       1.64064 2009-06-25     1.63678 2009-06-25   -41.6               1   \n",
      "20       1.63691 2009-06-26     1.65219 2009-06-26   149.8               1   \n",
      "21       1.65245 2009-06-29     1.65660 2009-06-29    38.5               1   \n",
      "22       1.65658 2009-06-30     1.64605 2009-06-30  -108.3               1   \n",
      "23       1.64607 2009-07-01     1.64775 2009-07-01    13.8               1   \n",
      "24       1.64776 2009-07-02     1.63926 2009-07-02   -88.0               1   \n",
      "25       1.63930 2009-07-03     1.63315 2009-07-03   -64.5               1   \n",
      "26       1.63209 2009-07-06     1.62855 2009-07-06   -38.4               1   \n",
      "27       1.62843 2009-07-07     1.61382 2009-07-07  -149.1               1   \n",
      "28       1.61380 2009-07-08     1.60706 2009-07-08   -70.4               1   \n",
      "29       1.60696 2009-07-09     1.63340 2009-07-09   261.4               1   \n",
      "..           ...        ...         ...        ...     ...             ...   \n",
      "595      1.65729 2014-01-23     1.66356 2014-01-23    59.7               1   \n",
      "596      1.66357 2014-01-24     1.64827 2014-01-24  -156.0               1   \n",
      "597      1.65023 2014-01-27     1.65824 2014-01-27    77.1               1   \n",
      "598      1.65821 2014-01-28     1.65779 2014-01-28    -7.2               1   \n",
      "599      1.65771 2014-01-29     1.65611 2014-01-29   -19.0               1   \n",
      "600      1.65615 2014-01-30     1.64824 2014-01-30   -82.1               1   \n",
      "601      1.64832 2014-01-31     1.64363 2014-01-31   -49.9               1   \n",
      "602      1.64178 2014-02-03     1.63027 2014-02-03  -118.1               1   \n",
      "603      1.63230 2014-02-05     1.63073 2014-02-05   -18.7               1   \n",
      "604      1.63200 2014-02-07     1.64115 2014-02-07    88.5               1   \n",
      "605      1.63965 2014-02-10     1.64007 2014-02-10     1.2               1   \n",
      "606      1.63991 2014-02-11     1.64481 2014-02-11    46.0               1   \n",
      "607      1.64488 2014-02-12     1.65947 2014-02-12   142.9               1   \n",
      "608      1.65949 2014-02-13     1.66566 2014-02-13    58.7               1   \n",
      "609      1.66572 2014-02-14     1.67476 2014-02-14    87.4               1   \n",
      "610      1.67653 2014-02-17     1.67116 2014-02-17   -56.7               1   \n",
      "611      1.67114 2014-02-18     1.66813 2014-02-18   -33.1               1   \n",
      "612      1.66820 2014-02-19     1.66773 2014-02-19    -7.7               1   \n",
      "613      1.66771 2014-02-20     1.66492 2014-02-20   -30.9               1   \n",
      "614      1.66491 2014-02-21     1.66144 2014-02-21   -37.7               1   \n",
      "615      1.66258 2014-02-24     1.66545 2014-02-24    25.7               1   \n",
      "616      1.66546 2014-02-25     1.66787 2014-02-25    21.1               1   \n",
      "617      1.66786 2014-02-26     1.66679 2014-02-26   -13.7               1   \n",
      "618      1.66682 2014-02-27     1.66870 2014-02-27    15.8               1   \n",
      "619      1.66873 2014-02-28     1.67414 2014-02-28    51.1               1   \n",
      "620      1.67096 2014-03-03     1.66639 2014-03-03   -48.7               1   \n",
      "621      1.66641 2014-03-04     1.66631 2014-03-04    -4.0               1   \n",
      "622      1.66633 2014-03-05     1.67195 2014-03-05    53.2               1   \n",
      "623      1.67193 2014-03-06     1.67380 2014-03-06    15.7               1   \n",
      "624      1.67385 2014-03-07     1.67121 2014-03-07   -29.4               1   \n",
      "\n",
      "    Type  \n",
      "0    BUY  \n",
      "1    BUY  \n",
      "2    BUY  \n",
      "3    BUY  \n",
      "4    BUY  \n",
      "5    BUY  \n",
      "6    BUY  \n",
      "7    BUY  \n",
      "8    BUY  \n",
      "9    BUY  \n",
      "10   BUY  \n",
      "11   BUY  \n",
      "12   BUY  \n",
      "13   BUY  \n",
      "14   BUY  \n",
      "15   BUY  \n",
      "16   BUY  \n",
      "17   BUY  \n",
      "18   BUY  \n",
      "19   BUY  \n",
      "20   BUY  \n",
      "21   BUY  \n",
      "22   BUY  \n",
      "23   BUY  \n",
      "24   BUY  \n",
      "25   BUY  \n",
      "26   BUY  \n",
      "27   BUY  \n",
      "28   BUY  \n",
      "29   BUY  \n",
      "..   ...  \n",
      "595  BUY  \n",
      "596  BUY  \n",
      "597  BUY  \n",
      "598  BUY  \n",
      "599  BUY  \n",
      "600  BUY  \n",
      "601  BUY  \n",
