NeuralStockPredictor_Eng.ipynb

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      "name": "NeuralStockPredictor Eng.ipynb",
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        "<a href=\"https://colab.research.google.com/github/tg-bomze/NeuralStockPredictor/blob/master/NeuralStockPredictor_Eng.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# <b><font color=\"red\" size=\"+3\">NeuralStockPredictor\n",
        "\n",
        "---\n",
        "\n",
        "<b><font color=\"red\">P.S. Sorry for my bad English. I translate using Google Translator</font></b>"
      ]
    },
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      "source": [
        "##**The research was conducted by:**\n",
        "GitHub: [@tg-bomze](https://github.com/tg-bomze)\n",
        "\n",
        "Telegram: [@bomze](https://t.me/bomze) \n",
        "\n",
        "Twitter: [@tg_bomze](https://twitter.com/tg_bomze)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJiuUSelZ4F5",
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      },
      "source": [
        "# <b><font color=\"red\" size=\"+2\">Research:\n",
        "\n",
        "Stock Price Prediction using Convolutional and Recurrent Neural Networks on Keras"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Install the all needed libraries {display-mode: \"form\"}\n",
        "%%time\n",
        "!pip install imgaug==0.2.5\n",
        "!pip install scipy==1.2\n",
        "!pip install talos\n",
        "!pip install --upgrade yfinance\n",
        "\n",
        "from IPython.display import clear_output\n",
        "\n",
        "import plotly \n",
        "import plotly.graph_objects as go\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.ticker as mticker\n",
        "import matplotlib.dates as mdates\n",
        "import tensorflow.keras as keras\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import yfinance as yf\n",
        "import datetime as dt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import talos as ta\n",
        "import math\n",
        "import time\n",
        "import sys\n",
        "import os\n",
        "\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import confusion_matrix, classification_report, matthews_corrcoef\n",
        "from sklearn.metrics import mean_squared_error, roc_auc_score, r2_score\n",
        "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
        "from talos.utils.gpu_utils import parallel_gpu_jobs\n",
        "from talos.model.normalizers import lr_normalizer\n",
        "from matplotlib.dates import bytespdate2num\n",
        "from google.colab import files\n",
        "from matplotlib import style\n",
        "from time import time\n",
        "\n",
        "from keras.models import Sequential, Model, load_model\n",
        "from keras.layers.core import Dense, Dropout, Activation, Flatten, Permute, Reshape\n",
        "from keras.callbacks import Callback, ModelCheckpoint, ReduceLROnPlateau, CSVLogger\n",
        "from keras.layers.convolutional import Conv1D, Conv2D, MaxPooling1D, MaxPooling2D\n",
        "from keras.layers import Embedding, Input, SpatialDropout1D, AtrousConvolution1D\n",
        "from keras.layers import GlobalMaxPooling1D, RepeatVector, AveragePooling1D\n",
        "from keras.layers.wrappers import Bidirectional, TimeDistributed\n",
        "from keras.optimizers import RMSprop, Adam, SGD, Nadam, Adamax\n",
        "from keras.utils import plot_model, np_utils, to_categorical\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.recurrent import LSTM, GRU, SimpleRNN\n",
        "from keras.layers.advanced_activations import *\n",
        "from keras.initializers import *\n",
        "from keras.constraints import *\n",
        "from keras import backend as K\n",
        "from keras import regularizers\n",
        "from keras import losses\n",
        "\n",
        "sns.despine()\n",
        "%matplotlib inline\n",
        "style.use('ggplot')\n",
        "clear_output()"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Declare the main variables and load the source data {display-mode: \"form\"}\n",
        "#@markdown The size of the input data (in days).\n",
        "WINDOW = 30   #@param {type:\"slider\", min:10, max:90, step:10}\n",
        "EMB_SIZE = 8  # колич. входных перем.\n",
        "#@markdown The size of the test data.\n",
        "PERCENT = 0.9   #@param {type:\"slider\", min:0, max:1, step:0.05}\n",
        "STEP = 1      # шаг\n",
        "#@markdown Prediction Range (in days).\n",
        "FORECAST = 1   #@param {type:\"slider\", min:1, max:10, step:1}\n",
        "#@markdown What stock price will we predict?\n",
        "TICKER = 'AMZN' #@param [\"GAZP.ME\", \"SBER.ME\", \"ROSN.ME\", \"VTBR.ME\", \"TSLA\", \"GOOG\", \"AMZN\", \"FB\"]\n",
        "#@markdown If in the drop-down list you did not find the promotion you need, then use the manual download of quotes. Data is proposed to be downloaded from https://finance.yahoo.com. Just enter the ticker you need in the search and download the csv file.\n",
        "upload_stock_quotes = False #@param {type:\"boolean\"}\n",
        "#@markdown The checkmark above will prompt you to download this file from your local.\n",
        "\n",
        "if upload_stock_quotes == False:\n",
        "  # --- Загружаем исторические данные тикер ---\n",
        "  tic = yf.Ticker(TICKER)\n",
        "  data_original = tic.history(period=\"max\")\n",
        "  # -------------------------------------------\n",
        "  # заменяем пустые данные нулем\n",
        "  data_original = data_original.fillna(0)\n",
        "  # удаляем те строки, когда данных по торгам небыло\n",
        "  data_original = data_original.loc[data_original['Volume'] > 0]\n",
        "  # удаляем столбцы, по которым небыло данных\n",
        "  del data_original['Dividends']\n",
        "  del data_original['Stock Splits']\n",
        "  # Вытаскиваем Дату из индекса\n",
        "  data_original.reset_index(inplace=True)\n",
        "  # создаем дополнительный датафрейм new_data для расчета результатов\n",
        "  new_data = pd.DataFrame(np.sort(data_original['Date'], axis=0), columns=['Date'])\n",
        "  # устанавливаем Дату как индекс обратно\n",
        "  data_original = data_original.set_index('Date')\n",
        "else:\n",
        "  # --- Загружаем исторические данные тикер ---\n",
        "  uploaded = files.upload()\n",
        "  for fn in uploaded.keys():\n",
        "    print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "        name=fn, length=len(uploaded[fn])))\n",
        "  TICKER = fn.split('.')[0]\n",
        "  data_original = pd.read_csv('{}.csv'.format(TICKER))\n",
        "  # -------------------------------------------\n",
        "  # заменяем пустые данные нулем\n",
        "  data_original.fillna(0)\n",
        "  # удаляем те строки, когда данных по торгам небыло\n",
        "  data_original = data_original.loc[data_original['Volume'] > 0]\n",
        "  # создаем дополнительный датафрейм new_data для расчета результатов\n",
        "  new_data = pd.DataFrame(np.sort(data_original['Date'], axis=0), columns=['Date'])\n",
        "  # устанавливаем Дату как индекс\n",
        "  data_original = data_original.set_index('Date')\n",
        "  # удаляем лишний столбец\n",
        "  del data_original['Adj Close']\n",
        "\n",
        "clear_output()\n",
        "fig = go.Figure(data=[go.Candlestick(x=data_original.index,\n",
        "                open=data_original['Open'],\n",
        "                high=data_original['High'],\n",
        "                low=data_original['Low'],\n",
        "                close=data_original['Close'])])\n",
        "\n",
        "fig.update_layout(xaxis_rangeslider_visible=False)\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Stock Price of {}'.format(TICKER),\n",
        "    yaxis_title='Price',\n",
        "    xaxis_title='Days')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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        "colab_type": "code",
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      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Declare all the functions for calculating additional parameters {display-mode: \"form\"}\n",
        "def simple_func(ishod):\n",
        "  result_res = []\n",
        "  for i in range(0,len(ishod)):\n",
        "    result_res.append(ishod[i])\n",
        "  return pd.DataFrame(result_res)\n",
        "\n",
        "def find_result(ishod):\n",
        "  result_res = []\n",
        "  result_res.append(0)\n",
        "  for i in range(0,len(ishod)-1):\n",
        "    result = 0 if ishod[0][i+1] < ishod[0][i] else 1\n",
        "    result_res.append(result)\n",
        "  return pd.DataFrame(result_res)\n",
        "\n",
        "def shuffle_in_unison(a, b):\n",
        "  assert len(a) == len(b)\n",
        "  shuffled_a = np.empty(a.shape, dtype=a.dtype)\n",
        "  shuffled_b = np.empty(b.shape, dtype=b.dtype)\n",
        "  permutation = np.random.permutation(len(a))\n",
        "  for old_index, new_index in enumerate(permutation):\n",
        "    shuffled_a[new_index] = a[old_index]\n",
        "    shuffled_b[new_index] = b[old_index]\n",
        "  return shuffled_a, shuffled_b\n",
        "\n",
        "def create_Xt_Yt(X, y, percentage=0.9):\n",
        "  p = int(len(X) * percentage)\n",
        "  X_train = X[0:p]\n",
        "  Y_train = y[0:p]\n",
        "  X_train, Y_train = shuffle_in_unison(X_train, Y_train)\n",
        "  X_test = X[p:]\n",
        "  Y_test = y[p:]\n",
        "  return X_train, X_test, Y_train, Y_test\n",
        "\n",
        "# --- Gap calculation function ---\n",
        "def find_gap(ishod):\n",
        "  # разница между текущим открытием и предыдущим закрытием\n",
        "  gap_res = []\n",
        "  gap_res.append(0)\n",
        "  for i in range(0,len(ishod)-1):\n",
        "    gap = abs(ishod[\"Open\"][i+1] - ishod[\"Close\"][i])\n",
        "    gap_res.append(gap)\n",
        "  return pd.DataFrame(gap_res)\n",
        "\n",
        "# --- Calculation Function (High-Low) ---\n",
        "def find_hldif(ishod):\n",
        "  # разница между максимумом и минимумом\n",
        "  hldif_res = []\n",
        "  for i in range(0,len(ishod)):\n",
        "    hldif = (ishod[\"High\"][i] - ishod[\"Low\"][i])\n",
        "    hldif_res.append(hldif)\n",
