05 correlation_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "import yfinance as yf\n",
    "# 한글폰트 출력 가능 코드\n",
    "from matplotlib import rc\n",
    "rc('font', family='AppleGothic') # Mac Os\n",
    "#rc('font', family='NanumGothic') # Windows Os\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Simple data 형성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29 46 49 49 25 19 25 17 42 34 28 12 20 31 41 12 49 20 39  9 15 30 17 27\n",
      " 12 31 34  3 17 16]\n",
      "[24 38 18 24 22 38 20 13 46 27 13 15 33 14 14 43  1 46 30 24 38 22 12 44\n",
      " 47  3  1 37 47 42]\n"
     ]
    }
   ],
   "source": [
    "# 랜덤한 데이터 형성 (시드 부여)\n",
    "np.random.seed(2022)\n",
    "\n",
    "x = np.random.randint(1,50,30)\n",
    "y = np.random.randint(1,50,30)\n",
    "\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-1. 데이터 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 데이터 프레임 형성\n",
    "sample = pd.DataFrame(list(zip(x,y)), columns=['x','y'])\n",
    "\n",
    "# 상관분석에 앞서 먼저 시각화를 이용하여 상관성을 육안으로 확인해본다.\n",
    "\n",
    "sns.pairplot(sample, kind='reg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-2. 상관분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. 공분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-61.68666666666666\n",
      "[[160.90666667 -61.68666667]\n",
      " [-61.68666667 200.91555556]]\n"
     ]
    }
   ],
   "source": [
    "# 하나씩 시행해보기\n",
    "\n",
    "# mu = mean (공통적으로 적용)\n",
    "mu_1 = np.sum(sample['x'])/len(sample['x'])\n",
    "mu_2 = np.sum(sample['y'])/len(sample['y'])\n",
    "\n",
    "# var = variance (잔차의 재곱합의 평균), 표준편차 = var의 제곱근\n",
    "var_1 = np.sum([pow(i - mu_1, 2) for i in sample['x']]) / (len(sample['x']))\n",
    "var_2 = np.sum([pow(i - mu_2, 2) for i in sample['y']]) / (len(sample['y']))\n",
    "std_1 = var_1 ** 0.5\n",
    "std_2 = var_2 ** 0.5\n",
    "\n",
    "# 공분산 (표본상관계수를 구하는 것이 아니고 모집단으로 도출하므로 자유도는 0으로 설정하였음.)\n",
    "covariance = sum( (sample['x'] - mu_1) * (sample['y'] - mu_2) ) / (len(sample['x']))\n",
    "\n",
    "# COV(X,Y) : 모집단인 경우 (numpy와 비교, 자유도 0 설정은 ddof=0)\n",
    "print(covariance)\n",
    "print(np.cov(sample['x'],sample['y'], ddof=0)) # 공분산 행렬의 (1,2), (2,1)이 COV(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. 피어슨 상관계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.34308166001608753\n"
     ]
    }
   ],
   "source": [
    "# 위의 공분산에서 이어짐\n",
    "correlation = covariance / (std_1 * std_2)\n",
    "\n",
    "# 피어슨 상관계수\n",
    "print(correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.3430816600160875, 0.0634428569239739)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 피어슨 상관계수를 구하는 다른 방법으로는 scipy라이브러리를 활용하는 법\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "# 귀무가설은 '두 데이터의 상관 계수가 0이다.(독립)'로 설정되어 있음.\n",
    "pearsonr(sample['x'], sample['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. 모집단으로 가정한 경우\n",
      "모집단 data 각각의 분산 값은 COV(X,X) = 160.907, COV(Y,Y) = 200.916(으)로 계산됩니다.\n",
      "모집단 data에 대한 공분산 값 : -61.687\n",
      "모집단 data에 대한 피어슨 상관계수 값 : -0.343\n",
      "Scipy로 구한 피어슨 상관계수는 -0.343입니다.\n",
      "Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 6.343999999999999%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 없습니다.\n",
      "\n",
      "2. 표본집단으로 가정한 경우\n",
      "표본 data 각각의 추정된 분산 값은 COV(x,x) = 166.455, COV(y,y) = 207.844(으)로 계산됩니다.\n",
      "표본 data로 추정한 모집단의 공분산 값 : -63.814\n",
      "표본 data로 추정한 모집단의 피어슨 상관계수 값 : -0.343\n",
      "Scipy로 구한 피어슨 상관계수는 -0.343입니다.\n",
      "Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 6.343999999999999%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 없습니다.\n"
     ]
    }
   ],
   "source": [
    "# 함수화 시행\n",
    "def cov_pearson(data1, data2, population = True):\n",
    "    # 라이브러리 호출\n",
    "    import numpy as np\n",
    "    from scipy.stats import pearsonr\n",
    "\n",
    "    # 변수를 설정하는 부분입니다.\n",
    "    mu_1 = np.sum(data1)/len(data1)\n",
    "    mu_2 = np.sum(data2)/len(data2)\n",
    "    var_1 = np.sum([pow(i - mu_1, 2) for i in data1]) / (len(data1))\n",
    "    var_2 = np.sum([pow(i - mu_2, 2) for i in data2]) / (len(data2))\n",
    "    std_1 = var_1 ** 0.5\n",
    "    std_2 = var_2 ** 0.5\n",
    "    \n",
    "    # 데이터가 모수인지 표본인지에 따라 자유도를 산입할지 여부를 결정합니다.\n",
    "    if population == True:\n",
    "        covariance = sum( (data1 - mu_1) * (data2 - mu_2) ) / (len(data1))\n",
    "        correlation = covariance / (std_1 * std_2)\n",
    "        print(f'모집단 data 각각의 분산 값은 COV(X,X) = {round(var_1,3)}, COV(Y,Y) = {round(var_2,3)}(으)로 계산됩니다.')\n",
    "        print(f'모집단 data에 대한 공분산 값 : {round(covariance,3)}')\n",
    "        print(f'모집단 data에 대한 피어슨 상관계수 값 : {round(correlation,3)}')\n",
    "    else:\n",
    "        var_1 = np.sum([pow(i - mu_1, 2) for i in data1]) / (len(data1) - 1)\n",
    "        var_2 = np.sum([pow(i - mu_2, 2) for i in data2]) / (len(data2) - 1)\n",
    "        std_1 = var_1 ** 0.5\n",
    "        std_2 = var_2 ** 0.5\n",
    "        covariance_ddof = sum( (data1 - mu_1) * (data2 - mu_2) ) / (len(data1) -1)\n",
    "        correlation_ddof = covariance_ddof / (std_1 * std_2)\n",
    "        print(f'표본 data 각각의 추정된 분산 값은 COV(x,x) = {round(var_1,3)}, COV(y,y) = {round(var_2,3)}(으)로 계산됩니다.')\n",
    "        print(f'표본 data로 추정한 모집단의 공분산 값 : {round(covariance_ddof,3)}')\n",
    "        print(f'표본 data로 추정한 모집단의 피어슨 상관계수 값 : {round(correlation_ddof,3)}')\n",
    "\n",
    "    # Scipy 라이브러리에서 구한 피어슨 계수를 통해 위에서 확인한 상관계수 값을 검증해봅니다.\n",
    "    print(f'Scipy로 구한 피어슨 상관계수는 {round(pearsonr(data1, data2)[0], 3)}입니다.')\n",
    "    \n",
    "    # Scipy에서 제공하는 피어슨 검정의 P값을 이용하여 데이터 간의 독립성에 대한 검정 결과 해석을 출력합니다.\n",
    "    if pearsonr(data1, data2)[1] < 0.05:\n",
