StockPricePredictorVer6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "Sd-JlrKinQZy",
    "outputId": "caf34978-0442-414a-bf84-31bc29403270"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-04-13</th>\n",
       "      <td>6.808000</td>\n",
       "      <td>6.570000</td>\n",
       "      <td>6.788000</td>\n",
       "      <td>6.718000</td>\n",
       "      <td>3248000.0</td>\n",
       "      <td>6.718000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-16</th>\n",
       "      <td>6.740000</td>\n",
       "      <td>6.418000</td>\n",
       "      <td>6.682000</td>\n",
       "      <td>6.450000</td>\n",
       "      <td>5498000.0</td>\n",
       "      <td>6.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-17</th>\n",
       "      <td>6.614000</td>\n",
       "      <td>6.408000</td>\n",
       "      <td>6.486000</td>\n",
       "      <td>6.448000</td>\n",
       "      <td>5577500.0</td>\n",
       "      <td>6.448000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-18</th>\n",
       "      <td>6.550000</td>\n",
       "      <td>6.306000</td>\n",
       "      <td>6.418000</td>\n",
       "      <td>6.532000</td>\n",
       "      <td>4115500.0</td>\n",
       "      <td>6.532000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-19</th>\n",
       "      <td>6.686000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>6.632000</td>\n",
       "      <td>3874500.0</td>\n",
       "      <td>6.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-06</th>\n",
       "      <td>1079.000000</td>\n",
       "      <td>1027.699951</td>\n",
       "      <td>1073.469971</td>\n",
       "      <td>1045.760010</td>\n",
       "      <td>29782800.0</td>\n",
       "      <td>1045.760010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-07</th>\n",
       "      <td>1076.589966</td>\n",
       "      <td>1021.539978</td>\n",
       "      <td>1052.390015</td>\n",
       "      <td>1057.260010</td>\n",
       "      <td>26482400.0</td>\n",
       "      <td>1057.260010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-08</th>\n",
       "      <td>1048.439941</td>\n",
       "      <td>1022.440002</td>\n",
       "      <td>1043.209961</td>\n",
       "      <td>1025.489990</td>\n",
       "      <td>18293300.0</td>\n",
       "      <td>1025.489990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-11</th>\n",
       "      <td>1008.469971</td>\n",
       "      <td>974.640015</td>\n",
       "      <td>980.400024</td>\n",
       "      <td>975.929993</td>\n",
       "      <td>19785700.0</td>\n",
       "      <td>975.929993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-12</th>\n",
       "      <td>1021.190002</td>\n",
       "      <td>976.599976</td>\n",
       "      <td>997.640015</td>\n",
       "      <td>986.950012</td>\n",
       "      <td>21948800.0</td>\n",
       "      <td>986.950012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2517 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close      Volume  \\\n",
       "Date                                                                         \n",
       "2012-04-13     6.808000     6.570000     6.788000     6.718000   3248000.0   \n",
       "2012-04-16     6.740000     6.418000     6.682000     6.450000   5498000.0   \n",
       "2012-04-17     6.614000     6.408000     6.486000     6.448000   5577500.0   \n",
       "2012-04-18     6.550000     6.306000     6.418000     6.532000   4115500.0   \n",
       "2012-04-19     6.686000     6.500000     6.550000     6.632000   3874500.0   \n",
       "...                 ...          ...          ...          ...         ...   \n",
       "2022-04-06  1079.000000  1027.699951  1073.469971  1045.760010  29782800.0   \n",
       "2022-04-07  1076.589966  1021.539978  1052.390015  1057.260010  26482400.0   \n",
       "2022-04-08  1048.439941  1022.440002  1043.209961  1025.489990  18293300.0   \n",
       "2022-04-11  1008.469971   974.640015   980.400024   975.929993  19785700.0   \n",
       "2022-04-12  1021.190002   976.599976   997.640015   986.950012  21948800.0   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "2012-04-13     6.718000  \n",
       "2012-04-16     6.450000  \n",
       "2012-04-17     6.448000  \n",
       "2012-04-18     6.532000  \n",
       "2012-04-19     6.632000  \n",
       "...                 ...  \n",
       "2022-04-06  1045.760010  \n",
       "2022-04-07  1057.260010  \n",
