Bollinger_Bands.ipynb

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  "metadata": {
    "colab": {
      "name": "Bollinger Bands",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrmWVWYcj5bC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "86744383-9c47-4e0f-efa1-cc44efb54165"
      },
      "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",
        "hdfc.tail()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(246, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>2019-03-22</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>2019-03-25</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>2019-03-26</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>2019-03-27</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>2019-03-28</td>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date         Open         High  ...        Close    Adj Close    Volume\n",
              "241  2019-03-22  1150.000000  1153.500000  ...  1138.074951  1128.533447  15201940\n",
              "242  2019-03-25  1130.000000  1143.925049  ...  1140.650024  1131.087036   4599630\n",
              "243  2019-03-26  1137.949951  1157.500000  ...  1155.675049  1145.985962   5893424\n",
              "244  2019-03-27  1159.275024  1164.025024  ...  1149.724976  1140.085938   6942650\n",
              "245  2019-03-28  1149.400024  1162.500000  ...  1151.400024  1141.746826   9777164\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruKPM_whutRu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "f39c4aa9-499f-491d-cbfc-05432470f612"
      },
      "source": [
        "infy=pd.read_csv('/content/INFY.NS.csv')\n",
        "print(infy.shape)\n",
        "infy.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(246, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-04-02</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-04-03</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-04-04</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-04-05</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-04-06</td>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date        Open        High  ...       Close   Adj Close   Volume\n",
              "0  2018-04-02  570.500000  574.775024  ...  568.575012  516.459229  8072702\n",
              "1  2018-04-03  567.349976  571.775024  ...  570.224976  517.957886  4077168\n",
              "2  2018-04-04  572.000000  572.275024  ...  562.099976  510.577576  4813302\n",
              "3  2018-04-05  569.775024  575.650024  ...  573.775024  521.182556  7763544\n",
              "4  2018-04-06  571.500000  573.000000  ...  563.500000  511.849365  5937742\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODsPMxaiuuHc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "957ba2f8-5c84-424a-cd5c-fec08e9bf94e"
      },
      "source": [
        "tata=pd.read_csv('/content/TATAMOTORS.NS.csv')\n",
        "print(tata.shape)\n",
        "tata.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(246, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-04-02</td>\n",
              "      <td>335.000000</td>\n",
              "      <td>340.799988</td>\n",
              "      <td>332.700012</td>\n",
              "      <td>339.149994</td>\n",
              "      <td>339.149994</td>\n",
              "      <td>9874486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-04-03</td>\n",
              "      <td>338.399994</td>\n",
              "      <td>344.500000</td>\n",
              "      <td>337.149994</td>\n",
              "      <td>343.100006</td>\n",
              "      <td>343.100006</td>\n",
              "      <td>6989584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-04-04</td>\n",
              "      <td>346.799988</td>\n",
              "      <td>363.700012</td>\n",
              "      <td>346.250000</td>\n",
              "      <td>355.600006</td>\n",
              "      <td>355.600006</td>\n",
              "      <td>27157199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-04-05</td>\n",
              "      <td>362.600006</td>\n",