    "        print(f'Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 {round(pearsonr(data1, data2)[1],5) * 100}%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 있습니다.')\n",
    "    else:\n",
    "        print(f'Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 {round(pearsonr(data1, data2)[1],5) * 100}%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 없습니다.')\n",
    "\n",
    "print('1. 모집단으로 가정한 경우')\n",
    "cov_pearson(sample['x'], sample['y'], True)\n",
    "\n",
    "print('\\n2. 표본집단으로 가정한 경우')\n",
    "cov_pearson(sample['x'], sample['y'], False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. 스피어만 상관계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1 : [14 17 18 18 17 19 17 10  2 12]\n",
      "y1 : [ 9 12 17  7  9 15 17 12  2  3]\n",
      "\n",
      "smaple1 dataframe은 다음과 같습니다.\n"
     ]
    },
    {
     "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x   y\n",
       "0  14   9\n",
       "1  17  12\n",
       "2  18  17\n",
       "3  18   7\n",
       "4  17   9\n",
       "5  19  15\n",
       "6  17  17\n",
       "7  10  12\n",
       "8   2   2\n",
       "9  12   3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 랜덤한 데이터 형성 (시드 부여)\n",
    "np.random.seed(2022)\n",
    "\n",
    "x1 = np.random.randint(1,20,10)\n",
    "y1 = np.random.randint(1,20,10)\n",
    "\n",
    "print('x1 :',x1)\n",
    "print('y1 :',y1)\n",
    "\n",
    "sample1 = pd.DataFrame(list(zip(x1,y1)), columns=['x','y'])\n",
    "print('\\nsmaple1 dataframe은 다음과 같습니다.')\n",
    "sample1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>rank_x</th>\n",
       "      <th>rank_y</th>\n",
       "      <th>diff</th>\n",
       "      <th>d^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>30.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>12.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>20.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x   y  rank_x  rank_y  diff    d^2\n",
       "0  14   9     4.0     4.5  -0.5   0.25\n",
       "1  17  12     6.0     6.5  -0.5   0.25\n",
       "2  18  17     8.5     9.5  -1.0   1.00\n",
       "3  18   7     8.5     3.0   5.5  30.25\n",
       "4  17   9     6.0     4.5   1.5   2.25\n",
       "5  19  15    10.0     8.0   2.0   4.00\n",
       "6  17  17     6.0     9.5  -3.5  12.25\n",
       "7  10  12     2.0     6.5  -4.5  20.25\n",
       "8   2   2     1.0     1.0   0.0   0.00\n",
       "9  12   3     3.0     2.0   1.0   1.00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1['rank_x'] = pd.DataFrame(sample1['x'].rank())\n",
    "sample1['rank_y'] = pd.DataFrame(sample1['y'].rank())\n",
    "sample1['diff'] = sample1['rank_x'] - sample1['rank_y']\n",
    "sample1['d^2'] = sample1['diff'] ** 2\n",
    "\n",
    "sample1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5666666666666667"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = len(sample1['d^2'])\n",
    "sq_distance = 6 * sample1['d^2'].sum()\n",
    "spearman = 1 - sq_distance / (n * ( n ** 2 - 1))\n",
    "spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.5559113444061977, pvalue=0.09519504230496476)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 스피어만 (변수가 모두 연속형이며, 정규성을 따르지 않는 경우에 사용한다)\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "spearmanr(sample1['x'], sample1['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1v/ctxpzb994v98h43xyk972tmc0000gn/T/ipykernel_3452/23926823.py:1: FutureWarning: Passing a set as an indexer is deprecated and will raise in a future version. Use a list instead.\n",
      "  sample1[{'x','y'}].corr(method='spearman')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.555911</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "x  1.000000  0.555911\n",
       "y  0.555911  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1[{'x','y'}].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sub1 = pd.DataFrame(sample1['x'].sort_values().values).rank(method='min', ascending=True)\n",
    "# sub2 = pd.DataFrame(sample1['y'].sort_values().values).rank(method='min', ascending=True)\n",
    "\n",
    "# ranked = pd.concat((sub1,sub2), axis=1)\n",
    "# ranked.columns = ['ranked_x1','ranked_y1']\n",
    "# ranked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. 켄달 상관계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KendalltauResult(correlation=-0.6, pvalue=0.23333333333333334)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x_rank</th>\n",
       "      <th>y_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x  y  x_rank  y_rank\n",
       "0  12  1     5.0     2.0\n",
       "1   2  4     2.0     4.0\n",
       "2   1  7     1.0     5.0\n",
       "3  11  2     4.0     3.0\n",
       "4   3  0     3.0     1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import kendalltau\n",
    "\n",
    "X = [12, 2, 1, 11, 3]\n",
    "Y = [1, 4, 7, 2, 0]\n",
    "\n",
    "print(kendalltau(X,Y))\n",
    "\n",
    "# 데이터 프레임 형성 (x, y + x, y의 rank)\n",
    "kendal_df = pd.DataFrame(list(zip(X,Y)), columns=['x','y'])\n",
    "kendal_df\n",
    "kendal_df['x_rank'] = kendal_df['x'].rank()\n",
    "kendal_df['y_rank'] = kendal_df['y'].rank()\n",
    "kendal_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x_rank</th>\n",
       "      <th>y_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x  y  x_rank  y_rank\n",
       "0   1  7     1.0     5.0\n",
       "1   2  4     2.0     4.0\n",
       "2   3  0     3.0     1.0\n",
       "3  11  2     4.0     3.0\n",
       "4  12  1     5.0     2.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x의 rank를 기준으로 재정렬\n",
    "sort_kendal = kendal_df.sort_values(by='x_rank').reset_index()\n",
    "sort_kendal.drop(columns='index', inplace=True)\n",
    "sort_kendal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x_rank</th>\n",
