BITCOIN_PRICE_FINAL.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUpjIx6_58Kh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6fc17e0-6c46-4623-a395-c4ab114a793d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRzjS0tz3TZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6aa8fb3f-7e66-469a-f282-1d27eaea3a21"
      },
      "source": [
        "!pip install -q plotly==4.2.1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 7.2MB 5.3MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BZFuB7rJexyq"
      },
      "source": [
        "## **IMPORT LIBRARIES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7NZXuykevYB"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMrmPSlQiNgb"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8tJ-CgHCU3J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98fa6536-3e3c-4c1d-da10-9aecca570a78"
      },
      "source": [
        "from statsmodels.tsa.stattools import adfuller, acf, pacf\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "import statsmodels.graphics.tsaplots as tsa\n",
        "import statsmodels.stats.diagnostic as diag"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqJdemmRV0iP",
        "outputId": "2faba091-343b-4879-96a3-96063f191a9b"
      },
      "source": [
        "!pip install chart_studio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting chart_studio\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/ce/330794a6b6ca4b9182c38fc69dd2a9cbff60fd49421cb8648ee5fee352dc/chart_studio-1.1.0-py3-none-any.whl (64kB)\n",
            "\r\u001b[K     |█████                           | 10kB 15.8MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 20kB 18.6MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 30kB 10.3MB/s eta 0:00:01\r\u001b[K     |████████████████████▍           | 40kB 8.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 51kB 4.2MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 61kB 4.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 71kB 3.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.3.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.15.0)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from chart_studio) (4.2.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from chart_studio) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2020.11.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (3.0.4)\n",
            "Installing collected packages: chart-studio\n",
            "Successfully installed chart-studio-1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nfy5uXTGFGgE",
        "outputId": "ae57329d-61fd-41c5-9366-6dc791a4c6a1"
      },
      "source": [
        "!pip install dash"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting dash\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/91/ae029886dda55b93b60ac04377bcb2ab9209dd73244e3b5e513124cc6778/dash-1.17.0.tar.gz (75kB)\n",
            "\r\u001b[K     |████▍                           | 10kB 16.2MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 20kB 20.1MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 30kB 10.7MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 40kB 8.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 51kB 4.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 61kB 4.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 71kB 4.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 81kB 3.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: Flask>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from dash) (1.1.2)\n",
            "Collecting flask-compress\n",
            "  Downloading https://files.pythonhosted.org/packages/b2/7a/9c4641f975fb9daaf945dc39da6a52fd5693ab3bbc2d53780eab3b5106f4/Flask_Compress-1.8.0-py3-none-any.whl\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from dash) (4.2.1)\n",
            "Collecting dash_renderer==1.8.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/72/fe/59a322edb128ad15205002c7b81e3f5e580f6791c4a100183289e05dbfcb/dash_renderer-1.8.3.tar.gz (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 8.3MB/s \n",
            "\u001b[?25hCollecting dash-core-components==1.13.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/48/3dd8c7bf93cff3a9dd9d360bc25c4ec0cb191acdce13375c3ff85f98e992/dash_core_components-1.13.0.tar.gz (3.5MB)\n",
            "\u001b[K     |████████████████████████████████| 3.5MB 42.9MB/s \n",
            "\u001b[?25hCollecting dash-html-components==1.1.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/02/ba/bb9427c62feb25bfbaf243894eeeb4e7c67a92b426ed0575a167100e436e/dash_html_components-1.1.1.tar.gz (188kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 40.6MB/s \n",
            "\u001b[?25hCollecting dash-table==4.11.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/46/cc839f897cabea3f584cf59ce9d86a5ea3828e54eefe2537ba8b640a9707/dash_table-4.11.0.tar.gz (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 39.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from dash) (0.16.0)\n",
            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->dash) (2.11.2)\n",
            "Requirement already satisfied: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->dash) (1.1.0)\n",
            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->dash) (7.1.2)\n",
            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->dash) (1.0.1)\n",
            "Collecting brotli\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b4/d3/7c98f05b7b9103e2f3a112ba42f269c798155b3e5404fb80bb8f823aaebe/Brotli-1.0.9-cp36-cp36m-manylinux1_x86_64.whl (357kB)\n",
            "\u001b[K     |████████████████████████████████| 358kB 45.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->dash) (1.3.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from plotly->dash) (1.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->Flask>=1.0.2->dash) (1.1.1)\n",
            "Building wheels for collected packages: dash, dash-renderer, dash-core-components, dash-html-components, dash-table\n",
            "  Building wheel for dash (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash: filename=dash-1.17.0-cp36-none-any.whl size=83728 sha256=208921b80d0a1d1e17d36d594ee2a0fb314b2001ea096e0e61200bb0ded8a76a\n",
            "  Stored in directory: /root/.cache/pip/wheels/e0/32/13/8a569b638a79cf80a4a721f56040d91198adec28e3bc8123fc\n",
            "  Building wheel for dash-renderer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-renderer: filename=dash_renderer-1.8.3-cp36-none-any.whl size=1013944 sha256=55242a8b4f11985d990fab0f9932148a7dd0764f923cc2951f0f6124cd37bc0d\n",
            "  Stored in directory: /root/.cache/pip/wheels/cf/2b/5f/0928a6d1b7ebf280f21a2e925f36d662e6ba83e00b82c6b6bf\n",
            "  Building wheel for dash-core-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-core-components: filename=dash_core_components-1.13.0-cp36-none-any.whl size=3513142 sha256=bea95141a7f9479a0812a94df95ca016d9aafe986226a63e9c1bb3351e64fccf\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/d9/90/177214625a5b5f6440b24ea807550e07fbf439a495eee4dd36\n",
            "  Building wheel for dash-html-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-html-components: filename=dash_html_components-1.1.1-cp36-none-any.whl size=427894 sha256=0000de5d5e8821943a6d468396fae931f76769b31ff5678cfab2dc951046888a\n",
            "  Stored in directory: /root/.cache/pip/wheels/8d/01/46/78e4de185a8a4a2da8ba31da16c52170f036d4cebeeb6e07a2\n",
            "  Building wheel for dash-table (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-table: filename=dash_table-4.11.0-cp36-none-any.whl size=1839767 sha256=4764e850a06112949343f090c18aa00787dfb7d5a5e2da78460e7f65454ff59b\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/37/90/bd45dcc5d6acbe6ac53f753a8f1732ecf5270017372e389cd8\n",
            "Successfully built dash dash-renderer dash-core-components dash-html-components dash-table\n",
            "Installing collected packages: brotli, flask-compress, dash-renderer, dash-core-components, dash-html-components, dash-table, dash\n",
            "Successfully installed brotli-1.0.9 dash-1.17.0 dash-core-components-1.13.0 dash-html-components-1.1.1 dash-renderer-1.8.3 dash-table-4.11.0 flask-compress-1.8.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ae7BsYFDVlwH"
      },
      "source": [
        "from plotly import tools\n",
        "import gc\n",
        "import datetime\n",
        "import dash\n",
        "import dash_core_components as dcc\n",
        "import dash_html_components as html\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import pandas as pd\n",
        "from plotly.subplots import make_subplots"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wk0-884hfDgO"
      },
      "source": [
        "## **READ THE DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BECt8L_xevb3",
        "outputId": "002b5ee8-1d00-4b4b-e84e-24e1f8f00161"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/SUL/BTC-USD.csv',header=0)\n",
        "print(df.head())\n",
        "\n",
        "df['Date']=pd.to_datetime(df['Date'])\n",
        "print(df.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         Date        Open        High  ...       Close   Adj Close    Volume\n",
            "0  2015-11-19  334.678986  335.334015  ...  326.148987  326.148987  45011100\n",
            "1  2015-11-20  326.411011  326.472992  ...  322.022003  322.022003  53152900\n",
            "2  2015-11-21  322.092010  328.158997  ...  326.927002  326.927002  28200500\n",
            "3  2015-11-22  326.975006  327.010010  ...  324.536011  324.536011  23439400\n",
            "4  2015-11-23  324.350006  325.118011  ...  323.045990  323.045990  27478900\n",
            "\n",
            "[5 rows x 7 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1828 entries, 0 to 1827\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   Date       1828 non-null   datetime64[ns]\n",
            " 1   Open       1828 non-null   float64       \n",
            " 2   High       1828 non-null   float64       \n",
            " 3   Low        1828 non-null   float64       \n",
            " 4   Close      1828 non-null   float64       \n",
            " 5   Adj Close  1828 non-null   float64       \n",
            " 6   Volume     1828 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
            "memory usage: 100.1 KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUaombxuhZ9-"
      },
      "source": [
        "## **EXPLORATORY DATA ANALYSIS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_bgK6_HB7mb"
      },
      "source": [
        "### **LJung Box Test for White Noise Detection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4f4Ox4p9CDT8"
      },
      "source": [
        "### Autocorrelation Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "SnroX9OPCZNx",
        "outputId": "95d49c61-8659-4447-97a9-466af97149b5"
      },
      "source": [
        "tsa.plot_acf(df['Close'], lags=40, alpha=0.05, title='Auto-correlation coefficients for lags 1 through 40')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQPXiJe_NLaI"
      },
      "source": [
        "**Testing using Ljung Box**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uhDfsc0C2My",
        "outputId": "a8484b33-b178-427c-822e-5570a7fe28ac"
      },
      "source": [
        "diag.acorr_ljungbox(df['Close'], lags=[40], boxpierce=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([59590.70658337]), array([0.]), array([58902.34933406]), array([0.]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vMNH8p_INo_G"
      },
      "source": [
        "Test Statistic for Ljungbox = 59590.70658337\n",
        "\n",
        "p-value for Ljungbox = 0 \n",
        "\n",
        "\n",
        "Test Statistic for Box-Pierce Test = 58902.34933406\n",
        "\n",
        "p-value for Box-Pierce = 0\n",
        "\n",
        "Both the p-values from Chi-Square table are less than 0.01 , So with 99% confidence , we say that the time series is not Pure White noise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04zi8nz8FfG2"
      },
      "source": [
        "**Price Trend**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "tqjOyNpbhL1F",
        "outputId": "83fcf6c4-39c2-4edb-995a-eb21614b58b6"
      },
      "source": [
        "\n",
        "plt.plot('Date', 'Close', data=df)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price')\n",
        "plt.title('Closing price trend')\n",
        "plt.xticks(rotation=45)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([735599., 735964., 736330., 736695., 737060., 737425., 737791.]),\n",
              " <a list of 7 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "z-oJyr2zW_e_",
        "outputId": "1c6dfab3-f739-4f62-ed8f-defe712f8abe"
      },
      "source": [
        "import datetime\n",
        "from datetime import timezone\n",
        "\n",
        "data = df\n",
        "\n",
        "start = datetime.datetime(2015,11,19)#2015-11-19\n",
        "end = datetime.datetime(2020,11,19)#2020-11-19\n",
        "\n",
        "# find rows between start and end time and find the first row (00:00 monday morning)\n",
        "weekly_rows = data[(data['Date'] >= start) & (data['Date'] <= end)].groupby([pd.Grouper(key='Date', freq='W-MON')]).first().reset_index()\n",
        "weekly_rows.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-11-23</td>\n",
              "      <td>334.678986</td>\n",
              "      <td>335.334015</td>\n",
              "      <td>325.273010</td>\n",
              "      <td>326.148987</td>\n",
              "      <td>326.148987</td>\n",
              "      <td>45011100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-11-30</td>\n",
              "      <td>323.014008</td>\n",
              "      <td>323.058014</td>\n",
              "      <td>318.118011</td>\n",
              "      <td>320.045990</td>\n",
              "      <td>320.045990</td>\n",
              "      <td>29362600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-12-07</td>\n",
              "      <td>377.414001</td>\n",
              "      <td>378.931000</td>\n",
              "      <td>356.562988</td>\n",
              "      <td>362.488007</td>\n",
              "      <td>362.488007</td>\n",
              "      <td>60452200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-12-14</td>\n",
              "      <td>395.753998</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>389.950012</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>57801400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-12-21</td>\n",
              "      <td>443.877991</td>\n",
              "      <td>465.321014</td>\n",
              "      <td>443.877991</td>\n",
              "      <td>465.321014</td>\n",
              "      <td>465.321014</td>\n",
              "      <td>83121104</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date        Open        High  ...       Close   Adj Close    Volume\n",
              "0 2015-11-23  334.678986  335.334015  ...  326.148987  326.148987  45011100\n",
              "1 2015-11-30  323.014008  323.058014  ...  320.045990  320.045990  29362600\n",
              "2 2015-12-07  377.414001  378.931000  ...  362.488007  362.488007  60452200\n",
              "3 2015-12-14  395.753998  415.562988  ...  415.562988  415.562988  57801400\n",
              "4 2015-12-21  443.877991  465.321014  ...  465.321014  465.321014  83121104\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W33igHJi0Ty1"
      },
      "source": [
        "## **VISUALIZATION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxrG2P9WFv7R"
      },
      "source": [
        "**HIGH PRICE MOVEMENT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6DUTUwbZmHaz",
        "outputId": "cb00ccf1-7a56-4788-8bbc-c8d6e510c346"
      },
      "source": [
        "# Load data\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "\n",
        "# Create figure\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(x = list(weekly_rows.Date),\n",
        "               y = list(weekly_rows.High)))\n",
        "\n",
        "# Set title\n",
        "fig.update_layout(\n",
        "    title_text=\"Time series with range slider and selectors\"\n",
        ")\n",
        "\n",
        "# Add range slider\n",
        "fig.update_layout(\n",
        "    xaxis=dict(\n",
        "        rangeselector=dict(\n",
        "            buttons=list([\n",
        "                dict(count=1,\n",
        "                     label=\"1m\",\n",
        "                     step=\"month\",\n",
        "                     stepmode=\"backward\"),\n",
        "                dict(count=6,\n",
