{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "expired-religious",
   "metadata": {},
   "source": [
    "# **Matplotlib OHLC Example**\n",
    "- https://github.com/matplotlib/mplfinance\n",
    "- **[Basics of OHLC charts with Python’s Matplotlib](https://towardsdatascience.com/basics-of-ohlc-charts-with-pythons-matplotlib-56d0e745a5be)**\n",
    "- **[mpl_finance 라이브러리 최신변경 및 신규모듈 추상화로 어렵다](https://github.com/matplotlib/mplfinance)**\n",
    "\n",
    "## **1 Loading the DataSet**\n",
    "데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "visible-overview",
   "metadata": {},
   "outputs": [
    {
     "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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>366000</td>\n",
       "      <td>367000</td>\n",
       "      <td>360500</td>\n",
       "      <td>364500</td>\n",
       "      <td>398383</td>\n",
       "      <td>0.004132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2021-05-06</td>\n",
       "      <td>360500</td>\n",
       "      <td>364500</td>\n",
       "      <td>359500</td>\n",
       "      <td>361500</td>\n",
       "      <td>632873</td>\n",
       "      <td>-0.008230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2021-05-07</td>\n",
       "      <td>360500</td>\n",
       "      <td>364000</td>\n",
       "      <td>360000</td>\n",
       "      <td>361000</td>\n",
       "      <td>444578</td>\n",
       "      <td>-0.001383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date    open    high     low   close  volume    change\n",
       "577 2021-05-04  366000  367000  360500  364500  398383  0.004132\n",
       "578 2021-05-06  360500  364500  359500  361500  632873 -0.008230\n",
       "579 2021-05-07  360500  364000  360000  361000  444578 -0.001383"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "file_save = \"stock/boillinger.csv\"\n",
    "data_df = pd.read_csv(file_save)\n",
    "data_df['Date'] = pd.to_datetime(data_df['Date']) # datetime 포맷 변경\n",
    "data_df.columns = [_.lower() for _ in data_df.columns]\n",
    "# data_df = data_df.set_index('date')\n",
    "data_df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emotional-governor",
   "metadata": {},
   "source": [
    "# **Matplotlib 을 활용한 그래프 그리기**\n",
    "**Initialization** matplotlib 초기화 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "actual-settle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found : D2Coding\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "# Loading ... Visualization Modules\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "plt.style.use('seaborn')\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "from matplotlib.font_manager import fontManager as fm # 한글폰트 확인 및 추가\n",
    "font_list_check  = ['D2Coding', 'NanumGothicCoding', 'NanumGothic']\n",
    "for _ in font_list_check:\n",
    "    font_counts = [f  for f in font_list_check if _ in [_.name for _ in fm.ttflist]]\n",
    "    if len(font_counts) > 1: print(f\"found : {_}\"); plt.rc('font', family=_); break "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "meaning-presence",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, <class 'pandas.core.series.Series'>, \n",
      "date    2020-12-10 00:00:00\n",
      "open                 288000\n",
      "Name: 0, dtype: object \n",
      "\n",
      "1, <class 'pandas.core.series.Series'>, \n",
      "date    2020-12-11 00:00:00\n",
      "open                 288000\n",
      "Name: 1, dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plotlib 에서 활용하는 Pandas 메서드\n",
    "# .iterrows() : index 순서대로 Series 순차로 출력\n",
    "df = data_df.iloc[-100:,:].reset_index(drop=True).copy()\n",
    "# df = data_df.copy()\n",
    "for idx, val in df.iloc[:2,:2].iterrows(): \n",
    "    print(f\"{idx}, {type(val)}, \\n{val} \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cleared-living",
   "metadata": {},
   "source": [
    "## **1 Candle Line | OHLC Chart**\n",
    "matplotlib 를 활용하여 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "seven-elephant",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 944
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.arange(0, len(df))\n",
    "fig, ax = plt.subplots(1, figsize=(16,4))\n",
    "for idx, val in df.iterrows():\n",
    "    plt.plot([x[idx], x[idx]], [val['low'], val['high']])\n",
    "    \n",
    "plt.xticks(x[::50], df.date.dt.date[::50])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polyphonic-tumor",