       "      <th>y_rank</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>downward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>downward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>upward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>downward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x  y  x_rank  y_rank direction\n",
       "0   1  7     1.0     5.0  downward\n",
       "1   2  4     2.0     4.0  downward\n",
       "2   3  0     3.0     1.0    upward\n",
       "3  11  2     4.0     3.0  downward\n",
       "4  12  1     5.0     2.0       NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = []\n",
    "for i in range(4):\n",
    "    if sort_kendal['y_rank'][i] < sort_kendal['y_rank'][i+1] :\n",
    "        s.append('upward')\n",
    "    else:\n",
    "        s.append('downward')\n",
    "\n",
    "\n",
    "sort_kendal = pd.concat([sort_kendal, pd.DataFrame(s, columns={'direction'})], axis=1)\n",
    "sort_kendal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kendal 상관계수의 값은 (c-d)/(c+d)입니다. 즉 -0.6로 도출됩니다.\n"
     ]
    }
   ],
   "source": [
    "C = [0, 0, 1, 0]\n",
    "D = [2, 1, 0, 1]\n",
    "\n",
    "kendal = pd.concat([sort_kendal, pd.DataFrame(list(zip(C,D)), columns=['c','d'])], axis=1)\n",
    "kendal\n",
    "\n",
    "c = kendal['c'].sum()\n",
    "d = kendal['d'].sum()\n",
    "\n",
    "print(f'kendal 상관계수의 값은 (c-d)/(c+d)입니다. 즉 {(c-d)/(c+d)}로 도출됩니다.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 실습 (데이터 불러오기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  2 of 2 completed\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Adj Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">High</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>005930.KS</th>\n",
       "      <th>^KS11</th>\n",
       "      <th>005930.KS</th>\n",
       "      <th>^KS11</th>\n",
       "      <th>005930.KS</th>\n",
       "      <th>^KS11</th>\n",
       "      <th>005930.KS</th>\n",
       "      <th>^KS11</th>\n",
       "      <th>005930.KS</th>\n",
       "      <th>^KS11</th>\n",
       "      <th>005930.KS</th>\n",
       "      <th>^KS11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>78700.0</td>\n",
       "      <td>2989.239990</td>\n",
       "      <td>78700.0</td>\n",
       "      <td>2989.239990</td>\n",
       "      <td>79200.0</td>\n",
       "      <td>2995.250000</td>\n",
       "      <td>78300.0</td>\n",
       "      <td>2973.080078</td>\n",
       "      <td>78800.0</td>\n",
       "      <td>2991.969971</td>\n",
       "      <td>12427416</td>\n",
       "      <td>621200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>77400.0</td>\n",
       "      <td>2953.969971</td>\n",
       "      <td>77400.0</td>\n",
       "      <td>2953.969971</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>2986.199951</td>\n",
       "      <td>76400.0</td>\n",
       "      <td>2936.729980</td>\n",
       "      <td>78800.0</td>\n",
       "      <td>2984.050049</td>\n",
       "      <td>25470640</td>\n",
       "      <td>786900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>76900.0</td>\n",
       "      <td>2920.530029</td>\n",
       "      <td>76900.0</td>\n",
       "      <td>2920.530029</td>\n",
       "      <td>77600.0</td>\n",
       "      <td>2952.540039</td>\n",
       "      <td>76600.0</td>\n",
       "      <td>2915.379883</td>\n",
       "      <td>76700.0</td>\n",
       "      <td>2925.399902</td>\n",
       "      <td>12931954</td>\n",
       "      <td>785500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>78300.0</td>\n",
       "      <td>2954.889893</td>\n",
       "      <td>78300.0</td>\n",
       "      <td>2954.889893</td>\n",
       "      <td>78400.0</td>\n",
       "      <td>2959.030029</td>\n",
       "      <td>77400.0</td>\n",
       "      <td>2933.100098</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>2933.780029</td>\n",
       "      <td>15163757</td>\n",
       "      <td>545800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-10</th>\n",
       "      <td>78000.0</td>\n",
       "      <td>2926.719971</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>2926.719971</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>2951.120117</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>2910.899902</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>2947.370117</td>\n",
       "      <td>9947422</td>\n",
       "      <td>477000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-11</th>\n",
       "      <td>78900.0</td>\n",
       "      <td>2927.379883</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>2927.379883</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>2943.570068</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>2909.820068</td>\n",
       "      <td>78400.0</td>\n",
       "      <td>2930.929932</td>\n",
       "      <td>13221123</td>\n",
       "      <td>565800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-12</th>\n",
       "      <td>78900.0</td>\n",
       "      <td>2972.479980</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>2972.479980</td>\n",
       "      <td>79600.0</td>\n",
       "      <td>2972.580078</td>\n",
       "      <td>78600.0</td>\n",
       "      <td>2950.310059</td>\n",
       "      <td>79500.0</td>\n",
       "      <td>2950.780029</td>\n",
       "      <td>11000502</td>\n",
       "      <td>519000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13</th>\n",
       "      <td>77900.0</td>\n",
       "      <td>2962.090088</td>\n",
       "      <td>77900.0</td>\n",
       "      <td>2962.090088</td>\n",
       "      <td>79300.0</td>\n",
       "      <td>2982.139893</td>\n",
       "      <td>77900.0</td>\n",
       "      <td>2958.389893</td>\n",
       "      <td>79300.0</td>\n",
       "      <td>2979.909912</td>\n",
       "      <td>13889401</td>\n",
       "      <td>604600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-14</th>\n",