        "  return pd.DataFrame(hldif_res)\n",
        "\n",
        "# --- The function of calculating the difference of two exponential moving averages ---\n",
        "def find_emad(ishod, fast, slow):\n",
        "  # находим из двух параметров быстрый и медленный\n",
        "  params = [fast,slow]\n",
        "  params = pd.DataFrame(params)\n",
        "  faster = int(params.min()) # период быстрой ЭкспСкользСредн\n",
        "  slower = int(params.max()) # период медленной ЭкспСкользСредн\n",
        "  # считаем медленную EMA\n",
        "  alpha_slow = 2 / (slower + 1)\n",
        "  sma_slow = []\n",
        "  ema_slow = []\n",
        "  for i in range(0,len(ishod)):\n",
        "    smas = 0 if (i < (slower-1)) else ishod[\"Close\"][i-(slower-1):i+1].mean()\n",
        "    sma_slow.append(smas)\n",
        "    if i < (slower-1):\n",
        "      emas = 0\n",
        "    elif i == (slower-1):\n",
        "      emas = ishod[\"Close\"][i-(slower-1):i+1].mean()\n",
        "    else:\n",
        "      emas = (alpha_slow * ishod[\"Close\"][i]) + ((1 - alpha_slow)*ema_slow[i-1])\n",
        "    ema_slow.append(emas)\n",
        "  # считаем быструю EMA\n",
        "  alpha_fast = 2 / (faster + 1)\n",
        "  sma_fast = []\n",
        "  ema_fast = []\n",
        "  for i in range(0,len(ishod)):\n",
        "    smaf = 0 if (i < (slower-1)) else ishod[\"Close\"][i-(faster-1):i+1].mean()\n",
        "    sma_fast.append(smaf)\n",
        "    if i < (slower-1):\n",
        "      emaf = 0\n",
        "    elif i == (slower-1):\n",
        "      emaf = ishod[\"Close\"][i-(faster-1):i+1].mean()\n",
        "    else:\n",
        "      emaf = (alpha_fast * ishod[\"Close\"][i]) + ((1 - alpha_fast)*ema_fast[i-1])\n",
        "    ema_fast.append(emaf)\n",
        "  emad_res = pd.DataFrame(ema_fast) - pd.DataFrame(ema_slow)\n",
        "  return emad_res\n",
        "\n",
        "# --- Stochastic Oscillator Calculation Function ---\n",
        "def find_stoch(ishod, k, smooth):\n",
        "  # находим из двух параметров k и период сглаживания\n",
        "  params = [k,smooth]\n",
        "  params = pd.DataFrame(params)\n",
        "  smooth_per = int(params.min()) # период сглаживания\n",
        "  k_per = int(params.max()) # период k\n",
        "  otstup = (k_per + smooth_per)-1 # необходимый отступ дней для анализа\n",
        "  # считаем показатель k\n",
        "  max_high = []\n",
        "  min_low = []\n",
        "  k_res = []\n",
        "  for i in range(0,len(ishod)):\n",
        "    high = 0 if (i < (k_per-1)) else ishod[\"High\"][i-(k_per-1):i+1].max()\n",
        "    low = 0 if (i < (k_per-1)) else ishod[\"Low\"][i-(k_per-1):i+1].min()\n",
        "    k_pokaz = 0 if (i < (k_per-1)) else (ishod[\"Close\"][i] - low) / (high - low)\n",
        "    max_high.append(high)\n",
        "    min_low.append(low)\n",
        "    k_res.append(k_pokaz) \n",
        "  # сглаживаем показатель k\n",
        "  stoch_k = pd.DataFrame(k_res)\n",
        "  stoch_res = []\n",
        "  for i in range(0,len(ishod)):\n",
        "    smooth_k = 0 if (i < (otstup-1)) else stoch_k[i-(smooth_per-1):i+1].mean()\n",
        "    stoch_res.append(float(smooth_k))\n",
        "  return pd.DataFrame(stoch_res)\n",
        "\n",
        "# --- Volatility calculation function ---\n",
        "def find_volat(ishod, period):\n",
        "  # значение волатильности за определенный период\n",
        "  volat_res = []\n",
        "  for i in range(0,len(ishod)):\n",
        "    volat = 0 if (i < (period-1)) else ishod[\"Close\"][i-(period-1):i+1].std()\n",
        "    volat_res.append(volat) \n",
        "  return pd.DataFrame(volat_res)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOKm_dlT5WQb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "eb5d1a84-17e0-4f41-ebe9-c7073a514bb7"
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Calculate additional parameters and normalize the data {display-mode: \"form\"}\n",
        "\n",
        "#@markdown Period of Fast EMA\n",
        "fast_emad = 8 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "#@markdown Period of Slow EMA\n",
        "slow_emad = 18 #@param {type:\"slider\", min:1, max:32, step:1}\n",
        "#@markdown K period for the stochastic oscillator\n",
        "k_stoch = 5 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "#@markdown Smoothing Period for Stochastic Oscillator\n",
        "smooth_stoch = 3 #@param {type:\"slider\", min:1, max:8, step:1}\n",
        "#@markdown Volatility Calculation Period\n",
        "period_volat = 6 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "#@markdown Autocorrelation period\n",
        "window_autocorr = 30 #@param {type:\"slider\", min:10, max:90, step:5}\n",
        "#@markdown Autocorrelation lag\n",
        "lag_autocorr = 4 #@param {type:\"slider\", min:1, max:10, step:1}\n",
        "\n",
        "data_original['log_ret'] = np.log(data_original['Close']).diff()\n",
        "data_original['Autocorr'] = data_original['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, \n",
        "                                                             center=False).apply(lambda x: x.autocorr(lag=lag_autocorr), raw=False)\n",
        "# заполняем расчетную таблицу\n",
        "new_data['AUTCOR'] = simple_func(data_original['Autocorr'])\n",
        "new_data['GAP'] = find_gap(data_original)\n",
        "new_data['HLDIF'] = find_hldif(data_original)\n",
        "new_data['CLOSE'] = simple_func(data_original['Close'])\n",
        "new_data['VOLUME'] = simple_func(data_original['Volume'])\n",
        "new_data['EMAD'] = find_emad(data_original, fast_emad, slow_emad)\n",
        "new_data['STOCH'] = find_stoch(data_original, k_stoch, smooth_stoch)\n",
        "new_data['VOLAT'] = find_volat(data_original, period_volat)\n",
        "del data_original[\"log_ret\"]\n",
        "del data_original[\"Autocorr\"]\n",
        "\n",
        "# определяем количество удаляемых строк\n",
        "lishnee = [fast_emad, slow_emad, k_stoch, smooth_stoch, period_volat, k_stoch+smooth_stoch-1, window_autocorr+1, lag_autocorr]\n",
        "lishnee = pd.DataFrame(lishnee)\n",
        "row_del = int(lishnee.max())\n",
        "\n",
        "# удаляем пустые строки\n",
        "new_data = new_data[row_del-1:]\n",
        "new_data.reset_index(inplace=True)\n",
        "del new_data[\"index\"]\n",
        "# заменяем пустые данные нулем\n",
        "new_data = new_data.fillna(0)\n",
        "\n",
        "# --- Нормализация данных ---\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "new_data['GAP'] = min_max_scaler.fit_transform(new_data.GAP.values.reshape(-1,1))\n",
        "new_data['HLDIF'] = min_max_scaler.fit_transform(new_data.HLDIF.values.reshape(-1,1))\n",
        "new_data['CLOSE'] = min_max_scaler.fit_transform(new_data.CLOSE.values.reshape(-1,1))\n",
        "new_data['VOLUME'] = min_max_scaler.fit_transform(new_data.VOLUME.values.reshape(-1,1))\n",
        "new_data['EMAD'] = min_max_scaler.fit_transform(new_data.EMAD.values.reshape(-1,1))\n",
        "new_data['STOCH'] = min_max_scaler.fit_transform(new_data.STOCH.values.reshape(-1,1))\n",
        "new_data['VOLAT'] = min_max_scaler.fit_transform(new_data.VOLAT.values.reshape(-1,1))\n",
        "new_data['AUTCOR'] = min_max_scaler.fit_transform(new_data.AUTCOR.values.reshape(-1,1))\n",
        "\n",
        "gapn = new_data.loc[:, 'GAP'].tolist()\n",
        "hldifn = new_data.loc[:, 'HLDIF'].tolist()\n",
        "closen = new_data.loc[:, 'CLOSE'].tolist()\n",
        "volumen = new_data.loc[:, 'VOLUME'].tolist()\n",
        "emadn = new_data.loc[:, 'EMAD'].tolist()\n",
        "stochn = new_data.loc[:, 'STOCH'].tolist()\n",
        "volatn = new_data.loc[:, 'VOLAT'].tolist()\n",
        "autocorn = new_data.loc[:, 'AUTCOR'].tolist()\n",
        "\n",
        "gapn, hldifn, closen, volumen = gapn[WINDOW:], hldifn[WINDOW:], closen[WINDOW:], volumen[WINDOW:]\n",
        "emadn, stochn, volatn, autocorn = emadn[WINDOW:], stochn[WINDOW:], volatn[WINDOW:], autocorn[WINDOW:]\n",
        "\n",
        "# выводим сводные статистичекие данные по расчетной таблице new_data\n",
        "new_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AUTCOR</th>\n",
              "      <th>GAP</th>\n",
              "      <th>HLDIF</th>\n",
              "      <th>CLOSE</th>\n",
              "      <th>VOLUME</th>\n",
              "      <th>EMAD</th>\n",
              "      <th>STOCH</th>\n",
              "      <th>VOLAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5704.000000</td>\n",
              "      <td>5704.000000</td>\n",
              "      <td>5704.000000</td>\n",
              "      <td>5704.000000</td>\n",
              "      <td>5704.000000</td>\n",
              "      <td>5704.000000</td>\n",
              "      <td>5704.000000</td>\n",
              "      <td>5704.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.478425</td>\n",
              "      <td>0.013625</td>\n",
              "      <td>0.043911</td>\n",
              "      <td>0.154128</td>\n",
              "      <td>0.068095</td>\n",
              "      <td>0.530957</td>\n",
              "      <td>0.532046</td>\n",
              "      <td>0.054355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.145529</td>\n",
              "      <td>0.038556</td>\n",
              "      <td>0.072862</td>\n",
              "      <td>0.237694</td>\n",
              "      <td>0.070291</td>\n",
              "      <td>0.062325</td>\n",
              "      <td>0.257906</td>\n",
              "      <td>0.094875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.378576</td>\n",
              "      <td>0.000996</td>\n",
              "      <td>0.007264</td>\n",
              "      <td>0.016778</td>\n",
              "      <td>0.030833</td>\n",
              "      <td>0.514853</td>\n",
              "      <td>0.310661</td>\n",
              "      <td>0.008229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.470592</td>\n",
              "      <td>0.003208</td>\n",
              "      <td>0.020327</td>\n",
              "      <td>0.036982</td>\n",
              "      <td>0.050173</td>\n",
              "      <td>0.522381</td>\n",
              "      <td>0.536679</td>\n",
              "      <td>0.022169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.571984</td>\n",
