tak-akashi/TSE_Foreign_Pos.ipynb

## TSE_Foreign_Pos.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://www.jpx.co.jp/markets/statistics-equities/investor-type/00-00-archives-00.html'\n",
    "url_last = 'http://www.jpx.co.jp/markets/statistics-equities/investor-type/00-00-archives-01.html'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rq = requests.get(url)\n",
    "rq_last = requests.get(url_last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rq.encoding = rq.apparent_encoding\n",
    "rq_last.encoding = rq_last.apparent_encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(rq.text, 'html.parser')\n",
    "soup_last = BeautifulSoup(rq_last.text, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_tags = soup.find_all('a')\n",
    "a_tags_last = soup_last.find_all('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_xls = []\n",
    "for a_tag in a_tags:\n",
    "    if ('xls' in a_tag.get('href')) and ('val' in a_tag.get('href')):\n",
    "        list_xls.append(a_tag.get('href'))\n",
    "for a_tag in a_tags_last:\n",
    "    if ('xls' in a_tag.get('href')) and ('val' in a_tag.get('href')):\n",
    "        list_xls.append(a_tag.get('href'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_url = 'http://www.jpx.co.jp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, x in enumerate(list_xls):\n",
    "    url = base_url + x\n",
    "    urllib.request.urlretrieve(url,'temp/temp'+ str(i) + '.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "foreign_pos = []\n",
    "for i in range(len(list_xls)):\n",
    "    book = xlrd.open_workbook('temp/temp' + str(i) + '.xls')\n",
    "    sheet = book.sheet_by_name('Tokyo & Nagoya')\n",
    "    dt_org = sheet.cell_value(3, 0)\n",
    "    dt_ = dt_org.split('年')[0] + '/' + dt_org.split(' ')[-2]\n",
    "    dt = datetime.strptime(dt_, '%Y/%m/%d')\n",
    "    foreign_pos.append([dt,\n",
    "                        int(''.join((sheet.cell_value(29,10) \n",
    "                                     + sheet.cell_value(30,10)).split(',')))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [row[0] for row in foreign_pos]\n",
    "y = [row[1] for row in foreign_pos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(x, y, width = 4, color='blue')\n",
    "plt.title('株式売買動向（海外投資家）as of ' + foreign_pos[0][0].strftime('%Y%m%d'))\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m'))\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}