        "                     label=\"6m\",\n",
        "                     step=\"month\",\n",
        "                     stepmode=\"backward\"),\n",
        "               \n",
        "                dict(count=1,\n",
        "                     label=\"1y\",\n",
        "                     step=\"year\",\n",
        "                     stepmode=\"backward\"),\n",
        "                dict(step=\"all\")\n",
        "            ])\n",
        "        ),\n",
        "        rangeslider=dict(\n",
        "            visible=True\n",
        "        ),\n",
        "        type=\"date\"\n",
        "    )\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"cff23006-7ecb-4024-9212-e88e8e9cd601\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"cff23006-7ecb-4024-9212-e88e8e9cd601\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'cff23006-7ecb-4024-9212-e88e8e9cd601',\n",
              "                        [{\"type\": \"scatter\", \"x\": [\"2015-11-23T00:00:00\", \"2015-11-30T00:00:00\", \"2015-12-07T00:00:00\", \"2015-12-14T00:00:00\", \"2015-12-21T00:00:00\", \"2015-12-28T00:00:00\", \"2016-01-04T00:00:00\", \"2016-01-11T00:00:00\", \"2016-01-18T00:00:00\", \"2016-01-25T00:00:00\", \"2016-02-01T00:00:00\", \"2016-02-08T00:00:00\", \"2016-02-15T00:00:00\", \"2016-02-22T00:00:00\", \"2016-02-29T00:00:00\", \"2016-03-07T00:00:00\", \"2016-03-14T00:00:00\", \"2016-03-21T00:00:00\", \"2016-03-28T00:00:00\", \"2016-04-04T00:00:00\", \"2016-04-11T00:00:00\", \"2016-04-18T00:00:00\", \"2016-04-25T00:00:00\", \"2016-05-02T00:00:00\", \"2016-05-09T00:00:00\", \"2016-05-16T00:00:00\", \"2016-05-23T00:00:00\", \"2016-05-30T00:00:00\", \"2016-06-06T00:00:00\", \"2016-06-13T00:00:00\", \"2016-06-20T00:00:00\", \"2016-06-27T00:00:00\", \"2016-07-04T00:00:00\", \"2016-07-11T00:00:00\", \"2016-07-18T00:00:00\", \"2016-07-25T00:00:00\", \"2016-08-01T00:00:00\", \"2016-08-08T00:00:00\", \"2016-08-15T00:00:00\", \"2016-08-22T00:00:00\", \"2016-08-29T00:00:00\", \"2016-09-05T00:00:00\", \"2016-09-12T00:00:00\", \"2016-09-19T00:00:00\", \"2016-09-26T00:00:00\", \"2016-10-03T00:00:00\", \"2016-10-10T00:00:00\", \"2016-10-17T00:00:00\", \"2016-10-24T00:00:00\", \"2016-10-31T00:00:00\", \"2016-11-07T00:00:00\", \"2016-11-14T00:00:00\", \"2016-11-21T00:00:00\", \"2016-11-28T00:00:00\", \"2016-12-05T00:00:00\", \"2016-12-12T00:00:00\", \"2016-12-19T00:00:00\", \"2016-12-26T00:00:00\", \"2017-01-02T00:00:00\", \"2017-01-09T00:00:00\", \"2017-01-16T00:00:00\", \"2017-01-23T00:00:00\", \"2017-01-30T00:00:00\", \"2017-02-06T00:00:00\", \"2017-02-13T00:00:00\", \"2017-02-20T00:00:00\", \"2017-02-27T00:00:00\", \"2017-03-06T00:00:00\", \"2017-03-13T00:00:00\", \"2017-03-20T00:00:00\", \"2017-03-27T00:00:00\", \"2017-04-03T00:00:00\", \"2017-04-10T00:00:00\", \"2017-04-17T00:00:00\", \"2017-04-24T00:00:00\", \"2017-05-01T00:00:00\", \"2017-05-08T00:00:00\", \"2017-05-15T00:00:00\", \"2017-05-22T00:00:00\", \"2017-05-29T00:00:00\", \"2017-06-05T00:00:00\", \"2017-06-12T00:00:00\", \"2017-06-19T00:00:00\", \"2017-06-26T00:00:00\", \"2017-07-03T00:00:00\", \"2017-07-10T00:00:00\", \"2017-07-17T00:00:00\", \"2017-07-24T00:00:00\", \"2017-07-31T00:00:00\", \"2017-08-07T00:00:00\", \"2017-08-14T00:00:00\", \"2017-08-21T00:00:00\", \"2017-08-28T00:00:00\", \"2017-09-04T00:00:00\", \"2017-09-11T00:00:00\", \"2017-09-18T00:00:00\", \"2017-09-25T00:00:00\", \"2017-10-02T00:00:00\", \"2017-10-09T00:00:00\", \"2017-10-16T00:00:00\", \"2017-10-23T00:00:00\", \"2017-10-30T00:00:00\", \"2017-11-06T00:00:00\", \"2017-11-13T00:00:00\", \"2017-11-20T00:00:00\", \"2017-11-27T00:00:00\", \"2017-12-04T00:00:00\", \"2017-12-11T00:00:00\", \"2017-12-18T00:00:00\", \"2017-12-25T00:00:00\", \"2018-01-01T00:00:00\", \"2018-01-08T00:00:00\", \"2018-01-15T00:00:00\", \"2018-01-22T00:00:00\", \"2018-01-29T00:00:00\", \"2018-02-05T00:00:00\", \"2018-02-12T00:00:00\", \"2018-02-19T00:00:00\", \"2018-02-26T00:00:00\", \"2018-03-05T00:00:00\", \"2018-03-12T00:00:00\", \"2018-03-19T00:00:00\", \"2018-03-26T00:00:00\", \"2018-04-02T00:00:00\", \"2018-04-09T00:00:00\", \"2018-04-16T00:00:00\", \"2018-04-23T00:00:00\", \"2018-04-30T00:00:00\", \"2018-05-07T00:00:00\", \"2018-05-14T00:00:00\", \"2018-05-21T00:00:00\", \"2018-05-28T00:00:00\", \"2018-06-04T00:00:00\", \"2018-06-11T00:00:00\", \"2018-06-18T00:00:00\", \"2018-06-25T00:00:00\", \"2018-07-02T00:00:00\", \"2018-07-09T00:00:00\", \"2018-07-16T00:00:00\", \"2018-07-23T00:00:00\", \"2018-07-30T00:00:00\", \"2018-08-06T00:00:00\", \"2018-08-13T00:00:00\", \"2018-08-20T00:00:00\", \"2018-08-27T00:00:00\", \"2018-09-03T00:00:00\", \"2018-09-10T00:00:00\", \"2018-09-17T00:00:00\", \"2018-09-24T00:00:00\", \"2018-10-01T00:00:00\", \"2018-10-08T00:00:00\", \"2018-10-15T00:00:00\", \"2018-10-22T00:00:00\", \"2018-10-29T00:00:00\", \"2018-11-05T00:00:00\", \"2018-11-12T00:00:00\", \"2018-11-19T00:00:00\", \"2018-11-26T00:00:00\", \"2018-12-03T00:00:00\", \"2018-12-10T00:00:00\", \"2018-12-17T00:00:00\", \"2018-12-24T00:00:00\", \"2018-12-31T00:00:00\", \"2019-01-07T00:00:00\", \"2019-01-14T00:00:00\", \"2019-01-21T00:00:00\", \"2019-01-28T00:00:00\", \"2019-02-04T00:00:00\", \"2019-02-11T00:00:00\", \"2019-02-18T00:00:00\", \"2019-02-25T00:00:00\", \"2019-03-04T00:00:00\", \"2019-03-11T00:00:00\", \"2019-03-18T00:00:00\", \"2019-03-25T00:00:00\", \"2019-04-01T00:00:00\", \"2019-04-08T00:00:00\", \"2019-04-15T00:00:00\", \"2019-04-22T00:00:00\", \"2019-04-29T00:00:00\", \"2019-05-06T00:00:00\", \"2019-05-13T00:00:00\", \"2019-05-20T00:00:00\", \"2019-05-27T00:00:00\", \"2019-06-03T00:00:00\", \"2019-06-10T00:00:00\", \"2019-06-17T00:00:00\", \"2019-06-24T00:00:00\", \"2019-07-01T00:00:00\", \"2019-07-08T00:00:00\", \"2019-07-15T00:00:00\", \"2019-07-22T00:00:00\", \"2019-07-29T00:00:00\", \"2019-08-05T00:00:00\", \"2019-08-12T00:00:00\", \"2019-08-19T00:00:00\", \"2019-08-26T00:00:00\", \"2019-09-02T00:00:00\", \"2019-09-09T00:00:00\", \"2019-09-16T00:00:00\", \"2019-09-23T00:00:00\", \"2019-09-30T00:00:00\", \"2019-10-07T00:00:00\", \"2019-10-14T00:00:00\", \"2019-10-21T00:00:00\", \"2019-10-28T00:00:00\", \"2019-11-04T00:00:00\", \"2019-11-11T00:00:00\", \"2019-11-18T00:00:00\", \"2019-11-25T00:00:00\", \"2019-12-02T00:00:00\", \"2019-12-09T00:00:00\", \"2019-12-16T00:00:00\", \"2019-12-23T00:00:00\", \"2019-12-30T00:00:00\", \"2020-01-06T00:00:00\", \"2020-01-13T00:00:00\", \"2020-01-20T00:00:00\", \"2020-01-27T00:00:00\", \"2020-02-03T00:00:00\", \"2020-02-10T00:00:00\", \"2020-02-17T00:00:00\", \"2020-02-24T00:00:00\", \"2020-03-02T00:00:00\", \"2020-03-09T00:00:00\", \"2020-03-16T00:00:00\", \"2020-03-23T00:00:00\", \"2020-03-30T00:00:00\", \"2020-04-06T00:00:00\", \"2020-04-13T00:00:00\", \"2020-04-20T00:00:00\", \"2020-04-27T00:00:00\", \"2020-05-04T00:00:00\", \"2020-05-11T00:00:00\", \"2020-05-18T00:00:00\", \"2020-05-25T00:00:00\", \"2020-06-01T00:00:00\", \"2020-06-08T00:00:00\", \"2020-06-15T00:00:00\", \"2020-06-22T00:00:00\", \"2020-06-29T00:00:00\", \"2020-07-06T00:00:00\", \"2020-07-13T00:00:00\", \"2020-07-20T00:00:00\", \"2020-07-27T00:00:00\", \"2020-08-03T00:00:00\", \"2020-08-10T00:00:00\", \"2020-08-17T00:00:00\", \"2020-08-24T00:00:00\", \"2020-08-31T00:00:00\", \"2020-09-07T00:00:00\", \"2020-09-14T00:00:00\", \"2020-09-21T00:00:00\", \"2020-09-28T00:00:00\", \"2020-10-05T00:00:00\", \"2020-10-12T00:00:00\", \"2020-10-19T00:00:00\", \"2020-10-26T00:00:00\", \"2020-11-02T00:00:00\", \"2020-11-09T00:00:00\", \"2020-11-16T00:00:00\", \"2020-11-23T00:00:00\"], \"y\": [335.33401499999997, 323.058014, 378.931, 415.56298799999996, 465.321014, 443.68798799999996, 432.98300199999994, 434.18200700000006, 448.18200700000006, 387.730011, 397.76599100000004, 375.882996, 377.246002, 408.945007, 439.85800199999994, 439.65301500000004, 416.24301099999997, 418.131012, 418.375, 426.203003, 424.256989, 427.277008, 436.01998899999995, 467.964996, 451.09698499999996, 461.928986, 455.071991, 447.10000599999995, 546.617981, 590.258972, 704.5040280000001, 735.882996, 659.2479860000001, 683.491028, 675.258972, 673.276978, 656.224976, 612.848022, 591.091003, 581.737976, 589.473999, 578.356995, 610.830017, 611.192993, 609.525024, 608.2479860000001, 612.054016, 642.080017, 640.736023, 664.4240110000001, 736.4520259999999, 712.987, 715.7180179999999, 753.869995, 737.471008, 765.6220089999999, 788.460022, 801.336975, 940.0479740000001, 1044.079956, 914.872986, 910.560974, 924.1450199999999, 972.018982, 1061.930054, 1011.51001, 1117.25, 1193.25, 1275.550049, 1244.810059, 1122.430054, 1064.650024, 1156.439941, 1208.069946, 1217.569946, 1267.579956, 1473.900024, 1833.4899899999998, 1785.939941, 2320.820068, 2301.959961, 2999.909912, 2789.040039, 2763.449951, 2552.449951, 2631.590088, 2413.469971, 2387.610107, 2768.080078, 2921.350098, 3484.850098, 4455.970215, 4128.759765999999, 4625.680176, 4427.839844, 4344.649902, 4094.070068, 3969.889893, 4432.470215, 4922.169922, 5800.350098, 5935.52002, 6470.430176, 7253.319823999999, 6764.97998, 8348.660156, 10125.700195, 12032.0, 17781.800781, 19177.800781, 16461.199219, 15444.599609, 15497.5, 13843.099609, 11377.599609, 11307.200195, 7850.700195, 8958.469727, 11958.5, 10878.5, 11500.099609, 9470.379883, 9051.019531, 8232.780273, 7530.939941, 6872.410156, 8285.959961, 9732.610352, 9255.879883, 9462.75, 8836.19043, 8423.25, 7526.419922, 7643.22998, 6907.959961, 6822.5, 6290.160156, 6671.370117, 6767.740234000001, 7387.240234000001, 8424.269531, 8181.529785, 7146.560059, 6287.939941, 6500.870117, 7109.560059, 7388.259765999999, 6398.919922, 6384.180176, 6603.640136999999, 6611.839844, 6661.410156, 6673.589844, 6506.009765999999, 6364.990234000001, 6463.549805, 6395.27002, 4951.609863000001, 3862.959961, 4075.627686, 3513.1850590000004, 3701.349365, 4089.561523, 3850.913818, 4109.020996, 3720.1533200000003, 3620.746582, 3476.0654299999997, 3478.148193, 3668.586914, 4010.8793950000004, 3891.578857, 3903.9167479999996, 3926.88916, 4082.216064, 3985.080811, 4905.95459, 5289.917969, 5238.945313, 5633.802246, 5363.257324, 5988.178223, 8268.712891, 8062.167969, 8807.016602, 8210.985352, 8026.394043000001, 9348.374023, 11790.916992, 10912.188477, 12779.131836, 10996.632813, 10346.748047, 9701.759766, 12273.821289, 11420.049805, 10947.041992, 10381.328125, 10736.104492, 10394.353516, 10296.771484, 9804.317383, 8497.692383, 8332.714844, 8410.714844, 8296.651367, 9516.180664, 9457.417969, 8853.768555, 8408.516602, 7320.230469, 7418.858887, 7424.022948999999, 6964.075195, 7535.716797, 7335.290039, 8178.21582, 8879.511719, 8755.706055, 9358.589844, 9331.265625, 10210.052734, 10161.935547, 9652.737305, 8901.598633, 8136.945312999999, 5371.348633, 6789.022948999999, 6504.515136999999, 7427.939453, 6958.557129000001, 6934.551758, 7814.527344, 9062.415039, 8949.898438, 9836.047852, 8991.967773, 10182.34082, 9836.369141, 9579.430664, 9670.541016, 9217.835938, 9360.617188, 9283.841797, 9407.262695, 11204.327148, 11385.381836, 11932.710938, 12335.707031, 11778.299805, 12067.081055, 10414.775391, 10938.631836, 10568.077148, 10858.939453, 10803.456055, 11548.984375, 11999.917969, 13759.668945, 13984.981445, 15450.329102000002, 17782.919922]}],\n",
              "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Time series with range slider and selectors\"}, \"xaxis\": {\"rangeselector\": {\"buttons\": [{\"count\": 1, \"label\": \"1m\", \"step\": \"month\", \"stepmode\": \"backward\"}, {\"count\": 6, \"label\": \"6m\", \"step\": \"month\", \"stepmode\": \"backward\"}, {\"count\": 1, \"label\": \"1y\", \"step\": \"year\", \"stepmode\": \"backward\"}, {\"step\": \"all\"}]}, \"rangeslider\": {\"visible\": true}, \"type\": \"date\"}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('cff23006-7ecb-4024-9212-e88e8e9cd601');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ef-YYZduF5Jk"
      },
      "source": [
        "**Volume Movement**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N84W_NzNmHTR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "a5c65176-9502-423a-af91-92aa10ec1cd0"
      },
      "source": [
        "  # Load data\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "\n",
        "# Create figure\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(x = list(weekly_rows.Date),\n",
        "               y = list(weekly_rows.Volume)))\n",
        "\n",
        "# Set title\n",
        "fig.update_layout(\n",
        "    title_text=\"Time series with range slider and selectors\"\n",
        ")\n",
        "\n",
        "# Add range slider\n",
        "fig.update_layout(\n",
        "    xaxis=dict(\n",
        "        rangeselector=dict(\n",
        "            buttons=list([\n",
        "                dict(count=1,\n",
        "                     label=\"1m\",\n",
        "                     step=\"month\",\n",
        "                     stepmode=\"backward\"),\n",
        "                dict(count=6,\n",
        "                     label=\"6m\",\n",
        "                     step=\"month\",\n",
        "                     stepmode=\"backward\"),\n",
        "                \n",
        "                dict(count=1,\n",
        "                     label=\"1y\",\n",
        "                     step=\"year\",\n",
        "                     stepmode=\"backward\"),\n",
        "                dict(step=\"all\")\n",
        "            ])\n",
        "        ),\n",
        "        rangeslider=dict(\n",
        "            visible=True\n",
        "        ),\n",
        "        type=\"date\"\n",
        "    )\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"174baf1d-c0d3-4c02-b843-9364f9677ebf\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"174baf1d-c0d3-4c02-b843-9364f9677ebf\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '174baf1d-c0d3-4c02-b843-9364f9677ebf',\n",
              "                        [{\"type\": \"scatter\", \"x\": [\"2015-11-23T00:00:00\", \"2015-11-30T00:00:00\", \"2015-12-07T00:00:00\", \"2015-12-14T00:00:00\", \"2015-12-21T00:00:00\", \"2015-12-28T00:00:00\", \"2016-01-04T00:00:00\", \"2016-01-11T00:00:00\", \"2016-01-18T00:00:00\", \"2016-01-25T00:00:00\", \"2016-02-01T00:00:00\", \"2016-02-08T00:00:00\", \"2016-02-15T00:00:00\", \"2016-02-22T00:00:00\", \"2016-02-29T00:00:00\", \"2016-03-07T00:00:00\", \"2016-03-14T00:00:00\", \"2016-03-21T00:00:00\", \"2016-03-28T00:00:00\", \"2016-04-04T00:00:00\", \"2016-04-11T00:00:00\", \"2016-04-18T00:00:00\", \"2016-04-25T00:00:00\", \"2016-05-02T00:00:00\", \"2016-05-09T00:00:00\", \"2016-05-16T00:00:00\", \"2016-05-23T00:00:00\", \"2016-05-30T00:00:00\", \"2016-06-06T00:00:00\", \"2016-06-13T00:00:00\", \"2016-06-20T00:00:00\", \"2016-06-27T00:00:00\", \"2016-07-04T00:00:00\", \"2016-07-11T00:00:00\", \"2016-07-18T00:00:00\", \"2016-07-25T00:00:00\", \"2016-08-01T00:00:00\", \"2016-08-08T00:00:00\", \"2016-08-15T00:00:00\", \"2016-08-22T00:00:00\", \"2016-08-29T00:00:00\", \"2016-09-05T00:00:00\", \"2016-09-12T00:00:00\", \"2016-09-19T00:00:00\", \"2016-09-26T00:00:00\", \"2016-10-03T00:00:00\", \"2016-10-10T00:00:00\", \"2016-10-17T00:00:00\", \"2016-10-24T00:00:00\", \"2016-10-31T00:00:00\", \"2016-11-07T00:00:00\", \"2016-11-14T00:00:00\", \"2016-11-21T00:00:00\", \"2016-11-28T00:00:00\", \"2016-12-05T00:00:00\", \"2016-12-12T00:00:00\", \"2016-12-19T00:00:00\", \"2016-12-26T00:00:00\", \"2017-01-02T00:00:00\", \"2017-01-09T00:00:00\", \"2017-01-16T00:00:00\", \"2017-01-23T00:00:00\", \"2017-01-30T00:00:00\", \"2017-02-06T00:00:00\", \"2017-02-13T00:00:00\", \"2017-02-20T00:00:00\", \"2017-02-27T00:00:00\", \"2017-03-06T00:00:00\", \"2017-03-13T00:00:00\", \"2017-03-20T00:00:00\", \"2017-03-27T00:00:00\", \"2017-04-03T00:00:00\", \"2017-04-10T00:00:00\", \"2017-04-17T00:00:00\", \"2017-04-24T00:00:00\", \"2017-05-01T00:00:00\", \"2017-05-08T00:00:00\", \"2017-05-15T00:00:00\", \"2017-05-22T00:00:00\", \"2017-05-29T00:00:00\", \"2017-06-05T00:00:00\", \"2017-06-12T00:00:00\", \"2017-06-19T00:00:00\", \"2017-06-26T00:00:00\", \"2017-07-03T00:00:00\", \"2017-07-10T00:00:00\", \"2017-07-17T00:00:00\", \"2017-07-24T00:00:00\", \"2017-07-31T00:00:00\", \"2017-08-07T00:00:00\", \"2017-08-14T00:00:00\", \"2017-08-21T00:00:00\", \"2017-08-28T00:00:00\", \"2017-09-04T00:00:00\", \"2017-09-11T00:00:00\", \"2017-09-18T00:00:00\", \"2017-09-25T00:00:00\", \"2017-10-02T00:00:00\", \"2017-10-09T00:00:00\", \"2017-10-16T00:00:00\", \"2017-10-23T00:00:00\", \"2017-10-30T00:00:00\", \"2017-11-06T00:00:00\", \"2017-11-13T00:00:00\", \"2017-11-20T00:00:00\", \"2017-11-27T00:00:00\", \"2017-12-04T00:00:00\", \"2017-12-11T00:00:00\", \"2017-12-18T00:00:00\", \"2017-12-25T00:00:00\", \"2018-01-01T00:00:00\", \"2018-01-08T00:00:00\", \"2018-01-15T00:00:00\", \"2018-01-22T00:00:00\", \"2018-01-29T00:00:00\", \"