              "      <td>0.010233</td>\n",
              "      <td>0.044403</td>\n",
              "      <td>0.152602</td>\n",
              "      <td>0.078071</td>\n",
              "      <td>0.537296</td>\n",
              "      <td>0.760805</td>\n",
              "      <td>0.050817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AUTCOR          GAP  ...        STOCH        VOLAT\n",
              "count  5704.000000  5704.000000  ...  5704.000000  5704.000000\n",
              "mean      0.478425     0.013625  ...     0.532046     0.054355\n",
              "std       0.145529     0.038556  ...     0.257906     0.094875\n",
              "min       0.000000     0.000000  ...     0.000000     0.000000\n",
              "25%       0.378576     0.000996  ...     0.310661     0.008229\n",
              "50%       0.470592     0.003208  ...     0.536679     0.022169\n",
              "75%       0.571984     0.010233  ...     0.760805     0.050817\n",
              "max       1.000000     1.000000  ...     1.000000     1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyepEuSq6R8k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Pre-preparation of data for training a neural network {display-mode: \"form\"}\n",
        "\n",
        "X, Y = [], []\n",
        "for i in range(0, len(new_data), STEP): \n",
        "    try:\n",
        "        g = gapn[i:i+WINDOW]\n",
        "        h = hldifn[i:i+WINDOW]\n",
        "        c = closen[i:i+WINDOW]\n",
        "        v = volumen[i:i+WINDOW]\n",
        "        e = emadn[i:i+WINDOW]\n",
        "        s = stochn[i:i+WINDOW]\n",
        "        b = volatn[i:i+WINDOW]\n",
        "        a = autocorn[i:i+WINDOW]\n",
        "\n",
        "        y_i = closen[i+WINDOW+FORECAST]  \n",
        "        x_i = np.column_stack((g, h, c, v, e, s, b, a))\n",
        "\n",
        "    except Exception as e:\n",
        "        break\n",
        "\n",
        "    X.append(x_i)\n",
        "    Y.append(y_i)\n",
        "\n",
        "X, Y = np.array(X), np.array(Y)\n",
        "X_train, X_test, Y_train, Y_test = create_Xt_Yt(X, Y, PERCENT)\n",
        "\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], EMB_SIZE))\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], EMB_SIZE))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5C-Yn8YszMUG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "53d989fb-2106-4e84-e3db-275ff488a808"
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Quick analysis (prediction and backtesting) {display-mode: \"form\"}\n",
        "\n",
        "#@markdown The amount of initial capital.\n",
        "CAPITAL = 100000 #@param {type:\"integer\"}\n",
        "#@markdown The share of capital to which we open the order.\n",
        "CHAST = 0.05 #@param {type:\"slider\", min:0.05, max:1, step:0.05}\n",
        "#@markdown The size of the commission for opening/closing an order (%).\n",
        "KOMISSIYA = 0.05 #@param {type:\"slider\", min:0, max:1, step:0.05}\n",
        "KOMISSIYA = KOMISSIYA / 100\n",
        "\n",
        "#@markdown Choose a pre-trained model for your stock\n",
        "MODEL = 'AMZN' #@param [\"GAZP\", \"SBER\", \"ROSN\", \"VTBR\", \"TSLA\", \"GOOG\", \"AMZN\", \"FB\"]\n",
        "#@markdown If you did not find the pre-trained model you need in the drop-down list, then you can train it yourself.\n",
        "new_training = False #@param {type:\"boolean\"}\n",
        "#@markdown If the quality of the trained model is low, it is therefore proposed to skip this block and proceed to the next.\n",
        "\n",
        "if new_training == True:\n",
        "  model = Sequential()\n",
        "  model.add(Conv1D(input_shape = (WINDOW, EMB_SIZE),filters=16,kernel_size=4,padding='same'))\n",
        "  model.add(MaxPooling1D(pool_size=3))\n",
        "  model.add(LeakyReLU())\n",
        "  model.add(Conv1D(filters=32,kernel_size=4,padding='same'))\n",
        "  model.add(MaxPooling1D(pool_size=3))\n",
        "  model.add(LeakyReLU())\n",
        "  model.add(LSTM(32,return_sequences=True))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(16))\n",
        "  model.add(LeakyReLU())\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(1))\n",
        "  model.add(Activation('linear'))\n",
        "  reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=25, min_lr=0.000001, verbose=1)\n",
        "  checkpointer = ModelCheckpoint(filepath=\"test.hdf5\", verbose=1, save_best_only=True)\n",
        "  model.compile(optimizer=Adam(lr=0.002), loss='mean_absolute_error')\n",
        "  history = model.fit(X_train, Y_train, \n",
        "                epochs = 100, \n",
        "                batch_size = 256, \n",
        "                verbose=1, \n",
        "                validation_data=(X_test, Y_test),\n",
        "                callbacks=[reduce_lr, checkpointer],\n",
        "                shuffle=True)\n",
        "else:\n",
        "  if MODEL == 'GAZP':\n",
        "    !gdown https://drive.google.com/uc?id=1LEFpCsKPK_LLvZDcs4exZ0hhQbUYgo7r\n",
        "  elif MODEL == 'SBER':\n",
        "    !gdown https://drive.google.com/uc?id=1V1LhIVUGLXUZ66ajbEpqwZvBiuCTQjba\n",
        "  elif MODEL == 'ROSN':\n",
        "    !gdown https://drive.google.com/uc?id=1XM6CcZVBstRTt9PEGnqaLmGw_87QNKOE\n",
        "  elif MODEL == 'VTBR':\n",
        "    !gdown https://drive.google.com/uc?id=19k4dOmq4gyQPbSwCMcX6d0lHSN3jXSYH\n",
        "  elif MODEL == 'TSLA':\n",
        "    !gdown https://drive.google.com/uc?id=1r5uYwTEiljCg4EBICk3kOFz2xI3FRQWK\n",
        "  elif MODEL == 'GOOG':\n",
        "    !gdown https://drive.google.com/uc?id=1NrfxLgei-lPCvNxJ6FRRPTlgj8W1l5W3\n",
        "  elif MODEL == 'AMZN':\n",
        "    !gdown https://drive.google.com/uc?id=1BO8Nv2LBnyf8RDwSkzDcV-dWIpd21D3i\n",
        "  elif MODEL == 'FB':\n",
        "    !gdown https://drive.google.com/uc?id=1ewHlSXJEZZkFFVaa6hxGIdVBV6D2ZXno\n",
        "  !unzip -u model.zip\n",
        "  model = load_model(\"model.h5\")\n",
        "  model.load_weights(\"weights.h5\")\n",
        "  !rm model.zip\n",
        "  !rm weights.h5\n",
        "  !rm model.h5\n",
        "\n",
        "pred = model.predict(X_test)\n",
        "predicted = pred\n",
        "original = Y_test\n",
        "\n",
        "data_original.reset_index(inplace=True) # Вытаскиваем Дату из индекса\n",
        "test_dates = pd.DataFrame(np.array(data_original[\"Date\"][-len(Y_test):]))\n",
        "test_price = pd.DataFrame(np.array(data_original[\"Close\"][-len(Y_test):]))\n",
        "data_original = data_original.set_index('Date') # устанавливаем Дату как индекс обратно\n",
        "test_signal = pd.DataFrame(np.array(find_result(pd.DataFrame(pred))))\n",
        "backtest = pd.concat([test_dates, test_price, test_signal], axis=1)\n",
        "backtest.columns = (['Date', 'Price', 'Signal'])\n",
        "\n",
        "balans = []\n",
        "balans.append(CAPITAL)\n",
        "capital_now = CAPITAL\n",
        "signal_now = backtest['Signal'][0]\n",
        "price_now = backtest['Price'][0]\n",
        "change_now = 0.0\n",
        "\n",
        "for i in range(1,len(backtest)): # перебираем все сигналы\n",
        "  if signal_now == backtest['Signal'][i]: # если текущий сигнал совпадает с изначальным, тогда\n",
        "    balans.append(capital_now) # ничего не меняем и переписываем текущий капитал\n",
        "  else: # если же сигнал поменялся, то\n",
        "    change_now = (backtest['Price'][i] - price_now) / price_now # считаем изменение цены\n",
        "    if signal_now > 0.5: # если изначально был сигнал на рост\n",
        "      #-------------------------------\n",
        "      total = capital_now * CHAST # считаем сумму для открытия ордера\n",
        "      capital_now = capital_now - total # при открытии отнимаем эту сумму от капитала\n",
        "      komis_open = total * KOMISSIYA # считаем комиссию за открытие сделки\n",
        "      total = total + (total * change_now) # расчитываем сумму с учетом изменения цены актива\n",
        "      komis_close = total * KOMISSIYA # считаем комиссию за закрытие сделки\n",
        "      total = total - komis_open - komis_close # считаем сумму за вычетом комиссий\n",
        "      capital_now = capital_now + total # при закрытии ордера прибавляем сумму от капитала\n",
        "      balans.append(capital_now) # и переписываем текущий капитал\n",
        "      #-------------------------------\n",
        "      signal_now = backtest['Signal'][i] # обновляем нынешний сигнал\n",
        "      price_now = backtest['Price'][i] # обновляем нынешнюю цену      \n",
        "    else: # если сигнал был на падение\n",
        "      #-------------------------------\n",
        "      total = capital_now * CHAST # считаем сумму для открытия ордера\n",
        "      capital_now = capital_now - total # при открытии отнимаем эту сумму от капитала\n",
        "      komis_open = total * KOMISSIYA # считаем комиссию за открытие сделки\n",
        "      total = total - (total * change_now) # расчитываем сумму с учетом изменения цены актива\n",
        "      komis_close = total * KOMISSIYA # считаем комиссию за закрытие сделки\n",
        "      total = total - komis_open - komis_close # считаем сумму за вычетом комиссий\n",
        "      capital_now = capital_now + total # при закрытии ордера прибавляем сумму от капитала\n",
        "      balans.append(capital_now) # и переписываем текущий капитал\n",
        "      #-------------------------------\n",
        "      signal_now = backtest['Signal'][i] # обновляем нынешний сигнал\n",
        "      price_now = backtest['Price'][i] # обновляем нынешнюю цену\n",
        "\n",
        "backtest['Capital'] = pd.DataFrame(balans)\n",
        "from IPython.display import clear_output\n",
        "clear_output()\n",
        "\n",
        "orig = pd.DataFrame(original)\n",
        "perd = pd.DataFrame(predicted)\n",
        "capi = pd.DataFrame(np.array(backtest['Capital']))\n",
        "itog = pd.concat([orig, perd, capi], axis=1)\n",
        "itog.columns = (['orig', 'perd', 'capi'])\n",
        "itog['orig'] = min_max_scaler.fit_transform(itog.orig.values.reshape(-1,1))\n",
        "itog['perd'] = min_max_scaler.fit_transform(itog.perd.values.reshape(-1,1))\n",
        "itog['capi'] = min_max_scaler.fit_transform(itog.capi.values.reshape(-1,1))\n",