       "2022-04-08  1025.489990  \n",
       "2022-04-11   975.929993  \n",
       "2022-04-12   986.950012  \n",
       "\n",
       "[2517 rows x 6 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import ticker\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import pandas_datareader as web\n",
    "import datetime\n",
    "from datetime import date\n",
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import time\n",
    "import requests\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow\n",
    "pd.core.common.is_list_like = pd.api.types.is_list_like\n",
    "import pandas_datareader\n",
    "# Set style for plotting\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# For reading stock data from yahoo\n",
    "# from pandas_datareader.data import Datareader # pip install pandas_datareader\n",
    "\n",
    "# Insert Stock data\n",
    "# ticker = \"AAPL\"\n",
    "start = datetime.now() - relativedelta(years=10) # Get date 10 years ago from today\n",
    "end = date.today()\n",
    "\n",
    "# Get the stock quote\n",
    "df = web.DataReader('TSLA', 'yahoo', start, end)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 338
    },
    "id": "xDzCmQrjqa3T",
    "outputId": "e3a21640-0fc6-43fc-be71-4286676a2628"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close Price USD ($)')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(df[\"Close\"], label = 'TSLA Closing Price History')\n",
    "plt.title('Closing Price History')\n",
    "plt.xlabel('Date', fontsize=16)\n",
    "plt.ylabel('Close Price USD ($)', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "8BtCkaz5sJpD",
    "outputId": "3565a838-aede-4b32-c898-b7ecd9c1cd2b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>MA for 10 days</th>\n",
       "      <th>MA for 30 days</th>\n",
       "      <th>MA for 60 days</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-04-13</th>\n",
       "      <td>6.808000</td>\n",
       "      <td>6.570000</td>\n",
       "      <td>6.788000</td>\n",
       "      <td>6.718000</td>\n",
       "      <td>3248000.0</td>\n",
       "      <td>6.718000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-16</th>\n",
       "      <td>6.740000</td>\n",
       "      <td>6.418000</td>\n",
       "      <td>6.682000</td>\n",
       "      <td>6.450000</td>\n",
       "      <td>5498000.0</td>\n",
       "      <td>6.450000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-17</th>\n",
       "      <td>6.614000</td>\n",
       "      <td>6.408000</td>\n",
       "      <td>6.486000</td>\n",
       "      <td>6.448000</td>\n",
       "      <td>5577500.0</td>\n",
       "      <td>6.448000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-18</th>\n",
       "      <td>6.550000</td>\n",
       "      <td>6.306000</td>\n",
       "      <td>6.418000</td>\n",
       "      <td>6.532000</td>\n",
       "      <td>4115500.0</td>\n",
       "      <td>6.532000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-19</th>\n",
       "      <td>6.686000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>6.632000</td>\n",
       "      <td>3874500.0</td>\n",
       "      <td>6.632000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-06</th>\n",
       "      <td>1079.000000</td>\n",
       "      <td>1027.699951</td>\n",
       "      <td>1073.469971</td>\n",
       "      <td>1045.760010</td>\n",
       "      <td>29782800.0</td>\n",
       "      <td>1045.760010</td>\n",
       "      <td>1075.461981</td>\n",
       "      <td>929.295323</td>\n",
       "      <td>928.544995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-07</th>\n",
       "      <td>1076.589966</td>\n",
       "      <td>1021.539978</td>\n",
       "      <td>1052.390015</td>\n",
       "      <td>1057.260010</td>\n",
       "      <td>26482400.0</td>\n",
       "      <td>1057.260010</td>\n",
       "      <td>1079.795984</td>\n",
       "      <td>937.844989</td>\n",
       "      <td>928.425995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-08</th>\n",
       "      <td>1048.439941</td>\n",
       "      <td>1022.440002</td>\n",
       "      <td>1043.209961</td>\n",
       "      <td>1025.489990</td>\n",
       "      <td>18293300.0</td>\n",
       "      <td>1025.489990</td>\n",
       "      <td>1081.280981</td>\n",
       "      <td>945.032322</td>\n",
       "      <td>927.080495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-11</th>\n",
       "      <td>1008.469971</td>\n",