              "      <td>372.350006</td>\n",
              "      <td>359.799988</td>\n",
              "      <td>362.200012</td>\n",
              "      <td>362.200012</td>\n",
              "      <td>28886070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-04-06</td>\n",
              "      <td>362.000000</td>\n",
              "      <td>368.350006</td>\n",
              "      <td>356.450012</td>\n",
              "      <td>364.100006</td>\n",
              "      <td>364.100006</td>\n",
              "      <td>13297051</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date        Open        High  ...       Close   Adj Close    Volume\n",
              "0  2018-04-02  335.000000  340.799988  ...  339.149994  339.149994   9874486\n",
              "1  2018-04-03  338.399994  344.500000  ...  343.100006  343.100006   6989584\n",
              "2  2018-04-04  346.799988  363.700012  ...  355.600006  355.600006  27157199\n",
              "3  2018-04-05  362.600006  372.350006  ...  362.200012  362.200012  28886070\n",
              "4  2018-04-06  362.000000  368.350006  ...  364.100006  364.100006  13297051\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEyuVeZtwETJ"
      },
      "source": [
        "!pip install yfinance\n",
        "!pip install ta\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdkS89etwMt8"
      },
      "source": [
        "import yfinance as yf\n",
        "import ta\n",
        "import pandas as pd\n",
        "from datetime import date, timedelta, datetime\n",
        "from IPython.display import clear_output"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3_jYzO2H3Xa"
      },
      "source": [
        "!pip3 install numpy==1.20.0\n",
        "!pip3 install pandas==1.1.4\n",
        "\n",
        "!pip3 install pandas-datareader==0.9.0\n",
        "\n",
        "!pip3 install matplotlib==3.3.3\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtuXcYbJI8u0"
      },
      "source": [
        "!pip3 install --upgrade pandas\n",
        "!pip3 install --upgrade pandas-datareader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "zrvyamclxwmF",
        "outputId": "1752ae18-3c61-40df-f924-7d1f0c41c84b"
      },
      "source": [
        "#https://www.learnpythonwithrune.org/pandas-calculate-and-plot-the-bollinger-bands-for-a-stock/\n",
        "import pandas_datareader as pdr\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "ticker = pdr.get_data_yahoo(\"HDFCBANK.NS\", dt.datetime(2018, 4, 1), dt.datetime(2019,3,31))[['Close', 'High', 'Low']]\n",
        "\n",
        "# Boillinger band calculations\n",
        "ticker['TP'] = (ticker['Close'] + ticker['Low'] + ticker['High'])/3\n",
        "ticker['std'] = ticker['TP'].rolling(20).std(ddof=0)\n",
        "ticker['MA-TP'] = ticker['TP'].rolling(20).mean()\n",
        "#Using Simple moving average filter->20\n",
        "ticker['BOLU'] = ticker['MA-TP'] + 2*ticker['std']\n",
        "#Upper Bollinger Band\n",
        "ticker['BOLD'] = ticker['MA-TP'] - 2*ticker['std']\n",
        "#Lower Bollinger Band\n",
        "ticker = ticker.dropna()\n",
        "\n",
        "ax = ticker[['Close', 'BOLU', 'BOLD']].plot(color=['blue', 'orange', 'yellow'])\n",
        "ax.fill_between(ticker.index, ticker['BOLD'], ticker['BOLU'], facecolor='orange', alpha=0.1)\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "mdIF2hVy2VoL",
        "outputId": "e0655a85-78b6-419f-cd61-85bd6128f8ad"
      },
      "source": [
        "#https://www.learnpythonwithrune.org/pandas-calculate-and-plot-the-bollinger-bands-for-a-stock/\n",
        "import pandas_datareader as pdr\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "ticker = pdr.get_data_yahoo(\"INFY.NS\", dt.datetime(2018, 4, 1), dt.datetime(2019,3,31))[['Close', 'High', 'Low']]\n",
        "\n",
        "# Boillinger band calculations\n",
        "ticker['TP'] = (ticker['Close'] + ticker['Low'] + ticker['High'])/3\n",
        "ticker['std'] = ticker['TP'].rolling(20).std(ddof=0)\n",
        "ticker['MA-TP'] = ticker['TP'].rolling(20).mean()\n",
