stock.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "stock.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP5D94od+Bpig/C4acjlNJW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sergio-cabrales/BitCoin/blob/master/stock.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shOcX8jdT4cG",
        "outputId": "59170416-68ee-442f-acb3-e9f5df145952"
      },
      "source": [
        "pip install pandas-datareader"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.7/dist-packages (0.9.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (4.2.6)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (2.23.0)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (1.1.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (2018.9)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (1.19.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas-datareader) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfDabEIOUunZ"
      },
      "source": [
        "import quandl\r\n",
        "mydata = quandl.get(\"FRED/GDP\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5RbWyAtUD0q"
      },
      "source": [
        "import pandas_datareader as pdr\r\n",
        "import datetime \r\n",
        "aapl = pdr.get_data_yahoo('AAPL',start=datetime.datetime(2010, 1, 1), end=datetime.datetime(2021, 1, 1))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7R4UqPzUPpf",
        "outputId": "fc94b817-366e-4f58-90ba-f9f2e1503ea0"
      },
      "source": [
        "print(aapl)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  High         Low  ...       Volume   Adj Close\n",
            "Date                                ...                         \n",
            "2010-01-04    7.660714    7.585000  ...  493729600.0    6.583586\n",
            "2010-01-05    7.699643    7.616071  ...  601904800.0    6.594968\n",
            "2010-01-06    7.686786    7.526786  ...  552160000.0    6.490066\n",
            "2010-01-07    7.571429    7.466071  ...  477131200.0    6.478067\n",
            "2010-01-08    7.571429    7.466429  ...  447610800.0    6.521136\n",
            "...                ...         ...  ...          ...         ...\n",
            "2020-12-24  133.460007  131.100006  ...   54930100.0  131.773087\n",
            "2020-12-28  137.339996  133.509995  ...  124486200.0  136.486053\n",
            "2020-12-29  138.789993  134.339996  ...  121047300.0  134.668762\n",
            "2020-12-30  135.990005  133.399994  ...   96452100.0  133.520477\n",
            "2020-12-31  134.740005  131.720001  ...   99116600.0  132.492020\n",
            "\n",
            "[2769 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "QGSQnwtsVpO_",
        "outputId": "37907101-2cab-410b-8f1b-0f477cbd0a4f"
      },
      "source": [
        "aapl.head()"
      ],
      "execution_count": 14,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>7.660714</td>\n",
              "      <td>7.585000</td>\n",
              "      <td>7.622500</td>\n",
              "      <td>7.643214</td>\n",
              "      <td>493729600.0</td>\n",
              "      <td>6.583586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>7.699643</td>\n",
              "      <td>7.616071</td>\n",
              "      <td>7.664286</td>\n",
              "      <td>7.656429</td>\n",
              "      <td>601904800.0</td>\n",
              "      <td>6.594968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>7.686786</td>\n",
              "      <td>7.526786</td>\n",
              "      <td>7.656429</td>\n",
              "      <td>7.534643</td>\n",
              "      <td>552160000.0</td>\n",
              "      <td>6.490066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466071</td>\n",
              "      <td>7.562500</td>\n",
              "      <td>7.520714</td>\n",
              "      <td>477131200.0</td>\n",
              "      <td>6.478067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466429</td>\n",
              "      <td>7.510714</td>\n",
              "      <td>7.570714</td>\n",
              "      <td>447610800.0</td>\n",
              "      <td>6.521136</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                High       Low      Open     Close       Volume  Adj Close\n",
              "Date                                                                      \n",
              "2010-01-04  7.660714  7.585000  7.622500  7.643214  493729600.0   6.583586\n",
              "2010-01-05  7.699643  7.616071  7.664286  7.656429  601904800.0   6.594968\n",
              "2010-01-06  7.686786  7.526786  7.656429  7.534643  552160000.0   6.490066\n",
