NeuralStockPredictor_Rus.ipynb

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      "name": "NeuralStockPredictor Rus.ipynb",
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        "<a href=\"https://colab.research.google.com/github/tg-bomze/NeuralStockPredictor/blob/master/NeuralStockPredictor_Rus.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",
        "*↓ Открой меня ↓*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1qlDrOlZBFL",
        "colab_type": "text"
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      "source": [
        "##**Краткая инструкция:**\n",
        "\n",
        "Для запуска нажмите **Open in playground** в верхнем левом углу:\n",
        "\n",
        "![alt text](https://d.radikal.ru/d39/2001/c1/4192dfd3a667.jpg)\n",
        "\n",
        "(*на предложение reset runtime отвечать утвердительно*)\n",
        "\n",
        "Далее, нажав на квадратные скобки выполняйте каждый блок поочередно, дожидаясь окончания выполнения предыдущего (*картинка представлена в качестве примера*):\n",
        "\n",
        "![alt text](https://d.radikal.ru/d18/2001/1a/b618f3778f86.jpg)\n",
        "\n",
        "*Блок считается выполненным, когда вокруг квадратных скобок* **[**_**]** *перестанет бегать \"змейка\"*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etBPwkyOZrOj",
        "colab_type": "text"
      },
      "source": [
        "##**Исследование провел:**\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",
        "colab_type": "text"
      },
      "source": [
        "# <b><font color=\"red\" size=\"+2\">Исследование:\n",
        "\n",
        "Прогнозирование цены акций с использованием сверточных и рекуррентных нейронных сетей на Keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1uzwFlprTEe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "afa54635-9a05-43c8-bf52-e5c8b3829f75"
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      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Устанавливаем необходимые библиотеки {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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3.34 s, sys: 845 ms, total: 4.18 s\n",
            "Wall time: 1min 49s\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "cm6XyRyC1hcc",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "6d6b940c-9ef4-439d-f26f-b37757f5bf5f"
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Объявляем основные переменные и загружаем исходные данные {display-mode: \"form\"}\n",
        "#@markdown Размер входных данных (в днях).\n",
        "WINDOW = 30   #@param {type:\"slider\", min:10, max:90, step:10}\n",
        "EMB_SIZE = 8  # колич. входных перем.\n",
        "#@markdown Размер тестовой выборки (в частях).\n",
        "PERCENT = 0.9   #@param {type:\"slider\", min:0, max:1, step:0.05}\n",
        "STEP = 1      # шаг\n",
        "#@markdown Дальность предсказания (в днях).\n",
        "FORECAST = 1   #@param {type:\"slider\", min:1, max:10, step:1}\n",
        "#@markdown Курс какой акции будем предсказывать?\n",
        "TICKER = 'GAZP.ME' #@param [\"GAZP.ME\", \"SBER.ME\", \"ROSN.ME\", \"VTBR.ME\", \"TSLA\", \"GOOG\", \"AMZN\", \"FB\"]\n",
        "#@markdown Если в выпадающем списке вы не нашли необходимую вам акцию, то воспользуйтесь ручной загрузкой котировок. Данные предлагается скачивать с https://finance.yahoo.com. Просто введите в поиске необходимый вам тикер и скачайте csv-файл.\n",
        "upload_stock_quotes = False #@param {type:\"boolean\"}\n",
        "#@markdown Установленная галочка сверху предложит вам загрузить этот файл с вашей локальной машины.\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='Курс акции {}'.format(TICKER),\n",
        "    yaxis_title='Цена',\n",
        "    xaxis_title='Дни')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 2,
      "outputs": [
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    {
      "cell_type": "code",
      "metadata": {
        "id": "px53JdUJ4mk-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Объявляем все функции для расчета дополнительных параметров {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",
        "  # (1 - рост, 0 - падение)\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",
        "# --- Функция расчета гэпа ---\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",
        "# --- Функция расчета (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",
        "# --- Функция расчета разницы двух экспоненциальных скользящих средних ---\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 ЭкспСкользСредн\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 ЭкспСкользСредн\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",
        "# --- Функция расчета стохастического осциллятора ---\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",
        "# --- Функция расчета волатильности ---\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": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOKm_dlT5WQb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "ece2b37a-cc5b-4702-cadb-1707b50f73a2"
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Расчитываем дополнительные параметры и проводим нормализацию данных {display-mode: \"form\"}\n",
        "\n",
        "#@markdown Период быстрой ЭкспСкользСредн\n",
        "fast_emad = 8 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "#@markdown Период медленной ЭкспСкользСредн\n",
        "slow_emad = 18 #@param {type:\"slider\", min:1, max:32, step:1}\n",
        "#@markdown Период K для стохастического осциллятора\n",
        "k_stoch = 5 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "#@markdown Период сглаживания для стохастического осциллятора\n",