       "      <td>77300.0</td>\n",
       "      <td>2921.919922</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>2921.919922</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>2944.969971</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>2914.729980</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>2937.610107</td>\n",
       "      <td>10096725</td>\n",
       "      <td>532300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-17</th>\n",
       "      <td>77500.0</td>\n",
       "      <td>2890.100098</td>\n",
       "      <td>77500.0</td>\n",
       "      <td>2890.100098</td>\n",
       "      <td>77800.0</td>\n",
       "      <td>2919.669922</td>\n",
       "      <td>76900.0</td>\n",
       "      <td>2875.979980</td>\n",
       "      <td>77600.0</td>\n",
       "      <td>2918.550049</td>\n",
       "      <td>8785122</td>\n",
       "      <td>592900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-18</th>\n",
       "      <td>77000.0</td>\n",
       "      <td>2864.239990</td>\n",
       "      <td>77000.0</td>\n",
       "      <td>2864.239990</td>\n",
       "      <td>77800.0</td>\n",
       "      <td>2902.790039</td>\n",
       "      <td>76600.0</td>\n",
       "      <td>2857.260010</td>\n",
       "      <td>77600.0</td>\n",
       "      <td>2898.979980</td>\n",
       "      <td>9592788</td>\n",
       "      <td>553700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-19</th>\n",
       "      <td>76300.0</td>\n",
       "      <td>2842.280029</td>\n",
       "      <td>76300.0</td>\n",
       "      <td>2842.280029</td>\n",
       "      <td>76900.0</td>\n",
       "      <td>2871.790039</td>\n",
       "      <td>76100.0</td>\n",
       "      <td>2832.179932</td>\n",
       "      <td>76500.0</td>\n",
       "      <td>2840.340088</td>\n",
       "      <td>10598290</td>\n",
       "      <td>463400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-20</th>\n",
       "      <td>76500.0</td>\n",
       "      <td>2862.679932</td>\n",
       "      <td>76500.0</td>\n",
       "      <td>2862.679932</td>\n",
       "      <td>76700.0</td>\n",
       "      <td>2862.699951</td>\n",
       "      <td>75900.0</td>\n",
       "      <td>2830.969971</td>\n",
       "      <td>76200.0</td>\n",
       "      <td>2842.280029</td>\n",
       "      <td>9708168</td>\n",
       "      <td>395400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Adj Close                  Close                   High  \\\n",
       "           005930.KS        ^KS11 005930.KS        ^KS11 005930.KS   \n",
       "Date                                                                 \n",
       "2022-01-04   78700.0  2989.239990   78700.0  2989.239990   79200.0   \n",
       "2022-01-05   77400.0  2953.969971   77400.0  2953.969971   79000.0   \n",
       "2022-01-06   76900.0  2920.530029   76900.0  2920.530029   77600.0   \n",
       "2022-01-07   78300.0  2954.889893   78300.0  2954.889893   78400.0   \n",
       "2022-01-10   78000.0  2926.719971   78000.0  2926.719971   78100.0   \n",
       "2022-01-11   78900.0  2927.379883   78900.0  2927.379883   79000.0   \n",
       "2022-01-12   78900.0  2972.479980   78900.0  2972.479980   79600.0   \n",
       "2022-01-13   77900.0  2962.090088   77900.0  2962.090088   79300.0   \n",
       "2022-01-14   77300.0  2921.919922   77300.0  2921.919922   78100.0   \n",
       "2022-01-17   77500.0  2890.100098   77500.0  2890.100098   77800.0   \n",
       "2022-01-18   77000.0  2864.239990   77000.0  2864.239990   77800.0   \n",
       "2022-01-19   76300.0  2842.280029   76300.0  2842.280029   76900.0   \n",
       "2022-01-20   76500.0  2862.679932   76500.0  2862.679932   76700.0   \n",
       "\n",
       "                              Low                   Open               \\\n",
       "                  ^KS11 005930.KS        ^KS11 005930.KS        ^KS11   \n",
       "Date                                                                    \n",
       "2022-01-04  2995.250000   78300.0  2973.080078   78800.0  2991.969971   \n",
       "2022-01-05  2986.199951   76400.0  2936.729980   78800.0  2984.050049   \n",
       "2022-01-06  2952.540039   76600.0  2915.379883   76700.0  2925.399902   \n",
       "2022-01-07  2959.030029   77400.0  2933.100098   78100.0  2933.780029   \n",
       "2022-01-10  2951.120117   77100.0  2910.899902   78100.0  2947.370117   \n",
       "2022-01-11  2943.570068   78000.0  2909.820068   78400.0  2930.929932   \n",
       "2022-01-12  2972.580078   78600.0  2950.310059   79500.0  2950.780029   \n",
       "2022-01-13  2982.139893   77900.0  2958.389893   79300.0  2979.909912   \n",
       "2022-01-14  2944.969971   77100.0  2914.729980   77700.0  2937.610107   \n",
       "2022-01-17  2919.669922   76900.0  2875.979980   77600.0  2918.550049   \n",
       "2022-01-18  2902.790039   76600.0  2857.260010   77600.0  2898.979980   \n",
       "2022-01-19  2871.790039   76100.0  2832.179932   76500.0  2840.340088   \n",
       "2022-01-20  2862.699951   75900.0  2830.969971   76200.0  2842.280029   \n",
       "\n",
       "              Volume          \n",
       "           005930.KS   ^KS11  \n",
       "Date                          \n",
       "2022-01-04  12427416  621200  \n",
       "2022-01-05  25470640  786900  \n",
       "2022-01-06  12931954  785500  \n",
       "2022-01-07  15163757  545800  \n",
       "2022-01-10   9947422  477000  \n",
       "2022-01-11  13221123  565800  \n",
       "2022-01-12  11000502  519000  \n",
       "2022-01-13  13889401  604600  \n",
       "2022-01-14  10096725  532300  \n",
       "2022-01-17   8785122  592900  \n",
       "2022-01-18   9592788  553700  \n",
       "2022-01-19  10598290  463400  \n",