      "602  BUY  \n",
      "603  BUY  \n",
      "604  BUY  \n",
      "605  BUY  \n",
      "606  BUY  \n",
      "607  BUY  \n",
      "608  BUY  \n",
      "609  BUY  \n",
      "610  BUY  \n",
      "611  BUY  \n",
      "612  BUY  \n",
      "613  BUY  \n",
      "614  BUY  \n",
      "615  BUY  \n",
      "616  BUY  \n",
      "617  BUY  \n",
      "618  BUY  \n",
      "619  BUY  \n",
      "620  BUY  \n",
      "621  BUY  \n",
      "622  BUY  \n",
      "623  BUY  \n",
      "624  BUY  \n",
      "\n",
      "[625 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "Q_learning.episode_review(220)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing (Out-of sample data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So how does this agent perform in a new environment?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./saved_tensor_models/dqn.ckpt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-08-27 18:38:17,848] Restoring parameters from ./saved_tensor_models/dqn.ckpt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of Testing, Total Reward is -195.8, Average Reward is -0.383170254403\n"
     ]
    }
   ],
   "source": [
    "Q_learning.test_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUMMARY OF EPISODE 1 \n",
      "\n",
      "Total Trades Taken: 511\n",
      "467 Buys, 44 Sells\n",
      "Total Reward: -195.8\n",
      "Reward: -118.90000000001109 pips(Buy), -76.90000000001204 pips(Sell) \n",
      "\n",
      "Average Reward: -0.383170254403\n",
      "Win Ratio: 49.3150684932 % \n",
      "\n"
     ]
    },
    {
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kZxAQAAAMAwvLq7QVMyE1QWwrFOM/OStHp7nbYdDQR7hnvDs7nLrczE8AV7Lp+dq/9+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\ntmTh7WzAw2QgrbhuyOsUQggxdDJL63hmw9H2n93Ha2y2UlBt5rfnxJGYX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24830f3d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Entry Price Entry Time  Exit Price  Exit Time  Profit  Trade Duration  \\\n",
      "0        1.67181 2014-03-10     1.66449 2014-03-10   -76.2               1   \n",
      "1        1.66445 2014-03-11     1.66146 2014-03-11   -32.9               1   \n",
      "2        1.66150 2014-03-12     1.66163 2014-03-12    -1.7               1   \n",
      "3        1.66165 2014-03-13     1.66220 2014-03-13     2.5               1   \n",
      "4        1.66222 2014-03-14     1.66483 2014-03-14    23.1               1   \n",
      "5        1.66405 2014-03-17     1.66338 2014-03-17    -9.7               1   \n",
      "6        1.66337 2014-03-18     1.65911 2014-03-18   -45.6               1   \n",
      "7        1.65921 2014-03-19     1.65300 2014-03-19   -65.1               1   \n",
      "8        1.65297 2014-03-20     1.65058 2014-03-20   -26.9               1   \n",
      "9        1.64957 2014-03-25     1.65285 2014-03-25    29.8               1   \n",
      "10       1.65300 2014-03-26     1.65759 2014-03-26    42.9               1   \n",
      "11       1.65768 2014-03-27     1.66025 2014-03-27    22.7               1   \n",
      "12       1.66045 2014-03-28     1.66392 2014-03-28    31.7               1   \n",
      "13       1.66500 2014-03-31     1.66605 2014-03-31     7.5               1   \n",
      "14       1.66605 2014-04-01     1.66272 2014-04-01   -36.3               1   \n",
      "15       1.66273 2014-04-02     1.66236 2014-04-02    -6.7               1   \n",
      "16       1.66239 2014-04-03     1.65963 2014-04-03   -30.6               1   \n",
      "17       1.65962 2014-04-04     1.65751 2014-04-04   -24.1               1   \n",
      "18       1.65691 2014-04-07     1.66052 2014-04-07    33.1               1   \n",
      "19       1.66053 2014-04-08     1.67461 2014-04-08   137.8               1   \n",
      "20       1.67456 2014-04-09     1.67918 2014-04-09    43.2               1   \n",
      "21       1.67918 2014-04-10     1.67828 2014-04-10   -12.0               1   \n",
      "22       1.67829 2014-04-11     1.67287 2014-04-11   -57.2               1   \n",
      "23       1.67181 2014-04-14     1.67272 2014-04-14     6.1               1   \n",
      "24       1.67278 2014-04-15     1.67276 2014-04-15    -3.2               1   \n",
      "25       1.67269 2014-04-16     1.67953 2014-04-16    65.4               1   \n",
      "26       1.67954 2014-04-17     1.67922 2014-04-17    -6.2               1   \n",
      "27       1.67916 2014-04-18     1.67971 2014-04-18     2.5               1   \n",
      "28       1.67889 2014-04-21     1.67897 2014-04-21    -2.2               1   \n",