        "smooth_stoch = 3 #@param {type:\"slider\", min:1, max:8, step:1}\n",
        "#@markdown Период расчета волатильности\n",
        "period_volat = 6 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "#@markdown Период автокорреляции\n",
        "window_autocorr = 30 #@param {type:\"slider\", min:10, max:90, step:5}\n",
        "#@markdown Лаг автокорреляции\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": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2573.000000</td>\n",
              "      <td>2573.000000</td>\n",
              "      <td>2573.000000</td>\n",
              "      <td>2573.000000</td>\n",
              "      <td>2573.000000</td>\n",
              "      <td>2573.000000</td>\n",
              "      <td>2573.000000</td>\n",
              "      <td>2573.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.504440</td>\n",
              "      <td>0.032235</td>\n",
              "      <td>0.032103</td>\n",
              "      <td>0.238695</td>\n",
              "      <td>0.128910</td>\n",
              "      <td>0.591640</td>\n",
              "      <td>0.510282</td>\n",
              "      <td>0.107841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.176439</td>\n",
              "      <td>0.062700</td>\n",
              "      <td>0.073340</td>\n",
              "      <td>0.206070</td>\n",
              "      <td>0.111369</td>\n",
              "      <td>0.076678</td>\n",
              "      <td>0.257329</td>\n",
              "      <td>0.099185</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>0.376999</td>\n",
              "      <td>0.007439</td>\n",
              "      <td>0.014305</td>\n",
              "      <td>0.118564</td>\n",
              "      <td>0.069037</td>\n",
              "      <td>0.556032</td>\n",
              "      <td>0.285937</td>\n",
              "      <td>0.051677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.502047</td>\n",
              "      <td>0.018066</td>\n",
              "      <td>0.019807</td>\n",
              "      <td>0.163262</td>\n",
              "      <td>0.105056</td>\n",
              "      <td>0.592989</td>\n",
              "      <td>0.505667</td>\n",
              "      <td>0.083121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.632832</td>\n",
              "      <td>0.034538</td>\n",
              "      <td>0.028702</td>\n",
              "      <td>0.262198</td>\n",
              "      <td>0.157495</td>\n",
              "      <td>0.624202</td>\n",
              "      <td>0.736629</td>\n",
              "      <td>0.126954</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  2573.000000  2573.000000  ...  2573.000000  2573.000000\n",
              "mean      0.504440     0.032235  ...     0.510282     0.107841\n",
              "std       0.176439     0.062700  ...     0.257329     0.099185\n",
              "min       0.000000     0.000000  ...     0.000000     0.000000\n",
              "25%       0.376999     0.007439  ...     0.285937     0.051677\n",
              "50%       0.502047     0.018066  ...     0.505667     0.083121\n",
              "75%       0.632832     0.034538  ...     0.736629     0.126954\n",
              "max       1.000000     1.000000  ...     1.000000     1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyepEuSq6R8k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Предподготовка данных к обучению нейронной сети {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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5C-Yn8YszMUG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "65d1d53f-52b0-420c-c7ab-e4f74330354f"
      },
      "source": [
        "#@title <b><font color=\"gree\" size=\"+2\">Быстрый анализ (предсказание и бэктестинг) {display-mode: \"form\"}\n",
        "\n",
        "#@markdown Размер первоначального капитала (в y.e.).\n",
        "CAPITAL = 100000 #@param {type:\"integer\"}\n",
        "#@markdown Доля капитала, на которую открываем ордер (в частях).\n",
        "CHAST = 0.05 #@param {type:\"slider\", min:0.05, max:1, step:0.05}\n",
        "#@markdown Размер комиссии за открытие/закрытие ордера (в %).\n",
        "KOMISSIYA = 0.05 #@param {type:\"slider\", min:0, max:1, step:0.05}\n",
        "KOMISSIYA = KOMISSIYA / 100\n",
        "\n",
        "#@markdown Выберите предобученную модель для вашей акции\n",
        "MODEL = 'GAZP' #@param [\"GAZP\", \"SBER\", \"ROSN\", \"VTBR\", \"TSLA\", \"GOOG\", \"AMZN\", \"FB\"]\n",
        "#@markdown Если в выпадающем списке вы не нашли необходимую вам предобученную модель, то можете обучить ее самостоятельно.\n",
        "new_training = False #@param {type:\"boolean\"}\n",
        "#@markdown Если качество обученной модели окажется низким, поэтому предлагается пропустить этот блок и преступить к следующему.\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(\"Изменение портфолио:\", round((itog['capi'][len(itog['capi'])-1] - itog['capi'][0])*100, 1),\"% за\", len(itog['capi']), \"дней\")\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": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Изменение портфолио: 94.6 % за 252 дней\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS0IIikYgxXd",
        "colab_type": "text"
      },
      "source": [
        "## <b><font color=\"gree\" size=\"+2\">Основной анализ (ручная настройка)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCp4ufg0PU5o",
        "colab_type": "text"
      },
      "source": [
        "Ниже в разделах \"***Строим архитектуру сети***\" и \"***Обучаем нейросеть***\" будет предложено вручную настроить весь процесс обучения нейронной сети. Помимо этого можно будет также загрузить архитектуру и веса сети, которые можно взять [**ТУТ**](https://drive.google.com/open?id=1ln7tEV5YQf-imL7DzoyLIU2BfoP1PqIF)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBC7b6ivrjdY",
