prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Market Prediction and Forecasting Using Stacked LSTM\n",
    "Using Keras and Tensorflow\n",
    "\n",
    "##### Dataset\n",
    "Yahoo Finance - Stock of Reliance Industries Limited\n",
    "NSE: RELIANCE: https://finance.yahoo.com/quote/RELIANCE.NS/history?period1=1433635200&period2=1591488000&interval=1d&filter=history&frequency=1d\n",
    "\n",
    "\n",
    "\n",
    "Amazing Tutorial Reference from Krish Naik: https://www.youtube.com/watch?v=H6du_pfuznE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import necessary Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Dense\n",
    "from tensorflow.python.keras.layers import LSTM\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-06-08</td>\n",
       "      <td>453.399994</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>441.549988</td>\n",
       "      <td>442.725006</td>\n",
       "      <td>425.550629</td>\n",
       "      <td>4958862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>441.774994</td>\n",
       "      <td>445.325012</td>\n",
       "      <td>438.674988</td>\n",
       "      <td>441.850006</td>\n",
       "      <td>424.709564</td>\n",
       "      <td>4184606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-10</td>\n",
       "      <td>443.000000</td>\n",
       "      <td>454.950012</td>\n",
       "      <td>442.500000</td>\n",
       "      <td>452.899994</td>\n",
       "      <td>435.330933</td>\n",
       "      <td>6176034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-06-11</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>454.750000</td>\n",
       "      <td>436.825012</td>\n",
       "      <td>438.575012</td>\n",
       "      <td>421.561676</td>\n",
       "      <td>6709574.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-06-12</td>\n",
       "      <td>439.500000</td>\n",
       "      <td>446.350006</td>\n",
       "      <td>439.125000</td>\n",
       "      <td>444.924988</td>\n",
       "      <td>427.665283</td>\n",
       "      <td>9849112.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2015-06-08  453.399994  455.000000  441.549988  442.725006  425.550629   \n",
       "1  2015-06-09  441.774994  445.325012  438.674988  441.850006  424.709564   \n",
       "2  2015-06-10  443.000000  454.950012  442.500000  452.899994  435.330933   \n",
       "3  2015-06-11  453.000000  454.750000  436.825012  438.575012  421.561676   \n",
       "4  2015-06-12  439.500000  446.350006  439.125000  444.924988  427.665283   \n",
       "\n",
       "      Volume  \n",
       "0  4958862.0  \n",
       "1  4184606.0  \n",
       "2  6176034.0  \n",
       "3  6709574.0  \n",
       "4  9849112.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import the data and remove rows containing NAN values\n",
    "df = pd.read_csv('Reliance_Stock.csv')\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>1538.349976</td>\n",
       "      <td>1475.949951</td>\n",
       "      <td>1520.349976</td>\n",
       "      <td>1520.349976</td>\n",
       "      <td>18434012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>1540.000000</td>\n",
       "      <td>1520.800049</td>\n",
       "      <td>1535.699951</td>\n",
       "      <td>1535.699951</td>\n",
       "      <td>10224049.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>1533.349976</td>\n",
       "      <td>1541.650024</td>\n",
       "      <td>1541.650024</td>\n",
       "      <td>11713461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>1589.500000</td>\n",
       "      <td>1541.000000</td>\n",
       "      <td>1579.800049</td>\n",
       "      <td>1579.800049</td>\n",
       "      <td>15784379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>1595.0</td>\n",
       "      <td>1618.000000</td>\n",
       "      <td>1573.699951</td>\n",
       "      <td>1581.699951</td>\n",
       "      <td>1581.699951</td>\n",
       "      <td>15246965.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open         High          Low        Close    Adj Close  \\\n",
       "1224  2020-06-01  1480.0  1538.349976  1475.949951  1520.349976  1520.349976   \n",
       "1225  2020-06-02  1526.0  1540.000000  1520.800049  1535.699951  1535.699951   \n",
       "1226  2020-06-03  1545.0  1560.000000  1533.349976  1541.650024  1541.650024   \n",
       "1227  2020-06-04  1544.0  1589.500000  1541.000000  1579.800049  1579.800049   \n",
       "1228  2020-06-05  1595.0  1618.000000  1573.699951  1581.699951  1581.699951   \n",
       "\n",
       "          Volume  \n",
       "1224  18434012.0  \n",
       "1225  10224049.0  \n",
       "1226  11713461.0  \n",
       "1227  15784379.0  \n",
       "1228  15246965.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_close = df['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1228,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a159e7c88>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_close)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since LSTM are sensitive to the scale of the data, so we apply MinMax Scaler to transform our values between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range = (0,1))\n",
    "df_close = scaler.fit_transform(np.array(df_close).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1228, 1)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02338571],\n",
       "       [0.0226536 ],\n",
       "       [0.0318991 ],\n",
       "       ...,\n",
       "       [0.94285362],\n",
       "       [0.97477365],\n",
       "       [0.97636329]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into train and test split\n",
    "training_size = int(len(df_close) * 0.75)\n",
    "test_size = len(df_close) - training_size\n",
    "train_data, test_data = df_close[0:training_size,:], df_close[training_size:len(df_close),:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, time_step = 1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - time_step - 1):\n",
    "        a = dataset[i:(i+time_step), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i+time_step, 0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 100\n",
    "x_train, y_train = create_dataset(train_data, time_step)\n",
    "x_test, y_test = create_dataset(test_data, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape the input to be [samples, time steps, features] which is the requirement of LSTM\n",
    "x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/rudrajikadra/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /Users/rudrajikadra/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n"
     ]
    }
   ],
   "source": [
    "#Create the LSTM Model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences = True, input_shape = (100,1)))\n",
    "model.add(LSTM(50, return_sequences = True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss = 'mean_squared_error', optimizer = 'adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, None, 50)          10400     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, None, 50)          20200     \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 820 samples, validate on 206 samples\n",
      "Epoch 1/100\n",
      "820/820 [==============================]820/820 [==============================] - 9s 11ms/step - loss: 0.0470 - val_loss: 0.0359\n",
      "\n",
      "Epoch 2/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 9ms/step - loss: 0.0072 - val_loss: 0.0123\n",
      "\n",
      "Epoch 3/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 0.0023 - val_loss: 0.0105\n",
      "\n",
      "Epoch 4/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 0.0013 - val_loss: 0.0091\n",
