stock_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Prediction with Deep Learning Models in TensorFlow\n",
    "\n",
    "## MA2132 Journal Project\n",
    "\n",
    "> Lee Jia Jie, Prannaya Gupta\n",
    "> \n",
    "> M19207\n",
    ">\n",
    "> NUS High School\n",
    ">\n",
    "> **20 September 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "sCbmbzAsCoJw",
    "outputId": "769ff17b-e319-45f0-93c2-ecf91b53b6bc",
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.14.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv1D, AveragePooling1D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "print(tf.__version__)\n",
    "\n",
    "if tf.test.is_gpu_available():\n",
    "    from tensorflow.keras.layers import CuDNNLSTM as LSTM, CuDNNGRU as GRU\n",
    "else:\n",
    "    print(\"GPU is not available\")\n",
    "    from tensorflow.keras.layers import LSTM, GRU\n",
    "\n",
    "DATA_DIR = \"/content/data\"\n",
    "FILE = \"NFLX.csv\"\n",
    "PATH = os.path.join(DATA_DIR, FILE)\n",
    "\n",
    "WINDOW = 100\n",
    "FEATURE_NAMES = [\"High\", \"Low\", \"Open\", \"Close\", \"Volume\", \"10ma\", \"30ma\"]\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "0QjWSs5YwSpN",
    "outputId": "fbc3b098-5bad-43ee-819e-ed1c8da051c1"
   },
   "outputs": [
    {
     "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-22</th>\n",
       "      <td>65.278572</td>\n",
       "      <td>62.697144</td>\n",
       "      <td>65.181427</td>\n",
       "      <td>63.254284</td>\n",
       "      <td>16169300.0</td>\n",
       "      <td>63.254284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-23</th>\n",
       "      <td>64.001427</td>\n",
       "      <td>62.928570</td>\n",
       "      <td>63.032856</td>\n",
       "      <td>63.414288</td>\n",
       "      <td>11471600.0</td>\n",
       "      <td>63.414288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-24</th>\n",
       "      <td>64.522858</td>\n",
       "      <td>63.220001</td>\n",
       "      <td>63.494286</td>\n",
       "      <td>64.365715</td>\n",
       "      <td>9551500.0</td>\n",
       "      <td>64.365715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-25</th>\n",
       "      <td>64.621429</td>\n",
       "      <td>63.202858</td>\n",
       "      <td>64.271431</td>\n",
       "      <td>63.355713</td>\n",
       "      <td>9839900.0</td>\n",
       "      <td>63.355713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-26</th>\n",
       "      <td>64.377144</td>\n",
       "      <td>63.412857</td>\n",
       "      <td>63.571430</td>\n",
       "      <td>64.107140</td>\n",
       "      <td>10404800.0</td>\n",
       "      <td>64.107140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close\n",
       "Date                                                                         \n",
       "2014-09-22  65.278572  62.697144  65.181427  63.254284  16169300.0  63.254284\n",
       "2014-09-23  64.001427  62.928570  63.032856  63.414288  11471600.0  63.414288\n",
       "2014-09-24  64.522858  63.220001  63.494286  64.365715   9551500.0  64.365715\n",
       "2014-09-25  64.621429  63.202858  64.271431  63.355713   9839900.0  63.355713\n",
       "2014-09-26  64.377144  63.412857  63.571430  64.107140  10404800.0  64.107140"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download and / or import data\n",
    "\n",
    "from pandas_datareader.data import DataReader\n",
    "\n",
    "YEARS = 5  # how many years of data to download\n",
    "NAME = \"NFLX\"\n",
    "DATA_SOURCE = \"yahoo\"\n",
    "\n",
    "if os.path.exists(PATH):\n",
    "    df = pd.read_csv(PATH)\n",
    "    df.set_index(\"Date\", inplace=True)\n",
    "else:\n",
    "    dt = datetime.datetime.now()\n",
    "    df = DataReader(NAME, DATA_SOURCE, start=dt-relativedelta(years=YEARS), end=dt)\n",
    "\n",
    "    if not os.path.isdir(DATA_DIR):\n",
    "        os.makedirs(DATA_DIR)\n",
    "    df.to_csv(PATH)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "0SzjdtRKCoJ3",
    "outputId": "9afc6efa-b434-4e76-db1c-693d398e0a48",
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>10ma</th>\n",
       "      <th>30ma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.025150</td>\n",
       "      <td>0.026576</td>\n",
       "      <td>0.025368</td>\n",
       "      <td>0.029174</td>\n",
       "      <td>0.183252</td>\n",
       "      <td>0.020205</td>\n",
       "      <td>0.035485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.025814</td>\n",
       "      <td>0.027007</td>\n",
       "      <td>0.028723</td>\n",
       "      <td>0.027508</td>\n",
       "      <td>0.155173</td>\n",
       "      <td>0.021367</td>\n",
       "      <td>0.034735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.022676</td>\n",
       "      <td>0.024546</td>\n",
       "      <td>0.026326</td>\n",
       "      <td>0.025356</td>\n",
       "      <td>0.094994</td>\n",
       "      <td>0.022035</td>\n",
       "      <td>0.033892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.022900</td>\n",
       "      <td>0.023622</td>\n",
       "      <td>0.025828</td>\n",
       "      <td>0.024439</td>\n",
       "      <td>0.116922</td>\n",
       "      <td>0.022263</td>\n",
       "      <td>0.032924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.021947</td>\n",
       "      <td>0.023994</td>\n",
       "      <td>0.022937</td>\n",
       "      <td>0.025555</td>\n",
       "      <td>0.081407</td>\n",
       "      <td>0.022273</td>\n",
       "      <td>0.032094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       High       Low      Open     Close    Volume      10ma      30ma\n",
       "0  0.025150  0.026576  0.025368  0.029174  0.183252  0.020205  0.035485\n",
       "1  0.025814  0.027007  0.028723  0.027508  0.155173  0.021367  0.034735\n",
       "2  0.022676  0.024546  0.026326  0.025356  0.094994  0.022035  0.033892\n",
       "3  0.022900  0.023622  0.025828  0.024439  0.116922  0.022263  0.032924\n",
       "4  0.021947  0.023994  0.022937  0.025555  0.081407  0.022273  0.032094"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocessing pipeline\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class FeatureUpdater(BaseEstimator, TransformerMixin):\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        assert isinstance(X, pd.DataFrame)\n",
    "        assert \"Close\" in X.columns\n",
    "\n",
    "        if \"Adj Close\" in X.columns:\n",
    "            X_copy = X.drop(\"Adj Close\", axis=1)\n",
    "        else:\n",
    "            X_copy = X.copy()\n",
    "        X_copy[\"10ma\"] = X_copy[\"Close\"].rolling(window=10).mean()\n",
    "        X_copy[\"30ma\"] = X_copy[\"Close\"].rolling(window=30).mean()\n",
    "\n",
    "        return X_copy\n",
    "\n",
    "class NanDropper(BaseEstimator, TransformerMixin):\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        if isinstance(X, pd.DataFrame):\n",
    "            return X.dropna(axis=0)\n",
    "        return pd.DataFrame(X).dropna(axis=0)\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    (\"feature_updater\", FeatureUpdater()),\n",
    "    (\"nan_dropper\", NanDropper()),\n",
    "    (\"scaler\", MinMaxScaler())\n",
    "])\n",
    "\n",
    "df_tr = pd.DataFrame(pipeline.fit_transform(df), columns=FEATURE_NAMES)\n",
    "\n",
    "df_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "_8rA8e4pCoJ1",
    "outputId": "97944f82-5a3a-4c00-b99a-0212b2df8b83",
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD8CAYAAAB9y7/cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot data\n",
    "\n",
    "def plot_data(df):\n",
    "    ax1 = plt.subplot2grid(shape=(6, 1), loc=(0, 0), rowspan=5)\n",
    "    ax2 = plt.subplot2grid(shape=(6, 1), loc=(5, 0), sharex=ax1)\n",
    "\n",
    "    df.plot(y=[\"High\", \"Low\", \"Open\", \"Close\", \"10ma\", \"30ma\"], ax=ax1)\n",
    "    df.plot(y=\"Volume\", ax=ax2, title=\"Volume\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_data(df_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "id": "15GQNKjkU609",
    "outputId": "f77b4985-aea2-4d54-a77d-34aa761c8fea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training input shape:  (791, 100, 7)\n",
      "Training output shape:  (791, 1)\n",
      "Validation input shape:  (169, 100, 7)\n",
      "Validation ouput shape:  (169, 1)\n",
      "Test input shape:  (170, 100, 7)\n",
      "Test output shape:  (170, 1)\n"
     ]
    }
   ],
   "source": [
    "# split data into training, validation and test sets\n",
    "\n",
    "\"\"\"\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=TEST_RATIO)\n",
    "train_X, val_X, train_y, val_y = train_test_split(train_X, train_y, test_size=VALIDATION_RATIO)\n",
    "\"\"\"\n",
    "\n",
    "TRAIN_RATIO = 0.7\n",
    "VALIDATION_RATIO = 0.15\n",
    "\n",
    "def split_input_output(df, target_feature=\"Open\"):\n",
    "    if not isinstance(df, pd.DataFrame):\n",
    "        df = pd.DataFrame(df, columns=FEATURE_NAMES)\n",
    "    X = np.array([df.iloc[i : i + WINDOW, :].values for i in range(len(df) - WINDOW)])\n",
    "    y = np.expand_dims(df[target_feature][WINDOW:].values, axis=1)\n",
    "    return X, y\n",
    "\n",
    "def split_train_val_test(X, y):\n",
    "    assert X.shape[0] == y.shape[0]\n",
    "\n",
    "    train_val_split = int(TRAIN_RATIO * X.shape[0])\n",
    "    val_test_split = int((TRAIN_RATIO + VALIDATION_RATIO) * X.shape[0])\n",
    "\n",
    "    train_X = X[:train_val_split]\n",
    "    train_y = y[:train_val_split]\n",
    "    val_X = X[train_val_split : val_test_split]\n",
    "    val_y = y[train_val_split : val_test_split]\n",
    "    test_X = X[val_test_split:]\n",
    "    test_y = y[val_test_split:]\n",
    "\n",
    "    return train_X, train_y, val_X, val_y, test_X, test_y\n",
    "\n",
    "X, y = split_input_output(df_tr)\n",
    "train_X, train_y, val_X, val_y, test_X, test_y = split_train_val_test(X, y)\n",
    "\n",
    "print(\"Training input shape: \", train_X.shape)\n",
    "print(\"Training output shape: \", train_y.shape)\n",
    "print(\"Validation input shape: \", val_X.shape)\n",
    "print(\"Validation ouput shape: \", val_y.shape)\n",
    "print(\"Test input shape: \", test_X.shape)\n",
    "print(\"Test output shape: \", test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tqm-eMs8U-L6"
   },
   "outputs": [],
   "source": [
    "# the models and tensorboard logs will be saved to the following folders\n",
    "\n",
    "LOG_DIR = \"/content/logs\"\n",
    "MODEL_DIR = \"/content/models\"\n",
    "\n",
    "if not os.path.isdir(LOG_DIR):\n",
    "    os.makedirs(LOG_DIR)\n",
    "\n",
    "if not os.path.isdir(MODEL_DIR):\n",
    "    os.makedirs(MODEL_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9NKIqd-6Rb20",
    "outputId": "8b4d5f59-4717-4dec-e6b7-f31ac05b751c",
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 700)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               89728     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 100,609\n",
      "Trainable params: 100,609\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 791 samples, validate on 169 samples\n",
      "Epoch 1/20\n",
      " 64/791 [=>............................] - ETA: 34s - loss: 0.2721WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.104016). Check your callbacks.\n",
      "791/791 [==============================] - 3s 4ms/sample - loss: 0.0288 - val_loss: 0.0280\n",
      "Epoch 2/20\n",
      "791/791 [==============================] - 0s 62us/sample - loss: 0.0017 - val_loss: 0.0139\n",
      "Epoch 3/20\n",
      "791/791 [==============================] - 0s 57us/sample - loss: 0.0014 - val_loss: 0.0264\n",
      "Epoch 4/20\n",
      "791/791 [==============================] - 0s 60us/sample - loss: 9.0126e-04 - val_loss: 0.0197\n",
      "Epoch 5/20\n",
      "791/791 [==============================] - 0s 59us/sample - loss: 8.3629e-04 - val_loss: 0.0069\n",
      "Epoch 6/20\n",
      "791/791 [==============================] - 0s 57us/sample - loss: 6.8322e-04 - val_loss: 0.0114\n",
      "Epoch 7/20\n",
      "791/791 [==============================] - 0s 58us/sample - loss: 5.4880e-04 - val_loss: 0.0065\n",
      "Epoch 8/20\n",
      "791/791 [==============================] - 0s 54us/sample - loss: 6.5188e-04 - val_loss: 0.0156\n",
      "Epoch 9/20\n",
      "791/791 [==============================] - 0s 65us/sample - loss: 7.8418e-04 - val_loss: 0.0186\n",
      "Epoch 10/20\n",
      "791/791 [==============================] - 0s 57us/sample - loss: 7.3360e-04 - val_loss: 0.0071\n",