        "print(\"Portfolio Change:\", round((itog['capi'][len(itog['capi'])-1] - itog['capi'][0])*100, 1),\"% in\", len(itog['capi']), \"days\")\n",
        "plt.plot(itog['orig'], color = 'black', label = 'Original data')\n",
        "plt.plot(itog['perd'], color = 'blue', label = 'Predicted data')\n",
        "plt.plot(itog['capi'], color = 'red', label = 'Capital')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Portfolio Change: 75.5 % in 565 days\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H8vImvN6uW8iZNTUIVmub1zRTFKGujsqiIrTGbf7SUnxtfK47vi/HkiWk3X67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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS0IIikYgxXd",
        "colab_type": "text"
      },
      "source": [
        "## <b><font color=\"gree\" size=\"+2\">Main analysis with fine tuning (training, prediction and backtesting)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCp4ufg0PU5o",
        "colab_type": "text"
      },
      "source": [
        "Below in the sections \"***Build the network architecture***\" and \"***Train the neural network***\" it will be proposed to manually configure the entire process of training the neural network. In addition, it will also be possible to download the architecture and network weights, which can be taken [**HERE**](https://drive.google.com/open?id=1ln7tEV5YQf-imL7DzoyLIU2BfoP1PqIF)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBC7b6ivrjdY",
        "colab_type": "text"
      },
      "source": [
        "### <b><font color=\"black\" size=\"+1\">Build the network architecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tued3U5Pg5BZ",
        "colab_type": "text"
      },
      "source": [
        "If you want to use the created architecture, you can upload the **model.h5**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuCUHr7xgmXr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# --- Upload the file to the server ---\n",
        "uploaded = files.upload()\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "# --------------------------------\n",
        "\n",
        "model = load_model(\"model.h5\")\n",
        "model.summary()\n",
        "plot_model(model, to_file='model.png', show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOZmOCV6hB6x",
        "colab_type": "text"
      },
      "source": [
        "If not, then build it yourself"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxKG1mNNCclb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''# --- New version ---\n",
        "model= Sequential()\n",
        "model.add(Conv1D(input_shape = (WINDOW, EMB_SIZE),filters=16,kernel_size=4,padding='same'))\n",
        "model.add(MaxPooling1D(pool_size=3))\n",
        "model.add(LeakyReLU())\n",
        "model.add(Conv1D(filters=32,kernel_size=4,padding='same'))\n",
        "model.add(MaxPooling1D(pool_size=3))\n",
        "model.add(LeakyReLU())\n",
        "model.add(LSTM(64,return_sequences=True))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(32))\n",
        "model.add(LeakyReLU())\n",
        "model.add(Dropout(0.7))\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('linear'))\n",
        "# --------------------'''\n",
        "\n",
        "# --- Old version (stable) ---\n",
        "model = Sequential()\n",
        "model.add(Conv1D(input_shape = (WINDOW, EMB_SIZE),filters=16,kernel_size=4,padding='same'))\n",
        "model.add(MaxPooling1D(2))\n",
        "model.add(LeakyReLU())\n",
        "model.add(Conv1D(filters=64,kernel_size=4,padding='same'))\n",
        "model.add(MaxPooling1D(2))\n",
        "model.add(LeakyReLU())\n",
        "model.add(Flatten())\n",
        "model.add(Dense(32))\n",
        "model.add(LeakyReLU())\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('linear'))\n",
        "# ---------------------\n",
        "\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=25, min_lr=0.000001, verbose=1)\n",
        "checkpointer = ModelCheckpoint(filepath=\"test.hdf5\", verbose=1, save_best_only=True)\n",
        "\n",
        "model.compile(optimizer=Adam(lr=0.001), loss='mean_squared_error') # mean_absolute_error, mean_squared_error\n",
        "\n",
        "model.summary()\n",
        "plot_model(model, to_file='model.png', show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38PHCymDHijB",
        "colab_type": "text"
      },
      "source": [
        "### <b><font color=\"black\" size=\"+1\">Train the neural network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSsdHxN6T5MS",
        "colab_type": "text"
      },
      "source": [
        "If you want to use an already trained network, you can upload the file file with **weights.h5**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlzw_G65T5VQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# --- Upload the file to the server ---\n",
        "uploaded = files.upload()\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "# --------------------------------\n",
        "\n",
        "model.load_weights(\"weights.h5\")\n",
        "pred = model.predict(X_test)\n",
        "predicted = pred\n",
        "original = Y_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQZAJ6N6Ue4b",
        "colab_type": "text"
      },
      "source": [
        "If not, then train yourself"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmKKS-dpCjFa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = time()\n",
        "history = model.fit(X_train, Y_train, \n",
        "              epochs = 100, \n",
        "              batch_size = 256, \n",
        "              verbose=1, \n",
        "              validation_data=(X_test, Y_test),\n",
        "              callbacks=[reduce_lr, checkpointer],\n",
        "              shuffle=True)\n",
        "finish = time()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5W4232TkJHj2",
        "colab_type": "text"
      },
      "source": [
        "Save prediction results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Bf81auVjiMs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(\"Seconds passed:\", finish - start)\n",
        "pred = model.predict(X_test)\n",
        "predicted = pred\n",
        "original = Y_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrT5tdmlN9w6",
        "colab_type": "text"
      },
      "source": [
        "Result visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnQi_v9bN7tA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "4f84cc73-0e02-4c6f-b0d2-3e192bff0e29"
      },
      "source": [
        "plt.title('Actual and predicted')\n",
        "plt.plot(original, color = 'black', label = 'Original data')\n",
        "plt.plot(predicted, color = 'blue', label = 'Predicted data')\n",
        "plt.xlabel('Days')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K2u0dgiIaCgoKlVJSUsIff/zBpk2bALjrrrsqtAkICAAsabeKaNwZKO4pBQWFSsnOzgYs\ns8A1Gg3t27ev0EZJu73zUERDQUGhUrKysgDw8PCgS5cuODo6Vmjj7+9Pw4YN2bdvX12bp1BPKKKh\noKBQKdY1br799luWL19eaRtJkujVqxcxMTEIIerSvGsiPz+fKVOmkJmZWd+m3HIooqGgoFAp1pFG\n06ZNcXZ2rrJdWFgYmZmZpKSk1JVp18yGDRtYuXIlK1asqG9TbjkU0VBQUKgU6/rf3t7e1bYLCwsD\nYNeuXbW+lhCCP//8s84q5m7evBmA33//vU6udzuhiIaCgkKlZGVl4eXlhVarrbZdQEAAfn5+1yQa\nkZGRPPnkk3Km1o1izZo1zJ07l507d+Lh4UFcXBynT5++ode83VBEQ0FBoVJycnLw8fG5ajtJkujY\nseM1Lci0Z88em/9vBEII3n//faZOnYrBYGDGjBkAVcZrFCqnzuZpHDx4kCVLlmA2mxk0aBAPPvig\nzf7s7Gy+/PJLLl68iKurK5MnT77qsFhBQeHGkZWVZZdogGW0ERUVhdlsRqWqeV90//79ANctC8tg\nMODg4IAkSfK2Y8eOkZ2dTd++fTEajTz88MNER0fzxRdf4O3tTXh4+HW59u1OnYw0zGYzixcvZtq0\nacydO5fo6GjOnTtn02bZsmX07duX//znPzzyyCP88MMPdWGaQj3w4osvsm7duvo2Q+EqZGdn2y0a\nLVu2xGAwkJ6ebtd533//fQoLCwEwmUzExsaiUqk4cuQIJSUl12R3VlYWHTp0YNy4ccTExMjXiYqK\nAizFF1etWoVWq2Xu3LkMGzaMjz76qMpMKiEEr7/+Ov/617+uya7bhToRjeTkZPz8/PD19UWj0RAW\nFsbevXtt2pw7d44OHToA0L59eyXv+zYlIyODX3/9lfXr19e3KQrVIIQgOzubhg0b2tX+8pnhVZGa\nmkp+fj4///wzCxcuZNasWQAkJiZSVFTE8OHDKS8v58iRI9dke2xsLCUlJURFRfHoo49yzz33yK8D\nAgJo1qyZ3Faj0fDOO+9gNBpZvHhxpef77rvvWLFiBUuWLKnQ2b0TqRP3VF5eno2rydvbm+PHj9u0\nad68OXv27GH48OHs2bOHkpISCgoKcHNzs2kXERFBREQEALNnz0av19fKJo1GU+tjbwfq6/6jo6MB\ni6ugPt//O/nzt+feL168iMFgoHnz5na9T9bKt1lZWVW279+/Px07dqS0tBSApUuXYjQaSUtLA+Ct\nt95iw4YNJCQkMHz48Jrckg0pKSmoVCoSExOJiIggPDychQsXsnv3bp544okK96/X6xk1ahTLli1j\n5syZNs+cgoICZs2aRa9evdi9ezerV6/mgw8+qLVtNwMazbU99m+a2lPjxo3jm2++Yfv27bRt2xYv\nL69KfaODBw9m8ODB8mvrBKSaotfra33s7UB93X9kZCRg6ZGePn0aFxeXOrcB7uzP3557t865cHFx\nset9cnBwwNHRkSNHjlTavqysjJSUFM6cOYNOp2PUqFGUlZWxbds2VCoVo0aNkrOwDh48eE2fze7d\nuwkKCsLJyYkRI0bw3Xff8Z///AdXV1dGjx6N0WiscP4nn3yS1atXs2zZMkaPHi1vj4iIoLi4mFde\neYXvvvuO//3vf3Tq1ImBAwfW2r76Rq/Xo9Ppan18nYiGl5cXubm58uvc3Fy8vLwqtHnjjTcASxBr\n9+7d9fZAUbhxHDhwQF6X4dixY4SGhta3SQqVYH2o2hvTsC7IVFUNqvT0dMxmM6WlpZSWltK/f3+b\nEutWmjVrxtmzZ2tvOBAXF0efPn3k12+99RZTpkzh448/pm3btpUe07VrV1q0aMHq1attRCMyMhJH\nR0e6deuGv78/zz77LOPGjePXX3+1WVfkTqJOYhqBgYGkp6eTlZWF0WgkJiaGrl272rS5ePGiPLFn\n7dq1DBgwoC5MU6hDzGYzhw4dol+/fgAcPXq0ni1SqArrbHB7RQOodhU/qxBYs5muLLFupWnTppw5\nc6YmpgKWUid79+4lKyuLzMxMOnbsKO/r1q0bUVFR9OrVq8rjJUni4YcfJiYmxmZRqaioKHr06IGj\noyOtWrVizZo1wPXL8roVqRPRUKvVPP3008yaNYvXXnuNXr160bRpU1auXCm/+QkJCbz66qu88sor\n5OfnM2rUqLowTaEOSUlJobCwkBEjRuDi4qKIxk2MtcKtvYFwsGRQnT59GoPBUGGfNYD85JNP0qdP\nHxo3blzpOZo1a0Z6ejplZWV2X3fTpk1Mnz6dMWPG8P777wPISTU14ZFHHkGn0zFkyBDWrFlDeno6\nSUlJ9O3bV27j7e2Nt7c3ycnJNT7/7UKdxTRCQ0MruCLGjBkj/92zZ88qex8KtwdWkejQoQPBwcEk\nJCTUs0UKVZGVlYVarcbT09PuY+666y6MRiNHjhyhW7duNvvOnj2LWq3mgw8+qDYQ26xZM4QQpKam\nyhlZlWEymRBCYDAYmD59Om3atMHZ2Zmff/6ZFi1a0KlTJ7vtvvzaf/zxB6+//jrvvPMODzzwAEAF\nr0fr1q2vaSLjrc5NEwhXuP1JTk5GkiQCAwPp1q0bixcvJjc3l6VLl5KSksKoUaNu6QDj7UROTg56\nvb5GE/WsLud9+/ZVKhr+/v5XzdyxpsOeOXOmWtGYMGECFy5coEOHDmRkZLBw4UK6dOlCUVERrq6u\ndtt8Ja1bt+aTTz5h8ODBclC8TZs2Nm1atWrFr7/+ihDCZvLgnYJSRkShzjh+/DhNmzbFycmJ+++/\nn/Lycl566SX+85//8Pvvv/Ppp5/Wt4kKf1OT2eBWfHx8aNGiRYU5WGARDXtW9rO2qS6uYZ1zERsb\ny9KlSxk3bhxdu3ZFkqRrEgwrwcHBPP744/j4+PDOO+9U2N+6dWvy8/NlF96dhiIaCnXG8ePHadWq\nFWBxZTRp0oSdO3fSoUMHwsPDOXToEPn5+fVspQJg18Q+IeDnn50YPlzP1q0OgCXovG/fvgpra5w9\ne5YmTZpc9bp+fn5otVoiIyP59NNPMZlMgCWj0vqQ3rdvH+Xl5QwZMoROnToxderU2txitXz00UdE\nR0dXWsooKCgI4I51USmioVAnmEwmTpw4If/gJEni/vvvB2Dy5Mn07dsXs9l8TZVSFa4f9pQQiYx0\nYPJkT+LitLz3XgOMRoto5ObmsmrVKjnucPLkSTIyMuwaaajVapo0acLvv//OnDlz2Lt3LwUFBTzw\nwAMMHDiQoqIiYmJiUKvVzJ8/n40bN9KgQYPrddsyKpWqypR/63f4ygnKdwpKTEOhTjhz5gylpaXy\nDw7ghRdewN/fn+HDh2M0GnFycmLnzp3ce++99WipgtlstqvC7dGjlsfHJ59cYMoUT37+2YkhQ4bw\nxRdf8Nprr5GcnIy/v7/s4mnevLld12/RogVnz55FkiQ2bdrEggULSEhIwGw289NPPxETE0OnTp2u\niyuqNvj6+uLq6nrHrouuiIZCnWDtlV0uGg0bNuTZZ58FQKfT0bNnT7Zv337HBhhvBoQQXLhwgfLy\n8quKRlqaGjc3M6NHl7BokSvff+/C6NE+REZGEh4eztKlS/H09KRDhw689NJLDB061C4bpk2bRl5e\nHgsXLmTZsmWUlJQwc+ZM1q1bx5w5c7h48SIvvvji9bjdWiFJEr6+vvJcljsNxT2lUCdYS19bYxqX\nk5WlwmyGESNGcPLkSXbs2FHX5in8zVNPPcW4ceOAq0/sS01V07ixCUmC/v1LOXxYi8FgcTGFh4dT\nUFDAmTNn+Mc//sEDDzyAo6OjXTa0a9eOPn36MGTIEEpKSggICGD8+PFMnjyZCxcuMGLEiHovY+7j\n43PHlqFRREPhhhMfH8///vc/7rvvPjw8PGz2nT6tpnt3XzZtcuShhx7Cz8+PBQsW1JOldzZ5eXls\n376dgwcPAvaJRqNGlkB1t25llJVJHDliqWkUGhpKSEgIfn5+DBs2rFb2DBs2jEaNGvHee++h1WoZ\nMmQIx44d4/PPP6/R/JEbgV6vV0YaCgo3itmzZ9OgQQNmz55dYd/WrY6Ul0skJWnQ6XQ8//zz7Nq1\n644NMtYnkZGRNmt0Xy17yjrSAItoAOzZYxENSZL4+uuv+fnnn6+6XGxV+Pj4sHfvXpsCpTdLPbqG\nDRsqIw0FhRtFWloa3bt3r1CkEuDPPy2pmhkZagC50JwyW7zu2bZtG56envIEu+pGGsXFEnl5l0TD\n29tMy5ZGdu/WkZdniUf5+fnZHfy+1dDr9XL5+DsNRTQUbjj5+fm4u7tX2G4wQEyMpWeakWH5KrZs\n2RLgjs1MqS/MZjPbt29nwIABPP744/j5+VX6mVlJS7OIvFU0ALp3L2XrVkc6dvRn3Tr74he3KlZB\nvbx6952CIhoKN5yqRGPPHgcMBhXOzmbS0y0PIScnJ3x9e5KcnFLXZt7RpKenk5ubS7du3Zg0aRLR\n0dHVZrClplYUjRdeKOKllwro3LmMN9/04PRp9Q232x7Ky2HzZgd27tRRgzqI1XLkSBjQ9Y6cFa6I\nhsINpby8nOLi4kpFIyZGh0YjuOceg+yeiozUkZm5i4iIf8qjD4Ubj3V97EaNGqFSqa6a6VSZaLRu\nbWT69AIWLTqP2Qxfflk/8yiuZMcOByZO9GbMGD1Ll157TCQ3V8WyZb2AH0lLU0YaCgrXlYKCAoBK\nZ+3u26ejQ4dyWrY0kZOjorwc4uIs7qrCwruYObNq94jC9cUqGn5+fna1T01Vo1IJfH1NFfY1aWIi\nLKyM6GiH62pjbcnMvDTiOXfu2kc/27c7IIQEtGLt2tszZlMdimgo3FCstaSuHGmUlcGBA1q6di3D\n39+EEBJZWWpOnlTj4lIEzGP9eieSk28OF8ftTkZGBmCfaBQVSaxf70hAgJGqEqN69y7lxAkNaWl1\n+4g5f17ixx+dubz01fnzFhsaNjSRm2v5Oy1NxYABPiQn13x+87ZtDnh7G4GN7NgRwmUJZ3cEimgo\n3FAuXrwIVBSNuDgtBoOKbt3K8POz9FbT01WcPKmhceNiYC5arYmFC28OF8ftTkZGBhqNptIMtyuZ\nPr0BJ09q+PDDqotL9u5dCkBMTN2ONpYvd+GNNzw4e/ZSZ+P8eRWOjoImTSwjWoDt2x1JStISEWG/\nfbt26XjySS+2bXNk4MAyHB1/pqjIjbi42qUU36oooqFwQ7GONK50T+3da3FD2YqGmpMnNQQFSUA2\nbdokERV1c7g4bncyMzNp2LDhVdfPSElRs2qVM+HhhfTuXXVUuW1bI56eJiIjb+znV1oKc+e6UlBg\nCdofPmx5gFtjLmARDQ8PM3q9iZwcy/bYWEu7/ft1dl9r7Von/vzTkYsXVdx7rwFf34OAmW3b7qzv\naJ2JxsGDB3nllVeYPHkyv/zyS4X9OTk5zJw5kzfffJM33niD2NjYujJN4QZS1UgjNlZHs2ZGfH3N\n+PtbxvcnTmjIyFDTvr2Wxo0bU1wcxblzGjnvX+HGkZGRYZdr6ptvXNHpBM89V1RtO5UKhg41sGaN\nM599duNGizt2OPCf/7izfLkzAEeOWMTAmo0HFpeVp6cZvd4sjzSsYhEba79oxMdr6dWrlM2bsxg6\n1ICfnxoXl0S2bbu904uvpE5Ew2w2s3jxYqZNm8bcuXOJjo6W1wy2smbNGnr16sXHH3/Mq6++yuLF\ni+vCNIUbTFWikZSkoV27cgA8Pc04OAj++svyA27Z0sQDDzzAyZOrATh4UMfq1U7XLV1SoSKZmZlX\nFY2LFyV++smJBx4ooWHDqzvyZ83KZ8SIEj7+2J2TJy0P8dRUlTzKvB4kJFhEYtUqZ/LyJM6etcQo\nrPNIwDLS8PQ04+1tJjdXxfnzkJSkxc/PREaG2q64i9EIiYlaOnUqp317I5IE/v7+mEzriY3VcuHC\nndOxqRPRSE5Oxs/PD19fXzQaDWFhYRVW95IkieLiYgCKi4vrvbaMwvXBKhqXu6fKyiyjiqAgIwCS\nBE2aGNm92zLMDwgw8sADD2A2W74jH3zgziuveLJ9+53lBqhLMjMz8fX1rbbNtm2OFBerGDu22K5z\nOjrClCmW7Lnduy1CMXeuG6NHe5OVdX0ePfHxFtE4dkzLDz9cSqe1HWmo5JGGySSxebPl2hMnWkZL\nVxttrFnjRFSUAwaDRPv25fL2iRMnYjD8iRASx47VPq5RVFR0S80srxPRyMvLs1kBy9vbm7y8PJs2\njz76KFFRUYSHh/Phhx/y9EKTn3EAACAASURBVNNP14VpCjeY/Pz8CgvanDqlwWiUaN3aKG97/fUC\nSkstvbWAABPt27enZUtvnJzOyT/IrCwlk+pGUFJSQn5+/lVFY/NmB/R6E6Gh9g/5goKMeHmZ5A7B\n6dMaysokFi++tvkSBoPlX0KClrvvLsXRUTB3rhsA/v4mm9GDVTR8fCyxsw0bJCRJ8MQTxTg4iGpF\nIyFBw8sve/Lii5ZObIcOl0Sja9euDB9uqWBw8GBpre9lzJgxTJs2rdbH1zU3zXoa0dHR9O/fnxEj\nRpCUlMT8+fOZM2dOhcBcREQEERERgKUQnl6vr9X1NBpNrY+9Hair+y8rK8PDw8OmjlFkpEUcund3\nRa+3PDyeeQbi403s3i3RvLmlgxEYGMiFC0cpKbEsE1pS4ope71zltfbv38/XX3+NXq9n4sSJckmS\nyriTP/8r7z05ORmwrHVS1XtSVgbbt2t56CEzvr41e9/69JHYt88JvV5DerqlA7B0qSvvveeAm1vt\n7uG++zRcvAinTqmYOBFeftnIhAkaWrUSBAZKZGU5oNfrEQIuXFDRuLEjLVtaxCEiQkVQELRu7UVo\nqODIEWf0+sqFY906S0eloECFg4OgRw8PNJc9NSdOHMzvvxdx9Ci1+j4VFxdz6NAhCgsL6+z7qNFc\n22O/TkTDy8vLpkZLbm5uhdS+bdu2yWrbunVrysvLKSgoqJB1M3jwYJuql7WtNKnX6+/YKpVQd/ef\nlZWFm5ubzbX273dFkjR4e+eQk3MpoX7aNMu609amWq0WnW4zLVoMIDtbxenTBnJyLlZ6nV27dvHI\nI4/g7OxMWVkZGzZsYMuWLVXadaPvv7i4GIPBYFcKa11z5b0nJiYC4OzsXOV7EhWlIz9fT9+++eTk\n1MyVctddLvz6awMOH87j3DlfOnYs48gRHRERF7n77spHLXFxGj77zI35889z5eT0oiKJ7dv9MBot\nnY/mzfMJCyslIkJNWRksWuTKvn2O5OTkkJ8vYTL54+BQiFZbCjQkL0/i7ruLycm5QMeO7ixd6kJa\nWg66K3SjtBS+/96XwYMNHD6spXFjExcu2L4/7u6uQCLx8bWrert//37MZjMpKSmcPXsWJyenGp+j\npuj1enRX3mwNqBP3VGBgIOnp6WRlZWE0GomJiaFr1642bfR6PXFxcQCcO3eO8vLyagumKVw/SkpK\nMJkqzuy9HlRWdyopSUuzZiacnESF9peXO3J2dkarXUl0dBa+vmY5XbIy1qxZg6urK/v27WPatGkk\nJCRw5syZ63YfNeWtt95iyJAhN72v2mw288MPPwDVL8caHe2AWi24++6au2F69LAIw2+/OVFeLjFk\niOU9SUysOg7wxx9O/P67E7t3O7BliwNbtzrIE/b27NFhNEo4OVmC8dY4Q7NmJlq1MuHvbyI3V43B\ncGlinzWmYaVDB4trNDS0DINB4ujRirZERjpw/rya8eOL+PHHXD799EKFNo0bNwYSOHeuds8q6zPP\nbDaTlJRUq3PUNXUiGmq1mqeffppZs2bx2muv0atXL5o2bcrKlSvZt28fYFkxbOvWrfzzn//ks88+\n46WXXlKW/KwjBgwYwP/+978q95ddQ9rSxYsXK4jG8eOXguDV4ezsTFGRJVjp43NpYtaVmEwmNm3a\nxKBBg2jQoAH33HMPQLUjjRtJaWkpmzZtIj09ndWrV8vvX1paGlFRUTbt6ltUFi1axJo1a3jjjTeq\nFY0DB3S0a1eOs3NFob8a7dqVo9MJNm60DBk6dSpHrzdx7FjVjo4TJywdhA0bHAkP9+Kpp7x55RXL\nAl4xMTq0WsFXX51nwoQiOWXbinVhqIwMtY1oeHqaUaks9ltjE6Ghlv+t8zaOHtUwdqwXixa58Ndf\nDuh0grCwUoKDjTYxOCseHh5oNMlcvNiA+PgzNS5gGBcXh1ptuVfriO9mp85iGqGhoYSGhtpsGzNm\njPx3kyZNeP/99+vKHIW/MZvNnD17tsp5MVFRUTzxxBM8+OCD/Pvf/65xVtvFixcJDAyUXxcUSBw/\nrmHQoKs/LJ2dneWMOm9vM8ePV/513bNnD3l5edx7772Apbx6UFAQW7Zs4ZlnnqmRvdeD6OhoioqK\ncHNz44MPPmD69Ol88sknbNmyhU2bNrF//358fHx44YUXSE9PZ8OGDbVeqOha2bVrF23atOHVV1+t\nso3JBAcPahk1qqRW19DpoG3bcnmBpiZNTLRpY6ww0igrQ3YRnThh+awtJUEkevUqZe1aJz76KJ/o\naAe6dCljwIBSBgyoOPLx9780WbS42NLx9PIyo1Zb/s/JUcui0aiRCV9fE4sXu7JokStnzlium5io\nxd/fROfOZRXcY5cjSRLe3llkZsKECbNp376Yb7/91u73Ji4ujh49ehAbG1vpGjLFxcV88MEHvPLK\nK1dNVKgrlBnhdzilpZYfXVXrV6xfvx6dTse6detYuHBhjc9/pXsqMtIBo1Gif/+ruzlcXFwwGAyY\nTCZ8fMxVjjT+/PNPtFotAwcOlLcNHjyYXbt21agn/9NPTqxcWTufsslkkus3bdq0CWdnZ/773//i\n7OyMt7c38+bNY/PmzZhMJn755Rdyc3PZunUrcXFx9TonKTU1lWbNmlU7qk9O1lBYqKpR1tSVdOxY\n/neRP0tl3ODgco4d08h1m+LjNbRp48/+/VqEgJMnNTg6CoSQaNOmnIkTizCbJfbt03LkiJawsKpt\nadLEIhr79ulsRhoAer2Zpk0FXl6W15JkKXly+rSaVq2MzJiRz4wZ+WRkqDlwQCe71qqjSRNLnC0t\nzZ3o6Gi7R+bl5eUkJibSuXNngoOD5ZFGXFwchYWFgOW79N1337FixQq7zlkXKKJxh1NSYuk9njp1\nqkJcQwjB1q1bGThwIF27dmXHjh12nfPixYtyRs6V7qmtWx1p0MBM165X/2E5OzvLNur1Js6fV1Ne\nXrFddnY2er0eV9dLM4/btGmD0WgkLS3NLpvBModg0aKazV7esmULGRkZfPzxx/Tu3Zu8vDy2bNlC\n//79GTJkCLGxsbz99tucPn0ao9GIv78/a9asYePGjZjNZtq2bcv7779Pjx49OHr0aI2ufT1IT0//\n2y9fNdaU1JCQ2otG586WD65BAzNuboLgYCPFxSrOnLG4Zn77zYmyMol165zIzlZRWKhi1CjLKHP0\n6GJ5Iujy5S6YzRI9elTd6QgIMDFokIFPP3Vjxw5Lqq9VNAYPNjB2rK07a/bsfGJjM1m2LI/w8CJG\njy6W3VjWZWyro2VLAZQBbSkuLubAgQN2vSepqamUlZURFBRE27ZtOXz4MFu3buXee+9l/vz5AHKm\n6ObNm+06Z12giMYdjlU0SktLKzxgjx49SkZGBoMGDaJv374cOXJEzoITQhAfH8+GDRs4duwYYCmD\nXlJSwujRo+nfvz/PPPOMzVoaZrOlQmi/fqVVVke9HKtoFBcXy0HMvLyKX9nCwkIbwQBo2rQpQIXK\nA1WRna3izBkNZ8+qbSqkVsfFixeZOHEiY8eO5euvv8ZgMDBv3jwyMzMZOnSo3G7kyJHo9XpCQ0MJ\nDw/nyJEjzJ07l4CAAFatWsXUqVNJT0/n119/te/C14nCwkIuXLhwVdE4cEBLgwZmWrasfbJEp06W\nh6813hAcbBGBXbssAe6ICIsPaPNmR1JSLC6i++838MsvOTzzTBHNm5twcjLzxx+OqNVCjkVUhiTB\nvHnn8fEx8fPPzqhUggYNLB/q228XMHOm7X24uAh8fC4JiZeXoEePMiRJ2NW5adLEDzgOtEOlUtnE\nrayYzWbWr18v/94AOf7h6+vL+PHjMRgMjB8/HiEE0dHRGI1Gtm/fjoODAwcPHpRHsvWNIhp3OJd/\nia90UW3duhWA/v37069fPwB27txJXl4eI0eOZMiQkbzwQjhPP/00qampdOrUiZCQEI4cOcL999/P\nX3/9xd133819990HWB4+2dlqu+IZcEk0ioqKZNHIzq74lS0qKqpSNM6ePWvXtQ4c0P59LhXnz9u6\nak6cOMGCBQswGm0DoUlJSQghSExMxGg04uHhwZIlS1Cr1QwaNEhu5+DgwOrVq1m4cCGPP/44Dz30\nEFlZWTzyyCN4enoyefJkQkJCiIyMrNS21NRUucd5PbF2Eho1alRtu4QELe3bl3OVWobV0rq1EZ1O\nyK6j4GAjrq5m3njDg4ce8iY+XktQUDlnz2rkgHnLlka6dStDq7XUsgoONmI0SnTsePWAvJeXYPXq\nXAIDy/HzM9XY9ilTCpg6tQAPj6v3ICzv31E0mo507tyZiIgI2e1r5Y8//iA8PJwPPvhA3mYVDb1e\nT8eOHZk5cyYqlYqQkBAOHz5MZGQkFy5cYNKkSUD9JXZciSIadzhXikZaWhrDhw/njz/+YOnSpXTv\n3h0/Pz86deqEh4cHf/75J6tXryY2NhZ39wxCQv7g1KlT/Pzzz5SVldG+fXtmzJjBwoULiY+PZ8WK\nFfz+eyhz5rjx00/OODqa5ZTLq3H5SMPaE8zNrZh2W1BQUEE0rCVrKhON48ePV3DFXV7t9Nw5S093\n5syZTJ06lUcffZQPP/yQmJgYm2OsKZKTJ0/mgw8+YNiwYZjNZrp3714hYSAoKIjGjRvj4uLCggUL\nOHLkCJMnT5b39+3bl0OHDnH+/PkK9s6dO5eJEydy4ULFlM9rITU1FaDakYYQlqB0q1ZXz3arDp0O\nXn65gNGjLS4nZ2fBzp1ZzJiRL7u//v1vS0XkH35wRqcT8qjEitVFZY/LCCwpuBs35vDLLzVfXS8s\nrIxJkwrtamsRjQSMxuY88MBojhw5wj333CNXeAb48ccfAfj222/ZuXMncEk0GjZsCFgySOPj43nj\njTcwmUzMmDEDR0dHnn32WRo2bHjTFHFVROMO50rRmDdvHocOHeK5554jLS1NzqpRq9Xce++9rF+/\nnh9//JE2bQZz8WIDEhMHAu58/fXXeHh4sGrVKsLDw+VzCgFLlrjw6adurF7tzP33G3B3t8//Yy09\nUlxcjLe35QFi70hDo9HQqFGjCu6pc+fOMXDgQKZPn26zPTZWh4uLRZjOnlVTUlLCV199xbJlyzAY\nDDg4OFTo7ScmJuLk5MSbb77JuHHjGDJkCICNa6oqPDw85FRLsIiGEILNmzdXGNHs27cPs9nMrl27\nrnremmAVjepGGrm5KvLzVQQGXptoALz2WiHDh1/qMPj4mAkPL2Lx4jz+8Y8C7r67jBkz8jGbLcFv\n9RX9A6todO9uf2zFxUXYLEl7I7CI7lFATZ8+T/PZZ5+RkpLC7t27AUvcaPv27Tz//PM0btyYL774\nArCIhiRJNhNA3dzc6Nq1K2q1mlOnTvHCCy/g7u5O48aNSU9Pv6H3YS+KaNzhWEVDpVKxdetWVqxY\nwT333INOpyM0NJS+ffvKbcPDwzEYDCQlJRESMu7v4zVoNOHk5OTQo0ePCmVfUlPV8qQ8g0Hiscfs\nK3YHlcc0KsugqiymAZY07itHGrt378ZsNvPZZ58xY8YM3nzzTQ4ejOfQIS1Dh1oeaGfPqklOTkYI\nwSeffMKOHTvo3bs3W7duRVwW8EhKSiIoKEi+54EDB/L+++/zxBNP2H2PVu666y4aNGjAlClTaNGi\nBX369CEvL4/z589z/PhxALmHer1IS0tDrVZXm8ppjS9cD9GoinvuKWXatAIkCcLDi4iJyeSbb/Iq\ntBs61MCoUcX07Vv7Ok83gsDAQJ56qjtgmYNk7TRYY31r167FbDYzYcIEHn74YaKiosjOziY7Oxsv\nL68KZT1cXV3p3Lkzvr6+/OMf/wAsFXWVmIbCTYFVNO699165vMtHH33EH3/8wXfffWeTihkUFCTP\nhfDx6Q9YZuNK0puANz169KhwfuukqXffzWfChCJ69rS/l3i5aLi5CRwcRKWzwqsSjaZNm1YYaezf\nvx8XFxd8fX357rvvWLt2LSNGfERRkYr77jPg5mbm3Dn13z/4STg7D0Kv1zNo0CBOnTpFSkqKfK6k\npCRat24tv9ZoNDz99NM2xRntRavV8tNPPzF79myeffZZTp48yZYtW+RMHE9Pz+suGqmpqfj5+VVb\ni8i6HOqNFI0r8fMz06hRxdLr/v5m5s+/gKtrzScY3khUKhX/+tcYJEmQnKzBzc0Nf39/2X25bds2\n2rVrR/PmzXnwwQcxm81s2LCBnJwc2TV1JQsWLGD16tXyd8nf318ZaSjcHFhF46233uLYsWMcOHAA\nX19fgoKCKq2bNGvWLJYsWUJmpp6GDU3MnXsek6kBEMFXX70pi4SVgwd1ODgIJkwoYtasfGoyyf/y\nQLgkgbd3xVnhQggKCwsrfVA3bdqUjIwMm6Dkvn37CA0NZdOmfURHHyIqKgqz+X7U6nL69i2laVMT\nZ89qSEw8Dsxh27YgAO655x60Wi1jxowhMjKS8+fPk5mZSXBwsP03dBU6dOjAuHHjePfdd/Hz8yMi\nIoL9+/fLFRWSk5Ov64MjNTX1qkHwlBTLfIkb7eK51XFyssRQkpIs3/82bdpw/PhxCgsL2bt3L/37\n95e3t23blnXr1pGVlVVlkcLmzZvbFNz09/ensLCQgoKCG34vV0MRjTscq2hYC6VdrXSLn58fQ4YM\n4dgxDW3aGGnf3sikSelIUkfS010qLO958KAl86Y29dEuj2kAlU7ws07+c6ukXGqTJpbquFbffVFR\nEUePHqVLly489ZQfb74ZiK+vHzrdI7i4/IWzs6BpUyPnzqk5fPg8oCMry/IQaNy4MatWrcLR0ZG3\n3nqLw4cPA9iMNK4XkiQxePBgduzYQUREBG3btpXjJdHR0dflGn/99Rfx8fHye1QVKSkaAgKMFeIL\nChVp1cooj8yCgoI4fvw4O3fuxGg0ytmHYCnbc/DgQdLS0myqP1eHv78/wE0x2rBbNFJTU1m9ejVf\nf/21/Pr06dM3zDCFuuFK0bAHs9my8l7r1pbA5FtvqTl+PItGjYyyDxygvNyyZnNtJ4Vd7p4CSymR\nK0XDOnO2KvcUIBcutAaUO3XqweHDEpGRjnz+uStlZU24eHE5eXl5NGli4uxZNcePW9wjGRmXrtet\nWzfefPNNzpw5w5QpU/D09KzUJXc9GDx4MEVFRcTFxfH000/Trl27GruozGazTaJDZmYmP/zwA08+\n+SQPP/wwnp6ess+8KlJSNLRsWXeuqVuZoCAjJ05oMBotnQmDwcCyZctwcnKiW7ducrvOnTtTXl5O\nRkbG7Ssau3bt4t133yUvL0+euGIwGFi6dOkNNU7hxlMb0Th3Tk1JiYo2bS49TJycBIGBJhvROHBA\nR0mJqkbZLpdjtenykUZ2tm2XtzrRaNWqFWAJSObk5PD222/j7e2Nh0dPzGbLiOrDD93x9S0GVjN6\n9GhSU7dTVKQiK6sNYCl6dznDhg3D19eXjIwMXn311UqvW1tiY7U8+qg3EyZ40adPH4YNG8bnn3/O\nmDFjUKlUhIWFERUVZROMr4758+fTvXt3ubr0I488wj//+U9+++03Jk+ezJYtW2jbtm2