      "\n",
      "Epoch 5/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 9.9732e-04 - val_loss: 0.0091\n",
      "\n",
      "Epoch 6/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 9.6192e-04 - val_loss: 0.0087\n",
      "\n",
      "Epoch 7/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 9.1813e-04 - val_loss: 0.0084\n",
      "\n",
      "Epoch 8/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 9.3331e-04 - val_loss: 0.0084\n",
      "\n",
      "Epoch 9/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.9997e-04 - val_loss: 0.0082\n",
      "\n",
      "Epoch 10/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 9.0700e-04 - val_loss: 0.0086\n",
      "\n",
      "Epoch 11/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.9309e-04 - val_loss: 0.0084\n",
      "\n",
      "Epoch 12/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.9646e-04 - val_loss: 0.0081\n",
      "\n",
      "Epoch 13/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.9618e-04 - val_loss: 0.0074\n",
      "\n",
      "Epoch 14/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.7144e-04 - val_loss: 0.0085\n",
      "\n",
      "Epoch 15/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.2659e-04 - val_loss: 0.0079\n",
      "\n",
      "Epoch 16/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.0239e-04 - val_loss: 0.0074\n",
      "\n",
      "Epoch 17/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.0344e-04 - val_loss: 0.0075\n",
      "\n",
      "Epoch 18/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 7.6666e-04 - val_loss: 0.0074\n",
      "\n",
      "Epoch 19/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 7.5774e-04 - val_loss: 0.0093\n",
      "\n",
      "Epoch 20/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.1861e-04 - val_loss: 0.0061\n",
      "\n",
      "Epoch 21/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 8.4991e-04 - val_loss: 0.0085\n",
      "\n",
      "Epoch 22/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 7ms/step - loss: 7.1697e-04 - val_loss: 0.0072\n",
      "\n",
      "Epoch 23/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 7.0766e-04 - val_loss: 0.0071\n",
      "\n",
      "Epoch 24/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 6.7462e-04 - val_loss: 0.0061\n",
      "\n",
      "Epoch 25/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 7.1459e-04 - val_loss: 0.0091\n",
      "\n",
      "Epoch 26/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 7.7475e-04 - val_loss: 0.0057\n",
      "\n",
      "Epoch 27/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 8.1911e-04 - val_loss: 0.0065\n",
      "\n",
      "Epoch 28/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 6.6701e-04 - val_loss: 0.0060\n",
      "\n",
      "Epoch 29/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 6.7205e-04 - val_loss: 0.0055\n",
      "\n",
      "Epoch 30/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 6.4202e-04 - val_loss: 0.0062\n",
      "\n",
      "Epoch 31/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 6.7588e-04 - val_loss: 0.0068\n",
      "\n",
      "Epoch 32/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 6.3994e-04 - val_loss: 0.0071\n",
      "\n",
      "Epoch 33/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 6.1196e-04 - val_loss: 0.0057\n",
      "\n",
      "Epoch 34/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 5.9172e-04 - val_loss: 0.0062\n",
      "\n",
      "Epoch 35/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 9ms/step - loss: 5.9897e-04 - val_loss: 0.0058\n",
      "\n",
      "Epoch 36/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 5.9116e-04 - val_loss: 0.0067\n",
      "\n",
      "Epoch 37/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 9ms/step - loss: 6.0887e-04 - val_loss: 0.0050\n",
      "\n",
      "Epoch 38/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 9ms/step - loss: 6.9668e-04 - val_loss: 0.0080\n",
      "\n",
      "Epoch 39/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 6.3513e-04 - val_loss: 0.0052\n",
      "\n",
      "Epoch 40/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 9ms/step - loss: 5.8963e-04 - val_loss: 0.0065\n",
      "\n",
      "Epoch 41/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 5.7319e-04 - val_loss: 0.0056\n",
      "\n",
      "Epoch 42/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 9ms/step - loss: 5.6937e-04 - val_loss: 0.0072\n",
      "\n",
      "Epoch 43/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 9ms/step - loss: 5.6246e-04 - val_loss: 0.0060\n",
      "\n",
      "Epoch 44/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.4989e-04 - val_loss: 0.0057\n",
      "\n",
      "Epoch 45/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.5056e-04 - val_loss: 0.0058\n",
      "\n",
      "Epoch 46/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.9990e-04 - val_loss: 0.0050\n",
      "\n",
      "Epoch 47/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.8013e-04 - val_loss: 0.0060\n",
      "\n",
      "Epoch 48/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.4631e-04 - val_loss: 0.0066\n",
      "\n",
      "Epoch 49/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 6.0195e-04 - val_loss: 0.0048\n",
      "\n",
      "Epoch 50/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.3686e-04 - val_loss: 0.0052\n",
      "\n",
      "Epoch 51/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.2665e-04 - val_loss: 0.0048\n",
      "\n",
      "Epoch 52/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.2816e-04 - val_loss: 0.0056\n",
      "\n",
      "Epoch 53/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.6201e-04 - val_loss: 0.0055\n",
      "\n",
      "Epoch 54/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.1622e-04 - val_loss: 0.0051\n",
      "\n",
      "Epoch 55/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.2734e-04 - val_loss: 0.0046\n",
      "\n",
      "Epoch 56/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.3875e-04 - val_loss: 0.0048\n",
      "\n",
      "Epoch 57/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.2832e-04 - val_loss: 0.0052\n",
      "\n",
      "Epoch 58/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.1186e-04 - val_loss: 0.0046\n",
      "\n",
      "Epoch 59/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.2837e-04 - val_loss: 0.0048\n",
      "\n",
      "Epoch 60/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.0545e-04 - val_loss: 0.0044\n",
      "\n",
      "Epoch 61/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.6904e-04 - val_loss: 0.0042\n",
      "\n",
      "Epoch 62/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.9806e-04 - val_loss: 0.0044\n",
      "\n",
      "Epoch 63/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.9912e-04 - val_loss: 0.0045\n",
      "\n",
      "Epoch 64/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.1250e-04 - val_loss: 0.0053\n",
      "\n",
      "Epoch 65/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.0349e-04 - val_loss: 0.0043\n",
      "\n",
      "Epoch 66/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.8436e-04 - val_loss: 0.0045\n",
      "\n",
      "Epoch 67/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.7824e-04 - val_loss: 0.0046\n",
      "\n",
      "Epoch 68/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.8799e-04 - val_loss: 0.0044\n",
      "\n",
      "Epoch 69/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.6824e-04 - val_loss: 0.0043\n",
      "\n",
      "Epoch 70/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.9029e-04 - val_loss: 0.0044\n",
