StockPricePredictorVer1.ipynb

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              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "2017-04-06    59.375999    60.388000    58.820000    59.740002    59.740002   \n",
              "2017-04-07    59.500000    60.537998    59.430000    60.507999    60.507999   \n",
              "2017-04-10    61.830002    62.745998    61.742001    62.478001    62.478001   \n",
              "2017-04-11    62.675999    62.694000    61.099998    61.742001    61.742001   \n",
              "2017-04-12    61.268002    61.689999    59.264000    59.368000    59.368000   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2022-03-30  1091.170044  1113.949951  1084.000000  1093.989990  1093.989990   \n",
              "2022-03-31  1094.569946  1103.140015  1076.640015  1077.599976  1077.599976   \n",
              "2022-04-01  1081.150024  1094.750000  1066.640015  1084.589966  1084.589966   \n",
              "2022-04-04  1089.380005  1149.910034  1072.530029  1145.449951  1145.449951   \n",
              "2022-04-05  1136.300049  1152.869995  1087.300049  1091.260010  1091.260010   \n",
              "\n",
              "              Volume Symbol  \n",
              "Date                         \n",
              "2017-04-06  27603000   TSLA  \n",
              "2017-04-07  22898000   TSLA  \n",
              "2017-04-10  38322500   TSLA  \n",
              "2017-04-11  28623000   TSLA  \n",
              "2017-04-12  30253500   TSLA  \n",
              "...              ...    ...  \n",
              "2022-03-30  19955000   TSLA  \n",
              "2022-03-31  16330900   TSLA  \n",
              "2022-04-01  18012900   TSLA  \n",
              "2022-04-04  27345300   TSLA  \n",
              "2022-04-05  26607000   TSLA  \n",
              "\n",
              "[1259 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>Open</th>\n",
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              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "    <tr>\n",
              "      <th>2017-04-06</th>\n",
              "      <td>59.375999</td>\n",
              "      <td>60.388000</td>\n",
              "      <td>58.820000</td>\n",
              "      <td>59.740002</td>\n",
              "      <td>59.740002</td>\n",
              "      <td>27603000</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2017-04-07</th>\n",
              "      <td>59.500000</td>\n",
              "      <td>60.537998</td>\n",
              "      <td>59.430000</td>\n",
              "      <td>60.507999</td>\n",
              "      <td>60.507999</td>\n",
              "      <td>22898000</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2017-04-10</th>\n",
              "      <td>61.830002</td>\n",
              "      <td>62.745998</td>\n",
              "      <td>61.742001</td>\n",
              "      <td>62.478001</td>\n",
              "      <td>62.478001</td>\n",
              "      <td>38322500</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2017-04-11</th>\n",
              "      <td>62.675999</td>\n",
              "      <td>62.694000</td>\n",
              "      <td>61.099998</td>\n",
              "      <td>61.742001</td>\n",
              "      <td>61.742001</td>\n",
              "      <td>28623000</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2017-04-12</th>\n",
              "      <td>61.268002</td>\n",
              "      <td>61.689999</td>\n",
              "      <td>59.264000</td>\n",
              "      <td>59.368000</td>\n",
              "      <td>59.368000</td>\n",
              "      <td>30253500</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>2022-03-30</th>\n",
              "      <td>1091.170044</td>\n",
              "      <td>1113.949951</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>19955000</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-31</th>\n",
              "      <td>1094.569946</td>\n",
              "      <td>1103.140015</td>\n",
              "      <td>1076.640015</td>\n",
              "      <td>1077.599976</td>\n",
              "      <td>1077.599976</td>\n",
              "      <td>16330900</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-01</th>\n",
              "      <td>1081.150024</td>\n",
              "      <td>1094.750000</td>\n",
              "      <td>1066.640015</td>\n",
              "      <td>1084.589966</td>\n",
              "      <td>1084.589966</td>\n",
              "      <td>18012900</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-04-04</th>\n",
              "      <td>1089.380005</td>\n",
              "      <td>1149.910034</td>\n",
              "      <td>1072.530029</td>\n",
              "      <td>1145.449951</td>\n",
              "      <td>1145.449951</td>\n",
              "      <td>27345300</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-04-05</th>\n",
