OBV.ipynb

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      "name": "OBV.ipynb",
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        "<a href=\"https://colab.research.google.com/github/gaditi123/Technical-Indicators-FA-Project/blob/main/OBV.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "(246, 7)\n"
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              "      <th>Open</th>\n",
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              "      <th>Adj Close</th>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "                   Open         High  ...    Adj Close    Volume\n",
              "Date                                  ...                       \n",
              "2019-03-22  1150.000000  1153.500000  ...  1128.533447  15201940\n",
              "2019-03-25  1130.000000  1143.925049  ...  1131.087036   4599630\n",
              "2019-03-26  1137.949951  1157.500000  ...  1145.985962   5893424\n",
              "2019-03-27  1159.275024  1164.025024  ...  1140.085938   6942650\n",
              "2019-03-28  1149.400024  1162.500000  ...  1141.746826   9777164\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "#importing required libraries\n",
        "hdfc=pd.read_csv('/content/HDFCBANK.NS.csv')\n",
        "print(hdfc.shape)\n",
        "\n",
        "hdfc.index = hdfc[\"Date\"].apply(lambda x: pd.Timestamp(x))\n",
        "hdfc.drop(\"Date\", axis=1, inplace=True)\n",
        "\n",
        "hdfc.tail()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "infy=pd.read_csv('/content/INFY.NS.csv')\n",
        "print(infy.shape)\n",
        "\n",
        "infy.index = infy[\"Date\"].apply(lambda x: pd.Timestamp(x))\n",
        "infy.drop(\"Date\", axis=1, inplace=True)\n",
        "infy.head()"
      ],
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            "(246, 7)\n"
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              "      <th>2018-04-06</th>\n",
              "      <td>571.500000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                  Open        High         Low       Close   Adj Close   Volume\n",
              "Date                                                                           \n",
              "2018-04-02  570.500000  574.775024  560.650024  568.575012  516.459229  8072702\n",
              "2018-04-03  567.349976  571.775024  564.049988  570.224976  517.957886  4077168\n",
              "2018-04-04  572.000000  572.275024  560.000000  562.099976  510.577576  4813302\n",
              "2018-04-05  569.775024  575.650024  564.549988  573.775024  521.182556  7763544\n",
              "2018-04-06  571.500000  573.000000  561.049988  563.500000  511.849365  5937742"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance\n",
        "!pip install ta"
      ],
      "metadata": {
        "id": "d06cvwTSd05N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import ta\n",
        "from datetime import date, timedelta, datetime\n",
        "from IPython.display import clear_output"
      ],
      "metadata": {
        "id": "sjBjvaxsd64n"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Visually show the stock price\n",
        "hdfc['Adj Close'].plot()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Adjusted Close\")\n",
        "plt.title(\"HDFC Price data\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "fMxz2xveeIfN",
        "outputId": "4fcfc809-bb49-4e15-c276-5c3c604dd03e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visually show the stock price\n",
        "infy['Adj Close'].plot()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Adjusted Close\")\n",
        "plt.title(\"Infosys Price data\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "L4G08gvsgxOG",
        "outputId": "1c01cf61-4b5d-45f6-a9e9-c941a6c7cb02"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#HDFC CALCULATIONS"
      ],
      "metadata": {
        "id": "hLFReeC3k0dm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate the On Balance Volume (OBV)\n",
        "OBV = []\n",
        "OBV.append(0)\n",
        "\n",
        "#Loop through the data set (close price) from the second row (index 1) to the end of the data set\n",
        "for i in range(1, len(hdfc.Close)):\n",
        "  if hdfc.Close[i] > hdfc.Close[i-1]:\n",
        "    OBV.append(OBV[-1] + hdfc.Volume[i])\n",
        "  elif hdfc.Close[i] < hdfc.Close[i-1]:\n",
        "      OBV.append(OBV[-1] - hdfc.Volume[i])\n",
        "  else:\n",
        "      OBV.append(OBV[-1])"
      ],
      "metadata": {
        "id": "ZOC2UUCvhA5P"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hdfc['OBV'] = OBV\n",
        "hdfc['OBV_EMA'] = hdfc['OBV'].ewm(span=20).mean()\n",
        "#Show the data\n",
        "hdfc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "wFCWa51PhkEg",
        "outputId": "951505a6-40b2-4a15-a6c7-dda69c8cd1d4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b1c92d8e-7ad0-4df4-aef5-62b958dd7a6a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>OBV</th>\n",