      "\n",
      "Epoch 71/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.7330e-04 - val_loss: 0.0059\n",
      "\n",
      "Epoch 72/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.6582e-04 - val_loss: 0.0044\n",
      "\n",
      "Epoch 73/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.0335e-04 - val_loss: 0.0042\n",
      "\n",
      "Epoch 74/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.7956e-04 - val_loss: 0.0051\n",
      "\n",
      "Epoch 75/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.1095e-04 - val_loss: 0.0040\n",
      "\n",
      "Epoch 76/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.6179e-04 - val_loss: 0.0043\n",
      "\n",
      "Epoch 77/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.5276e-04 - val_loss: 0.0040\n",
      "\n",
      "Epoch 78/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.4180e-04 - val_loss: 0.0041\n",
      "\n",
      "Epoch 79/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.6643e-04 - val_loss: 0.0040\n",
      "\n",
      "Epoch 80/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.2700e-04 - val_loss: 0.0037\n",
      "\n",
      "Epoch 81/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.4570e-04 - val_loss: 0.0043\n",
      "\n",
      "Epoch 82/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 5.1386e-04 - val_loss: 0.0040\n",
      "\n",
      "Epoch 83/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.8088e-04 - val_loss: 0.0041\n",
      "\n",
      "Epoch 84/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.7273e-04 - val_loss: 0.0038\n",
      "\n",
      "Epoch 85/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.4992e-04 - val_loss: 0.0036\n",
      "\n",
      "Epoch 86/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.6488e-04 - val_loss: 0.0038\n",
      "\n",
      "Epoch 87/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.2781e-04 - val_loss: 0.0037\n",
      "\n",
      "Epoch 88/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.2406e-04 - val_loss: 0.0041\n",
      "\n",
      "Epoch 89/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.9062e-04 - val_loss: 0.0045\n",
      "\n",
      "Epoch 90/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 5.8627e-04 - val_loss: 0.0037\n",
      "\n",
      "Epoch 91/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 4.2038e-04 - val_loss: 0.0035\n",
      "\n",
      "Epoch 92/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.0686e-04 - val_loss: 0.0034\n",
      "\n",
      "Epoch 93/100\n",
      "820/820 [==============================]820/820 [==============================] - 6s 8ms/step - loss: 4.7185e-04 - val_loss: 0.0040\n",
      "\n",
      "Epoch 94/100\n",
      "820/820 [==============================]820/820 [==============================] - 9s 11ms/step - loss: 4.1860e-04 - val_loss: 0.0033\n",
      "\n",
      "Epoch 95/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 4.5306e-04 - val_loss: 0.0042\n",
      "\n",
      "Epoch 96/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 10ms/step - loss: 4.3055e-04 - val_loss: 0.0033\n",
      "\n",
      "Epoch 97/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 9ms/step - loss: 3.8043e-04 - val_loss: 0.0033\n",
      "\n",
      "Epoch 98/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 8ms/step - loss: 3.7840e-04 - val_loss: 0.0032\n",
      "\n",
      "Epoch 99/100\n",
      "820/820 [==============================]820/820 [==============================] - 8s 9ms/step - loss: 3.7813e-04 - val_loss: 0.0032\n",
      "\n",
      "Epoch 100/100\n",
      "820/820 [==============================]820/820 [==============================] - 7s 9ms/step - loss: 3.9484e-04 - val_loss: 0.0037\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x1a15a02d68>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_data = (x_test, y_test), epochs = 100, batch_size = 64, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets predict and check performance metrics\n",
    "train_predict = model.predict(x_train)\n",
    "test_predict = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform back to original form\n",
    "train_predict = scaler.inverse_transform(train_predict)\n",
    "test_predict = scaler.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "799.7679903421658"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate RMSE performance metrics\n",
    "math.sqrt(mean_squared_error(y_train, train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1349.4805080273798"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Data RMSE\n",
    "math.sqrt(mean_squared_error(y_test, test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting\n",
    "\n",
    "#Shift train prediction for plotting\n",
    "look_back = 100\n",
    "trainPredictPlot = np.empty_like(df_close)\n",
    "trainPredictPlot[:,:] = np.nan\n",
    "trainPredictPlot[look_back:len(train_predict) + look_back, :] = train_predict\n",
    "\n",
    "#Shift test prediction for plotting\n",
    "testPredictPlot = np.empty_like(df_close)\n",
    "testPredictPlot[:,:] = np.nan\n",
    "testPredictPlot[len(train_predict) + (look_back * 2)+1:len(df_close) - 1, :] = test_predict\n",
    "\n",
    "#Plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(df_close))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the next 30 days Stock Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307, (206, 100, 1))"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data), x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_input = test_data[207:].reshape(1,-1)\n",
    "x_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_input = list(x_input)\n",
    "temp_input = temp_input[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92028046]\n",
      "101\n",
      "1 day input [0.94816663 0.9478738  0.94456879 0.93260407 0.92796036 0.939716\n",
      " 0.97577765 0.93507229 0.93636921 0.93590899 0.93047045 0.92603602\n",
      " 0.91348555 0.88436843 0.89114567 0.8609409  0.83408293 0.81220327\n",
      " 0.84642422 0.86491522 0.87269654 0.85249028 0.8541218  0.86847118\n",
      " 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463\n",
      " 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079 0.76463703\n",
      " 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654\n",
      " 0.6181313  0.5423683  0.57776065 0.50279251 0.49634993 0.46330038\n",
      " 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569\n",
      " 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997\n",
      " 0.65042781 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661\n",
      " 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714\n",
      " 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529\n",
      " 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462\n",
      " 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323\n",
      " 0.844458   0.862447   0.88478678 0.87821873 0.92503192 0.9378752\n",
      " 0.94285362 0.97477365 0.97636329 0.92028046]\n",
      "1 day output [[0.9243099]]\n",
      "2 day input [0.9478738  0.94456879 0.93260407 0.92796036 0.939716   0.97577765\n",
      " 0.93507229 0.93636921 0.93590899 0.93047045 0.92603602 0.91348555\n",
      " 0.88436843 0.89114567 0.8609409  0.83408293 0.81220327 0.84642422\n",
      " 0.86491522 0.87269654 0.85249028 0.8541218  0.86847118 0.88315526\n",
      " 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495\n",
      " 0.8619449  0.83805724 0.8176418  0.81283079 0.76463703 0.75417831\n",
      " 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654  0.6181313\n",
