Algorithmic_Trading_Using_Money_Flow_Index(MFI).ipynb

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  "metadata": {
    "colab": {
      "name": "Algorithmic Trading Using Money Flow Index(MFI).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMmFMgBvGzTsdH5ygXBMc7U",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Edugera/Templates_to_Articles/blob/main/Algorithmic_Trading_Using_Money_Flow_Index(MFI).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1YGo_aAaHPd"
      },
      "source": [
        "# Descrição: Este programa usa o índice de Fluxo financeiro para determinar quando comprar ou vender ma ação."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6HnygpBh2qxx",
        "outputId": "cc1c71ec-717a-4fb4-bb7d-0edcd446db2c"
      },
      "source": [
        "!pip install yfinance --upgrade --no-cache-dir\r\n",
        "import yfinance as yf\r\n",
        "#import pandas_datareader.data as web\r\n",
        "#yf.pdr_override()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/7a/e8/b9d7104d3a4bf39924799067592d9e59119fcfc900a425a12e80a3123ec8/yfinance-0.1.55.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.24 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied, skipping upgrade: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bd/78/56a7c88a57d0d14945472535d0df9fb4bbad7d34ede658ec7961635c790e/lxml-4.6.2-cp36-cp36m-manylinux1_x86_64.whl (5.5MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5MB 11.6MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22616 sha256=c9e8e08cdf070e5501cbeed08a9d0ce2952378f9d83cad2c5499763ce2c1d4a4\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ucuf9bjh/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.2 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZ0URFI22zrl",
        "outputId": "5f558a96-fb89-4588-f502-89203c6a5bdd"
      },
      "source": [
        "# Para corrgir o bug: AttributeError: 'numpy.int64' object has no attribute 'to_pydatetime'\r\n",
        "!pip install git+https://github.com/quantopian/pyfolio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/quantopian/pyfolio\n",
            "  Cloning https://github.com/quantopian/pyfolio to /tmp/pip-req-build-emun4xpq\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio /tmp/pip-req-build-emun4xpq\n",
            "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.6/dist-packages (from pyfolio==0.9.2+75.g4b901f6) (5.5.0)\n",
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            "Requirement already satisfied: pytz>=2014.10 in /usr/local/lib/python3.6/dist-packages (from pyfolio==0.9.2+75.g4b901f6) (2018.9)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from pyfolio==0.9.2+75.g4b901f6) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.16.1 in /usr/local/lib/python3.6/dist-packages (from pyfolio==0.9.2+75.g4b901f6) (0.22.2.post1)\n",
            "Requirement already satisfied: seaborn>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from pyfolio==0.9.2+75.g4b901f6) (0.11.1)\n",
            "Collecting empyrical>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/43/1b997c21411c6ab7c96dc034e160198272c7a785aeea7654c9bcf98bec83/empyrical-0.5.5.tar.gz (52kB)\n",
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            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (4.3.3)\n",
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            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (1.0.18)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->pyfolio==0.9.2+75.g4b901f6) (0.10.0)\n",
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            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.6/dist-packages (from empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (0.9.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (1.15.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (0.2.5)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (2.23.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (4.6.2)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (3.0.4)\n",
            "Building wheels for collected packages: pyfolio, empyrical\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp36-none-any.whl size=75764 sha256=51fd212599dead301c28069fa4c1829575a235bfd69ebdda6525f4c323304329\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-lcauxoah/wheels/62/7d/a7/3e462442ba7d63c35414176627c886340521dc3dbc0893ce9f\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp36-none-any.whl size=39765 sha256=9ebc386a818ca3861058ede3bf5125567abed8136dfd20c357064be8ed3dd948\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built pyfolio empyrical\n",
            "Installing collected packages: empyrical, pyfolio\n",
            "Successfully installed empyrical-0.5.5 pyfolio-0.9.2+75.g4b901f6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WuWI5OY2-Ye",