              "      <th>OBV_EMA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-04-02</th>\n",
              "      <td>945.250000</td>\n",
              "      <td>967.875000</td>\n",
              "      <td>945.250000</td>\n",
              "      <td>965.599976</td>\n",
              "      <td>951.427673</td>\n",
              "      <td>2298184</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-03</th>\n",
              "      <td>961.974976</td>\n",
              "      <td>962.849976</td>\n",
              "      <td>955.500000</td>\n",
              "      <td>957.950012</td>\n",
              "      <td>943.889954</td>\n",
              "      <td>2039942</td>\n",
              "      <td>-2039942</td>\n",
              "      <td>-1.070970e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-04</th>\n",
              "      <td>956.474976</td>\n",
              "      <td>962.474976</td>\n",
              "      <td>939.224976</td>\n",
              "      <td>941.625000</td>\n",
              "      <td>927.804565</td>\n",
              "      <td>1857054</td>\n",
              "      <td>-3896996</td>\n",
              "      <td>-2.108670e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-05</th>\n",
              "      <td>950.650024</td>\n",
              "      <td>957.000000</td>\n",
              "      <td>945.099976</td>\n",
              "      <td>954.450012</td>\n",
              "      <td>940.441345</td>\n",
              "      <td>2429446</td>\n",
              "      <td>-1467550</td>\n",
              "      <td>-1.923588e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-06</th>\n",
              "      <td>956.099976</td>\n",
              "      <td>963.224976</td>\n",
              "      <td>952.900024</td>\n",
              "      <td>961.700012</td>\n",
              "      <td>947.584900</td>\n",
              "      <td>1860004</td>\n",
              "      <td>392454</td>\n",
              "      <td>-1.363357e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-22</th>\n",
              "      <td>1150.000000</td>\n",
              "      <td>1153.500000</td>\n",
              "      <td>1135.400024</td>\n",
              "      <td>1138.074951</td>\n",
              "      <td>1128.533447</td>\n",
              "      <td>15201940</td>\n",
              "      <td>47022950</td>\n",
              "      <td>2.639791e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-25</th>\n",
              "      <td>1130.000000</td>\n",
              "      <td>1143.925049</td>\n",
              "      <td>1126.500000</td>\n",
              "      <td>1140.650024</td>\n",
              "      <td>1131.087036</td>\n",
              "      <td>4599630</td>\n",
              "      <td>51622580</td>\n",
              "      <td>2.880026e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-26</th>\n",
              "      <td>1137.949951</td>\n",
              "      <td>1157.500000</td>\n",
              "      <td>1136.300049</td>\n",
              "      <td>1155.675049</td>\n",
              "      <td>1145.985962</td>\n",
              "      <td>5893424</td>\n",
              "      <td>57516004</td>\n",
              "      <td>3.153509e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-27</th>\n",
              "      <td>1159.275024</td>\n",
              "      <td>1164.025024</td>\n",
              "      <td>1139.525024</td>\n",
              "      <td>1149.724976</td>\n",
              "      <td>1140.085938</td>\n",
              "      <td>6942650</td>\n",
              "      <td>50573354</td>\n",
              "      <td>3.334826e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-28</th>\n",
              "      <td>1149.400024</td>\n",
              "      <td>1162.500000</td>\n",
              "      <td>1143.925049</td>\n",
              "      <td>1151.400024</td>\n",
              "      <td>1141.746826</td>\n",
              "      <td>9777164</td>\n",
              "      <td>60350518</td>\n",
              "      <td>3.591990e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>246 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1c92d8e-7ad0-4df4-aef5-62b958dd7a6a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b1c92d8e-7ad0-4df4-aef5-62b958dd7a6a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b1c92d8e-7ad0-4df4-aef5-62b958dd7a6a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   Open         High  ...       OBV       OBV_EMA\n",
              "Date                                  ...                        \n",
              "2018-04-02   945.250000   967.875000  ...         0  0.000000e+00\n",
              "2018-04-03   961.974976   962.849976  ...  -2039942 -1.070970e+06\n",
              "2018-04-04   956.474976   962.474976  ...  -3896996 -2.108670e+06\n",