      "29       1.67897 2014-04-22     1.68225 2014-04-22    29.8               1   \n",
      "..           ...        ...         ...        ...     ...             ...   \n",
      "481      1.24318 2017-03-30     1.24674 2017-03-30    32.6               1   \n",
      "482      1.24673 2017-03-31     1.25495 2017-03-31    79.2               1   \n",
      "483      1.25437 2017-04-03     1.24847 2017-04-03   -62.0               1   \n",
      "484      1.24840 2017-04-04     1.24379 2017-04-04   -49.1               1   \n",
      "485      1.24379 2017-04-05     1.24817 2017-04-05    40.8               1   \n",
      "486      1.24820 2017-04-06     1.24667 2017-04-06   -18.3               1   \n",
      "487      1.24667 2017-04-07     1.23658 2017-04-07  -103.9               1   \n",
      "488      1.23750 2017-04-10     1.24136 2017-04-10    35.6               1   \n",
      "489      1.24136 2017-04-11     1.24893 2017-04-11    72.7               1   \n",
      "490      1.24900 2017-04-12     1.25357 2017-04-12    42.7               1   \n",
      "491      1.25356 2017-04-13     1.25013 2017-04-13   -37.3               1   \n",
      "492      1.25013 2017-04-14     1.25248 2017-04-14    20.5               1   \n",
      "493      1.25294 2017-04-17     1.25636 2017-04-17    31.2               1   \n",
      "494      1.25636 2017-04-18     1.28397 2017-04-18   273.1               1   \n",
      "495      1.28396 2017-04-19     1.27744 2017-04-19   -68.2               1   \n",
      "496      1.27745 2017-04-20     1.28112 2017-04-20    33.7               1   \n",
      "497      1.28083 2017-04-21     1.28246 2017-04-21    13.3               1   \n",
      "498      1.28369 2017-04-24     1.27931 2017-04-24   -46.8               1   \n",
      "499      1.27928 2017-04-25     1.28397 2017-04-25    43.9               1   \n",
      "500      1.28393 2017-04-26     1.28474 2017-04-26     5.1               1   \n",
      "501      1.28475 2017-04-27     1.29030 2017-04-27    52.5               1   \n",
      "502      1.28824 2017-05-02     1.29370 2017-05-02    51.6               1   \n",
      "503      1.29372 2017-05-03     1.28660 2017-05-03   -74.2               1   \n",
      "504      1.28651 2017-05-04     1.29205 2017-05-04    52.4               1   \n",
      "505      1.29207 2017-05-05     1.29800 2017-05-05    56.3               1   \n",
      "506      1.29825 2017-05-08     1.29396 2017-05-08   -45.9               1   \n",
      "507      1.29396 2017-05-09     1.29315 2017-05-09   -11.1               1   \n",
      "508      1.29277 2017-05-10     1.29357 2017-05-10     5.0               1   \n",
      "509      1.29356 2017-05-11     1.28833 2017-05-11   -55.3               1   \n",
      "510      1.28833 2017-05-12     1.28883 2017-05-12     2.0               1   \n",
      "\n",
      "    Type  \n",
      "0    BUY  \n",
      "1    BUY  \n",
      "2    BUY  \n",
      "3    BUY  \n",
      "4    BUY  \n",
      "5    BUY  \n",
      "6    BUY  \n",
      "7    BUY  \n",
      "8    BUY  \n",
      "9    BUY  \n",
      "10   BUY  \n",
      "11   BUY  \n",
      "12   BUY  \n",
      "13   BUY  \n",
      "14   BUY  \n",
      "15   BUY  \n",
      "16   BUY  \n",
      "17   BUY  \n",
      "18   BUY  \n",
      "19   BUY  \n",
      "20   BUY  \n",
      "21   BUY  \n",
      "22   BUY  \n",
      "23   BUY  \n",
      "24   BUY  \n",
      "25   BUY  \n",
      "26   BUY  \n",
      "27   BUY  \n",
      "28   BUY  \n",
      "29   BUY  \n",
      "..   ...  \n",
      "481  BUY  \n",
      "482  BUY  \n",
      "483  BUY  \n",
      "484  BUY  \n",
      "485  BUY  \n",
      "486  BUY  \n",
      "487  BUY  \n",
      "488  BUY  \n",
      "489  BUY  \n",
      "490  BUY  \n",
      "491  BUY  \n",
      "492  BUY  \n",
      "493  BUY  \n",
      "494  BUY  \n",
      "495  BUY  \n",
      "496  BUY  \n",
      "497  BUY  \n",
      "498  BUY  \n",
      "499  BUY  \n",
      "500  BUY  \n",
      "501  BUY  \n",
      "502  BUY  \n",
      "503  BUY  \n",
      "504  BUY  \n",
      "505  BUY  \n",
      "506  BUY  \n",
      "507  BUY  \n",
      "508  BUY  \n",
      "509  BUY  \n",
      "510  BUY  \n",
      "\n",
      "[511 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "Q_learning.episode_review(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not very well.. Can be further improved on more runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank You\n",
    "Do feedback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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