      "Epoch 11/20\n",
      "791/791 [==============================] - 0s 57us/sample - loss: 4.5494e-04 - val_loss: 0.0050\n",
      "Epoch 12/20\n",
      "791/791 [==============================] - 0s 55us/sample - loss: 4.8384e-04 - val_loss: 0.0082\n",
      "Epoch 13/20\n",
      "791/791 [==============================] - 0s 57us/sample - loss: 3.7052e-04 - val_loss: 0.0047\n",
      "Epoch 14/20\n",
      "791/791 [==============================] - 0s 58us/sample - loss: 3.7644e-04 - val_loss: 0.0083\n",
      "Epoch 15/20\n",
      "791/791 [==============================] - 0s 70us/sample - loss: 4.5082e-04 - val_loss: 0.0065\n",
      "Epoch 16/20\n",
      "791/791 [==============================] - 0s 72us/sample - loss: 3.3785e-04 - val_loss: 0.0041\n",
      "Epoch 17/20\n",
      "791/791 [==============================] - 0s 62us/sample - loss: 3.7349e-04 - val_loss: 0.0060\n",
      "Epoch 18/20\n",
      "791/791 [==============================] - 0s 59us/sample - loss: 3.8623e-04 - val_loss: 0.0047\n",
      "Epoch 19/20\n",
      "791/791 [==============================] - 0s 60us/sample - loss: 4.6347e-04 - val_loss: 0.0071\n",
      "Epoch 20/20\n",
      "791/791 [==============================] - 0s 56us/sample - loss: 4.9343e-04 - val_loss: 0.0101\n",
      "170/170 [==============================] - 0s 68us/sample - loss: 0.0085\n"
     ]
    }
   ],
   "source": [
    "# feedforward neural network\n",
    "\n",
    "name_ffnn = \"FFNN-\" + str(int(time.time()))\n",
    "\n",
    "model_ffnn = Sequential([\n",
    "    Flatten(input_shape=train_X.shape[1:]),\n",
    "    Dense(128, activation=\"relu\"),\n",
    "    Dense(64, activation=\"relu\"),\n",
    "    Dense(32, activation=\"relu\"),\n",
    "    Dense(16, activation=\"relu\"),\n",
    "    Dense(1)\n",
    "])\n",
    "model_ffnn.compile(optimizer=\"adam\", loss=\"mse\")\n",
    "\n",
    "model_ffnn.summary()\n",
    "\n",
    "history_ffnn = model_ffnn.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[TensorBoard(log_dir=os.path.join(LOG_DIR, name_ffnn))])\n",
    "\n",
    "model_ffnn.evaluate(test_X, test_y)\n",
    "\n",
    "model_ffnn.save(os.path.join(MODEL_DIR, name_ffnn + \".h5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_pk5ECeoiFdU",
    "outputId": "6b1c00c4-67ef-41cd-ce2b-4d51011b6924",
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "cu_dnnlstm (CuDNNLSTM)       (None, None, 128)         70144     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, None, 128)         0         \n",
      "_________________________________________________________________\n",
      "cu_dnnlstm_1 (CuDNNLSTM)     (None, None, 128)         132096    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, None, 128)         0         \n",
      "_________________________________________________________________\n",
      "cu_dnnlstm_2 (CuDNNLSTM)     (None, 128)               132096    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 342,657\n",
      "Trainable params: 342,657\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 791 samples, validate on 169 samples\n",
      "Epoch 1/20\n",
      "791/791 [==============================] - 4s 5ms/sample - loss: 0.0100 - val_loss: 0.0051\n",
      "Epoch 2/20\n",
      "791/791 [==============================] - 0s 370us/sample - loss: 0.0028 - val_loss: 0.0051\n",
      "Epoch 3/20\n",
      "791/791 [==============================] - 0s 381us/sample - loss: 0.0022 - val_loss: 0.0049\n",
      "Epoch 4/20\n",
      "791/791 [==============================] - 0s 369us/sample - loss: 0.0020 - val_loss: 0.0063\n",
      "Epoch 5/20\n",
      "791/791 [==============================] - 0s 368us/sample - loss: 0.0021 - val_loss: 0.0062\n",
      "Epoch 6/20\n",
      "791/791 [==============================] - 0s 371us/sample - loss: 0.0015 - val_loss: 0.0055\n",
      "Epoch 7/20\n",
      "791/791 [==============================] - 0s 373us/sample - loss: 0.0020 - val_loss: 0.0070\n",
      "Epoch 8/20\n",
      "791/791 [==============================] - 0s 376us/sample - loss: 0.0018 - val_loss: 0.0095\n",
      "Epoch 9/20\n",
      "791/791 [==============================] - 0s 361us/sample - loss: 0.0023 - val_loss: 0.0100\n",
      "Epoch 10/20\n",
      "791/791 [==============================] - 0s 381us/sample - loss: 0.0024 - val_loss: 0.0046\n",
      "Epoch 11/20\n",
      "791/791 [==============================] - 0s 365us/sample - loss: 0.0019 - val_loss: 0.0052\n",
      "Epoch 12/20\n",
      "791/791 [==============================] - 0s 369us/sample - loss: 0.0018 - val_loss: 0.0045\n",
      "Epoch 13/20\n",
      "791/791 [==============================] - 0s 378us/sample - loss: 0.0016 - val_loss: 0.0114\n",
      "Epoch 14/20\n",
      "791/791 [==============================] - 0s 376us/sample - loss: 0.0017 - val_loss: 0.0040\n",
      "Epoch 15/20\n",
      "791/791 [==============================] - 0s 370us/sample - loss: 0.0014 - val_loss: 0.0052\n",
      "Epoch 16/20\n",
      "791/791 [==============================] - 0s 364us/sample - loss: 0.0017 - val_loss: 0.0089\n",
      "Epoch 17/20\n",
      "791/791 [==============================] - 0s 372us/sample - loss: 0.0021 - val_loss: 0.0080\n",
      "Epoch 18/20\n",
      "791/791 [==============================] - 0s 367us/sample - loss: 0.0017 - val_loss: 0.0109\n",
      "Epoch 19/20\n",
      "791/791 [==============================] - 0s 374us/sample - loss: 0.0014 - val_loss: 0.0041\n",
      "Epoch 20/20\n",
      "791/791 [==============================] - 0s 371us/sample - loss: 0.0013 - val_loss: 0.0038\n",
      "170/170 [==============================] - 0s 315us/sample - loss: 0.0017\n"
     ]
    }
   ],
   "source": [
    "# lstm\n",
    "\n",
    "name_lstm = \"LSTM-\" + str(int(time.time()))\n",
    "\n",
    "model_lstm = Sequential([\n",
    "    LSTM(128, return_sequences=True, input_shape=(None, train_X.shape[2])),  # accept input of arbitary length\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    LSTM(128, return_sequences=True),\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    LSTM(128),\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    Flatten(),\n",
    "\n",
    "    Dense(64),\n",
    "    Dropout(0.2),\n",
    "\n",
    "    Dense(1)\n",
    "])\n",
    "model_lstm.compile(optimizer=Adam(lr=0.001, decay=1e-6), loss=\"mse\")\n",
    "\n",
    "model_lstm.summary()\n",
    "\n",
    "history_lstm = model_lstm.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[TensorBoard(log_dir=os.path.join(LOG_DIR, name_lstm))])\n",
    "\n",
    "model_lstm.evaluate(test_X, test_y)\n",
    "\n",
    "model_lstm.save(os.path.join(MODEL_DIR, name_lstm + \".h5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "nGOKedoDhy5J",
    "outputId": "6ac502a1-1099-422a-95db-726c6adfd20d",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "cu_dnngru (CuDNNGRU)         (None, None, 128)         52608     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, None, 128)         0         \n",
      "_________________________________________________________________\n",
      "cu_dnngru_1 (CuDNNGRU)       (None, None, 128)         99072     \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, None, 128)         0         \n",
      "_________________________________________________________________\n",
      "cu_dnngru_2 (CuDNNGRU)       (None, 128)               99072     \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 259,073\n",
      "Trainable params: 259,073\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 791 samples, validate on 169 samples\n",
      "Epoch 1/20\n",
      "791/791 [==============================] - 1s 1ms/sample - loss: 0.0174 - val_loss: 0.0090\n",
      "Epoch 2/20\n",
      "791/791 [==============================] - 0s 361us/sample - loss: 0.0039 - val_loss: 0.0053\n",
      "Epoch 3/20\n",
      "791/791 [==============================] - 0s 332us/sample - loss: 0.0027 - val_loss: 0.0038\n",
      "Epoch 4/20\n",
      "791/791 [==============================] - 0s 335us/sample - loss: 0.0022 - val_loss: 0.0023\n",
      "Epoch 5/20\n",
      "791/791 [==============================] - 0s 330us/sample - loss: 0.0020 - val_loss: 0.0028\n",
      "Epoch 6/20\n",
      "791/791 [==============================] - 0s 335us/sample - loss: 0.0018 - val_loss: 0.0022\n",
      "Epoch 7/20\n",
      "791/791 [==============================] - 0s 334us/sample - loss: 0.0016 - val_loss: 0.0024\n",
      "Epoch 8/20\n",
      "791/791 [==============================] - 0s 331us/sample - loss: 0.0015 - val_loss: 0.0020\n",
      "Epoch 9/20\n",
      "791/791 [==============================] - 0s 330us/sample - loss: 0.0015 - val_loss: 0.0018\n",
      "Epoch 10/20\n",
      "791/791 [==============================] - 0s 349us/sample - loss: 0.0016 - val_loss: 0.0017\n",
      "Epoch 11/20\n",
      "791/791 [==============================] - 0s 332us/sample - loss: 0.0013 - val_loss: 0.0037\n",
      "Epoch 12/20\n",
      "791/791 [==============================] - 0s 333us/sample - loss: 0.0016 - val_loss: 0.0086\n",
      "Epoch 13/20\n",
      "791/791 [==============================] - 0s 335us/sample - loss: 0.0018 - val_loss: 0.0018\n",
      "Epoch 14/20\n",
      "791/791 [==============================] - 0s 334us/sample - loss: 0.0014 - val_loss: 0.0019\n",
      "Epoch 15/20\n",
      "791/791 [==============================] - 0s 334us/sample - loss: 0.0014 - val_loss: 0.0040\n",
      "Epoch 16/20\n",
      "791/791 [==============================] - 0s 341us/sample - loss: 0.0013 - val_loss: 0.0035\n",
      "Epoch 17/20\n",
      "791/791 [==============================] - 0s 350us/sample - loss: 0.0013 - val_loss: 0.0033\n",
      "Epoch 18/20\n",
      "791/791 [==============================] - 0s 326us/sample - loss: 0.0014 - val_loss: 0.0015\n",
      "Epoch 19/20\n",
      "791/791 [==============================] - 0s 329us/sample - loss: 0.0011 - val_loss: 0.0023\n",
      "Epoch 20/20\n",
      "791/791 [==============================] - 0s 333us/sample - loss: 0.0011 - val_loss: 0.0028\n",
      "170/170 [==============================] - 0s 296us/sample - loss: 0.0019\n"
     ]
    }
   ],
   "source": [
    "# gru\n",
    "\n",
    "name_gru = \"GRU-\" + str(int(time.time()))\n",
    "\n",
    "model_gru = Sequential([\n",
    "    GRU(128, return_sequences=True, input_shape=(None, train_X.shape[2])),  # accept input of arbitary length\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    GRU(128, return_sequences=True),\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    GRU(128),\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    Flatten(),\n",
    "\n",
    "    Dense(64),\n",
    "    Dropout(0.2),\n",
    "\n",
    "    Dense(1)\n",
    "])\n",
    "model_gru.compile(optimizer=Adam(lr=0.001, decay=1e-6), loss=\"mse\")\n",
    "\n",
    "model_gru.summary()\n",
    "\n",
    "history_gru = model_gru.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[TensorBoard(log_dir=os.path.join(LOG_DIR, name_gru))])\n",
    "\n",
    "model_gru.evaluate(test_X, test_y)\n",
    "\n",
    "model_gru.save(os.path.join(MODEL_DIR, name_gru + \".h5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "8x4vMfgtv-Kz",
    "outputId": "177c088f-0e8c-45cf-a0d6-bf74f56a4093"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, 99, 128)           1920      \n",
      "_________________________________________________________________\n",
      "average_pooling1d (AveragePo (None, 98, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 97, 64)            16448     \n",
      "_________________________________________________________________\n",
      "average_pooling1d_1 (Average (None, 96, 64)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 95, 32)            4128      \n",
      "_________________________________________________________________\n",
      "average_pooling1d_2 (Average (None, 94, 32)            0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 3008)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 256)               770304    \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 793,057\n",
      "Trainable params: 793,057\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 791 samples, validate on 169 samples\n",
      "Epoch 1/20\n",
      "791/791 [==============================] - 2s 2ms/sample - loss: 0.2449 - val_loss: 0.0285\n",
      "Epoch 2/20\n",
      "791/791 [==============================] - 0s 75us/sample - loss: 0.0091 - val_loss: 0.0184\n",