2018-02-05T00:00:00\", \"2018-02-12T00:00:00\", \"2018-02-19T00:00:00\", \"2018-02-26T00:00:00\", \"2018-03-05T00:00:00\", \"2018-03-12T00:00:00\", \"2018-03-19T00:00:00\", \"2018-03-26T00:00:00\", \"2018-04-02T00:00:00\", \"2018-04-09T00:00:00\", \"2018-04-16T00:00:00\", \"2018-04-23T00:00:00\", \"2018-04-30T00:00:00\", \"2018-05-07T00:00:00\", \"2018-05-14T00:00:00\", \"2018-05-21T00:00:00\", \"2018-05-28T00:00:00\", \"2018-06-04T00:00:00\", \"2018-06-11T00:00:00\", \"2018-06-18T00:00:00\", \"2018-06-25T00:00:00\", \"2018-07-02T00:00:00\", \"2018-07-09T00:00:00\", \"2018-07-16T00:00:00\", \"2018-07-23T00:00:00\", \"2018-07-30T00:00:00\", \"2018-08-06T00:00:00\", \"2018-08-13T00:00:00\", \"2018-08-20T00:00:00\", \"2018-08-27T00:00:00\", \"2018-09-03T00:00:00\", \"2018-09-10T00:00:00\", \"2018-09-17T00:00:00\", \"2018-09-24T00:00:00\", \"2018-10-01T00:00:00\", \"2018-10-08T00:00:00\", \"2018-10-15T00:00:00\", \"2018-10-22T00:00:00\", \"2018-10-29T00:00:00\", \"2018-11-05T00:00:00\", \"2018-11-12T00:00:00\", \"2018-11-19T00:00:00\", \"2018-11-26T00:00:00\", \"2018-12-03T00:00:00\", \"2018-12-10T00:00:00\", \"2018-12-17T00:00:00\", \"2018-12-24T00:00:00\", \"2018-12-31T00:00:00\", \"2019-01-07T00:00:00\", \"2019-01-14T00:00:00\", \"2019-01-21T00:00:00\", \"2019-01-28T00:00:00\", \"2019-02-04T00:00:00\", \"2019-02-11T00:00:00\", \"2019-02-18T00:00:00\", \"2019-02-25T00:00:00\", \"2019-03-04T00:00:00\", \"2019-03-11T00:00:00\", \"2019-03-18T00:00:00\", \"2019-03-25T00:00:00\", \"2019-04-01T00:00:00\", \"2019-04-08T00:00:00\", \"2019-04-15T00:00:00\", \"2019-04-22T00:00:00\", \"2019-04-29T00:00:00\", \"2019-05-06T00:00:00\", \"2019-05-13T00:00:00\", \"2019-05-20T00:00:00\", \"2019-05-27T00:00:00\", \"2019-06-03T00:00:00\", \"2019-06-10T00:00:00\", \"2019-06-17T00:00:00\", \"2019-06-24T00:00:00\", \"2019-07-01T00:00:00\", \"2019-07-08T00:00:00\", \"2019-07-15T00:00:00\", \"2019-07-22T00:00:00\", \"2019-07-29T00:00:00\", \"2019-08-05T00:00:00\", \"2019-08-12T00:00:00\", \"2019-08-19T00:00:00\", \"2019-08-26T00:00:00\", \"2019-09-02T00:00:00\", \"2019-09-09T00:00:00\", \"2019-09-16T00:00:00\", \"2019-09-23T00:00:00\", \"2019-09-30T00:00:00\", \"2019-10-07T00:00:00\", \"2019-10-14T00:00:00\", \"2019-10-21T00:00:00\", \"2019-10-28T00:00:00\", \"2019-11-04T00:00:00\", \"2019-11-11T00:00:00\", \"2019-11-18T00:00:00\", \"2019-11-25T00:00:00\", \"2019-12-02T00:00:00\", \"2019-12-09T00:00:00\", \"2019-12-16T00:00:00\", \"2019-12-23T00:00:00\", \"2019-12-30T00:00:00\", \"2020-01-06T00:00:00\", \"2020-01-13T00:00:00\", \"2020-01-20T00:00:00\", \"2020-01-27T00:00:00\", \"2020-02-03T00:00:00\", \"2020-02-10T00:00:00\", \"2020-02-17T00:00:00\", \"2020-02-24T00:00:00\", \"2020-03-02T00:00:00\", \"2020-03-09T00:00:00\", \"2020-03-16T00:00:00\", \"2020-03-23T00:00:00\", \"2020-03-30T00:00:00\", \"2020-04-06T00:00:00\", \"2020-04-13T00:00:00\", \"2020-04-20T00:00:00\", \"2020-04-27T00:00:00\", \"2020-05-04T00:00:00\", \"2020-05-11T00:00:00\", \"2020-05-18T00:00:00\", \"2020-05-25T00:00:00\", \"2020-06-01T00:00:00\", \"2020-06-08T00:00:00\", \"2020-06-15T00:00:00\", \"2020-06-22T00:00:00\", \"2020-06-29T00:00:00\", \"2020-07-06T00:00:00\", \"2020-07-13T00:00:00\", \"2020-07-20T00:00:00\", \"2020-07-27T00:00:00\", \"2020-08-03T00:00:00\", \"2020-08-10T00:00:00\", \"2020-08-17T00:00:00\", \"2020-08-24T00:00:00\", \"2020-08-31T00:00:00\", \"2020-09-07T00:00:00\", \"2020-09-14T00:00:00\", \"2020-09-21T00:00:00\", \"2020-09-28T00:00:00\", \"2020-10-05T00:00:00\", \"2020-10-12T00:00:00\", \"2020-10-19T00:00:00\", \"2020-10-26T00:00:00\", \"2020-11-02T00:00:00\", \"2020-11-09T00:00:00\", \"2020-11-16T00:00:00\", \"2020-11-23T00:00:00\"], \"y\": [45011100, 29362600, 60452200, 57801400, 83121104, 50840400, 51596500, 34522600, 115607000, 46819800, 58147000, 40378700, 55318500, 73093104, 85244896, 74895800, 70311696, 66781700, 66813300, 75411504, 60718000, 70728800, 52810500, 78971904, 59366400, 58956100, 64100300, 65783100, 138450000, 107770000, 186694000, 309944000, 138384992, 130476000, 138172992, 61203300, 225135008, 330932992, 92228096, 58405200, 85349200, 70342400, 78529104, 86920600, 72710896, 49422400, 49801600, 103590000, 65546700, 90378800, 130527000, 79660800, 72038496, 129906000, 68511104, 116218000, 81645600, 99629296, 167308000, 185168000, 115808000, 155095008, 111349000, 164582000, 146007008, 137946000, 186868992, 184956000, 291256000, 245306000, 337391008, 326332000, 436310016, 216182000, 270524000, 242556000, 477337984, 1167920000, 959044992, 1378749952, 1443970048, 2089609984, 1781200000, 1854189952, 1489789952, 985516032, 1329760000, 1512450048, 1460089984, 1324669952, 1752760064, 3258050048, 3764239872, 2486080000, 2697969920, 1864530048, 1563980032, 1043740032, 1288019968, 1597139968, 1821570048, 2735699968, 2311379968, 2326340096, 3197110016, 4277609984, 6348819968, 6895260160, 14603799552, 16894499840, 13454300160, 16846600192, 16659999744, 18853799936, 9660609536, 8637859840, 13999800320, 5696719872, 9926540288, 6966179840, 6832169984, 5991139840, 6361789952, 5378250240, 5499700224, 4272750080, 6900879872, 10678800384, 7713019904, 7415869952, 6705710080, 5137010176, 5662660096, 4961739776, 4654380032, 4057029888, 3279759872, 4672309760, 4052430080, 5961950208, 7277689856, 5287530000, 4682800000, 5301700000, 3377180000, 4659940000, 4273640000, 3849910000, 4180090000, 4726180000, 3979260000, 3580810000, 4074800000, 3716150000, 3781100000, 4700040000, 4503800000, 8428290000, 5998720000, 5028069239, 4696765187, 5911325472, 6158207293, 4324200990, 5306593305, 5537192301, 5313623556, 5897159492, 5227549544, 6480384531, 9933626654, 7931218996, 10174126414, 9809887079, 9344919956, 10707678814, 21315047816, 14722104361, 11618660197, 15867308107, 13878964573, 18026409032, 32031452226, 25127245056, 24226919266, 24609731548, 17107279931, 15848210535, 24879684533, 31015895222, 28167921522, 24151199069, 17851916994, 13829811132, 23635107659, 16681503536, 15053082175, 14762609502, 19384917988, 14906809639, 15304603363, 25002886688, 15305343412, 15592264032, 15220412631, 16803377856, 28426779937, 26198609047, 20309769107, 21083613815, 21129505542, 14797485769, 18249031194, 22363804217, 22991622105, 21167946112, 28767291326, 44841784107, 24097418512, 34398744402, 29893183716, 37648059388, 47271023953, 42515259129, 42386715820, 42213940993, 38622642935, 48221910672, 32786468812, 44243482667, 34110434051, 32589741510, 33187959920, 43148462663, 42142717533, 39254288954, 29584186947, 39137252108, 23717842782, 21565537208, 17006433272, 15735797743, 13839652594, 18085038362, 18069581955, 28766551141, 21250197042, 27039782639, 26043227671, 26301509931, 27311555343, 33430927462, 32509451925, 23621787803, 20459870041, 22264958686, 24241420250, 30915821592, 33749878155, 29869951617, 25574938143, 39006849169]}],\n",
              "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Time series with range slider and selectors\"}, \"xaxis\": {\"rangeselector\": {\"buttons\": [{\"count\": 1, \"label\": \"1m\", \"step\": \"month\", \"stepmode\": \"backward\"}, {\"count\": 6, \"label\": \"6m\", \"step\": \"month\", \"stepmode\": \"backward\"}, {\"count\": 1, \"label\": \"1y\", \"step\": \"year\", \"stepmode\": \"backward\"}, {\"step\": \"all\"}]}, \"rangeslider\": {\"visible\": true}, \"type\": \"date\"}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('174baf1d-c0d3-4c02-b843-9364f9677ebf');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uS5bzbXehbsD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f6acc10-6c9b-4313-d884-8a10b454d212"
      },
      "source": [
        "df['Daily Lag']=df['Close'].shift(1)\n",
        "#df.head()\n",
        "df['Daily Returns']=(df['Daily Lag']/df['Close'])-1\n",
        "#df.head()\n",
        "#df['Daily Returns'].hist()\n",
        "mean=df['Daily Returns'].mean()\n",
        "std=df['Daily Returns'].std()\n",
        "print(\"\\n Mean: \",mean)\n",
        "print(\"\\n Standard deviation: \",std)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Mean:  -0.0014055857824751942\n",
            "\n",
            " Standard deviation:  0.040371527817143565\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKfmEpLBhl4w"
      },
      "source": [
        "## **MODELS FOR CLASSIFICATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edI8Qxy98kFZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "a57b4266-0d14-4150-9e7b-fd7e0a44c2ec"
      },
      "source": [
        "logRegData=df\n",
        "logRegData['PriceDifference']=logRegData['Open']-logRegData['Close']\n",
        "logRegData['UpDown']=np.where(logRegData['PriceDifference']>0,'-1','1')\n",
        "logRegData.head()\n",
        "logRegData['LagPrice1']=logRegData['Close'].shift(1)\n",
        "logRegData['LagPrice2']=logRegData['Close'].shift(2)\n",
        "logRegData['LagPrice3']=logRegData['Close'].shift(3)\n",
        "logRegData['LagPrice4']=logRegData['Close'].shift(4)\n",
        "logRegData['LagPrice5']=logRegData['Close'].shift(5)\n",
        "logRegData['MA_10']=logRegData['Close'].rolling(window=10).mean()\n",
        "logRegData['MA_20']=logRegData['Close'].rolling(window=20).mean()\n",
        "logRegData['STD_10']=logRegData['Close'].rolling(window=10).std()\n",
        "logRegData['STD_20']=logRegData['Close'].rolling(window=20).std()\n",
        "#logRegData['MACD_10']=logRegData['Close'].rolling(window=10).macd()\n",
        "logRegData['EMA_10']=logRegData['Close'].ewm(span=12,adjust=False).mean()\n",
        "logRegData['EMA_26']=logRegData['Close'].ewm(span=26,adjust=False).mean()\n",
        "logRegData['MACD']=logRegData['EMA_10']-logRegData['EMA_26']\n",
        "logRegData['UpperBollinger']=logRegData['MA_20']+2*logRegData['STD_20']\n",
        "logRegData['LowerBollinger']=logRegData['MA_20']-2*logRegData['STD_20']\n",
        "\n",
        "logRegData=logRegData.iloc[20:]\n",
        "logRegData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Daily Lag</th>\n",
              "      <th>Daily Returns</th>\n",
              "      <th>PriceDifference</th>\n",
              "      <th>UpDown</th>\n",
              "      <th>LagPrice1</th>\n",
              "      <th>LagPrice2</th>\n",
              "      <th>LagPrice3</th>\n",
              "      <th>LagPrice4</th>\n",
              "      <th>LagPrice5</th>\n",
              "      <th>MA_10</th>\n",
              "      <th>MA_20</th>\n",
              "      <th>STD_10</th>\n",
              "      <th>STD_20</th>\n",
              "      <th>EMA_10</th>\n",
              "      <th>EMA_26</th>\n",
              "      <th>MACD</th>\n",
              "      <th>UpperBollinger</th>\n",
              "      <th>LowerBollinger</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2015-12-09</td>\n",
              "      <td>414.441010</td>\n",
              "      <td>423.119995</td>\n",
              "      <td>406.290985</td>\n",
              "      <td>417.562988</td>\n",
              "      <td>417.562988</td>\n",
              "      <td>90917200</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>-0.004790</td>\n",
              "      <td>-3.121978</td>\n",
              "      <td>1</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>395.536011</td>\n",
              "      <td>388.782990</td>\n",
              "      <td>388.949005</td>\n",
              "      <td>363.183014</td>\n",
              "      <td>382.961902</td>\n",
              "      <td>360.69015</td>\n",
              "      <td>22.049595</td>\n",
              "      <td>30.432862</td>\n",
              "      <td>382.609519</td>\n",
              "      <td>362.791904</td>\n",
              "      <td>19.817615</td>\n",
              "      <td>421.555875</td>\n",
              "      <td>299.824426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2015-12-10</td>\n",
              "      <td>417.988007</td>\n",
              "      <td>419.509003</td>\n",
              "      <td>411.548004</td>\n",
              "      <td>415.479004</td>\n",
              "      <td>415.479004</td>\n",
              "      <td>52138900</td>\n",
              "      <td>417.562988</td>\n",
              "      <td>0.005016</td>\n",
              "      <td>2.509003</td>\n",
              "      <td>-1</td>\n",
              "      <td>417.562988</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>395.536011</td>\n",
              "      <td>388.782990</td>\n",
              "      <td>388.949005</td>\n",
              "      <td>386.777701</td>\n",
              "      <td>365.36300</td>\n",
              "      <td>24.165174</td>\n",
              "      <td>31.344357</td>\n",
              "      <td>387.666363</td>\n",
              "      <td>366.694652</td>\n",
              "      <td>20.971711</td>\n",
              "      <td>428.051714</td>\n",
              "      <td>302.674287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2015-12-11</td>\n",
              "      <td>415.281006</td>\n",
              "      <td>451.937988</td>\n",
              "      <td>415.281006</td>\n",
              "      <td>451.937988</td>\n",
              "      <td>451.937988</td>\n",
              "      <td>110944000</td>\n",
              "      <td>415.479004</td>\n",
              "      <td>-0.080673</td>\n",
              "      <td>-36.656982</td>\n",
              "      <td>1</td>\n",
              "      <td>415.479004</td>\n",
              "      <td>417.562988</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>395.536011</td>\n",
              "      <td>388.782990</td>\n",
              "      <td>395.722699</td>\n",
              "      <td>371.61355</td>\n",
              "      <td>30.021001</td>\n",
              "      <td>35.469350</td>\n",
              "      <td>397.554305</td>\n",
              "      <td>373.008974</td>\n",
              "      <td>24.545332</td>\n",
              "      <td>442.552249</td>\n",
              "      <td>300.674851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2015-12-12</td>\n",
              "      <td>452.334991</td>\n",
              "      <td>469.102997</td>\n",
              "      <td>410.740997</td>\n",
              "      <td>434.997009</td>\n",
              "      <td>434.997009</td>\n",
              "      <td>131969000</td>\n",
              "      <td>451.937988</td>\n",
              "      <td>0.038945</td>\n",
              "      <td>17.337982</td>\n",
              "      <td>-1</td>\n",
              "      <td>451.937988</td>\n",
              "      <td>415.479004</td>\n",
              "      <td>417.562988</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>395.536011</td>\n",
              "      <td>403.303699</td>\n",
              "      <td>377.13660</td>\n",
              "      <td>29.333796</td>\n",
              "      <td>36.342310</td>\n",
              "      <td>403.314721</td>\n",
              "      <td>377.600680</td>\n",
              "      <td>25.714041</td>\n",
              "      <td>449.821221</td>\n",
              "      <td>304.451979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2015-12-13</td>\n",
              "      <td>431.660004</td>\n",
              "      <td>441.679993</td>\n",
              "      <td>426.268005</td>\n",
              "      <td>433.755005</td>\n",
              "      <td>433.755005</td>\n",
              "      <td>55050600</td>\n",
              "      <td>434.997009</td>\n",
              "      <td>0.002863</td>\n",
              "      <td>-2.095001</td>\n",
              "      <td>1</td>\n",
              "      <td>434.997009</td>\n",
              "      <td>451.937988</td>\n",
              "      <td>415.479004</td>\n",
              "      <td>417.562988</td>\n",
              "      <td>415.562988</td>\n",
              "      <td>410.574600</td>\n",
              "      <td>382.67205</td>\n",
              "      <td>26.577250</td>\n",
              "      <td>36.100395</td>\n",
              "      <td>407.997842</td>\n",
              "      <td>381.760260</td>\n",
              "      <td>26.237582</td>\n",
              "      <td>454.872841</td>\n",
              "      <td>310.471260</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date        Open  ...  UpperBollinger  LowerBollinger\n",
              "20 2015-12-09  414.441010  ...      421.555875      299.824426\n",
              "21 2015-12-10  417.988007  ...      428.051714      302.674287\n",
              "22 2015-12-11  415.281006  ...      442.552249      300.674851\n",
              "23 2015-12-12  452.334991  ...      449.821221      304.451979\n",
              "24 2015-12-13  431.660004  ...      454.872841      310.471260\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKT2riEtilQx"