       "      <td>974.640015</td>\n",
       "      <td>980.400024</td>\n",
       "      <td>975.929993</td>\n",
       "      <td>19785700.0</td>\n",
       "      <td>975.929993</td>\n",
       "      <td>1069.689984</td>\n",
       "      <td>948.548989</td>\n",
       "      <td>926.153327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-12</th>\n",
       "      <td>1021.190002</td>\n",
       "      <td>976.599976</td>\n",
       "      <td>997.640015</td>\n",
       "      <td>986.950012</td>\n",
       "      <td>21948800.0</td>\n",
       "      <td>986.950012</td>\n",
       "      <td>1058.427991</td>\n",
       "      <td>952.634989</td>\n",
       "      <td>925.108995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2517 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close      Volume  \\\n",
       "Date                                                                         \n",
       "2012-04-13     6.808000     6.570000     6.788000     6.718000   3248000.0   \n",
       "2012-04-16     6.740000     6.418000     6.682000     6.450000   5498000.0   \n",
       "2012-04-17     6.614000     6.408000     6.486000     6.448000   5577500.0   \n",
       "2012-04-18     6.550000     6.306000     6.418000     6.532000   4115500.0   \n",
       "2012-04-19     6.686000     6.500000     6.550000     6.632000   3874500.0   \n",
       "...                 ...          ...          ...          ...         ...   \n",
       "2022-04-06  1079.000000  1027.699951  1073.469971  1045.760010  29782800.0   \n",
       "2022-04-07  1076.589966  1021.539978  1052.390015  1057.260010  26482400.0   \n",
       "2022-04-08  1048.439941  1022.440002  1043.209961  1025.489990  18293300.0   \n",
       "2022-04-11  1008.469971   974.640015   980.400024   975.929993  19785700.0   \n",
       "2022-04-12  1021.190002   976.599976   997.640015   986.950012  21948800.0   \n",
       "\n",
       "              Adj Close  MA for 10 days  MA for 30 days  MA for 60 days  \n",
       "Date                                                                     \n",
       "2012-04-13     6.718000             NaN             NaN             NaN  \n",
       "2012-04-16     6.450000             NaN             NaN             NaN  \n",
       "2012-04-17     6.448000             NaN             NaN             NaN  \n",
       "2012-04-18     6.532000             NaN             NaN             NaN  \n",
       "2012-04-19     6.632000             NaN             NaN             NaN  \n",
       "...                 ...             ...             ...             ...  \n",
       "2022-04-06  1045.760010     1075.461981      929.295323      928.544995  \n",
       "2022-04-07  1057.260010     1079.795984      937.844989      928.425995  \n",
       "2022-04-08  1025.489990     1081.280981      945.032322      927.080495  \n",
       "2022-04-11   975.929993     1069.689984      948.548989      926.153327  \n",
       "2022-04-12   986.950012     1058.427991      952.634989      925.108995  \n",
       "\n",
       "[2517 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Place moving averages for 10, 30 and 60 days in new columns in the dataframe\n",
    "ma_day = [10, 30, 60]\n",
    "\n",
    "for ma in ma_day:\n",
    "  column_name = f\"MA for {ma} days\"\n",
    "  df2 = df\n",
    "  df2[column_name] = df2['Close'].rolling(ma).mean()\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "aZ_6MvoeshYK",
    "outputId": "454b4abc-2780-4a94-8639-da15ae2c0993"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(df[\"Close\"], label='Closing Price')\n",
    "plt.plot(df[\"MA for 10 days\"], label='10 Day Moving Average')\n",
    "plt.plot(df[\"MA for 30 days\"], label='30 Day Moving Average')\n",
    "plt.plot(df[\"MA for 60 days\"], label='60 Day Moving Average')\n",
    "plt.title(\"Stock Closing Price and Moving Average\")\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_3KupqciwQ_5",
    "outputId": "b787b92f-676d-4462-94be-1b239844c63f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1762"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create training and test dataset\n",
    "# Create new dataframe with only Close column\n",
    "data = df.filter(['Close'])\n",
    "\n",
    "# Convert the dataframe to a numpy array\n",
    "dataset = data.values\n",
    "\n",
    "# Get number of rows to train the model on\n",
    "training_data_len = int(np.ceil(len(dataset)*.7))\n",
