stocks-trend.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1588c4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "907c70b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = \"2010-01-01\"\n",
    "end = \"2022-03-31\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1ccf6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.DataReader(\"AAPL\",\"yahoo\",start,end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fdc1ed59",
   "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>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>7.619643</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>7.611786</td>\n",
       "      <td>7.526071</td>\n",
       "      <td>352410800.0</td>\n",
       "      <td>6.434926</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>493729600.0</td>\n",
       "      <td>6.535085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>601904800.0</td>\n",
       "      <td>6.546382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>7.686786</td>\n",
       "      <td>7.526786</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.534643</td>\n",
       "      <td>552160000.0</td>\n",
       "      <td>6.442255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466071</td>\n",
       "      <td>7.562500</td>\n",
       "      <td>7.520714</td>\n",
       "      <td>477131200.0</td>\n",
       "      <td>6.430346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                High       Low      Open     Close       Volume  Adj Close\n",
       "Date                                                                      \n",
       "2009-12-31  7.619643  7.520000  7.611786  7.526071  352410800.0   6.434926\n",
       "2010-01-04  7.660714  7.585000  7.622500  7.643214  493729600.0   6.535085\n",
       "2010-01-05  7.699643  7.616071  7.664286  7.656429  601904800.0   6.546382\n",
       "2010-01-06  7.686786  7.526786  7.656429  7.534643  552160000.0   6.442255\n",
       "2010-01-07  7.571429  7.466071  7.562500  7.520714  477131200.0   6.430346"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb35c905",
   "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>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-25</th>\n",
       "      <td>175.279999</td>\n",
       "      <td>172.750000</td>\n",
       "      <td>173.880005</td>\n",
       "      <td>174.720001</td>\n",
       "      <td>80546200.0</td>\n",
       "      <td>174.463669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-28</th>\n",
       "      <td>175.729996</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>172.169998</td>\n",
       "      <td>175.600006</td>\n",
       "      <td>90371900.0</td>\n",
       "      <td>175.342377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-29</th>\n",
       "      <td>179.009995</td>\n",
       "      <td>176.339996</td>\n",
       "      <td>176.690002</td>\n",
       "      <td>178.960007</td>\n",
       "      <td>100589400.0</td>\n",
       "      <td>178.697449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-30</th>\n",
       "      <td>179.610001</td>\n",
       "      <td>176.699997</td>\n",
       "      <td>178.550003</td>\n",
       "      <td>177.770004</td>\n",
       "      <td>92633200.0</td>\n",
       "      <td>177.509201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>178.029999</td>\n",
       "      <td>174.399994</td>\n",
       "      <td>177.839996</td>\n",
       "      <td>174.610001</td>\n",
       "      <td>103049300.0</td>\n",
       "      <td>174.353836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close       Volume  \\\n",
       "Date                                                                      \n",
       "2022-03-25  175.279999  172.750000  173.880005  174.720001   80546200.0   \n",
       "2022-03-28  175.729996  172.000000  172.169998  175.600006   90371900.0   \n",
       "2022-03-29  179.009995  176.339996  176.690002  178.960007  100589400.0   \n",
       "2022-03-30  179.610001  176.699997  178.550003  177.770004   92633200.0   \n",
       "2022-03-31  178.029999  174.399994  177.839996  174.610001  103049300.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2022-03-25  174.463669  \n",
       "2022-03-28  175.342377  \n",
       "2022-03-29  178.697449  \n",
       "2022-03-30  177.509201  \n",
       "2022-03-31  174.353836  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f48c2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6aa562eb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>High</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>7.619643</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>7.611786</td>\n",
       "      <td>7.526071</td>\n",
       "      <td>352410800.0</td>\n",
       "      <td>6.434926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>493729600.0</td>\n",
       "      <td>6.535085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>601904800.0</td>\n",
       "      <td>6.546382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>7.686786</td>\n",
       "      <td>7.526786</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.534643</td>\n",
       "      <td>552160000.0</td>\n",
       "      <td>6.442255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466071</td>\n",
       "      <td>7.562500</td>\n",
       "      <td>7.520714</td>\n",
       "      <td>477131200.0</td>\n",
       "      <td>6.430346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Date      High       Low      Open     Close       Volume  Adj Close\n",
       "0 2009-12-31  7.619643  7.520000  7.611786  7.526071  352410800.0   6.434926\n",
       "1 2010-01-04  7.660714  7.585000  7.622500  7.643214  493729600.0   6.535085\n",
       "2 2010-01-05  7.699643  7.616071  7.664286  7.656429  601904800.0   6.546382\n",
       "3 2010-01-06  7.686786  7.526786  7.656429  7.534643  552160000.0   6.442255\n",
       "4 2010-01-07  7.571429  7.466071  7.562500  7.520714  477131200.0   6.430346"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d29e405a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"Date\",\"Adj Close\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b766159f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "      <th>0</th>\n",
       "      <td>7.619643</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>7.611786</td>\n",
       "      <td>7.526071</td>\n",