              "2010-01-07  7.571429  7.466071  7.562500  7.520714  477131200.0   6.478067\n",
              "2010-01-08  7.571429  7.466429  7.510714  7.570714  447610800.0   6.521136"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "wPPumAOzZ-EN",
        "outputId": "20d5c89d-ce1b-4ee6-cb20-beed3a6d2286"
      },
      "source": [
        "aapl.tail()"
      ],
      "execution_count": 15,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.320007</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>54930100.0</td>\n",
              "      <td>131.773087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>133.990005</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>124486200.0</td>\n",
              "      <td>136.486053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>138.050003</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>121047300.0</td>\n",
              "      <td>134.668762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>135.580002</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>96452100.0</td>\n",
              "      <td>133.520477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>134.080002</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>99116600.0</td>\n",
              "      <td>132.492020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  High         Low  ...       Volume   Adj Close\n",
              "Date                                ...                         \n",
              "2020-12-24  133.460007  131.100006  ...   54930100.0  131.773087\n",
              "2020-12-28  137.339996  133.509995  ...  124486200.0  136.486053\n",
              "2020-12-29  138.789993  134.339996  ...  121047300.0  134.668762\n",
              "2020-12-30  135.990005  133.399994  ...   96452100.0  133.520477\n",
              "2020-12-31  134.740005  131.720001  ...   99116600.0  132.492020\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "2tOETlvYaR4C",
        "outputId": "894bd4c3-e70d-40e8-c7c9-fee099848c59"
      },
      "source": [
        "aapl.describe()"
      ],
      "execution_count": 16,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2769.000000</td>\n",
              "      <td>2769.000000</td>\n",
              "      <td>2769.000000</td>\n",
              "      <td>2769.000000</td>\n",
              "      <td>2.769000e+03</td>\n",
              "      <td>2769.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>34.114230</td>\n",
              "      <td>33.413543</td>\n",
              "      <td>33.764375</td>\n",
              "      <td>33.777537</td>\n",
              "      <td>2.866177e+08</td>\n",
              "      <td>32.072065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>24.895185</td>\n",
              "      <td>24.220789</td>\n",
              "      <td>24.561595</td>\n",
              "      <td>24.572747</td>\n",
              "      <td>2.289451e+08</td>\n",
              "      <td>25.012138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.794643</td>\n",
              "      <td>6.870357</td>\n",
              "      <td>6.858929</td>\n",
              "      <td>4.544800e+07</td>\n",
              "      <td>5.908031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.695356</td>\n",
              "      <td>17.333929</td>\n",
              "      <td>17.518213</td>\n",
              "      <td>17.484285</td>\n",
              "      <td>1.211120e+08</td>\n",
              "      <td>15.446707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27.225000</td>\n",
              "      <td>26.705000</td>\n",
              "      <td>26.975000</td>\n",
              "      <td>26.987499</td>\n",
              "      <td>2.029972e+08</td>\n",
              "      <td>24.945536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43.367500</td>\n",
              "      <td>42.605000</td>\n",
              "      <td>43.012501</td>\n",
              "      <td>43.007500</td>\n",
              "      <td>3.890376e+08</td>\n",
              "      <td>41.592369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>138.050003</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>1.880998e+09</td>\n",
              "      <td>136.486053</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              High          Low  ...        Volume    Adj Close\n",
              "count  2769.000000  2769.000000  ...  2.769000e+03  2769.000000\n",
              "mean     34.114230    33.413543  ...  2.866177e+08    32.072065\n",
              "std      24.895185    24.220789  ...  2.289451e+08    25.012138\n",
              "min       7.000000     6.794643  ...  4.544800e+07     5.908031\n",