        "outputId": "60204dda-e360-4b8c-fe05-a76f853f758b"
      },
      "source": [
        "#tickers = [\"ABEV3.SA\", \"ITSA4.SA\", \"WEGE3.SA\", \"USIM5.SA\", \"VALE3.SA\", '^BVSP']\r\n",
        "#dados_yahoo = web.get_data_yahoo(tickers, period=\"5y\")[\"Adj Close\"]\r\n",
        "\r\n",
        "tickers = \"AAPL\"\r\n",
        "df = yf.download(tickers=tickers, period=\"5y\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "d28HkeoA5Dg9",
        "outputId": "e1272208-f694-4349-a4c1-29500e9dd56f"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "    <tr>\n",
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              "  <tbody>\n",
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              "      <th>2016-01-21</th>\n",
              "      <td>24.264999</td>\n",
              "      <td>24.469999</td>\n",
              "      <td>23.735001</td>\n",
              "      <td>24.075001</td>\n",
              "      <td>22.304800</td>\n",
              "      <td>208646000</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-22</th>\n",
              "      <td>24.657499</td>\n",
              "      <td>25.365000</td>\n",
              "      <td>24.592501</td>\n",
              "      <td>25.355000</td>\n",
              "      <td>23.490686</td>\n",
              "      <td>263202000</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-25</th>\n",
              "      <td>25.379999</td>\n",
              "      <td>25.382500</td>\n",
              "      <td>24.802500</td>\n",
              "      <td>24.860001</td>\n",
              "      <td>23.032082</td>\n",
              "      <td>207178000</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-26</th>\n",
              "      <td>24.982500</td>\n",
              "      <td>25.219999</td>\n",
              "      <td>24.517500</td>\n",
              "      <td>24.997499</td>\n",
              "      <td>23.159468</td>\n",
              "      <td>300308000</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-27</th>\n",
              "      <td>24.010000</td>\n",
              "      <td>24.157499</td>\n",
              "      <td>23.334999</td>\n",
              "      <td>23.355000</td>\n",
              "      <td>21.637739</td>\n",
              "      <td>533478800</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-13</th>\n",
              "      <td>128.759995</td>\n",
              "      <td>131.449997</td>\n",
              "      <td>128.490005</td>\n",
              "      <td>130.889999</td>\n",
              "      <td>130.889999</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-14</th>\n",
              "      <td>130.800003</td>\n",
              "      <td>131.000000</td>\n",
              "      <td>128.759995</td>\n",
              "      <td>128.910004</td>\n",
              "      <td>128.910004</td>\n",
              "      <td>90221800</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-15</th>\n",
              "      <td>128.779999</td>\n",
              "      <td>130.220001</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>127.139999</td>\n",
              "      <td>127.139999</td>\n",
              "      <td>111598500</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-19</th>\n",
              "      <td>127.779999</td>\n",
              "      <td>128.710007</td>\n",
              "      <td>126.940002</td>\n",
              "      <td>127.830002</td>\n",
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              "      <th>2021-01-20</th>\n",
              "      <td>128.660004</td>\n",
              "      <td>132.490005</td>\n",
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              "      <td>132.029999</td>\n",
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            "text/plain": [
              "                  Open        High  ...   Adj Close     Volume\n",
              "Date                                ...                       \n",
              "2016-01-21   24.264999   24.469999  ...   22.304800  208646000\n",
              "2016-01-22   24.657499   25.365000  ...   23.490686  263202000\n",
              "2016-01-25   25.379999   25.382500  ...   23.032082  207178000\n",
              "2016-01-26   24.982500   25.219999  ...   23.159468  300308000\n",
              "2016-01-27   24.010000   24.157499  ...   21.637739  533478800\n",
              "...                ...         ...  ...         ...        ...\n",
              "2021-01-13  128.759995  131.449997  ...  130.889999   88636800\n",
              "2021-01-14  130.800003  131.000000  ...  128.910004   90221800\n",
              "2021-01-15  128.779999  130.220001  ...  127.139999  111598500\n",
              "2021-01-19  127.779999  128.710007  ...  127.830002   90296400\n",