       "2022-01-20   9708168  395400  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 삼성전자, KOSPI 데이터 불러오기\n",
    "stock_price = yf.download({'005930.KS','^KS11'}, '2022-01-01', '2022-01-21')\n",
    "\n",
    "stock_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 상관분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. 공분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상관분석에 앞서 먼저 시각화를 이용하여 상관성을 육안으로 확인해본다.\n",
    "sub = stock_price['Adj Close']\n",
    "\n",
    "# 각 데이터의 히스토그램과 산점도를 도출(산점도에는 회귀선까지 구현)\n",
    "sns.pairplot(sub, kind='reg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. 피어슨 상관 계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32063.144155649043\n",
      "[[764230.76923077  32063.14415565]\n",
      " [ 32063.14415565   2088.44589524]]\n",
      "0.8025687284641172\n"
     ]
    }
   ],
   "source": [
    "# 하나씩 시행해보기\n",
    "# 변수 지정\n",
    "x1 = stock_price['Adj Close']['005930.KS']\n",
    "x2 = stock_price['Adj Close']['^KS11']\n",
    "\n",
    "# 만약 표본상관계수를 도출하게 된다면 (n-1로 또 나눠주어야 함)\n",
    "# mu = mean (공통적으로 적용)\n",
    "mu_1 = np.sum(x1)/len(x1)\n",
    "mu_2 = np.sum(x2)/len(x2)\n",
    "# 분산을 구하는 부분에서도 모분산 추정이므로 자유도 1 설정\n",
    "var_1 = np.sum([pow(i - mu_1, 2) for i in x1]) / (len(x1) - 1)\n",
    "var_2 = np.sum([pow(i - mu_2, 2) for i in x2]) / (len(x2) - 1)\n",
    "std_1 = var_1 ** 0.5\n",
    "std_2 = var_2 ** 0.5\n",
    "\n",
    "covariance_ddof = sum( (x1 - mu_1) * (x2 - mu_2) ) / (len(x1) -1)\n",
    "correlation_ddof = covariance_ddof / (std_1 * std_2)\n",
    "\n",
    "# COV(X,Y) : 표본인 경우 (numpy와 비교)\n",
    "print(covariance_ddof)\n",
    "print(np.cov(x1,x2))\n",
    "# 피어슨 상관계수\n",
    "print(correlation_ddof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8025687284641172, 0.0009595619740597607)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 피어슨 상관계수를 구하는 다른 방법으로는 scipy라이브러리를 활용하는 법\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "# 귀무가설은 '두 데이터의 상관 계수가 0이다.(독립)'로 설정되어 있음.\n",
    "pearsonr(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. 모집단으로 가정한 경우\n",
      "모집단 data 각각의 분산 값은 COV(X,X) = 705443.787, COV(Y,Y) = 1927.796(으)로 계산됩니다.\n",
      "모집단 data에 대한 공분산 값 : 29596.748\n",
      "모집단 data에 대한 피어슨 상관계수 값 : 0.803\n",
      "Scipy로 구한 피어슨 상관계수는 0.803입니다.\n",
      "Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 0.096%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 있습니다.\n",
      "\n",
      "2. 표본집단으로 가정한 경우\n",
      "표본 data 각각의 추정된 분산 값은 COV(x,x) = 764230.769, COV(y,y) = 2088.446(으)로 계산됩니다.\n",
      "표본 data로 추정한 모집단의 공분산 값 : 32063.144\n",
      "표본 data로 추정한 모집단의 피어슨 상관계수 값 : 0.803\n",
      "Scipy로 구한 피어슨 상관계수는 0.803입니다.\n",
      "Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 0.096%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 있습니다.\n"
     ]
    }
   ],
   "source": [
    "def cov_pearson(data1, data2, population = True):\n",
    "    # 라이브러리 호출\n",
    "    import numpy as np\n",
    "    from scipy.stats import pearsonr\n",
    "\n",
    "    # 변수를 설정하는 부분입니다.\n",
    "    mu_1 = np.sum(data1)/len(data1)\n",
    "    mu_2 = np.sum(data2)/len(data2)\n",
    "    var_1 = np.sum([pow(i - mu_1, 2) for i in data1]) / (len(data1))\n",
    "    var_2 = np.sum([pow(i - mu_2, 2) for i in data2]) / (len(data2))\n",
    "    std_1 = var_1 ** 0.5\n",
    "    std_2 = var_2 ** 0.5\n",
    "    \n",
    "    # 데이터가 모수인지 표본인지에 따라 자유도를 산입할지 여부를 결정합니다.\n",
    "    if population == True:\n",
    "        covariance = sum( (data1 - mu_1) * (data2 - mu_2) ) / (len(data1))\n",
    "        correlation = covariance / (std_1 * std_2)\n",
    "        print(f'모집단 data 각각의 분산 값은 COV(X,X) = {round(var_1,3)}, COV(Y,Y) = {round(var_2,3)}(으)로 계산됩니다.')\n",
    "        print(f'모집단 data에 대한 공분산 값 : {round(covariance,3)}')\n",
    "        print(f'모집단 data에 대한 피어슨 상관계수 값 : {round(correlation,3)}')\n",
    "    else:\n",
    "        var_1 = np.sum([pow(i - mu_1, 2) for i in data1]) / (len(data1) - 1)\n",
    "        var_2 = np.sum([pow(i - mu_2, 2) for i in data2]) / (len(data2) - 1)\n",
    "        std_1 = var_1 ** 0.5\n",
    "        std_2 = var_2 ** 0.5\n",
    "        covariance_ddof = sum( (data1 - mu_1) * (data2 - mu_2) ) / (len(data1) -1)\n",
    "        correlation_ddof = covariance_ddof / (std_1 * std_2)\n",
    "        print(f'표본 data 각각의 추정된 분산 값은 COV(x,x) = {round(var_1,3)}, COV(y,y) = {round(var_2,3)}(으)로 계산됩니다.')\n",
    "        print(f'표본 data로 추정한 모집단의 공분산 값 : {round(covariance_ddof,3)}')\n",
    "        print(f'표본 data로 추정한 모집단의 피어슨 상관계수 값 : {round(correlation_ddof,3)}')\n",
    "\n",
    "    # Scipy 라이브러리에서 구한 피어슨 계수를 통해 위에서 확인한 상관계수 값을 검증해봅니다.\n",
    "    print(f'Scipy로 구한 피어슨 상관계수는 {round(pearsonr(data1, data2)[0], 3)}입니다.')\n",
    "    \n",
    "    # Scipy에서 제공하는 피어슨 검정의 P값을 이용하여 데이터 간의 독립성에 대한 검정 결과 해석을 출력합니다.\n",
    "    if pearsonr(data1, data2)[1] < 0.05:\n",
    "        print(f'Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 {round(pearsonr(data1, data2)[1],5) * 100}%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 있습니다.')\n",
    "    else:\n",
    "        print(f'Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 {round(pearsonr(data1, data2)[1],5) * 100}%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 없습니다.')\n",
    "\n",
    "print('1. 모집단으로 가정한 경우')\n",
    "cov_pearson(x1, x2, True)\n",
    "\n",
    "print('\\n2. 표본집단으로 가정한 경우')\n",
    "cov_pearson(x1, x2, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. 스피어만 상관 계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8434065934065934"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 스피어만 계수는 순위합으로 도출되기 때문에, 이를 직접 구현하기 위해선 ordinalencoder로 순위를 구하고 순위합을 도출해야 함\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "encoder = OrdinalEncoder()\n",
    "\n",
    "# transform data (데이터를 인코딩하는 작업)\n",
    "result = encoder.fit_transform(stock_price['Adj Close'])\n",