              "25%      17.695356    17.333929  ...  1.211120e+08    15.446707\n",
              "50%      27.225000    26.705000  ...  2.029972e+08    24.945536\n",
              "75%      43.367500    42.605000  ...  3.890376e+08    41.592369\n",
              "max     138.789993   134.339996  ...  1.880998e+09   136.486053\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SH0d_NZQagg7",
        "outputId": "4b01a24c-4513-4e40-9c08-e4df40b6c2ba"
      },
      "source": [
        "# Inspect the index \r\n",
        "aapl.index"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muecxGcObAhj",
        "outputId": "3b08730b-edda-4537-d4d1-1568f8d2b0af"
      },
      "source": [
        "# Inspect the columns\r\n",
        "aapl.columns"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUfKc3rHbKdC"
      },
      "source": [
        "# Select only the last 10 observations of `Close`\r\n",
        "ts = aapl['Close'][-10:]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPYlzRlcbP08",
        "outputId": "45470af6-9e01-48eb-f72f-b7c4127365a4"
      },
      "source": [
        "# Check the type of `ts` \r\n",
        "type(ts)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtUv5aVAbTke",
        "outputId": "5ac46145-93c7-4431-e563-fc97f75a5294"
      },
      "source": [
        "# Inspect November 2006\r\n",
        "print(aapl.iloc[22:43])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                High       Low      Open     Close       Volume  Adj Close\n",
            "Date                                                                      \n",
            "2010-02-04  7.084643  6.841786  7.026071  6.858929  757652000.0   5.908031\n",
            "2010-02-05  7.000000  6.816071  6.879643  6.980714  850306800.0   6.012933\n",
            "2010-02-08  7.067143  6.928571  6.988929  6.932857  478270800.0   5.971709\n",
            "2010-02-09  7.053571  6.955357  7.015000  7.006786  632886800.0   6.035388\n",
            "2010-02-10  7.021429  6.937857  6.996071  6.968571  370361600.0   6.002471\n",
            "2010-02-11  7.133929  6.930714  6.960000  7.095357  550345600.0   6.111681\n",
            "2010-02-12  7.201429  6.982143  7.075357  7.156429  655468800.0   6.164287\n",
            "2010-02-16  7.274643  7.197143  7.212143  7.264286  543737600.0   6.257191\n",
            "2010-02-17  7.296786  7.173571  7.292500  7.233929  436396800.0   6.231041\n",
            "2010-02-18  7.281786  7.175714  7.201071  7.247500  422825200.0   6.242731\n",
            "2010-02-19  7.257143  7.182500  7.209286  7.202500  415469600.0   6.203969\n",
            "2010-02-22  7.232143  7.113929  7.226429  7.157857  390563600.0   6.165515\n",
            "2010-02-23  7.190357  6.989643  7.142857  7.037857  575094800.0   6.062153\n",
            "2010-02-24  7.194286  7.065714  7.079643  7.166429  460566400.0   6.172900\n",
            "2010-02-25  7.245000  7.031786  7.049286  7.214286  665126000.0   6.214124\n",
            "2010-02-26  7.327500  7.214286  7.227857  7.307857  507460800.0   6.294721\n",
            "2010-03-01  7.482143  7.337500  7.348214  7.463929  550093600.0   6.429156\n",
            "2010-03-02  7.529643  7.419286  7.497500  7.458929  566546400.0   6.424848\n",
            "2010-03-03  7.495357  7.426429  7.462143  7.476071  372052800.0   6.439613\n",
            "2010-03-04  7.532857  7.451071  7.474286  7.525357  366041200.0   6.482066\n",
            "2010-03-05  7.846429  7.665357  7.676429  7.819643  899620400.0   6.735554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OV3FlKZ-btIV",
        "outputId": "32eaf921-2a81-43cb-b679-9fb099426070"
      },
      "source": [
        "# Inspect the 'Open' and 'Close' values at 2006-11-01 and 2006-12-01\r\n",
        "print(aapl.iloc[[22,43], [0, 3]])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                High     Close\n",
            "Date                          \n",
            "2010-02-04  7.084643  6.858929\n",
            "2010-03-08  7.860357  7.824286\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcJumxaRb6QL",
        "outputId": "bf125cb4-b90d-4607-8cd5-b091d1934718"
      },
      "source": [
        "# Sample 20 rows\r\n",
        "sample = aapl.sample(20)\r\n",
        "\r\n",
        "# Print `sample`\r\n",
        "print(sample)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  High         Low  ...       Volume   Adj Close\n",
            "Date                                ...                         \n",
            "2011-05-10   12.488929   12.380714  ...  282091600.0   10.750122\n",
            "2016-12-21   29.350000   29.195000  ...   95132800.0   27.675074\n",