Vx+flSZw+\nrZYn4ilUT1BQOaWlEmfOqAkKsrg1t2/fzrBhw3BwuDQC79y5s/x3TUXjZgiG2yUaP/30E++88w7P\nP/+8nCnSvHlzTp06dSNtU6gDrKLhWF1VtitITLQIQ5s2tg+TVq3KSUnRyDOq//yz9uW0wZLm6+jo\nKFe61etN5OaqbGZsVycaer0eX19f4uPjef/998nMzOTbb7/l7FnLGi2vvFLAkCElbNx4gfvvD+PC\nhQtERs79+2hLMc2CAhVFRZdcdjqdjkmTJhEaGsq4ceNsrldQIDFunJdcGqMmGI0wbpw3e/fq2LLF\nkcxMV77++msefPBBuU2fPn3IyMhg5syZcjlzK0lJSRXKvPz+++/k5eXx7rvvsnLlSk6cOMH8+fNJ\nT09n6tSpVw3Yb9niiNksMXjwzZWtdLNirdycnKyhdevWSJJEcHAw//d//2fTrkmTJvI8HntFw5rh\nVpOyODcKu0QjPz+/QtlkSZKU0uW3ASUlJTg5OdXos7QG+64sFR0YaKSwUCWv/7xjhwMhIeVyCYfa\ncHmlW73eTHm5xMWLl2y1ikZVD8D27duTkJDAjh07GD58OKGhoSQmatHpBK+9VsCSJefx9YX//e9/\nfPzxxxQXxwBG4FKAOz3d9mfy9NNPs379epveI1hmTm/b5sgff9gvwFYOHdJy4YKKt96yFL+r7Bx9\n+vQB4KuvvmLWrFk2ExQ/+eQTnnvuOcr/Ls6VnZ1NXFwcTZs25ddff+XNN98kJCSEhx56yO4Owu+/\nO9GkiZGOHZWRhj1YReP4cS0NGjRgyZIl/PjjjxXibZIkyaMNe0XDwcGyEuEt455q2bJlhRIH0dHR\n8vBf4dbFKho14dgxDf7+pgqT9AIDLQ+x5GQNubkqDh/W0r//ta0X4eLiYiMaYDvBzyoalQXCAdq1\na8fRo0fJzs4mLCxMtj84WFSof9WnTx/c3XVA3N/XtlwvM9O+kUNursWuI0dsT7x5swOFhdWLclSU\nRYDGjCmhQ4cyNm6s+JkEBAQwadIkHn30US5cuMChQ4fkfYmJiRQVFckBeuvv9fPPP2fu3LlMmjSJ\nOXPm2N05KCiQiIx0YNgwQ40y3u5k3N0Fvr4muYT/PffcU2VKbadOnQD7RQMsSSi3jGhMnDiRFStW\n8O6771JaWsqsWbNYuXIl48ePv9H2Kdxgaica2kr93IGBlm0pKRoiIhwQQqp0vYOacOVIA2xLiVTn\nngKLaFixikZioob27SuOfnQ63d8LOO0FkIvVXRnXqAqraBw+rCU/XyItTcWxYxomTvTm448tolZV\nOGLnTgc6dCjDy8vMsGEG9u3T8dVXLowcqeejjyzHSpLE22+/zb/+9S8kSZLdUQaDQXYVW2eNb9++\nHS8vL0JCQhg9ejRvv/02bdq0ses+ANavt1Sdve++2q2hcacSFGSsct2Xyxk5ciQDBw4kICDA7nP7\n+/sTGRlJ7969Ky03U1fYJRqNGzdm3rx5DB06lMcee4z+/fszZ84cOTijcOtSU9EwmSyiUNkqZv7+\nZpyczCQlaVi92pkWLYxySezaYisaFUuJ2OOeAkupjGbNmnHxokRaWuWiAfDyyy8zdKg3cKlcRU1F\nIyVFw9NPezFihA/btllGECtWOPPmmw0YOtSHK6qEUFwssX+/Tl4v+6mniujWrZT33mvA/v2WGMfl\neHl50blzZ1k0kpOTMf+9MEVMTAynTp3it99+Y+jQoRVm6NuDtfRL27bldO2quKZqQnBwOYmJGhsX\namW0bdtWzqyylwkTJjBgwABOnTp13UvK1AS7vlF5eXmUl5cTFhbGyJEj6d27N0ajkby8ilP9FW4t\naioap0+rMRikCkFwsFQivfvuUlascCYmxoFHHim+ZtfG5Uu+VlZKxLqvKvdUQEAArq6u9OnTB0mS\nSEtT/7298uu1atWKadP6oFYLevUqw83NbJN2Wx3Wsu1CSPz1lwMZGWq++MIVDw8zRUUqvv/ehfh4\nLTExtrGQffu0lJVJ9O5tGZVZK7R+8UUejz5aLKdxXk6/fv3Yv38/8+bNk1d86927N3v27OG1115D\no9Hwxhtv2GX3lezdqyMhQcvEiUWKa6qGjBpVgsGgYvVq5+t+7v79+7No0SIcHR3566+/rvv57cWu\n5V4/+eQTXnzxRRsXQF5eHgsXLqyQGVAVBw8eZMmSJZjNZgYNGmSTFQKW6o/x8fGAZU3q/Pz8Gi2b\nqFA7aioax45ZZ7xWnrs/a1Y+gwY1pKQEHn742l0bzs7OcnVXLy8zkmRbSsQ6Q9Y6p+NK1Go1P/30\nk1zJ1Rqk9/WtOjjfqpWRw4cz8PAQ+PmZajTScHMzU1CgwtHRUvYkL0/NM88UUlws4eAAq1c78e23\nznz8sRvPPVfIAw8Y2L3bAZXKdu0GjQYeeMCAwSCxapUzZ86obdazeOGFFzhx4gSffPIJDRs2RKvV\nEh4ezvjx49m7dy+zZs3Cz8/PLruvZNkyZ9zdzbVe3vVOpnPnckJCyvj2W+cai25JCfz4owtjxxZx\nRY6FjE6nIyQkhD179lwfg2uBXaKRlpZGs2bNbLY1a9ZMnml7NcxmM4sXL+add97B29ubt99+m65d\nu9rMRp0wYYL898aNGzl58qRd51a4NkpKSmxW1rsahw5pkSQhZ4pcSaNGZr744jxxcVqaNbv20hOX\nB8I1GssKbJePNKx1p6pzw3Tu3JmoKB3p6Wp5ZnN1ogHI6yj4+ZlJT7dXNCxLhhYUWJazNRgkli93\noW/fUjlttbBQknuhq1c7/y0aOjp0KMfNraJNVjfg8eNaG9Fo0KABCxcupKSkhIiICNq0acPAgQNJ\nSkrCZDLVev5Ifr7E7787MWZMMU5ON1eNp1uFZ54pYtIkT0aP9ua//z2Pv3/FOlqV8eefjsyY0QCd\nTvDkk5UX9rx4UUKne4O4uAVkZxfi43P95gnZi13jbnd39wqTSjIyMqp0CVxJcnIyfn5+8hoHYWFh\n7N27t8r20dHRcnqhwo3FYDDYPdIoL4effnKmX79SXFyqfqAMGFDK5Mn2rUVwNS6PaUDFUiKVlUWv\njM8+c+PDD93leIi9nfDmzS2lIa50D1VGbq4Kb28zW7Zk8+67FwkPL+Sxx4ro0+dSMsBjjxWj0wla\nty5n1y4dhYUSBw7oqpwAaRXnpKRL/TuzGXlt7enTp6NSqeQgt5OT0zVNOFy3zgmDQWLMGPurESvY\n8uCDJfzf/11g/34dn31m3zMSLBWhAb791qXKhImVK53ZsWMUQmzj44/rpw6VXSONAQMGMGfOHB57\n7DF5NuzKlSsZOHCgXRfJy8vD29tbfu3t7S2Xe76S7OxssrKy6NChQ6X7IyIi5HUNZs+eXWXBr6uh\n0WhqfeztgPX+S0tL8fDwsOu9WL1aRWammoULzXX23nl5ef29Rrjlev7+avLz1fLrsrIy3N3dq7VH\nCEhM1HL+vEROjiuOU9DKRQAAIABJREFUjgJPTw0m09Xv4d57VSxfruLMGR+6d6++533hgoZu3VQ0\namQ5b8OG8N13AJeuM2IEZGaWExEh8eijKlau9MFgkBgyxKHSe9DroUkTwZkzLuj1loD4xIlqLlyQ\nWLvWiF6vZ9WqVQQGBtr9mVT33f/tNw3t25sZOLDBbRvPqIvf/uuvw549gq1bnfHy0tq1cuD58xbR\nOHpUS1KSD717V/y+xceradTIRFHRU3TuPBK9vmONbauuqrFdx9vT6MEHH0Sj0bBs2TJyc3Px9vZm\n4MCB3H///dd08cqIjo6mZ8+eVbobBg8ezODBg+XXOTk5tbqOXq+v9bG3A9b7LyoqQqVS2fVefPWV\nF82aQZcu2dTVW6dSqSgsLJQXrPHx8WDbNgeysnJQqSwdEmdn52rtT0tTcf68ZWjx118mfHwEJpPZ\nrnvu3FkF+PHrryW0bFn16EkIyM72x8WlmJycq/cAO3SQUKv9+L//U6FWC4KDc8nJqdyNERjoRWys\nmn37ztOsmYnffvOjrAwyMnLQaKBnz56A/b+Fqr775eWwb58/Tz1VRG7uRbvOdStSV7/9AQOc+OUX\nT7Zty+euu66ehZaS4kmjRloKClQsXFhOmzYVV2rctashXbqU8+WXHyNJUq3uQ6/Xo6tNBdG/scs9\npVKpGDlyJPPmzWPZsmXMmzePkSNH2p3O5+XlRW7upSUXres2VEZMTAy9e/e267wK105NAuGJiVp6\n9Cir04qnAQEBmM1meRW0vn1Lyc1Vc+iQJSBfVVn0y0lIuDTZLj5eKy8daw9eXmY6dChj584qIpN/\nU1AgUV4u4e1t37nd3YXskpo//3y1x7VrZyQxUUvv3r58950z+fkqSkpUckXV68WxYxoMBom77qpd\nrTAFWwYNMqBWC7srBKSlWZId7ruvhN9/dyQ/XyIhQUNBgcSCBa5s3OjIuXMaQkLK6rUaR5XfuoSE\nBHliVFxcXJUnqMqNdDmBgYGkp6eTlZWFl5cXMTExvPzyyxXapaamUlRUdEPKTStUjr2iUVJima/Q\nvHndVjwdMWIEM2fOZMmSJfTs2ZP+/Q2oVIKtWx0JCSmnsLCwQombKzl69JJolJVJNGxoAuxXvr59\nS/nqK1eKiyWcnSu6DP79b3fi4y3X8PKyX5C+/PI85eVUuuDQ5bzySgHdupXx6qsezJ59KWnh0CEt\nwcHX7/M4dMjS++zUSZmbcT3w9LRkxFk6HFcffaalqenbt5RRo0pYscKFwYN9SEvToFYLTCYJlcry\n3QsNrV9Rr1I0Fi9ezJw5cwD48ssvK20jSRILFiy46kWsi8jMmjULs9nMgAEDaNq0KStXriQwMJCu\nXbsCFtdUWFiYUtOqjigvL6e8vNyuWkRnz1q+Ki1a1O1iPE5OTjz22GN89dVXpKen4+/vT2hoOVu3\nOvDGGwXk5ORw1113VXuOhAQtfn4msrJUmM3S3yMN+0WjW7cyvvjC0uurbLLbtm0OHD9uEQ17RxqA\n3SMeNzfB0KEGRowo4fvvXfDzM1FYKHH4sI4xY65fWuyhQ1oaNDDX+Wd8O9OhQzk//uiM2YxNXKOg\nQGLbNgdGjrSUaTEaLengjRqZ6NWrDH9/E+npaiZNKqCoSKJdOyNTpzZAqxV06FC/ol6laFgFAywl\nlmszs/RyQkNDCQ0Ntdk2ZswYm9ejR4++pmso1AyDwVIXyp6RxqlTlodsXY80AMaNG8fChQtZu3Yt\nL730EgMHFvPxxx7Ex5vJzs6W52BUxdGjGjp1KiMuTktamubvkYa22mMux9qbP3ZMW0E0zOZLggo1\nE42a8uijFtHo0aOUrKxLLrrrxaFDWjp1Kr9tA+D1QXCwkeJiFefOqW1S0N97z50VK1zw8ckhLKyM\nzEw1/9/encdFWe0PHP/MwiqIDiiogAtoarnjhnuimWb509Jb5lp2S2292u16r+TVTC2XFm3HUm8W\neSvT0kzcSijTitSUFEUlGXZF2ZmZ5/fHXKYmBhhhhvX7fr18vZznOTNzDg/Md845z/kek0lF69ZG\n1GpYv/4KBQXWaXiuXVORnKzlBrP+OFylkcBkMjFt2jRL9kzRcNzIXhoXL9ZOT8P8nu3o1asX27dv\nJzo6mo0bBwFXeOopc70r2rK0uBjOnzfvMhgYaK576cpyewUGGvH0NPHrr2W/Y6Wnqyks/P1T1plB\nIyysmFmzcpk+PZ8ePUo4dcqFYgeNVBQUqEhIcKFHD5nPcKTSzAml2wmAOThHR5vX6nz8sfl3uDRT\nQevW5t/RAQOKy+Rte/hh85bJta3SoKFWq2ndunWd2JtWOJY9QePoURfCw1sSF+eKl5eJ5s2d96FY\nkbvuuotffvmFhQsX4u5+Dfg7J0/6Ar9x6lT5N05cvKjFaFTRsePvQaNlyxtrg1ptXgGfkFD2m31p\nL6NfvyI8PEyW/FjOoFLBc89dY8CAYrp3L6aoSGW1fqM69u93w2D4PZWJcIzSxZmlmRQAXn/dnFpm\n7NgCPv/cg4IC8x1+8HvQqMvsGnMaPHgwq1at4uDBg5w4cYKTJ09a/on6y56g8cor3ly8qOWrrzxo\n29ZYa0MX48ePR6VS0bJlS6KiooB3aNduFVDCkSPlZ28t3UkwJOT3oNGixY3/Yd50U4nNnsbFi+Zv\niKtW5XDwYEaNDR2UJoIsnbyurh07PPD1NRIeLj0NR/L2VggMNFj97hw75sqwYUXMmJFHbq6a/fvd\ny/Q06jK7vqZ89dVXAGzbts3quL0T4aJuqixonDunYf9+d1xcFEpKVLUyn1EqICCAtWvX0rVrV7p2\n7Yq7uxuXLy8GmnD+/FwUxXaen9LbUkNCDHTsaECtVmjTpipBw8CHH2rIzFRbDW9duqRBpVJo29ZQ\nbr4gZ2jb1oiPj4mff3Zh6tTqvVZenoqYGDcmTy6gmuu+hA1/7KWmpKjR6zX06lVC377FqFSKZeGp\nt7fJZiqZusauX5ENGzY4ux6iFpQmiCwvsd0HHzTB1VVhyZIcFi1qRrt2tRc0wPpGiXbt2pGQkICH\nRyJ5eeaJxqCgssEgMVGLv78Rb2+Fu+4qoFu3EgICbnyI7ffJcC1+fr9/G794UUtAgKlGAwaYh6q6\ndy/h+PEbnwzPyYEpU3xZvDiHW24xsGePO4WFasaPlwSFztC5cwlff+3G9evmlDFgvm3WzQ1atTJy\n8aKG7Gx1rf992avC4SlFUYiJiWHjxo0cPHiwhqokasrWrVvp3LmzZc+JP0tI0NK5cwn33pvPnXcW\ncNtt1duFz5E6dOgAgL9/GmC+Q8qWc+e0hISY/xg1GspNtFiZm28uQatVeOstL/6wyyrJyTW/dqVU\njx7FJCS4UHiDl+XgQRWHD7uxapV5zcemTU1o187AgAEyNOUM48YVUlKi4q23vPjxR1dcXRVuvtk8\nvNi2rZFLlzQkJWlp377uD01BJUFjy5YtfPTRR1y9epWtW7fy0Ucf1VS9hJP99NNP/Pzzz0ydOrXc\ndTFJSVratTPi6mpeiFaXNuQpDRrt25sT6/1x1XcpRTEHjdDQ6n+o+/qa+Pe/c4iJceeJJ5qRnW3+\nmV28qHVINt+q6N69hJISlc0J+oocOWL+s9+/353Nmz05dsyV6dPz7MqPJG5cjx4ljBtXwJtvNmHP\nHnduvrnE0jMNDjZy7pyW5GRNw+hpfPvttyxZsoSnnnqKyMhIDh8+XFP1Ek62fft2tFotEydOtHm+\npAR++63u/iKXBo3g4Oa0bWt9Z1N0tAfvv+9JVpaanBy1padRXTNm5PPkk9f57DMPxoxpwU8/uZCa\nqnHY69+o0nxGN7pe48gR8yZaPj4m/vGPZri7mySrrZP9/e/XaNpUISlJy7Bhv9+hFhxsIDtbg9Go\non37uvm39mcVzmnk5+db7oEPDAy0bK0p6r/ExEQCAwNp1qyZzfOXL2swGFR1NmiU7q3cunVrunQx\nj+0bDOY9N955x4tLlzRoteZJxa5dHdNDUqlgwYLrDBlSxKRJvkye7IuXl4n77qudD9zWrY34+hpv\naF6jpAR++EHF1KlFzJqVx5EjrgQFGS37hwjnCAkxcvRoGrm5Kry8fv9Zt237ey+1vgxPVRg0FEUh\nPT0d5X/J3U0mk9VjAH9/f+fWUDhFUlJShTmbLlww/2rU1V/kLl260KVLF/r370+rVoV8+aUHzzzj\nw4sv5pCSoiE3V01kpA9BQY4fq+/fv5ipU/P5z3+asGDBtRvKN+VIKpV56OPnn13JyVGRna2u8Hq9\n8II3Bw64UVCgok+fYtq1M9KunUx+1xSVijJ3R/1xPqxB9DSKiop49NFHrY79+XF0dLTjayWcLikp\niXHjxpV7vjRtSF3taXh7e1v2Venbt4Bff9WyYYM3kyYVcPWqedQ1N1fNo4/mOmWs/l//ukaXLiW1\nPqzTvXsJhw65MXu2jvh4F3btyrS5FW9REWzc2ITr19WoVAp9+sikd11Q2tPw9jY5NZuAI1UYNCQg\nNEw5OTlkZ2dX2NNIStLi6Wm6oTTitWnKlHw2bPDm4EHzDGPnziVcuqThL39xzoe6t7fCzJm1Pw/Q\no0cxRqOK774zt/vhh5uzfXsmPj7W32hjY924fl3N2rVX6NHDizZt6sd1beh0OhNNmpho185Qb3J+\nyf0SjVBycjJApcNT7drV3grwGxUcbESjUYiNNX94LluWw7ffpt9wnqn6pjSNuaurwoYNV0hK0jJl\niq/l7q5Su3e74+VlYsKEAgYPlvmLukKlgl69SujTp+7cmVgZWf/ZwO3du5f27dsTGhpqOXbhwgUA\ngoODy31eUpLG5jBHXeXiAkFBRsudREFBxgYfMAACAky0b2+gX79iJkwowNvbxJw5OmbO9CU6OhMP\nD/jhBxe++MKDiIjCGl+EKCq3dWtWvflyBtLTaLCKi4tZvXo1M2fOZMWKFVbnLl26BJQfNIqKzD2N\nqi6Eqy0dOhgwmVSoVAoBAXVzAt8Zdu3KYOVK89agI0cW8eqrV/jxRxcWL/YhPt6FiRP9aNrUxBNP\nyN2PdZFGQ71aIyM9jQbo2LFjzJ49m6ysLNzd3Tl16pTV+YsXL+Lr60vTpk1tPv/sWXNm2M6d60+X\nGX6/+8Tf34SLY7eaqNOaNrUebho3rpA5c/J4550mnDrlgpeXwu7dGTRvLsNSovrsim9xcXE2j8sK\n8brp66+/Jisri02bNvHYY49x6dIlq9T2ly5dsiyOs6V0oVyXLvWvpwH1I1Oosz3ySC6uruYsuHPm\n5ErAEA5jV09j69ateHh40KtXL6tj8fHxdu+2Fx8fz7vvvovJZGLkyJFMmDChTJm4uDi2bduGSqWi\nbdu2PP7443Y2Q/xRSkoKLVu2JCIiwrKm5vTp0+h0OkJDQ0lISGD06NHlPj8hwQU3N6Xe3DdeqkMH\nc7CQoGHeM2TmzDyioz2YNSuvtqsjGhC7ehrPPPMMb7/9NqdPnwZg06ZNHD9+nMjISLvexGQyERUV\nxaJFi1i3bh2xsbH89ttvVmX0ej3bt29n2bJlrF27lpkzZ95YS4RFSkqKZSV/165dAYiMjGTYsGEc\nOHCA9PR0qy8Af5aQYM7XVN/SZEtPw9qiRdf49tv0MrffClEddn0sBAYGsmDBAl588UVuuukmMjMz\niYyMxNPT0643SUxMJCAgwLJ6PDw8nKNHjxIYGGgps2/fPm677Ta8vLwA8PHxudG2iP9JSUmhY8eO\ngDnNRrNmzThx4gRg3u8dKLNfeymTCU6fdqmXO7i1bm1kwoR8Ro+uO9l4a5NGU3YFshDVVW7QsLUr\n34gRI4iJiWHOnDmcP38egFtuuaXSN8nOzsbX19fy2NfXl7Nnz1qVSUlJAWDx4sWYTCbuueceevbs\nWea1YmJiLCuBV65ciZ+fX6Xvb4tWq63yc+syRVHQ6/Xcdtttlvb16NGDQ4cO4ebmxpEjR1CpVPTu\n3Rt3d3er50ZFqXnsMXPOqT59XOvlz8e8HtX2BP8fNdTrb4/G3HaQ9murOYRQ7rNff/11m8ddXFx4\n7733AMfu3GcymdDr9Tz77LNkZ2fz7LPPsnr1apo0aWJVLiIigoiICMvjzMzMKr2fn59flZ9bl129\nepXc3Fx0Op2lfSNHjkRRFNq3b8+mTZsIDQ3F3d3dqv0ZGWqeeaYlHTsaaNfOwNCh18jMbLjDPA31\n+tujMbcdpP1+fn64ulZ9m+Byg4Yjd+vT6XRkZWVZHmdlZaHT6cqU6dixI1qtlpYtW9KqVSv0er3V\nojRRudIeW+mcBsADDzzAAw88wO7du9m0aRPdunUr87yXXvKmoEDFG29cccj+E0KIhsmuifALFy6U\nicyZmZmWlcWVCQkJQa/Xk56ejsFgIC4ujrCwMKsy/fr1s2w/eu3aNfR6vWTQrQJbQaPUwIED8fb2\nZtCgQWXOHTniypAhRRIwhBAVsitovPrqqxiN1kMVBoPB7qEpjUbD7NmzWb58OU8++SQDBw4kKCiI\n6Ohojh07BpjH3b29vXnyySf597//zf3334+3t/cNNqdxKigoYMWKFeTm5lYYNJo1a8axY8fK3CZd\nUmLe4a5Ll/q1mE8IUfPsmhHJzMws860/ICCAjIwMu9+od+/eZe7YmTJliuX/KpWKGTNmMGPGDLtf\nU5jFxcWxfv16OnToQEpKimWIzxYvLy8UxbwVaqkLF7QUF6vqVa4pIUTtsKunodPpLHdLlTp//jzN\nmzd3SqXEjUlLSwPMwSMlJQV/f380Go1VmZIS+P57VwoKVNxzjy8PPfT7+YQE83eHm26SnoYQomJ2\n9TTGjRvHiy++yJ133om/vz9paWns3Lmz3P2lRc0qDRqxsbGYTCa6d+9epkxkpA+bNzehZUsj6eka\nvvtO4ZFHNPj4mEhIcEGtVmQ+QwhRKbuCRkREBE2aNGH//v1kZWXh6+vL9OnTGTBggLPrJ+yQmpoK\nmFfVA9x///2Wc7/8oiU62pPNm5swfHgh33/vyvTpebz/viczZug4c8YFPz8j7doZ+dOyDSGEKMPu\nVR4DBw5k4MCBzqyLqKLU1FS8vLzIzc0lKCiIESNGWM7Nnq1Dr9dwxx0FrF9/BTDvPZGb68Enn7jQ\nooWRjAwN/frJXtFCiMrZHTQOHDjA119/TXZ2NjqdjqFDh1p9OInak5aWRt++fSkuLmbSpEmW+YyS\nErh8WcMTT+SyYMF1q+esW2fg9ttz6d+/mMmTfRk+vP6lDRFC1Dy7gsYnn3zCoUOHGD9+vGU15Y4d\nO7hy5YrMa9QBaWlp3HLLLaxevdrqeGqqBkVR0aZN2ZXdAQEwdqw5R9O+ffbfBSeEaNzsChr79u1j\nyZIltGjRwnKsR48ePPvssxI0apnBYCAjI4OAgIAy51JSzD0OyfoqhHAUu265LSoqKrPLm7e3N8XF\nxU6plLBfeno6iqLYXD0vQUMI4Wh2BY2ePXvyyiuvkJKSQnFxMZcvX2b9+vX06NHD2fUTlSi93dZW\n0Lh8WYKGEMKx7Bqemj17Nhs3bmTBggUYjUa0Wi0DBw5k1qxZzq6fqERp0ChveKpZMxNNmsieCkII\nx7AraHh6ejJ//nzmzp3L9evX8fb2Rq22q5MinKx0jUZ5PQ3pZQghHMmuT/7SHoVarcbHx8cSMB58\n8EHn1UzYRa/Xl7upTEqKBA0hhGPZFTT+nOEWzHftmEwmh1dI3JiEhARCQkLK5JoCCRpCCMercHgq\nMjISlUpFSUkJzz77rNW5rKwsOnXq5NTKicqdPn2avn37ljmel6fi6lW1zTUaQghRVRUGjVtvvRWA\nxMREq9XfKpUKHx8fu/YHF85z7do1Ll++zPTp08uc0+vlzikhhONVGDSGDx8OQMeOHWnTpk1N1Efc\ngISEBAC6dOlS5pxebx55DAiQoCGEcJwKg8b58+fRarUEBwcD5m+27733HsnJyXTs2JHp06fjLqlR\na82pU6cA20EjLc3c05CgIYRwpAonwt977z2uXr1qefzGG2+g1+sZOXIkycnJ/Oc//7H7jeLj43n8\n8cd59NFH2b59e5nzBw8e5IEHHmDhwoUsXLiQffv23UAzGqfTp0/TrFkzWrVqVeZcampp0JCbFYQQ\njlNhT+Py5cuWb7F5eXn89NNPrFmzhtatWxMWFsbixYvtuu3WZDIRFRXFv/71L3x9ffnHP/5BWFgY\ngYGBVuXCw8N54IEHqtGcxuXs2bPcdNNNqFSqMufS0tQ0bWrC01MW9gkhHKfCnkbp6m8wf0A1a9aM\n1q1bA+Dn50deXp5db5KYmEhAQAD+/v5otVrCw8M5evRoNasurl27Vu6Wu6mpGvz9ZWhKCOFYFfY0\ngoKC+PbbbwkPDyc2NpZu3bpZzmVnZ+Pp6WnXm2RnZ+Pr62t57Ovry9mzZ8uUO3LkCKdPn6ZVq1bM\nmDHD5oK1mJgYYmJiAFi5cqXNMvYob0FcfWIwGGjatKnNdmRlaQkKotw2NoT2V0djbn9jbjtI+0s7\nAlV+fkUnp06dyqpVq3j77bdRq9UsW7bMci4uLo6bbrqpWm/+R3369GHQoEG4uLiwd+9eNmzYUGZt\nCJi3no2IiLA8zszMrNL7le4LUp8VFBSgKIrNdiQntyQ8vJjMzKs2ntkw2l8djbn9jbntIO338/PD\n1dW1ys+vMGh07tyZ1157Db1eT6tWrfDw8LCc6927N+Hh4Xa9iU6nIysry/I4KysLnU5nVcbb29vy\n/5EjR97QJHtjVVRUhJubW5njJhOkp2vkzikhhMNVmkbEw8ODDh06WAUMgNatW5f54C9PSEgIer2e\n9PR0DAYDcXFxhIWFWZW5cuWK5f/Hjh0rM0kuyiouLrYZNLKy1BgMKgkaQgiHq97glp00Gg2zZ89m\n+fLlmEwmRowYQVBQENHR0YSEhBAWFsbu3bs5duwYGo0GLy8v5s6dWxNVq9fK62mkpZUu7JPbbYUQ\njlUjQQPMw1m9e/e2OjZlyhTL/++77z7uu+++mqpOvacoCoWFhTaDRukaDbl7SgjhaLIpRj1VUlIC\nYHNCS4KGEMJZJGjUU0VFRQA2exoZGebL2rKlDE8JIRxLgkY9VVxcDNgOGunpGpo3N+LiUtO1EkI0\ndBI06qnCwkKg/J6G9DKEEM4gQaOeqnh4SoOfnwQNIYTjSdCop0qHp2xNhJt7GjIJLoRwPAka9VRF\nPY30dLX0NIQQTiFBo54qbyI8L09FQYHMaQghnEOCRj1V3kR4err5krZoIcNTQgjHk6BRT5U3PJWR\nYV7Y16KF9DSEEI4nQaOeKm8ivHRhn/Q0hBDOIEGjniq/p1EaNKSnIYRwPAka9VR5E+Hp6RrUagVf\nXwkaQgjHk6BRT5U3EZ6ZqcbX14RGUxu1EkI0dBI06qnyhqf0eo0MTQkhnEaCRj1layJcUeDECRe6\ndi2prWoJIRo4CRr1lK2eRkqKhowMDb16FddWtYQQDVyNBY34+Hgef/xxHn30UbZv315uue+++47J\nkydz7ty5mqpavVRcXIyrqysqlcpy7KefzLnQe/aUnoYQwjlqJGiYTCaioqJYtGgR69atIzY2lt9+\n+61MuYKCAnbv3k3Hjh1rolr1mq2tXuPjXXF1VejSRYKGEMI5aiRoJCYmEhAQgL+/P1qtlvDwcI4e\nPVqmXHR0NHfddRcusntQpYqKimwEDRduvrkEGzkMhRDCIbQ18SbZ2dn4+vpaHvv6+nL27FmrMufP\nnyczM5PevXuzY8eOcl8rJiaGmJgYAFauXImfn1+V6qTVaqv83LpArVbj4eFhaYOiwMmTLtx3n8mu\ndtX39ldXY25/Y247SPu12up97NdI0KiMyWRi8+bNzJ07t9KyERERREREWB5nZmZW6T39/Pyq/Ny6\nICcnB61Wa2nD1asqrl9vhb9/LpmZeZU+v763v7oac/sbc9tB2u/n52dzHx571UjQ0Ol0ZGVlWR5n\nZWWh0+ksjwsLC0lOTubf//43AFevXuWFF17g6aefJiQkpCaqWO8UFxfj7u5ueZyaal7NFxAgOaeE\nEM5TI0EjJCQEvV5Peno6Op2OuLg4HnvsMct5T09PoqKiLI+XLFnCtGnTJGBUoLCw0OrbQmnQaNVK\nFvYJIZynRoKGRqNh9uzZLF++HJPJxIgRIwgKCiI6OpqQkBDCwsJqohoNyp8nwlNTzfc0SE9DCOFM\nNTan0bt3b3r37m11bMqUKTbLLlmypAZqVL8VFxdbBQ293tzT8PeXoCGEcB5ZEV5Ple1paNDpjHK7\nrRDCqSRo1FN/7mmkpmoICJD5DCGEc0nQqKf+vCI8NVUt8xlCCKeToFGH7diRx8yZGVy7VvacreEp\nCRpCCGerE4v7RFmLFmWyaVN3AAYPvsyBAxqr3fhKExYClJRAZqYMTwkhnE96GnWQwWDg/fc9cHM7\nxoABz5OV5c9jj6VYlfljTyM9XRb2CSFqhgSNOui//92LwdCRcePUfPTRNHS6GA4e7Mj589mWMn+c\nCE9JkTUaQoiaIcNTDpSVlYWLiwtNmza1Oh4fH8+WLVtYtWoVGo2WVau80es1vPzyVatyOTk5PPfc\nc+zZYwAeYNKkIDSaElatasGcOd7cffcxtm0LoXnz5lZB4+JF82Vs21aChnAuRVEoLCzEZDJZ7eVS\nn6SlpVk2MWvIFEVBrVbj7u7u0GslQcNBrl+/zm233UafPn148803rc4tX76cuLg4xo0bx8mT43n1\nVW9UqiJWrCjE0/P3/FG7du1i69at6HQvAtCzpwGAsWNbMXDgGb79diZDh74MPAVgSSF//rwWtVoh\nONhQAy0VjVlhYSEuLi7VzpRam7RaLRqNprarUSMMBgOFhYV4eHg47DVleMpBnn/+efR6PSdPnrQ6\nfvz4ceLi4gCIjv6c1as9gSsoihuLFm2wKnvo0CECAgLo23ceHToYaNZMsZyLjvbinnt+Ax7H3/9r\nQIO3tzcASUlagoONVCNxpRB2MZlM9TpgNDZarRaTybE3yEjQcIBLly6xefNmfHx8uHTpEoWFhZZz\nGzduxMvLi/9hsf9YAAAX4klEQVT7v/9j165CjEYXQkP3A7Bt2w/Ex8cDYDQa+eabbxg8eAjx8a70\n7Gm9z7dGAy+9pGbx4hzS0gbx6KPmbXEBzp/X0L699DKE89XXIanGzNHXTIKGA+zcuROAefPmYTKZ\nOHcuicOHXVEU857nw4cPZ/bs2ZhMY9Fq83nzzQEAuLndbMnue/LkSa5evUqbNlNIS9MweLDtMde/\n/jWPLl1K+PLLHri7e6Io5uGpDh0kaAghnE+ChgPs3LmTXr16MXz4cAA2b9YwZYofn31WTHJyMj17\n9qRXr974+s5g9GgIDVWh1Sp07jyWnTt3kp6ezv795t5HYuIIvL1NjB9faPO9VCqYPz+Xs2dd2LnT\nnfR0Nfn5agkaotFISUlh1qxZDBo0iPDwcCIjIykuLrZZNjU1lTlz5lT6mtOmTSMnJ6dK9VmzZg1v\nvPFGpeU6duxY4fmcnBzee++9KtWhJknQqKakpCROnDjB+PHj6dChA6Dmiy+6AvDZZ+beQvfu3fnl\nFy1ZWW5ERBSh1UJgoBFf334YDAZmzvyV9et70qHDevbta87EiQV4eirlvucddxTQrVsxixY1IzbW\nfAdV+/Zy55Ro+BRFYc6cOYwZM4bY2Fi++eYb8vLyWLVqVZmyBoOBgIAA3n777Upfd8uWLfj4+Dij\nyna7du0amzdvrtU62ENmtKrBZDIRGRmJq6sr48eP/9+e3bPIzGyJj4+JY8eaA+ag8f775g/3YcPM\ngaRtWwOZmT6MGrWGr76aAhRx/rwbLVsamT274u1atVrYsOEKY8a0YOHCZgDS0xA1LjIyklOnTjn0\nNbt27crSpUvLPX/48GHc3Nws2ypoNBqWLFnCgAEDWLBgATt27GD37t3k5eVhMpl46aWXmDFjBvv3\n76egoIAnnniCM2fO0KFDB9LS0li+fDk9evSgf//+lufdf//99OvXj2PHjhEQEMDGjRvx8PDg/fff\n5/3336e4uJj27dvzyiuvVHhX0qVLl5g3bx75+fmMHj3acjwvL49Zs2aRk5ODwWDg6aef5rbbbuP5\n55/n4sWLjBo1iqFDh/LUU0/ZLFfbpKdRRUajkeXLl7N//36effZZWrdu/b/jj6DR/Eb//vvJzm5J\ncPBgvL29iY11IzS0xJLqo21bI+fPazl+fD5a7Rn69BnN8eN6fvwxjdDQygNASIiRzZuzCQw00LKl\nkdatpachGr4zZ87QrVs3q2Pe3t60adOGpKQkAE6cOMFbb73Fxx9/bFVu06ZN+Pj48M0337Bw4UKO\nHz9u8z2SkpKYMWMGBw4coGnTpuzatQuA22+/nV27dhETE0NoaCgffPBBhXWNjIxk+vTp7Nu3D39/\nf8txNzc3oqKi2LNnD9u2bWPp0qUoisKiRYto27Yte/fuZfHixeWWq23S06iCoqIiZs+ezcGDB5k6\ndSozZswAIClJw5UrfYB/8dVXnwK/4Ot7H8XF8N13rtxzT4HlNdq2NZCbq0algg8/dKF793do0uTG\nfiEGDixm374MiopUNJLbzkUdUlGPoDYNHTqU5s2blzn+/fff88ADDwDQuXNnunTpYvP5QUFB3HLL\nLYB5lCA5ORmAX3/9lRdeeIFr166Rl5fHsGHDKqzH0aNHLUNjkyZNYvny5YB5iG3lypUcOXIElUpF\namoqGRkZZZ5fXrmWLVva+ZNwjhoLGvHx8bz77ruYTCZGjhzJhAkTrM5/9dVX7Nmzx7KC8a9//SuB\ngYE1VT27KYrCggULOHjwICtWrGDatGmWW9o++MATjUZh8+ZRNG8+mHvuSSYr6y/89JOR/Hy11R1R\ngwYV061bMS+8kEP37m5A1XZP0mpBq639bx9C1ISOHTvyxRdfWB27fv06ly9fpn379pw4cQJPT89q\nvccfs0drNBrLLfRPPvkkUVFR3HzzzURHR/Ptt99W+lq2bnf95JNPyMrKYvfu3bi4uNC/f3+bK9Tt\nLVfTamR4ymQyERUVxaJFi1i3bh2xsbH89ttvVmUGDx7MmjVrePHFF7nrrrvYtGlTTVTthsXGxvLJ\nJ5+wYMECpk+fbvmluH5dxfvvN2HUqEKGD+9Ijx7dWbnSi0uXmvPII81xd1cYOPD3C96tWwlffplJ\n9+4ltdUUIeqdIUOGUFBQwLZt2wDzMPHSpUuZPHlypaue+/bta7k9/syZMyQkJNzQe+fm5uLv709J\nSQmffvpppeX79u3LZ599BpgDQKnr16/j5+eHi4uL1WdhkyZNyM3NrbRcbauRoJGYmEhAQAD+/v5o\ntVrCw8M5evSoVZk/fjsoLCx06iIik8lUZuW2vT7++GO8vb15+OGHuX5dxQcfePLGG01Yu9abq1fV\nPPro7xf9rrsKCA0tISdHRVRUNs2bS49AiOpQqVS88847fP755wwaNIghQ4bg5ubGM888U+lzZ8yY\nQVZWFkOGDOGFF16gU6dOlqwK9li4cCF33HEHEyZMIDQ0tNLyS5cu5b333mPkyJGkpqZajk+cOJGf\nf/6ZkSNH8t///tfyWjqdjr59+3LrrbeybNmycsvVNpVSAzMr3333HfHx8Tz88MMAfP3115w9e9Yy\nvljqyy+/5IsvvsBgMBAZGUmrVq3KvFZMTAwxMTEArFy5stz7syuydOlS1q5dy5EjRywXQqXSkJEB\nf5ivKiM/P5+goCDuvvtu3nzzTe6+W8vOnb/H3bFjTXz6qfUk9uXLUFQEHTrccDWdSqvVYjA03juu\nGnP7q9P2tLQ0q+Gb+sRoNFJSUoK7uzsXLlzg7rvvJi4uzrIvTUNVVFRkNRGv1WpRq6veX6hTE+Fj\nxoxhzJgxHD58mI8//pj58+eXKRMREUFERITlcWZm5g2/z6RJk3j99deZMGECGRkZNG3anubNvyAh\noQ3796cTEmL7TqQtW7aQm5vL2LFjOX06m127/JkzJ5f7788jOtqTqVPzycy0fq6bm/lfFarpVH5+\nflX62TUUjbn91Wl7UVFRvU32l5ubyz333IPBYEBRFJ5//nnUanWD//JQVFRkdb39/PyqFShrZHhK\np9ORlZVleZyVlYVOpyu3vK3hK0fy9/dn/fr1nD9/nk6d+pGR8REnT7bAYFDx5Ze2x0Xj4uKIjIyk\nX79+DBw4kO3bPTAaVdx3Xz6hoUb++c/rtGsnt70KUVd5eXmxe/duDhw4QExMDLfeemttV6leqpGg\nERISgl6vJz09HYPBQFxcHGFhYVZl9Hq95f8//vijzaEpR5o0aRLfffcdTZvuxGjsCIynffsr7Nnj\nbrP82rVrLQt9VCo1H33kSY8exXTq1LC/pQghxB/VyPCURqNh9uzZLF++HJPJxIgRIwgKCiI6OpqQ\nkBDCwsL48ssvOXHiBBqNBi8vL+bNm+f0eqnVbTlwwINHH83k9dcP4OcXy7Fj48jIUNOihXU64YSE\nBMaOHUvz5s05etSVU6dcWLHiajmvLIQQDVONzWn07t2b3r17Wx0rTQUAMGvWrJqqikVMjLlXMWmS\niWPH+pKaGoWi3MGXX7ozbVq+pVxWVhZXrlyxTJq/804TfHxM3H13gc3XFUKIhqpRpxGJiXGnXTsD\noaEGbr31VpKSthMQkMEHH1gvDjp79ixgXliUmqpm92537r03v8KkgkII0RA12qCRlwexsW6MHFmI\nSgVTp05l0KBBpKYu5eefXXnxxRhL2T8GjV27zBPg996bX95LCyGcKCgoiFGjRnHrrbfy0EMPUVBQ\n9R5/XFwc06dPB8xZKdavX19u2aqmLm9oqdMbbdDYuFFNUZGKcePMKQJ8fHz44IMPWLHiZlSqQl57\nzUB+vjkwnD17Fk9PT1q3bs2uXe506lRiV1JBIYTjubu7s3fvXvbv34+rq2uZdOKKolRpi9PRo0fb\nvM2/VG2nLq/t9y9Vp9Zp1JTcXBWrVmkYNKiI/v1/Xxyo0WiYPv1OvvoqnQMH7uHZZxdw7VoK586d\nIzQ0lOxsLUeOuPLYY7kVvLoQjUNkZFNOnXJx6Gt27VrC0qXX7C7fr18/Tp8+TXJyMvfddx+9evXi\nxIkTbNmyhXPnzrF69WqKi4tp27Yt69ato0mTJuzfv59//etfeHh40K9fP8trRUdHc/z4cZYvX05G\nRgbPPPMMFy9eBGDFihVs3LjRKnX54sWLef3119m5cyfFxcWMGTOGBQsWAPDyyy+zbds2/Pz8aN26\nNd27dy9T9/qaOr1RBo133mlCRoaKjRtt/3I+8YQHBw54s3WrCfgcMC/9373bHZNJxe23ywS4ELXN\nYDBw4MABy46ZSUlJvPTSS/Tp04fs7GxefvlloqOj8fT0ZMOGDbz11ls88sgj/O1vfyM6Opr27dtb\nslT82eLFixkwYABRUVEYjUby8vJYtGgRv/76K3v37gXg0KFDJCUl8cUXX6AoCjNnzuS7777D09OT\nHTt2sHfvXgwGA2PGjLEZNEpTp99zzz1Ww06lKdG9vb3Jzs5m/PjxjB49usz7GwwGm+WcvY97owwa\nf/lLPsHBnvTubTtZYJ8+JQQH55CX90/eeecOHnvsUQYNGsy773rSuXMJN98sQ1NC3EiPwJEKCwsZ\nNWoUAP379+fee+8lLS2NwMBA+vTpA8APP/zAmTNnuOuuuwAoKSmhT58+JCYmEhwc/L9dNs3rtf7z\nn/+UeY/Y2FhefvllwDwC0bRp0zLbwR46dIhDhw5Zegn5+fkkJSWRm5vLmDFjLAkUS+v6Z/U1dXqj\nDBoBASYeeshUbmoPlQr++leFf/6zHSpVE7799lt++MGVkyddWbHiKk4O5EKICpTOafzZH5OeKorC\n0KFDee2116zKVDVRqS2KojB//nymTZtmddye7WVL1cfU6Y12IrwykycX0KyZibfe8iIlRcuyZT54\neZmYOFGGpoSo6/r06cPRo0ctu/nl5+db5iaTk5O5cOECANu3b7f5/MGDB1smnY1GI9euXSuTunz4\n8OFER0eTl2fenlmv15OZmcmAAQPYs2cPBQUF5Obm2gxwUH9TpzfKnoY9PD0V7r8/j/Xrvdm1ywMP\nDxPPP5+Dl5eszRCirvP19WXdunXMmzfPkgn76aefJiQkhNWrVzN9+nQ8PDzo37+/1QdxqaVLl/L0\n00/z4YcfolarWbFiBWFhYZbU5SNGjGDx4sWcPXuWO++8EzD3dF599VW6devG+PHjGTVqFH5+fvTs\n2dNmHZcuXcq8efN47bXXrCbCJ06cyIwZMxg5ciTdu3e3mTp9xIgRzJs3z2Y5Z6uR1OjOlJKSUqXn\n2ZPpMytLzcqV3gQGGpk4sYCgoIaTkLAxZ3mFxt3+6rQ9Pz+/2jvj1bbGlhb/z9esullupadRAV9f\nEy++mFN5QSGEaCRkTkMIIYTdJGgIIexWz0ezGyVHXzMJGkIIuzWGne4aEoPBUK2tXW2ROQ0hhN3c\n3d0pLCykqKjI6SuPncXNza1G1jPUNkVRUKvVuLvb3liuqiRoCCHsplKpLCud66vGfOecI8jwlBBC\nCLtJ0BBCCGE3CRpCCCHsVu9XhAshhKg5jban8cwzz9R2FWqVtL/xtr8xtx2k/dVtf6MNGkIIIW6c\nBA0hhBB20yxZsmRJbVeitpTu3tVYSfsbb/sbc9tB2l+d9stEuBBCCLvJ8JQQQgi7SdAQQghht0aZ\neyo+Pp53330Xk8nEyJEjmTBhQm1XyanmzZuHu7s7arUajUbDypUryc3NZd26dWRkZNCiRQuefPJJ\nvLy8aruqDvHaa6/x448/4uPjw5o1awDKba+iKLz77rv89NNPuLm5MXfu3Ho/3m2r/R999BH79u2j\nadOmANx777307t0bgE8//ZT9+/ejVquZNWtWuduT1geZmZls2LCBq1evolKpiIiIYOzYsY3m+pfX\nfodef6WRMRqNyvz585XU1FSlpKREWbBggZKcnFzb1XKquXPnKjk5OVbHtmzZonz66aeKoijKp59+\nqmzZsqU2quYUv/zyi3Lu3Dnlqaeeshwrr70//PCDsnz5csVkMim//vqr8o9//KNW6uxIttofHR2t\nfPbZZ2XKJicnKwsWLFCKi4uVtLQ0Zf78+YrRaKzJ6jpUdna2cu7cOUVRFCU/P1957LHHlOTk5EZz\n/ctrvyOvf6MbnkpMTCQgIAB/f3+0Wi3h4eEcPXq0tqtV444ePcqwYcMAGDZsWIP6GXTt2rVMr6m8\n9h47doyhQ4eiUqno1KkTeXl5XLlypcbr7Ei22l+eo0ePEh4ejouLCy1btiQgIIDExEQn19B5mjdv\nbukpeHh40KZNG7KzsxvN9S+v/eWpyvVvdEEjOzsbX19fy2NfX98Kf6gNxfLly/n73/9OTEwMADk5\nOTRv3hyAZs2akZPTsPdCL6+92dnZ+Pn5Wco15N+HPXv2sGDBAl577TVyc3OBsn8POp2uwbQ/PT2d\npKQkQkNDG+X1/2P7wXHXv1HOaTQ2y5YtQ6fTkZOTw3PPPUfr1q2tzqtUqnq7oU5VNLb2AowePZq7\n774bgOjoaDZv3szcuXNruVbOU1hYyJo1a5g5cyaenp5W5xrD9f9z+x15/RtdT0On05GVlWV5nJWV\nhU6nq8UaOV9p+3x8fOjbty+JiYn4+PhYuuFXrlyxTJA1VOW1V6fTWW3I01B/H5o1a4ZarUatVjNy\n5EjOnTsHlP17yM7OrvftNxgMrFmzhiFDhtC/f3+gcV1/W+135PVvdEEjJCQEvV5Peno6BoOBuLg4\nwsLCartaTlNYWEhBQYHl/8ePHyc4OJiwsDAOHToEwKFDh+jbt29tVtPpymtvWFgYX3/9NYqicObM\nGTw9PS3DGA3JH8fpv//+e4KCggBz++Pi4igpKSE9PR29Xm8ZzqiPFEXhjTfeoE2bNtxxxx2W443l\n+pfXfkde/0a5IvzHH39k06ZNmEwmRowYwcSJE2u7Sk6TlpbG6tWrATAajQwePJiJEydy/fp11q1b\nR2ZmZoO75fall17i1KlTXL9+HR8fHyZPnkzfvn1ttldRFKKiovj5559xdXVl7ty5hISE1HYTqsVW\n+3/55RcuXLiASqWiRYsWPPTQQ5YPx08++YQDBw6gVquZOXMmvXr1quUWVF1CQgKRkZEEBwdbhqDu\nvfdeOnbs2Ciuf3ntj42Nddj1b5RBQwghRNU0uuEpIYQQVSdBQwghhN0kaAghhLCbBA0hhBB2k6Ah\nhBDCbhI0hBBC2E3SiAhRgXnz5nH16lU0Gg1qtZrAwECGDh1KREQEarV85xKNjwQNISrx97//ne7d\nu5Ofn8+pU6d49913SUxMbNC5m4QojwQNIezk6elJWFgYzZo145///Cd33HEHmZmZfPjhh6SlpeHp\n6cmIESOYPHkyACtWrKBnz57cfvvtltdYsGCBZYX6pk2bOHz4MCUlJfj5+fH4448THBxcW80Twi4S\nNIS4QaGhoeh0OhISEmjTpg3z588nMDCQ5ORknnvuOdq1a0e/fv0YNmwYn3/+uSVoXLhwgezsbHr3\n7s3PP//M6dOnefnll/H09OTy5cs0adKkllsmROVkUFaIKtDpdOTm5nLzzTcTHByMWq2mbdu2DBo0\niFOnTgHmZHB6vR69Xg/A119/TXh4OFqtFq1WS2FhIZcvX0ZRFAIDA+t1ojzReEhPQ4gqyM7OxsvL\ni7Nnz7J161YuXbqEwWDAYDAwYMAAAFxdXRk4cCDffPMNd999N7Gxsfztb38D4JZbbuG2224jKiqK\nzMxM+vXrx7Rp08rs/SBEXSM9DSFuUGJiItnZ2XTu3JlXXnmFPn368Prrr7Np0yZGjRrFH3OADh8+\nnG+++YaTJ0/i5uZGp06dLOfGjh3LqlWrWLt2LXq9nh07dtRGc4S4IRI0hLBTfn4+P/zwAy+//DJD\nhgwhODiYgoICvLy8cHV1JTExkcOHD1s9p1OnTqjVajZv3szQoUMtxxMTEzl79iwGgwE3NzdcXFzk\nFl5RL8jwlBCVWLVqFRqNBpVKRWBgIOPGjWP06NEAPPjgg2zevJmNGzfStWtXBg4cSF5entXzhw4d\nSnR0NAsXLrQcKygoYNOmTaSlpeHq6kqPHj248847a7RdQlSF7KchhJMdOnSImJgYli1bVttVEaLa\npD8shBMVFRXx1VdfERERUdtVEcIhJGgI4STx8fE8+OCD+Pj4MHjw4NqujhAOIcNTQggh7CY9DSGE\nEHaToCGEEMJuEjSEEELYTYKGEEIIu0nQEEIIYbf/Bwomjmzc4LdeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNK2yBpCJQ-_",
        "colab_type": "text"
      },
      "source": [
        "### <b><font color=\"black\" size=\"+1\">Evaluate the accuracy of the model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWtdi6GZzjvb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predic = pd.DataFrame(predicted)\n",
        "fact = pd.DataFrame(original)\n",
        "itog = pd.concat([predic, fact], axis=1)\n",
        "itog.columns = (['pred', 'fact'])\n",
        "itog[\"predUD\"] = find_result(predic)\n",
        "itog[\"factUD\"] = find_result(fact)\n",
        "\n",
        "true_long = 0\n",
        "true_short = 0\n",
        "vern = 0\n",
        "\n",
        "for i in range(0,len(itog)):\n",
        "  if itog[\"predUD\"][i] > 0.5 and itog[\"factUD\"][i] > 0.5:\n",
        "    true_long = true_long + 1\n",
        "  elif itog[\"predUD\"][i] < 0.5 and itog[\"factUD\"][i] < 0.5:\n",
        "    true_short = true_short + 1\n",
        "  \n",
        "  if itog[\"predUD\"][i] == itog[\"factUD\"][i]:\n",
        "    vern = vern + 1\n",
        "    \n",
        "per_long = round((true_long/len(itog[itog[\"factUD\"]>0.5]))*100,2)\n",
        "per_short = round((true_short/len(itog[itog[\"factUD\"]<0.5]))*100,2)\n",
        "per_vern = round((vern/len(itog))*100,2)\n",
        "\n",
        "print(\"Percentage of open orders for increase:\", per_long)\n",
        "print(\"Percentage of open orders for decrease:\", per_short)\n",
        "print(\"\")\n",
        "print(\"The percentage of correct forecasts:\", per_vern)\n",
        "print(\"--------------------------------------\")\n",
        "itog.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cC3p7gwfNjuO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b3a9f456-8e7d-4c20-daad-9ebb7d76d353"
      },
      "source": [
        "print (\"MSE =\", np.mean(np.square(predicted - original)))\n",
        "print (\"MAE =\", np.mean(np.abs(predicted - original)))\n",
        "print (\"MAPE =\", np.mean(np.abs((original - predicted) / original)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE = 0.07131233666676697\n",
            "MAE = 0.2025770944113796\n",
            "MAPE = 0.35941051355341186\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKqhPbJ7SHd9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "82aceced-5b7e-49a9-d265-3b46cc634c00"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v37x5M+eff34wsusnfRZCiJPl448/5uOPP+aiiy7i4osvZu/evZSWljJp0iQ++eQTVq1a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\nFKZMmeIfKQTeacq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ISABg9uzZbN26NSBYFBQUcNVVVwGQlpbGs88+i6ZphIWFMWnSJA4d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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyXfhrzrPENc",
        "colab_type": "text"
      },
      "source": [
        "### <b><font color=\"black\" size=\"+1\">Backtesting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBpmE2p65ZxM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "cae4d890-e16c-46f3-d2a0-40c5109e3529"
      },
      "source": [
        "data_original.reset_index(inplace=True)\n",
        "test_dates = pd.DataFrame(np.array(data_original[\"Date\"][-len(Y_test):]))\n",
        "test_price = pd.DataFrame(np.array(data_original[\"Close\"][-len(Y_test):]))\n",
        "data_original = data_original.set_index('Date')\n",
        "test_signal = pd.DataFrame(np.array(find_result(pd.DataFrame(pred))))\n",
        "backtest = pd.concat([test_dates, test_price, test_signal], axis=1)\n",
        "backtest.columns = (['Date', 'Price', 'Signal'])\n",
        "\n",
        "CAPITAL = 100000.0 # amount of initial capital\n",
        "CHAST = 1 # 0.05 = 5% share of the capital for which we open the order\n",
        "KOMISSIYA = 0.0005 # 0.0005 = 0.05% commission for opening/closing an order\n",
        "\n",
        "balans = []\n",
        "balans.append(CAPITAL)\n",
        "capital_now = CAPITAL\n",
        "signal_now = backtest['Signal'][0]\n",
        "price_now = backtest['Price'][0]\n",
        "change_now = 0.0\n",
        "\n",
        "for i in range(1,len(backtest)):\n",
        "  if signal_now == backtest['Signal'][i]: # if the current signal matches the original, then\n",
        "    balans.append(capital_now) # do not change anything and rewrite current capital\n",
        "  else: # if the signal has changed, then\n",
        "    change_now = (backtest['Price'][i] - price_now) / price_now # calculate the price change\n",
        "    if signal_now > 0.5: # if a signal to increase stock prices\n",
        "      #-------------------------------\n",
        "      total = capital_now * CHAST # calculate the amount to open an order\n",
        "      capital_now = capital_now - total # at the opening we subtract this amount from the capital\n",
        "      komis_open = total * KOMISSIYA # we calculate the commission for opening an order\n",
        "      total = total + (total * change_now) # calculate the amount taking into account changes in the price of an asset\n",
        "      komis_close = total * KOMISSIYA # we calculate the commission for closing an order\n",
        "      total = total - komis_open - komis_close # calculate the amount minus commissions\n",
        "      capital_now = capital_now + total # when closing an order, add the amount of capital\n",
        "      balans.append(capital_now) # and rewrite current capital\n",
        "      #-------------------------------\n",
        "      signal_now = backtest['Signal'][i] # update current signal\n",
        "      price_now = backtest['Price'][i] # update current price   \n",
        "    else: # if a signal to decrease stock prices\n",
        "      #-------------------------------\n",
        "      total = capital_now * CHAST # calculate the amount to open an order\n",
        "      capital_now = capital_now - total # at the opening we subtract this amount from the capital\n",
        "      komis_open = total * KOMISSIYA # we calculate the commission for opening an order\n",
        "      total = total - (total * change_now) # calculate the amount taking into account changes in the price of an asset\n",
        "      komis_close = total * KOMISSIYA # we calculate the commission for closing an order\n",
        "      total = total - komis_open - komis_close # calculate the amount minus commissions\n",
        "      capital_now = capital_now + total # when closing an order, add the amount of capital\n",
        "      balans.append(capital_now) # and rewrite current capital\n",
        "      #-------------------------------\n",
        "      signal_now = backtest['Signal'][i] # update current signal\n",
        "      price_now = backtest['Price'][i] # update current price\n",
        "\n",
        "backtest['Capital'] = pd.DataFrame(balans)\n",
        "plt.plot(backtest['Capital'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/Etv/+SHKZkPv3Ynx5KPol36JTkrG9n9LzV0Pu7OzHcwhwH4/+lQt9M9oPT7A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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDzjyRXBP4TB",
        "colab_type": "text"
      },
      "source": [
        "### <b><font color=\"black\" size=\"+1\">Save the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45iYYd0RP8GQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save the weight and architecture of the neural network\n",
        "model.save_weights('weights.h5', overwrite=True)\n",
        "model.save('model.h5', overwrite=True)\n",
        "\n",
        "# Save the network architecture in JSON format\n",
        "model_json = model.to_json()\n",
        "json_file = open(\"test.json\", \"w\")\n",
        "json_file.write(model_json)\n",
        "json_file.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TnGQ1L2Ro0O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Download file to local\n",
        "from google.colab import files\n",
        "files.download(\"weights.h5\")\n",
        "files.download(\"model.h5\")\n",
        "files.download(\"test.json\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}