              "2018-04-05   950.650024   957.000000  ...  -1467550 -1.923588e+06\n",
              "2018-04-06   956.099976   963.224976  ...    392454 -1.363357e+06\n",
              "...                 ...          ...  ...       ...           ...\n",
              "2019-03-22  1150.000000  1153.500000  ...  47022950  2.639791e+07\n",
              "2019-03-25  1130.000000  1143.925049  ...  51622580  2.880026e+07\n",
              "2019-03-26  1137.949951  1157.500000  ...  57516004  3.153509e+07\n",
              "2019-03-27  1159.275024  1164.025024  ...  50573354  3.334826e+07\n",
              "2019-03-28  1149.400024  1162.500000  ...  60350518  3.591990e+07\n",
              "\n",
              "[246 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create and plot the graph\n",
        "plt.figure(figsize=(12.2, 4.5))\n",
        "plt.plot(hdfc['OBV'], label = 'OBV', color = 'orange')\n",
        "plt.plot(hdfc['OBV_EMA'], label = 'OBV_EMA', color = 'purple')\n",
        "\n",
        "plt.title('HDFC OBV curves')\n",
        "plt.xlabel('Date', fontsize = 18)\n",
        "plt.ylabel('Volume', fontsize = 18)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "AIzJzii2htGF",
        "outputId": "add99b09-fb45-4d3d-8c19-8a0cb9380a06"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 878.4x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a function to signal when to buy and sell an asset\n",
        "#If OBV > OBV_EMA Then Buy\n",
        "#If OBV < OBV_EMA Then Sell\n",
        "#Else Do nothing\n",
        "def buy_sell(signal, col1, col2):\n",
        "  sigPriceBuy = []\n",
        "  sigPriceSell = []\n",
        "  flag = -1 #A flag for the trend upward/downward\n",
        "  #Loop through the length of the data set\n",
        "  for i in range(0,len(signal)):\n",
        "    #if OBV > OBV_EMA  and flag != 1 then buy else sell\n",
        "      if signal[col1][i] > signal[col2][i] and flag != 1:\n",
        "          sigPriceBuy.append(signal['Close'][i])\n",
        "          sigPriceSell.append(np.nan)\n",
        "          flag = 1\n",
        "      #else  if OBV < OBV_EMA  and flag != 0 then sell else buy\n",
        "      elif signal[col1][i] < signal[col2][i] and flag != 0:    \n",
        "          sigPriceSell.append(signal['Close'][i])\n",
        "          sigPriceBuy.append(np.nan)\n",
        "          flag = 0\n",
        "      #else   OBV == OBV_EMA  so append NaN \n",
        "      else: \n",
        "        sigPriceBuy.append(np.nan)\n",
        "        sigPriceSell.append(np.nan)\n",
        "  \n",
        "  return (sigPriceBuy, sigPriceSell)"
      ],
      "metadata": {
        "id": "mPaeE4e2i4fb"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = buy_sell(hdfc, 'OBV','OBV_EMA' )\n",
        "hdfc['Buy_Signal_Price'] = x[0]\n",
        "hdfc['Sell_Signal_Price'] = x[1]\n",
        "#Show the data frame\n",
        "hdfc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "o6On55bEjAu1",
        "outputId": "add56c10-e803-4682-b1bd-f2f15592e432"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-71db00dc-2d56-4453-ae8d-7013c7afca31\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>OBV</th>\n",
              "      <th>OBV_EMA</th>\n",
              "      <th>Buy_Signal_Price</th>\n",
              "      <th>Sell_Signal_Price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-04-02</th>\n",
              "      <td>945.250000</td>\n",
              "      <td>967.875000</td>\n",
              "      <td>945.250000</td>\n",
              "      <td>965.599976</td>\n",
              "      <td>951.427673</td>\n",
              "      <td>2298184</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-03</th>\n",
              "      <td>961.974976</td>\n",
              "      <td>962.849976</td>\n",
              "      <td>955.500000</td>\n",
              "      <td>957.950012</td>\n",
              "      <td>943.889954</td>\n",
              "      <td>2039942</td>\n",
              "      <td>-2039942</td>\n",
              "      <td>-1.070970e+06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>957.950012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-04</th>\n",
              "      <td>956.474976</td>\n",
              "      <td>962.474976</td>\n",
              "      <td>939.224976</td>\n",
              "      <td>941.625000</td>\n",
              "      <td>927.804565</td>\n",
              "      <td>1857054</td>\n",
              "      <td>-3896996</td>\n",
              "      <td>-2.108670e+06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-05</th>\n",
              "      <td>950.650024</td>\n",
              "      <td>957.000000</td>\n",
              "      <td>945.099976</td>\n",
              "      <td>954.450012</td>\n",
              "      <td>940.441345</td>\n",
              "      <td>2429446</td>\n",
              "      <td>-1467550</td>\n",
              "      <td>-1.923588e+06</td>\n",