   "metadata": {},
   "source": [
    "## **2 Candle Stick | OHLC Chart**\n",
    "matplotlib 를 활용하여 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "raising-improvement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 944
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, len(df))\n",
    "fix, ax = plt.subplots(1, figsize=(16,4))\n",
    "\n",
    "for _, val in df.iterrows():\n",
    "    plt.plot([ x[_], x[_]], [val['low'], val['high']], color=\"black\") # High, Low Marker\n",
    "    plt.plot([ x[_], x[_]-.1], [val['open'], val['open']], color=\"black\") # Open Marker\n",
    "    plt.plot([ x[_], x[_]+.1], [val['close'], val['close']], color=\"black\") # close Marker\n",
    "    \n",
    "plt.xticks(x[::20], df.date.dt.date[::20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strong-orbit",
   "metadata": {},
   "source": [
    "## **3 Candle Stick in Color | OHLC Chart**\n",
    "matplotlib 를 활용하여 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "adapted-practitioner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 944
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, len(df))\n",
    "fix, ax = plt.subplots(1, figsize=(16,4))\n",
    "\n",
    "for _, val in df.iterrows():\n",
    "    if val['close'] > val['open']: color = \"#db1102\" # 종가 상승\n",
    "    else: color = \"#0e40c9\"                          # 종가 하락\n",
    "    plt.plot([ x[_], x[_]], [val['low'], val['high']], color=color)     # High, Low Marker\n",
    "    plt.plot([ x[_], x[_]-.1], [val['open'], val['open']], color=color) # Open Marker\n",
    "    plt.plot([ x[_], x[_]+.1], [val['close'], val['close']], color=color) # close Marker\n",
    "    \n",
    "plt.xticks(x[::20], df.date.dt.date[::20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crucial-athletics",
   "metadata": {},
   "source": [
    "## **4 Candle Stick in Color 2 | OHLC Chart**\n",
    "matplotlib 를 활용하여 그리기 **Adding the Chart Style**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "coral-walker",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 268,
       "width": 957
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, len(df))\n",
    "fix, ax = plt.subplots(1, figsize=(16,4))\n",
    "\n",
    "for _, val in df.iterrows():\n",
    "    if val['close'] > val['open']: color = \"#db1102\" # 종가 상승\n",
    "    else: color = \"#0e40c9\"                          # 종가 하락\n",
    "    plt.plot([ x[_], x[_]], [val['low'], val['high']], color=color)     # High, Low Marker\n",
    "    plt.plot([ x[_], x[_]-.1], [val['open'], val['open']], color=color) # Open Marker\n",
    "    plt.plot([ x[_], x[_]+.1], [val['close'], val['close']], color=color) # close Marker\n",
    "    \n",
    "plt.xticks(x[::10], df.date.dt.date[::10]) # X-axis : datetime ticks\n",
    "ax.set_xticks(x, minor=True)\n",
    "plt.ylabel('원')  # labels\n",
    "ax.xaxis.grid(color='black', linestyle='dashed', which='both', alpha=.1)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.title('KOSPI Stock Price', loc=\"left\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seeing-kinase",
   "metadata": {},
   "source": [
    "## **4 Candle Chart | Customizing**\n",
    "volume 데이터 테이블 추가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "hindu-founder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 356,
       "width": 982
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, len(df))\n",
    "fix, (ax1, ax2) = plt.subplots(2, figsize=(16,6), gridspec_kw={'height_ratios':[4,1]})\n",
    "\n",
    "for _, val in df.iterrows():\n",
    "    if val['close'] > val['open']: color = \"#db1102\" # 종가 상승\n",
    "    else: color = \"#0e40c9\"                          # 종가 하락\n",
    "    ax1.plot([ x[_], x[_]], [val['low'], val['high']], color=color)     # High, Low Marker\n",
    "    ax1.plot([ x[_], x[_]-.1], [val['open'], val['open']], color=color) # Open Marker\n",
    "    ax1.plot([ x[_], x[_]+.1], [val['close'], val['close']], color=color) # close Marker\n",
    "\n",
    "# ticks top plot\n",
    "ax2.set_xticks(x[::10])\n",
    "ax2.set_xticklabels(df.date.dt.date[::10]) # X-axis : datetime ticks\n",
    "ax1.set_xticks(x, minor=True)\n",
    "\n",
    "# labels\n",
    "ax1.set_ylabel('원')\n",
    "ax2.set_ylabel('거래량')\n",
    "# ax1.xaxis.grid(color='black', linestyle='dashed', which)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "functioning-henry",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}