gdp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:31.078542Z",
     "start_time": "2023-07-15T08:40:30.979635Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.tsa.x13 import x13_arima_analysis\n",
    "import numpy as np, matplotlib.pyplot as plt, seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "\n",
    "Data = pd.read_excel('data2.xlsx', index_col = 0).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "          소비자물가 - 농축수산물 - 공업제품 - 집세 - 공공서비스 - 개인서비스 근원물가 생활물가     수출  \\\n1995\\n01월   5.2     7.5    1.8  3.6    10.0     8.2  4.6    -     78   \n1995\\n02월   4.5     6.0    1.8  3.3     8.7     7.0  4.0    -     84   \n1995\\n03월   4.6     6.7    2.2  3.4     7.6     7.1  4.1    -    100   \n1995\\n04월   5.1     8.0    2.2  3.6     8.9     7.6  4.5    -    102   \n1995\\n05월   5.2     6.8    2.7  3.9     9.1     7.5  4.5    -    106   \n...         ...     ...    ...  ...     ...     ...  ...  ...    ...   \n2023\\n01월   5.2     1.1    6.0  1.3     0.8     5.9  5.0  6.1  464.0   \n2023\\n02월   4.8     1.1    5.1  1.1     0.9     5.7  4.8  5.5  500.0   \n2023\\n03월   4.2     3.0    2.9  0.9     1.2     5.8  4.8  4.4  550.0   \n2023\\n04월   3.7     1.0    2.0  0.8     1.0     6.1  4.6  3.7  495.0   \n2023\\n05월   3.3    -0.3    1.8  0.6     1.0     5.6  4.3  3.2  522.0   \n\n          (전년동월대비 수출 증감률)     수입 (전년동월대비 수입 증감률)   무역수지  \n1995\\n01월            28.0     90            19.0    -12  \n1995\\n02월            35.9     99            47.6    -15  \n1995\\n03월            31.3    117            40.3    -17  \n1995\\n04월            32.5    111            37.5     -9  \n1995\\n05월            35.5    118            39.7    -12  \n...                   ...    ...             ...    ...  \n2023\\n01월           -16.4  589.0            -2.8 -125.0  \n2023\\n02월            -7.7  553.0             3.5  -53.0  \n2023\\n03월           -13.8  597.0            -6.5  -47.0  \n2023\\n04월           -14.4  522.0           -13.3  -27.0  \n2023\\n05월           -15.2  543.0           -14.0  -21.0  \n\n[341 rows x 13 columns]",
      "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>소비자물가</th>\n      <th>- 농축수산물</th>\n      <th>- 공업제품</th>\n      <th>- 집세</th>\n      <th>- 공공서비스</th>\n      <th>- 개인서비스</th>\n      <th>근원물가</th>\n      <th>생활물가</th>\n      <th>수출</th>\n      <th>(전년동월대비 수출 증감률)</th>\n      <th>수입</th>\n      <th>(전년동월대비 수입 증감률)</th>\n      <th>무역수지</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1995\\n01월</th>\n      <td>5.2</td>\n      <td>7.5</td>\n      <td>1.8</td>\n      <td>3.6</td>\n      <td>10.0</td>\n      <td>8.2</td>\n      <td>4.6</td>\n      <td>-</td>\n      <td>78</td>\n      <td>28.0</td>\n      <td>90</td>\n      <td>19.0</td>\n      <td>-12</td>\n    </tr>\n    <tr>\n      <th>1995\\n02월</th>\n      <td>4.5</td>\n      <td>6.0</td>\n      <td>1.8</td>\n      <td>3.3</td>\n      <td>8.7</td>\n      <td>7.0</td>\n      <td>4.0</td>\n      <td>-</td>\n      <td>84</td>\n      <td>35.9</td>\n      <td>99</td>\n      <td>47.6</td>\n      <td>-15</td>\n    </tr>\n    <tr>\n      <th>1995\\n03월</th>\n      <td>4.6</td>\n      <td>6.7</td>\n      <td>2.2</td>\n      <td>3.4</td>\n      <td>7.6</td>\n      <td>7.1</td>\n      <td>4.1</td>\n      <td>-</td>\n      <td>100</td>\n      <td>31.3</td>\n      <td>117</td>\n      <td>40.3</td>\n      <td>-17</td>\n    </tr>\n    <tr>\n      <th>1995\\n04월</th>\n      <td>5.1</td>\n      <td>8.0</td>\n      <td>2.2</td>\n      <td>3.6</td>\n      <td>8.9</td>\n      <td>7.6</td>\n      <td>4.5</td>\n      <td>-</td>\n      <td>102</td>\n      <td>32.5</td>\n      <td>111</td>\n      <td>37.5</td>\n      <td>-9</td>\n    </tr>\n    <tr>\n      <th>1995\\n05월</th>\n      <td>5.2</td>\n      <td>6.8</td>\n      <td>2.7</td>\n      <td>3.9</td>\n      <td>9.1</td>\n      <td>7.5</td>\n      <td>4.5</td>\n      <td>-</td>\n      <td>106</td>\n      <td>35.5</td>\n      <td>118</td>\n      <td>39.7</td>\n      <td>-12</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023\\n01월</th>\n      <td>5.2</td>\n      <td>1.1</td>\n      <td>6.0</td>\n      <td>1.3</td>\n      <td>0.8</td>\n      <td>5.9</td>\n      <td>5.0</td>\n      <td>6.1</td>\n      <td>464.0</td>\n      <td>-16.4</td>\n      <td>589.0</td>\n      <td>-2.8</td>\n      <td>-125.0</td>\n    </tr>\n    <tr>\n      <th>2023\\n02월</th>\n      <td>4.8</td>\n      <td>1.1</td>\n      <td>5.1</td>\n      <td>1.1</td>\n      <td>0.9</td>\n      <td>5.7</td>\n      <td>4.8</td>\n      <td>5.5</td>\n      <td>500.0</td>\n      <td>-7.7</td>\n      <td>553.0</td>\n      <td>3.5</td>\n      <td>-53.0</td>\n    </tr>\n    <tr>\n      <th>2023\\n03월</th>\n      <td>4.2</td>\n      <td>3.0</td>\n      <td>2.9</td>\n      <td>0.9</td>\n      <td>1.2</td>\n      <td>5.8</td>\n      <td>4.8</td>\n      <td>4.4</td>\n      <td>550.0</td>\n      <td>-13.8</td>\n      <td>597.0</td>\n      <td>-6.5</td>\n      <td>-47.0</td>\n    </tr>\n    <tr>\n      <th>2023\\n04월</th>\n      <td>3.7</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.8</td>\n      <td>1.0</td>\n      <td>6.1</td>\n      <td>4.6</td>\n      <td>3.7</td>\n      <td>495.0</td>\n      <td>-14.4</td>\n      <td>522.0</td>\n      <td>-13.3</td>\n      <td>-27.0</td>\n    </tr>\n    <tr>\n      <th>2023\\n05월</th>\n      <td>3.3</td>\n      <td>-0.3</td>\n      <td>1.8</td>\n      <td>0.6</td>\n      <td>1.0</td>\n      <td>5.6</td>\n      <td>4.3</td>\n      <td>3.2</td>\n      <td>522.0</td>\n      <td>-15.2</td>\n      <td>543.0</td>\n      <td>-14.0</td>\n      <td>-21.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>341 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:31.361698Z",
     "start_time": "2023-07-15T08:40:31.355407Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "Data.index = pd.date_range(start = '1995-01-01', end = '2023-06-01', freq = 'M')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:31.841619Z",
     "start_time": "2023-07-15T08:40:31.839921Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "           소비자물가 - 농축수산물 - 공업제품 - 집세 - 공공서비스 - 개인서비스 근원물가 생활물가     수출  \\\n1995-01-31   5.2     7.5    1.8  3.6    10.0     8.2  4.6    -     78   \n1995-02-28   4.5     6.0    1.8  3.3     8.7     7.0  4.0    -     84   \n1995-03-31   4.6     6.7    2.2  3.4     7.6     7.1  4.1    -    100   \n1995-04-30   5.1     8.0    2.2  3.6     8.9     7.6  4.5    -    102   \n1995-05-31   5.2     6.8    2.7  3.9     9.1     7.5  4.5    -    106   \n...          ...     ...    ...  ...     ...     ...  ...  ...    ...   \n2023-01-31   5.2     1.1    6.0  1.3     0.8     5.9  5.0  6.1  464.0   \n2023-02-28   4.8     1.1    5.1  1.1     0.9     5.7  4.8  5.5  500.0   \n2023-03-31   4.2     3.0    2.9  0.9     1.2     5.8  4.8  4.4  550.0   \n2023-04-30   3.7     1.0    2.0  0.8     1.0     6.1  4.6  3.7  495.0   \n2023-05-31   3.3    -0.3    1.8  0.6     1.0     5.6  4.3  3.2  522.0   \n\n           (전년동월대비 수출 증감률)     수입 (전년동월대비 수입 증감률)   무역수지  \n1995-01-31            28.0     90            19.0    -12  \n1995-02-28            35.9     99            47.6    -15  \n1995-03-31            31.3    117            40.3    -17  \n1995-04-30            32.5    111            37.5     -9  \n1995-05-31            35.5    118            39.7    -12  \n...                    ...    ...             ...    ...  \n2023-01-31           -16.4  589.0            -2.8 -125.0  \n2023-02-28            -7.7  553.0             3.5  -53.0  \n2023-03-31           -13.8  597.0            -6.5  -47.0  \n2023-04-30           -14.4  522.0           -13.3  -27.0  \n2023-05-31           -15.2  543.0           -14.0  -21.0  \n\n[341 rows x 13 columns]",
      "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>소비자물가</th>\n      <th>- 농축수산물</th>\n      <th>- 공업제품</th>\n      <th>- 집세</th>\n      <th>- 공공서비스</th>\n      <th>- 개인서비스</th>\n      <th>근원물가</th>\n      <th>생활물가</th>\n      <th>수출</th>\n      <th>(전년동월대비 수출 증감률)</th>\n      <th>수입</th>\n      <th>(전년동월대비 수입 증감률)</th>\n      <th>무역수지</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1995-01-31</th>\n      <td>5.2</td>\n      <td>7.5</td>\n      <td>1.8</td>\n      <td>3.6</td>\n      <td>10.0</td>\n      <td>8.2</td>\n      <td>4.6</td>\n      <td>-</td>\n      <td>78</td>\n      <td>28.0</td>\n      <td>90</td>\n      <td>19.0</td>\n      <td>-12</td>\n    </tr>\n    <tr>\n      <th>1995-02-28</th>\n      <td>4.5</td>\n      <td>6.0</td>\n      <td>1.8</td>\n      <td>3.3</td>\n      <td>8.7</td>\n      <td>7.0</td>\n      <td>4.0</td>\n      <td>-</td>\n      <td>84</td>\n      <td>35.9</td>\n      <td>99</td>\n      <td>47.6</td>\n      <td>-15</td>\n    </tr>\n    <tr>\n      <th>1995-03-31</th>\n      <td>4.6</td>\n      <td>6.7</td>\n      <td>2.2</td>\n      <td>3.4</td>\n      <td>7.6</td>\n      <td>7.1</td>\n      <td>4.1</td>\n      <td>-</td>\n      <td>100</td>\n      <td>31.3</td>\n      <td>117</td>\n      <td>40.3</td>\n      <td>-17</td>\n    </tr>\n    <tr>\n      <th>1995-04-30</th>\n      <td>5.1</td>\n      <td>8.0</td>\n      <td>2.2</td>\n      <td>3.6</td>\n      <td>8.9</td>\n      <td>7.6</td>\n      <td>4.5</td>\n      <td>-</td>\n      <td>102</td>\n      <td>32.5</td>\n      <td>111</td>\n      <td>37.5</td>\n      <td>-9</td>\n    </tr>\n    <tr>\n      <th>1995-05-31</th>\n      <td>5.2</td>\n      <td>6.8</td>\n      <td>2.7</td>\n      <td>3.9</td>\n      <td>9.1</td>\n      <td>7.5</td>\n      <td>4.5</td>\n      <td>-</td>\n      <td>106</td>\n      <td>35.5</td>\n      <td>118</td>\n      <td>39.7</td>\n      <td>-12</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-01-31</th>\n      <td>5.2</td>\n      <td>1.1</td>\n      <td>6.0</td>\n      <td>1.3</td>\n      <td>0.8</td>\n      <td>5.9</td>\n      <td>5.0</td>\n      <td>6.1</td>\n      <td>464.0</td>\n      <td>-16.4</td>\n      <td>589.0</td>\n      <td>-2.8</td>\n      <td>-125.0</td>\n    </tr>\n    <tr>\n      <th>2023-02-28</th>\n      <td>4.8</td>\n      <td>1.1</td>\n      <td>5.1</td>\n      <td>1.1</td>\n      <td>0.9</td>\n      <td>5.7</td>\n      <td>4.8</td>\n      <td>5.5</td>\n      <td>500.0</td>\n      <td>-7.7</td>\n      <td>553.0</td>\n      <td>3.5</td>\n      <td>-53.0</td>\n    </tr>\n    <tr>\n      <th>2023-03-31</th>\n      <td>4.2</td>\n      <td>3.0</td>\n      <td>2.9</td>\n      <td>0.9</td>\n      <td>1.2</td>\n      <td>5.8</td>\n      <td>4.8</td>\n      <td>4.4</td>\n      <td>550.0</td>\n      <td>-13.8</td>\n      <td>597.0</td>\n      <td>-6.5</td>\n      <td>-47.0</td>\n    </tr>\n    <tr>\n      <th>2023-04-30</th>\n      <td>3.7</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.8</td>\n      <td>1.0</td>\n      <td>6.1</td>\n      <td>4.6</td>\n      <td>3.7</td>\n      <td>495.0</td>\n      <td>-14.4</td>\n      <td>522.0</td>\n      <td>-13.3</td>\n      <td>-27.0</td>\n    </tr>\n    <tr>\n      <th>2023-05-31</th>\n      <td>3.3</td>\n      <td>-0.3</td>\n      <td>1.8</td>\n      <td>0.6</td>\n      <td>1.0</td>\n      <td>5.6</td>\n      <td>4.3</td>\n      <td>3.2</td>\n      <td>522.0</td>\n      <td>-15.2</td>\n      <td>543.0</td>\n      <td>-14.0</td>\n      <td>-21.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>341 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:32.236877Z",
     "start_time": "2023-07-15T08:40:32.229906Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "df = Data.iloc[:,[0,-4,-2]]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:32.730471Z",
     "start_time": "2023-07-15T08:40:32.727682Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "df = df.astype(float) * 0.01"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:33.163020Z",
     "start_time": "2023-07-15T08:40:33.157716Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "            소비자물가  (전년동월대비 수출 증감률)  (전년동월대비 수입 증감률)\n1995-01-31  0.052            0.280            0.190\n1995-02-28  0.045            0.359            0.476\n1995-03-31  0.046            0.313            0.403\n1995-04-30  0.051            0.325            0.375\n1995-05-31  0.052            0.355            0.397\n...           ...              ...              ...\n2023-01-31  0.052           -0.164           -0.028\n2023-02-28  0.048           -0.077            0.035\n2023-03-31  0.042           -0.138           -0.065\n2023-04-30  0.037           -0.144           -0.133\n2023-05-31  0.033           -0.152           -0.140\n\n[341 rows x 3 columns]",