              "2021-01-20  128.660004  132.490005  ...  132.029999   98769961\n",
              "\n",
              "[1259 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cunQ6LJt0MYd"
      },
      "source": [
        "# Imprte as blibliotecas\r\n",
        "import warnings\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "plt.style.use('fivethirtyeight')\r\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2IZKr0o04DR"
      },
      "source": [
        "# Baixe os dados da ação da APPLE\r\n",
        "#from google.colab import files\r\n",
        "#files.upload()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BamWP4rb30JB"
      },
      "source": [
        "# Pegue os dados\r\n",
        "#df = pd.read_csv('AApl.csv')\r\n",
        "\r\n",
        "# Indexe\r\n",
        "# df = df.set_index(pd.DatetimeIndex(df['Date'].values))\r\n",
        "# df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "lO1d8cZ55XnV",
        "outputId": "4f758103-8df0-47dd-a7ea-6f95833b67b5"
      },
      "source": [
        "# Visualize os Dados \r\n",
        "plt.figure(figsize=(12.2, 4.5))\r\n",
        "plt.plot(df['Close'], label = 'Close Price')\r\n",
        "plt.title('Apple Close Price')\r\n",
        "plt.xlabel('Date')\r\n",
        "plt.ylabel('Close Price USD ($)')\r\n",
        "plt.legend(df.columns.values, loc='upper left')\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 878.4x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RM6GyLbS6qxJ",
        "outputId": "9f389d76-4fb2-40da-8bba-ec481021c93e"
      },
      "source": [
        "# Calcule o preço típico\r\n",
        "typical_price = (df['Close'] + df['High'] + df['Low']) / 3\r\n",
        "typical_price"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2016-01-21     24.093334\n",
              "2016-01-22     25.104167\n",
              "2016-01-25     25.015000\n",
              "2016-01-26     24.911666\n",
              "2016-01-27     23.615833\n",
              "                 ...    \n",
              "2021-01-13    130.276667\n",
              "2021-01-14    129.556666\n",
              "2021-01-15    128.120000\n",
              "2021-01-19    127.826670\n",
              "2021-01-20    131.023336\n",
              "Length: 1259, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7fsWRoo7HBm"
      },
      "source": [
        "# Pegue o período\r\n",
        "period = 14"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPG13qx87PD4",
        "outputId": "14769b56-ae22-4a01-8ee3-4cf5be52321a"
      },
      "source": [
        "# Calcule o fluxo de dinheiro\r\n",
        "money_flow = typical_price * df['Volume']\r\n",
        "money_flow"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2016-01-21    5.026978e+09\n",
              "2016-01-22    6.607467e+09\n",
              "2016-01-25    5.182558e+09\n",
              "2016-01-26    7.481173e+09\n",
              "2016-01-27    1.259855e+10\n",
              "                  ...     \n",
              "2021-01-13    1.154731e+10\n",
              "2021-01-14    1.168884e+10\n",
              "2021-01-15    1.429800e+10\n",
              "2021-01-19    1.154229e+10\n",
              "2021-01-20    1.294117e+10\n",
              "Length: 1259, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97qWp-2N7htN"
      },
      "source": [
        "# Pegue os fluxos de dinheiro positivos e negativos\r\n",
        "positive_flow = []\r\n",
        "negative_flow = []\r\n",
        "\r\n",
        "# loop through the typical price\r\n",
        "for i in range(1, len(typical_price)):\r\n",
        "  if typical_price[i] > typical_price[i-1]:\r\n",
        "    positive_flow.append(money_flow[i-1])\r\n",
        "    negative_flow.append(0)\r\n",
        "  elif typical_price[i] < typical_price[i-1]:\r\n",
        "    negative_flow.append(money_flow[i-1])\r\n",
        "    positive_flow.append(0)\r\n",
        "  else:\r\n",
        "    positive_flow.append(0)\r\n",
        "    negative_flow.append(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nc2ESwAb837f"
      },
      "source": [
        "# Pegue todos os fluxos positivos e negativos whitin the time period\r\n",
        "positive_mf = []\r\n",
        "negative_mf = []\r\n",
        "\r\n",
        "for i in range(period-1, len(positive_flow)):\r\n",
        "  positive_mf.append( sum(positive_flow[i + 1 - period : i+1]) )\r\n",
        "for i in range(period-1, len(negative_flow)):\r\n",
        "  negative_mf.append( sum(negative_flow[i + 1 - period : i+1]) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_sfBNgb9mX5",
        "outputId": "caeb9a5d-b033-4470-f14d-50a7ccf2b1bb"
      },
      "source": [
        "# Calcule o índice do fluxo de dinheiro\r\n",
        "mfi = 100 * (np.array(positive_mf) / (np.array(positive_mf) + np.array(negative_mf) ))\r\n",
