Exponential_Moving_Average.ipynb

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    "colab": {
      "name": "Exponential Moving Average.ipynb",
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      "authorship_tag": "ABX9TyPEoHJ5kLR4g4MoiYxkKqy5",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ishika-mittal/Stock-Prices-Analytics-Dashboard/blob/main/Exponential_Moving_Average.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MekUePD1Vsdp"
      },
      "source": [
        "### **I2: Exponential Moving Average (EMA)**\n",
        "\n",
        "EMA is similar to SMA with just one difference - it gives more weight to recent prices in an attempt to make it more responsive to new information. \n",
        "\n",
        "Details: https://www.investopedia.com/terms/m/movingaverage.asp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dp21u475VOos"
      },
      "source": [
        "#Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas_datareader import data\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "from datetime import timedelta\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOj3MA-WVSuo",
        "outputId": "0fbf9161-f7e9-4227-d4a6-7e77004dccd0"
      },
      "source": [
        "#Installing yfinance library for fetching historical stock data\n",
        "!pip install yfinance\n",
        "import yfinance as yf"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/a7/ee/315752b9ef281ba83c62aa7ec2e2074f85223da6e7e74efb4d3e11c0f510/yfinance-0.1.59.tar.gz\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/c0/d0526314971fc661b083ab135747dc68446a3022686da8c16d25fcf6ef07/lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3MB 6.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.59-py2.py3-none-any.whl size=23455 sha256=7934a9e4b718aefd43af8240dbca93aa7bfb2dae883a7be8f0cf16cb08815a68\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/2a/0f/4b5a86e1d52e451757eb6bc17fd899629f0925c777741b6d04\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.3 yfinance-0.1.59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KG1wsOF_VUi3"
      },
      "source": [
        "#Function to get the stock data\n",
        "def stock_data(stock, start_date, end_date):\n",
        "\n",
        "    tickerSymbol = stock\n",
        "\n",
        "    #get data on this ticker\n",
        "    tickerData = yf.Ticker(tickerSymbol)\n",
        "\n",
        "    #get the historical prices for this ticker\n",
        "    tickerDf = tickerData.history(period='1d', start=start_date, end=end_date)\n",
        "\n",
        "    return tickerDf"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "iAwZHdFmVbh0",
        "outputId": "adc40383-01d6-4845-deed-ebaff978583d"
      },
      "source": [
        "#Fetching a 1Y dataset for better representation\n",
        "\n",
        "df = stock_data(\"ITC.BO\", \"2020-06-08\", \"2021-06-08\")\n",
        "df.reset_index(level=\"Date\", inplace=True)\n",
        "df.drop(columns = ['Dividends', 'Stock Splits'], inplace=True)\n",
        "\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>182.945035</td>\n",
              "      <td>184.074883</td>\n",
              "      <td>178.199672</td>\n",
              "      <td>178.742004</td>\n",
              "      <td>744734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-06-09</td>\n",
              "      <td>179.826649</td>\n",
              "      <td>186.289389</td>\n",
              "      <td>178.696801</td>\n",
              "      <td>179.510300</td>\n",
              "      <td>1341970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-06-10</td>\n",
              "      <td>180.052627</td>\n",
              "      <td>183.668142</td>\n",
              "      <td>179.284339</td>\n",
              "      <td>180.097824</td>\n",
              "      <td>1024793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-06-11</td>\n",
              "      <td>180.775730</td>\n",
              "      <td>181.905578</td>\n",
              "      <td>174.990912</td>\n",
              "      <td>175.894791</td>\n",
              "      <td>855456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-12</td>\n",
              "      <td>171.375387</td>\n",
              "      <td>176.482296</td>\n",
              "      <td>169.974373</td>\n",
              "      <td>175.352448</td>\n",
              "      <td>980250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date        Open        High         Low       Close   Volume\n",
              "0 2020-06-08  182.945035  184.074883  178.199672  178.742004   744734\n",