      " 0.5423683  0.57776065 0.50279251 0.49634993 0.46330038 0.42079615\n",
      " 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369\n",
      " 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781\n",
      " 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326\n",
      " 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714 0.84788844\n",
      " 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096\n",
      " 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462 0.87403524\n",
      " 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323 0.844458\n",
      " 0.862447   0.88478678 0.87821873 0.92503192 0.9378752  0.94285362\n",
      " 0.97477365 0.97636329 0.92028046 0.92430991]\n",
      "2 day output [[0.9224974]]\n",
      "3 day input [0.94456879 0.93260407 0.92796036 0.939716   0.97577765 0.93507229\n",
      " 0.93636921 0.93590899 0.93047045 0.92603602 0.91348555 0.88436843\n",
      " 0.89114567 0.8609409  0.83408293 0.81220327 0.84642422 0.86491522\n",
      " 0.87269654 0.85249028 0.8541218  0.86847118 0.88315526 0.88637653\n",
      " 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449\n",
      " 0.83805724 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181\n",
      " 0.77388248 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683\n",
      " 0.57776065 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508\n",
      " 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375\n",
      " 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781 0.67368794\n",
      " 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465\n",
      " 0.79387957 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434\n",
      " 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387\n",
      " 0.95987202 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081\n",
      " 0.83178201 0.85253205 0.85884916 0.85073323 0.844458   0.862447\n",
      " 0.88478678 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365\n",
      " 0.97636329 0.92028046 0.92430991 0.92249739]\n",
      "3 day output [[0.91839176]]\n",
      "4 day input [0.93260407 0.92796036 0.939716   0.97577765 0.93507229 0.93636921\n",
      " 0.93590899 0.93047045 0.92603602 0.91348555 0.88436843 0.89114567\n",
      " 0.8609409  0.83408293 0.81220327 0.84642422 0.86491522 0.87269654\n",
      " 0.85249028 0.8541218  0.86847118 0.88315526 0.88637653 0.89763007\n",
      " 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724\n",
      " 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181  0.77388248\n",
      " 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065\n",
      " 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125\n",
      " 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307\n",
      " 0.55696863 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772\n",
      " 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957\n",
      " 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434 0.86803608\n",
      " 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202\n",
      " 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201\n",
      " 0.85253205 0.85884916 0.85073323 0.844458   0.862447   0.88478678\n",
      " 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329\n",
      " 0.92028046 0.92430991 0.92249739 0.91839176]\n",
      "4 day output [[0.9137675]]\n",
      "5 day input [0.92796036 0.939716   0.97577765 0.93507229 0.93636921 0.93590899\n",
      " 0.93047045 0.92603602 0.91348555 0.88436843 0.89114567 0.8609409\n",
      " 0.83408293 0.81220327 0.84642422 0.86491522 0.87269654 0.85249028\n",
      " 0.8541218  0.86847118 0.88315526 0.88637653 0.89763007 0.88980697\n",
      " 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418\n",
      " 0.81283079 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482\n",
      " 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251\n",
      " 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929\n",
      " 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863\n",
      " 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546\n",
      " 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875\n",
      " 0.83855924 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843\n",
      " 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192\n",
      " 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205\n",
      " 0.85884916 0.85073323 0.844458   0.862447   0.88478678 0.87821873\n",
      " 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046\n",
      " 0.92430991 0.92249739 0.91839176 0.91376752]\n",
      "5 day output [[0.90935344]]\n",
      "6 day input [0.939716   0.97577765 0.93507229 0.93636921 0.93590899 0.93047045\n",
      " 0.92603602 0.91348555 0.88436843 0.89114567 0.8609409  0.83408293\n",
      " 0.81220327 0.84642422 0.86491522 0.87269654 0.85249028 0.8541218\n",
      " 0.86847118 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882\n",
      " 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079\n",
      " 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139\n",
      " 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251 0.49634993\n",
      " 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473\n",
      " 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854\n",
      " 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546 0.63026342\n",
      " 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924\n",
      " 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165\n",
      " 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482\n",
      " 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916\n",
      " 0.85073323 0.844458   0.862447   0.88478678 0.87821873 0.92503192\n",
      " 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046 0.92430991\n",
      " 0.92249739 0.91839176 0.91376752 0.90935344]\n",
      "6 day output [[0.9053698]]\n",
      "7 day input [0.97577765 0.93507229 0.93636921 0.93590899 0.93047045 0.92603602\n",
      " 0.91348555 0.88436843 0.89114567 0.8609409  0.83408293 0.81220327\n",
      " 0.84642422 0.86491522 0.87269654 0.85249028 0.8541218  0.86847118\n",
      " 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463\n",
      " 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079 0.76463703\n",
      " 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654\n",
      " 0.6181313  0.5423683  0.57776065 0.50279251 0.49634993 0.46330038\n",
      " 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569\n",
      " 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997\n",
      " 0.65042781 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661\n",
      " 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714\n",
      " 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529\n",
      " 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462\n",
      " 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323\n",
      " 0.844458   0.862447   0.88478678 0.87821873 0.92503192 0.9378752\n",
      " 0.94285362 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739\n",
      " 0.91839176 0.91376752 0.90935344 0.90536982]\n",
      "7 day output [[0.9018251]]\n",
      "8 day input [0.93507229 0.93636921 0.93590899 0.93047045 0.92603602 0.91348555\n",
      " 0.88436843 0.89114567 0.8609409  0.83408293 0.81220327 0.84642422\n",
      " 0.86491522 0.87269654 0.85249028 0.8541218  0.86847118 0.88315526\n",
      " 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495\n",
      " 0.8619449  0.83805724 0.8176418  0.81283079 0.76463703 0.75417831\n",
      " 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654  0.6181313\n",