      },
      "source": [
        "## **TRAIN TEST SPLIT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_gHbc9T85Pi"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "#X=logRegData[['MA_10','MA_20','STD_10','STD_20','MACD','UpperBollinger','LowerBollinger']]\n",
        "X=logRegData[['MA_10','STD_10','MACD','UpperBollinger','LowerBollinger']]\n",
        "y=logRegData[['UpDown']]\n",
        "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.75)\n",
        "y_train=y_train['UpDown'].to_list()\n",
        "y_test=y_test['UpDown'].to_list()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6xakySZzis57"
      },
      "source": [
        "## **LOGISTIC REGRESSION MODEL** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTSH6CqZiDGz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7786f25-1d57-4a76-fdc9-424e77d7c58f"
      },
      "source": [
        "#building logistic regression model\n",
        "#training logistic regression model\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "corr_matrix=X.corr()\n",
        "print(corr_matrix)\n",
        "\n",
        "log_model=LogisticRegression(max_iter=150)\n",
        "log_model.fit(X_train,y_train)\n",
        "y_pred=log_model.predict(X_test)\n",
        "print(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   MA_10    STD_10      MACD  UpperBollinger  LowerBollinger\n",
            "MA_10           1.000000  0.685691  0.251131        0.988094        0.972817\n",
            "STD_10          0.685691  1.000000  0.278971        0.746742        0.553428\n",
            "MACD            0.251131  0.278971  1.000000        0.195052        0.115652\n",
            "UpperBollinger  0.988094  0.746742  0.195052        1.000000        0.946731\n",
            "LowerBollinger  0.972817  0.553428  0.115652        0.946731        1.000000\n",
            "['1' '1' '1' ... '-1' '1' '1']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gz30cfMTiHHz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "2ea30e43-5c1f-4ddd-dd21-991fa286a131"
      },
      "source": [
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))\n",
        "plot_confusion_matrix(log_model,X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[167 435]\n",
            " [193 561]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.46      0.28      0.35       602\n",
            "           1       0.56      0.74      0.64       754\n",
            "\n",
            "    accuracy                           0.54      1356\n",
            "   macro avg       0.51      0.51      0.49      1356\n",
            "weighted avg       0.52      0.54      0.51      1356\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9a9c9e6828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcrP5xDviJgz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88bb2a31-9e02-427b-88fa-11a36f9d39c2"
      },
      "source": [
        "f1_log_reg = f1_score(y_test,y_pred,zero_division='warn',average=None).mean()*100\n",
        "print (accuracy_score(y_test,y_pred)*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "53.687315634218294\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1aLXhN0jj14"
      },
      "source": [
        "## **SUPPORT VECTOR CLASSIFIER MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrXWvQDvjOR3"
      },
      "source": [
        "from matplotlib import style\n",
        "style.use(\"ggplot\")\n",
        "from sklearn import svm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQMM-u_vT6RN",
        "outputId": "3774e9fb-716e-44a4-e281-bcbe03d04d21"
      },
      "source": [
        "y_train = [int(element) for element in y_train]\n",
        "print(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, -1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJmFKZTGUv7E"
      },
      "source": [
        "y_test = [int(element) for element in y_test]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8GB-yA-qdqa"
      },
      "source": [
        "**RBF Kernel**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9cPvkiBjida",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ab05b89-1d1a-47d4-f73e-91128a1f4d48"
      },
      "source": [
        "clf = svm.SVC(kernel='rbf', random_state=1, max_iter=100, gamma=0.1, C=0.02)\n",
        "clf.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:231: ConvergenceWarning:\n",
            "\n",
            "Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=0.02, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
              "    max_iter=100, probability=False, random_state=1, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Tgq0m6WUNHI"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV,StratifiedShuffleSplit\n",
        "\n",
        "C_range = np.logspace(-2, 10, 13)\n",
        "gamma_range = np.logspace(-9, 3, 13)\n",
        "iter_values = [100,150,200,250]\n",
        "\n",
        "param_grid = dict(max_iter = iter_values, gamma=gamma_range, C=C_range)\n",
        "\n",
        "cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n",
        "grid_svm = GridSearchCV(svm.SVC(kernel='rbf'), param_grid=param_grid, cv=cv,scoring='f1')\n",
        "\n",
        "grid_svm.fit(X_train,y_train)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_HoqTV5zG91",
        "outputId": "d82601d9-52bf-49f3-f1a0-dedfde1525e1"
      },
      "source": [
        "print(\"The best parameters are %s with a score of %0.2f\" % (grid_svm.best_params_, grid_svm.best_score_))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The best parameters are {'C': 0.01, 'gamma': 1e-09, 'max_iter': 200} with a score of 0.71\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kjke8DNjkHmV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "babe606c-c65a-441b-c2e9-fc5b145d9ea2"
      },
      "source": [
        "y_pred_svc = grid_svm.predict(X_test)\n",
        "y_pred_svc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1, -1,  1, ...,  1,  1,  1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2r_vc_YslvRb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "882418a6-f26a-4220-ad34-4b7c9ad6f5ba"
      },
      "source": [
        "print(confusion_matrix(y_test,y_pred_svc))\n",
        "print(classification_report(y_test,y_pred_svc))\n",
        "\n",
        "plot_confusion_matrix(grid_svm,X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 92 510]\n",
            " [100 654]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.48      0.15      0.23       602\n",
            "           1       0.56      0.87      0.68       754\n",
            "\n",
            "    accuracy                           0.55      1356\n",
            "   macro avg       0.52      0.51      0.46      1356\n",
            "weighted avg       0.53      0.55      0.48      1356\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9aa84e1780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QzuEKlWU-Vf",
        "outputId": "f6dab841-20d9-492d-fe50-29aa5e22b3ac"
      },
      "source": [
        "y_pred_svc = clf.predict(X_test)\n",
        "y_pred_svc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "__MA121rVCct",
        "outputId": "a192c3a8-9338-4053-d7bc-9e7ae9642b4e"
      },
      "source": [
        "print(confusion_matrix(y_test,y_pred_svc))\n",
        "print(classification_report(y_test,y_pred_svc))\n",
        "\n",
        "plot_confusion_matrix(clf,X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 91 511]\n",
            " [126 628]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.42      0.15      0.22       602\n",
            "           1       0.55      0.83      0.66       754\n",
            "\n",
            "    accuracy                           0.53      1356\n",
            "   macro avg       0.49      0.49      0.44      1356\n",
            "weighted avg       0.49      0.53      0.47      1356\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9a9de6db70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBMXRXsw2yK8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43870d38-8e7d-4a92-c1a3-47915ba3a56a"
      },
      "source": [
        "f1_svc = f1_score(y_test,y_pred_svc,zero_division='warn',average=None).mean()*100\n",
        "print(accuracy_score(y_test,y_pred_svc)*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "53.02359882005899\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4-lgQ5CrLfK"
      },
      "source": [
        "**Polynomial Kernel**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsCFMt-vmKW9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0ab3c33-f876-4704-985e-1d336d8d38a7"
      },
      "source": [
        "poly = svm.SVC(kernel='poly', random_state=1, gamma=0.1, C=0.02, max_iter=100)\n",
        "poly.fit(X_train,y_train)\n",
        "y_pred_svc_poly = poly.predict(X_test)\n",
        "y_pred_svc_poly"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:231: ConvergenceWarning:\n",
            "\n",
            "Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  1,  1, ..., -1,  1, -1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caGn467FrSyh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "73a2843a-5105-4531-8396-c1f8c293d7b2"
      },
      "source": [
        "print(confusion_matrix(y_test,y_pred_svc_poly))\n",
        "print(classification_report(y_test,y_pred_svc_poly))\n",
        "print(accuracy_score(y_test,y_pred_svc_poly)*100)\n",
        "plot_confusion_matrix(poly,X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[467 135]\n",
            " [607 147]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.43      0.78      0.56       602\n",
            "           1       0.52      0.19      0.28       754\n",
            "\n",
            "    accuracy                           0.45      1356\n",
            "   macro avg       0.48      0.49      0.42      1356\n",
            "weighted avg       0.48      0.45      0.41      1356\n",
            "\n",
            "45.28023598820059\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9a99dce550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXub7aEN28Jg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8f0758b-0b67-465f-9714-7fb083bbaa82"
      },
      "source": [
        "f1_svc_poly = f1_score(y_test,y_pred_svc_poly,zero_division='warn',average=None).mean()*100\n",
        "print(accuracy_score(y_test,y_pred_svc_poly)*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45.28023598820059\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vifK1ghnmK_T"
      },
      "source": [
        "## **RANDOM FOREST CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhMyX0m8mOK6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b11f0a19-1c6f-4579-b547-0f13dd467ba3"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "\n",
        "rf = RandomForestClassifier(max_depth=2, random_state=0)\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred_rf = rf.predict(X_test)\n",
        "y_pred_rf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  1, -1, ...,  1,  1,  1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "192A0RuQmY5j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "2d9a3e28-aaee-4533-fa5c-1529c7f96348"
      },
      "source": [
        "\n",
        "print(confusion_matrix(y_test,y_pred_rf))\n",
        "print(classification_report(y_test,y_pred_rf))\n",
        "\n",
        "plot_confusion_matrix(rf,X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[151 451]\n",
            " [160 594]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.49      0.25      0.33       602\n",
            "           1       0.57      0.79      0.66       754\n",
            "\n",
            "    accuracy                           0.55      1356\n",
            "   macro avg       0.53      0.52      0.50      1356\n",
            "weighted avg       0.53      0.55      0.51      1356\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9a975aae48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3nP_F3_3H6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73564243-7f48-4cb4-b67a-b35c23d05909"
      },
      "source": [
        "f1_rf = f1_score(y_test,y_pred_rf,zero_division='warn',average=None).mean()*100\n",
        "print (accuracy_score(y_test,y_pred_rf)*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "54.9410029498525\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SktFCw7-3UWj"
      },
      "source": [
        "## **COMPARISON**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_4Z0dOr3Zwi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "outputId": "d1af4ba7-03c7-485c-81ec-eb18d247c2f7"
      },
      "source": [
        "f1_names = ['LOGISTIC REGRESSION','SVC RBF','SVC POLY','RANDOM FOREST']\n",
        "f1_data = [f1_log_reg, f1_svc, f1_svc_poly, f1_rf]\n",
        "f1_df = pd.DataFrame()\n",
        "f1_df['MODELS'] = f1_names\n",
        "f1_df['F1 SCORES'] = f1_data\n",
        "print(f1_df)\n",
        "fig = px.bar(f1_df,x = 'MODELS', y ='F1 SCORES', color =['time','medal','lifeExp','smoker'],\n",
        "             title = \"F1 Scores Comparison\")\n",
        "fig.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                MODELS  F1 SCORES\n",
            "0  LOGISTIC REGRESSION  49.416810\n",
            "1              SVC RBF  44.285966\n",
            "2             SVC POLY  42.053151\n",
            "3        RANDOM FOREST  49.557226\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"2b379825-9338-4af5-8757-e74016b481e9\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"2b379825-9338-4af5-8757-e74016b481e9\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '2b379825-9338-4af5-8757-e74016b481e9',\n",
              "                        [{\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"color=time<br>MODELS=%{x}<br>F1 SCORES=%{y}\", \"legendgroup\": \"color=time\", \"marker\": {\"color\": \"#636efa\"}, \"name\": \"color=time\", \"offsetgroup\": \"color=time\", \"orientation\": \"v\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [\"LOGISTIC REGRESSION\"], \"xaxis\": \"x\", \"y\": [49.41681021681022], \"yaxis\": \"y\"}, {\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"color=medal<br>MODELS=%{x}<br>F1 SCORES=%{y}\", \"legendgroup\": \"color=medal\", \"marker\": {\"color\": \"#EF553B\"}, \"name\": \"color=medal\", \"offsetgroup\": \"color=medal\", \"orientation\": \"v\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [\"SVC RBF\"], \"xaxis\": \"x\", \"y\": [44.285965839056175], \"yaxis\": \"y\"}, {\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"color=lifeExp<br>MODELS=%{x}<br>F1 SCORES=%{y}\", \"legendgroup\": \"color=lifeExp\", \"marker\": {\"color\": \"#00cc96\"}, \"name\": \"color=lifeExp\", \"offsetgroup\": \"color=lifeExp\", \"orientation\": \"v\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [\"SVC POLY\"], \"xaxis\": \"x\", \"y\": [42.05315100303167], \"yaxis\": \"y\"}, {\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"color=smoker<br>MODELS=%{x}<br>F1 SCORES=%{y}\", \"legendgroup\": \"color=smoker\", \"marker\": {\"color\": \"#ab63fa\"}, \"name\": \"color=smoker\", \"offsetgroup\": \"color=smoker\", \"orientation\": \"v\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [\"RANDOM FOREST\"], \"xaxis\": \"x\", \"y\": [49.557226328123136], \"yaxis\": \"y\"}],\n",
              "                        {\"barmode\": \"relative\", \"height\": 600, \"legend\": {\"tracegroupgap\": 0}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"F1 Scores Comparison\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.98], \"title\": {\"text\": \"MODELS\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"F1 SCORES\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('2b379825-9338-4af5-8757-e74016b481e9');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajIR9SgXMB-_"
      },
      "source": [
        "## **TIME SERIES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erNxhN-3o_er",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e670bf3e-1d93-4852-ee2e-e404415fc0ef"
      },
      "source": [
        "timeSeriesData=df[['Date','Close']]\n",
        "print(timeSeriesData.head())\n",
        "print(len(timeSeriesData))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        Date       Close\n",
            "0 2015-11-19  326.148987\n",
            "1 2015-11-20  322.022003\n",
            "2 2015-11-21  326.927002\n",
            "3 2015-11-22  324.536011\n",
            "4 2015-11-23  323.045990\n",
            "1828\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9x901NQLXj0z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef048f80-ba19-4540-d4f7-82698f4d38d2"
      },
      "source": [
        "train_data=timeSeriesData[0:1371] \n",