      "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>소비자물가</th>\n      <th>(전년동월대비 수출 증감률)</th>\n      <th>(전년동월대비 수입 증감률)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1995-01-31</th>\n      <td>0.052</td>\n      <td>0.280</td>\n      <td>0.190</td>\n    </tr>\n    <tr>\n      <th>1995-02-28</th>\n      <td>0.045</td>\n      <td>0.359</td>\n      <td>0.476</td>\n    </tr>\n    <tr>\n      <th>1995-03-31</th>\n      <td>0.046</td>\n      <td>0.313</td>\n      <td>0.403</td>\n    </tr>\n    <tr>\n      <th>1995-04-30</th>\n      <td>0.051</td>\n      <td>0.325</td>\n      <td>0.375</td>\n    </tr>\n    <tr>\n      <th>1995-05-31</th>\n      <td>0.052</td>\n      <td>0.355</td>\n      <td>0.397</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-01-31</th>\n      <td>0.052</td>\n      <td>-0.164</td>\n      <td>-0.028</td>\n    </tr>\n    <tr>\n      <th>2023-02-28</th>\n      <td>0.048</td>\n      <td>-0.077</td>\n      <td>0.035</td>\n    </tr>\n    <tr>\n      <th>2023-03-31</th>\n      <td>0.042</td>\n      <td>-0.138</td>\n      <td>-0.065</td>\n    </tr>\n    <tr>\n      <th>2023-04-30</th>\n      <td>0.037</td>\n      <td>-0.144</td>\n      <td>-0.133</td>\n    </tr>\n    <tr>\n      <th>2023-05-31</th>\n      <td>0.033</td>\n      <td>-0.152</td>\n      <td>-0.140</td>\n    </tr>\n  </tbody>\n</table>\n<p>341 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:33.565872Z",
     "start_time": "2023-07-15T08:40:33.562498Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "df.columns = ['CPI','export','import']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:34.239584Z",
     "start_time": "2023-07-15T08:40:34.235145Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "df['label'] = np.nan"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:34.600255Z",
     "start_time": "2023-07-15T08:40:34.593975Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "              CPI  export  import  label\n1995-01-31  0.052   0.280   0.190    NaN\n1995-02-28  0.045   0.359   0.476    NaN\n1995-03-31  0.046   0.313   0.403    NaN\n1995-04-30  0.051   0.325   0.375    NaN\n1995-05-31  0.052   0.355   0.397    NaN\n...           ...     ...     ...    ...\n2023-01-31  0.052  -0.164  -0.028    NaN\n2023-02-28  0.048  -0.077   0.035    NaN\n2023-03-31  0.042  -0.138  -0.065    NaN\n2023-04-30  0.037  -0.144  -0.133    NaN\n2023-05-31  0.033  -0.152  -0.140    NaN\n\n[341 rows x 4 columns]",
      "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>CPI</th>\n      <th>export</th>\n      <th>import</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1995-01-31</th>\n      <td>0.052</td>\n      <td>0.280</td>\n      <td>0.190</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1995-02-28</th>\n      <td>0.045</td>\n      <td>0.359</td>\n      <td>0.476</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1995-03-31</th>\n      <td>0.046</td>\n      <td>0.313</td>\n      <td>0.403</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1995-04-30</th>\n      <td>0.051</td>\n      <td>0.325</td>\n      <td>0.375</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1995-05-31</th>\n      <td>0.052</td>\n      <td>0.355</td>\n      <td>0.397</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-01-31</th>\n      <td>0.052</td>\n      <td>-0.164</td>\n      <td>-0.028</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2023-02-28</th>\n      <td>0.048</td>\n      <td>-0.077</td>\n      <td>0.035</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2023-03-31</th>\n      <td>0.042</td>\n      <td>-0.138</td>\n      <td>-0.065</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2023-04-30</th>\n      <td>0.037</td>\n      <td>-0.144</td>\n      <td>-0.133</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2023-05-31</th>\n      <td>0.033</td>\n      <td>-0.152</td>\n      <td>-0.140</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>341 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:34.974847Z",
     "start_time": "2023-07-15T08:40:34.971834Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "df.loc[(df['CPI'] < 0.04) & (df['import'] > 0), 'label'] = 1 # 호황기\n",
    "df.loc[(df['CPI'] < 0.04) & (df['import'] <= 0), 'label'] = 2 # 후퇴기\n",
    "df.loc[(df['CPI'] >= 0.04) & (df['import'] > 0), 'label'] = 0 # 회복기\n",
    "df.loc[(df['CPI'] >= 0.04) & (df['import'] <= 0), 'label'] = 3 # 불경기"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:35.656361Z",