        "#Using Simple moving average filter->20\n",
        "ticker['BOLU'] = ticker['MA-TP'] + 2*ticker['std']\n",
        "#Upper Bollinger Band\n",
        "ticker['BOLD'] = ticker['MA-TP'] - 2*ticker['std']\n",
        "#Lower Bollinger Band\n",
        "ticker = ticker.dropna()\n",
        "print(ticker)\n",
        "\n",
        "ax = ticker[['Close', 'BOLU', 'BOLD']].plot(color=['blue', 'orange', 'yellow'])\n",
        "ax.fill_between(ticker.index, ticker['BOLD'], ticker['BOLU'], facecolor='orange', alpha=0.1)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 Close        High  ...        BOLU        BOLD\n",
            "Date                                ...                        \n",
            "2018-04-27  592.599976  595.799988  ...  594.966590  549.786739\n",
            "2018-04-30  599.750000  603.974976  ...  598.956285  548.798707\n",
            "2018-05-02  598.525024  600.799988  ...  602.089599  548.457894\n",
            "2018-05-03  591.275024  602.349976  ...  604.260045  549.197448\n",
            "2018-05-04  586.549988  593.000000  ...  605.125312  549.768013\n",
            "...                ...         ...  ...         ...         ...\n",
            "2019-03-25  735.250000  742.400024  ...  753.289684  702.341985\n",
            "2019-03-26  727.750000  735.250000  ...  752.827964  702.168704\n",
            "2019-03-27  729.700012  732.299988  ...  749.790874  703.229127\n",
            "2019-03-28  737.799988  745.000000  ...  748.430293  703.841374\n",
            "2019-04-01  755.099976  760.000000  ...  751.420661  702.274338\n",
            "\n",
            "[228 rows x 8 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "-trTgXbg2b_i",
        "outputId": "30d9dec5-7b6a-499a-aa84-8026e1f093ed"
      },
      "source": [
        "#https://www.learnpythonwithrune.org/pandas-calculate-and-plot-the-bollinger-bands-for-a-stock/\n",
        "import pandas_datareader as pdr\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "ticker = pdr.get_data_yahoo(\"TATAMOTORS.NS\", dt.datetime(2018, 4, 1), dt.datetime(2019,3,31))[['Close', 'High', 'Low']]\n",
        "\n",
        "# Boillinger band calculations\n",
        "ticker['TP'] = (ticker['Close'] + ticker['Low'] + ticker['High'])/3\n",
        "ticker['std'] = ticker['TP'].rolling(20).std(ddof=0)\n",
        "ticker['MA-TP'] = ticker['TP'].rolling(20).mean()\n",
        "#Using Simple moving average filter->20\n",
        "ticker['BOLU'] = ticker['MA-TP'] + 2*ticker['std']\n",
        "#Upper Bollinger Band\n",
        "ticker['BOLD'] = ticker['MA-TP'] - 2*ticker['std']\n",
        "#Lower Bollinger Band\n",
        "ticker = ticker.dropna()\n",
        "print(ticker)\n",
        "\n",
        "ax = ticker[['Close', 'BOLU', 'BOLD']].plot(color=['blue', 'orange', 'yellow'])\n",
        "ax.fill_between(ticker.index, ticker['BOLD'], ticker['BOLU'], facecolor='orange', alpha=0.1)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 Close        High  ...        BOLU        BOLD\n",
            "Date                                ...                        \n",
            "2018-04-27  336.899994  337.649994  ...  368.876333  321.558665\n",
            "2018-04-30  340.399994  342.750000  ...  368.864432  321.828900\n",
            "2018-05-02  336.049988  351.649994  ...  368.854009  321.770989\n",
            "2018-05-03  333.500000  335.899994  ...  367.876858  320.514805\n",
            "2018-05-04  334.399994  337.299988  ...  364.740248  320.546415\n",
            "...                ...         ...  ...         ...         ...\n",
            "2019-03-25  171.300003  173.800003  ...  191.485936  171.124065\n",
            "2019-03-26  173.100006  173.600006  ...  191.451635  171.186700\n",
            "2019-03-27  169.850006  175.000000  ...  191.870017  170.343318\n",
            "2019-03-28  170.550003  171.399994  ...  192.446697  168.796638\n",
            "2019-04-01  187.350006  189.750000  ...  192.789835  168.916833\n",
            "\n",
            "[228 rows x 8 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}