    "training_data_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vfy_U6lRxhgJ",
    "outputId": "90824d66-c88e-4334-895f-972163b629b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00122317],\n",
       "       [0.00100434],\n",
       "       [0.0010027 ],\n",
       "       ...,\n",
       "       [0.83308426],\n",
       "       [0.79261688],\n",
       "       [0.80161509]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale the data\n",
    "# !pip install sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(dataset)\n",
    "\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0FyVVVW5yAui",
    "outputId": "e3ce304d-948f-4176-d8e3-6d48c14cc05d"
   },
   "outputs": [],
   "source": [
    "# Create the training dataset\n",
    "# Create the scaled training into data set\n",
    "train_data = scaled_data[0:int(training_data_len), :]\n",
    "# Split the data into x_train and y_train datasets\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(60, len(train_data)):\n",
    "  x_train.append(train_data[i-60:i,0])\n",
    "  y_train.append(train_data[i,0])\n",
    "\n",
    "# Convert the x_train and y_train to numpy arrays\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "\n",
    "# Reshape the data\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "# x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7MuuwjXhzBm3",
    "outputId": "24cd5e6d-50d0-4142-e8f8-1ef0e790aa01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1702/1702 [==============================] - 24s 13ms/step - loss: 2.3374e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2a0fd197f10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "\n",
    "# Build the LSTM model - (Long short-term memory (LSTM) is an artificial recurrent neural network (RNN))\n",
    "model = Sequential()\n",
    "#Ver1\n",
    "# model.add(LSTM(128, return_sequences=True, input_shape=(x_train.shape[1],1)))\n",
    "# model.add(LSTM(64, return_sequences=False))\n",
    "# model.add(Dense(25))\n",
    "# model.add(Dense(1))\n",
    "\n",
    "#Ver3\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1],1)))\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))\n",
    "\n",
    "#Ver5\n",
    "# model.add(LSTM(units=50, activation = 'relu', return_sequences=True, 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))\n",
    "\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_train, y_train, batch_size=1, epochs=1)\n",
    "\n",
    "# Wait 45 seconds or so for model fit to run, it will spool up at about 20 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "h6rg5YxDEkkT"
   },
   "outputs": [],
   "source": [
    "# Create the testing dataset\n",
    "# Create a new array containing scaled values from index 1543 to 2002\n",
    "test_data = scaled_data[training_data_len - 60: , :]\n",
    "\n",
    "# Create the data sets x_test and y_test\n",
    "x_test = []\n",
    "y_test = dataset[training_data_len:, :]\n",
    "for i in range(60, len(test_data)):\n",
    "  x_test.append(test_data[i-60:i, 0])\n",
    "\n",
    "# Convert the data to a numpy array\n",
    "x_test = np.array(x_test)\n",
    "\n",
    "# Reshape the data\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "\n",
    "# Get the models predicted price values\n",
    "predictions = model.predict(x_test)\n",
    "predictions = scaler.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gsmXqMmdFzFf",
    "outputId": "66dbc0ec-7b16-473d-e2c1-d3c31c2fb34a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.27433127361401"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the root mean squared error (RMSE)\n",
    "rmse = np.sqrt(np.mean(((predictions - y_test) ** 2)))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mUOxQeN3GBtC",
    "outputId": "7d9843d9-403a-427b-920d-bc2efbc3fe9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.397676225640061"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape = np.mean(np.abs((y_test - predictions) / y_test)) * 100\n",
    "mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "id": "qZj2Qvi4GKce",
    "outputId": "ed91751f-7143-46cf-b59a-857aa74c67f1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove pandas warning on copy of a slice which is immaterial in this case\n",
    "pd.options.mode.chained_assignment = None # default='warn'\n",
    "# Plot the data\n",
    "train = data[:training_data_len]\n",
    "valid = data[training_data_len:]\n",