        "test_data=timeSeriesData[1371:]\n",
        "print(train_data.head())\n",
        "print(train_data.tail())\n",
        "print(test_data.head())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        Date       Close\n",
            "0 2015-11-19  326.148987\n",
            "1 2015-11-20  322.022003\n",
            "2 2015-11-21  326.927002\n",
            "3 2015-11-22  324.536011\n",
            "4 2015-11-23  323.045990\n",
            "           Date         Close\n",
            "1366 2019-08-16  10374.338867\n",
            "1367 2019-08-17  10231.744141\n",
            "1368 2019-08-18  10345.810547\n",
            "1369 2019-08-19  10916.053711\n",
            "1370 2019-08-20  10763.232422\n",
            "           Date         Close\n",
            "1371 2019-08-21  10138.049805\n",
            "1372 2019-08-22  10131.055664\n",
            "1373 2019-08-23  10407.964844\n",
            "1374 2019-08-24  10159.960938\n",
            "1375 2019-08-25  10138.517578\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etYYFhPEMRST"
      },
      "source": [
        "## **SIMPLE MOVING AVERAGE METHOD**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oS73uDSMMBQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ffbcfb5-8e8c-4a06-92c5-c74cf025f9c9"
      },
      "source": [
        "#simple moving average method\n",
        "timeSeriesData=df[['Date','Close']]\n",
        "for i in range(0,timeSeriesData.shape[0]-4):\n",
        "    timeSeriesData.loc[timeSeriesData.index[i+4],'SMA_5'] = np.round(((timeSeriesData.iloc[i,1]+ timeSeriesData.iloc[i+1,1] +timeSeriesData.iloc[i+2,1]+timeSeriesData.iloc[i+3,1]+timeSeriesData.iloc[i+4,1])/5),1)\n",
        "timeSeriesData=timeSeriesData.iloc[5:]\n",
        "print(timeSeriesData.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:1596: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "        Date       Close  SMA_5\n",
            "5 2015-11-24  320.045990  323.3\n",
            "6 2015-11-25  328.205994  324.6\n",
            "7 2015-11-26  352.683990  329.7\n",
            "8 2015-11-27  358.041992  336.4\n",
            "9 2015-11-28  357.381012  343.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzNBURmwMXWQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "c5f09a11-14eb-4f1d-edb5-1b1e70b5bf3d"
      },
      "source": [
        "plt.figure(figsize=[15,10])\n",
        "plt.grid(True)\n",
        "plt.plot(timeSeriesData['Close'],label='Actual price')\n",
        "plt.plot(timeSeriesData['SMA_5'],label='SMA 5 days')\n",
        "plt.legend(loc=2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZjyTK06Mb8F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53065fb0-ea92-4cd1-e5ce-71068064110e"
      },
      "source": [
        "train_data=timeSeriesData[0:1371] \n",
        "test_data=timeSeriesData[1371:]\n",
        "print(train_data.head())\n",
        "print(train_data.tail())\n",
        "print(test_data.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        Date       Close  SMA_5\n",
            "5 2015-11-24  320.045990  323.3\n",
            "6 2015-11-25  328.205994  324.6\n",
            "7 2015-11-26  352.683990  329.7\n",
            "8 2015-11-27  358.041992  336.4\n",
            "9 2015-11-28  357.381012  343.3\n",
            "           Date         Close    SMA_5\n",
            "1371 2019-08-21  10138.049805  10479.0\n",
            "1372 2019-08-22  10131.055664  10458.8\n",
            "1373 2019-08-23  10407.964844  10471.3\n",
            "1374 2019-08-24  10159.960938  10320.1\n",
            "1375 2019-08-25  10138.517578  10195.1\n",
            "           Date         Close    SMA_5\n",
            "1376 2019-08-26  10370.820313  10241.7\n",
            "1377 2019-08-27  10185.500000  10252.6\n",
            "1378 2019-08-28   9754.422852  10121.8\n",
            "1379 2019-08-29   9510.200195   9991.9\n",
            "1380 2019-08-30   9598.173828   9883.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJhnn-YxMe1-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "504c54a4-bd20-401e-d726-50a1efee4fb9"
      },
      "source": [
        "import math\n",
        "y=test_data['Close'].to_list()\n",
        "y_hat=test_data['SMA_5'].to_list()\n",
        "sum_=0\n",
        "for i in range (0,len(test_data)):  \n",
        "  diff=y[i]-y_hat[i]\n",
        "  squared_diff=diff**2  \n",
        "  sum_=sum_+squared_diff\n",
        "rmse=math.sqrt(sum_/len(test_data))\n",
        "print(\"The Root Mean Square Error is: \",rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Root Mean Square Error is:  338.1719351330977\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YIpXZX8MpMe"
      },
      "source": [
        "## **Augmented Dicky Fuller Test**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgpkP1xOMhwY"
      },
      "source": [
        "def test_stationarity(timeseries):\n",
        "    \n",
        "    #Determing rolling statistics\n",
        "    rolmean = timeseries.rolling(window=52,center=False).mean() \n",
        "    rolstd = timeseries.rolling(window=52,center=False).std()\n",
        "\n",
        "    #Plot rolling statistics:\n",
        "    orig = plt.plot(timeseries, color='green',label='Bitcoin Data')\n",
        "    mean_ = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
        "    std_ = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
        "    plt.legend(loc='best')\n",
        "    plt.xlabel('Time(Days)',fontsize=24)\n",
        "    plt.ylabel('$(Dollar)',fontsize=24)\n",
        "    plt.title('Rolling Mean & Standard Deviation')\n",
        "    plt.show(block=False)\n",
        "    \n",
        "    #Perform Dickey-Fuller test:\n",
        "    print('Results of Dickey-Fuller Test:')\n",
        "    dftest = adfuller(timeseries, autolag='AIC')\n",
        "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
        "    for key,value in dftest[4].items():\n",
        "        dfoutput['Critical Value (%s)'%key] = value\n",
        "    print(dfoutput)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CA6pGtFQMtnI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "9d5b4ca0-6e00-4699-aae7-264f66179bcc"
      },
      "source": [
        "indexed_data = df.set_index('Date')\n",
        "\n",
        "daily_target_prices = indexed_data['Close']\n",
        "\n",
        "test_stationarity(daily_target_prices)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey-Fuller Test:\n",
            "Test Statistic                   -1.015591\n",
            "p-value                           0.747535\n",
            "#Lags Used                       20.000000\n",
            "Number of Observations Used    1807.000000\n",
            "Critical Value (1%)              -3.433974\n",
            "Critical Value (5%)              -2.863141\n",
            "Critical Value (10%)             -2.567622\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HzESfHfMvba"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiNXTDghwE-O"
      },
      "source": [
        "Since the p-value is larger than 0.05 , the the moving average is not constant over time and the null hypothesis of the Dickey-Fuller test cannot be rejected.The Daily Time Series is not stationary . "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGfjs3kAwTRR"
      },
      "source": [
        "In order to apply ARIMA , we have to transform the time-series into a stationary one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGby6DvfM6cV"
      },
      "source": [
        "daily_target_prices_log = np.log(daily_target_prices)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvPMasRIM66_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "909a48e3-74c5-43f2-ab63-ff20fc1cea00"
      },
      "source": [
        "test_stationarity(daily_target_prices_log)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey-Fuller Test:\n",
            "Test Statistic                   -1.429118\n",
            "p-value                           0.568240\n",
            "#Lags Used                        0.000000\n",
            "Number of Observations Used    1827.000000\n",
            "Critical Value (1%)              -3.433934\n",
            "Critical Value (5%)              -2.863123\n",
            "Critical Value (10%)             -2.567613\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3kFiqNlxOio"
      },
      "source": [
        "The series is still non-stationary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4tM96kPKvCSv"
      },
      "source": [
        "**Decomposition of the series**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHdWz19GvfpL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "8350ce91-96d7-4af6-e54b-2439a1e1b395"
      },
      "source": [
        "decomposition = seasonal_decompose(daily_target_prices)\n",
        "\n",
        "trend = decomposition.trend\n",
        "seasonal = decomposition.seasonal\n",
        "residual = decomposition.resid\n",
        "\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.subplot(411)\n",
        "plt.plot(daily_target_prices_log[-80:], label='Original')\n",
        "plt.legend(loc='best')\n",
        "plt.subplot(412)\n",
        "plt.plot(trend[-80:], label='Trend')\n",
        "plt.legend(loc='best')\n",
        "plt.subplot(413)\n",
        "plt.plot(seasonal[-80:],label='Seasonality')\n",
        "plt.legend(loc='best')\n",
        "plt.subplot(414)\n",
        "plt.plot(residual[-80:], label='Residuals')\n",
        "plt.legend(loc='best')\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJiXYZ2-NEdm"
      },
      "source": [
        "**Removing Trend and Seasonality with Differencing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LuYiiaaNG0O"
      },
      "source": [
        "daily_prices_log_diff = daily_target_prices_log - daily_target_prices_log.shift()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppSddSG6NJYq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "05e7c451-9b17-4855-8e2b-a88f2013b34c"
      },
      "source": [
        "plt.plot(daily_prices_log_diff)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9a95753390>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTIZZMu2NK4k"
      },
      "source": [
        "daily_prices_log_diff.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9JJV5ZSNMzG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "eff09e28-a9bb-4da3-fa57-032e1afc3a3e"
      },
      "source": [
        "test_stationarity(daily_prices_log_diff)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey-Fuller Test:\n",
            "Test Statistic                  -43.952255\n",
            "p-value                           0.000000\n",
            "#Lags Used                        0.000000\n",
            "Number of Observations Used    1826.000000\n",
            "Critical Value (1%)              -3.433936\n",
            "Critical Value (5%)              -2.863124\n",
            "Critical Value (10%)             -2.567613\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "us-p3QDfT_oR"
      },
      "source": [
        "Since the p-value is less than 0.05 , the series is stationary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIfnz2TaNYKd"
      },
      "source": [
        "## **Auto Regressive Integrated Moving Average Model : ARIMA MODEL**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssUGZpcPNhBH"
      },
      "source": [
        "### **TRAIN TEST SPLIT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0llLvSMNZXc"
      },
      "source": [
        "\n",
        "size = int(len(daily_target_prices_log)*(0.7))\n",
        "train, test = daily_target_prices_log[0:size], daily_target_prices_log[size:len(daily_target_prices_log)]\n",
        "\n",
        "train_log_diff, test_log_diff = daily_prices_log_diff[0:size], daily_prices_log_diff[size:len(daily_prices_log_diff)]\n",
        "\n",
        "#predictions = list()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDLkeYenNomO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "8037dd40-ba3c-4ee4-a52d-d0df5c43e796"
      },
      "source": [
        "\n",
        "#Model Fitting\n",
        "model = ARIMA(daily_target_prices_log,order=(2,1,0))\n",
        "results_ARIMA = model.fit(disp=-1)  \n",
        "plt.plot(daily_prices_log_diff)\n",
        "plt.plot(results_ARIMA.fittedvalues, color='red')\n",
        "plt.title('RSS: %.7f'% sum((results_ARIMA.fittedvalues-daily_prices_log_diff)**2))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning:\n",
            "\n",
            "No frequency information was provided, so inferred frequency D will be used.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning:\n",
            "\n",
            "No frequency information was provided, so inferred frequency D will be used.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YVx1ZLIOSto"
      },
      "source": [
        "## **MODEL TESTING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcTRoWgiNqGd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0469dd6d-2a58-4815-deed-bc4f00316091"
      },
      "source": [
        "history = [price for price in train]\n",
        "\n",
        "original_values = list()\n",
        "errors = list()\n",
        "predictions = list()\n",
        "\n",
        "print('Predicted vs Expected values')\n",
        "\n",
        "for t in range(len(test)):\n",
        "  model = ARIMA(history,order=(2,1,0))\n",
        "  model_fit = model.fit(disp=-1)\n",
        "    \n",
        "  output = model_fit.forecast()\n",
        "    \n",
        "  pred = output[0]\n",
        "    \n",
        "        \n",
        "  original_value = test[t]\n",
        "  history.append(original_value)\n",
        "    \n",
        "  pred = np.exp(pred)\n",
        "    \n",
        "    \n",
        "  original_value = np.exp(original_value)\n",
        "    \n",
        "  # Calculating the error\n",
        "  error = ((abs(pred - original_value)) / original_value) * 100\n",
        "  errors.append(error)\n",
        "  print('predicted = %f,   expected = %f,   error = %f ' % (pred, original_value, error), '%')\n",
        "    \n",
        "  predictions.append(float(pred))\n",
        "  original_values.append(float(original_value))\n",
        "    \n",
        "# After iterating over whole test set the overall mean error is calculated.   \n",
        "print('\\n Mean Error in Predicting Test Case Articles : %f ' % (sum(errors)/float(len(errors))), '%')\n",
        "plt.figure(figsize=(8, 6))\n",
        "test_day = [t for t in range(len(test))]\n",
        "labels={'Orginal','Predicted'}\n",
        "print(len(test_day))\n",
        "print(len(predictions))\n",
        "plt.plot(test_day, predictions, color= 'green')\n",
        "plt.plot(test_day, original_values, color = 'orange')\n",
        "plt.title('Expected Vs Predicted Forecasting')\n",
        "plt.xlabel('Day')\n",
        "plt.ylabel('Closing Price')\n",
        "plt.legend(labels)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted vs Expected values\n",
            "predicted = 8016.694099,   expected = 7963.327637,   error = 0.670153  %\n",
            "predicted = 7978.376723,   expected = 7680.066406,   error = 3.884215  %\n",
            "predicted = 7697.251558,   expected = 7881.846680,   error = 2.342029  %\n",
            "predicted = 7895.775512,   expected = 7987.371582,   error = 1.146761  %\n",
            "predicted = 8011.435395,   expected = 8052.543945,   error = 0.510504  %\n",
            "predicted = 8074.627402,   expected = 8673.215820,   error = 6.901574  %\n",
            "predicted = 8698.657694,   expected = 8805.778320,   error = 1.216481  %\n",
            "predicted = 8842.037814,   expected = 8719.961914,   error = 1.399959  %\n",
            "predicted = 8744.086580,   expected = 8659.487305,   error = 0.976955  %\n",
            "predicted = 8679.074203,   expected = 8319.472656,   error = 4.322408  %\n",
            "predicted = 8337.300033,   expected = 8574.501953,   error = 2.766364  %\n",
            "predicted = 8589.668041,   expected = 8564.016602,   error = 0.299526  %\n",
            "predicted = 8590.485478,   expected = 8742.958008,   error = 1.743947  %\n",
            "predicted = 8765.135798,   expected = 8208.995117,   error = 6.774772  %\n",
            "predicted = 8231.042777,   expected = 7707.770996,   error = 6.788886  %\n",
            "predicted = 7713.842517,   expected = 7824.231445,   error = 1.410860  %\n",
            "predicted = 7833.779245,   expected = 7822.023438,   error = 0.150291  %\n",
            "predicted = 7843.088329,   expected = 8043.951172,   error = 2.497067  %\n",