            "2012-04-18   22.151787   21.525356  ...  954531200.0   18.714354\n",
            "2011-05-25   12.091429   11.887500  ...  294224000.0   10.360356\n",
            "2016-06-30   23.942499   23.575001  ...  143345600.0   22.364946\n",
            "2014-10-28   26.684999   26.337500  ...  192243600.0   24.167643\n",
            "2011-01-11   12.320000   12.123929  ...  444108000.0   10.509866\n",
            "2014-06-13   23.110001   22.719999  ...  218100000.0   20.564962\n",
            "2015-11-02   30.340000   29.902500  ...  128813200.0   27.906120\n",
            "2011-10-12   14.616071   14.290714  ...  622286000.0   12.372562\n",
            "2020-09-08  118.989998  112.680000  ...  231366600.0  112.457649\n",
            "2017-06-28   36.527500   35.790001  ...   88329600.0   34.769218\n",
            "2011-08-26   13.707143   13.242857  ...  641477200.0   11.800060\n",
            "2013-10-24   19.016787   18.658930  ...  384764800.0   16.824930\n",
            "2010-03-02    7.529643    7.419286  ...  566546400.0    6.424848\n",
            "2011-07-14   12.914643   12.726429  ...  430533600.0   11.006072\n",
            "2014-06-17   23.174999   22.950001  ...  118904000.0   20.745197\n",
            "2017-05-12   39.105000   38.667500  ...  130108000.0   37.217823\n",
            "2013-05-28   16.111071   15.744643  ...  386145200.0   13.871668\n",
            "2018-09-11   56.075001   54.139999  ...  142996000.0   54.390709\n",
            "\n",
            "[20 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKh6DFhncOng",
        "outputId": "6c7b220f-d14a-4e04-883b-e56bc51f756e"
      },
      "source": [
        "# Add a column `diff` to `aapl` \r\n",
        "aapl['diff'] = aapl.Open - aapl.Close\r\n",
        "\r\n",
        "print(aapl)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  High         Low  ...   Adj Close      diff\n",
            "Date                                ...                      \n",
            "2010-01-04    7.660714    7.585000  ...    6.583586 -0.020714\n",
            "2010-01-05    7.699643    7.616071  ...    6.594968  0.007857\n",
            "2010-01-06    7.686786    7.526786  ...    6.490066  0.121786\n",
            "2010-01-07    7.571429    7.466071  ...    6.478067  0.041786\n",
            "2010-01-08    7.571429    7.466429  ...    6.521136 -0.060000\n",
            "...                ...         ...  ...         ...       ...\n",
            "2020-12-24  133.460007  131.100006  ...  131.773087 -0.649994\n",
            "2020-12-28  137.339996  133.509995  ...  136.486053 -2.699997\n",
            "2020-12-29  138.789993  134.339996  ...  134.668762  3.180008\n",
            "2020-12-30  135.990005  133.399994  ...  133.520477  1.860001\n",
            "2020-12-31  134.740005  131.720001  ...  132.492020  1.389999\n",
            "\n",
            "[2769 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "0exklMZycZIv",
        "outputId": "2788b767-c040-4067-c120-c42248acd846"
      },
      "source": [
        "# Import Matplotlib's `pyplot` module as `plt`\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Plot the closing prices for `aapl`\r\n",
        "aapl['Close'].plot(grid=True)\r\n",
        "\r\n",
        "# Show the plot\r\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "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/"
        },
        "id": "h234ZCqact13",
        "outputId": "550f6003-04ec-4509-fb89-2c091c2c6d52"
      },
      "source": [
        "# Import `numpy` as `np`\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "# Assign `Adj Close` to `daily_close`\r\n",
        "daily_close = aapl[['Adj Close']]\r\n",
        "\r\n",
        "# Daily returns\r\n",
        "daily_pct_change = daily_close.pct_change()\r\n",
        "\r\n",
        "# Replace NA values with 0\r\n",
        "daily_pct_change.fillna(0, inplace=True)\r\n",
        "\r\n",
        "# Inspect daily returns\r\n",
        "print(daily_pct_change)\r\n",
        "\r\n",
        "# Daily log returns\r\n",
        "daily_log_returns = np.log(daily_close.pct_change()+1)\r\n",
        "\r\n",
        "# Print daily log returns\r\n",
        "print(daily_log_returns)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            Adj Close\n",
            "Date                 \n",
            "2010-01-04   0.000000\n",
            "2010-01-05   0.001729\n",
            "2010-01-06  -0.015906\n",
            "2010-01-07  -0.001849\n",
            "2010-01-08   0.006648\n",
            "...               ...\n",
            "2020-12-24   0.007712\n",
            "2020-12-28   0.035766\n",