              "1 2020-06-09  179.826649  186.289389  178.696801  179.510300  1341970\n",
              "2 2020-06-10  180.052627  183.668142  179.284339  180.097824  1024793\n",
              "3 2020-06-11  180.775730  181.905578  174.990912  175.894791   855456\n",
              "4 2020-06-12  171.375387  176.482296  169.974373  175.352448   980250"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 250
        },
        "id": "su4YCUcnVe7P",
        "outputId": "c3bf6c25-fb51-47ca-cdaf-040d86e41b96"
      },
      "source": [
        "#Creating a dataset with only Closing Price column and Date as index\n",
        "\n",
        "data = df.sort_index(ascending=True,axis=0)\n",
        "df1 = pd.DataFrame(index=range(0,len(df)),columns=['Date','Close'])\n",
        "for i in range(0,len(data)):\n",
        "    df1[\"Date\"][i]=data['Date'][i]\n",
        "    df1[\"Close\"][i]=data[\"Close\"][i]\n",
        "\n",
        "df1.index=df1.Date\n",
        "df1.drop(\"Date\",axis=1,inplace=True)\n",
        "print(df1.shape)\n",
        "df1.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-06-08</th>\n",
              "      <td>178.742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-09</th>\n",
              "      <td>179.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-10</th>\n",
              "      <td>180.098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-11</th>\n",
              "      <td>175.895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-12</th>\n",
              "      <td>175.352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Close\n",
              "Date               \n",
              "2020-06-08  178.742\n",
              "2020-06-09   179.51\n",
              "2020-06-10  180.098\n",
              "2020-06-11  175.895\n",
              "2020-06-12  175.352"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZU9zQhsAVgwf"
      },
      "source": [
        "#Defining Function\n",
        "\n",
        "def EMA(data, window_size):\n",
        "\n",
        "  data1 = data\n",
        "  data1 = pd.Series(data1[\"Close\"])\n",
        "\n",
        "  #Calculating Exponential Moving average for a given time period\n",
        "  exponential_ma = data1.ewm(span = window_size, adjust = False).mean()\n",
        "  #Parsing the results as a new column in the existing data frame\n",
        "  data[\"Exponential_MA\"] = exponential_ma\n",
        "\n",
        "  return data"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "li7t6tCzVjvR",
        "outputId": "661b9da0-ee5a-4ea9-b0c1-ff8cc52369f7"
      },
      "source": [
        "#Implementing the function\n",
        "EMA(df1,30)\n",
        "df1.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Exponential_MA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-06-08</th>\n",
              "      <td>178.742</td>\n",
              "      <td>178.742004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-09</th>\n",
              "      <td>179.51</td>\n",
              "      <td>178.791572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-10</th>\n",
              "      <td>180.098</td>\n",
              "      <td>178.875846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-11</th>\n",
              "      <td>175.895</td>\n",
              "      <td>178.683520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-12</th>\n",
              "      <td>175.352</td>\n",
              "      <td>178.468612</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Close  Exponential_MA\n",
              "Date                               \n",
              "2020-06-08  178.742      178.742004\n",
              "2020-06-09   179.51      178.791572\n",
              "2020-06-10  180.098      178.875846\n",
              "2020-06-11  175.895      178.683520\n",
              "2020-06-12  175.352      178.468612"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "LQ4YQ-HNVlYg",
        "outputId": "b7448167-8257-491b-92bf-41be5acb50df"
      },
      "source": [
        "#Plotting Exponential Moving Average wrt to Closing Price\n",
        "\n",
        "#Setting labels\n",
        "plt.figure(figsize=(17,8))\n",
        "plt.xlabel(\"Year\")\n",
        "\n",
        "plt.ylabel(\"Closing Price in INR\")\n",
        "plt.title(\"EXPONENTIAL MOVING AVERAGE INDICATOR\")\n",
        "\n",
        "#Let's go\n",
        "plt.plot(df1[['Close', 'Exponential_MA']])\n",
        "plt.legend([\"Closing Price\",\"EMA\"], loc =\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}