      " 0.5423683  0.57776065 0.50279251 0.49634993 0.46330038 0.42079615\n",
      " 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369\n",
      " 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781\n",
      " 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326\n",
      " 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714 0.84788844\n",
      " 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096\n",
      " 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462 0.87403524\n",
      " 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323 0.844458\n",
      " 0.862447   0.88478678 0.87821873 0.92503192 0.9378752  0.94285362\n",
      " 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176\n",
      " 0.91376752 0.90935344 0.90536982 0.90182507]\n",
      "8 day output [[0.8986501]]\n",
      "9 day input [0.93636921 0.93590899 0.93047045 0.92603602 0.91348555 0.88436843\n",
      " 0.89114567 0.8609409  0.83408293 0.81220327 0.84642422 0.86491522\n",
      " 0.87269654 0.85249028 0.8541218  0.86847118 0.88315526 0.88637653\n",
      " 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449\n",
      " 0.83805724 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181\n",
      " 0.77388248 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683\n",
      " 0.57776065 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508\n",
      " 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375\n",
      " 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781 0.67368794\n",
      " 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465\n",
      " 0.79387957 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434\n",
      " 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387\n",
      " 0.95987202 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081\n",
      " 0.83178201 0.85253205 0.85884916 0.85073323 0.844458   0.862447\n",
      " 0.88478678 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365\n",
      " 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752\n",
      " 0.90935344 0.90536982 0.90182507 0.89865011]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 day output [[0.8957563]]\n",
      "10 day input [0.93590899 0.93047045 0.92603602 0.91348555 0.88436843 0.89114567\n",
      " 0.8609409  0.83408293 0.81220327 0.84642422 0.86491522 0.87269654\n",
      " 0.85249028 0.8541218  0.86847118 0.88315526 0.88637653 0.89763007\n",
      " 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724\n",
      " 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181  0.77388248\n",
      " 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065\n",
      " 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125\n",
      " 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307\n",
      " 0.55696863 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772\n",
      " 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957\n",
      " 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434 0.86803608\n",
      " 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202\n",
      " 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201\n",
      " 0.85253205 0.85884916 0.85073323 0.844458   0.862447   0.88478678\n",
      " 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329\n",
      " 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344\n",
      " 0.90536982 0.90182507 0.89865011 0.8957563 ]\n",
      "10 day output [[0.8930589]]\n",
      "11 day input [0.93047045 0.92603602 0.91348555 0.88436843 0.89114567 0.8609409\n",
      " 0.83408293 0.81220327 0.84642422 0.86491522 0.87269654 0.85249028\n",
      " 0.8541218  0.86847118 0.88315526 0.88637653 0.89763007 0.88980697\n",
      " 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418\n",
      " 0.81283079 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482\n",
      " 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251\n",
      " 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929\n",
      " 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863\n",
      " 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546\n",
      " 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875\n",
      " 0.83855924 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843\n",
      " 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192\n",
      " 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205\n",
      " 0.85884916 0.85073323 0.844458   0.862447   0.88478678 0.87821873\n",
      " 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046\n",
      " 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344 0.90536982\n",
      " 0.90182507 0.89865011 0.8957563  0.8930589 ]\n",
      "11 day output [[0.8904815]]\n",
      "12 day input [0.92603602 0.91348555 0.88436843 0.89114567 0.8609409  0.83408293\n",
      " 0.81220327 0.84642422 0.86491522 0.87269654 0.85249028 0.8541218\n",
      " 0.86847118 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882\n",
      " 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079\n",
      " 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139\n",
      " 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251 0.49634993\n",
      " 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473\n",
      " 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854\n",
      " 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546 0.63026342\n",
      " 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924\n",
      " 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165\n",
      " 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482\n",
      " 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916\n",
      " 0.85073323 0.844458   0.862447   0.88478678 0.87821873 0.92503192\n",
      " 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046 0.92430991\n",
      " 0.92249739 0.91839176 0.91376752 0.90935344 0.90536982 0.90182507\n",
      " 0.89865011 0.8957563  0.8930589  0.89048147]\n",
      "12 day output [[0.8879634]]\n",
      "13 day input [0.91348555 0.88436843 0.89114567 0.8609409  0.83408293 0.81220327\n",
      " 0.84642422 0.86491522 0.87269654 0.85249028 0.8541218  0.86847118\n",
      " 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463\n",
      " 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079 0.76463703\n",
      " 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654\n",
      " 0.6181313  0.5423683  0.57776065 0.50279251 0.49634993 0.46330038\n",
      " 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569\n",
      " 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997\n",
      " 0.65042781 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661\n",
      " 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714\n",
      " 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529\n",
      " 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462\n",
      " 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323\n",
      " 0.844458   0.862447   0.88478678 0.87821873 0.92503192 0.9378752\n",
      " 0.94285362 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739\n",
      " 0.91839176 0.91376752 0.90935344 0.90536982 0.90182507 0.89865011\n",
      " 0.8957563  0.8930589  0.89048147 0.88796341]\n",
      "13 day output [[0.88545513]]\n",
      "14 day input [0.88436843 0.89114567 0.8609409  0.83408293 0.81220327 0.84642422\n",
      " 0.86491522 0.87269654 0.85249028 0.8541218  0.86847118 0.88315526\n",
      " 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495\n",
      " 0.8619449  0.83805724 0.8176418  0.81283079 0.76463703 0.75417831\n",