              "      <td>1136.300049</td>\n",
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              "      <td>1087.300049</td>\n",
              "      <td>1091.260010</td>\n",
              "      <td>1091.260010</td>\n",
              "      <td>26607000</td>\n",
              "      <td>TSLA</td>\n",
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          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "from matplotlib import ticker\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "# !pip install yfinance\n",
        "import yfinance as yf\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",
        "\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_date = datetime.now() - relativedelta(years=5) # Get date 5 years ago from today\n",
        "end_date = date.today()\n",
        "\n",
        "# Create empty dataframe\n",
        "stock_df = pd.DataFrame()\n",
        "\n",
        "# Download Stock Price Data\n",
        "stock = []\n",
        "# stock = yf.download(tickers = \"AAPL MSFT AMZN TSLA NVDA GOOG GOOGL FB ASML AVGO\", start=start_date, end=end_date)\n",
        "ticker = \"TSLA\"\n",
        "stock = yf.download(ticker, start=start_date, end=end_date)\n",
        "stock_df = stock_df.append(stock, sort=False)\n",
        "# Add stock symbol column\n",
        "stock_df['Symbol'] = ticker\n",
        "\n",
        "stock_df"
      ]
    },
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      "source": [
        "plt.figure(figsize=(13,4))\n",
        "plt.plot(stock_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 ($)', fontsize=16)"
      ],
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        "id": "xDzCmQrjqa3T",
        "outputId": "e3a21640-0fc6-43fc-be71-4286676a2628"
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      "execution_count": 52,
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              "Text(0, 0.5, 'Close Price USD ($)')"
            ]
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          "execution_count": 52
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          "data": {
            "text/plain": [
              "<Figure size 936x288 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set style for plotting\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "LZcLcQI8rmFN"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        "  stock_df2 = stock_df\n",
        "  stock_df2[column_name] = stock_df2['Adj Close'].rolling(ma).mean()\n",
        "\n",
        "stock_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "8BtCkaz5sJpD",
        "outputId": "3565a838-aede-4b32-c898-b7ecd9c1cd2b"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "2017-04-06    59.375999    60.388000    58.820000    59.740002    59.740002   \n",
              "2017-04-07    59.500000    60.537998    59.430000    60.507999    60.507999   \n",
              "2017-04-10    61.830002    62.745998    61.742001    62.478001    62.478001   \n",
              "2017-04-11    62.675999    62.694000    61.099998    61.742001    61.742001   \n",
              "2017-04-12    61.268002    61.689999    59.264000    59.368000    59.368000   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2022-03-30  1091.170044  1113.949951  1084.000000  1093.989990  1093.989990   \n",
              "2022-03-31  1094.569946  1103.140015  1076.640015  1077.599976  1077.599976   \n",
              "2022-04-01  1081.150024  1094.750000  1066.640015  1084.589966  1084.589966   \n",
              "2022-04-04  1089.380005  1149.910034  1072.530029  1145.449951  1145.449951   \n",
              "2022-04-05  1136.300049  1152.869995  1087.300049  1091.260010  1091.260010   \n",
              "\n",
              "              Volume Symbol  MA for 10 days  MA for 30 days  MA for 60 days  \n",
              "Date                                                                         \n",
              "2017-04-06  27603000   TSLA             NaN             NaN             NaN  \n",
              "2017-04-07  22898000   TSLA             NaN             NaN             NaN  \n",
              "2017-04-10  38322500   TSLA             NaN             NaN             NaN  \n",
              "2017-04-11  28623000   TSLA             NaN             NaN             NaN  \n",
              "2017-04-12  30253500   TSLA             NaN             NaN             NaN  \n",
              "...              ...    ...             ...             ...             ...  \n",
              "2022-03-30  19955000   TSLA     1000.119983      889.216325      927.592161  \n",
              "2022-03-31  16330900   TSLA     1020.719983      894.356657      926.392328  \n",
              "2022-04-01  18012900   TSLA     1038.639978      901.297990      926.333494  \n",
              "2022-04-04  27345300   TSLA     1061.068976      910.913656      927.679327  \n",
