AliceBlue_Sample_Program_6_Coding_and_Plotting_Exponential_Moving_Average_Indicator_of_Infosys_Using_Python_By_Unofficed.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPmBoWny9sJUcxEqHimbRY8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aeron7/alice_blue_pro/blob/main/AliceBlue_Sample_Program_6_Coding_and_Plotting_Exponential_Moving_Average_Indicator_of_Infosys_Using_Python_By_Unofficed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's install the Library\n",
        "!pip install alice_blue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lu6dFBeb3vOX",
        "outputId": "94d266f2-a0a1-474d-b508-7d25f0e9eda5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/krishnavelu/alice_blue.git@develop\n",
            "  Cloning https://github.com/krishnavelu/alice_blue.git (to revision develop) to /tmp/pip-req-build-ypx8fep2\n",
            "  Running command git clone -q https://github.com/krishnavelu/alice_blue.git /tmp/pip-req-build-ypx8fep2\n",
            "  Running command git checkout -b develop --track origin/develop\n",
            "  Switched to a new branch 'develop'\n",
            "  Branch 'develop' set up to track remote branch 'develop' from 'origin'.\n",
            "Collecting cryptography\n",
            "  Downloading cryptography-38.0.1-cp36-abi3-manylinux_2_24_x86_64.whl (4.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0 MB 15.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from alice-blue==2.0.3) (2022.2.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from alice-blue==2.0.3) (2.23.0)\n",
            "Collecting websocket_client\n",
            "  Downloading websocket_client-1.4.1-py3-none-any.whl (55 kB)\n",
            "\u001b[K     |████████████████████████████████| 55 kB 3.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.7/dist-packages (from cryptography->alice-blue==2.0.3) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography->alice-blue==2.0.3) (2.21)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->alice-blue==2.0.3) (3.0.4)\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->alice-blue==2.0.3) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->alice-blue==2.0.3) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->alice-blue==2.0.3) (2.10)\n",
            "Building wheels for collected packages: alice-blue\n",
            "  Building wheel for alice-blue (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alice-blue: filename=alice_blue-2.0.3-py2.py3-none-any.whl size=35040 sha256=291812c016c03deea8b0692a11a75d689f49004b902c106e98d1f4b89bfcd65d\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-g6m2dy4q/wheels/f6/c8/7c/5fa1c2f0d20829ef7bf42b48667da66482e0971bf2f1cb4dd0\n",
            "Successfully built alice-blue\n",
            "Installing collected packages: websocket-client, cryptography, alice-blue\n",
            "Successfully installed alice-blue-2.0.3 cryptography-38.0.1 websocket-client-1.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Copying the base code to get the alice function\n",
        "\n",
        "#Importing AliceBlue Library\n",
        "from alice_blue import *\n",
        "\n",
        "#Only Replace it with your credentails\n",
        "#Follow https://unofficed.com/how-to-activate-the-api-in-alice-blue-for-automated-trading/ for generating api_ID and api_Secret \n",
        "\n",
        "#Only Replace it with your credentails\n",
        "username    = \"enter_your_user_name\" \n",
        "password    = \"enter_your_password\" \n",
        "twoFA       = \"enter_byear\"\n",
        "app_id      = \"enter_app_id\"\n",
        "api_secret  = \"enter_api_secret\"\n",
        "\n",
        "#Generating Session ID\n",
        "session_id = AliceBlue.login_and_get_sessionID(username=username,password=password,twoFA=twoFA,app_id=app_id,api_secret=api_secret)\n",
        "\n",
        "#Generate an Alice object using the session_id\n",
        "alice = AliceBlue(username = username, session_id = session_id, master_contracts_to_download=['NSE', 'BSE','NFO','MCX'])"
      ],
      "metadata": {
        "id": "_1_eUIe830Z1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "from matplotlib import pyplot as plt \n",
        "\n",
        "payload = alice.historical_data(alice.get_instrument_by_symbol(\"NSE\", \"INFY-EQ\"),datetime.datetime(2022, 9, 15),datetime.datetime(2022, 9, 20),HistoricalDataType.Minute)\n",
        "df = pd.DataFrame.from_records(payload[\"result\"])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "dVqrNoPmA-6m",
        "outputId": "3e4a0254-309a-481e-a530-ce588b86c255"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      volume     high      low                 time    close     open\n",
              "0    13692.0  1435.30  1434.70  2022-09-15 11:22:59  1435.25  1434.85\n",
              "1    17590.0  1435.95  1435.25  2022-09-15 11:23:59  1435.80  1435.30\n",
              "2    18878.0  1436.00  1435.00  2022-09-15 11:24:59  1435.85  1435.80\n",
              "3    15943.0  1436.45  1435.40  2022-09-15 11:25:59  1435.40  1435.95\n",