      " 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654  0.6181313\n",
      " 0.5423683  0.57776065 0.50279251 0.49634993 0.46330038 0.42079615\n",
      " 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369\n",
      " 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781\n",
      " 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326\n",
      " 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714 0.84788844\n",
      " 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096\n",
      " 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462 0.87403524\n",
      " 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323 0.844458\n",
      " 0.862447   0.88478678 0.87821873 0.92503192 0.9378752  0.94285362\n",
      " 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176\n",
      " 0.91376752 0.90935344 0.90536982 0.90182507 0.89865011 0.8957563\n",
      " 0.8930589  0.89048147 0.88796341 0.88545513]\n",
      "14 day output [[0.88291955]]\n",
      "15 day input [0.89114567 0.8609409  0.83408293 0.81220327 0.84642422 0.86491522\n",
      " 0.87269654 0.85249028 0.8541218  0.86847118 0.88315526 0.88637653\n",
      " 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449\n",
      " 0.83805724 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181\n",
      " 0.77388248 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683\n",
      " 0.57776065 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508\n",
      " 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375\n",
      " 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781 0.67368794\n",
      " 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465\n",
      " 0.79387957 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434\n",
      " 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387\n",
      " 0.95987202 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081\n",
      " 0.83178201 0.85253205 0.85884916 0.85073323 0.844458   0.862447\n",
      " 0.88478678 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365\n",
      " 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752\n",
      " 0.90935344 0.90536982 0.90182507 0.89865011 0.8957563  0.8930589\n",
      " 0.89048147 0.88796341 0.88545513 0.88291955]\n",
      "15 day output [[0.88032967]]\n",
      "16 day input [0.8609409  0.83408293 0.81220327 0.84642422 0.86491522 0.87269654\n",
      " 0.85249028 0.8541218  0.86847118 0.88315526 0.88637653 0.89763007\n",
      " 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724\n",
      " 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181  0.77388248\n",
      " 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065\n",
      " 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125\n",
      " 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307\n",
      " 0.55696863 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772\n",
      " 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957\n",
      " 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434 0.86803608\n",
      " 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202\n",
      " 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201\n",
      " 0.85253205 0.85884916 0.85073323 0.844458   0.862447   0.88478678\n",
      " 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329\n",
      " 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344\n",
      " 0.90536982 0.90182507 0.89865011 0.8957563  0.8930589  0.89048147\n",
      " 0.88796341 0.88545513 0.88291955 0.88032967]\n",
      "16 day output [[0.8776674]]\n",
      "17 day input [0.83408293 0.81220327 0.84642422 0.86491522 0.87269654 0.85249028\n",
      " 0.8541218  0.86847118 0.88315526 0.88637653 0.89763007 0.88980697\n",
      " 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418\n",
      " 0.81283079 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482\n",
      " 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251\n",
      " 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929\n",
      " 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863\n",
      " 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546\n",
      " 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875\n",
      " 0.83855924 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843\n",
      " 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192\n",
      " 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205\n",
      " 0.85884916 0.85073323 0.844458   0.862447   0.88478678 0.87821873\n",
      " 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046\n",
      " 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344 0.90536982\n",
      " 0.90182507 0.89865011 0.8957563  0.8930589  0.89048147 0.88796341\n",
      " 0.88545513 0.88291955 0.88032967 0.87766743]\n",
      "17 day output [[0.8749213]]\n",
      "18 day input [0.81220327 0.84642422 0.86491522 0.87269654 0.85249028 0.8541218\n",
      " 0.86847118 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882\n",
      " 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079\n",
      " 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139\n",
      " 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251 0.49634993\n",
      " 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473\n",
      " 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854\n",
      " 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546 0.63026342\n",
      " 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924\n",
      " 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165\n",
      " 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482\n",
      " 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916\n",
      " 0.85073323 0.844458   0.862447   0.88478678 0.87821873 0.92503192\n",
      " 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046 0.92430991\n",
      " 0.92249739 0.91839176 0.91376752 0.90935344 0.90536982 0.90182507\n",
      " 0.89865011 0.8957563  0.8930589  0.89048147 0.88796341 0.88545513\n",
      " 0.88291955 0.88032967 0.87766743 0.87492132]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 day output [[0.87208486]]\n",
      "19 day input [0.84642422 0.86491522 0.87269654 0.85249028 0.8541218  0.86847118\n",
      " 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463\n",
      " 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079 0.76463703\n",
      " 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654\n",
      " 0.6181313  0.5423683  0.57776065 0.50279251 0.49634993 0.46330038\n",
      " 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569\n",
      " 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997\n",
      " 0.65042781 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661\n",
      " 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714\n",
      " 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529\n",
      " 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462\n",
      " 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323\n",
      " 0.844458   0.862447   0.88478678 0.87821873 0.92503192 0.9378752\n",
      " 0.94285362 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739\n",
      " 0.91839176 0.91376752 0.90935344 0.90536982 0.90182507 0.89865011\n",
      " 0.8957563  0.8930589  0.89048147 0.88796341 0.88545513 0.88291955\n",
      " 0.88032967 0.87766743 0.87492132 0.87208486]\n",
      "19 day output [[0.8691555]]\n",
      "20 day input [0.86491522 0.87269654 0.85249028 0.8541218  0.86847118 0.88315526\n",
      " 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495\n",