              "2022-04-05  26607000   TSLA     1070.796979      919.904655      928.750995  \n",
              "\n",
              "[1259 rows x 10 columns]"
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              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
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              "      <th>2017-04-06</th>\n",
              "      <td>59.375999</td>\n",
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              "      <th>2017-04-07</th>\n",
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              "      <td>NaN</td>\n",
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              "      <th>2017-04-11</th>\n",
              "      <td>62.675999</td>\n",
              "      <td>62.694000</td>\n",
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              "      <td>28623000</td>\n",
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              "    <tr>\n",
              "      <th>2017-04-12</th>\n",
              "      <td>61.268002</td>\n",
              "      <td>61.689999</td>\n",
              "      <td>59.264000</td>\n",
              "      <td>59.368000</td>\n",
              "      <td>59.368000</td>\n",
              "      <td>30253500</td>\n",
              "      <td>TSLA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-30</th>\n",
              "      <td>1091.170044</td>\n",
              "      <td>1113.949951</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>19955000</td>\n",
              "      <td>TSLA</td>\n",
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              "      <td>927.592161</td>\n",
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              "      <th>2022-03-31</th>\n",
              "      <td>1094.569946</td>\n",
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              "      <td>1076.640015</td>\n",
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              "      <th>2022-04-01</th>\n",
              "      <td>1081.150024</td>\n",
              "      <td>1094.750000</td>\n",
              "      <td>1066.640015</td>\n",
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              "      <th>2022-04-04</th>\n",
              "      <td>1089.380005</td>\n",
              "      <td>1149.910034</td>\n",
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              "      <th>2022-04-05</th>\n",
              "      <td>1136.300049</td>\n",
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              "      <td>1087.300049</td>\n",
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              "      <td>919.904655</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(13,4))\n",
        "plt.plot(stock_df[\"Close\"], label='Closing Price')\n",
        "plt.plot(stock_df[\"MA for 10 days\"], label='10 Day Moving Average')\n",
        "plt.plot(stock_df[\"MA for 30 days\"], label='30 Day Moving Average')\n",
        "plt.plot(stock_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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "aZ_6MvoeshYK",
        "outputId": "454b4abc-2780-4a94-8639-da15ae2c0993"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Daily Return - gives us an idea if this will be normally ditributed or skewed and as to the accuracy\n",
        "sns.displot(stock_df[\"Daily Return\"].dropna(), bins=100, color='mediumblue', height =3, aspect = 23/6)\n",
        "stock_df['Daily Return'] = stock_df['Adj Close'].pct_change()\n",
        "plt.title(\"Daily Return\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "lsloHYZ6u0-J",
        "outputId": "04a47a82-8b4d-4e0a-b9b0-b42e63dd6e70"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 828x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create training and test dataset\n",
        "# Create new dataframe with only Close column\n",
        "data = stock_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)*.95))\n",
        "training_data_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3KupqciwQ_5",
        "outputId": "b787b92f-676d-4462-94be-1b239844c63f"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1197"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfy_U6lRxhgJ",
        "outputId": "90824d66-c88e-4334-895f-972163b629b9"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.02005333],\n",
              "       [0.02069648],\n",
              "       [0.02234624],\n",
              "       ...,\n",
              "       [0.87830323],\n",
              "       [0.92926979],\n",
              "       [0.88388899]])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "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",
        "  if i<=61:\n",
        "    print(x_train)\n",
        "    print(y_train)\n",
        "    print()\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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0FyVVVW5yAui",