    "valid['Predictions'] = predictions\n",
    "\n",
    "# Visualize the data\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Stock Price Forecast LSTM Model')\n",
    "plt.xlabel('Date', fontsize=13)\n",
    "plt.ylabel('Close Price USD ($)', fontsize=13)\n",
    "plt.plot(train['Close'])\n",
    "plt.plot(valid[['Close', 'Predictions']])\n",
    "plt.legend(['Train', 'Val', 'Predictions'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-16</th>\n",
       "      <td>54.672001</td>\n",
       "      <td>59.518547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-17</th>\n",
       "      <td>54.245998</td>\n",
       "      <td>59.374699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-18</th>\n",
       "      <td>54.652000</td>\n",
       "      <td>59.292786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-22</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>59.299801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-23</th>\n",
       "      <td>52.779999</td>\n",
       "      <td>59.089558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-06</th>\n",
       "      <td>1045.760010</td>\n",
       "      <td>1038.621216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-07</th>\n",
       "      <td>1057.260010</td>\n",
       "      <td>1037.681152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-08</th>\n",
       "      <td>1025.489990</td>\n",
       "      <td>1034.479858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-11</th>\n",
       "      <td>975.929993</td>\n",
       "      <td>1027.627319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-12</th>\n",
       "      <td>986.950012</td>\n",
       "      <td>1015.265320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close  Predictions\n",
       "Date                                \n",
       "2019-04-16    54.672001    59.518547\n",
       "2019-04-17    54.245998    59.374699\n",
       "2019-04-18    54.652000    59.292786\n",
       "2019-04-22    52.549999    59.299801\n",
       "2019-04-23    52.779999    59.089558\n",
       "...                 ...          ...\n",
       "2022-04-06  1045.760010  1038.621216\n",
       "2022-04-07  1057.260010  1037.681152\n",
       "2022-04-08  1025.489990  1034.479858\n",
       "2022-04-11   975.929993  1027.627319\n",
       "2022-04-12   986.950012  1015.265320\n",
       "\n",
       "[755 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1002.949]]\n"
     ]
    }
   ],
   "source": [
    "# Try to predict closing price for next closing price\n",
    "\n",
    "# Get the quote\n",
    "# df = web.DataReader('TSLA', data_source='yahoo', start='2012-01-01', end='2022-04-12')\n",
    "\n",
    "\n",
    "# Create a new dataframe\n",
    "new_df = df.filter(['Close'])\n",
    "# Get the last 60 days closing price values and convert the dataframe to an array\n",
    "last_60_days = new_df[-60:].values\n",
    "# Scale the data to be values between 0 and 1\n",
    "last_60_days_scaled = scaler.transform(last_60_days)\n",
    "# Create an empty list\n",
    "X_test = []\n",
    "# Append the past 60 days\n",
    "X_test.append(last_60_days_scaled)\n",
    "# Convert X_test dataset to a numpy array\n",
    "X_test = np.array(X_test)\n",
    "# Reshape the data\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "# Get the predicted scaled price\n",
    "predicted_price = model.predict(X_test)\n",
    "# Undo the sdcaling\n",
    "predicted_price = scaler.inverse_transform(predicted_price)\n",
    "print(predicted_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2022-04-11    975.929993\n",
      "2022-04-12    986.950012\n",
      "Name: Close, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Get actual price\n",
    "actual_price = web.DataReader('TSLA', data_source='yahoo', start='2022-04-12', end='2022-04-12')\n",
    "print(actual_price['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00081653])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(y_test, 'b', label = 'Original Price')\n",
    "y_predicted = model.predict(x_test)\n",
    "scale_factor = 1/0.00081653\n",
    "y_predict0ed = y_predicted * scale_factor\n",
    "plt.plot(y_predicted, 'r', label = 'Predicted Price')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "StockPricePredictor.ipynb",
   "provenance": []
  },
  "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": "3.10.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}