            "predicted = 8064.370582,   expected = 7954.127930,   error = 1.385980  %\n",
            "predicted = 7977.326787,   expected = 7688.077148,   error = 3.762314  %\n",
            "predicted = 7703.447694,   expected = 8000.329590,   error = 3.710871  %\n",
            "predicted = 8015.616606,   expected = 7927.714355,   error = 1.108797  %\n",
            "predicted = 7952.631553,   expected = 8145.857422,   error = 2.372075  %\n",
            "predicted = 8164.877996,   expected = 8230.923828,   error = 0.802411  %\n",
            "predicted = 8255.647228,   expected = 8693.833008,   error = 5.040191  %\n",
            "predicted = 8718.965220,   expected = 8838.375000,   error = 1.351038  %\n",
            "predicted = 8870.809991,   expected = 8994.488281,   error = 1.375045  %\n",
            "predicted = 9020.602314,   expected = 9320.352539,   error = 3.216082  %\n",
            "predicted = 9348.690492,   expected = 9081.762695,   error = 2.939163  %\n",
            "predicted = 9110.127418,   expected = 9273.521484,   error = 1.761942  %\n",
            "predicted = 9292.329976,   expected = 9527.160156,   error = 2.464850  %\n",
            "predicted = 9556.367510,   expected = 10144.556641,   error = 5.798076  %\n",
            "predicted = 10179.487290,   expected = 10701.691406,   error = 4.879641  %\n",
            "predicted = 10747.893074,   expected = 10855.371094,   error = 0.990091  %\n",
            "predicted = 10897.577244,   expected = 11011.102539,   error = 1.031008  %\n",
            "predicted = 11044.387449,   expected = 11790.916992,   error = 6.331395  %\n",
            "predicted = 11831.729115,   expected = 13016.231445,   error = 9.100194  %\n",
            "predicted = 13085.574965,   expected = 11182.806641,   error = 17.015123  %\n",
            "predicted = 11228.031430,   expected = 12407.332031,   error = 9.504869  %\n",
            "predicted = 12391.207734,   expected = 11959.371094,   error = 3.610864  %\n",
            "predicted = 12026.548698,   expected = 10817.155273,   error = 11.180328  %\n",
            "predicted = 10842.095819,   expected = 10583.134766,   error = 2.446922  %\n",
            "predicted = 10588.853461,   expected = 10801.677734,   error = 1.970289  %\n",
            "predicted = 10824.307025,   expected = 11961.269531,   error = 9.505366  %\n",
            "predicted = 11994.739139,   expected = 11215.437500,   error = 6.948473  %\n",
            "predicted = 11270.459226,   expected = 10978.459961,   error = 2.659747  %\n",
            "predicted = 10996.336765,   expected = 11208.550781,   error = 1.893323  %\n",
            "predicted = 11232.978485,   expected = 11450.846680,   error = 1.902638  %\n",
            "predicted = 11483.906993,   expected = 12285.958008,   error = 6.528193  %\n",
            "predicted = 12319.739794,   expected = 12573.812500,   error = 2.020650  %\n",
            "predicted = 12622.033833,   expected = 12156.512695,   error = 3.829397  %\n",
            "predicted = 12195.482726,   expected = 11358.662109,   error = 7.367246  %\n",
            "predicted = 11385.114600,   expected = 11815.986328,   error = 3.646515  %\n",
            "predicted = 11833.898998,   expected = 11392.378906,   error = 3.875574  %\n",
            "predicted = 11431.647863,   expected = 10256.058594,   error = 11.462388  %\n",
            "predicted = 10280.205774,   expected = 10895.089844,   error = 5.643681  %\n",
            "predicted = 10904.997729,   expected = 9477.641602,   error = 15.060246  %\n",
            "predicted = 9525.520802,   expected = 9693.802734,   error = 1.735974  %\n",
            "predicted = 9688.269404,   expected = 10666.482422,   error = 9.170905  %\n",
            "predicted = 10687.477734,   expected = 10530.732422,   error = 1.488456  %\n",
            "predicted = 10573.512263,   expected = 10767.139648,   error = 1.798318  %\n",
            "predicted = 10790.593128,   expected = 10599.105469,   error = 1.806640  %\n",
            "predicted = 10631.877393,   expected = 10343.106445,   error = 2.791917  %\n",
            "predicted = 10370.009287,   expected = 9900.767578,   error = 4.739448  %\n",
            "predicted = 9926.569811,   expected = 9811.925781,   error = 1.168415  %\n",
            "predicted = 9831.060768,   expected = 9911.841797,   error = 0.814995  %\n",
            "predicted = 9934.596962,   expected = 9870.303711,   error = 0.651381  %\n",
            "predicted = 9897.142172,   expected = 9477.677734,   error = 4.425815  %\n",
            "predicted = 9504.463694,   expected = 9552.860352,   error = 0.506620  %\n",
            "predicted = 9570.223588,   expected = 9519.145508,   error = 0.536583  %\n",
            "predicted = 9544.334792,   expected = 9607.423828,   error = 0.656670  %\n",
            "predicted = 9630.041282,   expected = 10085.627930,   error = 4.517187  %\n",
            "predicted = 10107.786175,   expected = 10399.668945,   error = 2.806654  %\n",
            "predicted = 10430.841875,   expected = 10518.174805,   error = 0.830305  %\n",
            "predicted = 10548.871771,   expected = 10821.726563,   error = 2.521361  %\n",
            "predicted = 10848.744917,   expected = 10970.184570,   error = 1.106997  %\n",
            "predicted = 11002.041949,   expected = 11805.653320,   error = 6.807005  %\n",
            "predicted = 11832.070375,   expected = 11478.168945,   error = 3.083257  %\n",
            "predicted = 11524.823439,   expected = 11941.968750,   error = 3.493103  %\n",
            "predicted = 11962.965128,   expected = 11966.407227,   error = 0.028765  %\n",
            "predicted = 12005.935739,   expected = 11862.936523,   error = 1.205428  %\n",
            "predicted = 11895.555460,   expected = 11354.024414,   error = 4.769508  %\n",
            "predicted = 11386.583930,   expected = 11523.579102,   error = 1.188825  %\n",
            "predicted = 11543.270656,   expected = 11382.616211,   error = 1.411402  %\n",
            "predicted = 11416.454010,   expected = 10895.830078,   error = 4.778194  %\n",
            "predicted = 10926.005038,   expected = 10051.704102,   error = 8.698037  %\n",
            "predicted = 10075.345171,   expected = 10311.545898,   error = 2.290643  %\n",
            "predicted = 10320.849852,   expected = 10374.338867,   error = 0.515590  %\n",
            "predicted = 10404.387174,   expected = 10231.744141,   error = 1.687328  %\n",
            "predicted = 10259.642135,   expected = 10345.810547,   error = 0.832882  %\n",
            "predicted = 10368.357499,   expected = 10916.053711,   error = 5.017346  %\n",
            "predicted = 10940.822936,   expected = 10763.232422,   error = 1.649974  %\n",
            "predicted = 10801.371156,   expected = 10138.049805,   error = 6.542889  %\n",
            "predicted = 10166.361445,   expected = 10131.055664,   error = 0.348491  %\n",
            "predicted = 10147.295195,   expected = 10407.964844,   error = 2.504521  %\n",
            "predicted = 10431.623977,   expected = 10159.960938,   error = 2.673859  %\n",
            "predicted = 10191.333470,   expected = 10138.517578,   error = 0.520943  %\n",
            "predicted = 10160.464449,   expected = 10370.820313,   error = 2.028344  %\n",
            "predicted = 10394.434505,   expected = 10185.500000,   error = 2.051294  %\n",
            "predicted = 10215.726397,   expected = 9754.422852,   error = 4.729173  %\n",
            "predicted = 9779.576967,   expected = 9510.200195,   error = 2.832504  %\n",
            "predicted = 9529.565074,   expected = 9598.173828,   error = 0.714810  %\n",
            "predicted = 9617.554142,   expected = 9630.664063,   error = 0.136127  %\n",
            "predicted = 9655.120356,   expected = 9757.970703,   error = 1.054014  %\n",
            "predicted = 9781.288250,   expected = 10346.760742,   error = 5.465213  %\n",
            "predicted = 10369.693296,   expected = 10623.540039,   error = 2.389474  %\n",
            "predicted = 10656.768863,   expected = 10594.493164,   error = 0.587812  %\n",
            "predicted = 10625.019930,   expected = 10575.533203,   error = 0.467936  %\n",
            "predicted = 10601.679577,   expected = 10353.302734,   error = 2.399011  %\n",
            "predicted = 10380.281880,   expected = 10517.254883,   error = 1.302365  %\n",
            "predicted = 10539.185511,   expected = 10441.276367,   error = 0.937712  %\n",
            "predicted = 10470.010116,   expected = 10334.974609,   error = 1.306588  %\n",
            "predicted = 10360.275208,   expected = 10115.975586,   error = 2.414988  %\n",
            "predicted = 10140.925167,   expected = 10178.372070,   error = 0.367907  %\n",
            "predicted = 10199.941697,   expected = 10410.126953,   error = 2.019046  %\n",
            "predicted = 10435.075947,   expected = 10360.546875,   error = 0.719355  %\n",
            "predicted = 10389.636932,   expected = 10358.048828,   error = 0.304962  %\n",
            "predicted = 10382.943834,   expected = 10347.712891,   error = 0.340471  %\n",
            "predicted = 10373.247722,   expected = 10276.793945,   error = 0.938559  %\n",
            "predicted = 10302.410566,   expected = 10241.272461,   error = 0.596978  %\n",
            "predicted = 10265.688925,   expected = 10198.248047,   error = 0.661299  %\n",
            "predicted = 10223.037115,   expected = 10266.415039,   error = 0.422523  %\n",
            "predicted = 10290.503851,   expected = 10181.641602,   error = 1.069201  %\n",
            "predicted = 10208.043611,   expected = 10019.716797,   error = 1.879562  %\n",
            "predicted = 10044.068020,   expected = 10070.392578,   error = 0.261405  %\n",
            "predicted = 10092.334494,   expected = 9729.324219,   error = 3.731094  %\n",
            "predicted = 9755.787719,   expected = 8620.566406,   error = 13.168756  %\n",
            "predicted = 8641.677457,   expected = 8486.993164,   error = 1.822604  %\n",
            "predicted = 8492.613468,   expected = 8118.967773,   error = 4.602133  %\n",
            "predicted = 8136.516058,   expected = 8251.845703,   error = 1.397622  %\n",
            "predicted = 8264.028679,   expected = 8245.915039,   error = 0.219668  %\n",
            "predicted = 8266.833462,   expected = 8104.185547,   error = 2.006962  %\n",
            "predicted = 8122.900999,   expected = 8293.868164,   error = 2.061368  %\n",
            "predicted = 8309.541507,   expected = 8343.276367,   error = 0.404336  %\n",
            "predicted = 8365.162691,   expected = 8393.041992,   error = 0.332172  %\n",
            "predicted = 8412.729769,   expected = 8259.992188,   error = 1.849125  %\n",
            "predicted = 8280.005126,   expected = 8205.939453,   error = 0.902586  %\n",
            "predicted = 8222.519352,   expected = 8151.500488,   error = 0.871237  %\n",
            "predicted = 8169.163534,   expected = 7988.155762,   error = 2.265952  %\n",
            "predicted = 8005.738469,   expected = 8245.623047,   error = 2.909235  %\n",
            "predicted = 8260.377059,   expected = 8228.783203,   error = 0.383943  %\n",
            "predicted = 8251.639573,   expected = 8595.740234,   error = 4.003153  %\n",
            "predicted = 8613.396853,   expected = 8586.473633,   error = 0.313554  %\n",
            "predicted = 8612.386633,   expected = 8321.756836,   error = 3.492409  %\n",
            "predicted = 8341.495168,   expected = 8336.555664,   error = 0.059251  %\n",
            "predicted = 8351.029230,   expected = 8321.005859,   error = 0.360814  %\n",
            "predicted = 8340.037106,   expected = 8374.686523,   error = 0.413740  %\n",
            "predicted = 8393.065416,   expected = 8205.369141,   error = 2.287481  %\n",
            "predicted = 8225.323989,   expected = 8047.526855,   error = 2.209339  %\n",
            "predicted = 8063.374260,   expected = 8103.911133,   error = 0.500214  %\n",
            "predicted = 8119.145702,   expected = 7973.207520,   error = 1.830357  %\n",
            "predicted = 7992.311200,   expected = 7988.560547,   error = 0.046950  %\n",
            "predicted = 8004.029410,   expected = 8222.078125,   error = 2.651990  %\n",
            "predicted = 8239.670106,   expected = 8243.720703,   error = 0.049136  %\n",
            "predicted = 8265.874508,   expected = 8078.203125,   error = 2.323182  %\n",
            "predicted = 8097.139631,   expected = 7514.671875,   error = 7.751074  %\n",
            "predicted = 7530.394169,   expected = 7493.488770,   error = 0.492500  %\n",
            "predicted = 7501.079632,   expected = 8660.700195,   error = 13.389455  %\n",
            "predicted = 8675.477423,   expected = 9244.972656,   error = 6.160053  %\n",
            "predicted = 9281.305010,   expected = 9551.714844,   error = 2.831008  %\n",
            "predicted = 9582.253652,   expected = 9256.148438,   error = 3.523120  %\n",
            "predicted = 9280.824793,   expected = 9427.687500,   error = 1.557781  %\n",
            "predicted = 9445.944729,   expected = 9205.726563,   error = 2.609443  %\n",
            "predicted = 9228.873858,   expected = 9199.584961,   error = 0.318372  %\n",
            "predicted = 9217.902965,   expected = 9261.104492,   error = 0.466484  %\n",
            "predicted = 9282.273115,   expected = 9324.717773,   error = 0.455184  %\n",
            "predicted = 9346.911757,   expected = 9235.354492,   error = 1.207937  %\n",
            "predicted = 9257.235786,   expected = 9412.612305,   error = 1.650727  %\n",
            "predicted = 9433.155848,   expected = 9342.527344,   error = 0.970064  %\n",
            "predicted = 9366.150776,   expected = 9360.879883,   error = 0.056308  %\n",
            "predicted = 9381.461349,   expected = 9267.561523,   error = 1.229016  %\n",
            "predicted = 9288.937329,   expected = 8804.880859,   error = 5.497592  %\n",
            "predicted = 8823.359844,   expected = 8813.582031,   error = 0.110940  %\n",
            "predicted = 8827.707335,   expected = 9055.526367,   error = 2.515801  %\n",
            "predicted = 9076.235016,   expected = 8757.788086,   error = 3.636157  %\n",
            "predicted = 8780.232436,   expected = 8815.662109,   error = 0.401895  %\n",
            "predicted = 8831.891678,   expected = 8808.262695,   error = 0.268259  %\n",
            "predicted = 8828.698752,   expected = 8708.094727,   error = 1.384965  %\n",
            "predicted = 8727.438273,   expected = 8491.992188,   error = 2.772566  %\n",
            "predicted = 8509.573004,   expected = 8550.760742,   error = 0.481685  %\n",
            "predicted = 8567.124772,   expected = 8577.975586,   error = 0.126496  %\n",
            "predicted = 8597.701203,   expected = 8309.286133,   error = 3.470997  %\n",
            "predicted = 8327.804182,   expected = 8206.145508,   error = 1.482531  %\n",
            "predicted = 8220.907193,   expected = 8027.268066,   error = 2.412267  %\n",
            "predicted = 8043.334537,   expected = 7642.750000,   error = 5.241366  %\n",
            "predicted = 7656.513532,   expected = 7296.577637,   error = 4.932941  %\n",
            "predicted = 7306.430978,   expected = 7397.796875,   error = 1.235042  %\n",
            "predicted = 7409.119172,   expected = 7047.916992,   error = 5.124949  %\n",
            "predicted = 7063.293470,   expected = 7146.133789,   error = 1.159233  %\n",
            "predicted = 7156.242708,   expected = 7218.371094,   error = 0.860698  %\n",
            "predicted = 7234.862247,   expected = 7531.663574,   error = 3.940714  %\n",
            "predicted = 7549.068348,   expected = 7463.105957,   error = 1.151831  %\n",
            "predicted = 7483.115306,   expected = 7761.243652,   error = 3.583554  %\n",
            "predicted = 7777.022957,   expected = 7569.629883,   error = 2.739805  %\n",
            "predicted = 7589.814916,   expected = 7424.292480,   error = 2.229471  %\n",
            "predicted = 7437.013333,   expected = 7321.988281,   error = 1.570954  %\n",
            "predicted = 7335.038113,   expected = 7320.145508,   error = 0.203447  %\n",
            "predicted = 7333.874202,   expected = 7252.034668,   error = 1.128504  %\n",
            "predicted = 7266.947253,   expected = 7448.307617,   error = 2.434920  %\n",
            "predicted = 7463.029796,   expected = 7546.996582,   error = 1.112585  %\n",
            "predicted = 7565.854855,   expected = 7556.237793,   error = 0.127273  %\n",
            "predicted = 7573.519785,   expected = 7564.345215,   error = 0.121287  %\n",
            "predicted = 7580.322318,   expected = 7400.899414,   error = 2.424339  %\n",