      "Epoch 3/20\n",
      "791/791 [==============================] - 0s 77us/sample - loss: 0.0034 - val_loss: 0.0338\n",
      "Epoch 4/20\n",
      "791/791 [==============================] - 0s 73us/sample - loss: 0.0017 - val_loss: 0.0388\n",
      "Epoch 5/20\n",
      "791/791 [==============================] - 0s 73us/sample - loss: 0.0014 - val_loss: 0.0496\n",
      "Epoch 6/20\n",
      "791/791 [==============================] - 0s 73us/sample - loss: 0.0012 - val_loss: 0.0355\n",
      "Epoch 7/20\n",
      "791/791 [==============================] - 0s 68us/sample - loss: 0.0010 - val_loss: 0.0244\n",
      "Epoch 8/20\n",
      "791/791 [==============================] - 0s 75us/sample - loss: 9.0494e-04 - val_loss: 0.0159\n",
      "Epoch 9/20\n",
      "791/791 [==============================] - 0s 81us/sample - loss: 6.7020e-04 - val_loss: 0.0177\n",
      "Epoch 10/20\n",
      "791/791 [==============================] - 0s 74us/sample - loss: 5.5483e-04 - val_loss: 0.0101\n",
      "Epoch 11/20\n",
      "791/791 [==============================] - 0s 77us/sample - loss: 7.1344e-04 - val_loss: 0.0158\n",
      "Epoch 12/20\n",
      "791/791 [==============================] - 0s 73us/sample - loss: 6.0090e-04 - val_loss: 0.0195\n",
      "Epoch 13/20\n",
      "791/791 [==============================] - 0s 71us/sample - loss: 5.2538e-04 - val_loss: 0.0225\n",
      "Epoch 14/20\n",
      "791/791 [==============================] - 0s 72us/sample - loss: 4.9215e-04 - val_loss: 0.0179\n",
      "Epoch 15/20\n",
      "791/791 [==============================] - 0s 78us/sample - loss: 4.7078e-04 - val_loss: 0.0262\n",
      "Epoch 16/20\n",
      "791/791 [==============================] - 0s 74us/sample - loss: 5.6437e-04 - val_loss: 0.0205\n",
      "Epoch 17/20\n",
      "791/791 [==============================] - 0s 74us/sample - loss: 4.6883e-04 - val_loss: 0.0113\n",
      "Epoch 18/20\n",
      "791/791 [==============================] - 0s 73us/sample - loss: 3.9596e-04 - val_loss: 0.0137\n",
      "Epoch 19/20\n",
      "791/791 [==============================] - 0s 68us/sample - loss: 3.3587e-04 - val_loss: 0.0120\n",
      "Epoch 20/20\n",
      "791/791 [==============================] - 0s 73us/sample - loss: 3.4992e-04 - val_loss: 0.0083\n",
      "170/170 [==============================] - 0s 1ms/sample - loss: 0.0057\n"
     ]
    }
   ],
   "source": [
    "# 1d cnn\n",
    "\n",
    "name_cnn = \"CNN-\" + str(int(time.time()))\n",
    "\n",
    "model_cnn = Sequential([\n",
    "    Conv1D(128, 2, use_bias=True, activation=\"relu\", kernel_initializer=\"VarianceScaling\", input_shape=train_X.shape[1:]),\n",
    "    AveragePooling1D(strides=1),\n",
    "\n",
    "    Conv1D(64, 2, use_bias=True, activation=\"relu\", kernel_initializer=\"VarianceScaling\"),\n",
    "    AveragePooling1D(strides=1),\n",
    "\n",
    "    Conv1D(32, 2, use_bias=True, activation=\"relu\", kernel_initializer=\"VarianceScaling\"),\n",
    "    AveragePooling1D(strides=1),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(256, activation=\"relu\"),\n",
    "    Dense(1)\n",
    "])\n",
    "model_cnn.compile(optimizer=\"adam\", loss=\"mse\")\n",
    "\n",
    "model_cnn.summary()\n",
    "\n",
    "history_cnn = model_cnn.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[TensorBoard(log_dir=os.path.join(LOG_DIR, name_cnn))])\n",
    "\n",
    "model_cnn.evaluate(test_X, test_y)\n",
    "\n",
    "model_cnn.save(os.path.join(LOG_DIR, name_cnn + \".h5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "id": "W-lLoJ3ClFgl",
    "outputId": "5b10f401-001d-4383-d62b-ed5ef6524622"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-04d2ad154617>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mdays\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_tr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mWINDOW\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mactual\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mmodels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel_ffnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_lstm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_gru\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_cnn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FFNN\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"LSTM\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"GRU\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"CNN\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model_ffnn' is not defined"
     ]
    }
   ],
   "source": [
    "# visualise model predictions\n",
    "\n",
    "def visualize_predictions(days, actual, models, X, labels):\n",
    "    assert len(models) == len(labels)\n",
    "\n",
    "    plt.plot(days, actual, label=\"Actual Price\")\n",
    "    for i, model in enumerate(models):\n",
    "        plt.plot(days, model.predict(X), label=labels[i])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "visualize_predictions(\n",
    "    days=df_tr.index[WINDOW:].values,\n",
    "    actual=y,\n",
    "    models=[model_ffnn, model_lstm, model_gru, model_cnn],\n",
    "    X=X,\n",
    "    labels=[\"FFNN\", \"LSTM\", \"GRU\", \"CNN\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "id": "yc5_p--7nfyY",
    "outputId": "71465688-7eb8-410b-abb1-83a621cf5ffe"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-eb59555420c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m plot_losses(\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mhistories\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhistory_ffnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory_lstm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory_gru\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory_cnn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FFNN\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"LSTM\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"GRU\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"CNN\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'history_ffnn' is not defined"
     ]
    }
   ],
   "source": [
    "# plot model losses and training\n",
    "\n",
    "def plot_losses(histories, labels):\n",
    "    assert len(histories) == len(labels)\n",
    "\n",
    "    ax_loss = plt.subplot2grid(shape=(1, 2), loc=(0, 0))\n",
    "    ax_val_loss = plt.subplot2grid(shape=(1, 2), loc=(0, 1))\n",
    "\n",
    "    for i, history in enumerate(histories):\n",
    "        ax_loss.plot(range(EPOCHS), history.history[\"loss\"], label=labels[i])\n",
    "        ax_val_loss.plot(range(EPOCHS), history.history[\"val_loss\"], label=labels[i])\n",
    "    ax_loss.legend()\n",
    "    ax_val_loss.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_losses(\n",
    "    histories=[history_ffnn, history_lstm, history_gru, history_cnn],\n",
    "    labels=[\"FFNN\", \"LSTM\", \"GRU\", \"CNN\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "id": "lWbx0_JvKVtV",
    "outputId": "61603ad9-f7f0-4256-8df8-95ab1a3fd2ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.113695). Check your callbacks.\n",
      "LSTM-256-1-0-256-1569067585           0.000771384166471441\n",
      "LSTM-256-1-0-512-1569067602           0.0005244185411891736\n",
      "LSTM-256-1-0.2-256-1569067615         0.0013613891733043333\n",
      "LSTM-256-1-0.2-512-1569067627         0.00048368569148485275\n",
      "LSTM-256-1-0.4-256-1569067640         0.0005854476181625882\n",
      "LSTM-256-1-0.4-512-1569067653         0.0004048840252353865\n",
      "LSTM-256-2-0-256-1569067667           0.001741345419877154\n",
      "LSTM-256-2-0-512-1569067694           0.001724570045959862\n",
      "LSTM-256-2-0.2-256-1569067721         0.0017416157424176002\n",
      "LSTM-256-2-0.2-512-1569067750         0.0013493808801285923\n",
      "LSTM-256-2-0.4-256-1569067778         0.0007730655355022892\n",
      "LSTM-256-2-0.4-512-1569067807         0.002853274502901032\n",
      "LSTM-256-3-0-256-1569067836           0.0014699550619458452\n",
      "LSTM-256-3-0-512-1569067880           0.01192138028495452\n",
      "LSTM-256-3-0.2-256-1569067924         0.001133636458133183\n",
      "LSTM-256-3-0.2-512-1569067969         0.0015488734078483984\n",
      "LSTM-256-3-0.4-256-1569068016         0.0012772853999836917\n",
      "LSTM-256-3-0.4-512-1569068062         0.0011163409022093916\n",
      "LSTM-512-1-0-256-1569068110           0.0005519506833105184\n",
      "LSTM-512-1-0-512-1569068138           0.0003458335901514682\n",
      "LSTM-512-1-0.2-256-1569068167         0.0004661964119055911\n",
      "LSTM-512-1-0.2-512-1569068196         0.00044594442160279655\n",
      "LSTM-512-1-0.4-256-1569068227         0.000499082130023881\n",
      "LSTM-512-1-0.4-512-1569068257         0.0004734005162711529\n",
      "LSTM-512-2-0-256-1569068287           0.0007059807084528182\n",
      "LSTM-512-2-0-512-1569068345           0.0006991481048958923\n",
      "LSTM-512-2-0.2-256-1569068404         0.000888560847242308\n",
      "LSTM-512-2-0.2-512-1569068464         0.004082041272126576\n",
      "LSTM-512-2-0.4-256-1569068524         0.0011935651382801178\n",
      "LSTM-512-2-0.4-512-1569068585         0.0007682317196775008\n",
      "LSTM-512-3-0-256-1569068646           0.0011334099481567082\n",
      "LSTM-512-3-0-512-1569068736           0.001307406705831561\n",
      "LSTM-512-3-0.2-256-1569068826         0.0035181937081848872\n",
      "LSTM-512-3-0.2-512-1569068919         0.0013878320275312838\n",
      "LSTM-512-3-0.4-256-1569069012         0.0011858843578546143\n",
      "LSTM-512-3-0.4-512-1569069106         0.0009717383766067488\n"
     ]
    }
   ],
   "source": [
    "# experimenting with different architectures for the lstm\n",
    "\n",
    "lstm_sizes = [256, 512]\n",
    "lstm_layer_nums = [1, 2, 3]\n",
    "dropout_rates = [0, 0.2, 0.4]\n",
    "dense_sizes = [256, 512]\n",
    "\n",
    "for lstm_size in lstm_sizes:\n",
    "    for lstm_layer_num in lstm_layer_nums:\n",
    "        for dropout_rate in dropout_rates:\n",
    "            for dense_size in dense_sizes:\n",
    "                name = f\"LSTM-{lstm_size}-{lstm_layer_num}-{dropout_rate}-{dense_size}-{int(time.time())}\"\n",
    "\n",
    "                model = Sequential()\n",
    "\n",
    "                if lstm_layer_num == 1:\n",
    "                    model.add(LSTM(lstm_size, input_shape=(None, train_X.shape[2])))\n",
    "                    model.add(Dropout(dropout_rate))\n",
    "                else:\n",
    "                    model.add(LSTM(lstm_size, return_sequences=True, input_shape=(None, train_X.shape[2])))\n",
    "                    model.add(Dropout(dropout_rate))\n",
    "\n",
    "                    for _ in range(lstm_layer_num - 2):\n",
    "                        model.add(LSTM(lstm_size, return_sequences=True))\n",
    "                        model.add(Dropout(dropout_rate))\n",
    "                    \n",
    "                    model.add(LSTM(lstm_size))\n",
    "                    model.add(Dropout(dropout_rate))\n",
    "                    \n",
    "                model.add(Flatten())\n",
    "\n",
    "                model.add(Dense(dense_size))\n",
    "                model.add(Dropout(dropout_rate))\n",
    "\n",
    "                model.add(Dense(1))\n",
    "\n",
    "                model.compile(optimizer=Adam(lr=0.001, decay=1e-6), loss=\"mse\")\n",
    "\n",
    "                model.fit(\n",
    "                    train_X,\n",
    "                    train_y,\n",
    "                    validation_data=(val_X, val_y),\n",
    "                    batch_size=BATCH_SIZE,\n",
    "                    epochs=EPOCHS,\n",
    "                    callbacks=[TensorBoard(log_dir=os.path.join(LOG_DIR, name))],\n",
    "                    verbose=0\n",
    "                )\n",
    "\n",
    "                loss = model.evaluate(test_X, test_y, verbose=0)\n",
    "                print(name.ljust(37), loss)\n",
    "\n",
    "                model.save(os.path.join(MODEL_DIR, name + \".h5\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TC46ADzJe6Mj"
   },
   "source": [
    "We will now use tensorboard to compare the models. This notebook was run on google colab to train the models using a hosted gpu server, and hence localtunnel is used to help run tensorboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "id": "xcFA_TFMahNh",
    "outputId": "b8f26380-729a-4c8f-e9ae-bc4302fbf0d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K\u001b[?25h/tools/node/bin/lt -> /tools/node/lib/node_modules/localtunnel/bin/lt.js\n",