              "      <td>954.450012</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-06</th>\n",
              "      <td>956.099976</td>\n",
              "      <td>963.224976</td>\n",
              "      <td>952.900024</td>\n",
              "      <td>961.700012</td>\n",
              "      <td>947.584900</td>\n",
              "      <td>1860004</td>\n",
              "      <td>392454</td>\n",
              "      <td>-1.363357e+06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-22</th>\n",
              "      <td>1150.000000</td>\n",
              "      <td>1153.500000</td>\n",
              "      <td>1135.400024</td>\n",
              "      <td>1138.074951</td>\n",
              "      <td>1128.533447</td>\n",
              "      <td>15201940</td>\n",
              "      <td>47022950</td>\n",
              "      <td>2.639791e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-25</th>\n",
              "      <td>1130.000000</td>\n",
              "      <td>1143.925049</td>\n",
              "      <td>1126.500000</td>\n",
              "      <td>1140.650024</td>\n",
              "      <td>1131.087036</td>\n",
              "      <td>4599630</td>\n",
              "      <td>51622580</td>\n",
              "      <td>2.880026e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-26</th>\n",
              "      <td>1137.949951</td>\n",
              "      <td>1157.500000</td>\n",
              "      <td>1136.300049</td>\n",
              "      <td>1155.675049</td>\n",
              "      <td>1145.985962</td>\n",
              "      <td>5893424</td>\n",
              "      <td>57516004</td>\n",
              "      <td>3.153509e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-27</th>\n",
              "      <td>1159.275024</td>\n",
              "      <td>1164.025024</td>\n",
              "      <td>1139.525024</td>\n",
              "      <td>1149.724976</td>\n",
              "      <td>1140.085938</td>\n",
              "      <td>6942650</td>\n",
              "      <td>50573354</td>\n",
              "      <td>3.334826e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-28</th>\n",
              "      <td>1149.400024</td>\n",
              "      <td>1162.500000</td>\n",
              "      <td>1143.925049</td>\n",
              "      <td>1151.400024</td>\n",
              "      <td>1141.746826</td>\n",
              "      <td>9777164</td>\n",
              "      <td>60350518</td>\n",
              "      <td>3.591990e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>246 rows × 10 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   Open         High  ...  Buy_Signal_Price  Sell_Signal_Price\n",
              "Date                                  ...                                     \n",
              "2018-04-02   945.250000   967.875000  ...               NaN                NaN\n",
              "2018-04-03   961.974976   962.849976  ...               NaN         957.950012\n",
              "2018-04-04   956.474976   962.474976  ...               NaN                NaN\n",
              "2018-04-05   950.650024   957.000000  ...        954.450012                NaN\n",
              "2018-04-06   956.099976   963.224976  ...               NaN                NaN\n",
              "...                 ...          ...  ...               ...                ...\n",
              "2019-03-22  1150.000000  1153.500000  ...               NaN                NaN\n",
              "2019-03-25  1130.000000  1143.925049  ...               NaN                NaN\n",
              "2019-03-26  1137.949951  1157.500000  ...               NaN                NaN\n",
              "2019-03-27  1159.275024  1164.025024  ...               NaN                NaN\n",
              "2019-03-28  1149.400024  1162.500000  ...               NaN                NaN\n",
              "\n",
              "[246 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visually Show The Stock buy and sell signals\n",
        "#Create and plot the graph\n",
        "plt.figure(figsize=(12.2,4.5))\n",
        "plt.scatter(hdfc.index, hdfc['Buy_Signal_Price'], color = 'green', label='Buy Signal', marker = '^', alpha = 1)\n",
        "plt.scatter(hdfc.index, hdfc['Sell_Signal_Price'], color = 'red', label='Sell Signal', marker = 'v', alpha = 1)\n",
        "plt.plot( hdfc['Close'],  label='Close Price', alpha = 0.35)#plt.plot( X-Axis , Y-Axis, line_width, alpha_for_blending,  label)\n",
        "plt.xticks(rotation=45)\n",
        "plt.title('HDFC Buy / Sell Signals')\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Close Price',fontsize=18)\n",
        "plt.legend( loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "eX-mH5asjK7B",
        "outputId": "18f0854c-2837-49e2-a975-f4e6c08321fc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 878.4x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#INFOSYS CALCULATIONS"