    "result = pd.DataFrame(result, columns={'Samsung', 'KOSPI'})\n",
    "# 순위 척도의 차이를 도출하는 부분\n",
    "result['diff'] = result['Samsung'] - result['KOSPI']\n",
    "# 데이터 개수 지정\n",
    "n = len(result['diff'])\n",
    "\n",
    "# 스피어만 계수 산출하는 부분 (스피어만 계수 산출 공식 적용)\n",
    "sum_diff = 6 * sum([pow(i, 2) for i in result['diff'].tolist()])\n",
    "spearman = 1 - sum_diff/(n * (n ** 2 -1))\n",
    "spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.8308123402999015, pvalue=0.0004349404431727389)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 스피어만 (변수가 모두 연속형이며, 정규성을 따르지 않는 경우에 사용한다)\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "spearmanr(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모집단 data 각각의 분산 값은 COV(X,X) = 14.0, COV(Y,Y) = 13.148(으)로 계산됩니다.\n",
      "모집단 data에 대한 공분산 값 : 11.385\n",
      "모집단 data에 대한 피어슨 상관계수 값 : 0.839\n",
      "Scipy로 구한 피어슨 상관계수는 0.839입니다.\n",
      "Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 0.034%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 있습니다.\n",
      "\n",
      "스피어만 계수와 비교\n",
      "모집단 data 각각의 분산 값은 COV(X,X) = 705443.787, COV(Y,Y) = 1927.796(으)로 계산됩니다.\n",
      "모집단 data에 대한 공분산 값 : 29596.748\n",
      "모집단 data에 대한 피어슨 상관계수 값 : 0.803\n",
      "Scipy로 구한 피어슨 상관계수는 0.803입니다.\n",
      "Scipy는 각 데이터의 상관계수가 유의한지 판단합니다. 위 검정의 p-value는 0.096%로 두 데이터의 연관성이 없다는 귀무가설을 5%의 유의수준으로 기각할 수 있습니다.\n"
     ]
    }
   ],
   "source": [
    "# 스피어만 계수는 순위 척도를 계산한 후에는 피어슨 계수와 구하는 공식이 동일하기 때문에 위의 피어슨 계수에 이를 입력하여도 동일한 결과를 가져옴\n",
    "# 따라서 밑에는 피어슨 계수로 값이 출력되었지만, 사실은 스피어만 계수임 (따라서 공분산 값과 분산은 확인하지 않아도 됨)\n",
    "cov_pearson(result['Samsung'], result['KOSPI'], True)\n",
    "\n",
    "print('\\n스피어만 계수와 비교')\n",
    "cov_pearson(x1, x2, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. 켄달 상관계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KendalltauResult(correlation=-0.6, pvalue=0.23333333333333334)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simple data 부분 잠시 리뷰\n",
    "from scipy.stats import kendalltau\n",
    "\n",
    "X = [12, 2, 1, 11, 3]\n",
    "Y = [1, 4, 7, 2, 0]\n",
    "\n",
    "kendalltau(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KendalltauResult(correlation=0.6838852006725745, pvalue=0.0011973714876856232)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kendalltau(x1, x2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 참고) heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/AQAAAABgZIT/AAAAAAAwMsJ/AAAAAAAYGeE/AAAAAACMjPAfAAAAAABGRvgPAAAAAAAjI/wHAAAAAICREf4DAAAAAMDICP8BAAAAAGBkhP8AAAAAADAywn8AAAAAABgZ4T8AAAAAAIyM8B8AAAAAAEZG+A8AAAAAACMj/AcAAAAAgJER/gMAAAAAwMgI/wEAAAAAYGSE/wAAAAAAMDLCfwAAAAAAGBnhPwAAAAAAjIzwHwAAAAAARmbJUCeuqvcn6e3tStLd/bihagMAAAAAwDQbLPxP8sYk703ya9kU+AMAAAAAABMwWPjf3V+oqq919w+HqgEAAAAAAFzdoD3/u/vhQ54fAAAAAAC4Og/8BQAAAACAkRH+AwAAAADAyAj/AQAAAABgZAZ74G9VfS7J7PZ2JenuvstQtQEAAAAAYJoNFv4neVySf+vuwwesAQAAAAAAbGOwtj/dfWaSTw91fgAAAAAAYPsG7fnf3c8c8vwAAAAAAMDVeeAvAAAAAACMzCDhf1XdsqrutcX2gVX1z1X131X1uqrafYi6AAAAAADAcCv//0+Si7fYfl2Sbya5a5LTkrxhoLoAAAAAADD1lgx03n26+2tJUlV7JrlbksO6e2OSlVV10kB1AQAAAABg6g218n/jFq8fneRf54L/zWqgugAAAAAAMPWGWvn/kap6a5LPJfmzJA/YvKOq7pHkzIHqAgAAAADA1Btk5X93vz7Jh5IcmuQ3u/v0LXbfLsmLh6gLAAAAAAAMtPK/qh7T3f+Q5N+2s/sTSd6S5CFD1AYAAAAAgGk3VM//R1TVC7ccqKrdqurPsyn8f/tAdQEAAAAAYOoNFf4/Msl+VfX2qpqtqgcl+Z8keyT55e7+yEB1AQAAAABg6g3S9qe7O8nzqupJSU5PcnaSh3f3N4eoBwAAAAAA/NRQK/+TJN399iRPTrImyUVD1gIAAAAAADYZ6oG/X0jSWwztneTfquqyJJVNfxxwlyFqAwAAAADAtBuq7c9RSVJVt0ry4O5+/RB1AAAAAACAqxsk/E+SqnpUkht392uHqgEAAAAAAFzdUG1/npJko+AfAAAAAAAmb6gH/r49yf5V9ZsDnR8AAAAAALgGg4T/3b2hu49LsraqnjFEDQAAAAAAYPsG6/mfJN39yaqqIWsAAAAAAABbG6rtz1W6uze/rqqHDl0PAAAAAACm3eDh/zaOmXA9AAAAAACYOpMO/7UAAgAAAACAgQ3a8387fjjheruE2mu/LP+D56f23Du96oqseudx6Usuutpxyx72pMze4vap2SXZcMbpWf2BNyYbNy7AjAEAAAAAWMwmsvK/qg6tqvt19+9X1S9W1e6TqLurWPbIp2XNR0/IFcc9Pas/8rbs/oinXe2Y2dvcMbVin1z5mmfliuOenszOZskd7rYAswUAAAAAYLEbPPyvqicmWZnkuLmhpUleNXTdXcbyPTOz597Z8INvJUk2nnF6snzPTT9bqJrNxp9ccNV2X35J+uILAgAAAAAAP6tJrPz//e7+9SSXJkl3/1eS202g7i5h5oCDs/GCs7ca6wvPycwBB281tv4bX0ztvjzLHvn0LHvUM9OXXZwN3//WJKcKAAAAAMBITCL8X19VM0k6SapqWa7lWQNVdXRVnVxVJ69cuXICUxxSpXs7w9sMzt78iGR2SdZ+5kNZ+/F3pfY7MEuPesBkpggAAAAAwKhMIvx/Y5KPJDmoqn4nyb/kWtr+dPfK7j6yu488+uijJzDF4Wy86NzMHHjIVmN1wCHZeNG5W40te9iTs+ZDx6cvOi99xaVZ809vzdK7P2SSUwUAAAAAYCQGD/+7+yNJnp3krUn2SvLU7v740HV3GVdelqxdk5kb3SJJMnPozdOXX5pasjS7P/aYqw7rtas3rf6fs+TIe2XjuWdOfLoAAAAAACx+87bf2Rmq6n5JLujut1TVq5K8v6qO6e4vD117V7H6g2/O8sc/L1m2PL3q8qw+4dXJsj226vu/+r2vz+6PeVbqN56QpLPxvLOy+gNvXLhJAwAAAACwaFVvtyH9TixQ9eUkd09yiyRPS/JXSd7f3ffcwVP0pU+511DTY2B7H3/SQk8BAAAAAHYVtdATYHpM5IG/3b0myUOTnNDd5yRZP4G6AAAAAAAwlSYR/v+wql6W5CHd/eWqul6SKyZQFwAAAAAAptIkwv/fT/KVbFr5nyQHJ3neBOoCAAAAAMBUmkT4nyR3SvLhqvpskkcn+f6E6gIAAAAAwNSZRPj/5iTf7e67dvc9kpyR5C0TqAsAAAAAAFNpEuH/L3T3323e6O53JDlsAnUBAAAAAGAqTSL8X19Vh2zeqKqD44G/AAAAAAAwmCUTqPHHSf65qr4+t314kidPoC4AAAAAAEylQcL/qtpti83Tk9wnyU2TbMimnv8bhqgLAAAAAAAMt/L/U0k6Sc39uz33Hqg2AAAAAABMtaHC/8cmuUV3f27bHVV1xyTnDFQXAAAAAACm3lAP/D0uye7XsG+3JC8fqC4AAAAAAEy9ocL/m3X3p7e3o7s/n+RGA9UFAAAAAICpN1T4f22WLVBdAAAAAAAYvaHC/29X1QO3t6Oq7pvkBwPVBQAAAACAqTfUA3//JMmHq+o+SU5IcmaSGyZ5fJI7J3nYQHUBAAAAAGDqDRL+d/clcyv8H53k+Umun+SCJCcmOba71w9RFwAAAAAAGG7lf7p7Y5L3z/0AAAAAAAATslAP/AUAAAAAAAYi/AcAAAAAgJEZrO3PZlW12zXsWtfdPXR9AAAAAACYNoOF/1X1V919bJJPJekktc0he1XVl7v76UPNAQAAAAAAptGQD/w9du7fe13TMVX1haHqAwAAAADAtBok/K+qe1zbMd392SRPGqI+AAAAAABMs6FW/j9oi9d3T3JWku8n2SfJQ5N8JMlnu/uUgeoDAAAAAMDUGiT87+4Xbn5dVZ/q7sdssf2SJCcMURcAAAAAAEhmJlBjxZYb3X1hkn0nUBcAAAAAAKbSJML/91bVP1bVXavqV6vqhCSfnkBdAAAAAACYSkP1/L9Kd/9NVX0tycOSLE3y/u7+5NB1AQAAAABgWk1i5X+6+wvd/fwkb05yu6r6wiTqAgAAAADANBp85X9V3SrJI5L8ZpILk3w0yaOGrgsAAAAAANNqkPC/qu6Q5OFJ7pbku0k+luQB3f3gIeoBAAAAAAA/NdTK/48nOSXJn3T315Kkqo4ZqBYAAAAAALCFQcL/7r5hVd01ye9X1RuSfDHJ3kPUAgAAAAAAtjZYz//u/nySzyfJ3BcBy6rqy0lOTvKR7j5xqNoAAAAAADDNBn/gb7LdLwIemUT4DwAAAAAAA5hI+L+lLb8IAAAAAAAAdr6ZhZ4AAAAAAACwcwn/AQAAAABgZIT/AAAAAAAwMsJ/AAAAAAAYGeE/AAAAAACMjPAfAAAAAABGRvgPAAAAAAAjI/wHAAAAAICREf4DAAAAAMDICP8BAAAAAGBkhP8AAAAAADAywn8AAAAAABgZ4T8AAAAAAIyM8B8AAAAAAEZG+A8AAAAAACMj/AcAAAAAgJER/gMAAAAAwMgI/wEAAAAAYGSE/wAAAAAAMDLCfwAAAAAAGBnhPwAAAAAAjIzwHwAAAAAARkb4DwAAAAAAIyP8BwAAAACAkRH+AwAAAADAyAj/AQAAAABgZKq7F3oO12aXnyAAAAAAwA6ohZ4A02PJQk9gR7yvDl/oKXAdPa5Py6VPuddCT4PrYO/jT1roKQAAAAAA15G2PwAAAAAAMDLCfwAAAAAAGBnhPwAAAAAAjIzwHwAAAAAARkb4DwAAAAAAIyP8BwAAAACAkRH+AwAAAADAyAj/AQAAAABgZIT/AAAAAAAwMsJ/AAAAAAAYGeE/AAAAAACMjPAfAAAAAABGRvgPAAAAAAAjI/wHAAAAAICREf4DAAAAAMDICP8BAAAAAGBkhP8AAAAAADAywn8AAAAAABgZ4T8AAAAAAIyM8B8AAAAAAEZG+A8AAAAAACMj/AcAAAAAgJER/gMAAAAAwMgI/wEAAAAAYGSE/wAAAAAAMDJLhjpxVe2RZJ/u/tHcdiV5ZpJfSPL/uvujQ9UGAAAAAIBpNuTK/zcmuecW2y9N8qAkn07yqKo6ZsDaAAAAAAAwtQZb+Z/kdt39pCSpqpkkj0vyK919SVV9Msm/J/nrAesDAAAAAMBUGnLl/xVbvL5fkpO7+5Ik6e4NSdYOWBsAAAAAAKbWkOH/96rqMVV1SJK/TLJy846qukmE/wAAAAAAMIghw/8/SnLbbAr939bdn9li3zOSvGLA2gAAAAAAMLWG7Pl/4+5+8TXse1GSZyf53ID1AQAAAABgKg258v89VXWPbQfnxk5OcsMBawMAAAAAwNQacuX/g5L8Q1W9s7vfU1XXT/K6JDdP8oTu/tqAtQEAAAAAYGoNtvK/uy9M8utJ7l9V70vyn0k+m+Sugn8AAAAAABjOkG1/0t1ruvvxSU7Npv7+b+/uHrImAAAAAABMu8Ha/lTV+5NsDvoryd2T/GdVfW9uu7v7cUPVBwAAAACAaTVkz/8XDHhuAAAAAADgGgwW/nf3GUOdGwAAAAAAuGaD9vy/JlX13qr6ykLUBgAAAACAsRuy7c98Hp9ktwWqDQAAAAAAozaR8L+qbpPksGx6APB3uvubSVZNojYAAAAAAEybQcP/qrp1ktcn+X6S78wNP7iqDk3ynO7+zjW+GQAAAAAAuE6GXvm/MsnvdfcPthycC//fleQ+A9cHAAAAAICpM/QDf3fbNvhPku4+O8nuA9cGAAAAAICpNHT4//2qeti2g1X1kCTnDFwbAAAAAACm0tBtf/4wyUuq6nlJLpgbOzDJl5I8ceDaAAAAAAAwlQYN/7v7yiQvSJKq2jdJdfdPhqwJAAAAAADTbui2P1fp7ouTXDz3JQAAAAAAADCQQcP/qrpNVX20qo6rqsOTfCXJe6vqM1V1kyFrAwAAAADAtBq65/+bkzwjyRFJPpLkod19elUdkeQNSX5r4PoAAAAAADB1hm77s7G7v9nd/5RkQ3efniTd/c0k+w5cGwAAAAAAptLQ4f+VW7y+YPOLqqokyweuDQAAAAAAU2no8P9TVXXPuddP2GL84Uk+OHBtAAAAAACYSoP2/O/uN2/x+gdbvP6nIesCAAAAAMA0G/qBv6mq+yV5apKbJLkkyf8meXV3nzN0bQAAAAAAmEaDtv2pqmckeUSSo5P8vyTHJjkxyT9V1T2GrA0AAAAAANNq6JX/j+rueyZJVf1Fknd39yOq6stJ/jXJXQauDwAAAAAAU2foB/5epbtXJTlw7vWFSVZNqjYAAAAAAEyTocP/z1fVX1XVL1XVa5P8f0lSVQcnuXTg2gAAAAAAMJUGDf+7+0VJTk3yh0lOS/LiuV0/SfI7Q9YGAAAAAIBpNXTP/3T3u5O8e5ux1UPXXSyOeMHRWfuTS/Od4/9hoafCNai99svyP3h+as+906uuyKp3Hpe+5KKrHbfsYU/K7C1un5pdkg1nnJ7VH3hjsnHjAswYAAAAAJh2g678r6r9t9l+YlW9oap+Y8i6i8HyQw7K/b/wgdzqj5+w0FPhWix75NOy5qMn5Irjnp7VH3lbdn/E0652zOxt7phasU+ufM2zcsVxT09mZ7PkDndbgNkCAAAAAAzf8/8fN7+oqj9PckSS9yR5UFVdPUGdIqvOOT8nHvXofO25r17oqTCf5XtmZs+9s+EH30qSbDzj9GT5npt+tlA1m40/ueCq7b78kvTFFwQAAAAAYCEM3fantnh9r+7+tSSpqq8mOSnJWweuDz+XmQMOzsYLzt5qrC88Z9P4md+5amz9N76YZYffIcse+fSkZtIXnZsN3//WpKcLAAAAAJBk+JX/vcXrq5qkd/fGJPr+swhUurczvM3g7M2PSGaXZO1nPpS1H39Xar8Ds/SoB0xmigAAAAAA2xg6/N+7qo6uqqOTpKoeOPfv/kmuvKY3zb3n5Ko6eeXKlQNPEa7ZxovOzcyBh2w1Vgccko0XnbvV2LKHPTlrPnR8+qLz0ldcmjX/9NYsvftDJjlVAAAAAICrDB3+vzLJmrmff03y7bnx/ZK84Jre1N0ru/vI7j7y6KOPHniKMI8rL0vWrsnMjW6RJJk59Obpyy9NLVma3R97zFWH9drVm1b/z1ly5L2y8dwzJz5dAAAAAIBk4J7/3f2haxj/zvbGYVe0+oNvzvLHPy9Ztjy96vKsPuHVybI9MnPAwT895r2vz+6PeVbqN56QpLPxvLOy+gNvXLhJAwAAAABTrXq7Dc130smrZpI8NckjsumLhk6yPskHk6zsHSve76vDB5sjw3pcn5ZLn3KvhZ4G18Hex5+00FMAAAAAGJta6AkwPYZu+/PWJLNJHtDd9+jueyZ54Ny+Nw1cGwAAAAAAptLQ4f8vdvebunvd5oHuXtfdxye5w8C1AQAAAABgKg0d/ndV7bvt4NzY0oFrAwAAAADAVBr0gb9JnpPkX6rqE0lOy6ae/4cneUiS5w1cGwAAAAAAptKg4X93f6Gq7pNNff4Py6YHWpya5A3dvWbI2gAAAAAAMK2GXvmf7l6f5GNVtc+mzb506JoAAAAAADDNBg3/q2qPJH+R5KgkP940VNdL8rkkL+vuVUPWBwAAAACAaTT0yv8Tkryru7fq719VD0jyd0keO3B9AAAAAACYOjMDn/9G3f3RbQe7+1NJDh24NgAAAAAATKWhw//VVXWTbQer6sYD1wUAAAAAgKk1dNufo5P8bVWdkeS0JJ3k8CS3SPL0gWsDAAAAAMBUGjT87+7Tkzyoqm6b5LAkleSk7v6fIesCAAAAAMA0G3rlf5Kku7+R5BuTqAUAAAAAANNu6J7/V1NVT5l0TQAAAAAAmCYTD/+TPHoBagIAAAAAwNRYiPC/FqAmAAAAAABMjYUI/7+8ADUBAAAAAGBqTOSBv0lSVXdKcpvufn5V3SvJ/3T3jydVHwAAAAAApsVEVv5X1Z8lOTrJU+eGzkryuknUBgAAAACAaTOptj/37u4nJbkySbr720luMqHaAAAAAAAwVSYV/q+vqj2SdJJU1b6bXwMAAAAAADvXpHr+/0WSjyW5cVUdm+T+SZ47odoAAAAAADBVJhL+d/fnq+q3ktwtyYYkb+ruSydRGwAAAAAAps2kHvj7u0lu2d0fT/L4JF+qqgdPojYAAAAAAEybSfX8f3aSk6vqjkm+l+TIJMdOqDYAAAAAAEyViT3wt7s7ycOTfKC7r0iyZkK1AQAAAABgqkwq/D+5qt6d5I7d/b9VdeMkF0yoNgAAAAAATJWJPPA3yR8luX2S0+a2K5taAQEAAAAAADvZpML/fZM8N8lNq6qTfD/JMROqDQAAAAAAU2VSbX/+Nsk7u/tu3X33JO9KsnJCtQEAAAAAYKpMKvw/sLv/bfPG3OsDJ1QbAAAAAACmyqTC/8ur6nabN+Zee+AvAAAAAAAMYFI9/5+W5B1VVXPba5I8eUK1AQAAAABgqgwW/lfVLbcZOmauXidZn2SfJOcNVR8AAAAAAKbVkCv//zzJg5N8OEllU+ifudf3S/L5JI8esD4AAAAAAEylIcP/5Unu291f2XZHVd0mm74cAAAAAAAAdrIhH/i73/aC/yTp7lOS7DtgbQAAAAAAmFpDhv+z17J/xYC1AQAAAABgag0Z/n+5qp64vR1V9fgk2/2rAAAAAAAA4OczZM//FyU5oaoemOSEJGcmuWGSx8/VfdyAtQEAAAAAYGoNFv5399okj62qo5I8OMlvJrkwyd90938MVRcAAAAAAKbdkCv/kyTd/YUkXxi6DgAAAAAAsMmQPf8BAAAAAIAFIPwHAAAAAICREf4DAAAAAMDICP8BAAAAAGBkhP8AAAAAADAywn8AAAAAABgZ4T8AAAAAAIyM8B8AAAAAAEZG+A8AAAAAACMj/AcAAAAAgJER/gMAAAAAwMgI/wEAAAAAYGSE/wAAAAAAMDLCfwAAAAAAGBnhPwAAAAAAjIzwHwAAAAAARkb4DwAAAAAAIyP8BwAAAACAkRH+AwAAAADAAquqF1bVU+fZ/+dVdXJVfbWqHn5t5xP+AwAAAADAAqmqQ6vqi0n+eJ5jHpRk/+4+Msldkjy9qg6Y77zCfwAAAAAAWCDdfXZ33znJc+c57ElJjps7fnWStyV57HznFf4DAAAAAMCu7frdfc4W299Octh8b1gy7Hx2jsf1aQs9BX4Oex9/0kJPAQAAAAAYmffV4b3Qc7g2v5PTn5Lk6C2GVnb3yp10+nk//6II/y99yr0WegpcR3sff1LeV4cv9DS4Dh7Xp7n3FjFfugEAAAAsvLmgf2eE/RdW1Q26+9y57Vsm+c58b9D2BwAAAAAAdiFVdVBVvWWLoRMy90yAqtotyeOTvH++cyyKlf8AAAAAALClmXEvbd8ryc03b3T3h6vqTlX1pWxq9/OK7r5ovhMI/wEAAAAAYIF19zu3eP3dJA/aZv8Lf5bzjfu7EQAAAAAAmEJW/gMAAAAAsOiMvO3Pz83lAQAAAACAkRH+AwAAAADAyGj7AwAAAADAoqPtz/xcHgAAAAAAGBnhPwAAAAAAjIy2PwAAAAAALDra/szP5QEAAAAAgJER/gMAAAAAwMgI/wEAAAAAYGT0/AcAAAAAYNGZqYWewa7Nyn8AAAAAABgZ4T8AAAAAAIyMtj8AAAAAACw6M5a2z8vlAQAAAACAkRH+AwAAAADAyGj7AwAAAADAoqPtz/xcHgAAAAAAGBnhPwAAAAAAjIy2PwAAAAAALDra/szP5QEAAAAAgJER/gMAAAAAwMho+wMAAAAAwKKj7c/8XB4AAAAAABgZ4T8AAAAAAIyM8B8AAAAAAEZGz38AAAAAABYdPf/n5/IAAAAAAMDICP8BAAAAAGBktP0BAAAAAGDR0fZnfi4PAAAAAACMjPAfAAAAAABGRtsfAAAAAAAWHW1/5ufyAAAAAADAyAj/AQAAAABgZAZt+1NVn0syu71dSbq77zJkfQAAAAAAxqmqFnoKu7She/4/Lsm/dffhA9cBAAAAAADmDNr2p7vPTPLpIWsAAAAAAABbG3rlf7r7mUPXAAAAAABgusx4ou28XB4AAAAAABgZ4T8AAAAAAIzM4G1/AAAAAABgZ9P2Z36Dhv9V9T9J9kty1ra7knR332XI+gAAAAAAMI2GXvl/9yRfFPIDAAAAAMDkDPqHEd19aZL3