            "2020-12-29  -0.013315\n",
            "2020-12-30  -0.008527\n",
            "2020-12-31  -0.007703\n",
            "\n",
            "[2769 rows x 1 columns]\n",
            "            Adj Close\n",
            "Date                 \n",
            "2010-01-04        NaN\n",
            "2010-01-05   0.001727\n",
            "2010-01-06  -0.016034\n",
            "2010-01-07  -0.001850\n",
            "2010-01-08   0.006626\n",
            "...               ...\n",
            "2020-12-24   0.007683\n",
            "2020-12-28   0.035141\n",
            "2020-12-29  -0.013404\n",
            "2020-12-30  -0.008563\n",
            "2020-12-31  -0.007732\n",
            "\n",
            "[2769 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "gKQLUKY1dLhB",
        "outputId": "58dc947c-a760-4208-9aa2-5a212033a79b"
      },
      "source": [
        "# Import matplotlib\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Plot the distribution of `daily_pct_c`\r\n",
        "daily_pct_change.hist(bins=50)\r\n",
        "\r\n",
        "# Show the plot\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# Pull up summary statistics\r\n",
        "print(daily_pct_change.describe())"
      ],
      "execution_count": 34,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "         Adj Close\n",
            "count  2769.000000\n",
            "mean      0.001244\n",
            "std       0.017827\n",
            "min      -0.128647\n",
            "25%      -0.006947\n",
            "50%       0.000952\n",
            "75%       0.010405\n",
            "max       0.119808\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mU0GApI0d1Y1",
        "outputId": "d794962d-4986-4c9d-82a4-438beadc880b"
      },
      "source": [
        "# Isolate the adjusted closing prices \r\n",
        "adj_close_px = aapl['Adj Close']\r\n",
        "\r\n",
        "# Calculate the moving average\r\n",
        "moving_avg = adj_close_px.rolling(window=20).mean()\r\n",
        "\r\n",
        "# Inspect the result\r\n",
        "print(moving_avg[-10:])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date\n",
            "2020-12-17    121.288256\n",
            "2020-12-18    121.688658\n",
            "2020-12-21    122.232345\n",
            "2020-12-22    123.132500\n",
            "2020-12-23    123.920823\n",
            "2020-12-24    124.716634\n",
            "2020-12-28    125.720135\n",
            "2020-12-29    126.509954\n",
            "2020-12-30    127.059134\n",
            "2020-12-31    127.538917\n",
            "Name: Adj Close, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "g8OOI8-dd9v7",
        "outputId": "3660a936-b6c1-45e7-8140-60a51a8ea0eb"
      },
      "source": [
        "# Import matplotlib \r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Short moving window rolling mean\r\n",
        "aapl['42'] = adj_close_px.rolling(window=40).mean()\r\n",
        "\r\n",
        "# Long moving window rolling mean\r\n",
        "aapl['252'] = adj_close_px.rolling(window=252).mean()\r\n",
        "\r\n",
        "# Plot the adjusted closing price, the short and long windows of rolling means\r\n",
        "aapl[['Adj Close', '42', '252']].plot()\r\n",
        "\r\n",
        "# Show plot\r\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "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": 465
        },
        "id": "g92KR7dZeFwv",
        "outputId": "503fdb5e-d97e-408d-fb83-c72a7644fbc8"
      },
      "source": [
        "# Import matplotlib\r\n",
        "import matplotlib.pyplot as plt \r\n",
        "\r\n",
        "# Define the minumum of periods to consider \r\n",
        "min_periods = 20 \r\n",
        "\r\n",
        "# Calculate the volatility\r\n",
        "vol = daily_pct_change.rolling(min_periods).std() * np.sqrt(min_periods) \r\n",
        "\r\n",
        "# Plot the volatility\r\n",
        "vol.plot(figsize=(10, 8))\r\n",
        "\r\n",
        "# Show the plot\r\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "Pcm0nTyRezAx",
        "outputId": "254a363a-73ec-4a47-b919-8fbf6b7b568a"
      },
      "source": [
        "# Initialize the short and long windows\r\n",
        "short_window = 40\r\n",
        "long_window = 100\r\n",
        "\r\n",
        "# Initialize the `signals` DataFrame with the `signal` column\r\n",
        "signals = pd.DataFrame(index=aapl.index)\r\n",
        "signals['signal'] = 0.0\r\n",
        "\r\n",
        "# Create short simple moving average over the short window\r\n",
        "signals['short_mavg'] = aapl['Close'].rolling(window=short_window, min_periods=1, center=False).mean()\r\n",