      ],
      "metadata": {
        "id": "IwchzJBIk4k7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate the On Balance Volume (OBV)\n",
        "OBV = []\n",
        "OBV.append(0)\n",
        "\n",
        "#Loop through the data set (close price) from the second row (index 1) to the end of the data set\n",
        "for i in range(1, len(infy.Close)):\n",
        "  if infy.Close[i] > infy.Close[i-1]:\n",
        "    OBV.append(OBV[-1] + infy.Volume[i])\n",
        "  elif infy.Close[i] < infy.Close[i-1]:\n",
        "      OBV.append(OBV[-1] - infy.Volume[i])\n",
        "  else:\n",
        "      OBV.append(OBV[-1])"
      ],
      "metadata": {
        "id": "rFMzksmVk7rl"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "infy['OBV'] = OBV\n",
        "infy['OBV_EMA'] = infy['OBV'].ewm(span=20).mean()\n",
        "#Show the data\n",
        "infy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "yNHlqixalLAC",
        "outputId": "477fb483-1393-41b1-fc20-fe8cfaee3a94"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-76796801-1652-4f8a-ad64-ad5774a423f8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>OBV</th>\n",
              "      <th>OBV_EMA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-04-02</th>\n",
              "      <td>570.500000</td>\n",
              "      <td>574.775024</td>\n",
              "      <td>560.650024</td>\n",
              "      <td>568.575012</td>\n",
              "      <td>516.459229</td>\n",
              "      <td>8072702</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-03</th>\n",
              "      <td>567.349976</td>\n",
              "      <td>571.775024</td>\n",
              "      <td>564.049988</td>\n",
              "      <td>570.224976</td>\n",
              "      <td>517.957886</td>\n",
              "      <td>4077168</td>\n",
              "      <td>4077168</td>\n",
              "      <td>2.140513e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-04</th>\n",
              "      <td>572.000000</td>\n",
              "      <td>572.275024</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>562.099976</td>\n",
              "      <td>510.577576</td>\n",
              "      <td>4813302</td>\n",
              "      <td>-736134</td>\n",
              "      <td>1.084226e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-05</th>\n",
              "      <td>569.775024</td>\n",
              "      <td>575.650024</td>\n",
              "      <td>564.549988</td>\n",
              "      <td>573.775024</td>\n",
              "      <td>521.182556</td>\n",
              "      <td>7763544</td>\n",
              "      <td>7027410</td>\n",
              "      <td>2.799931e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-06</th>\n",
              "      <td>571.500000</td>\n",
              "      <td>573.000000</td>\n",
              "      <td>561.049988</td>\n",
              "      <td>563.500000</td>\n",
              "      <td>511.849365</td>\n",
              "      <td>5937742</td>\n",
              "      <td>1089668</td>\n",
              "      <td>2.386233e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-22</th>\n",
              "      <td>745.000000</td>\n",
              "      <td>746.349976</td>\n",
              "      <td>732.299988</td>\n",
              "      <td>743.099976</td>\n",
              "      <td>691.513062</td>\n",
              "      <td>8883260</td>\n",
              "      <td>387905584</td>\n",
              "      <td>3.752094e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-25</th>\n",
              "      <td>742.400024</td>\n",
              "      <td>742.400024</td>\n",
              "      <td>730.549988</td>\n",
              "      <td>735.250000</td>\n",
              "      <td>684.208008</td>\n",
              "      <td>7644966</td>\n",
              "      <td>380260618</td>\n",
              "      <td>3.756905e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-26</th>\n",
              "      <td>735.250000</td>\n",
              "      <td>735.250000</td>\n",
              "      <td>719.099976</td>\n",
              "      <td>727.750000</td>\n",
              "      <td>677.228577</td>\n",
              "      <td>9406261</td>\n",
              "      <td>370854357</td>\n",
              "      <td>3.752299e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-27</th>\n",
              "      <td>730.000000</td>\n",
              "      <td>732.299988</td>\n",
              "      <td>727.150024</td>\n",
              "      <td>729.700012</td>\n",
              "      <td>679.043274</td>\n",
              "      <td>7387487</td>\n",
              "      <td>378241844</td>\n",
              "      <td>3.755167e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-28</th>\n",
              "      <td>730.000000</td>\n",
              "      <td>745.000000</td>\n",