            "predicted = 7416.207843,   expected = 7278.119629,   error = 1.897306  %\n",
            "predicted = 7290.663851,   expected = 7217.427246,   error = 1.014719  %\n",
            "predicted = 7230.413570,   expected = 7243.134277,   error = 0.175624  %\n",
            "predicted = 7257.184599,   expected = 7269.684570,   error = 0.171947  %\n",
            "predicted = 7285.068238,   expected = 7124.673828,   error = 2.251253  %\n",
            "predicted = 7139.423325,   expected = 7152.301758,   error = 0.180060  %\n",
            "predicted = 7164.856053,   expected = 6932.480469,   error = 3.351983  %\n",
            "predicted = 6946.617742,   expected = 6640.515137,   error = 4.609621  %\n",
            "predicted = 6650.076738,   expected = 7276.802734,   error = 8.612656  %\n",
            "predicted = 7287.644578,   expected = 7202.844238,   error = 1.177317  %\n",
            "predicted = 7226.772068,   expected = 7218.816406,   error = 0.110207  %\n",
            "predicted = 7232.589584,   expected = 7191.158691,   error = 0.576137  %\n",
            "predicted = 7206.097309,   expected = 7511.588867,   error = 4.066937  %\n",
            "predicted = 7526.651026,   expected = 7355.628418,   error = 2.325058  %\n",
            "predicted = 7375.355431,   expected = 7322.532227,   error = 0.721379  %\n",
            "predicted = 7335.482133,   expected = 7275.155762,   error = 0.829211  %\n",
            "predicted = 7289.637356,   expected = 7238.966797,   error = 0.699969  %\n",
            "predicted = 7253.133846,   expected = 7290.088379,   error = 0.506915  %\n",
            "predicted = 7304.476174,   expected = 7317.990234,   error = 0.184669  %\n",
            "predicted = 7333.677547,   expected = 7422.652832,   error = 1.198699  %\n",
            "predicted = 7438.243708,   expected = 7292.995117,   error = 1.991618  %\n",
            "predicted = 7309.433367,   expected = 7193.599121,   error = 1.610241  %\n",
            "predicted = 7206.536909,   expected = 7200.174316,   error = 0.088367  %\n",
            "predicted = 7213.440790,   expected = 6985.470215,   error = 3.263496  %\n",
            "predicted = 6999.729474,   expected = 7344.884277,   error = 4.699254  %\n",
            "predicted = 7356.319016,   expected = 7410.656738,   error = 0.733238  %\n",
            "predicted = 7430.930037,   expected = 7411.317383,   error = 0.264631  %\n",
            "predicted = 7427.402176,   expected = 7769.219238,   error = 4.399632  %\n",
            "predicted = 7784.937584,   expected = 8163.692383,   error = 4.639504  %\n",
            "predicted = 8186.156551,   expected = 8079.862793,   error = 1.315539  %\n",
            "predicted = 8102.335599,   expected = 7879.071289,   error = 2.833637  %\n",
            "predicted = 7894.474617,   expected = 8166.554199,   error = 3.331633  %\n",
            "predicted = 8180.870126,   expected = 8037.537598,   error = 1.783289  %\n",
            "predicted = 8058.273060,   expected = 8192.494141,   error = 1.638342  %\n",
            "predicted = 8207.809838,   expected = 8144.194336,   error = 0.781115  %\n",
            "predicted = 8163.433628,   expected = 8827.764648,   error = 7.525473  %\n",
            "predicted = 8844.967895,   expected = 8807.010742,   error = 0.430988  %\n",
            "predicted = 8835.908534,   expected = 8723.786133,   error = 1.285249  %\n",
            "predicted = 8742.306346,   expected = 8929.038086,   error = 2.091286  %\n",
            "predicted = 8946.771874,   expected = 8942.808594,   error = 0.044318  %\n",
            "predicted = 8964.862898,   expected = 8706.245117,   error = 2.970486  %\n",
            "predicted = 8725.406912,   expected = 8657.642578,   error = 0.782711  %\n",
            "predicted = 8673.076982,   expected = 8745.894531,   error = 0.832591  %\n",
            "predicted = 8763.782575,   expected = 8680.875977,   error = 0.955049  %\n",
            "predicted = 8700.629945,   expected = 8406.515625,   error = 3.498647  %\n",
            "predicted = 8423.812840,   expected = 8445.434570,   error = 0.256017  %\n",
            "predicted = 8459.634690,   expected = 8367.847656,   error = 1.096901  %\n",
            "predicted = 8386.060463,   expected = 8596.830078,   error = 2.451713  %\n",
            "predicted = 8613.589987,   expected = 8909.819336,   error = 3.324751  %\n",
            "predicted = 8931.608538,   expected = 9358.589844,   error = 4.562453  %\n",
            "predicted = 9383.126785,   expected = 9316.629883,   error = 0.713744  %\n",
            "predicted = 9342.996924,   expected = 9508.993164,   error = 1.745676  %\n",
            "predicted = 9528.909484,   expected = 9350.529297,   error = 1.907701  %\n",
            "predicted = 9373.556409,   expected = 9392.875000,   error = 0.205673  %\n",
            "predicted = 9410.952583,   expected = 9344.365234,   error = 0.712594  %\n",
            "predicted = 9365.173127,   expected = 9293.521484,   error = 0.770985  %\n",
            "predicted = 9312.911103,   expected = 9180.962891,   error = 1.437194  %\n",
            "predicted = 9200.048342,   expected = 9613.423828,   error = 4.299982  %\n",
            "predicted = 9632.155307,   expected = 9729.801758,   error = 1.003581  %\n",
            "predicted = 9757.077146,   expected = 9795.943359,   error = 0.396758  %\n",
            "predicted = 9818.888545,   expected = 9865.119141,   error = 0.468627  %\n",
            "predicted = 9887.534712,   expected = 10116.673828,   error = 2.264965  %\n",
            "predicted = 10139.678602,   expected = 9856.611328,   error = 2.871852  %\n",
            "predicted = 9881.883266,   expected = 10208.236328,   error = 3.196958  %\n",
            "predicted = 10226.369620,   expected = 10326.054688,   error = 0.965374  %\n",
            "predicted = 10353.797617,   expected = 10214.379883,   error = 1.364916  %\n",
            "predicted = 10238.665840,   expected = 10312.116211,   error = 0.712273  %\n",
            "predicted = 10333.146723,   expected = 9889.424805,   error = 4.486832  %\n",
            "predicted = 9913.003643,   expected = 9934.433594,   error = 0.215714  %\n",
            "predicted = 9950.102547,   expected = 9690.142578,   error = 2.682726  %\n",
            "predicted = 9712.156002,   expected = 10141.996094,   error = 4.238220  %\n",
            "predicted = 10159.428011,   expected = 9633.386719,   error = 5.460606  %\n",
            "predicted = 9662.519796,   expected = 9608.475586,   error = 0.562464  %\n",
            "predicted = 9621.705108,   expected = 9686.441406,   error = 0.668319  %\n",
            "predicted = 9706.711069,   expected = 9663.181641,   error = 0.450467  %\n",
            "predicted = 9685.251302,   expected = 9924.515625,   error = 2.410841  %\n",
            "predicted = 9944.837639,   expected = 9650.174805,   error = 3.053446  %\n",
            "predicted = 9675.808582,   expected = 9341.705078,   error = 3.576472  %\n",
            "predicted = 9358.634969,   expected = 8820.522461,   error = 6.100687  %\n",
            "predicted = 8835.569590,   expected = 8784.494141,   error = 0.581427  %\n",
            "predicted = 8794.948372,   expected = 8672.455078,   error = 1.412441  %\n",
            "predicted = 8690.085239,   expected = 8599.508789,   error = 1.053275  %\n",
            "predicted = 8615.640145,   expected = 8562.454102,   error = 0.621154  %\n",
            "predicted = 8579.012414,   expected = 8869.669922,   error = 3.276982  %\n",
            "predicted = 8886.909722,   expected = 8787.786133,   error = 1.127970  %\n",
            "predicted = 8811.171175,   expected = 8755.246094,   error = 0.638761  %\n",
            "predicted = 8772.170462,   expected = 9078.762695,   error = 3.377027  %\n",
            "predicted = 9096.521225,   expected = 9122.545898,   error = 0.285279  %\n",
            "predicted = 9146.791146,   expected = 8909.954102,   error = 2.658117  %\n",
            "predicted = 8929.619907,   expected = 8108.116211,   error = 10.131869  %\n",
            "predicted = 8122.444442,   expected = 7923.644531,   error = 2.508945  %\n",
            "predicted = 7927.921221,   expected = 7909.729492,   error = 0.229992  %\n",
            "predicted = 7922.687755,   expected = 7911.430176,   error = 0.142295  %\n",
            "predicted = 7927.007132,   expected = 4970.788086,   error = 59.471838  %\n",
            "predicted = 4978.681826,   expected = 5563.707031,   error = 10.515025  %\n",
            "predicted = 5519.408174,   expected = 5200.366211,   error = 6.134990  %\n",
            "predicted = 5233.165887,   expected = 5392.314941,   error = 2.951405  %\n",
            "predicted = 5387.077785,   expected = 5014.479980,   error = 7.430438  %\n",
            "predicted = 5037.325585,   expected = 5225.629395,   error = 3.603467  %\n",
            "predicted = 5217.773068,   expected = 5238.438477,   error = 0.394496  %\n",
            "predicted = 5253.880847,   expected = 6191.192871,   error = 15.139442  %\n",
            "predicted = 6177.553783,   expected = 6198.778320,   error = 0.342399  %\n",
            "predicted = 6244.373506,   expected = 6185.066406,   error = 0.958876  %\n",
            "predicted = 6197.059753,   expected = 5830.254883,   error = 6.291404  %\n",
            "predicted = 5848.802433,   expected = 6416.314941,   error = 8.844836  %\n",
            "predicted = 6399.779069,   expected = 6734.803711,   error = 4.974527  %\n",
            "predicted = 6759.583361,   expected = 6681.062988,   error = 1.175268  %\n",
            "predicted = 6705.004054,   expected = 6716.440430,   error = 0.170274  %\n",
            "predicted = 6726.623768,   expected = 6469.798340,   error = 3.969605  %\n",
            "predicted = 6489.034212,   expected = 6242.193848,   error = 3.954385  %\n",
            "predicted = 6251.989647,   expected = 5922.042969,   error = 5.571501  %\n",
            "predicted = 5933.432442,   expected = 6429.841797,   error = 7.720398  %\n",
            "predicted = 6418.096997,   expected = 6438.644531,   error = 0.319128  %\n",
            "predicted = 6466.564827,   expected = 6606.776367,   error = 2.122238  %\n",
            "predicted = 6615.150479,   expected = 6793.624512,   error = 2.627081  %\n",
            "predicted = 6807.179919,   expected = 6733.387207,   error = 1.095923  %\n",
            "predicted = 6753.425250,   expected = 6867.527344,   error = 1.661473  %\n",
            "predicted = 6875.235799,   expected = 6791.129395,   error = 1.238474  %\n",
            "predicted = 6810.068453,   expected = 7271.781250,   error = 6.349377  %\n",
            "predicted = 7270.512975,   expected = 7176.414551,   error = 1.311218  %\n",
            "predicted = 7208.241146,   expected = 7334.098633,   error = 1.716059  %\n",
            "predicted = 7341.019625,   expected = 7302.089355,   error = 0.533139  %\n",
            "predicted = 7322.117325,   expected = 6865.493164,   error = 6.651003  %\n",
            "predicted = 6888.513715,   expected = 6859.083008,   error = 0.429076  %\n",
            "predicted = 6858.816461,   expected = 6971.091797,   error = 1.610585  %\n",
            "predicted = 6981.200742,   expected = 6845.037598,   error = 1.989224  %\n",
            "predicted = 6864.668451,   expected = 6842.427734,   error = 0.325041  %\n",
            "predicted = 6851.483835,   expected = 6642.109863,   error = 3.152221  %\n",
            "predicted = 6659.532381,   expected = 7116.804199,   error = 6.425241  %\n",
            "predicted = 7110.633627,   expected = 7096.184570,   error = 0.203617  %\n",
            "predicted = 7125.691595,   expected = 7257.665039,   error = 1.818401  %\n",
            "predicted = 7266.531348,   expected = 7189.424805,   error = 1.072499  %\n",
            "predicted = 7210.153420,   expected = 6881.958496,   error = 4.768918  %\n",
            "predicted = 6900.881951,   expected = 6880.323242,   error = 0.298804  %\n",
            "predicted = 6883.803442,   expected = 7117.207520,   error = 3.279433  %\n",
            "predicted = 7124.266054,   expected = 7429.724609,   error = 4.111304  %\n",
            "predicted = 7443.482112,   expected = 7550.900879,   error = 1.422595  %\n",
            "predicted = 7572.463509,   expected = 7569.936035,   error = 0.033388  %\n",
            "predicted = 7587.905206,   expected = 7679.867188,   error = 1.197442  %\n",
            "predicted = 7692.511926,   expected = 7795.601074,   error = 1.322402  %\n",
            "predicted = 7811.296509,   expected = 7807.058594,   error = 0.054283  %\n",
            "predicted = 7825.632234,   expected = 8801.038086,   error = 11.082850  %\n",
            "predicted = 8791.966757,   expected = 8658.553711,   error = 1.540824  %\n",
            "predicted = 8712.819491,   expected = 8864.766602,   error = 1.714057  %\n",
            "predicted = 8872.425756,   expected = 8988.596680,   error = 1.292426  %\n",
            "predicted = 9010.405535,   expected = 8897.468750,   error = 1.269314  %\n",
            "predicted = 8921.877997,   expected = 8912.654297,   error = 0.103490  %\n",
            "predicted = 8927.333897,   expected = 9003.070313,   error = 0.841229  %\n",
            "predicted = 9019.401778,   expected = 9268.761719,   error = 2.690326  %\n",
            "predicted = 9283.580457,   expected = 9951.518555,   error = 6.711921  %\n",
            "predicted = 9963.422227,   expected = 9842.666016,   error = 1.226865  %\n",
            "predicted = 9888.670407,   expected = 9593.896484,   error = 3.072515  %\n",
            "predicted = 9616.720759,   expected = 8756.430664,   error = 9.824666  %\n",
            "predicted = 8786.785132,   expected = 8601.795898,   error = 2.150589  %\n",
            "predicted = 8597.193891,   expected = 8804.477539,   error = 2.354298  %\n",
            "predicted = 8812.138960,   expected = 9269.987305,   error = 4.939040  %\n",
            "predicted = 9284.052480,   expected = 9733.721680,   error = 4.619705  %\n",
            "predicted = 9758.564195,   expected = 9328.197266,   error = 4.613613  %\n",
            "predicted = 9370.750244,   expected = 9377.013672,   error = 0.066796  %\n",
            "predicted = 9382.665161,   expected = 9670.739258,   error = 2.978822  %\n",
            "predicted = 9684.976574,   expected = 9726.575195,   error = 0.427680  %\n",
            "predicted = 9754.327250,   expected = 9729.038086,   error = 0.259935  %\n",
            "predicted = 9750.676550,   expected = 9522.981445,   error = 2.391006  %\n",
            "predicted = 9547.385173,   expected = 9081.761719,   error = 5.127017  %\n",
            "predicted = 9104.279613,   expected = 9182.577148,   error = 0.852675  %\n",
            "predicted = 9185.559994,   expected = 9209.287109,   error = 0.257643  %\n",
            "predicted = 9230.297185,   expected = 8790.368164,   error = 5.004671  %\n",
            "predicted = 8818.317871,   expected = 8906.934570,   error = 0.994918  %\n",
            "predicted = 8909.491179,   expected = 8835.052734,   error = 0.842535  %\n",
            "predicted = 8857.846737,   expected = 9181.017578,   error = 3.519989  %\n",
            "predicted = 9188.759286,   expected = 9525.750977,   error = 3.537692  %\n",
            "predicted = 9547.859237,   expected = 9439.124023,   error = 1.151963  %\n",
            "predicted = 9470.728096,   expected = 9700.414063,   error = 2.367795  %\n",
            "predicted = 9711.218910,   expected = 9461.058594,   error = 2.644105  %\n",
            "predicted = 9493.753243,   expected = 10167.268555,   error = 6.624349  %\n",
            "predicted = 10162.348103,   expected = 9529.803711,   error = 6.637539  %\n",
            "predicted = 9587.143357,   expected = 9656.717773,   error = 0.720477  %\n",
            "predicted = 9653.133669,   expected = 9800.636719,   error = 1.505035  %\n",
            "predicted = 9820.900048,   expected = 9665.533203,   error = 1.607432  %\n",
            "predicted = 9693.239046,   expected = 9653.679688,   error = 0.409785  %\n",
            "predicted = 9669.412102,   expected = 9758.852539,   error = 0.916506  %\n",
            "predicted = 9775.556898,   expected = 9771.489258,   error = 0.041628  %\n",
            "predicted = 9794.370710,   expected = 9795.700195,   error = 0.013572  %\n",
            "predicted = 9815.409974,   expected = 9870.094727,   error = 0.554045  %\n",
            "predicted = 9889.018598,   expected = 9321.781250,   error = 6.085075  %\n",
            "predicted = 9356.888816,   expected = 9480.843750,   error = 1.307425  %\n",