      "\u001b[K\u001b[?25h+ localtunnel@2.0.0\n",
      "added 35 packages from 21 contributors in 2.303s\n"
     ]
    }
   ],
   "source": [
    "# install localtunnel\n",
    "\n",
    "!npm install -g localtunnel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 663
    },
    "id": "QUCZCdvuXv3V",
    "outputId": "e10b1647-cccb-434e-9344-cbb9dd6bfb87"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "your url is: https://ordinary-hound-56.localtunnel.me\n",
      "/tools/node/lib/node_modules/localtunnel/bin/lt.js:81\n",
      "    throw err;\n",
      "    ^\n",
      "\n",
      "Error: connection refused: localtunnel.me:36158 (check your firewall settings)\n",
      "    at Socket.remote.on.err (/tools/node/lib/node_modules/localtunnel/lib/TunnelCluster.js:52:11)\n",
      "    at emitOne (events.js:116:13)\n",
      "    at Socket.emit (events.js:211:7)\n",
      "    at emitErrorNT (internal/streams/destroy.js:64:8)\n",
      "    at _combinedTickCallback (internal/process/next_tick.js:138:11)\n",
      "    at process._tickCallback (internal/process/next_tick.js:180:9)\n",
      "your url is: https://odd-parrot-5.localtunnel.me\n",
      "your url is: https://mean-falcon-4.localtunnel.me\n",
      "/tools/node/lib/node_modules/localtunnel/bin/lt.js:81\n",
      "    throw err;\n",
      "    ^\n",
      "\n",
      "Error: connection refused: localtunnel.me:41770 (check your firewall settings)\n",
      "    at Socket.remote.on.err (/tools/node/lib/node_modules/localtunnel/lib/TunnelCluster.js:52:11)\n",
      "    at emitOne (events.js:116:13)\n",
      "    at Socket.emit (events.js:211:7)\n",
      "    at emitErrorNT (internal/streams/destroy.js:64:8)\n",
      "    at _combinedTickCallback (internal/process/next_tick.js:138:11)\n",
      "    at process._tickCallback (internal/process/next_tick.js:180:9)\n",
      "/tools/node/lib/node_modules/localtunnel/bin/lt.js:81\n",
      "    throw err;\n",
      "    ^\n",
      "\n",
      "Error: connection refused: localtunnel.me:37579 (check your firewall settings)\n",
      "    at Socket.remote.on.err (/tools/node/lib/node_modules/localtunnel/lib/TunnelCluster.js:52:11)\n",
      "    at emitOne (events.js:116:13)\n",
      "    at Socket.emit (events.js:211:7)\n",
      "    at emitErrorNT (internal/streams/destroy.js:64:8)\n",
      "    at _combinedTickCallback (internal/process/next_tick.js:138:11)\n",
      "    at process._tickCallback (internal/process/next_tick.js:180:9)\n",
      "your url is: https://fluffy-chipmunk-54.localtunnel.me\n",
      "your url is: https://mighty-vampirebat-2.localtunnel.me\n"
     ]
    }
   ],
   "source": [
    "# tensorboard\n",
    "\n",
    "get_ipython().system_raw(f'tensorboard --logdir {LOG_DIR} --host 0.0.0.0 --port 6006 &')\n",
    "get_ipython().system_raw('lt --port 6006 >> url.txt 2>&1 &')\n",
    "\n",
    "!cat url.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cgq5EH--cpH7"
   },
   "source": [
    "It is apparent that the model with 256 lstm nodes, 1 lstm layer, a dropout rate of 0.4 and with 512 dense layers produces the best results, with the lowest test and validation losses (0.00042 and 0.00100 respectively)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rww-ezVU2COA"
   },
   "outputs": [],
   "source": [
    "BEST_MODEL = os.path.join(MODEL_DIR, \"LSTM-256-1-0.4-512-1568463412.h5\")  # change the model name if the timestamp is different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6l6dKu_YzBik"
   },
   "outputs": [],
   "source": [
    "# download the best model\n",
    "\n",
    "from google.colab import files\n",
    "\n",
    "files.download(BEST_MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cQck5cE3gANz"
   },
   "outputs": [],
   "source": [
    "# test the final model on other companies' stocks\n",
    "\n",
    "def test_company(name):\n",
    "    dt = datetime.datetime.now()\n",
    "    df = DataReader(name, \"yahoo\", start=dt-relativedelta(years=5), end=dt)\n",
    "    df_tr = pd.DataFrame(pipeline.fit_transform(df), columns=FEATURE_NAMES)\n",
    "    X, y = split_input_output(df_tr)\n",
    "\n",
    "    model = tf.keras.models.load_model(BEST_MODEL)\n",
    "    model.evaluate(X, y)\n",
    "\n",
    "    visualize_predictions(\n",
    "        days=df_tr.index[WINDOW:].values,\n",
    "        actual=y,\n",
    "        models=[model],\n",
    "        X=X,\n",
    "        labels=[\"LSTM Predictions\"],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "8nfZi2li0KwG",
    "outputId": "0962b506-7eda-49b7-fc1f-20755a0d53e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1130/1130 [==============================] - 0s 344us/sample - loss: 4.1550e-04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhw3+vhapFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# apple stocks\n",
    "\n",
    "test_company(\"AAPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "V7kGS4d302BA",
    "outputId": "64617164-8f45-4ff6-9977-7c1d3db7b69b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1130/1130 [==============================] - 0s 372us/sample - loss: 3.9997e-04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXl4VNX5xz/nzpqdkAQCCZCw7yQS\nQUAEUQQ3rFZUXIrYau3P3Vq1WtGqbV2oVq1WrVVbF1ywVVQUQdkUQQKENWyBAAlk32cy+/n9cSeT\nhCRkXwjn8zw+z8y955577hC/88573kVIKVEoFApF90Lr7AUoFAqFou1R4q5QKBTdECXuCoVC0Q1R\n4q5QKBTdECXuCoVC0Q1R4q5QKBTdECXuCoVC0Q1R4q5QKBTdECXuCoVC0Q0xdtaNo6OjZUJCQmfd\nXqFQKE5JNm/eXCCljGlsXKeJe0JCAqmpqZ11e4VCoTglEUIcbso45ZZRKBSKbogSd4VCoeiGKHFX\nKBSKbkin+dzrw+12k5WVhcPh6OylKNoBq9VKfHw8JpOps5eiUHR7upS4Z2VlERYWRkJCAkKIzl6O\nog2RUlJYWEhWVhaJiYmdvRyFotvTqFtGCPGmECJPCLGzgfNCCPGiEOKAEGK7EOKMli7G4XAQFRWl\nhL0bIoQgKipK/SpTKDqIpvjc3wZmn+T8hcAQ/3+3AP9ozYKUsHdf1L+tQtFxNCruUsq1QNFJhlwG\n/EfqbAB6CCH6tNUCFQpF1+SbXTnklqlfYl2VtoiWiQOO1nif5T9WByHELUKIVCFEan5+fhvcun34\n9NNPEUKwZ8+eRse+/fbbHDt2rMX3Wr16NZdcckm9xyMiIkhKSmLEiBH88Y9/rPf6Y8eOceWVV7b4\n/gpFS3B7fdzyzmaue2NjZy9F0QAdGgoppXxdSpkipUyJiWk0e7bTWLx4MWeffTaLFy9udGxrxf1k\nTJ06lbS0NFJTU3n33XfZsmVLrfMej4e+ffuyZMmSdrm/QtEQdqcXgKxieyevpPVIKTlSeOo/x4m0\nhbhnA/1qvI/3Hzslqaio4Pvvv+df//oXH3zwQa1zTz/9NGPGjGHcuHE8+OCDLFmyhNTUVK677jqS\nkpKorKwkISGBgoICAFJTU5k+fToAP/30E5MmTSI5OZnJkyezd+/eJq8pJCSE8ePHc+DAAd5++23m\nzJnDjBkzOO+888jMzGT06NEAeL1e7rvvPkaPHs3YsWN56aWXANi8eTPTpk1j/PjxzJo1i+PHj7fB\nJ6U4nbG5PACYDKd+qsy/vj/EOc+uIv14WWcvpU1pi1DIpcDtQogPgIlAqZSy1erxx893sftY237Y\nI/uG8+ilo0465rPPPmP27NkMHTqUqKgoNm/ezPjx4/nqq6/47LPP2LhxI8HBwRQVFdGzZ0/+/ve/\ns2jRIlJSUk467/Dhw1m3bh1Go5GVK1fy0EMP8cknnzRp3YWFhWzYsIFHHnmETZs2sWXLFrZv307P\nnj3JzMwMjHv99dfJzMwkLS0No9FIUVERbrebO+64g88++4yYmBg+/PBDHn74Yd58880m3VuhqA+7\nX9zN3UDcP9+m//KucHo6eSVtS6PiLoRYDEwHooUQWcCjgAlASvkqsAy4CDgA2IEF7bXYjmDx4sXc\nddddAFxzzTUsXryY8ePHs3LlShYsWEBwcDAAPXv2bNa8paWlzJ8/n/379yOEwO12N3rNunXrSE5O\nRtM0HnzwQUaNGsWmTZuYOXNmvfdfuXIlt956K0ajMbDGnTt3snPnTmbOnAno1n2fPmq/W9E6XlmV\nAZz6lrvXJ9mWVUokZVg02dnLaVMaFXcp5bxGzkvgtjZbkZ/GLOz2oKioiO+++44dO3YghMDr9SKE\n4Nlnn23yHEajEZ/PB1ArpvuRRx7h3HPP5X//+x+ZmZkBd83JmDp1Kl988UWd4yEhIU1ej5SSUaNG\n8eOPPzb5GoWiMf67Vfe8moyndnjroYIKwrCz1XoruZt+Df2f6ewltRmn9tduG7NkyRJuuOEGDh8+\nTGZmJkePHiUxMZF169Yxc+ZM3nrrLex2feOlqEiPDg0LC6O8vDwwR0JCAps3bwao5XYpLS0lLk4P\nInr77bfbZf0zZ87ktddew+PxBNY4bNgw8vPzA+LudrvZtWtXu9xfcfphMRrYmV3KR6nVAXPrMwp4\n8ovdnbiqplPm8BAr9P+XIw5/08mraVuUuNdg8eLFXH755bWO/fznP2fx4sXMnj2bOXPmkJKSQlJS\nEosWLQLgxhtv5NZbbw1sqD766KPcddddpKSkYDAYAvPcf//9/P73vyc5OTkgvm3Nr371K/r378/Y\nsWMZN24c77//PmazmSVLlvDAAw8wbtw4kpKSWL9+fbvcX3F6YcRDmFkw99UfuX/JdkrsLgCu/edG\n3vj+EC6Pr5NX2DhllW6ihL635zGFdfJq2hahe1U6npSUFHlis4709HRGjBjRKetRdAzq37h7kPDg\nl3xnvhez2czZFU8B8P7NE5mQ0JPBD38FwI+/n0GfiKDOXGajfL7tGN9/9BxPm/5Jcd9pRN6ytLOX\n1ChCiM1SypNHcKAsd4VC0SIkA7Uc4j1HAkfu+TCN3HJn4H1+jdddlTKHm3FC3xx2WaM7eTVtixJ3\nhULRbMKpTvoZJQ7xo+V2xlSsp9ReHQVW4ej6oYVllR7GagcB0Lzdq5SCEneFQtFsBoVUC+GVhrX0\nEUW8Yf4rJeUVjBUZZFqvxVDQ9Tfun/46nQSRA4BwtzxL1eeTXS4JSom7QqFoNhGyWsimajsCr7dv\n38xcwxoAojK/6vB1NYdyh5seVBAq9C8qg6eyxXO9tvYgF76wju1ZJW21vFajxF2hUDSbMF9p4PVg\n7RjuED0xTubuIkz4LWBXeX2XdhkOF9qJEwWB95qn5Zb75sPFABwrafwLoqCiY/YilLgrFIpm00PW\ndkE4EmfikRrhZRn0E3rFV6OjuDOW1mQyC23E+8X9qC8GzdMan3vjUYdZxXYueH4NKU+u5J0Nh1tx\nr6ahxP0EQkND6xzbu3cv06dPD5TfveWWW1i+fDlJSUkkJSURGhrKsGHDSEpK4he/+AWrV69GCMEb\nb7wRmCMtLQ0hRCA+viaPPfYYcXFxJCUlMXr0aJYubV041vTp06kKM73ooosoKWn4p+Knn37K7t3V\nCScLFy5k5cqVrbq/ovsT7hf3dF9/AETvkeQSSYgzp1rcnV1H3H0+yeOf72Z/bvWvicwCGyO1w0ih\nsUf2x9AKy70qotzmr5ZZH6+uyWBfbgUCH9OHtn9VXCXuTeDOO+/knnvuIS0tjfT0dO644w5mzZpF\nWloaaWlppKSk8N5775GWlsZ//vMfAEaPHs1HH30UmGPx4sWMGzeuwXtUzf/xxx9z0003BUoYVNHS\nxKdly5bRo0ePBs+fKO6PP/44559/fovupTh96CHL8QgTt7vv4A/uBRiTryFXRhIvc+gldGPC3IXE\nfX9eBW/+cIi7P0wLHMsstDPJtB9n9GjyZA8M3pb73C0mLXCfhnB7JME42N/jTvpl1S0r0tYocW8C\nx48fJz4+PvB+zJgxjV4zYMAAHA4Hubm5SCn5+uuvufDCCxu9bsSIERiNRgoKCgLZrxMnTuT+++/H\nZrNx0003MWHCBJKTk/nss88AqKys5JprrmHEiBFcfvnlVFZW/5HWLEH8n//8J5C9esMNN7B+/XqW\nLl3K7373O5KSksjIyODGG28M1If/9ttvSU5OZsyYMdx00004nc7AnI8++ihnnHEGY8aMCTQ1WbNm\nTeDXTHJycq2yDIruRRCVuAzBZMg43vXOxBLSgywZQ4q2DwC3NGBxd53NxUK/nzvYbEBKSXZJJZkF\nNoaILFxRw6nE3CpxDzLpZbp+OlTY4JicMgdLQ/6E0VEE4X1bfK+m0hYlf9uHrx6EnB2Nj2sOsWPg\nwqeafdk999zDjBkzmDx5MhdccAELFiw4qTVcxZVXXsnHH39McnIyZ5xxBhaLpdFrNm7ciKZpVDUz\nycrKYv369RgMBh566CFmzJjBm2++SUlJCRMmTOD888/ntddeIzg4mPT0dLZv384ZZ9TtUb5r1y6e\nfPJJ1q9fT3R0dKBk8Zw5c7jkkkvqdHNyOBzceOONfPvttwwdOpRf/OIX/OMf/+Duu+8GIDo6mi1b\ntvDKK6+waNEi3njjDRYtWsTLL7/MlClTqKiowGq1NuXjVZxiSCmx4sJj0LNPQ8wGhBDkBw8Gp17D\naJcYzAj3kZNN06EU2PTSCBFBZl5fe5C/fLWHCCqItBZTFDUcO9v1aBkpoQW9fn1+v0xWQcPhkEH2\nbAZ7M2DcPBgwpWUP0gyU5d4EFixYQHp6OnPnzmX16tWcddZZASv2ZFx11VV8/PHHLF68mHnzTlpc\nk+eff56kpCTuu+8+Pvzww0Az6blz5wZq1HzzzTc89dRTJCUlMX36dBwOB0eOHGHt2rVcf/31AIwd\nO5axY8fWmf+7775j7ty5REfrWXiNlSzeu3cviYmJDB06FID58+ezdu3awPkrrrgCgPHjxwdqyk+Z\nMoV7772XF198kZKSkkDpYUX3wuuTWHHi0ay8f/NEvrl3GgDJs+cHxuw0j8XiqwR3tTX85fbjvNsB\nG4n1UZVQZTYK/vKV/ktzhKZ/+Xijh1MprQgktHBT1euT/J/hU9Z5r8edW397zki7/8su+YYWfYE0\nl677f18LLOz2pG/fvtx0003cdNNNjB49mp07dzJ+/PiTXhMbG4vJZGLFihW88MILJy3Ydc8993Df\nfffVOV6zvK+Ukk8++YRhw4a1/EHaiKpfIQaDIbAf8OCDD3LxxRezbNkypkyZwvLlyxk+fHhnLlPR\nDqQeLiYYJx5DEJMHVafsJ41LYXX6I5w5ZgS2lRvBBdiLIEKvhnrb+3qbyNFxEST1a/yXb1tS4XTz\niflRHHnDWMa1+jrEIQA8vcdiZ7k+0GUHU/Pr4Xil5C7j/7AID+WHN2PqXfvvftXePLylR/VOGBHx\n9U/SxijLvQl8/fXXgeYaOTk5FBYWBsr3Nsbjjz/O008/XatCZEuZNWsWL730ElXF3rZu3QrAOeec\nw/vvvw/Azp072b59e51rZ8yYwccff0xhoe4TbKhkcRXDhg0jMzOTAwcOAPDOO+8wbdq0k64vIyOD\nMWPG8MADD3DmmWc2qcG44tRCSsk1r28gCBceQ223m6YJpl97HyFjLoZg/y9Du/73VumqiiKRHCnq\n2H6lf1u5jx93HWS8tp8ppfpGpgUX47QM3KFxiJAYKjHrg1uYperzVYdCugsO1jm/4K1NxIlCfIgO\n8beDEvc62O124uPjA/8999xzfPPNN4wePZpx48Yxa9Ysnn32WWJjY5s03+TJk/nZz37WJmt75JFH\ncLvdjB07llGjRvHII48A8Jvf/IaKigpGjBjBwoUL6/1FMWrUKB5++GGmTZvGuHHjuPfeewG929Sz\nzz5LcnIyGRkZgfFWq5W33nqLuXPnMmbMGDRN49Zbbz3p+v72t78F+reaTKYmbSArTi1uf38rJjxM\nMuzGa2h4T8UQ4rfo/eKeWWhjtDjIAcsNBB1Y1hFLDfC3lfvJObI/8D6KUlItv+FSwwaM/c5A06BS\n+vfDWijuRo8Ni9ANQHtZYa1yx7lluqunLwVUmKLBYGrhkzRzTR1yl1OIE0MQq3juuecavGb16tW1\n3k+fPr3eTkuPPfZYvdc3dPzEph5BQUG89tprdcYFBQXVaeZdRc0eq/Pnz2f+/Pm1zk+ZMqVWKGTN\ne5533nmBXwcNzZmSkhJ4/qqG3Iruy5c7jvM7ox5NddzQl34NjDOF6wEB3op8DMCWI8XMNazBKHz0\nLvgR+FWHrNfrt6irGnIAzDWsIUzoewGi/yQMQmDHL+4uW4vuY/VWh0B+v/MQX9o28faCCUgpmfjn\nbwEYrh2h2BpPeIvu0HyUuCsUimYxQdPdbSvibmNCA2OMobq4u8ryWZ+ey8P/28knZn0zNdiR0xHL\nBMDh1t1BfUV1iOJMg94pjblvw/BLMDgldvy/Qloo7kZvtcUfKipZt7+A859bQ3yk7r9PEgcYo2WS\nHndJi+Zv0Zo67E4KhaIbIBkmsvhAzuSO2XWjsqrQQnrilQJvRQG//DIVkAzX9FZ8wc6GY8Hbmkq/\nuM8zfBc4Nl7bjyt8AOZRVV3XXByX/j2CkpaFb5prxMiHob8+VGDjUIH+ZTFZ0ytkDr/k7hbN3xK6\nnM+9szpDKdof9W/behxuLylPruR2f+RJe3Mwv4KD+dUuh74UEi7s0GsU4daGfcdWs5Eiwvn8B92t\nFy8KCPGLXrC7A8Xd5WWUOMRoLVN/L/0bpz0HBcZEBJk4KnvhkgbyDrUst6ZK3D1S0z8fPwLdzZso\njlNp7Y0Ibf+yA1V0KXG3Wq0UFhYqEeiGSCkpLCxUiU2t5DfvbqagwskX24+TdrR9M0CllMz46xrO\ne25N4Ngwv/V91JR40muDTAaOyF4kiFz9OqFbxGliBKGe4upiLO2Mw+3lLC0dgJzzX6JARgDUClUU\nQnDxuH5kyljStv7UovuYfbqgZ8i+JGsHmKLtYLqWxiHr9YwSh+iv5eEK69/Kp2keXcotEx8fT1ZW\nFvn5+Z29FEU7YLVaa5VxUDSfHw8W0l/kcr/xA458uoKkO/7WbveqcOr5CzV1eKhR95fPvXDGSa+1\nGA3s88VzsWEjJjzcaNDjyLcZx5HkTgdHKQS1f6y7w+0jWTuAPSQew7irCVlxPwCi35m1xgWZDOyV\n/Riv7UP6fAiteXavyadHxOyT8Qwji/fMfwmce9n0IglaLoXhV7TyaZpHlxJ3k8lEYuLJLQKF4nTm\nvBG9Gbn7fS4xbITCjUD7iXuJ3U0EFfQXeYFj0aIMrzCSEN9QnIyO0+NjhW8884yr+Nr8AIO04wDk\nmeLADdgKOkTcK91eYkQJrtA4IoJMrPcNZbphG/SrvRX8/YECTL4RXGrYwKerfmBtYRjPXZXU5PsY\npB4GeVD2qXMuQdN/vUTEdEx8exVdyi2jUChOTondxaTgbACcWv2ZlJ9vO8YzX7c+gay00s175j/z\nueUP+EqPUeH0EO4txW6MbDR93icl63xj2eIbHBB2AIclSn9hy2vgyrYjr8zBgrd+IpJypDUSs1Fj\n+1nPs3/m23WyRK0mjY2+EQCs+fZL/rslu1nuYc2ni3uhbDjQ0ZiyoNnP0BqUuCsUpxDlDg8DPXoG\npMVXCb669cPvWLyVV1Zn1DneXMoq3dUbkQc3cN0bG+kpynBYTl6XCGDmyN6YzRYed/8icOy54Yvx\nBvuTmyraX9z/vuoANpeXSFGODNa/VO68eDxDplxeZ+z7N59FpozFKwUJmu56cnnrz3mpD03qLqwV\n3hRe9szhBc8VZMloNvn02kxFw6+F6MGtfaRmocRdoTiFCHLk08NXTI7opR