        "mfi\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([29.71680915, 23.60889759, 30.42270695, ..., 45.69999684,\n",
              "       40.49191171, 38.8601607 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "iGk7o1wF-Kwu",
        "outputId": "c17fef91-6b83-41af-ffee-9071cb54dcdb"
      },
      "source": [
        "# Visualize o MFI\r\n",
        "df2 = pd.DataFrame()\r\n",
        "df2['MFI'] = mfi\r\n",
        "\r\n",
        "# Crie o gráfico\r\n",
        "plt.figure(figsize=(12.2, 4.5))\r\n",
        "plt.plot(df2['MFI'], label = 'MFI')\r\n",
        "plt.axhline(10, linestyle= '--', color = 'orange')\r\n",
        "plt.axhline(20, linestyle= '--', color = 'blue')\r\n",
        "plt.axhline(80, linestyle= '--', color = 'blue')\r\n",
        "plt.axhline(90, linestyle= '--', color = 'orange')\r\n",
        "plt.title('MFI')\r\n",
        "plt.ylabel('MFI Values')\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 878.4x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMUMaga3A_Rn"
      },
      "source": [
        "# Crie um novo conjunto de dados\r\n",
        "new_df = pd.DataFrame()\r\n",
        "new_df = df[period:]\r\n",
        "new_df['MFI'] = mfi "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "tH2GiR-GBkT9",
        "outputId": "95abca7f-4ccc-4264-9ec4-b6e07340e9d6"
      },
      "source": [
        "# Mostre o novo cnjunto de dados\r\n",
        "new_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>MFI</th>\n",
              "      <th>Buy</th>\n",
              "      <th>Sell</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-02-10</th>\n",
              "      <td>23.980000</td>\n",
              "      <td>24.087500</td>\n",
              "      <td>23.525000</td>\n",
              "      <td>23.567499</td>\n",
              "      <td>21.953094</td>\n",
              "      <td>169374400</td>\n",
              "      <td>29.716809</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-11</th>\n",
              "      <td>23.447500</td>\n",
              "      <td>23.680000</td>\n",
              "      <td>23.147499</td>\n",
              "      <td>23.424999</td>\n",
              "      <td>21.820358</td>\n",
              "      <td>200298800</td>\n",
              "      <td>23.608898</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-12</th>\n",
              "      <td>23.547501</td>\n",
              "      <td>23.625000</td>\n",
              "      <td>23.252501</td>\n",
              "      <td>23.497499</td>\n",
              "      <td>21.887894</td>\n",
              "      <td>161405600</td>\n",
              "      <td>30.422707</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-16</th>\n",
              "      <td>23.754999</td>\n",
              "      <td>24.212500</td>\n",
              "      <td>23.652500</td>\n",
              "      <td>24.160000</td>\n",
              "      <td>22.505007</td>\n",
              "      <td>196231600</td>\n",
              "      <td>36.106917</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-17</th>\n",
              "      <td>24.167500</td>\n",
              "      <td>24.552500</td>\n",
              "      <td>24.037500</td>\n",
              "      <td>24.530001</td>\n",
              "      <td>22.849665</td>\n",
              "      <td>179452800</td>\n",
              "      <td>44.115661</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-13</th>\n",
              "      <td>128.759995</td>\n",
              "      <td>131.449997</td>\n",
              "      <td>128.490005</td>\n",
              "      <td>130.889999</td>\n",
              "      <td>130.889999</td>\n",
              "      <td>88636800</td>\n",
              "      <td>48.984520</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-14</th>\n",
              "      <td>130.800003</td>\n",
              "      <td>131.000000</td>\n",
              "      <td>128.759995</td>\n",
              "      <td>128.910004</td>\n",
              "      <td>128.910004</td>\n",
              "      <td>90221800</td>\n",
              "      <td>51.697177</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-15</th>\n",
              "      <td>128.779999</td>\n",
              "      <td>130.220001</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>127.139999</td>\n",
              "      <td>127.139999</td>\n",
              "      <td>111598500</td>\n",
              "      <td>45.699997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-19</th>\n",
              "      <td>127.779999</td>\n",
              "      <td>128.710007</td>\n",
              "      <td>126.940002</td>\n",
              "      <td>127.830002</td>\n",
              "      <td>127.830002</td>\n",
              "      <td>90296400</td>\n",