        "colab_type": "text"
      },
      "source": [
        "### <b><font color=\"black\" size=\"+1\">Строим архитектуру сети"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tued3U5Pg5BZ",
        "colab_type": "text"
      },
      "source": [
        "**Если хотите воспользоваться готовой архитектурой, то можно загрузить файл model.h5**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuCUHr7xgmXr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# --- Загружаем файл на сервер ---\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": [
        "**Если нет, то строим самостоятельно**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxKG1mNNCclb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''# --- Новая версия ---\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",
        "# --- Старая версия (стабильная) ---\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\">Обучаем нейросеть"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSsdHxN6T5MS",
        "colab_type": "text"
      },
      "source": [
        "**Если хотите воспользоваться уже обученной сетью, то можно загрузить файл файл с весами weights.h5**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlzw_G65T5VQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# --- Загружаем файл на сервер ---\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": [
        "**Если нет, то обучаем самостоятельно**"
      ]
    },
    {
      "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": [
        "**Сохраняем результаты предсказания**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Bf81auVjiMs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2cf7fa0a-3d5a-4262-e485-e4433a1f0527"
      },
      "source": [
        "print(\"Затрачено секунд:\", finish - start)\n",
        "pred = model.predict(X_test)\n",
        "predicted = pred\n",
        "original = Y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Затрачено секунд: 5.736419916152954\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrT5tdmlN9w6",
        "colab_type": "text"
      },
      "source": [
        "**Визуализация результата**"
      ]
    },
    {
      "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\">Оценка точности модели"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWtdi6GZzjvb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "b5a37de5-7db9-4f60-a705-fb6d804db824"
      },
      "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(\"Процент вырных ордеров на повышение:\", per_long)\n",
        "print(\"Процент верный ордеров на понижение:\", per_short)\n",
        "print(\"\")\n",
        "print(\"Процент верных прогнозов:\", per_vern)\n",
        "print(\"--------------------------------------\")\n",
        "itog.head()\n",
        "\n",
        "# для перебора каждых вторых данных нужно удалить \"2\" из строк:\n",
        "# for i in range(0,len(itog),2):\n",
        "# print(\"Процент вырных ордеров на повышение:\", per_long*2)\n",
        "# print(\"Процент верный ордеров на понижение:\", per_short*2)\n",
        "# print(\"Процент верных прогнозов:\", per_vern*2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Процент вырных ордеров на повышение: 56.3\n",
            "Процент верный ордеров на понижение: 55.37\n",
            "\n",
            "Процент верных прогнозов: 55.83\n",
            "--------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pred</th>\n",
              "      <th>fact</th>\n",
              "      <th>predUD</th>\n",
              "      <th>factUD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.260672</td>\n",
              "      <td>0.298881</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.269898</td>\n",
              "      <td>0.291848</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.286241</td>\n",
              "      <td>0.295089</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.291665</td>\n",
              "      <td>0.292704</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.295423</td>\n",
              "      <td>0.275579</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pred      fact  predUD  factUD\n",
              "0  0.260672  0.298881       0       0\n",
              "1  0.269898  0.291848       1       0\n",
              "2  0.286241  0.295089       1       1\n",
              "3  0.291665  0.292704       1       0\n",
              "4  0.295423  0.275579       1       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "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\">Бэктестинг инструмента"
      ]
    },
    {
      "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) # 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",
        "CAPITAL = 100000.0 # размер первоначального капитала\n",
        "CHAST = 1 # 0.05 = 5% доля капитала, на которую открываем ордер\n",
        "KOMISSIYA = 0.0005 # 0.0005 = 0.05% комиссия за открытие/закрытие ордера\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",
        "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\">Сохраняем модель"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45iYYd0RP8GQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Сохраняем веса и архетектуру нейросети\n",
        "model.save_weights('weights.h5', overwrite=True)\n",
        "model.save('model.h5', overwrite=True)\n",
        "\n",
        "# Сохраняем архетектуру сети в формате JSON\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": [
        "# Качам файла на локальный диск\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": []
    }
  ]
}