     "start_time": "2023-07-15T08:40:35.654840Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "              CPI  export  import  label\n1995-01-31  0.052   0.280   0.190    0.0\n1995-02-28  0.045   0.359   0.476    0.0\n1995-03-31  0.046   0.313   0.403    0.0\n1995-04-30  0.051   0.325   0.375    0.0\n1995-05-31  0.052   0.355   0.397    0.0\n...           ...     ...     ...    ...\n2023-01-31  0.052  -0.164  -0.028    3.0\n2023-02-28  0.048  -0.077   0.035    0.0\n2023-03-31  0.042  -0.138  -0.065    3.0\n2023-04-30  0.037  -0.144  -0.133    2.0\n2023-05-31  0.033  -0.152  -0.140    2.0\n\n[341 rows x 4 columns]",
      "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>CPI</th>\n      <th>export</th>\n      <th>import</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1995-01-31</th>\n      <td>0.052</td>\n      <td>0.280</td>\n      <td>0.190</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1995-02-28</th>\n      <td>0.045</td>\n      <td>0.359</td>\n      <td>0.476</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1995-03-31</th>\n      <td>0.046</td>\n      <td>0.313</td>\n      <td>0.403</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1995-04-30</th>\n      <td>0.051</td>\n      <td>0.325</td>\n      <td>0.375</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1995-05-31</th>\n      <td>0.052</td>\n      <td>0.355</td>\n      <td>0.397</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-01-31</th>\n      <td>0.052</td>\n      <td>-0.164</td>\n      <td>-0.028</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2023-02-28</th>\n      <td>0.048</td>\n      <td>-0.077</td>\n      <td>0.035</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2023-03-31</th>\n      <td>0.042</td>\n      <td>-0.138</td>\n      <td>-0.065</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2023-04-30</th>\n      <td>0.037</td>\n      <td>-0.144</td>\n      <td>-0.133</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2023-05-31</th>\n      <td>0.033</td>\n      <td>-0.152</td>\n      <td>-0.140</td>\n      <td>2.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>341 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:36.085340Z",
     "start_time": "2023-07-15T08:40:36.082587Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[:,:3].plot()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:37.596013Z",
     "start_time": "2023-07-15T08:40:37.370118Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.CPI.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:40:38.836146Z",
     "start_time": "2023-07-15T08:40:38.685847Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "df.to_csv('Labeling.csv',sep=',')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:42:15.979903Z",
     "start_time": "2023-07-15T08:42:15.972348Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "Timestamp('2002-01-01 00:00:00')"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "from pykrx import stock\n",
    "import FinanceDataReader as fdr\n",
    "\n",
    "def get_business_day(df, year, month, index=0):\n",
    "    str_month = f\"{year}-{month}\"\n",
    "    return df.loc[str_month].index[index]\n",
    "\n",
    "df = fdr.DataReader(symbol='KS11')\n",
    "get_business_day(df, 2002, 1, 0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:35:47.421021Z",
     "start_time": "2023-07-15T08:35:46.212185Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "def momentum(df, year=2010, month=1, period=12):\n",
    "    # 투자 시작일, 종료일\n",
    "    str_day = f\"{year}-{month}-01\"\n",
    "    start = datetime.datetime.strptime(str_day, \"%Y-%m-%d\")\n",
    "    end = start + relativedelta(months=period-1)\n",
    "    inv_start = get_business_day(df, start.year, start.month, 0)   # 첫 번째 거래일의 종가\n",
    "    inv_end = get_business_day(df, end.year, end.month, -1)\n",
    "    inv_start = inv_start.strftime(\"%Y%m%d\")\n",
    "    inv_end = inv_end.strftime(\"%Y%m%d\")\n",
    "    #print(inv_start, inv_end)\n",
    "\n",
    "    # 모멘텀 계산 시작일, 종료일\n",
    "    end = start - relativedelta(months=1)     # 역추세 1개월 제외\n",
    "    start = start - relativedelta(months=12)\n",
    "    mom_start = get_business_day(df, start.year, start.month, 0)   # 첫 번째 거래일의 종가\n",
    "    mom_end = get_business_day(df, end.year, end.month, -1)\n",
    "    mom_start = mom_start.strftime(\"%Y%m%d\")\n",
    "    mom_end = mom_end.strftime(\"%Y%m%d\")\n",
    "    print(mom_start, mom_end, \" | \", inv_start, inv_end)\n",