       "      <td>352410800.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>493729600.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>601904800.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.686786</td>\n",
       "      <td>7.526786</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.534643</td>\n",
       "      <td>552160000.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466071</td>\n",
       "      <td>7.562500</td>\n",
       "      <td>7.520714</td>\n",
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      "text/plain": [
       "       High       Low      Open     Close       Volume\n",
       "0  7.619643  7.520000  7.611786  7.526071  352410800.0\n",
       "1  7.660714  7.585000  7.622500  7.643214  493729600.0\n",
       "2  7.699643  7.616071  7.664286  7.656429  601904800.0\n",
       "3  7.686786  7.526786  7.656429  7.534643  552160000.0\n",
       "4  7.571429  7.466071  7.562500  7.520714  477131200.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d266915e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6b7f6c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "MA100 = df.Close.rolling(100).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fcb3788e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25842aeb2e0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(df.Close)\n",
    "plt.plot(MA100, \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4940b5b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "MA200 = df.Close.rolling(200).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d3cc77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25842b61a90>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "plt.plot(df.Close)\n",
    "plt.plot(MA100, \"r\")\n",
    "plt.plot(MA200, \"g\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "676b6c4f",
   "metadata": {},
   "source": [
    "## Splitting Data into Training & Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3120d193",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_training = pd.DataFrame(df[\"Close\"][0:int(len(df)*0.70)])\n",
    "data_testing = pd.DataFrame(df[\"Close\"][int(len(df)*0.70):int(len(df))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3309b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66ee0e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_training_array = scaler.fit_transform(data_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55fead9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2c6ae08",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(100, data_training_array.shape[0]):\n",
    "    x_train.append(data_training_array[i-100: i])\n",
    "    y_train.append(data_training_array[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4771b1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = np.array(x_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe93f86",
   "metadata": {},
   "source": [
    "# ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d77e9d59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9a5b3324",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units = 50, activation=\"relu\", return_sequences = True,\n",
    "               input_shape = (x_train.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units = 60, activation=\"relu\", return_sequences = True))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(LSTM(units = 80, activation=\"relu\", return_sequences = True))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(LSTM(units = 120, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(units = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf7d6001",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 100, 50)           10400     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 100, 50)           0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 120)               96480     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 120)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 121       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 178,761\n",
      "Trainable params: 178,761\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0439cdbf",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "65/65 [==============================] - 90s 1s/step - loss: 0.0347\n",
      "Epoch 2/50\n",
      "65/65 [==============================] - 70s 1s/step - loss: 0.0078\n",
      "Epoch 3/50\n",
      "65/65 [==============================] - 73s 1s/step - loss: 0.0059\n",
      "Epoch 4/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0055\n",
      "Epoch 5/50\n",
      "65/65 [==============================] - 76s 1s/step - loss: 0.0052\n",
      "Epoch 6/50\n",
      "65/65 [==============================] - 69s 1s/step - loss: 0.0061\n",
      "Epoch 7/50\n",
      "65/65 [==============================] - 70s 1s/step - loss: 0.0051\n",
      "Epoch 8/50\n",
      "65/65 [==============================] - 70s 1s/step - loss: 0.0047\n",
      "Epoch 9/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0050\n",
      "Epoch 10/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0048\n",
      "Epoch 11/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0048\n",
      "Epoch 12/50\n",
      "65/65 [==============================] - 70s 1s/step - loss: 0.0038\n",
      "Epoch 13/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0044\n",
      "Epoch 14/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0038\n",
      "Epoch 15/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0035\n",
      "Epoch 16/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0037\n",
      "Epoch 17/50\n",
      "65/65 [==============================] - 70s 1s/step - loss: 0.0034\n",
      "Epoch 18/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0036\n",
      "Epoch 19/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0032\n",
      "Epoch 20/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0032\n",
      "Epoch 21/50\n",
      "65/65 [==============================] - 75s 1s/step - loss: 0.0034\n",
      "Epoch 22/50\n",
      "65/65 [==============================] - 75s 1s/step - loss: 0.0028\n",
      "Epoch 23/50\n",
      "65/65 [==============================] - 73s 1s/step - loss: 0.0034\n",
      "Epoch 24/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0028\n",
      "Epoch 25/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0026\n",