              "      <td>40.491912</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-20</th>\n",
              "      <td>128.660004</td>\n",
              "      <td>132.490005</td>\n",
              "      <td>128.550003</td>\n",
              "      <td>132.029999</td>\n",
              "      <td>132.029999</td>\n",
              "      <td>98769961</td>\n",
              "      <td>38.860161</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1245 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low  ...        MFI  Buy  Sell\n",
              "Date                                            ...                      \n",
              "2016-02-10   23.980000   24.087500   23.525000  ...  29.716809  NaN   NaN\n",
              "2016-02-11   23.447500   23.680000   23.147499  ...  23.608898  NaN   NaN\n",
              "2016-02-12   23.547501   23.625000   23.252501  ...  30.422707  NaN   NaN\n",
              "2016-02-16   23.754999   24.212500   23.652500  ...  36.106917  NaN   NaN\n",
              "2016-02-17   24.167500   24.552500   24.037500  ...  44.115661  NaN   NaN\n",
              "...                ...         ...         ...  ...        ...  ...   ...\n",
              "2021-01-13  128.759995  131.449997  128.490005  ...  48.984520  NaN   NaN\n",
              "2021-01-14  130.800003  131.000000  128.759995  ...  51.697177  NaN   NaN\n",
              "2021-01-15  128.779999  130.220001  127.000000  ...  45.699997  NaN   NaN\n",
              "2021-01-19  127.779999  128.710007  126.940002  ...  40.491912  NaN   NaN\n",
              "2021-01-20  128.660004  132.490005  128.550003  ...  38.860161  NaN   NaN\n",
              "\n",
              "[1245 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOFlc1NQB3M1"
      },
      "source": [
        "# Crie uma funçaõ que pegue os sinais de compra e venda\r\n",
        "def get_signal(data, High, Low):\r\n",
        "  buy_signal = []\r\n",
        "  sell_signal = []\r\n",
        "\r\n",
        "  for i in range(len(data['MFI'])):\r\n",
        "    if data['MFI'][i] > High:\r\n",
        "      buy_signal.append(np.nan)\r\n",
        "      sell_signal.append(data['Close'][i])\r\n",
        "    elif data['MFI'][i] < Low:\r\n",
        "      buy_signal.append(data['Close'][i])\r\n",
        "      sell_signal.append(np.nan)\r\n",
        "    else:\r\n",
        "      sell_signal.append(np.nan)\r\n",
        "      buy_signal.append(np.nan)\r\n",
        "\r\n",
        "    return (buy_signal, sell_signal)\r\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fl587vtVJSqr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "PAxtOmnyDP3R",
        "outputId": "88f43d25-4d8c-4d2b-b7e2-cfe0569da0c6"
      },
      "source": [
        "# Adicione nova coluna (Compra e Venda)\r\n",
        "new_df['Buy'] = pd.Series(get_signal(new_df, 80, 20))\r\n",
        "new_df['Sell'] = pd.Series(get_signal(new_df, 80, 20))\r\n",
        "\r\n",
        "# Mostre os dados\r\n",
        "new_df\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>MFI</th>\n",
              "      <th>Buy</th>\n",
              "      <th>Sell</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-02-10</th>\n",
              "      <td>23.980000</td>\n",
              "      <td>24.087500</td>\n",
              "      <td>23.525000</td>\n",
              "      <td>23.567499</td>\n",
              "      <td>21.953094</td>\n",
              "      <td>169374400</td>\n",
              "      <td>29.716809</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-11</th>\n",
              "      <td>23.447500</td>\n",
              "      <td>23.680000</td>\n",
              "      <td>23.147499</td>\n",
              "      <td>23.424999</td>\n",
              "      <td>21.820358</td>\n",
              "      <td>200298800</td>\n",
              "      <td>23.608898</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-12</th>\n",
              "      <td>23.547501</td>\n",
              "      <td>23.625000</td>\n",
              "      <td>23.252501</td>\n",
              "      <td>23.497499</td>\n",
              "      <td>21.887894</td>\n",
              "      <td>161405600</td>\n",
              "      <td>30.422707</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-16</th>\n",
              "      <td>23.754999</td>\n",
              "      <td>24.212500</td>\n",
              "      <td>23.652500</td>\n",
              "      <td>24.160000</td>\n",
              "      <td>22.505007</td>\n",
              "      <td>196231600</td>\n",
              "      <td>36.106917</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-17</th>\n",
              "      <td>24.167500</td>\n",
              "      <td>24.552500</td>\n",
              "      <td>24.037500</td>\n",
              "      <td>24.530001</td>\n",
              "      <td>22.849665</td>\n",