            "predicted = 9478.697640,   expected = 9475.277344,   error = 0.036097  %\n",
            "predicted = 9499.521601,   expected = 9386.788086,   error = 1.200981  %\n",
            "predicted = 9407.822086,   expected = 9450.702148,   error = 0.453724  %\n",
            "predicted = 9465.197810,   expected = 9538.024414,   error = 0.763540  %\n",
            "predicted = 9556.888861,   expected = 9480.254883,   error = 0.808354  %\n",
            "predicted = 9503.572281,   expected = 9411.840820,   error = 0.974639  %\n",
            "predicted = 9430.751064,   expected = 9288.018555,   error = 1.536738  %\n",
            "predicted = 9307.746616,   expected = 9332.340820,   error = 0.263537  %\n",
            "predicted = 9345.919679,   expected = 9303.629883,   error = 0.454552  %\n",
            "predicted = 9324.323867,   expected = 9648.717773,   error = 3.362042  %\n",
            "predicted = 9657.751490,   expected = 9629.658203,   error = 0.291737  %\n",
            "predicted = 9660.505203,   expected = 9313.610352,   error = 3.724601  %\n",
            "predicted = 9339.962309,   expected = 9264.813477,   error = 0.811121  %\n",
            "predicted = 9275.000991,   expected = 9162.917969,   error = 1.223224  %\n",
            "predicted = 9182.269219,   expected = 9045.390625,   error = 1.513241  %\n",
            "predicted = 9063.221285,   expected = 9143.582031,   error = 0.878876  %\n",
            "predicted = 9155.340111,   expected = 9190.854492,   error = 0.386410  %\n",
            "predicted = 9210.821830,   expected = 9137.993164,   error = 0.796988  %\n",
            "predicted = 9158.881098,   expected = 9228.325195,   error = 0.752510  %\n",
            "predicted = 9242.488069,   expected = 9123.410156,   error = 1.305191  %\n",
            "predicted = 9146.950313,   expected = 9087.303711,   error = 0.656373  %\n",
            "predicted = 9102.912225,   expected = 9132.488281,   error = 0.323855  %\n",
            "predicted = 9148.117204,   expected = 9073.942383,   error = 0.817449  %\n",
            "predicted = 9094.703190,   expected = 9375.474609,   error = 2.994744  %\n",
            "predicted = 9383.930373,   expected = 9252.277344,   error = 1.422926  %\n",
            "predicted = 9283.219538,   expected = 9428.333008,   error = 1.539121  %\n",
            "predicted = 9438.302849,   expected = 9277.967773,   error = 1.728127  %\n",
            "predicted = 9305.790441,   expected = 9278.807617,   error = 0.290801  %\n",
            "predicted = 9292.397319,   expected = 9240.346680,   error = 0.563297  %\n",
            "predicted = 9259.582607,   expected = 9276.500000,   error = 0.182368  %\n",
            "predicted = 9292.554472,   expected = 9243.614258,   error = 0.529449  %\n",
            "predicted = 9263.790150,   expected = 9243.213867,   error = 0.222610  %\n",
            "predicted = 9260.337019,   expected = 9192.836914,   error = 0.734268  %\n",
            "predicted = 9212.192698,   expected = 9132.227539,   error = 0.875637  %\n",
            "predicted = 9150.142393,   expected = 9151.392578,   error = 0.013661  %\n",
            "predicted = 9166.848117,   expected = 9159.040039,   error = 0.085250  %\n",
            "predicted = 9177.315901,   expected = 9185.817383,   error = 0.092550  %\n",
            "predicted = 9203.268174,   expected = 9164.231445,   error = 0.425968  %\n",
            "predicted = 9183.530750,   expected = 9374.887695,   error = 2.041165  %\n",
            "predicted = 9386.796078,   expected = 9525.363281,   error = 1.454718  %\n",
            "predicted = 9546.800521,   expected = 9581.072266,   error = 0.357703  %\n",
            "predicted = 9603.242269,   expected = 9536.892578,   error = 0.695716  %\n",
            "predicted = 9558.608029,   expected = 9677.113281,   error = 1.224593  %\n",
            "predicted = 9691.005518,   expected = 9905.166992,   error = 2.162119  %\n",
            "predicted = 9923.147977,   expected = 10990.873047,   error = 9.714652  %\n",
            "predicted = 10991.513250,   expected = 10912.823242,   error = 0.721078  %\n",
            "predicted = 10973.213026,   expected = 11100.467773,   error = 1.146391  %\n",
            "predicted = 11115.648808,   expected = 11111.213867,   error = 0.039914  %\n",
            "predicted = 11139.774917,   expected = 11323.466797,   error = 1.622223  %\n",
            "predicted = 11341.435022,   expected = 11759.592773,   error = 3.555886  %\n",
            "predicted = 11779.501965,   expected = 11053.614258,   error = 6.566972  %\n",
            "predicted = 11108.117745,   expected = 11246.348633,   error = 1.229118  %\n",
            "predicted = 11241.881595,   expected = 11205.892578,   error = 0.321162  %\n",
            "predicted = 11236.171315,   expected = 11747.022461,   error = 4.348771  %\n",
            "predicted = 11754.787367,   expected = 11779.773438,   error = 0.212110  %\n",
            "predicted = 11821.258501,   expected = 11601.472656,   error = 1.894465  %\n",
            "predicted = 11631.344084,   expected = 11754.045898,   error = 1.043911  %\n",
            "predicted = 11768.443802,   expected = 11675.739258,   error = 0.793993  %\n",
            "predicted = 11706.918229,   expected = 11878.111328,   error = 1.441248  %\n",
            "predicted = 11894.582569,   expected = 11410.525391,   error = 4.242199  %\n",
            "predicted = 11453.043471,   expected = 11584.934570,   error = 1.138471  %\n",
            "predicted = 11588.877164,   expected = 11784.137695,   error = 1.656978  %\n",
            "predicted = 11808.614278,   expected = 11768.871094,   error = 0.337698  %\n",
            "predicted = 11800.042107,   expected = 11865.698242,   error = 0.553327  %\n",
            "predicted = 11887.049220,   expected = 11892.803711,   error = 0.048386  %\n",
            "predicted = 11919.786909,   expected = 12254.402344,   error = 2.730573  %\n",
            "predicted = 12270.591975,   expected = 11991.233398,   error = 2.329690  %\n",
            "predicted = 12034.974752,   expected = 11758.283203,   error = 2.353163  %\n",
            "predicted = 11780.855410,   expected = 11878.372070,   error = 0.820960  %\n",
            "predicted = 11892.119429,   expected = 11592.489258,   error = 2.584692  %\n",
            "predicted = 11627.984632,   expected = 11681.825195,   error = 0.460892  %\n",
            "predicted = 11694.189693,   expected = 11664.847656,   error = 0.251542  %\n",
            "predicted = 11692.122483,   expected = 11774.595703,   error = 0.700434  %\n",
            "predicted = 11795.139626,   expected = 11366.134766,   error = 3.774413  %\n",
            "predicted = 11404.046690,   expected = 11488.363281,   error = 0.733930  %\n",
            "predicted = 11495.303221,   expected = 11323.397461,   error = 1.518146  %\n",
            "predicted = 11354.954905,   expected = 11542.500000,   error = 1.624822  %\n",
            "predicted = 11554.333153,   expected = 11506.865234,   error = 0.412518  %\n",
            "predicted = 11538.453939,   expected = 11711.505859,   error = 1.477623  %\n",
            "predicted = 11728.381051,   expected = 11680.820313,   error = 0.407170  %\n",
            "predicted = 11712.242358,   expected = 11970.478516,   error = 2.157275  %\n",
            "predicted = 11985.657242,   expected = 11414.034180,   error = 5.008072  %\n",
            "predicted = 11462.120628,   expected = 10245.296875,   error = 11.876901  %\n",
            "predicted = 10279.883598,   expected = 10511.813477,   error = 2.206374  %\n",
            "predicted = 10489.538716,   expected = 10169.567383,   error = 3.146361  %\n",
            "predicted = 10207.484008,   expected = 10280.351563,   error = 0.708804  %\n",
            "predicted = 10286.275675,   expected = 10369.563477,   error = 0.803195  %\n",
            "predicted = 10391.072572,   expected = 10131.516602,   error = 2.561867  %\n",
            "predicted = 10160.802790,   expected = 10242.347656,   error = 0.796154  %\n",
            "predicted = 10251.510693,   expected = 10363.138672,   error = 1.077164  %\n",
            "predicted = 10383.682408,   expected = 10400.915039,   error = 0.165684  %\n",
            "predicted = 10424.214513,   expected = 10442.170898,   error = 0.171960  %\n",
            "predicted = 10462.745829,   expected = 10323.755859,   error = 1.346312  %\n",
            "predicted = 10348.626451,   expected = 10680.837891,   error = 3.110350  %\n",
            "predicted = 10687.644803,   expected = 10796.951172,   error = 1.012382  %\n",
            "predicted = 10826.898705,   expected = 10974.905273,   error = 1.348591  %\n",
            "predicted = 10995.495867,   expected = 10948.990234,   error = 0.424748  %\n",
            "predicted = 10977.285763,   expected = 10944.585938,   error = 0.298776  %\n",
            "predicted = 10965.590452,   expected = 11094.346680,   error = 1.160557  %\n",
            "predicted = 11112.012909,   expected = 10938.271484,   error = 1.588381  %\n",
            "predicted = 10969.192569,   expected = 10462.259766,   error = 4.845347  %\n",
            "predicted = 10491.058417,   expected = 10538.459961,   error = 0.449796  %\n",
            "predicted = 10541.834001,   expected = 10225.864258,   error = 3.089907  %\n",
            "predicted = 10256.852725,   expected = 10745.548828,   error = 4.547893  %\n",
            "predicted = 10740.742753,   expected = 10702.290039,   error = 0.359294  %\n",
            "predicted = 10742.014501,   expected = 10754.437500,   error = 0.115515  %\n",
            "predicted = 10772.642985,   expected = 10774.426758,   error = 0.016556  %\n",
            "predicted = 10796.752086,   expected = 10721.327148,   error = 0.703504  %\n",
            "predicted = 10744.547551,   expected = 10848.830078,   error = 0.961233  %\n",
            "predicted = 10864.705876,   expected = 10787.618164,   error = 0.714594  %\n",
            "predicted = 10814.761237,   expected = 10623.330078,   error = 1.801988  %\n",
            "predicted = 10646.821432,   expected = 10585.164063,   error = 0.582489  %\n",
            "predicted = 10601.572843,   expected = 10565.493164,   error = 0.341486  %\n",
            "predicted = 10585.393697,   expected = 10684.428711,   error = 0.926910  %\n",
            "predicted = 10701.195450,   expected = 10804.000977,   error = 0.951551  %\n",
            "predicted = 10825.665912,   expected = 10621.664063,   error = 1.920620  %\n",
            "predicted = 10651.459764,   expected = 10679.136719,   error = 0.259168  %\n",
            "predicted = 10692.339037,   expected = 10923.627930,   error = 2.117327  %\n",
            "predicted = 10939.839731,   expected = 11079.465820,   error = 1.260224  %\n",
            "predicted = 11104.947355,   expected = 11296.361328,   error = 1.694475  %\n",
            "predicted = 11317.659980,   expected = 11384.181641,   error = 0.584334  %\n",
            "predicted = 11411.447340,   expected = 11555.363281,   error = 1.245447  %\n",
            "predicted = 11576.335730,   expected = 11425.899414,   error = 1.316626  %\n",
            "predicted = 11457.835991,   expected = 11429.506836,   error = 0.247860  %\n",
            "predicted = 11447.800791,   expected = 11495.349609,   error = 0.413635  %\n",
            "predicted = 11516.355685,   expected = 11322.123047,   error = 1.715514  %\n",
            "predicted = 11351.532143,   expected = 11358.101563,   error = 0.057839  %\n",
            "predicted = 11373.813441,   expected = 11483.359375,   error = 0.953954  %\n",
            "predicted = 11503.650993,   expected = 11742.037109,   error = 2.030194  %\n",
            "predicted = 11762.120693,   expected = 11916.334961,   error = 1.294142  %\n",
            "predicted = 11943.822353,   expected = 12823.689453,   error = 6.861263  %\n",
            "predicted = 12829.522294,   expected = 12965.891602,   error = 1.051754  %\n",
            "predicted = 13020.152916,   expected = 12931.539063,   error = 0.685254  %\n",
            "predicted = 12963.571171,   expected = 13108.062500,   error = 1.102309  %\n",
            "predicted = 13128.699900,   expected = 13031.173828,   error = 0.748406  %\n",
            "predicted = 13065.766271,   expected = 13075.248047,   error = 0.072517  %\n",
            "predicted = 13098.106147,   expected = 13654.218750,   error = 4.072826  %\n",
            "predicted = 13667.328532,   expected = 13271.285156,   error = 2.984213  %\n",
            "predicted = 13328.278081,   expected = 13437.882813,   error = 0.815640  %\n",
            "predicted = 13447.958332,   expected = 13546.522461,   error = 0.727597  %\n",
            "predicted = 13576.859467,   expected = 13780.995117,   error = 1.481284  %\n",
            "predicted = 13806.380333,   expected = 13737.109375,   error = 0.504262  %\n",
            "predicted = 13774.500049,   expected = 13550.489258,   error = 1.653156  %\n",
            "predicted = 13582.138271,   expected = 13950.300781,   error = 2.639101  %\n",
            "predicted = 13961.215629,   expected = 14133.707031,   error = 1.220426  %\n",
            "predicted = 14171.319700,   expected = 15579.848633,   error = 9.040710  %\n",
            "predicted = 15577.184833,   expected = 15565.880859,   error = 0.072620  %\n",
            "predicted = 15650.862286,   expected = 14833.753906,   error = 5.508440  %\n",
            "predicted = 14884.561533,   expected = 15479.567383,   error = 3.843814  %\n",
            "predicted = 15469.508287,   expected = 15332.315430,   error = 0.894795  %\n",
            "predicted = 15390.065551,   expected = 15290.902344,   error = 0.648511  %\n",
            "predicted = 15319.697862,   expected = 15701.339844,   error = 2.430633  %\n",
            "predicted = 15721.305837,   expected = 16276.343750,   error = 3.410090  %\n",
            "predicted = 16308.232706,   expected = 16317.808594,   error = 0.058684  %\n",
            "predicted = 16370.391099,   expected = 16068.138672,   error = 1.881067  %\n",
            "predicted = 16110.852712,   expected = 15955.587891,   error = 0.973106  %\n",
            "predicted = 15984.964841,   expected = 16716.111328,   error = 4.373903  %\n",
            "predicted = 16726.046659,   expected = 17645.406250,   error = 5.210192  %\n",
            "predicted = 17682.973874,   expected = 17804.005859,   error = 0.679802  %\n",
            "predicted = 17868.793243,   expected = 17945.328125,   error = 0.426489  %\n",
            "\n",
            " Mean Error in Predicting Test Case Articles : 2.395210  %\n",
            "549\n",
            "549\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ExB_L0MUX2mU"
      },
      "source": [
        "Prediction for 30 days into the future..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTTFlPF3OeIw"
      },
      "source": [
        "future_data = pd.read_csv('/content/drive/MyDrive/SUL/future_data.csv',parse_dates=True)\n",
        "\n",
        "future_data.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PxBpqJMijL37"
      },
      "source": [
        "future_data['Date'] = pd.to_datetime(future_data['Date'],infer_datetime_format=True)\n",
        "future_data.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJ7pwy66jkw3"
      },
      "source": [
        "#Prediction \n",
        "\n",
        "full_history = [price for price in daily_target_prices_log]\n",
        "\n",
        "future_d = [data for data in future_data['Date']]\n",
        "\n",
        "predictions_future = list()\n",
        "\n",
        "for t in range(len(future_data)):\n",
        "  model = ARIMA(full_history,order=(2,1,0))\n",
        "  model_fit = model.fit(disp=-1)\n",
        "    \n",
        "  output = model_fit.forecast()\n",
        "    \n",
        "  pred = output[0]\n",
        "  print(pred)\n",
        "    \n",
        "  pred = np.exp(pred)\n",
        "  print(float(pred))\n",
        "  \n",
        "  full_history.append(float(pred))\n",
        "    \n",
        "  print('predicted = %f' % (pred), '%')\n",
        "    \n",
        "  predictions_future.append(float(pred))\n",
        "  \n",
        "    \n",
        "   \n",
        "plt.figure(figsize=(8, 6))\n",
        "test_day = [date for date in future_data['Date']]\n",
        "labels={'Predicted'}\n",
        "\n",
        "plt.plot(test_day, predictions, color= 'green')\n",
        "plt.title('Predicted Forecasting for next 30 days')\n",
        "plt.xlabel('Day')\n",
        "plt.ylabel('Closing Price')\n",
        "plt.legend(labels)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}