      " 0.8619449  0.83805724 0.8176418  0.81283079 0.76463703 0.75417831\n",
      " 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654  0.6181313\n",
      " 0.5423683  0.57776065 0.50279251 0.49634993 0.46330038 0.42079615\n",
      " 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369\n",
      " 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781\n",
      " 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326\n",
      " 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714 0.84788844\n",
      " 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096\n",
      " 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462 0.87403524\n",
      " 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323 0.844458\n",
      " 0.862447   0.88478678 0.87821873 0.92503192 0.9378752  0.94285362\n",
      " 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176\n",
      " 0.91376752 0.90935344 0.90536982 0.90182507 0.89865011 0.8957563\n",
      " 0.8930589  0.89048147 0.88796341 0.88545513 0.88291955 0.88032967\n",
      " 0.87766743 0.87492132 0.87208486 0.86915553]\n",
      "20 day output [[0.8661327]]\n",
      "21 day input [0.87269654 0.85249028 0.8541218  0.86847118 0.88315526 0.88637653\n",
      " 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449\n",
      " 0.83805724 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181\n",
      " 0.77388248 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683\n",
      " 0.57776065 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508\n",
      " 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375\n",
      " 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781 0.67368794\n",
      " 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465\n",
      " 0.79387957 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434\n",
      " 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387\n",
      " 0.95987202 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081\n",
      " 0.83178201 0.85253205 0.85884916 0.85073323 0.844458   0.862447\n",
      " 0.88478678 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365\n",
      " 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752\n",
      " 0.90935344 0.90536982 0.90182507 0.89865011 0.8957563  0.8930589\n",
      " 0.89048147 0.88796341 0.88545513 0.88291955 0.88032967 0.87766743\n",
      " 0.87492132 0.87208486 0.86915553 0.86613268]\n",
      "21 day output [[0.8630176]]\n",
      "22 day input [0.85249028 0.8541218  0.86847118 0.88315526 0.88637653 0.89763007\n",
      " 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724\n",
      " 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181  0.77388248\n",
      " 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065\n",
      " 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125\n",
      " 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307\n",
      " 0.55696863 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772\n",
      " 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957\n",
      " 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434 0.86803608\n",
      " 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202\n",
      " 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201\n",
      " 0.85253205 0.85884916 0.85073323 0.844458   0.862447   0.88478678\n",
      " 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329\n",
      " 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344\n",
      " 0.90536982 0.90182507 0.89865011 0.8957563  0.8930589  0.89048147\n",
      " 0.88796341 0.88545513 0.88291955 0.88032967 0.87766743 0.87492132\n",
      " 0.87208486 0.86915553 0.86613268 0.86301762]\n",
      "22 day output [[0.859812]]\n",
      "23 day input [0.8541218  0.86847118 0.88315526 0.88637653 0.89763007 0.88980697\n",
      " 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418\n",
      " 0.81283079 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482\n",
      " 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251\n",
      " 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929\n",
      " 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863\n",
      " 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546\n",
      " 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875\n",
      " 0.83855924 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843\n",
      " 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192\n",
      " 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205\n",
      " 0.85884916 0.85073323 0.844458   0.862447   0.88478678 0.87821873\n",
      " 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046\n",
      " 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344 0.90536982\n",
      " 0.90182507 0.89865011 0.8957563  0.8930589  0.89048147 0.88796341\n",
      " 0.88545513 0.88291955 0.88032967 0.87766743 0.87492132 0.87208486\n",
      " 0.86915553 0.86613268 0.86301762 0.85981202]\n",
      "23 day output [[0.856518]]\n",
      "24 day input [0.86847118 0.88315526 0.88637653 0.89763007 0.88980697 0.88072882\n",
      " 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079\n",
      " 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139\n",
      " 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251 0.49634993\n",
      " 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473\n",
      " 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854\n",
      " 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546 0.63026342\n",
      " 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924\n",
      " 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165\n",
      " 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482\n",
      " 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916\n",
      " 0.85073323 0.844458   0.862447   0.88478678 0.87821873 0.92503192\n",
      " 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046 0.92430991\n",
      " 0.92249739 0.91839176 0.91376752 0.90935344 0.90536982 0.90182507\n",
      " 0.89865011 0.8957563  0.8930589  0.89048147 0.88796341 0.88545513\n",
      " 0.88291955 0.88032967 0.87766743 0.87492132 0.87208486 0.86915553\n",
      " 0.86613268 0.86301762 0.85981202 0.85651797]\n",
      "24 day output [[0.8531367]]\n",
      "25 day input [0.88315526 0.88637653 0.89763007 0.88980697 0.88072882 0.91118463\n",
      " 0.8962495  0.8619449  0.83805724 0.8176418  0.81283079 0.76463703\n",
      " 0.75417831 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654\n",
      " 0.6181313  0.5423683  0.57776065 0.50279251 0.49634993 0.46330038\n",
      " 0.42079615 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569\n",
      " 0.54454369 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997\n",
      " 0.65042781 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661\n",
      " 0.69364326 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714\n",
      " 0.84788844 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529\n",
      " 0.90198096 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462\n",
      " 0.87403524 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323\n",
      " 0.844458   0.862447   0.88478678 0.87821873 0.92503192 0.9378752\n",
      " 0.94285362 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739\n",
      " 0.91839176 0.91376752 0.90935344 0.90536982 0.90182507 0.89865011\n",
      " 0.8957563  0.8930589  0.89048147 0.88796341 0.88545513 0.88291955\n",
      " 0.88032967 0.87766743 0.87492132 0.87208486 0.86915553 0.86613268\n",
      " 0.86301762 0.85981202 0.85651797 0.85313672]\n",
      "25 day output [[0.8496698]]\n",