              "      <td>730.000000</td>\n",
              "      <td>737.799988</td>\n",
              "      <td>686.580994</td>\n",
              "      <td>9277241</td>\n",
              "      <td>387519085</td>\n",
              "      <td>3.766598e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>246 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76796801-1652-4f8a-ad64-ad5774a423f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-76796801-1652-4f8a-ad64-ad5774a423f8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-76796801-1652-4f8a-ad64-ad5774a423f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  Open        High  ...        OBV       OBV_EMA\n",
              "Date                                ...                         \n",
              "2018-04-02  570.500000  574.775024  ...          0  0.000000e+00\n",
              "2018-04-03  567.349976  571.775024  ...    4077168  2.140513e+06\n",
              "2018-04-04  572.000000  572.275024  ...    -736134  1.084226e+06\n",
              "2018-04-05  569.775024  575.650024  ...    7027410  2.799931e+06\n",
              "2018-04-06  571.500000  573.000000  ...    1089668  2.386233e+06\n",
              "...                ...         ...  ...        ...           ...\n",
              "2019-03-22  745.000000  746.349976  ...  387905584  3.752094e+08\n",
              "2019-03-25  742.400024  742.400024  ...  380260618  3.756905e+08\n",
              "2019-03-26  735.250000  735.250000  ...  370854357  3.752299e+08\n",
              "2019-03-27  730.000000  732.299988  ...  378241844  3.755167e+08\n",
              "2019-03-28  730.000000  745.000000  ...  387519085  3.766598e+08\n",
              "\n",
              "[246 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create and plot the graph\n",
        "plt.figure(figsize=(12.2, 4.5))\n",
        "plt.plot(infy['OBV'], label = 'OBV', color = 'orange')\n",
        "plt.plot(infy['OBV_EMA'], label = 'OBV_EMA', color = 'purple')\n",
        "\n",
        "plt.title('INFOSYS OBV curves')\n",
        "plt.xlabel('Date', fontsize = 18)\n",
        "plt.ylabel('Volume', fontsize = 18)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "SL0DlijX4eo-",
        "outputId": "b55f85db-d722-450b-8a1d-5457c7be041e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 878.4x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = buy_sell(infy, 'OBV','OBV_EMA' )\n",
        "infy['Buy_Signal_Price'] = x[0]\n",
        "infy['Sell_Signal_Price'] = x[1]\n",
        "#Show the data frame\n",
        "infy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "w3EvY8gl4rnW",
        "outputId": "23cb8a7b-fc09-43a0-8f40-4affa56236a8"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c21e722f-c09c-4b02-a751-ba44d4eebf2f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>OBV</th>\n",
              "      <th>OBV_EMA</th>\n",
              "      <th>Buy_Signal_Price</th>\n",
              "      <th>Sell_Signal_Price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-04-02</th>\n",
              "      <td>570.500000</td>\n",
              "      <td>574.775024</td>\n",
              "      <td>560.650024</td>\n",
              "      <td>568.575012</td>\n",
              "      <td>516.459229</td>\n",
              "      <td>8072702</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-03</th>\n",
              "      <td>567.349976</td>\n",
              "      <td>571.775024</td>\n",
              "      <td>564.049988</td>\n",
              "      <td>570.224976</td>\n",
              "      <td>517.957886</td>\n",
              "      <td>4077168</td>\n",
              "      <td>4077168</td>\n",
              "      <td>2.140513e+06</td>\n",
              "      <td>570.224976</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-04</th>\n",
              "      <td>572.000000</td>\n",
              "      <td>572.275024</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>562.099976</td>\n",
              "      <td>510.577576</td>\n",
              "      <td>4813302</td>\n",
              "      <td>-736134</td>\n",
              "      <td>1.084226e+06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>562.099976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-05</th>\n",
              "      <td>569.775024</td>\n",
              "      <td>575.650024</td>\n",
              "      <td>564.549988</td>\n",
              "      <td>573.775024</td>\n",
              "      <td>521.182556</td>\n",
              "      <td>7763544</td>\n",
              "      <td>7027410</td>\n",
              "      <td>2.799931e+06</td>\n",
              "      <td>573.775024</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-06</th>\n",
              "      <td>571.500000</td>\n",
              "      <td>573.000000</td>\n",
              "      <td>561.049988</td>\n",
              "      <td>563.500000</td>\n",
              "      <td>511.849365</td>\n",