      "Epoch 26/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0027\n",
      "Epoch 27/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0029\n",
      "Epoch 28/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0027\n",
      "Epoch 29/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0027\n",
      "Epoch 30/50\n",
      "65/65 [==============================] - 77s 1s/step - loss: 0.0027\n",
      "Epoch 31/50\n",
      "65/65 [==============================] - 73s 1s/step - loss: 0.0025\n",
      "Epoch 32/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0023\n",
      "Epoch 33/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0023\n",
      "Epoch 34/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0025\n",
      "Epoch 35/50\n",
      "65/65 [==============================] - 74s 1s/step - loss: 0.0025\n",
      "Epoch 36/50\n",
      "65/65 [==============================] - 75s 1s/step - loss: 0.0022\n",
      "Epoch 37/50\n",
      "65/65 [==============================] - 70s 1s/step - loss: 0.0022\n",
      "Epoch 38/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0023\n",
      "Epoch 39/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0022\n",
      "Epoch 40/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0022\n",
      "Epoch 41/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0022\n",
      "Epoch 42/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0022\n",
      "Epoch 43/50\n",
      "65/65 [==============================] - 72s 1s/step - loss: 0.0019\n",
      "Epoch 44/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0020\n",
      "Epoch 45/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0020\n",
      "Epoch 46/50\n",
      "65/65 [==============================] - 70s 1s/step - loss: 0.0020\n",
      "Epoch 47/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0020\n",
      "Epoch 48/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0019\n",
      "Epoch 49/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0020\n",
      "Epoch 50/50\n",
      "65/65 [==============================] - 71s 1s/step - loss: 0.0018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2584ce8be80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n",
    "model.fit(x_train, y_train, epochs = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a479b580",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"stocks-trend.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0bd2bc5e",
   "metadata": {},
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>47.477501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>47.572498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2160</th>\n",
       "      <td>50.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>51.847500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>51.997501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Close\n",
       "2158  47.477501\n",
       "2159  47.572498\n",
       "2160  50.375000\n",
       "2161  51.847500\n",
       "2162  51.997501"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_testing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b9fa4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "past_100_days = data_training.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "866814f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = past_100_days.append(data_testing, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4461996d",
   "metadata": {},
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43.757500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.235001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.994999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.992500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>174.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>175.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>178.960007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>177.770004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>174.610001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Close\n",
       "0      43.757500\n",
       "1      44.235001\n",
       "2      44.994999\n",
       "3      45.430000\n",
       "4      44.992500\n",
       "...          ...\n",
       "1021  174.720001\n",
       "1022  175.600006\n",
       "1023  178.960007\n",
       "1024  177.770004\n",
       "1025  174.610001\n",
       "\n",
       "[1026 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f2dae0be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0560553 ],\n",
       "       [0.05931553],\n",
       "       [0.06450456],\n",
       "       ...,\n",
       "       [0.97917564],\n",
       "       [0.97105068],\n",
       "       [0.94947516]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data = scaler.fit_transform(final_df)\n",
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "81752358",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "\n",
    "for i in range(100, input_data.shape[0]):\n",
    "    x_test.append(input_data[i-100: i])\n",
    "    y_test.append(input_data[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "716c0fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test, y_test = np.array(x_test), np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab4d13b",
   "metadata": {},
   "source": [
    "# Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "05024568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 8s 294ms/step\n"
     ]
    }
   ],
   "source": [
    "y_predicted = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b272988d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(926, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b60f5a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00682769])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "de2f178c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_factor = 1/0.00682769\n",
    "\n",
    "y_predicted = y_predicted * scale_factor\n",
    "\n",
    "y_test = y_test * scale_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d8b58a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.plot(y_test, \"b\", label=\"Original Price\")\n",
    "plt.plot(y_predicted, \"r\", label=\"Predicted Price\")\n",
    "\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Price\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42327fe3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}