        "\r\n",
        "# Create long simple moving average over the long window\r\n",
        "signals['long_mavg'] = aapl['Close'].rolling(window=long_window, min_periods=1, center=False).mean()\r\n",
        "\r\n",
        "# Create signals\r\n",
        "signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:]  > signals['long_mavg'][short_window:], 1.0, 0.0)   \r\n",
        "\r\n",
        "# Generate trading orders\r\n",
        "signals['positions'] = signals['signal'].diff()\r\n",
        "\r\n",
        "# Print `signals`\r\n",
        "print(signals)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-4ad561ac2f30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Initialize the `signals` DataFrame with the `signal` column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0msignals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maapl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0msignals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'signal'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "jW7wO7f7fgIr",
        "outputId": "26a81b0f-cbf9-4cc1-8dbc-1bba6ef83198"
      },
      "source": [
        "# Import `pyplot` module as `plt`\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Initialize the plot figure\r\n",
        "fig = plt.figure()\r\n",
        "\r\n",
        "# Add a subplot and label for y-axis\r\n",
        "ax1 = fig.add_subplot(111,  ylabel='Price in $')\r\n",
        "\r\n",
        "# Plot the closing price\r\n",
        "aapl['Close'].plot(ax=ax1, color='r', lw=2.)\r\n",
        "\r\n",
        "# Plot the short and long moving averages\r\n",
        "signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)\r\n",
        "\r\n",
        "# Plot the buy signals\r\n",
        "ax1.plot(signals.loc[signals.positions == 1.0].index, \r\n",
        "         signals.short_mavg[signals.positions == 1.0],\r\n",
        "         '^', markersize=10, color='m')\r\n",
        "         \r\n",
        "# Plot the sell signals\r\n",
        "ax1.plot(signals.loc[signals.positions == -1.0].index, \r\n",
        "         signals.short_mavg[signals.positions == -1.0],\r\n",
        "         'v', markersize=10, color='k')\r\n",
        "         \r\n",
        "# Show the plot\r\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-46-76b784ce7616>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# Plot the short and long moving averages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0msignals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'short_mavg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'long_mavg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Plot the buy signals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'signals' is not defined"
          ]
        },
        {
          "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": 231
        },
        "id": "qwo7fkc_foDJ",
        "outputId": "7a7a85d3-bd21-4926-97a1-0b4fbb812532"
      },
      "source": [
        "# Set the initial capital\r\n",
        "initial_capital= float(100000.0)\r\n",
        "\r\n",
        "# Create a DataFrame `positions`\r\n",
        "positions = pd.DataFrame(index=signals.index).fillna(0.0)\r\n",
        "\r\n",
        "# Buy a 100 shares\r\n",
        "positions['AAPL'] = 100*signals['signal']   \r\n",
        "  \r\n",
        "# Initialize the portfolio with value owned   \r\n",
        "portfolio = positions.multiply(aapl['Adj Close'], axis=0)\r\n",
        "\r\n",
        "# Store the difference in shares owned \r\n",
        "pos_diff = positions.diff()\r\n",
        "\r\n",
        "# Add `holdings` to portfolio\r\n",
        "portfolio['holdings'] = (positions.multiply(aapl['Adj Close'], axis=0)).sum(axis=1)\r\n",
        "\r\n",
        "# Add `cash` to portfolio\r\n",
        "portfolio['cash'] = initial_capital - (pos_diff.multiply(aapl['Adj Close'], axis=0)).sum(axis=1).cumsum()   \r\n",
        "\r\n",
        "# Add `total` to portfolio\r\n",
        "portfolio['total'] = portfolio['cash'] + portfolio['holdings']\r\n",
        "\r\n",
        "# Add `returns` to portfolio\r\n",
        "portfolio['returns'] = portfolio['total'].pct_change()\r\n",
        "\r\n",
        "# Print the first lines of `portfolio`\r\n",
        "print(portfolio.head())"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-47-810b4aadbf4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Create a DataFrame `positions`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpositions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Buy a 100 shares\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    }
  ]
}