              "      <td>5937742</td>\n",
              "      <td>1089668</td>\n",
              "      <td>2.386233e+06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>563.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-22</th>\n",
              "      <td>745.000000</td>\n",
              "      <td>746.349976</td>\n",
              "      <td>732.299988</td>\n",
              "      <td>743.099976</td>\n",
              "      <td>691.513062</td>\n",
              "      <td>8883260</td>\n",
              "      <td>387905584</td>\n",
              "      <td>3.752094e+08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-25</th>\n",
              "      <td>742.400024</td>\n",
              "      <td>742.400024</td>\n",
              "      <td>730.549988</td>\n",
              "      <td>735.250000</td>\n",
              "      <td>684.208008</td>\n",
              "      <td>7644966</td>\n",
              "      <td>380260618</td>\n",
              "      <td>3.756905e+08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-26</th>\n",
              "      <td>735.250000</td>\n",
              "      <td>735.250000</td>\n",
              "      <td>719.099976</td>\n",
              "      <td>727.750000</td>\n",
              "      <td>677.228577</td>\n",
              "      <td>9406261</td>\n",
              "      <td>370854357</td>\n",
              "      <td>3.752299e+08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>727.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-27</th>\n",
              "      <td>730.000000</td>\n",
              "      <td>732.299988</td>\n",
              "      <td>727.150024</td>\n",
              "      <td>729.700012</td>\n",
              "      <td>679.043274</td>\n",
              "      <td>7387487</td>\n",
              "      <td>378241844</td>\n",
              "      <td>3.755167e+08</td>\n",
              "      <td>729.700012</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-28</th>\n",
              "      <td>730.000000</td>\n",
              "      <td>745.000000</td>\n",
              "      <td>730.000000</td>\n",
              "      <td>737.799988</td>\n",
              "      <td>686.580994</td>\n",
              "      <td>9277241</td>\n",
              "      <td>387519085</td>\n",
              "      <td>3.766598e+08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>246 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c21e722f-c09c-4b02-a751-ba44d4eebf2f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "          document.querySelector('#df-c21e722f-c09c-4b02-a751-ba44d4eebf2f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c21e722f-c09c-4b02-a751-ba44d4eebf2f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  Open        High  ...  Buy_Signal_Price  Sell_Signal_Price\n",
              "Date                                ...                                     \n",
              "2018-04-02  570.500000  574.775024  ...               NaN                NaN\n",
              "2018-04-03  567.349976  571.775024  ...        570.224976                NaN\n",
              "2018-04-04  572.000000  572.275024  ...               NaN         562.099976\n",
              "2018-04-05  569.775024  575.650024  ...        573.775024                NaN\n",
              "2018-04-06  571.500000  573.000000  ...               NaN         563.500000\n",
              "...                ...         ...  ...               ...                ...\n",
              "2019-03-22  745.000000  746.349976  ...               NaN                NaN\n",
              "2019-03-25  742.400024  742.400024  ...               NaN                NaN\n",
              "2019-03-26  735.250000  735.250000  ...               NaN         727.750000\n",
              "2019-03-27  730.000000  732.299988  ...        729.700012                NaN\n",
              "2019-03-28  730.000000  745.000000  ...               NaN                NaN\n",
              "\n",
              "[246 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visually Show The Stock buy and sell signals\n",
        "#Create and plot the graph\n",
        "plt.figure(figsize=(12.2,4.5))\n",
        "plt.scatter(infy.index, infy['Buy_Signal_Price'], color = 'green', label='Buy Signal', marker = '^', alpha = 1)\n",
        "plt.scatter(infy.index, infy['Sell_Signal_Price'], color = 'red', label='Sell Signal', marker = 'v', alpha = 1)\n",
        "plt.plot(infy['Close'],  label='Close Price', alpha = 0.35)\n",
        "plt.xticks(rotation=45)\n",
        "plt.title('INFOSYS Buy / Sell Signals')\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Close Price',fontsize=18)\n",
        "plt.legend( loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "21AKFIQp4z-f",
        "outputId": "3e7be251-8db7-4dde-d9d6-aa12c1a6a3fe"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 878.4x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}