mrbkdad/craw_kix_current_price_and_cal_movingmean.ipynb

## craw_kix_current_price_and_cal_movingmean.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KRX 주식 > 종목정보 > 일자별 시세  읽어오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from io import StringIO\n",
    "from datetime import datetime\n",
    "\n",
    "def stock_price_krx(isu_cd):\n",
    "    # STEP 01: Generate OTP\n",
    "    gen_otp_url = 'http://marketdata.krx.co.kr/contents/COM/GenerateOTP.jspx'\n",
    "    gen_otp_data = {\n",
    "        'name':'fileDown',\n",
    "        'filetype':'csv',\n",
    "        'url':'MKD/04/0402/04020100/mkd04020100t3_02',\n",
    "        'isu_cd':isu_cd,#'KR7005930003' 삼성전자\n",
    "        'fromdate':'20161001',\n",
    "        'todate':'20170430',\n",
    "    }\n",
    "    \n",
    "    r = requests.post(gen_otp_url, gen_otp_data)\n",
    "    code = r.content\n",
    "    \n",
    "    # STEP 02: download\n",
    "    down_url = 'http://file.krx.co.kr/download.jspx'\n",
    "    down_data = {\n",
    "        'code': code,\n",
    "    }\n",
    "    \n",
    "    r = requests.post(down_url, down_data)\n",
    "    r.encoding = \"utf-8-sig\"\n",
    "    #with open(\"data/A005930.csv\", 'w') as f:\n",
    "    #    f.write(r.text)\n",
    "    raw_csv = r.text\n",
    "    if raw_csv[-1] != '\"':\n",
    "        raw_csv = raw_csv + '\"'\n",
    "    df = pd.read_csv(StringIO(raw_csv), header=0, thousands=',')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = stock_price_krx('KR7005930003')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>년/월/일</th>\n",
       "      <th>종가</th>\n",
       "      <th>대비</th>\n",
       "      <th>거래량(주)</th>\n",
       "      <th>거래대금(원)</th>\n",
       "      <th>시가</th>\n",
       "      <th>고가</th>\n",
       "      <th>저가</th>\n",
       "      <th>시가총액(백만)</th>\n",
       "      <th>상장주식수(주)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/04/25</td>\n",
       "      <td>2135000</td>\n",
       "      <td>73000</td>\n",
       "      <td>385992</td>\n",
       "      <td>816104381800</td>\n",
       "      <td>2073000</td>\n",
       "      <td>2137000</td>\n",
       "      <td>2066000</td>\n",
       "      <td>298172684</td>\n",
       "      <td>139659337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/04/24</td>\n",
       "      <td>2062000</td>\n",
       "      <td>24000</td>\n",
       "      <td>179803</td>\n",
       "      <td>369972154800</td>\n",
       "      <td>2063000</td>\n",
       "      <td>2063000</td>\n",
       "      <td>2046000</td>\n",
       "      <td>287977553</td>\n",
       "      <td>139659337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/04/21</td>\n",
       "      <td>2038000</td>\n",
       "      <td>24000</td>\n",
       "      <td>302610</td>\n",
       "      <td>619834871224</td>\n",
       "      <td>2024000</td>\n",
       "      <td>2070000</td>\n",
       "      <td>2024000</td>\n",
       "      <td>284625729</td>\n",
       "      <td>139659337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/04/20</td>\n",
       "      <td>2014000</td>\n",
       "      <td>-31000</td>\n",
       "      <td>422977</td>\n",
       "      <td>852639858358</td>\n",
       "      <td>2029000</td>\n",
       "      <td>2040000</td>\n",
       "      <td>2004000</td>\n",
       "      <td>283328185</td>\n",
       "      <td>140679337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/04/19</td>\n",
       "      <td>2045000</td>\n",
       "      <td>-30000</td>\n",
       "      <td>235258</td>\n",
       "      <td>482680770572</td>\n",
       "      <td>2065000</td>\n",
       "      <td>2071000</td>\n",
       "      <td>2045000</td>\n",
       "      <td>287689244</td>\n",
       "      <td>140679337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        년/월/일       종가     대비  거래량(주)       거래대금(원)       시가       고가  \\\n",
       "0  2017/04/25  2135000  73000  385992  816104381800  2073000  2137000   \n",
       "1  2017/04/24  2062000  24000  179803  369972154800  2063000  2063000   \n",
       "2  2017/04/21  2038000  24000  302610  619834871224  2024000  2070000   \n",
       "3  2017/04/20  2014000 -31000  422977  852639858358  2029000  2040000   \n",
       "4  2017/04/19  2045000 -30000  235258  482680770572  2065000  2071000   \n",
       "\n",
       "        저가   시가총액(백만)   상장주식수(주)  \n",
       "0  2066000  298172684  139659337  \n",
       "1  2046000  287977553  139659337  \n",
       "2  2024000  284625729  139659337  \n",
       "3  2004000  283328185  140679337  \n",
       "4  2045000  287689244  140679337  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/04/25</td>\n",
       "      <td>2135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/04/24</td>\n",
       "      <td>2062000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/04/21</td>\n",
       "      <td>2038000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/04/20</td>\n",
       "      <td>2014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/04/19</td>\n",
       "      <td>2045000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    close\n",
       "0  2017/04/25  2135000\n",
       "1  2017/04/24  2062000\n",
       "2  2017/04/21  2038000\n",
       "3  2017/04/20  2014000\n",
       "4  2017/04/19  2045000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = df[['년/월/일','종가']]\n",
    "data_df.columns = ['date','close']\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\python35\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "date     datetime64[ns]\n",
       "close             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df['date']=pd.to_datetime(data_df['date'])\n",
    "data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>2135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>2062000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>2038000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>2014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>2045000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close\n",
       "date               \n",
       "2017-04-25  2135000\n",
       "2017-04-24  2062000\n",
       "2017-04-21  2038000\n",
       "2017-04-20  2014000\n",
       "2017-04-19  2045000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.set_index('date',inplace=True)\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>1657500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-09</th>\n",
       "      <td>1579250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-14</th>\n",
       "      <td>1585333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-19</th>\n",
       "      <td>1611333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>1591800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close\n",
       "date               \n",
       "2016-10-04  1657500\n",
       "2016-10-09  1579250\n",
       "2016-10-14  1585333\n",
       "2016-10-19  1611333\n",
       "2016-10-24  1591800"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 5일 평균 구하기\n",
    "data_df.resample('5D',label='left').mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>2135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>2062000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>2038000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>2014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>2045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>2075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>2078000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>2101000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>2121000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>2095000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close\n",
       "date               \n",
       "2017-04-25  2135000\n",
       "2017-04-24  2062000\n",
       "2017-04-21  2038000\n",
       "2017-04-20  2014000\n",
       "2017-04-19  2045000\n",
       "2017-04-18  2075000\n",
       "2017-04-17  2078000\n",
       "2017-04-14  2101000\n",
       "2017-04-13  2121000\n",
       "2017-04-12  2095000"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.dropna()\n",
    "data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#pd.rolling_mean(data_df,5).plot(figsize=(12,6))\n",
    "d5_mean_df = data_df.rolling(5).mean()\n",
    "d10_mean_df = data_df.rolling(10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9de68d0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mdpXsioiIiIi4ydELR3l64dNUKFiBrzt/neh9uuAc9dKtG/zxhyowiySFljGL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\noZNgNyIiIiIiIoZOgt2IiIiIiIgYOv8Hj+oYEG5TnZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9da27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([d5_mean_df,d10_mean_df],axis=1).plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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