Relative_Strength_Index.ipynb

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      "name": "Relative Strength Index.ipynb",
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      "authorship_tag": "ABX9TyNmPWgAFWbYzp7IPBT6472j",
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    {
      "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/Relative_Strength_Index.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8mPG7QtWYCZ"
      },
      "source": [
        "### **I3: Relative Strength Index (RSI)**\n",
        "\n",
        "1. It's a momentum indicator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions.\n",
        "\n",
        "2. Generally, an asset is considered overbought when the RSI is > 70% and oversold when it's < 30%\n",
        "\n",
        "Details: https://www.investopedia.com/terms/r/rsi.asp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRra6kEsWVKT"
      },
      "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": {
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        },
        "id": "tbtJvlMeWdst",
        "outputId": "e751c551-f790-4002-e8e7-f7443f90a687"
      },
      "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 8.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement 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: 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: 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: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2020.12.5)\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=a478d363407033cc54747ed28f416f34cd529bbbbdffeed5fe34cb55de476f71\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": "7tKx77z_WfYf"
      },
      "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": "kuOBlXluWgpP",
        "outputId": "b58ca209-3ae9-4a07-f2df-239c5a5a5160"
      },
      "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",
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            ],
            "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": {
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          "height": 250
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        "id": "UU5twKjFWijp",
        "outputId": "23babb71-10a8-4908-f4ee-da1cfd55cb7f"
      },
      "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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              "  <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": "qhQR5L8DWkXd"
      },
      "source": [
        "#Defining Function\n",
        "\n",
        "def get_RSI(data):\n",
        "  \n",
        "  data1 = data\n",
        "  diff = data1['Close'].diff()\n",
        "    \n",
        "  #Up change is equal to the positive difference, otherwise equal to zero\n",
        "  Up = diff.clip(lower=0)\n",
        "  #Down change is equal to negative deifference, otherwise equal to zero\n",
        "  Down = -1*diff.clip(upper=0)\n",
        "\n",
        "  Up_Change_Avg = Up.ewm(com = 13, adjust = False).mean()\n",
        "  Down_Change_Avg = Down.ewm(com = 13, adjust = False).mean()\n",
        "\n",
        "  Relative_Strength = (Up_Change_Avg/Down_Change_Avg)\n",
        "  RSI = 100 - (100/(1+Relative_Strength))\n",
        "  data['RSI'] = RSI \n",
        "\n",
        "  return data"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "PT1hx1l8Wmj3",
        "outputId": "336a1eb1-e932-49c5-ca6a-f5d7b947e77f"
      },
      "source": [
        "#Implementing the function\n",
        "get_RSI(df1)\n",
        "df1.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "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",
              "      <th>RSI</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-09</th>\n",
              "      <td>179.51</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-10</th>\n",
              "      <td>180.098</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-11</th>\n",
              "      <td>175.895</td>\n",
              "      <td>70.027602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-12</th>\n",
              "      <td>175.352</td>\n",
              "      <td>67.227558</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Close         RSI\n",
              "Date                           \n",
              "2020-06-08  178.742         NaN\n",
              "2020-06-09   179.51  100.000000\n",
              "2020-06-10  180.098  100.000000\n",
              "2020-06-11  175.895   70.027602\n",
              "2020-06-12  175.352   67.227558"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "id": "y3WSJ0DaWovh",
        "outputId": "4fb79b6a-7b0e-4b3b-c63b-a3aa7fb67156"
      },
      "source": [
        "#Plotting RSI \n",
        "\n",
        "#Plotting Closing Price\n",
        "plt.figure(figsize=(17,8))\n",
        "plt.plot(df1['Close'])\n",
        "plt.title('Closing Price Chart')\n",
        "plt.show()\n",
        "\n",
        "#Plotting corresponding RSI values and significant levels\n",
        "plt.figure(figsize=(17,8))\n",
        "plt.title('RSI chart')\n",
        "plt.plot(df1['RSI'])\n",
        "\n",
        "#Significant Level A\n",
        "plt.axhline(0, linestyle='--', alpha=0.1, color = 'g')\n",
        "plt.axhline(20, linestyle='--', alpha=0.5, color = 'r')\n",
        "plt.axhline(30, linestyle='--')\n",
        "\n",
        "#Significant Level B\n",
        "plt.axhline(70, linestyle='--')\n",
        "plt.axhline(80, linestyle='--', alpha=0.5, color = 'g')\n",
        "plt.axhline(100, linestyle='--', alpha=0.1, color = 'r')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "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"
          }
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}