9wNVw/vLWU2l2B147i\nbLYdLSFalFFpatydYjJo7Hp8NpOm6r0B0nwDKQnqj6zKXLW1/76aUdMQ+IikAuEX94boHW7lXzdN\nJltGkyhysOCqUTKhcQx+y92OhWc91xB50UJyF/zELa57+d47ClvSL1v1LC1BibtCcQoR69ITgdKN\n/mgPZ8Pi3tqos09WV0eOuEuOAdCTMuzGxi33KqYO681Yxz+5wfUQmhBoITH4pOgQcTcbNcKwYxQ+\ntJDoRscnRoeQKWOZY/iRvdYbcWXVzc5uiCqfu9u/jXnhmL6MT4iimHCudz9Mv+EpLXuIVqDEXaE4\nhQj36DHix4z+Dc2TZFRuzypt8NzJ2JtTjt3lgZzqAnSyPAeQRItSbMaIJs81eVA0X95/CU5DKPMm\n9Cc6PBgbVjz2lq2tOTg93oBbyRjauLj36xlMeciAwHvP4aaHRVa5ZaYM0zdUg8x6ocBRfcN57NKR\nTZ6nLelS0TIKheLk9PAWgYB8o79wnad29yBvjeqEl738A5lPXdys+aWUzPqbXrf/LkMmPik4IPsS\nUZHDqKBiQqSTAcOSmzVnv57B7PuTXkRu9/FSbFiRFSXtXmNla8YxPjX/GaiuddMYhZZ48Jd0/2HN\nN8yeeithJ0nWsrs8HMy3BdwyL147gf35FYRadGn98s6prXiC1qEsd4XiFEFKSYSvCLdmxWaK1A+6\nazeXqHR7CcLBQuN/mKM13D+gIWw1/MyjtEyOGePIlLGU5mfRy6m7hKIHNk/caxIbHoRNWnHZGu5Y\n1BJW783j7Ke/C9SSAbDYqqN0TL2GNGmeUkt1Ke+fG9bx3rebTjr+5//4kUte+p5yux0PRoIsRsbG\nd2yt+oZQ4q5QnCK4vZIYSrCbo/BVldv1l6h1e318teM4dpeHSwwbuMn4NS+a/97sTNCySnfg9Ujt\nMH2HTyCfSKJlUXXxrVY0m4gMMVFOED5n2/bXXfjZLrKKKzlWUv1LJsKtR+Q4pAnRY0BDl9aiMqh3\n4LUmJEOK1jY49pFPd+LKSecT86OMEYfwiq7lCGmSuAshZgsh9gohDgghHqznfH8hxCohxFYhxHYh\nxEVtv1SF4vRFSskTX+wmhhKc1l54Nb+4+9vCvfjtfn7z3ha+3pnDCFGj+FUz65OXOdyAJI584kUB\nWp9xlJui6Skq6C9y8WGAsKb1MqiPHkFmbDIIp62UTZlFjV/QRNz+sEWX18fhQn0fIsqji/tM1zPQ\nxIzT6H56l7NVYZeS5hvImOMfNzj23Y2HucnwNeO1/Zxj2BHYTO0qNPrEQggD8DJwITASmCeEOHGH\n4A/AR1LKZOAa4JW2XqhCcTqTkV/BOxsO01cUYugRh8/oT2Dy9yg9kKdHzeSVOeklatScqSxu1n3K\nKj1coa3jB+td+oGB0/CE6NZskpaBzRIDWsu7ikUEmbBhpaKsmLmv/tjieU6kStyv++dGpj27GrfX\nRy9ZAECubHp0T9/YWJIdr/Je1F3sMIwmsvJIg79+Zo7ozVmG9MD7UNl+YaktoSlfZxOAA1LKg1JK\nF/ABcNkJYyQE9kcigGNtt0SFQuH2SjR89BUFRMYNQRr9zSX8lnuVr9njk4RRw1qvbF5xMbvLwxTD\nTn0uNOiTBGF6BEiy2I/N2nKrHfRMUBtBhPp3LWtuAJ/IjxmFlNSItT8ZLo+PM8Ueymz6s5c7PPQR\nheTLCFw0vfOR1aRRTDhOr48ya19M0tVgwpXZfpxB4hivei6lVAazxnpek+/TETRF3OOAozXeZ/mP\n1eQx4HohRBawDLijTVanUCgAvVZLkjiAWXjRYkfXsdwdbt1yLbG7CBd2PPit62Za7g63j94UUyqD\nWTT8IxCCHeWhAJiFl4rQpvmuG0IIQaUIIsTfCanQ5qx3nNvrY94/N/CLN5sWjni29yc+tjzOdYaV\nAGw+XExfUYjNGss7v2yopUhdLEb9c3N6fFSG+PcWSg7XOzauQq/R/vNf3M6Z3jexXvXPJt+nI2ir\nDdV5wNtSynjgIuAdIUSduYUQtwghUoUQqaryo0LRdBxuL+ca0pDCAIPPq2O5u91O/mV6luj8DYRh\nJ1fzbww6mme5O9xeIoSNytgz+d1VenbpUS2efT7dniuLGNHqZ7GLYEL8lnu5w1PvGKe/B2lTY/WH\nSb3cQrQoJRwbN/8nlVhRhDWqP1OHNL2GusWkBe7vDvPnEhTXFfeM/ApE8SEAYgaMYt+TFzJx4Mmz\nYDuapoh7NtRqlRjvP1aTXwIfAUgpfwSsQJ2sASnl61LKFCllSkxMxxWtVyhOdRxuL9O0bVT0Gg9B\nkRjMwfoJv+Ue79jPeYat3JbzCOHCTqHJX52w2Za7lwhshEfGYND04mBms5l73LfxjPtqos65udXP\nUimCsAgPg0VWgyn+NRtMN0ZhhTPg5pmjrWe79WYWGL6ijyhCRJzoZDg5VfHp4VYjItJff70kE4C8\ncgf3fJhGkc3Fbe9tob/Io1SEgyWsWffoKJoi7puAIUKIRCGEGX3DdOkJY44A5wEIIUagi7syzRWK\nNqDI5uLGtzaRKHJwRo8CwGy26mn8HgdlDjda4X4AvOgp96VWf3nZFoh7uLCjBdXOQt0lE3jFexkD\nYhvP9GyMw2597pWW+5F56fWOaY64r9tfQIx/E7m/psvO3cZPCBOVGHs0L2xzSK9QnrhsFM9dlUSP\niAjyZA/chZkAPPFFOv/bms0ZT6xgT045I7VM8ox1S/x2FRoVdymlB7gdWA6ko0fF7BJCPC6EmOMf\n9lvgZiHENmAxcKNU7ZQUijbho9SjhGEnTFRCuC5WQWYjDsxIdyVZRZWBCBmD9BAkXDiDYnFKE7Ki\neTZWpctDOHYMwZGBY239v/I2X3WLu/eXfER2SWWdMc0Rd7vLSwy13TcR/lZ3EYOaV9NFCMENkxKI\nCbMQHWqhUIbhLi/0r8nLVG07fzC+wzBxhCTtINmGrtt8pkmBmVLKZegbpTWPLazxejcwpW2XplAo\nACocHhKEXoaWHrqrIMhsoBIzFlclxXZXQNyDhB5dYgrpQY6MJK70WLOir32OcjQhIbg6y3La0Bi2\nZZXy/s0T2+R5MmR104rp2ja+S8/lhkkJtca4vI1XZCxzuHG6feSXO5lQI/zzbc8FzDGsZ3P0Zcwc\nOK3F64wKNWPHisehJ1wt35VLpvUpAGzoeQYfhl7P9BbfoX1RGaoKRRen0ObiUoMeEx4+UG8PZzUZ\ncGDG66qkyOZioDhe6xpDcA9y6Imv9MTtsbp8m55LQYUetSKcuhtHq9El6a7zh7L+wRlMHtR6lwyA\nD41LnU+y1DuJ87XNWLx2tmeVMOax5eSX6+twNsFyn/38Ws7800ocHm+t2P5+Ey7jYsu/mf5/f29y\n8lJ9xIRasEkrXoeeFFXV7BpghrYVm7Rw31UzWzx/e6PEXaHo4hwpKGe+YTll8dMxx+htKINMBhzS\njM9lo9juIlEcr5UhKUJjKJJhUHnyLNCCCie//Hcqt7+/Rb+u0u/eqCHuBk3Qt0f9XZ9ayg45kE+8\n52AQkuiKPby+9iDlDg8/HNATj1weH4NENkNEVoNzHCvVN1E9zkrChZ03PBeyyD2XGRdfzY8PnY/J\n0Dp56xFswo4V4daTk+JEQeDcGC0TGdGPQb265mYqKHFXKLo8ZQXZWISH8LGXBI5ZTRqVWPA5bRRV\nOOktijloGho4r4X1oVSGIBrxuUBrAAAgAElEQVQJhdx1rIxh4gha3m79Oqe/oJe16WV9W8phqTcc\niS3YgOZv2yfR/fsuj49vLb9jheX+Rufx+vcV9st4/u69HGFoetLSyTAbNWxYMXpsHC2yM0TU/hVk\njmpdzH97o8RdoejCuL0+DOX+hO8aBbuMBs0v3qUcPHoMq3CTaamufBgS059SQtAcJ48T35ldynLL\ng7zvuRd8XgyuKnFv/8qGx6Tu5tH2LcPr37T1+T0ftdwynvoTneLI52Hju6Tt1JOJTta/tCVYDAZs\n0orPaWPqM6s4S9uNGyM2qecYmKIS2/R+bY0Sd4WiC2N3eulb5Q6oKe6aoIQQNGcJ6fv3AbDfUB2F\nMqh/PKUyBIPPWacscE1ySqvP7U9bh8nl/zLoAMvdhYml3knEiQK+3K5/gVXF5egFzPxU5Na5VkrJ\nncb/cbNxGQtN7wAwNXkkL1yT1GbrMxs17Fgx+/TIm0HiGMfNA7Cji7uIHd1m92oPlLgrFF0Ym8tT\nb6ldgyYolaFolcX0FvomaDa9WeEdz9eDHiHEYsRh8luyJ3HNuN3VVvHnn7zDkeP+jdmg9rfcX5qX\nzHbfQMJEJeHYmWtYjcmuC3lhRY2aMifUdvnvlizOe24NFej7AEmanp06//wzuSypeUlLJ8Ns1LBJ\nC1bcGPDSRxThDulDMP7PrM+4NrtXe6DEXaHogmw4WMi36bnY/eLuMQbXcpUYNUEpIcjKYnqhi3uu\njORm92/xjrsOAGnxj6+RyLQnp4w5f/+eg/n6JmFNt800wzaStQx8aGBu/43C6cNiyJf6L4QR4gjP\nml7n3PXzASgpq9HM4wTL/d6PtnEw30YPcUJN+JC2zXo3aHodHIBIKkgUOSQMGc0TnhvwSgG9Oqd9\nXlNR4q5QdEGueX0Dv/x3KgUVLvqKQhzBfcG/6Qi68JTLYMzCS39Nt2xzfbpQBlv8RcOqvgxqVIb8\ny7I9XJbzd/Z/qsdrm13V0TTjtf1cYtiAhq9VIYRNxWI0kI++xvtNHwAQXqnXKHTbqr+Q9P6tOjZn\ndS2aISKbvT7914xTmqCqJEMb4tB0cT9H20awcGIYNJ0PvDMY5HwPTNY2v19bosRdoegCVDg9tYRL\n4GOmlsrOjCP0FQW4Q2u7G4yaRhm6mA0Sx/CYwijzmQEIMftDIqtcKzXcMlv3ZfJL41fMyn4JAJNT\nt9wfdc9nhy+hPR6tDnPG9WXqkGhMBkGJ1CtOjtf21xojanwh/eOL9YF69RVOD/Eij6/MDzBWO0TP\n0TMZ6/gnKc5/tMtaXX5xv9q4Gp9mgoTO64naXLpW6xCF4jRl9KPLMRs09v3pQhxuL5dr3/Oc+VVS\nt26iryjEF3ZWrfG65a4Lz2BxDHdIb1x2PcIk2Kxb7h6zf1O0hlCemOxkcesW8ibfMP7tncVNhq8Y\nOWgAV7bLU+q8OK+6B2vVF1QtvB4Mzuo1z5I/sHXLBgbPPh+Xx8dEsYcRmm7hy/C+/PfeiwJFztqa\nSi0MfDBR2wP9p4IllJX3noPnJHXouwpK3BWKLoLL302o0OYKNMxIsa8DAYUn9C01GgTlAcs9G3fI\nJFxltcXda6m9oery+Eg8QdyD3LrlXix1H3tqn2u4a17blBloChWynuSoihyMrmpxH6QdJ37TfJid\njdPjY4BW7aYRYX0Y3Cu03dZXbuwJVXu7oy4HYHAXTlyqiXLLKBRdjKIKF0NENoWiuj64IbJfrTFV\nPnfQm2jI0N64vbo1GeIvWystuuVeWKBvSJY73CRq1eLucTmwFevnitEF8r+/mUxEUNskATWFqogX\ngBIZAsCX7/yVIFftzFqLV3fLuDw+Boga0TPtHI5YbqzRoq8VjcE7AyXuCkUX4DxtMx+aHwefl/wK\nB/1FHiUDquuWGHv2rzXeqFVb7gCExQYqKVZZ7hazmTIZzNINevZpmcNDfI0U+iPHc4kU5TiFhSV3\nnMfWR2ZibGXKfnPx+J0HldLMTOczAFxc8CZRtgMAfOiZHhhbZrOxMj2XASKXA76+/M59C4be7Rux\n4rJUV8ckvG/DA7sgStwVik6mzOHmFdOLTNT2IPPSycvLpYew0av/cJ5wX0eGrw+WAbVbxdX0uQNo\n4X1IiNbFPti/oaoJQakMIULoha/KHW6ia5TGLSnKI0aUIEN6MzougsgQc3s/ar3Mdz3A+c5nySeS\nL736c452b6dMBvGA52YWuecC8NHqzTy3Yh99RCGpvqF87J2O1dzyZt1NwWqp8ZmEt10MfUegxF2h\n6GR+zCjE5bdgczO2sW6j3jc0JHYQ//JezHmuv2KyhtS6xqhptSx3Y0Qf3v3VRN5ecGZgc7HS7aWU\nECLQxb2s0kOUqI4fLy8poDclyNDWNb1uLWt848hGj1F/2jMPgMHyCAUyAhDskAMB8BQcwoiHGErJ\nQXeXVH2RtRebMms0OwmKbHhgF0RtqCoUncC7Px5i4MpfETrkHH6dNp79Fn3XbtWGn4gp8YEJtNjR\nvHVjKOszCupcb9Bq+6vNcePoFWal17Dq2Os+EVZKZQiRmi7uxXYXg0UZe0hgOJkYyrLpLYogfHA7\nP23jTB0SzV/njuO8P38eOFaIviG8yx+iOag8lV6koAnJ0MHDeH5cx2SIznM9jFdqfCTaJyKnvVDi\nrlB0At+s/Jr/eFNhTyqTtIcxCb05RZg9iysNB3BHj8QUmci5PQXnDu9V7xw+NC53/pEbe2dwWVRd\ngb512iBWrA6hr3aMhAe/BCT7LWWsN5/NEOdhKg5vpbcoRoR1vi85JtRCr3Arv71kPKzUj+VJPU6/\ngAg2+4YQlbeeWKFb8SOGjSAxuWM2OH/0jWJkn7YtStYRKLeMQtHBFNtcJDs2Bd4vNv8JgFzZgz6e\nowzXjmIaPrtWRuqJVHW+2yqHUJByb71jzUYNrD0I8emRJuHYMAkvlcFxpMnBJBasJkQ4ERGd1wf0\nmSvHAnp7O4DIUEvgXOSQSYHXGb6+9BFFXGlYA4A9pGO+kG6dphdje/PGMzvkfm2JEneFooP5ZncO\nMw2bORwyhkxf78Dx9b5RjBd7MeJttChVzRyak/U4tWlhfp+7DETK5NOD3b4BDNP0RhjG8M4T96q1\nV+UgmQwa67x6eOPYlHP49LYpTB0SzXGi6COKuMqwhm+84xk5pnm9UVvKgxcOJ/Opi4mN6NqlBupD\nuWUUig5m9/bNXK1lYk95jGnLezFF20nUsMmE7vsfVAV/9Bl70jkkTcuQtBtCsQg3VlycpaUDsJ1h\n9JXVxbgMPTovCqTqS6qqWYdRE/yf+24u8/3AE8PPJUkz0DvcSrbUY/6Nwsc3vhQuOMX8352BEneF\nogNZuy8fcfA7MEHwGVfxTF8TZsMsJiT25L6FG6oHRp68EcSQJmZJ2jXdVxwripii7aTQEs8OWzh7\nvUlQlasU1nmWu6/Kcvf7EKoyb9/1zuRJTf+mu3/WMH67tTqh65iMqjOPoi7KLaNQdCBr9uUzQhzB\naekJ4X05d1gvpgyOxmTQMETrm6J2Q/hJ/e2gx7kvmJLQ6P3W+0YB8LTpn5xn2ErQ8Jk8dcUYsolh\nhXc8LmmAngNb/VwtpcpyFyc877nDqsv39gq36v1g/eTL9q813x1QlrtC0YH0DDEzUsvE1HdsHQH3\n9h7D+4Xn0nP4TGY3Ya4bJyfwbXoel45reHNxc3kkxVqoXvgKCD73Xs7r0ZvoUAu/qbgLI172dKKL\n40Sfe1WbPe2ENfks1eL+2m8u7JC1neooy12h6EBcbhdDRTYidkzdk5qBhzw3k9PvoibNNSAqhLX3\nn0vv8IY3+yQy0Fv0B+8o6FFdxsCDEQeWhi7tEHqF6fcf0FNP0qrqpXqiJX/tOdWf18B+tevsKOpH\nibtC0YEE2bKwCDei14g657x+H0WYte0KdzncPp70XI9NWoi54c3A8aR+umvj5WvPaLN7tYRZo2J5\n68YzuelsfY+hypI/scSNrNkZSm2mNgnlllEoOoCVu45TuPxpelRtXtaTyl5aqTeF7tPGYXerfUmM\ncr5FxuBhgWMvXJPEnpxyxg/o3JR6IWonaQV88NQWcKPJQrqvH2nh5zKvIxd4CqPEXaEAjpVUEmw2\n0CO47YtnHS+t5Iv3XuRv5regqrSLpW4N8kcuGcnraw9yZmLPOufagpoNLUIsxk4X9vrwBSz3E8Td\nILjQ9TQTw3oqcW8iyi2jOO35LC2bO59+hUXvfd744Baw7WgptxpPmNtcV9yH9g5j0dxxmDq47G5X\nYubI3lyd0o9HL61dytfs/0zaq+NSd+T0/StSKPz87cOvWGJ5nLuz7m54kMcJxYdbNH9hYT7DtaOs\nspxbfbAecW8PUrqgdX4yLEYDT185ll4nbBIbDbqoK3FvOkrcFac1r67J4BrDdwBE1yiHW5MNBwv5\n+LEr4YWx4Kxo1vxur4/Pl38NgBj1s+oT9bhl2oMPbjmL80fUX3jsVMKoKcu9uSifu+K0pcTu4oOv\nV7Ha8iUAPgSa10Oxw4fVZCDI3wjif1uyedzwo35RXjr0a3oRqV3HyhgjDgLg7nMmZTKIcFEJIR0j\nuEaDxus3pDSxWEHXpaq/bKhFSVZTUZa74rTlp0NF/N64GIDV3nFoSHCUkPzECn728g+BcZrPiUXo\nkSwUHWzWPUrsLqZouzjqi8Ec0YtZzme4IehlMHScSGmaOOUt3gqHB1Di3hyaJO5CiNlCiL1CiANC\niAcbGHOVEGK3EGKXEOL9tl2mQtH2FNucjNf2sTHiQn6KuEA/aC8EYG9ueWBcL8eh6otKjzTrHmU2\nO5O1nZQnzqJ3uIXjRDF4ZHKr1366YXcpcW8ujX5SQggD8DIwE8gCNgkhlkopd9cYMwT4PTBFSlks\nhDj1nXyKbo+ttJBoUUZEymRW7gyFCvAUHCBF7GGIlg1cDEDPsj3VF5UcbdY9vIWZmIWX/iMmEBob\nzne/nUbfHkGNX6ioxdVn9mP3sTJun9H5XaNOFZryNTgBOCClPAgghPgAuAzYXWPMzcDLUspiACll\nXlsvVKFoa5z5GQCYYgaRExSOVwrWr/2WJZZ/6QPkX/H4JORsp9wQxBHRh1GlTRf346WV7Nm+EQBr\nnF6jfGBMx2ykdjfCrCaeuzqps5dxStEUt0wcUPMvOst/rCZDgaFCiB+EEBuEEE2pe6RQdCol2fv1\nF5EJHKkQHJBxxGUvqx5QnsN3e/IYpR0mXfbnsDcaT3HTxX3uqz8SXJyOFw1j7MjGL1Ao2pC22lA1\nAkOA6cA84J9CiDp1OYUQtwghUoUQqfn5+W10a4Wi+VS6vBhK/XHrPQawN7ecHXIgg7Tj1YOKDrJi\n1zFGiMMcMQ8mW0YjS45U97hrhKxiOxO1PeSa4sGkXDGKjqUp4p4N1CzDFu8/VpMsYKmU0i2lPATs\nQxf7WkgpX5dSpkgpU2JiYk48rVB0CE6Pl+8PFDBYZGG39gZLKA63j3Sf/md+1Of/2yw6iKvgMCHC\nyaUXzCRbRmPyOcHWNMNkgtjDWVo6rnHXt9ejKBQN0hRx3wQMEUIkCiHMwDXA0hPGfIputSOEiEZ3\n0zQvZkyh6AA+Tj3KrKeXUbF4AZdqP2LrOSpwbpNvOADvhCzAjRGKMjCX69a9pdcQjlv9m3nHtzXp\nXpNDdBsoYfpNbfgECkXTaFTcpZQe4HZgOZAOfCSl3CWEeFwIMcc/bDlQKITYDawCfielLGyvRSsU\nLcHh9rLkkw/5t/NeLjf8gEl48fbW64RHh1rYLgeR5HiN3AEXkSVjoOggoTa/jz0ykXW2eLxSULJ/\nfZ25X1l9gI0Hq//kpZTEug5jN0ZASHSHPJ9CUZMm+dyllMuklEOllIOklH/yH1sopVzqfy2llPdK\nKUdKKcdIKT9oz0UrFC3h+RX7mG9czgCtOpjLMlIPd/z8jikAlBDGsNgwDvl6487PoI/vOB7NDGF9\nsGNln4xHO7al1rxSSp75ei9Xv17dA7XC6WEgWZSEDFL1xxWdgspQVZw2ZORVMFFLZ4n3HMY43uA8\n57NEDpkIQJ+I6g3PXmFWDsve+AozGCBysQfHBzo4b/UNxnQsFel2BMaXVro5Q+zjOsNKdqz4N+6s\nraRmFjFUZOHoUWfrSaHoEFS6l+K0IdaTRZQoJ1UOp5xgymVwveMuHB3LM/+NxeKr5AxtH56IlMC5\nb3xncq1vFc6nBmJ58CA+g4XskkqeML3FKO0w/AD8ABvc8zjXZCM/eli991Ao2htluStOG/o59bj2\nP9xyXb3n0xbOJG3hTEIsRnyRetu3GFGGFqNb39sfuwDL8Av4xHs2Fq+Nr5/7JT979DUufvF7YkUR\nAB94pgPwe5Nes8bYe3h7PpJC0SDKclecNgRV5gAQ2msg7/wyrpYrBqjVhckTkQj+6r7WAbrlHm41\n8Y/rU7j6ld/y84LvmV35BbMNX7BH9CNKlLNxyD1k97wSNur++52+BPoNndQBT6ZQ1EWJu+K0QEpJ\nUGUOlVoIQdZwpjbiCtd6Dghkc1j7j68+rgmunTKU0k+DiRB2AIZrekTN0LGTKCSKh7+/iQhsvOK9\njMwe7dMyT6FoDOWWUZwW7D5eRrgrF2dwbJPG94oI5XPvWewTA6HnwFrnBseEca3rDzzm/kXg2HZf\nIsFDpjJlUDSfaBdgOfd3bHlkZps+g0LRHJTlrjgtuPjF71lqLkSG92t8MNA73MId7jsYERnCVyeE\nMsaEWdglE9jlHYBEsMw7gXwiybQGYwF2PjYL42ncB1XRNVB/gYrThj6iEC0ivklj9bK8ghFxdXuQ\nRoVW+eYFnxgvJp/aY5SwK7oCynJXnBac1T+EmLwyiE1s0vgpg6N57qpxzBzZu845k0Hjz5ePYUJi\nJAlRIQx++Ku2Xq5C0WqUuCtOC/poeqgi4SdWq64fgya44oyGrfxrJ/YPvH73lxPxNrFSpELRUShx\nV5wWhDv9JQcimibuzeHsIap2jKLroZyDitOCCLdf3JtouSsUpzpK3BWnBUHeMv8LFXeuOD1Q4q44\nLTB79YQjLKqHqeL0QIm74rTA4rPjESYwWjp7KQpFh6DEXdHtkVIiHeU4tPqrQCoU3REl7opuz47s\nUkJFJeU+a2cvRaHoMJS4K7o9uWVO+ogirD37dvZSFIoOQ4m7ottTUOEkQeRg6aW6IilOH1QSk6Lb\nU1JSQqwoxtNrcGcvRaHoMJTlrugWbDqYz+LXn8JXUVDnnK/oEADGGCXuitMHJe6KbsFb/3qZecf+\nQs6/rq5zzlSaqb84oS67QtGdUeKuOOWxOT1coa0GILI0HU4o4hVUnqm/UOKuOI1Q4q445bnhmfeY\noaVRJoMI8tnAVts1E2I7QoWhB1gjOmmFCkXHo8Rd0eW595Ul/PDcPPC4ah33eH1kFduZ6lgDwOsh\nv9ZPFOwNjFmzL5+B3kNUhDWtjrtC0V1Q0TKKLk2F08OdOQ+RoOVC7g6Iq25W/df//UBc2vP8n3EN\nxyLGkWE5A+zgyd3DRys3ktLLx9qKM3hEy8CT9HAnPoVC0fEocVd0afbnljNcFOtvSo4GxH3Jv1/k\ngUOPBP6CK0bdgCM7lkqs7PnyH1yrHYAsGG+eDYBx5JzOWL5C0Wkot4yiS7MvpwwzbgB8xYcB+Olg\nIUkZ/wDgkK83i9xzGXb+fCKCzez39SFZOxC4/iLX1xSY4yFmWMcvXqHoRJTlrujSlBXmYhB69Iun\n8BBmIGf7CiZox/it61a2Rl3ILVMHIgwm4iKDyJc9AHjZM4eelDPPuIrMqHOIFqITn0Kh6HiUuCva\nlS+3HychOphRfVsWqWIu2Rd4LYsy+X5fPnFbFlGihXHP3fcT3ysqcH5AzxBe8lyOAR9nX/cH5v47\nnW1yEJPG3EhKq59EoTi1UG4ZRbty2/tbuOTFtS2+PrxUF/dU31BE6WG+WbmM8dp+dg39TS1hB5iT\n1Jc0OZgb3Q8wbsQwbjt/FHlDrmHOpNGtegaF4lREibui3ahwerjf+AGHrNfjWf9ys671+STffvgS\nl+e8QJEMZbNvCIayoyTl/heXZmXKFXfUucZqMtR6f9f5Q3jzxjMRyiWjOA1R4q5oN978/hD/Z1wK\ngPGbh5p17VP/XcdZu58AYLXpHI7KXhh8bq4Qq3GNuAKs4W2+XoWiO9EkcRdCzBZC7BVCHBBCPHiS\ncT8XQkghhHJxKli7cmntAz5vk647VGDDsvXfhAgn30z/jF5Xv8ReX7/A+dAL/tDgtS9ck8RHv57U\novUqFN2JRsVdCGEAXgYuBEYC84QQI+sZFwbcBWxs60UqTk1uMK6kRIbwhPs6/UDRwQbHZqX/RMXe\nVQAcL63kEsMGSntP5ILp0+nTw8omOYzXPBfzvnUeRMQ1OM9lSXFMSOzZps+hUJyKNMVynwAckFIe\nlFK6gA+Ay+oZ9wTwNOBow/UpThG2HCnmQF45AA63l0MFNsaIg6z3jULG+X/I+UvvnsjOzFziP5xJ\n6OKfUbbmFYpLShgisvH0nwpAfGQQIPiL5zom3LSoIx5HoTjlaYq4xwFHa7zP8h8LIIQ4A+gnpfzy\nZBMJIW4RQqQKIVLz8/ObvVhF55FbVIK9tG6t9Cr++uprbHhxPpQc4YmP17PrhSsYqOUwdOxEiIgH\nwFuahcfrq3Ptlg3fBl6Hr/o9tuV/RhMSa/xYACxGAwumJAAwuFdoGz6VQtF9afWGqhBCA54DftvY\nWCnl61LKFCllSkxMTGtvreggnG43Gc9fiOW5wbBH//6WUvLWD4fILqnE6fFyt/ETrjd+i3xxPFfs\nuZdLDBsACOo3juCovnil4IOVGxj6h6/qzO/JTQfgSudCjvpiuMr1CQAhA5IDYx69dBSZT13c3o+q\nUHQbmiLu2UC/Gu/j/ceqCANGA6uFEJnAWcBStanaffjDcy8z2bBbzxT94Fo4tpX8cie7l73C/hfm\ncCS/jD6iiO2+RPK8wYzX9uGTgj+5ryVq3EWM7RdNLpFMqlzDL7Uv6tZbLzmAnSAMAybxvOfnAKT5\nBkKP/p3xuApFt6Ap4r4JGCKESBRCmIFrgEAYhJSyVEoZLaVMkFImABuAOVLK1HZZ8WnCyt25rNqb\n19nLACClfBXlMojznM9SKc3kf3QXx0sqedb0OtPlT2z59/3EiwKWeidzofMpFrrn82vLUyRfsxBr\nUAjRYRZyZE8Gajk8bHq/1sZqfrmTeM8RKsIH8uGtk/nCN4kn3Nezf8YbnfjECsWpT6PlB6SUHiHE\n7cBywAC8KaXcJYR4HEiVUi49+QyKlvCr/+jfjZ3pitiZXcqvXvqMlZYN5PWdwbGs/iz03MizJa/j\n/Ob2wLirHR+RJaMZOPMW3hg4gOR+19RKHAq3GjkqQ6onrsiDqEEA7M0pZ4iWjYw+F4C3f3U2v1sS\nzs3JozrmIRWKbkqTastIKZcBy044trCBsdNbv6zTh2eX7yG3zMmiueM6eyl1eGPdQW4wrsCCm7g5\nC0nvM4Lb343my71pXJz1BQAfeqZzmeEHKi79J9emJNU7T6jFRC9RUn3AVv2LJCPrGGeLYmxxenTt\n5MHR/PDgjPZ7KIXiNEFlqHYyr6zaz/rNacgT/NAt5a0fDpHw4Jf1RqU0l1CrkeHiKAeIw9JnBAB3\nXzCc29x3s9A9nxc8l/OA5xbGO1+lx5DJDc4TZjWyyHMV+VLPKs3OymTBq9+x+fkrCU7/GICQPqok\nr0LRlihx70CWbjvG2n21Q0CvN6xkvfVObDvqjyJtruj/5as9mPBQVFbW4nVWEWw2MlRkUWCtblE3\nuFco6Y/PxpNyM1nj7gHARhC9wy0nmcfAKl8yE52v4JWCVWvX8FbO5YwvXcHcvBf1QX43jUKhaBuU\nuHcg9y/ewB/e+hyH28uK3blsPlzMWdpuALx5e2qNDaGSsSIDj6954t7Pd5z91l8Q9UqdJOJmE2X2\n0E/LJ/mMibWOB5kN/PnyMcyfnADA2wtOXpyr6pwPjSLCud74bd1BkarHqULRlqh67h1AdkklX24/\nxt3G/3Kr8XPW/v07bs6dB8AHZj2rU5ZU54lJKfm18XPuNH6KI2sSpgFnNvle04W+EWtw21q9bq2y\nCICQ6Ph6z4+Oi2j2hm+BjCBGlLLcm0KBjOA647fkaLHEmoNbvV6FQlGNstw7gAc/2c6ry37icsM6\nACaXfEkfCgnCQSy6gBoLqi13t1dyptDrmHsL60/Zr4+0oyVM0XZVH/A4W7dwZykAIqhH6+apwbOe\nqzjs68X3iXex2qdvIpcMUv1NFYq2RlnuHUAPXwnrLXdgFW5WeMcz07CZry0PECHsgTHWwl3g84Gm\nUeny4kN3ZXgrSxqaNkBqZhEH8yooqrBxg5aOQ5qwCjdU5LYqEUhz+v321pZ1UaqP73xn8J0rmUML\nLibx9xrnO5/h+alXttn8CoVCR1nuHcBA9wGsws1n3sk84L6ZfBlRS9jXesdgdJdDid4AutJdXRpX\n2k8u7tuzSkh9404u/XI8I1bdQohwstykx4xTntO6hTt0yx1r6y33L+44m49vrSrFK/x+eMEBGU+f\nnqpejELR1ijLvR3ZviONyNUPM7bcBEDe5EcpWlPIYdmbGFHKRt9wtoedw5riKM4x7IDiQ9AzkUq3\nF7NwAyArS+udW0rJU8t24Vn/Co+YPgdgmmE7AGtM53CZ55tWi7u1LFN/Ed5wid2mMjpOt/5H9gln\nbHztXwJRIeZWz69QKGqjxL2dWJa6n4u+mAbohXkcWjA3z55I0ohivvrnBFK0ffw0+lEunzmdfz29\nRL+oOBOASpeXcPyWvaN+cV+3v4D+Py7kOpMeeXKn6zYqCMKEl/KIAVCJ7pZpIQePF3BG6QoKjL2J\nDm27Im/L7poaeH3XeUP4bk+eaoOnULQDyi3TTmz9oVZCL6UhiSAEA6NDeNM7mwmOl7njqouIjwxm\nQMIg3Jiqxd3tIUL4o10c9btlMo4VMNewGru08JFnGst8E/nOdwaTLp6PwxyJFw3Kj7do7Q63lyWv\n/pERHCJj1F0tmqMp3KDk+h0AABWJSURBVDNzKJ/fcXa7za9QnM4oy72NWbU3j4mJPYmv2I1XCv7q\nuYr7TR+SHX8RvYGIIBMSjTwiA9f0CLGQo/WiZ+5BQoBKly9guQtnA26Z49sxCy+3u+7kG9+Z7Pzj\nLEIt+j/n17tyKNF6ElVe13KXUuL2SszGhr/XNx4sYAGfcjx6MhMvv63lH4ZCoeg0lOXehmQW2Fjw\n1iZufHMTAxzpFAQl8g/vpVzqfBLfhFsBMBr0jzzcWv29GhVqIcMdTca+naw/UIDdYSdY6GGMhgbE\n3ZK7GYCEcdP47LYpAWEHvblFkRZZr+X+5Gfb+OzxKyhJXdLgcxzYu4sYUUb0hLnN/AQUCkVXQVnu\nbUhVlEtqZgFJlgOUx16ILNHYIQeSkhgdGLf87nOIjbAG3s8c2Zsjm3sx3bCNRZt+4n/b8rjAf1pz\nVQTGldhdLN12jAmJPQkv3EZpUCwPXX1u3XW4vGQ6w+lbmE3ICedK9v3AXG0VfLEKxv8c6vF3Wwr1\nWHlTXNcrZqZQKJqGstzbEJPfKp+opdND2JCJ0+odNyw2jIgg0/+3d+/RUVX3Ase/v3nkwSNPHgYC\nIUgAwUeBFAltEREBX+ht1QtifZTWti5rLWVVrK2tXnuvVm1teyvI0t5LXa2i1CdS8Yq61PrgIW8k\nEEBIIEBISMhzJjNn3z/OSTLEPCY4YTLD77PWrJyzzz6TvbMnvzmz55zfaV6fOrI/LwTtumbbS5wl\n9oVNpSYDl7+6ud4f1uzmvle285+rdjJaitsNvkVlNRw1aQRPHOZYjY+Jv3mLTcX23P25wW0tFe9P\ng1bn0fsCQcr3bCBoBAZ8+RQGSqno0OAeQS7nIHiGaz11JpGEc6/iyW9P4K/fmdjhfiLCVjOcPVYW\nk1w7+Efi/QAUWkNwB2qpqmukxhcgwWX4u/dBvr3vbnKllOTs89p8vr5JHo6YdFKsSj7cdYij1T6e\neKeIA+V15NVvpdhqOfvl+PLb7YunHK9tLmWsfM4eMwi8yV/yL6KUihadlokgy8ngeJ5rH9tNDl9J\nS2VmZvjvn7tNNrPc65rXt5scLgps4YIHVgPCBVLEPYk7WnYYcE6bz/Pwt87n5aftC48ajh9moWc5\nfXbV80L63fxAdrEhbRbfOHI993v+l5s/fx2KP4Ec+wKj372xg1WuXfhHXNbF3iulehI9co+goAVu\ngoyR/YweP6V5miYct089mwNmQPN64zVLqTK9EQy98OHC4keel07eaWDbdyuaNDyTaq89x199eA93\neF7hFs+bfHPdDfQWH5OnXcVNBcN4LGB/YRrc/1HzvlNSSkmTWgZcMCvstiuleh4N7hEUsCzOlkP0\nEh99csPP5Ajw4+l5FFot9yH3XnA9NWJflj9IjrE36UamuzeevFNG+znQ6xLs4J559GMAfMZDrss+\nNdIzrID7Z49l3Mhciq3+1B1oed6MKueTwZCOp5KUUj2bBvcIsiwYLQcAkLPO79K+CW4X71shc+gi\nVLozAXja+2hz8Xz/T/kgOJZ1qTPB0/5l+4OG2PnRz621j8rvbPwRACWuwZCajYhw1/Q8dpgcXEe2\nAhAIWgz07cPvSobUIW0/sVIqJuicewQFLIuzXYcI4sLdxTsLiQhHSWdJ4EoScwu4FSh39wMLclz2\nPUfn+3/K/n4XcePR8dwwLIeOPhv0zRxEWVEqIwJF+CSJTb0KGFcziq/mpLHUqTM4LZl3rRxmVG8A\nXw2ltS5GUEJ137PJdOn7vlKxTP+DI8gyhrPlEP4+Q8DT/m3nOvJQ4AY+S7Xzrxz05FBh7KmZxYGr\nWGNN4Pr8bEBI7OAKU4C+yQm853wS2GkN5k/zvsrI3GE8OK/lvPiM3gmsM6MQDFX/eoqS4/XkuQ4S\nyNT7mSoV6zS4R1AgaMiVw9SnROaWcSUnGjlgBgKwxRoOQHmNH+g8k6IvYLE8cDE+4+HwyG8zMTeD\n5d8vYEBKy8VTHreLD62xbLOGEdi0nPc2FzJAKkkc1PYXtUqp2KHBPYKCxpBCHcGk9M4rt+GmghwA\n+iS2XOC0sPH7PBGYzc/vuot3F07lmnGD8bqF2Rd0nIa3vMbHWnMOY31/4eJ/bz/51+J5E/jIGkNK\ndRGl6+3UwWl5Be3WV0rFBg3u7ThUWY8xXbs5ddAyJIsP4z21+4H+8soxLLh0JAtmjGwuKzLZ/DYw\nhyEDMhjWrzfnZKWw+zeXMzSz499x6Rj7iP/h6yZ0mCRs1rlncYQMvMbPY97FfGYNheyunemjlOp5\nNLi34ciJBiY/9DaPvlkY9j7GGH7x8jZ6cerB3et2cecleSclATtVU0cNYN9/Xc63JrR9c+smIkKV\nxz4rxy2GVwf9BNzeDvdRSvV8Gtwdj64uZOWWQwBUNwQAWPbhfijfA2EcwT/z8X6mVf6DXl/iyL09\nIwac2m3owr0JRqmn5Q3grlvnntLvUkr1LBrcgaq6Rta9+xryws3gr8MfsHOtTGjcAH8aT+P7j3f6\nHHv2F/Mr7zP2SoSD+0u3T47o87X2Qc0g/h6Yxsb8R0hM1HwySsUDDe7AXcs38ljCYq5wr4VDn+IL\nBPmGawtLvb8DoGLvxk6eAQbVbG1elghNazQlIuub1N3TJMLPA98lfdK8bv49SqnTRS9iAhoaLQLG\nDQLBozvx9TuHG91v4cOL33igvqLD/Y0xmJJPAXg88E1mj/pWRNr1/t3TqPcHI/Jc4QjNMa+Uim0a\n3LFT5PqdP4X/8E58aRZ5UkJJxiQOH6vgK/XH2t3XH7B4edNBRgZ3UyjZPB64lqt692u3flcMTjs9\nUyTPf7+A93aVkeR1n5bfp5TqfhrcAX/QIl3sOx6ZskJW+g7xc6nBP2AwRcfqcTW2f+T+xzW7+e93\ndrMhcQ9rguMB+wbTsWRibgYTczOi3QylVATpnDv2+em9aQDAW7GbFRsOkEotnj4ZNJBIfV0N97y4\ntc19dx+tJluOkSnVTJh8Cfdefg5jslJOZ/OVUuoLwgruIjJLRApFpEhEFrWxfYGI7BCRLSKyRkRy\nIt/UbmQF6CU+fMaLt7aUn3mW4xKDq1c6flcSvWjg2bV2tseq+kaWPfITqh7Lh9pjCMJ17ncBGDpu\nOt+bMjzsUxCVUqq7dBrcRcQN/Bm4DBgDzBWR1jfX3AjkG2POB1YAv410Q7uTN1gHwEYzAoAfeuzL\n8FOyx9IgSSTja667etth5tYsI7V6N+azlfirj3GH+2VKh1yBN0tzsiileoZwjtwnAkXGmL3GGD/w\nHHB1aAVjzDvGmDpn9WOg48sie5jEYD0ArwcvZJNlp+o1c5fjHjUDnySRIEE8BLAsw5FjZSSIPad+\ncP1rpFTuwC2GrItvi1r7lVKqtXC+UB0MFIeslwAXdlB/PvDPL9Oo0y3RqgXguOnLtf5fcZYc54NR\n9m3m6rGzLz7lfYzKsvGc2LMWgFKTQUrpvziLbBAgc0RU2q6UUm2J6BeqInIjkA880s7220RkvYis\nLysri+Sv/lISnGmZGpIJ4OH1X7ZczHM8aF9tOtW9mU9fW0Lq4Q8J4uLBxhtJkXquMO9i4YI+Z0Wl\n7Uop1ZZwgvtBIPSea9lO2UlEZDpwLzDbGONrvR3AGLPUGJNvjMnv37//qbS3WyRZdnAfnTOIl26f\nTGqvlitCDwb6Ni+XfL6Li12bqOw3nrWWfUOL81yfU5vQD9x6VqlSqucIJ7ivA/JEJFdEEoA5wKuh\nFURkHPAkdmA/Gvlmdq+maZlFV+czbujJudjLTWrz8lTXZsa69mONuJQ5F0/AZ+yAXpc04PQ1Viml\nwtBpcDfGBIA7gNXAZ8DzxpjtIvKAiMx2qj0C9AFeEJFNIvJqO0/XIyU50zIk9v3Ctp1mKMsCl/Jy\ncDLDXEcASM+/notGDaTU2KlyG5IHnra2KqVUOMKaSzDGrAJWtSq7L2R5eoTbdVolGvtsGRK+mFq3\nEQ+/CtxKFuVc4/4QAE+/4aQEqzlKGsM4Ql166zNDlVIqunSiGPCYRnvB3f59SUvJZEngKtLOv4w5\nQEqSl8cDM0nwBBg97Tunp6FKKRUmTT8AuIx9c46O7kA0fmgaDwXm0jDYzq2ekuxhlTWJa/z/QdKA\n4aejmUopFTY9cgc8TcHd1X5wf+62Ap75eD/zJtmZFZI1g6JSqgfT4E7Ikbur/YCd4HEx/+u5zesi\nwq+vGsOEHM2mqJTqeTS4A24TICAePF1M+HXL13I7r6SUUlGgc+7YR+6Wvs8ppeKIBnfAbYIERYO7\nUip+aHAH3ASwNLgrpeKIBnfsOfeg6NkvSqn4ocEdO7hbrraP3E/XTaqVUiqSdC4CcBNsd1rm7YUX\nYcxpbpBSSn1JGtyxL2JqL7gnenS6RikVe874aZmGxiAuE8B0cHWqUkrFmjM+uO8pqyEJP57EXtFu\nilJKRUxcBPfNxZWMue8Njpxo6PK+pZUN9JcqXH31hhtKqfgRF8H9uXXF1PmDvLHtcJf3raj1018q\ncafoPVCVUvEjLr5QzU63T1csOV7X5X0rqmvoJydoTBsU6WYppVTUxN6Re1khfLzkpKKm9LsHK+u7\n/HQNlfat87ypeuSulIofMRfcg7vehDfuhuP7m8saAkFmutYxovT1sJ5jU3ElNT47za91wpnK6avB\nXSkVP2IuuC87NgqAio0t9+D2+f08mfB7FtQ8RqDk0w7331pSxTV//hdzln5kF9Q4wb2PBnelVPyI\nueB+xcVT2GmG0rBuWXOZt66sebl07YvNywcr66n3B3lvVxnHa/0UV9Tx+Fu7uMX9BrcefYgTVRUE\nT9jTMvTRs2WUUvEj5r5QHZiSxHv9ruC68sVQVQKp2XgaKpq3u/asAR7kQHkdUx555wv7D3WX87T3\nrwAc3LACT+0R8KLBXSkVV2IuuAP4Bl0I5YtZ9KdljJ91E8HacgA+sM5lcs12LvzFco4E+nCn+0Wm\nuTdyvuzlfes87mi8kym0TNvs3fIBQ10VNCQPJMmTGK3uKKVUxMVkcE8cfB6NW9zc4n+Wp16qwm88\nkAAN51yLq3Abk62NbJazWeBd0bzPRe4tbHV/F4DK5CHsqOlLRsUmUsSNu39etLqilFLdIubm3AEG\n90tjrTWa0a5iHvU+yc2eNwHIKpgDwO8TFvN24kIaXUmwsAjfj7fzqTWief/U869ka+8Cxrr2c4Fr\nL94hE6LSD6WU6i4xeeQ+OD2Zaxp/xIyUYyyoeZQJrt3Up+SSk9WflcFJXOn+GADvzAegT38SgdE/\nW8OBAzsZuO8VEif9gO9Nz2LvC0KOrxD3hT+MboeUUirCxEQpWXl+fr5Zv379Ke+/paSSvAF9ufPX\nD/KE9w9UXfYE/SbNIW/RK8wfA4sKekPe9Ai2WCmlok9ENhhj8jutF6vBvcmB8jqeX7eXBTPG4nIJ\nQcvgEhCRCLRSKaV6lnCDe0xOy4QamtmLhbPObV53uzSoK6VUTH6hqpRSqmMa3JVSKg5pcFdKqTgU\nVnAXkVkiUigiRSKyqI3tiSKy3Nn+iYgMi3RDlVJKha/T4C4ibuDPwGXAGGCuiIxpVW0+cNwYMwL4\nPfBwpBuqlFIqfOEcuU8Eiowxe40xfuA54OpWda4GmtI0rgAuET0XUSmloiac4D4YKA5ZL3HK2qxj\njAkAVUBmJBqolFKq607rF6oicpuIrBeR9WVlZZ3voJRS6pSEcxHTQWBIyHq2U9ZWnRIR8QCpQHnr\nJzLGLAWWAohImYjsb12nB+oHHIt2I7qB9iu2aL9iS3f2KyecSuEE93VAnojkYgfxOcANreq8CtwM\nfARcC7xtOslrYIzpH04Do01E1odzqW+s0X7FFu1XbOkJ/eo0uBtjAiJyB7AacAN/McZsF5EHgPXG\nmFeBp4FnRKQIqMB+A1BKKRUlYeWWMcasAla1KrsvZLkBuC6yTVNKKXWq9ArVzi2NdgO6ifYrtmi/\nYkvU+xW1lL9KKaW6jx65K6VUHDrjg7uIDBGRd0Rkh4hsF5EfO+UZIvJ/IrLb+ZnulIuI/NHJo7NF\nRMZHtwftExG3iGwUkZXOeq6T+6fIyQWU4JTHTG4gEUkTkRUislNEPhORgjgZq584r79tIvKsiCTF\n4niJyF9E5KiIbAsp6/L4iMjNTv3dInJzNPoSqp1+PeK8DreIyEsikhay7R6nX4UiMjOkvMM8XRFl\njDmjH0AWMN5Z7gvsws6h81tgkVO+CHjYWb4c+CcgwCTgk2j3oYO+LQD+Dqx01p8H5jjLS4AfOsu3\nA0uc5TnA8mi3vYM+LQO+6ywnAGmxPlbYV3jvA5JDxumWWBwvYAowHtgWUtal8QEygL3Oz3RnOb0H\n9msG4HGWHw7p1xhgM5AI5AJ7sM80dDvLw53X7mZgTLe1Odovhp72AF4BLgUKgSynLAsodJafBOaG\n1G+u15Me2BebrQGmASudf6BjIS/GAmC1s7waKHCWPU49iXYf2uhTqhMEpVV5rI9VU/qODOfvvxKY\nGavjBQxrFQS7ND7AXODJkPKT6vWUfrXa9m/A35zle4B7QratdsaveQzbqhfpxxk/LRPK+Xg7DvgE\nGGiMKXU2HQYGOsvh5NrpCR4HfgZYznomUGns3D9wcrtjJTdQLlAG/I8z3fSUiPQmxsfKGHMQeBQ4\nAJRi//03EPvj1aSr4xMT49bKd7A/hUAP6ZcGd4eI9AH+AdxljDkRus3Yb7Mxc1qRiFwJHDXGbIh2\nWyLMg/3ReLExZhxQi/0xv1msjRWAMwd9Nfab1yCgNzArqo3qJrE4Pp0RkXuBAPC3aLcllAZ3QES8\n2IH9b8aYF53iIyKS5WzPAo465eHk2om2rwGzReRz7BTN04A/AGlO7h84ud3NfeooN1APUAKUGGM+\ncdZXYAf7WB4rgOnAPmNMmTGmEXgRewxjfbyadHV8YmXcEJFbgCuBec4bF/SQfp3xwV1EBDt9wmfG\nmN+FbGrKl4Pz85WQ8pucb/onAVUhHzl7BGPMPcaYbGPMMOwv3N42xswD3sHO/QNf7FNTX8PKDRQN\nxpjDQLGIjHKKLgF2EMNj5TgATBKRXs7rsalfMT1eIbo6PquBGSKS7nyqmeGU9SgiMgt76nO2MaYu\nZNOrwBznrKZcIA9YS0ieLufMpzlO3e4R7S8pov0Avo79MXELsMl5XI49h7kG2A28BWQ49QX7zlR7\ngK1AfrT70En/ptJytsxw50VWBLwAJDrlSc56kbN9eLTb3UF/vgKsd8brZeyzKWJ+rID7gZ3ANuAZ\n7DMtYm68gGexvzdoxP6kNf9Uxgd7DrvIedzaQ/tVhD2H3hQ3loTUv9fpVyFwWUj55dhn5O0B7u3O\nNusVqkopFYfO+GkZpZSKRxrclVIqDmlwV0qpOKTBXSml4pAGd6WUikMa3JVSKg5pcFdKqTikwV0p\npeLQ/wM2Sw7WgV2wiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# google stocks\n",
    "\n",
    "test_company(\"GOOGL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "QywhyeJT1LmX",
    "outputId": "3f3311ae-c68a-403b-a3d1-36bc2be8fbdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1130/1130 [==============================] - 0s 406us/sample - loss: 3.0817e-04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwtD/AF74fMTxDgYIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nvidia stocks\n",
    "\n",
    "test_company(\"NVDA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "XtXS63jW7yWS",
    "outputId": "ddee3150-4600-4493-8f59-e25e49707c93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1130/1130 [==============================] - 0s 435us/sample - loss: 8.5428e-04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# disney stocks\n",
    "\n",
    "test_company(\"DIS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "xY-ZFTju93lD",
    "outputId": "44951241-e2e7-4a59-ff52-c8339ae95a7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1125/1125 [==============================] - 1s 454us/sample - loss: 3.0266e-04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niro0Yf0zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dbs stocks\n",
    "\n",
    "test_company(\"D05.SI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9mJe0X7q83mR"
   },
   "source": [
    "It is apparent that the model generates accurate predictions even for the stock prices of other companies it was not trained on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2PkmggXP8Gax"
   },
   "outputs": [],
   "source": [
    "# get the actual price and predictions\n",
    "\n",
    "def unscale_price(data, feature=\"Open\"):\n",
    "    \"\"\"data is a 1-D array of opening prices\"\"\"\n",
    "    scaler = pipeline.named_steps[\"scaler\"]\n",
    "    scaler_copy = MinMaxScaler()\n",
    "    col_no = FEATURE_NAMES.index(feature)\n",
    "    scaler_copy.min_, scaler_copy.scale_ = scaler.min_[col_no], scaler.scale_[col_no]\n",
    "    return scaler_copy.inverse_transform(data)\n",
    "\n",
    "def test_company_table(name):\n",
    "    dt = datetime.datetime.now()\n",
    "    df = DataReader(name, \"yahoo\", start=dt-relativedelta(years=5), end=dt)\n",
    "    df_tr = pd.DataFrame(pipeline.fit_transform(df), columns=FEATURE_NAMES)\n",
    "    X, y = split_input_output(df_tr)\n",
    "\n",
    "    model = tf.keras.models.load_model(BEST_MODEL)  # change the model name if the timestamp is different\n",
    "    predictions_unscaled = unscale_price(model.predict(X)).flatten()\n",
    "    actual_unscaled = unscale_price(y).flatten()\n",
    "\n",