        "outputId": "e3ce304d-948f-4176-d8e3-6d48c14cc05d"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0.02005333, 0.02069648, 0.02234624, 0.02172988, 0.0197418 ,\n",
            "       0.02094101, 0.02051224, 0.02031293, 0.0211956 , 0.02069146,\n",
            "       0.02120899, 0.02161599, 0.02258072, 0.02197442, 0.02171649,\n",
            "       0.02262762, 0.02409481, 0.02343491, 0.02211678, 0.01951067,\n",
            "       0.02166959, 0.0214753 , 0.02383185, 0.02449511, 0.02414004,\n",
            "       0.02442644, 0.02293077, 0.02312003, 0.02129441, 0.02245845,\n",
            "       0.02208496, 0.02200456, 0.02091757, 0.02198279, 0.02308989,\n",
            "       0.02448171, 0.02614989, 0.02713975, 0.02703255, 0.02694546,\n",
            "       0.02819659, 0.0291228 , 0.03026172, 0.03199522, 0.02987147,\n",
            "       0.03015453, 0.03299177, 0.03378064, 0.0328896 , 0.0322297 ,\n",
            "       0.03196172, 0.03237039, 0.03306714, 0.03410724, 0.03424793,\n",
            "       0.0332497 , 0.03071728, 0.0322029 , 0.03044595, 0.03058999])]\n",
            "[0.029084278516964525]\n",
            "\n",
            "[array([0.02005333, 0.02069648, 0.02234624, 0.02172988, 0.0197418 ,\n",
            "       0.02094101, 0.02051224, 0.02031293, 0.0211956 , 0.02069146,\n",
            "       0.02120899, 0.02161599, 0.02258072, 0.02197442, 0.02171649,\n",
            "       0.02262762, 0.02409481, 0.02343491, 0.02211678, 0.01951067,\n",
            "       0.02166959, 0.0214753 , 0.02383185, 0.02449511, 0.02414004,\n",
            "       0.02442644, 0.02293077, 0.02312003, 0.02129441, 0.02245845,\n",
            "       0.02208496, 0.02200456, 0.02091757, 0.02198279, 0.02308989,\n",
            "       0.02448171, 0.02614989, 0.02713975, 0.02703255, 0.02694546,\n",
            "       0.02819659, 0.0291228 , 0.03026172, 0.03199522, 0.02987147,\n",
            "       0.03015453, 0.03299177, 0.03378064, 0.0328896 , 0.0322297 ,\n",
            "       0.03196172, 0.03237039, 0.03306714, 0.03410724, 0.03424793,\n",
            "       0.0332497 , 0.03071728, 0.0322029 , 0.03044595, 0.03058999]), array([0.02069648, 0.02234624, 0.02172988, 0.0197418 , 0.02094101,\n",
            "       0.02051224, 0.02031293, 0.0211956 , 0.02069146, 0.02120899,\n",
            "       0.02161599, 0.02258072, 0.02197442, 0.02171649, 0.02262762,\n",
            "       0.02409481, 0.02343491, 0.02211678, 0.01951067, 0.02166959,\n",
            "       0.0214753 , 0.02383185, 0.02449511, 0.02414004, 0.02442644,\n",
            "       0.02293077, 0.02312003, 0.02129441, 0.02245845, 0.02208496,\n",
            "       0.02200456, 0.02091757, 0.02198279, 0.02308989, 0.02448171,\n",
            "       0.02614989, 0.02713975, 0.02703255, 0.02694546, 0.02819659,\n",
            "       0.0291228 , 0.03026172, 0.03199522, 0.02987147, 0.03015453,\n",
            "       0.03299177, 0.03378064, 0.0328896 , 0.0322297 , 0.03196172,\n",
            "       0.03237039, 0.03306714, 0.03410724, 0.03424793, 0.0332497 ,\n",
            "       0.03071728, 0.0322029 , 0.03044595, 0.03058999, 0.02908428])]\n",
            "[0.029084278516964525, 0.024808309803719543]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "\n",
        "# Build the LSTM model - (Long short-term memory (LSTM) is an artificial recurrent neural network (RNN))\n",
        "model = Sequential()\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",
        "# 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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MuuwjXhzBm3",
        "outputId": "24cd5e6d-50d0-4142-e8f8-1ef0e790aa01"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1137/1137 [==============================] - 49s 41ms/step - loss: 0.0026\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc21d297250>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "h6rg5YxDEkkT"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the root mean squared error (RMSE)\n",
        "rmse = np.sqrt(np.mean(((predictions - y_test) ** 2)))\n",
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsmXqMmdFzFf",
        "outputId": "66dbc0ec-7b16-473d-e2c1-d3c31c2fb34a"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66.62246704626929"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mape = np.mean(np.abs((y_test - predictions) / y_test)) * 100\n",
        "mape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUOxQeN3GBtC",
        "outputId": "7d9843d9-403a-427b-920d-bc2efbc3fe9e"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.053116619031624"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "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=(13,5))\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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "qZj2Qvi4GKce",
        "outputId": "ed91751f-7143-46cf-b59a-857aa74c67f1"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}