      "26 day input [0.88637653 0.89763007 0.88980697 0.88072882 0.91118463 0.8962495\n",
      " 0.8619449  0.83805724 0.8176418  0.81283079 0.76463703 0.75417831\n",
      " 0.7765181  0.77388248 0.74999482 0.71640139 0.5851654  0.6181313\n",
      " 0.5423683  0.57776065 0.50279251 0.49634993 0.46330038 0.42079615\n",
      " 0.50467508 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369\n",
      " 0.51513375 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781\n",
      " 0.67368794 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326\n",
      " 0.6882465  0.79387957 0.7999875  0.83855924 0.84922714 0.84788844\n",
      " 0.84688434 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096\n",
      " 0.9474387  0.95987202 0.87902192 0.90503482 0.85441462 0.87403524\n",
      " 0.85843081 0.83178201 0.85253205 0.85884916 0.85073323 0.844458\n",
      " 0.862447   0.88478678 0.87821873 0.92503192 0.9378752  0.94285362\n",
      " 0.97477365 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176\n",
      " 0.91376752 0.90935344 0.90536982 0.90182507 0.89865011 0.8957563\n",
      " 0.8930589  0.89048147 0.88796341 0.88545513 0.88291955 0.88032967\n",
      " 0.87766743 0.87492132 0.87208486 0.86915553 0.86613268 0.86301762\n",
      " 0.85981202 0.85651797 0.85313672 0.84966981]\n",
      "26 day output [[0.8461178]]\n",
      "27 day input [0.89763007 0.88980697 0.88072882 0.91118463 0.8962495  0.8619449\n",
      " 0.83805724 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181\n",
      " 0.77388248 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683\n",
      " 0.57776065 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508\n",
      " 0.39264125 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375\n",
      " 0.5848307  0.55696863 0.55445854 0.6620997  0.65042781 0.67368794\n",
      " 0.64791772 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465\n",
      " 0.79387957 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434\n",
      " 0.86803608 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387\n",
      " 0.95987202 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081\n",
      " 0.83178201 0.85253205 0.85884916 0.85073323 0.844458   0.862447\n",
      " 0.88478678 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365\n",
      " 0.97636329 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752\n",
      " 0.90935344 0.90536982 0.90182507 0.89865011 0.8957563  0.8930589\n",
      " 0.89048147 0.88796341 0.88545513 0.88291955 0.88032967 0.87766743\n",
      " 0.87492132 0.87208486 0.86915553 0.86613268 0.86301762 0.85981202\n",
      " 0.85651797 0.85313672 0.84966981 0.84611779]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 day output [[0.8424807]]\n",
      "28 day input [0.88980697 0.88072882 0.91118463 0.8962495  0.8619449  0.83805724\n",
      " 0.8176418  0.81283079 0.76463703 0.75417831 0.7765181  0.77388248\n",
      " 0.74999482 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065\n",
      " 0.50279251 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125\n",
      " 0.44229929 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307\n",
      " 0.55696863 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772\n",
      " 0.61503546 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957\n",
      " 0.7999875  0.83855924 0.84922714 0.84788844 0.84688434 0.86803608\n",
      " 0.84250843 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202\n",
      " 0.87902192 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201\n",
      " 0.85253205 0.85884916 0.85073323 0.844458   0.862447   0.88478678\n",
      " 0.87821873 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329\n",
      " 0.92028046 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344\n",
      " 0.90536982 0.90182507 0.89865011 0.8957563  0.8930589  0.89048147\n",
      " 0.88796341 0.88545513 0.88291955 0.88032967 0.87766743 0.87492132\n",
      " 0.87208486 0.86915553 0.86613268 0.86301762 0.85981202 0.85651797\n",
      " 0.85313672 0.84966981 0.84611779 0.84248072]\n",
      "28 day output [[0.83875823]]\n",
      "29 day input [0.88072882 0.91118463 0.8962495  0.8619449  0.83805724 0.8176418\n",
      " 0.81283079 0.76463703 0.75417831 0.7765181  0.77388248 0.74999482\n",
      " 0.71640139 0.5851654  0.6181313  0.5423683  0.57776065 0.50279251\n",
      " 0.49634993 0.46330038 0.42079615 0.50467508 0.39264125 0.44229929\n",
      " 0.55847473 0.54504569 0.54454369 0.51513375 0.5848307  0.55696863\n",
      " 0.55445854 0.6620997  0.65042781 0.67368794 0.64791772 0.61503546\n",
      " 0.63026342 0.67707661 0.69364326 0.6882465  0.79387957 0.7999875\n",
      " 0.83855924 0.84922714 0.84788844 0.84688434 0.86803608 0.84250843\n",
      " 0.86360165 0.86368529 0.90198096 0.9474387  0.95987202 0.87902192\n",
      " 0.90503482 0.85441462 0.87403524 0.85843081 0.83178201 0.85253205\n",
      " 0.85884916 0.85073323 0.844458   0.862447   0.88478678 0.87821873\n",
      " 0.92503192 0.9378752  0.94285362 0.97477365 0.97636329 0.92028046\n",
      " 0.92430991 0.92249739 0.91839176 0.91376752 0.90935344 0.90536982\n",
      " 0.90182507 0.89865011 0.8957563  0.8930589  0.89048147 0.88796341\n",
      " 0.88545513 0.88291955 0.88032967 0.87766743 0.87492132 0.87208486\n",
      " 0.86915553 0.86613268 0.86301762 0.85981202 0.85651797 0.85313672\n",
      " 0.84966981 0.84611779 0.84248072 0.83875823]\n",
      "29 day output [[0.8349492]]\n",
      "[[0.9202804565429688], [0.9243099093437195], [0.9224973917007446], [0.918391764163971], [0.9137675166130066], [0.9093534350395203], [0.9053698182106018], [0.9018250703811646], [0.8986501097679138], [0.8957563042640686], [0.8930588960647583], [0.8904814720153809], [0.8879634141921997], [0.8854551315307617], [0.8829195499420166], [0.8803296685218811], [0.8776674270629883], [0.8749213218688965], [0.8720848560333252], [0.8691555261611938], [0.8661326766014099], [0.8630176186561584], [0.8598120212554932], [0.8565179705619812], [0.8531367182731628], [0.8496698141098022], [0.8461177945137024], [0.8424807190895081], [0.8387582302093506], [0.8349491953849792]]\n"
     ]
    }
   ],
   "source": [
    "lst_output=[]\n",
    "n_steps=100\n",
    "nextNumberOfDays = 30\n",
    "i=0\n",
    "\n",
    "while(i<nextNumberOfDays):\n",
    "    \n",
    "    if(len(temp_input)>100):\n",
    "        x_input=np.array(temp_input[1:])\n",
    "        print(\"{} day input {}\".format(i,x_input))\n",
    "        x_input=x_input.reshape(1,-1)\n",
    "        x_input = x_input.reshape((1, n_steps, 1))\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(\"{} day output {}\".format(i,yhat))\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        temp_input=temp_input[1:]\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "    else:\n",
    "        x_input = x_input.reshape((1, n_steps,1))\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(yhat[0])\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        print(len(temp_input))\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "    \n",
    "\n",
    "print(lst_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_new = np.arange(1,101)\n",
    "day_pred = np.arange(101,131)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df_close.tolist()\n",
    "df3.extend(lst_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1228"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a17e8eeb8>]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(day_new, scaler.inverse_transform(df_close[1128:]))\n",
    "plt.plot(day_pred, scaler.inverse_transform(lst_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}