              "4    10990.0  1436.00  1435.30  2022-09-15 11:26:59  1435.70  1435.40\n",
              "..       ...      ...      ...                  ...      ...      ...\n",
              "995  58053.0  1390.15  1389.70  2022-09-19 15:25:59  1389.75  1390.10\n",
              "996  40059.0  1390.70  1389.65  2022-09-19 15:26:59  1390.15  1390.00\n",
              "997  35528.0  1390.45  1389.95  2022-09-19 15:27:59  1390.15  1390.00\n",
              "998  30427.0  1391.00  1390.15  2022-09-19 15:28:59  1390.80  1390.20\n",
              "999  21458.0  1391.20  1390.00  2022-09-19 15:29:59  1391.20  1390.75\n",
              "\n",
              "[1000 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-54ed9ac4-f33d-4f4f-9e8c-f2a7a0406f84\">\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>volume</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>time</th>\n",
              "      <th>close</th>\n",
              "      <th>open</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13692.0</td>\n",
              "      <td>1435.30</td>\n",
              "      <td>1434.70</td>\n",
              "      <td>2022-09-15 11:22:59</td>\n",
              "      <td>1435.25</td>\n",
              "      <td>1434.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17590.0</td>\n",
              "      <td>1435.95</td>\n",
              "      <td>1435.25</td>\n",
              "      <td>2022-09-15 11:23:59</td>\n",
              "      <td>1435.80</td>\n",
              "      <td>1435.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18878.0</td>\n",
              "      <td>1436.00</td>\n",
              "      <td>1435.00</td>\n",
              "      <td>2022-09-15 11:24:59</td>\n",
              "      <td>1435.85</td>\n",
              "      <td>1435.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15943.0</td>\n",
              "      <td>1436.45</td>\n",
              "      <td>1435.40</td>\n",
              "      <td>2022-09-15 11:25:59</td>\n",
              "      <td>1435.40</td>\n",
              "      <td>1435.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10990.0</td>\n",
              "      <td>1436.00</td>\n",
              "      <td>1435.30</td>\n",
              "      <td>2022-09-15 11:26:59</td>\n",
              "      <td>1435.70</td>\n",
              "      <td>1435.40</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>58053.0</td>\n",
              "      <td>1390.15</td>\n",
              "      <td>1389.70</td>\n",
              "      <td>2022-09-19 15:25:59</td>\n",
              "      <td>1389.75</td>\n",
              "      <td>1390.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>40059.0</td>\n",
              "      <td>1390.70</td>\n",
              "      <td>1389.65</td>\n",
              "      <td>2022-09-19 15:26:59</td>\n",
              "      <td>1390.15</td>\n",
              "      <td>1390.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>35528.0</td>\n",
              "      <td>1390.45</td>\n",
              "      <td>1389.95</td>\n",
              "      <td>2022-09-19 15:27:59</td>\n",
              "      <td>1390.15</td>\n",
              "      <td>1390.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>30427.0</td>\n",
              "      <td>1391.00</td>\n",
              "      <td>1390.15</td>\n",
              "      <td>2022-09-19 15:28:59</td>\n",
              "      <td>1390.80</td>\n",
              "      <td>1390.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>21458.0</td>\n",
              "      <td>1391.20</td>\n",
              "      <td>1390.00</td>\n",
              "      <td>2022-09-19 15:29:59</td>\n",
              "      <td>1391.20</td>\n",
              "      <td>1390.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54ed9ac4-f33d-4f4f-9e8c-f2a7a0406f84')\"\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-54ed9ac4-f33d-4f4f-9e8c-f2a7a0406f84 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-54ed9ac4-f33d-4f4f-9e8c-f2a7a0406f84');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's write the function for Exponential Moving Average \n",
        "\n",
        "def plot_ema(stock_symbol, df, MA_period = 20):\n",
        "\n",
        "  df['moving_avg_exp'] = df[\"close\"].ewm(span=MA_period,min_periods=MA_period-1).mean()\n",
        "\n",
        "  # plotting and visualization of simple moving average\n",
        "  df['close'].plot(c = 'k', figsize = (20,10), lw = 2, fontsize = 12)\n",
        "  df['moving_avg_exp'].plot(c = 'b', figsize = (20, 10), lw = 1)\n",
        "\n",
        "  # show plot\n",
        "  plt.title('Exponential Moving Average: {}'.format(stock_symbol), fontsize = 20)\n",
        "  plt.ylabel('Price in INR(₹)',fontsize = 15 )\n",
        "  plt.xlabel('Date', fontsize = 15 )\n",
        "  plt.legend()\n",
        "  plt.grid()\n",
        "  plt.show()\n",
        "\n",
        "plot_ema(\"INFY\", df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "XXzDd5zYM9-X",
        "outputId": "eb724aad-64b4-4fa6-fdad-247ef37c5225"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}