    "    table = pd.DataFrame({\"Date\": df.index[WINDOW + 29:].values, \"Predicted\": predictions_unscaled, \"Actual\": actual_unscaled})\n",
    "    table.set_index(\"Date\", inplace=True)\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "aMiI3Dq-3kOm",
    "outputId": "91f45734-b7df-4717-99d3-76ba7db70762"
   },
   "outputs": [
    {
     "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>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-27</th>\n",
       "      <td>58.167995</td>\n",
       "      <td>59.330002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-30</th>\n",
       "      <td>57.471416</td>\n",
       "      <td>59.715714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>57.224842</td>\n",
       "      <td>60.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>57.108532</td>\n",
       "      <td>59.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-02</th>\n",
       "      <td>56.827667</td>\n",
       "      <td>59.071430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Predicted     Actual\n",
       "Date                            \n",
       "2015-03-27  58.167995  59.330002\n",
       "2015-03-30  57.471416  59.715714\n",
       "2015-03-31  57.224842  60.110001\n",
       "2015-04-01  57.108532  59.642857\n",
       "2015-04-02  56.827667  59.071430"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx_table = test_company_table(\"NFLX\")\n",
    "nflx_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "vgc2i_ldRoBt",
    "outputId": "2dbe2c69-007b-46ac-eb9c-dc4880e1e6ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.5992601824801005"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean absolute error\n",
    "\n",
    "np.mean(np.abs(nflx_table[\"Predicted\"] - nflx_table[\"Actual\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nm1ky2DwLkd0"
   },
   "source": [
    "As you can see, the actual error of the model in terms of money is around 20 cents, which is too high and not practical in stock trading.\n",
    "\n",
    "However, predicting the range of prices for that day (the high and low prices) would be more useful as valuable information can still be obtained even if those predictions are not accurate.\n",
    "\n",
    "We will now modify the lstm model to predict the highest and lowest prices of the day. A separate model will be trained for each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 983
    },
    "id": "nOdbjloaGJYz",
    "outputId": "701b7f79-3495-4bb5-d4c3-0d631f1f7578"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 791 samples, validate on 169 samples\n",
      "Epoch 1/20\n",
      "791/791 [==============================] - 1s 1ms/sample - loss: 0.0159 - val_loss: 0.0018\n",
      "Epoch 2/20\n",
      "791/791 [==============================] - 0s 229us/sample - loss: 0.0031 - val_loss: 0.0041\n",
      "Epoch 3/20\n",
      "791/791 [==============================] - 0s 203us/sample - loss: 0.0018 - val_loss: 0.0022\n",
      "Epoch 4/20\n",
      "791/791 [==============================] - 0s 208us/sample - loss: 0.0013 - val_loss: 0.0016\n",
      "Epoch 5/20\n",
      "791/791 [==============================] - 0s 203us/sample - loss: 0.0012 - val_loss: 0.0018\n",
      "Epoch 6/20\n",
      "791/791 [==============================] - 0s 202us/sample - loss: 9.1537e-04 - val_loss: 0.0014\n",
      "Epoch 7/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 9.9727e-04 - val_loss: 0.0014\n",
      "Epoch 8/20\n",
      "791/791 [==============================] - 0s 205us/sample - loss: 9.8865e-04 - val_loss: 0.0013\n",
      "Epoch 9/20\n",
      "791/791 [==============================] - 0s 202us/sample - loss: 9.5909e-04 - val_loss: 0.0031\n",
      "Epoch 10/20\n",
      "791/791 [==============================] - 0s 213us/sample - loss: 0.0011 - val_loss: 0.0020\n",
      "Epoch 11/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 9.3523e-04 - val_loss: 0.0011\n",
      "Epoch 12/20\n",
      "791/791 [==============================] - 0s 215us/sample - loss: 8.1261e-04 - val_loss: 0.0018\n",
      "Epoch 13/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 6.9173e-04 - val_loss: 0.0012\n",
      "Epoch 14/20\n",
      "791/791 [==============================] - 0s 203us/sample - loss: 8.7234e-04 - val_loss: 0.0011\n",
      "Epoch 15/20\n",
      "791/791 [==============================] - 0s 207us/sample - loss: 7.6112e-04 - val_loss: 0.0019\n",
      "Epoch 16/20\n",
      "791/791 [==============================] - 0s 201us/sample - loss: 7.3311e-04 - val_loss: 0.0014\n",
      "Epoch 17/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 6.4047e-04 - val_loss: 0.0020\n",
      "Epoch 18/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 8.0069e-04 - val_loss: 9.5259e-04\n",
      "Epoch 19/20\n",
      "791/791 [==============================] - 0s 207us/sample - loss: 7.0957e-04 - val_loss: 0.0016\n",
      "Epoch 20/20\n",
      "791/791 [==============================] - 0s 206us/sample - loss: 6.9054e-04 - val_loss: 9.5337e-04\n",
      "170/170 [==============================] - 0s 130us/sample - loss: 5.1280e-04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuBtjTByy4m6MMXHo//guKSvDNloHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model to predict day high\n",
    "\n",
    "X, y = split_input_output(df_tr, target_feature=\"High\")\n",
    "train_X, train_y, val_X, val_y, test_X, test_y = split_train_val_test(X, y)\n",
    "\n",
    "model_high = Sequential([\n",
    "    LSTM(256, input_shape=(None, train_X.shape[2])),\n",
    "    Dropout(0.4),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(512),\n",
    "    Dropout(0.4),\n",
    "\n",
    "    Dense(1)\n",
    "])\n",
    "model_high.compile(optimizer=Adam(lr=0.001, decay=1e-6), loss=\"mse\")\n",
    "\n",
    "model_high.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=BATCH_SIZE, epochs=EPOCHS)\n",
    "\n",
    "model_high.evaluate(test_X, test_y)\n",
    "\n",
    "model_high.save(os.path.join(MODEL_DIR, \"LSTM-high.h5\"))\n",
    "\n",
    "# plot predictions\n",
    "\n",
    "visualize_predictions(\n",
    "    days=df_tr.index[WINDOW:].values,\n",
    "    actual=y,\n",
    "    models=[model_high],\n",
    "    X=X,\n",
    "    labels=[\"LSTM\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 983
    },
    "id": "wTpMwh6-QIcf",
    "outputId": "1c7a14fa-f9d4-4635-f483-f814b574da29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 791 samples, validate on 169 samples\n",
      "Epoch 1/20\n",
      "791/791 [==============================] - 1s 2ms/sample - loss: 0.0231 - val_loss: 0.0217\n",
      "Epoch 2/20\n",
      "791/791 [==============================] - 0s 246us/sample - loss: 0.0035 - val_loss: 0.0108\n",
      "Epoch 3/20\n",
      "791/791 [==============================] - 0s 227us/sample - loss: 0.0020 - val_loss: 0.0030\n",
      "Epoch 4/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 0.0016 - val_loss: 0.0020\n",
      "Epoch 5/20\n",
      "791/791 [==============================] - 0s 205us/sample - loss: 0.0015 - val_loss: 0.0029\n",
      "Epoch 6/20\n",
      "791/791 [==============================] - 0s 202us/sample - loss: 0.0012 - val_loss: 0.0047\n",
      "Epoch 7/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 0.0014 - val_loss: 0.0025\n",
      "Epoch 8/20\n",
      "791/791 [==============================] - 0s 207us/sample - loss: 0.0011 - val_loss: 0.0017\n",
      "Epoch 9/20\n",
      "791/791 [==============================] - 0s 205us/sample - loss: 0.0012 - val_loss: 0.0020\n",
      "Epoch 10/20\n",
      "791/791 [==============================] - 0s 203us/sample - loss: 0.0010 - val_loss: 0.0027\n",
      "Epoch 11/20\n",
      "791/791 [==============================] - 0s 207us/sample - loss: 9.1126e-04 - val_loss: 0.0027\n",
      "Epoch 12/20\n",
      "791/791 [==============================] - 0s 204us/sample - loss: 9.6085e-04 - val_loss: 0.0016\n",
      "Epoch 13/20\n",
      "791/791 [==============================] - 0s 207us/sample - loss: 7.7065e-04 - val_loss: 0.0015\n",
      "Epoch 14/20\n",
      "791/791 [==============================] - 0s 203us/sample - loss: 0.0010 - val_loss: 0.0023\n",
      "Epoch 15/20\n",
      "791/791 [==============================] - 0s 206us/sample - loss: 7.8661e-04 - val_loss: 0.0019\n",
      "Epoch 16/20\n",
      "791/791 [==============================] - 0s 206us/sample - loss: 0.0011 - val_loss: 0.0016\n",
      "Epoch 17/20\n",
      "791/791 [==============================] - 0s 217us/sample - loss: 9.0151e-04 - val_loss: 0.0036\n",
      "Epoch 18/20\n",
      "791/791 [==============================] - 0s 207us/sample - loss: 8.1273e-04 - val_loss: 0.0021\n",
      "Epoch 19/20\n",
      "791/791 [==============================] - 0s 207us/sample - loss: 9.1088e-04 - val_loss: 0.0015\n",
      "Epoch 20/20\n",
      "791/791 [==============================] - 0s 205us/sample - loss: 8.1074e-04 - val_loss: 0.0015\n",
      "170/170 [==============================] - 0s 141us/sample - loss: 7.2225e-04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model to predict day low\n",
    "\n",
    "X, y = split_input_output(df_tr, target_feature=\"Low\")\n",
    "train_X, train_y, val_X, val_y, test_X, test_y = split_train_val_test(X, y)\n",
    "\n",
    "model_low = Sequential([\n",
    "    LSTM(256, input_shape=(None, train_X.shape[2])),\n",
    "    Dropout(0.4),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(512),\n",
    "    Dropout(0.4),\n",
    "\n",
    "    Dense(1)\n",
    "])\n",
    "model_low.compile(optimizer=Adam(lr=0.001, decay=1e-6), loss=\"mse\")\n",
    "\n",
    "model_low.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=BATCH_SIZE, epochs=EPOCHS)\n",
    "\n",
    "model_low.evaluate(test_X, test_y)\n",
    "\n",
    "model_low.save(os.path.join(MODEL_DIR, \"LSTM-low.h5\"))\n",
    "\n",
    "# plot predictions\n",
    "\n",
    "visualize_predictions(\n",
    "    days=df_tr.index[WINDOW:].values,\n",
    "    actual=y,\n",
    "    models=[model_low],\n",
    "    X=X,\n",
    "    labels=[\"LSTM\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "BaTvkxXOMFhA",
    "outputId": "10351226-474c-4e0b-e676-ab65f047dfc7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_tr.index[WINDOW:].values, model_high.predict(X), label=\"Predicted high\")\n",
    "plt.plot(df_tr.index[WINDOW:].values, model_low.predict(X), label=\"Predicted low\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "tdSLRS_V6Y5G",
    "outputId": "bfb0b68b-38f9-4b64-a500-c1a8c6a6ebc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.0251502  0.02657588 0.02536826 ... 0.18325192 0.02020532 0.03548474]\n",
      "  [0.02581434 0.02700677 0.02872307 ... 0.15517288 0.02136674 0.03473482]\n",
      "  [0.02267579 0.02454561 0.02632569 ... 0.09499381 0.02203479 0.0338918 ]\n",
      "  ...\n",
      "  [0.04333636 0.0435905  0.04191442 ... 0.14844398 0.04105003 0.0496866 ]\n",
      "  [0.04225476 0.04121474 0.04603291 ... 0.19474343 0.04031633 0.04924247]\n",
      "  [0.03629264 0.03889333 0.0379061  ... 0.14018425 0.0391203  0.04873724]]\n",
      "\n",
      " [[0.02581434 0.02700677 0.02872307 ... 0.15517288 0.02136674 0.03473482]\n",
      "  [0.02267579 0.02454561 0.02632569 ... 0.09499381 0.02203479 0.0338918 ]\n",
      "  [0.0228997  0.02362171 0.02582798 ... 0.11692226 0.02226277 0.03292381]\n",
      "  ...\n",
      "  [0.04225476 0.04121474 0.04603291 ... 0.19474343 0.04031633 0.04924247]\n",
      "  [0.03629264 0.03889333 0.0379061  ... 0.14018425 0.0391203  0.04873724]\n",
      "  [0.03469869 0.03667285 0.03711205 ... 0.13836765 0.0381801  0.04815849]]\n",
      "\n",
      " [[0.02267579 0.02454561 0.02632569 ... 0.09499381 0.02203479 0.0338918 ]\n",
      "  [0.0228997  0.02362171 0.02582798 ... 0.11692226 0.02226277 0.03292381]\n",
      "  [0.02194712 0.02399437 0.02293667 ... 0.08140674 0.02227312 0.03209361]\n",
      "  ...\n",
      "  [0.03629264 0.03889333 0.0379061  ... 0.14018425 0.0391203  0.04873724]\n",
      "  [0.03469869 0.03667285 0.03711205 ... 0.13836765 0.0381801  0.04815849]\n",
      "  [0.03660383 0.03882345 0.03813786 ... 0.11329587 0.03820397 0.04755808]]\n",
      "\n",
      " [[0.0228997  0.02362171 0.02582798 ... 0.11692226 0.02226277 0.03292381]\n",
      "  [0.02194712 0.02399437 0.02293667 ... 0.08140674 0.02227312 0.03209361]\n",
      "  [0.02191676 0.02475136 0.02500351 ... 0.07471867 0.0222389  0.03120273]\n",
      "  ...\n",
      "  [0.03469869 0.03667285 0.03711205 ... 0.13836765 0.0381801  0.04815849]\n",
      "  [0.03660383 0.03882345 0.03813786 ... 0.11329587 0.03820397 0.04755808]\n",
