shinseitaro/businessquarter.ipynb

## businessquarter.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BusinessQuarter.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOhGy0kDJE5+g2Dmfc0beU5",
      "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/gist/shinseitaro/e4b96e202ed6e31a062f7fa7ea7d80a2/businessquarter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Kp66pAk8nyz"
      },
      "source": [
        "# インストール\n",
        "!pip install yfinance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1YXOeWw81A3"
      },
      "source": [
        "import yfinance as yf\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bv3kGwM_o-jf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "60aa5d90-d244-40c0-becf-e881da8b9337"
      },
      "source": [
        "data = yf.download(\"SPY\")\n",
        "adjclose = data[[\"Adj Close\"]]\n",
        "adjclose"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        },
        {
          "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>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1993-01-29</th>\n",
              "      <td>25.884184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-02-01</th>\n",
              "      <td>26.068277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-02-02</th>\n",
              "      <td>26.123499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-02-03</th>\n",
              "      <td>26.399649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-02-04</th>\n",
              "      <td>26.510111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-23</th>\n",
              "      <td>422.600006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-24</th>\n",
              "      <td>425.100006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-25</th>\n",
              "      <td>426.609985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-28</th>\n",
              "      <td>427.470001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-29</th>\n",
              "      <td>427.700012</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7156 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Adj Close\n",
              "Date                  \n",
              "1993-01-29   25.884184\n",
              "1993-02-01   26.068277\n",
              "1993-02-02   26.123499\n",
              "1993-02-03   26.399649\n",
              "1993-02-04   26.510111\n",
              "...                ...\n",
              "2021-06-23  422.600006\n",
              "2021-06-24  425.100006\n",
              "2021-06-25  426.609985\n",
              "2021-06-28  427.470001\n",
              "2021-06-29  427.700012\n",
              "\n",
              "[7156 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXgKTj56b_6N"
      },
      "source": [
        "import pandas as pd\n",
        "df_qf = adjclose.pct_change(-1).resample(\"BQS\").first()\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "7VxTHYa8e2rI",
        "outputId": "4bed422d-7478-4809-a5c0-66f5496990da"
      },
      "source": [
        "df_qf['month'] = df_qf.index.strftime(\"%m\")#%b\n",
        "df_qf.groupby(\"month\").mean().plot.bar(grid=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5863128510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "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": 297
        },
        "id": "3cbRFHKpfkeA",
        "outputId": "77b459f4-f1d0-412d-f08f-0a57572c7fda"
      },
      "source": [
        "df_qf.groupby(\"month\").describe().T"
      ],
      "execution_count": 7,
      "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>month</th>\n",
              "      <th>01</th>\n",
              "      <th>04</th>\n",
              "      <th>07</th>\n",
              "      <th>10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">Adj Close</th>\n",
              "      <th>count</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.001405</td>\n",
              "      <td>-0.004076</td>\n",
              "      <td>0.001481</td>\n",
              "      <td>0.001499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.014261</td>\n",
              "      <td>0.013726</td>\n",
              "      <td>0.010063</td>\n",
              "      <td>0.013443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.045833</td>\n",
              "      <td>-0.028407</td>\n",
              "      <td>-0.016173</td>\n",
              "      <td>-0.021453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.006285</td>\n",
              "      <td>-0.010740</td>\n",
              "      <td>-0.004088</td>\n",
              "      <td>-0.004090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.002640</td>\n",
              "      <td>-0.003585</td>\n",
              "      <td>0.000387</td>\n",
              "      <td>-0.000596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000551</td>\n",
              "      <td>-0.000490</td>\n",
              "      <td>0.005508</td>\n",
              "      <td>0.008028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.040697</td>\n",
              "      <td>0.034514</td>\n",
              "      <td>0.028059</td>\n",
              "      <td>0.037639</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "month                   01         04         07         10\n",
              "Adj Close count  29.000000  29.000000  28.000000  28.000000\n",
              "          mean   -0.001405  -0.004076   0.001481   0.001499\n",
              "          std     0.014261   0.013726   0.010063   0.013443\n",
              "          min    -0.045833  -0.028407  -0.016173  -0.021453\n",
              "          25%    -0.006285  -0.010740  -0.004088  -0.004090\n",
              "          50%    -0.002640  -0.003585   0.000387  -0.000596\n",
              "          75%     0.000551  -0.000490   0.005508   0.008028\n",
              "          max     0.040697   0.034514   0.028059   0.037639"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6tmx-G4AgmMK",
        "outputId": "0518cf6e-ebdf-41bd-80b7-d70c247429f5"
      },
      "source": [
        "month=\"07\"\n",
        "df_by_month = df_qf[(df_qf[\"month\"] == month)]\n",
        "len(df_by_month[df_by_month[\"Adj Close\"] > 0]) / len(df_by_month)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5357142857142857"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "tvlvshJzivHq",
        "outputId": "3fc2b7ea-f772-4432-bc24-b17cad59daf4"
      },
      "source": [
        "df_by_month.plot.bar(figsize=(20,5), grid=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f585ea29d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}