    "\n",
    "    # momentum 계산\n",
    "    df1 = stock.get_market_ohlcv(mom_start)\n",
    "    df2 = stock.get_market_ohlcv(mom_end)\n",
    "    mon_df = df1.join(df2, lsuffix=\"l\", rsuffix=\"r\")\n",
    "    mon_df['등락률'] = (mon_df['종가r'] - mon_df['종가l'])/mon_df['종가l']*100\n",
    "\n",
    "    # 우선주 제외\n",
    "    mon_df = mon_df.filter(regex=\"0$\", axis=0)\n",
    "    mon20 = mon_df.sort_values(by=\"등락률\", ascending=False)[:20]\n",
    "    #print(mon20)\n",
    "    mon20 = mon20[['등락률']]\n",
    "\n",
    "    # 투자 기간 수익률\n",
    "    df3 = stock.get_market_ohlcv(inv_start)\n",
    "    df4 = stock.get_market_ohlcv(inv_end)\n",
    "    inv_df = df3.join(df4, lsuffix=\"l\", rsuffix=\"r\")\n",
    "    inv_df['수익률'] = inv_df['종가r'] / inv_df['종가l']    # 수익률 = 매도가 / 매수가\n",
    "    inv_df = inv_df[['수익률']]\n",
    "\n",
    "    # join\n",
    "    result_df = mon20.join(inv_df)\n",
    "    result = result_df['수익률'].fillna(0).mean()\n",
    "    return year, result"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:35:47.438653Z",
     "start_time": "2023-07-15T08:35:47.427167Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20091102 20101029  |  20101101 20110429\n",
      "20091201 20101130  |  20101201 20110531\n",
      "20100104 20101230  |  20110103 20110630\n",
      "20100201 20110131  |  20110201 20110729\n",
      "20100302 20110228  |  20110302 20110831\n",
      "20100401 20110331  |  20110401 20110930\n",
      "20100503 20110429  |  20110502 20111031\n",
      "20100601 20110531  |  20110601 20111130\n",
      "20100701 20110630  |  20110701 20111229\n",
      "20100802 20110729  |  20110801 20120131\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "year = 2010\n",
    "month = 11\n",
    "period = 6\n",
    "total = 10\n",
    "\n",
    "str_day = f\"{year}-{month}-01\"\n",
    "start = datetime.datetime.strptime(str_day, \"%Y-%m-%d\")\n",
    "\n",
    "data = []\n",
    "for i in range(1,total+1):\n",
    "    ret = momentum(df, start.year, start.month, period)\n",
    "    data.append(ret)\n",
    "    start += relativedelta(months=1)\n",
    "    time.sleep(1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:36:24.748512Z",
     "start_time": "2023-07-15T08:35:47.429474Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "[(2010, 1.0426344124068856),\n (2010, 1.156145523255193),\n (2011, 1.18179907243972),\n (2011, 1.0272759037889423),\n (2011, 1.1152122365276846),\n (2011, 0.8086165874381119),\n (2011, 0.7838387393311954),\n (2011, 0.7554210946971405),\n (2011, 0.6911336892687744),\n (2011, 0.7422522214139247)]"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:36:24.762758Z",
     "start_time": "2023-07-15T08:36:24.758759Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "def momentum2(df, year=2010, month=11, period=12):\n",
    "    # 투자 시작일, 종료일\n",
    "    str_day = f\"{year}-{month}-01\"\n",
    "    start = datetime.datetime.strptime(str_day, \"%Y-%m-%d\")\n",
    "    end = start + relativedelta(months=period-1)\n",
    "    inv_start = get_business_day(df, start.year, start.month, 0)   # 첫 번째 거래일의 종가\n",
    "    inv_end = get_business_day(df, end.year, end.month, -1)\n",
    "    inv_start = inv_start.strftime(\"%Y%m%d\")\n",
    "    inv_end = inv_end.strftime(\"%Y%m%d\")\n",
    "    #print(inv_start, inv_end)\n",
    "\n",
    "    # 모멘텀 계산 시작일, 종료일\n",
    "    end = start - relativedelta(months=2)     # 역추세 1개월 제외\n",
    "    start = start - relativedelta(months=period)\n",
    "    mom_start = get_business_day(df, start.year, start.month, 0)   # 첫 번째 거래일의 종가\n",
    "    mom_end = get_business_day(df, end.year, end.month, -1)\n",
    "    mom_start = mom_start.strftime(\"%Y%m%d\")\n",
    "    mom_end = mom_end.strftime(\"%Y%m%d\")\n",
    "    print(mom_start, mom_end, \" | \", inv_start, inv_end)\n",
    "\n",
    "    # momentum 계산\n",
    "    df1 = stock.get_market_ohlcv(mom_start)\n",
    "    df2 = stock.get_market_ohlcv(mom_end)\n",
    "    mon_df = df1.join(df2, lsuffix=\"l\", rsuffix=\"r\")\n",
    "    mon_df['등락률'] = (mon_df['종가r'] - mon_df['종가l'])/mon_df['종가l']*100\n",
    "\n",
    "    # 우선주 제외\n",
    "    mon_df = mon_df.filter(regex=\"0$\", axis=0)\n",
    "    mon20 = mon_df.sort_values(by=\"등락률\", ascending=False)[:20]\n",