      "  [0.03622052 0.0390719  0.03918649 ... 0.12278028 0.03813116 0.04682333]]\n",
      "\n",
      " [[0.02194712 0.02399437 0.02293667 ... 0.08140674 0.02227312 0.03209361]\n",
      "  [0.02191676 0.02475136 0.02500351 ... 0.07471867 0.0222389  0.03120273]\n",
      "  [0.02351071 0.0246349  0.02483635 ... 0.07867844 0.02255204 0.03034357]\n",
      "  ...\n",
      "  [0.03660383 0.03882345 0.03813786 ... 0.11329587 0.03820397 0.04755808]\n",
      "  [0.03622052 0.0390719  0.03918649 ... 0.12278028 0.03813116 0.04682333]\n",
      "  [0.0345355  0.03702611 0.0379441  ... 0.12658357 0.03773327 0.045969  ]]]\n",
      "[[[0.03469869 0.03667285]\n",
      "  [0.03660383 0.03882345]\n",
      "  [0.03622052 0.0390719 ]\n",
      "  [0.0345355  0.03702611]\n",
      "  [0.03406111 0.037457  ]\n",
      "  [0.03629264 0.03637782]\n",
      "  [0.03735906 0.04066739]\n",
      "  [0.04335913 0.04376906]\n",
      "  [0.04343504 0.04629622]\n",
      "  [0.04837626 0.05127678]]\n",
      "\n",
      " [[0.03660383 0.03882345]\n",
      "  [0.03622052 0.0390719 ]\n",
      "  [0.0345355  0.03702611]\n",
      "  [0.03406111 0.037457  ]\n",
      "  [0.03629264 0.03637782]\n",
      "  [0.03735906 0.04066739]\n",
      "  [0.04335913 0.04376906]\n",
      "  [0.04343504 0.04629622]\n",
      "  [0.04837626 0.05127678]\n",
      "  [0.05977298 0.0597278 ]]\n",
      "\n",
      " [[0.03622052 0.0390719 ]\n",
      "  [0.0345355  0.03702611]\n",
      "  [0.03406111 0.037457  ]\n",
      "  [0.03629264 0.03637782]\n",
      "  [0.03735906 0.04066739]\n",
      "  [0.04335913 0.04376906]\n",
      "  [0.04343504 0.04629622]\n",
      "  [0.04837626 0.05127678]\n",
      "  [0.05977298 0.0597278 ]\n",
      "  [0.05939348 0.06071768]]\n",
      "\n",
      " [[0.0345355  0.03702611]\n",
      "  [0.03406111 0.037457  ]\n",
      "  [0.03629264 0.03637782]\n",
      "  [0.03735906 0.04066739]\n",
      "  [0.04335913 0.04376906]\n",
      "  [0.04343504 0.04629622]\n",
      "  [0.04837626 0.05127678]\n",
      "  [0.05977298 0.0597278 ]\n",
      "  [0.05939348 0.06071768]\n",
      "  [0.05822837 0.06056242]]\n",
      "\n",
      " [[0.03406111 0.037457  ]\n",
      "  [0.03629264 0.03637782]\n",
      "  [0.03735906 0.04066739]\n",
      "  [0.04335913 0.04376906]\n",
      "  [0.04343504 0.04629622]\n",
      "  [0.04837626 0.05127678]\n",
      "  [0.05977298 0.0597278 ]\n",
      "  [0.05939348 0.06071768]\n",
      "  [0.05822837 0.06056242]\n",
      "  [0.09155702 0.08325246]]]\n"
     ]
    }
   ],
   "source": [
    "# model to predict the high and low of the next 10 days\n",
    "# the number of output neurons is now 20, which may affect the accuracy of the model\n",
    "\n",
    "NEXT = 10\n",
    "\n",
    "X = np.array([df_tr.iloc[i : i + WINDOW, :].values for i in range(len(df_tr) - WINDOW - NEXT)])\n",
    "y = np.array([df_tr.loc[i + WINDOW : i + WINDOW + NEXT - 1, [\"High\", \"Low\"]].values for i in range(len(df_tr) - WINDOW - NEXT)])\n",
    "\n",
    "print(X[:5])\n",
    "print(y[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 731
    },
    "id": "JnpPtKgpTLt8",
    "outputId": "9a177e2e-0c72-4f08-ab14-3acbcac8e5ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 784 samples, validate on 168 samples\n",
      "Epoch 1/20\n",
      "784/784 [==============================] - 2s 2ms/sample - loss: 0.0217 - val_loss: 0.0083\n",
      "Epoch 2/20\n",
      "784/784 [==============================] - 0s 254us/sample - loss: 0.0051 - val_loss: 0.0070\n",
      "Epoch 3/20\n",
      "784/784 [==============================] - 0s 211us/sample - loss: 0.0037 - val_loss: 0.0051\n",
      "Epoch 4/20\n",
      "784/784 [==============================] - 0s 216us/sample - loss: 0.0030 - val_loss: 0.0050\n",
      "Epoch 5/20\n",
      "784/784 [==============================] - 0s 209us/sample - loss: 0.0028 - val_loss: 0.0049\n",
      "Epoch 6/20\n",
      "784/784 [==============================] - 0s 209us/sample - loss: 0.0026 - val_loss: 0.0049\n",
      "Epoch 7/20\n",
      "784/784 [==============================] - 0s 212us/sample - loss: 0.0023 - val_loss: 0.0049\n",
      "Epoch 8/20\n",
      "784/784 [==============================] - 0s 215us/sample - loss: 0.0022 - val_loss: 0.0049\n",
      "Epoch 9/20\n",
      "784/784 [==============================] - 0s 215us/sample - loss: 0.0022 - val_loss: 0.0048\n",
      "Epoch 10/20\n",
      "784/784 [==============================] - 0s 216us/sample - loss: 0.0021 - val_loss: 0.0047\n",
      "Epoch 11/20\n",
      "784/784 [==============================] - 0s 208us/sample - loss: 0.0020 - val_loss: 0.0051\n",
      "Epoch 12/20\n",
      "784/784 [==============================] - 0s 205us/sample - loss: 0.0019 - val_loss: 0.0047\n",
      "Epoch 13/20\n",
      "784/784 [==============================] - 0s 210us/sample - loss: 0.0019 - val_loss: 0.0051\n",
      "Epoch 14/20\n",
      "784/784 [==============================] - 0s 213us/sample - loss: 0.0017 - val_loss: 0.0053\n",
      "Epoch 15/20\n",
      "784/784 [==============================] - 0s 217us/sample - loss: 0.0018 - val_loss: 0.0099\n",
      "Epoch 16/20\n",
      "784/784 [==============================] - 0s 215us/sample - loss: 0.0018 - val_loss: 0.0052\n",
      "Epoch 17/20\n",
      "784/784 [==============================] - 0s 207us/sample - loss: 0.0016 - val_loss: 0.0046\n",
      "Epoch 18/20\n",
      "784/784 [==============================] - 0s 216us/sample - loss: 0.0015 - val_loss: 0.0058\n",
      "Epoch 19/20\n",
      "784/784 [==============================] - 0s 217us/sample - loss: 0.0018 - val_loss: 0.0047\n",
      "Epoch 20/20\n",
      "784/784 [==============================] - 0s 210us/sample - loss: 0.0018 - val_loss: 0.0056\n",
      "168/168 [==============================] - 0s 135us/sample - loss: 0.0031\n"
     ]
    }
   ],
   "source": [
    "train_X, train_y, val_X, val_y, test_X, test_y = split_train_val_test(X, y.reshape(y.shape[0], -1))\n",
    "\n",
    "model_next = Sequential([\n",
    "    LSTM(256, input_shape=(None, train_X.shape[2])),\n",
    "    Dropout(0.4),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(512),\n",
    "    Dropout(0.4),\n",
    "\n",
    "    Dense(NEXT * 2)\n",
    "])\n",
    "\n",
    "model_next.compile(optimizer=Adam(lr=0.001, decay=1e-6), loss=\"mse\")\n",
    "\n",
    "model_next.fit(train_X, train_y, validation_data=(val_X, val_y), batch_size=BATCH_SIZE, epochs=EPOCHS)\n",
    "\n",
    "model_next.evaluate(test_X, test_y)\n",
    "\n",
    "model_next.save(os.path.join(MODEL_DIR, f\"LSTM-next-{NEXT}-high-low.h5\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r7-4NtneFO2j"
   },
   "source": [
    "As you can see, the accuracy of the model is clearly affected due to the higher amount of predictions and hence greater complexity, thus a larger model may be required to make accurate predictions futher into the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "lYzOZy28A9u4",
    "outputId": "0c7ffee4-cf98-44bf-c114-f6753482f0fa"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = model_next.predict(np.expand_dims(df_tr.iloc[-100:, :].values, axis=0)).flatten()\n",
    "high = predicted[:NEXT]\n",
    "low = predicted[NEXT:]\n",
    "\n",
    "plt.plot(df_tr.index[WINDOW:].values, df_tr[\"High\"][WINDOW:].values, label=\"Actual High\")\n",
    "plt.plot(df_tr.index[WINDOW:].values, df_tr[\"Low\"][WINDOW:].values, label=\"Actual Low\")\n",
    "plt.plot(range(df_tr.index[-1], df_tr.index[-1] + NEXT), high, label=\"Predicted High\")\n",
    "plt.plot(range(df_tr.index[-1], df_tr.index[-1] + NEXT), low, label=\"Predicted Low\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "G3ang3ceCzVg",
    "outputId": "240b14a8-ee9a-4a49-e5d3-5034afc9210c"
   },
   "outputs": [
    {
     "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>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>302.169800</td>\n",
       "      <td>293.899261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>296.907776</td>\n",
       "      <td>293.083130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>292.739288</td>\n",
       "      <td>295.500458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>296.282959</td>\n",
       "      <td>294.505859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303.337860</td>\n",
       "      <td>294.262360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>299.599915</td>\n",
       "      <td>291.348450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>300.364197</td>\n",
       "      <td>297.488983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>297.884888</td>\n",
       "      <td>290.553009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>301.414459</td>\n",
       "      <td>296.423737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>305.891571</td>\n",
       "      <td>290.927673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         High         Low\n",
       "0  302.169800  293.899261\n",
       "1  296.907776  293.083130\n",
       "2  292.739288  295.500458\n",
       "3  296.282959  294.505859\n",
       "4  303.337860  294.262360\n",
       "5  299.599915  291.348450\n",
       "6  300.364197  297.488983\n",
       "7  297.884888  290.553009\n",
       "8  301.414459  296.423737\n",
       "9  305.891571  290.927673"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(df)\n",
    "high_low_predictions_df = pd.DataFrame({\"High\": unscale_price(np.expand_dims(high, axis=1), feature=\"High\").flatten(),\n",
    "                                        \"Low\": unscale_price(np.expand_dims(low, axis=1), feature=\"Low\").flatten()})\n",
    "high_low_predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "UBDZsV6JHUCM",
    "outputId": "c1a1fe5f-90df-40fd-c2a0-3e851980a576"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[287.181]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict the next day's opening price\n",
    "\n",
    "model_lstm = tf.keras.models.load_model(BEST_MODEL)\n",
    "pipeline.fit(df)\n",
    "unscale_price(model_lstm.predict(np.expand_dims(df_tr.iloc[-100:, :].values, axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7l78tL5oNCuI"
   },
   "outputs": [],
   "source": [
    "# sentiment analysis\n",
    "\n",
    "import tweepy\n",
    "from textblob import TextBlob\n",
    "\n",
    "CONSUMER_KEY = \"\"  # missing; the code therefore cannot run\n",
    "CONSUMER_SECRET = \"\"\n",
    "ACCESS_TOKEN = \"\"\n",
    "ACCESS_TOKEN_SECRET = \"\"\n",
    "auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n",
    "auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)\n",
    "api = tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 375
    },
    "id": "RCbAYTH9kCBk",
    "outputId": "e2d929fb-f650-4d91-fc6a-ed86b8a555d9"
   },
   "outputs": [
    {
     "ename": "TweepError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTweepError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-159-8f57ec5a5a99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpublic_tweets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Facebook\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtweet\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpublic_tweets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0manalysis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtweet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tweepy/binder.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[0;31m# Set pagination mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tweepy/binder.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    232\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mRateLimitError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mTweepError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mapi_error_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;31m# Parse the response payload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTweepError\u001b[0m: [{'code': 215, 'message': 'Bad Authentication data.'}]"
     ]
    }
   ],
   "source": [
    "THRESHOLD = 0\n",
    "\n",
    "score = 0\n",
    "\n",
    "public_tweets = api.search(\"Facebook\")\n",
    "for tweet in public_tweets:\n",
    "    analysis = TextBlob(tweet.text)\n",
    "    if analysis.sentiment.polarity >= THRESHOLD:\n",
    "        score += 1\n",
    "    else:\n",
    "        score -= 1\n",
    "\n",
    "score"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "statistics project.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}