              "      <td>179452800</td>\n",
              "      <td>44.115661</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-13</th>\n",
              "      <td>128.759995</td>\n",
              "      <td>131.449997</td>\n",
              "      <td>128.490005</td>\n",
              "      <td>130.889999</td>\n",
              "      <td>130.889999</td>\n",
              "      <td>88636800</td>\n",
              "      <td>48.984520</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-14</th>\n",
              "      <td>130.800003</td>\n",
              "      <td>131.000000</td>\n",
              "      <td>128.759995</td>\n",
              "      <td>128.910004</td>\n",
              "      <td>128.910004</td>\n",
              "      <td>90221800</td>\n",
              "      <td>51.697177</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-15</th>\n",
              "      <td>128.779999</td>\n",
              "      <td>130.220001</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>127.139999</td>\n",
              "      <td>127.139999</td>\n",
              "      <td>111598500</td>\n",
              "      <td>45.699997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-19</th>\n",
              "      <td>127.779999</td>\n",
              "      <td>128.710007</td>\n",
              "      <td>126.940002</td>\n",
              "      <td>127.830002</td>\n",
              "      <td>127.830002</td>\n",
              "      <td>90296400</td>\n",
              "      <td>40.491912</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-20</th>\n",
              "      <td>128.660004</td>\n",
              "      <td>132.490005</td>\n",
              "      <td>128.550003</td>\n",
              "      <td>132.029999</td>\n",
              "      <td>132.029999</td>\n",
              "      <td>98769961</td>\n",
              "      <td>38.860161</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1245 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low  ...        MFI  Buy  Sell\n",
              "Date                                            ...                      \n",
              "2016-02-10   23.980000   24.087500   23.525000  ...  29.716809  NaN   NaN\n",
              "2016-02-11   23.447500   23.680000   23.147499  ...  23.608898  NaN   NaN\n",
              "2016-02-12   23.547501   23.625000   23.252501  ...  30.422707  NaN   NaN\n",
              "2016-02-16   23.754999   24.212500   23.652500  ...  36.106917  NaN   NaN\n",
              "2016-02-17   24.167500   24.552500   24.037500  ...  44.115661  NaN   NaN\n",
              "...                ...         ...         ...  ...        ...  ...   ...\n",
              "2021-01-13  128.759995  131.449997  128.490005  ...  48.984520  NaN   NaN\n",
              "2021-01-14  130.800003  131.000000  128.759995  ...  51.697177  NaN   NaN\n",
              "2021-01-15  128.779999  130.220001  127.000000  ...  45.699997  NaN   NaN\n",
              "2021-01-19  127.779999  128.710007  126.940002  ...  40.491912  NaN   NaN\n",
              "2021-01-20  128.660004  132.490005  128.550003  ...  38.860161  NaN   NaN\n",
              "\n",
              "[1245 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QslOpYNm7pPr",
        "outputId": "837a401a-b919-4b4e-f798-22fb5ccc210e"
      },
      "source": [
        "print(new_df.Buy.cou)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1245,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "njCmbNj-JrKV",
        "outputId": "8a297ced-d96c-4d3c-c24f-bf5d193ac893"
      },
      "source": [
        "# Plote os dados\r\n",
        "plt.figure(figsize=(16, 7.7))\r\n",
        "plt.plot(new_df['Close'], label = 'Close Price', alpha = 0.5)\r\n",
        "plt.scatter(new_df.index, new_df['Buy'], color = 'green', label= 'Buy Signal', marker = '^', alpha = 1)\r\n",
        "plt.scatter(new_df.index, new_df['Sell'], color = 'red', label= 'Sell Signal', marker = 'v', alpha = 1)\r\n",
        "plt.title('Apple Close Price')\r\n",
        "plt.xlabel('Date')\r\n",
        "plt.ylabel('Close Price USD ($)')\r\n",
        "plt.legend(loc='upper left')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# Crie o gráfico\r\n",
        "plt.figure(figsize=(12.2, 4.5))\r\n",
        "plt.plot(new_df['MFI'], label = 'MFI')\r\n",
        "plt.axhline(10, linestyle= '--', color = 'orange')\r\n",
        "plt.axhline(20, linestyle= '--', color = 'blue')\r\n",
        "plt.axhline(80, linestyle= '--', color = 'blue')\r\n",
        "plt.axhline(90, linestyle= '--', color = 'orange')\r\n",
        "plt.title('MFI')\r\n",
        "plt.ylabel('MFI Values')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x554.4 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 878.4x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}