    "    mon20 = mon20[['등락률']]\n",
    "\n",
    "    # 투자 기간 수익률\n",
    "    df3 = stock.get_market_ohlcv(inv_start)\n",
    "    df4 = stock.get_market_ohlcv(inv_end)\n",
    "    inv_df = df3.join(df4, lsuffix=\"l\", rsuffix=\"r\")\n",
    "    inv_df['수익률'] = inv_df['종가r'] / inv_df['종가l']    # 수익률 = 매도가 / 매수가\n",
    "    inv_df = inv_df[['수익률']]\n",
    "\n",
    "    # join\n",
    "    result_df = mon20.join(inv_df)\n",
    "    result = result_df['수익률'].fillna(0).mean()\n",
    "    return year, result"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:36:24.801755Z",
     "start_time": "2023-07-15T08:36:24.764945Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20100503 20100930  |  20101101 20110429\n",
      "20110502 20110930  |  20111101 20120430\n",
      "20120502 20120928  |  20121101 20130430\n",
      "20130502 20130930  |  20131101 20140430\n",
      "20140502 20140930  |  20141103 20150430\n",
      "20150504 20150930  |  20151102 20160429\n",
      "20160502 20160930  |  20161101 20170428\n",
      "20170502 20170929  |  20171101 20180430\n",
      "20180502 20180928  |  20181101 20190430\n",
      "20190502 20190930  |  20191101 20200429\n",
      "20200504 20200929  |  20201102 20210430\n",
      "20210503 20210930  |  20211101 20220429\n"
     ]
    }
   ],
   "source": [
    "year = 2010\n",
    "month = 11\n",
    "period = 6\n",
    "total = 12\n",
    "\n",
    "str_day = f\"{year}-{month}-01\"\n",
    "start = datetime.datetime.strptime(str_day, \"%Y-%m-%d\")\n",
    "\n",
    "data = []\n",
    "for i in range(1,total+1):\n",
    "    ret = momentum2(df, start.year, start.month, period)\n",
    "    data.append(ret)\n",
    "    start += relativedelta(years=1)\n",
    "    time.sleep(1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:37:10.194342Z",
     "start_time": "2023-07-15T08:36:24.787157Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "[(2010, 1.06956194112967),\n (2011, 0.9607535438798692),\n (2012, 1.4577543978916438),\n (2013, 1.076926167292763),\n (2014, 1.6950272211482353),\n (2015, 0.9574101694987929),\n (2016, 0.9057471928263879),\n (2017, 1.3873152308101184),\n (2018, 1.0123607433408783),\n (2019, 0.7643145979893636),\n (2020, 1.1151789381829516),\n (2021, 0.9088089091211803)]"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-15T08:37:10.214161Z",
     "start_time": "2023-07-15T08:37:10.198916Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 50672 (\\N{HANGUL SYLLABLE YEON}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 46020 (\\N{HANGUL SYLLABLE DO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 48708 (\\N{HANGUL SYLLABLE BI}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 51473 (\\N{HANGUL SYLLABLE JUNG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 51088 (\\N{HANGUL SYLLABLE JA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 49328 (\\N{HANGUL SYLLABLE SAN}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 44400 (\\N{HANGUL SYLLABLE GUN}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 48324 (\\N{HANGUL SYLLABLE BYEOL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 48320 (\\N{HANGUL SYLLABLE BYEON}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/3414042650.py:32: UserWarning: Glyph 54868 (\\N{HANGUL SYLLABLE HWA}) missing from current font.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 가상의 데이터 (예시)\n",
    "asset_classes = ['주식', '채권', '현금', '부동산', '기타']\n",
    "years = [2020, 2021, 2022, 2023]\n",
    "weights = {\n",
    "    '주식': [0.4, 0.35, 0.3, 0.25],\n",
    "    '채권': [0.3, 0.35, 0.35, 0.35],\n",
    "    '현금': [0.2, 0.15, 0.1, 0.05],\n",
    "    '부동산': [0.08, 0.1, 0.15, 0.2],\n",
    "    '기타': [0.02, 0.05, 0.1, 0.15]\n",
    "}\n",
    "\n",
    "# 각 연도별 자산군의 비중을 정규화하여 총합이 1이 되도록 조정\n",
    "for asset in asset_classes:\n",
    "    total_weight = sum(weights[asset])\n",
    "    weights[asset] = [w / total_weight for w in weights[asset]]\n",
    "\n",
    "# area plot 생성\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.stackplot(years, [weights[asset] for asset in asset_classes], baseline='zero')\n",
    "\n",
    "# 그래프 설정\n",
    "plt.xlabel('연도')\n",
    "plt.ylabel('비중')\n",
    "plt.title('자산군별 비중 변화')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xticks(years)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# 출력\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:59:19.549976Z",
     "start_time": "2023-07-19T14:59:19.441306Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 50672 (\\N{HANGUL SYLLABLE YEON}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 46020 (\\N{HANGUL SYLLABLE DO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 45572 (\\N{HANGUL SYLLABLE NU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 51201 (\\N{HANGUL SYLLABLE JEOG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 44032 (\\N{HANGUL SYLLABLE GA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 52824 (\\N{HANGUL SYLLABLE CI}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 49884 (\\N{HANGUL SYLLABLE SI}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 49688 (\\N{HANGUL SYLLABLE SU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 51061 (\\N{HANGUL SYLLABLE IG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 47456 (\\N{HANGUL SYLLABLE RYUL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 53804 (\\N{HANGUL SYLLABLE TU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 51088 (\\N{HANGUL SYLLABLE JA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 50896 (\\N{HANGUL SYLLABLE WEON}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/var/folders/_x/jd8cnzcs4v9dgfmy2l73m8b80000gn/T/ipykernel_31939/1767047061.py:29: UserWarning: Glyph 44552 (\\N{HANGUL SYLLABLE GEUM}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 45572 (\\N{HANGUL SYLLABLE NU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 51201 (\\N{HANGUL SYLLABLE JEOG}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 44032 (\\N{HANGUL SYLLABLE GA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 52824 (\\N{HANGUL SYLLABLE CI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 49884 (\\N{HANGUL SYLLABLE SI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 49688 (\\N{HANGUL SYLLABLE SU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 51061 (\\N{HANGUL SYLLABLE IG}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 47456 (\\N{HANGUL SYLLABLE RYUL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 53804 (\\N{HANGUL SYLLABLE TU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50896 (\\N{HANGUL SYLLABLE WEON}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/tommylee/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 44552 (\\N{HANGUL SYLLABLE GEUM}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 가상의 데이터 (예시)\n",
    "years = [2020, 2021, 2022, 2023, 2024, 2025]\n",
    "investment = 10000\n",
    "cumulative_returns = [0.1, 0.2, 0.15, 0.05, -0.1, -0.05]\n",
    "\n",
    "# 누적 수익률 계산\n",
    "cumulative_values = [investment * (1 + r) for r in cumulative_returns]\n",
    "\n",
    "# 데이터프레임 생성\n",
    "df = pd.DataFrame({'Year': years, 'Cumulative Value': cumulative_values})\n",
    "\n",
    "# underwater plot 그리기\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df['Year'], df['Cumulative Value'], label='누적 수익률', color='blue')\n",
    "plt.axhline(y=investment, linestyle='--', color='red', label='투자 원금')\n",
    "plt.fill_between(df['Year'], df['Cumulative Value'], investment, where=(df['Cumulative Value'] < investment), interpolate=True, color='red', alpha=0.3)\n",
    "\n",
    "# 그래프 설정\n",
    "plt.xlabel('연도')\n",
    "plt.ylabel('누적 가치')\n",
    "plt.title('Underwater Plot 예시')\n",
    "plt.legend()\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# 출력\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T15:32:53.504119Z",
     "start_time": "2023-07-19T15:32:53.192548Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MDD: 0.30000000000000004\n"
     ]
    }
   ],
   "source": [
    "def calculate_mdd(returns):\n",
    "    max_value = 0\n",
    "    mdd = 0\n",
    "\n",
    "    for r in returns:\n",
    "        max_value = max(max_value, r)\n",
    "        drawdown = max_value - r\n",
    "        mdd = max(mdd, drawdown)\n",
    "\n",
    "    return mdd\n",
    "\n",
    "# 가상의 데이터 (예시)\n",
    "returns = [0.1, 0.2, 0.15, 0.05, -0.1, -0.05]\n",
    "\n",
    "# MDD 계산\n",
    "mdd = calculate_mdd(returns)\n",
    "print(\"MDD:\", mdd)"
   ],
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