DFkDAAAAAADY2uA9/7v7FUPXAAAAAABguuj5Pz+XBwAAAAAARmaw8L+qfrWqHrbF9s2r6qtV9d2q+lBV7T9UbQAAAAAAmGZDrvx/ZZKvb7H9+iTHd/cvJHlbkjcPWBsAAAAAgBGbmdn1fxb0+gx47iXd/b0kqarrJTmiu49Pku7+ZJKDBqwNAAAAAABTa9DvHqpq8/kfn+Qft9m9dMjaAAAAAAAwrZYMeO7jk/xzVX05yZOT3G3zjqp6eJL/HbA2AAAAAAAjttBtdXZ1g4X/3f3eqjo5ye2THNXd526xezbJS4aqDQAAAAAA02yw70aq6vndfVp3/+M2wX+SnJ3kxKFqAwAAAADANBuy7c/BVXV8kqd198YkqaoDkrwmyRFJnjJgbQAAAAAARkzbn/kNdnm6+9lJ/ifJv1bVPlX19CRfnvu5c3efPFRtAAAAAACYZkOu/E93/01VfS/JaUn+M5tC//OHrAkAAAAAANNusPC/qnabe/mZJL+e5K+THFhVF28+prvXDlV/V1J77Zflf/D81J57p1ddkVXvPC59yUVXO27Zw56U2VvcPjW7JBvOOD2rP/DGZOPGBZgxO+qIFxydtT+5NN85/h8Weipsh3sPAAAAYLy0/ZnfkCv/P5Wk515XknVJ3jw3VnP/3nvA+ruMZY98WtZ89IRs+MG3MnOTW2b3Rzwtq97x8q2Omb3NHVMr9smVr3lWkmT33/2TLLnD3bL+a59diClzLZYfclDu/qE3ZcUv3Dhf/7O/XujpcA3cewAAAABMq8HC/+6+V5JU1T2THNndrxuq1i5t+Z6Z2XPvbPjBt5IkG884PVm+56afVVdcdVjVbDb+5IKrtvvyS9IXX3C107FrWHXO+TnxqEfnZr//W5ndfdlCT4ftce8BAAAAMMUG7flfVcckuWhqg/8kMwccnI0XnL3VWF94zqbxM79z1dj6b3wxyw6/Q5Y98ulJzaQvOjcbvv+tSU8XRsO9BwAAAMA0G6wrUlU9Lcnl3f2eoWosDpXu7QxvMzh78yOS2SVZ+5kPZe3H35Xa78AsPeoBk5kijJJ7DwAAAGDMZmZ2/Z8FvT5Dnbi735qkq+opP+t7q+roqjq5qk5euXLlALObnI0XnZuZAw/ZaqwOOCQbLzp3q7FlD3ty1nzo+PRF56WvuDRr/umtWXr3h0xyqjAq7j0AAAAAptmg3z10998l+WJV/dHP+L6V3X1kdx959NFHDzS7CbnysmTtmszc6BZJkplDb56+/NLUkqXZ/bHHXHVYr129aQXynCVH3isbzz1z4tOF0XDvAQAAADDFBu35nyTd/T9J/mfoOruy1R98c5Y//nnJsuXpVZdn9QmvTpbtkZkDDv7pMe99fXZ/zLNSv/GEJJ2N552V1R9448JNGkbAvQcAAAAwXjO10DPYtVVvtyn2gAWrntLdx/8Mb+lLn3KvwebDsPY+/qS8rw5f6GlwHTyuT4t7b/Ha+/iTFnoKAAAAwNWJq3ei/zniVpMNt6+DO3zz1AX7nS/EIwcevQA1AQAAAABgagze9mc7fLsFAAAAAMDPZWYhlrYvIgtxeb68ADUBAAAAAGBqTGzlf1XdKcltuvv5VXWvJP/T3T+eVH0AAAAAAJgWEwn/q+rPktwkye2S/H2Ss5K8LskTJlEfAAAAAIBx0fZnfpO6PPfu7icluTJJuvvb2fRlAAAAAAAAsJNNKvxfX1V7JOkkqap9N78GAAAAAAB2rkn1/P+LJB9LcuOqOjbJ/ZM8d0K1AQAAAAAYGW1/5jeR8L+7P19Vv5Xkbkk2JHlTd186idoAAAAAADBtJvLdSFX9bpJbdvfHkzw+yZeq6sGTqA0AAAAAANNmUn8Y8ewkJ1fVHZN8L8mRSY6dUG0AAAAAAJgqk+r5v767u6oenuS93X1FVa2ZUG0AAAAAAEZmZqYWegq7tEmt/D+5qt6d5I7d/b9VdeMkF0yoNgAAAAAATJVJrfz/oyS3T3La3HZlUysgAAAAAABgJ5tU+L9vkucmuWlVdZLvJzlmQrUBAAAAABiZmtX2Zz6Tavvzt0ne2d136+67J3lXkpUTqg0AAAAAAFNlUuH/gd39b5s35l4fOKHaAAAAAAAwVSbV9ufyqrpdd/9vklTV7eKBvwAAAAAAXEc1o+3PfCYV/j8tyTuqavNvY02SJ0+oNgAAAAAATJXBwv+quuU2Q8fM1esk65Psk+S8oeoDAAAAAMC0GnLl/58neXCSDyepbAr9M/f6fkk+n+TRA9YHAAAAAGCktP2Z35Dh//Ik9+3ur2y7o6puk01fDgAAAAAAADvZzIDn3m97wX+SdPcpSfYdsDYAAAAAAEytIVf+z17L/hUD1gYAAAAAYMRqVtuf+Qy58v/LVfXE7e2oqscn2e5fBQAAAAAAAD+fIVf+vyjJCVX1wCQnJDkzyQ2TPH6u7uMGrA0AAAAAAFNrsPC/u9cmeWxVHZXkwUl+M8mFSf6mu/9jqLoAAAAAAIxfzWj7M58hV/4nSbr7C0m+MHQdAAAAAABgkyF7/gMAAAAAAAtA+A8AAAAAACMzeNsfAAAAAADY2WpWz//5WPkPAAAAAAAjI/wHAAAAAICR0fYHAAAAAIBFZ2ZG25/5WPkPAAAAAAAjI/wHAAAAAICR0fYHAAAAAIBFp7T9mZeV/wAAAAAAMDLCfwAAAAAAGBltfwAAAAAAWHRqVtuf+Vj5DwAAAAAAIyP8BwAAAACAkdH2BwAAAACARadmrG2fj6sDAAAAAAAjI/wHAAAAAICREf4DAAAAAMDI6PkPAAAAAMCiU7O10FPYpVn5DwAAAAAAIyP8BwAAAACAkdH2BwAAAACARadmtP2Zj5X/AAAAAAAwMsJ/AAAAAAAYGW1/AAAAAABYdLT9mZ+V/wAAAAAAMDLCfwAAAAAAGBltfwAAAAAAWHRqVtuf+Vj5DwAAAAAAIyP8BwAAAACAkdH2BwAAAACARWdmRtuf+Vj5DwAAAAAAIyP8BwAAAACAkdH2BwAAAACARae0/ZmXlf8AAAAAADAywn8AAAAAABiZ6u6FnsO12eUnCAAAAACwA/Sp2YkueMxddvns+MB/+M8F+50vip7/Zz7wlxd6ClxHN/rkV3P6HY9Y6GlwHdzyv77p3lvE3HuL2y3/65sLPQUAAADY5dWs71Lmo+0PAAAAAACMjPAfAAAAAABGZlG0/QEAAAAAgC3VjLY/87HyHwAAAAAARkb4DwAAAAAAI6PtDwAAAAAAi07NavszHyv/AQAAAABgZIT/AAAAAAAwMtr+AAAAAACw6NSMte3zcXUAAAAAAGBkhP8AAAAAADAy2v4AAAAAALDo1Ewt9BR2aVb+AwAAAADAyAj/AQAAAABgZIT/AAAAAAAwMnr+AwAAAACw6MzM6vk/Hyv/AQAAAABggVTV9avqE1X1par6dFUdcg3HvaKq/r+54/6mquZd3C/8BwAAAACAhfO6JH/R3b+a5IVJXrvtAVX1wCQHdPfd5o5bmuSh851U2x8AAAAAABadmln8bX+qat8k+3f3l5Kku0+uqn2qat/uvniLQ2eTnLXF9oVJzp7v3Fb+AwAAAADAAKrq6Ko6eYufo7c55GZJvrPN2Pfmxrf0f5PsVVX/p6r+Osn53f3F+Wpb+Q8AAAAAAAPo7pVJVs5zSCXp7b11m+07Z1OrnzckuTzJC6vqD7r7ndd0YuE/AAAAAACLTs0u/rY/SX6Q5LBtxm4+N76lVya5f3evTZKq+tMkn0/yzms6sbY/AAAAAACwALr7x0murKpfTpKqun2Si5LsVlVv2eLQK7Jp9f9mj0py6nzntvIfAAAAAAAWzrOTvKOqViS5JMkTkuyVTX8BsNlTk7ypql6WTS2BTk/yrPlOKvwHAAAAAGDRqZlRtP1Jd5+V5AHbDJ+X5EFbHHNmkof9LOfV9gcAAAAAAEZG+A8AAAAAACMz0bY/VXVgkkOTfHPzU4kBAAAAAOBnNjuOtj9DGWzlf1X9QVU9eYvt+yX5epJjk3yxqn5pqNoAAAAAADDNhlz5/9Qk991i+2VJfqu7v1hVN07ytlz9IQYAAAAAAMDPacie/6u6+/IkqapbJJnt7i8mSXf/MBNuOQQAAAAAANNiyAB+XVXt390XJXlmkndv3lFVS5PsNmBtAAAAAABGrGb0/J/PkOH/i5J8rKouS7I8yfO32PfiJP86YG0AAAAAAJhaQ4b/X+nuo6rqwO6+YJt9JyTxtQwAAAAAAAxgyPD/I1X1mG2D/6pakuQxSX4/yeED1gcAAAAAYKxmh3yk7eI35NU5IcmJVXWDzQNVdZ8k/51k/yS/MmBtAAAAAACYWoOt/O/uf66qH2bTXwC8JMmTkhyc5HHd/fWh6gIAAAAAwLQbsu1Puvvkqnpkko8m+c/uftSQ9QAAAAAAmBIzHis7n8HC/6q6/xabL0/yqqp6ZpLTNw9294lD1QcAAAAAgGk15Mr/o7bZflc29frfPN5JhP8AAAAAALCTDdnz/6VDnRsAAAAAgOlWs9r+zGdmIYpW1ROr6oULURsAAAAAAMZu0Af+zuPMJGsXqDYAAAAAAIzagoT/HvQLAAAAAMDPZUbbn/kMGv5X1UySpyZ5xBa11iX5YJKV3d1D1gcAAAAAgGk0dM//tyaZTfKA7r5Hd98jyQPn9r1p4NoAAAAAADCVhm7784vd/ZQtB7p7XZLjq+pzA9cGAAAAAGCsZrX9mc/QK/+7qvbddnBubOnAtQEAAAAAYCoNvfL/OUn+pao+keS0JJ3k8CQPSfK8gWsDAAAAAMBUGjT87+4vVNV9sqnP/2FJKsmpSd7Q3WuGrA0AAAAAANNq6JX/6e71ST42dB0AAAAAAKZHzej5P59Be/5X1WxV/XZV3a6qdquqV1bVR6rqGUPWBQAAAACAaTb0A3//Jsmdk7wgyVuSnJvkyUn2q6qXD1wbAAAAAACm0tBtf27X3Xepqj2SfLe7nzw3/vKqOmng2gAAAAAAjNXs0GvbF7ehr85FSdLdVyY5fZt9yweuDQAAAAAAU2no8P/cqrrp3Ov3bx6sql9O8r2BawMAAAAAwFQauu3PizK3wr+7/3aL8UuTPGvg2gAAAAAAjFTN1EJPYZc2aPjf3RdU1W5V9ZtJDktycZKvdvd/D1kXAAAAAACm2aBtf6rqdklOTPILSR6RTX8F8KKq+qeq2nfI2gAAAAAAMK2GbvvzmiSP6+5zquoTSZ7S3Y+qqgcleWuSxw5cHwAAAACAMZrV9mc+Qz/wd7fuPidJuvvUJLebe/2JJDcZuDYAAAAAAEyloVf+X15VR3T3N6vqyGx60G+qaibJFQPXXlAz+14v1/uTl2Zmr73TV16Ri177kmz88YVXO26fP3hmlt3ul5LZJVn37W/lJ299TbJxQ5JkxW//Tva4633S69dl3fe/nYv/9rWT/hhTafZ6++cGf/6KzOy9TzZecXnO/YsXZsOFF1ztuP2f/uzs8Uu/kixZmjWnnpLzX/uKZMOGLLnBITnoecdmZvke6fXrc8FfvyZrv3P6AnyS6eTeW9zcfwAAAAA7x9Dh/zOT/G1V7ZfkyiRPmhu/cZJXDlx7Qe179B/n0vccn7WnfSNLb3Hr7Pvk5+THrzp2q2N2/5W7ZGbvfXP+nzwxSbLfMS/O8qPumVWf/0z2+LUHZskNbpjzn/ukZOPGzF7/kIX4GFPpwGc/LxetfEtWn/L1LLv1bXLgMc/NuX/2vK2O2eOou2V2331z5pN/L0ly/WNfmhX3uHcuP+nTuf4L/zwXvPG1Wfvdb2fJgQflBi9/bc7+oyel165diI8zddx7i5v7DwAAANhh2v7Ma9C2P939w+5+cHcf1d336e7vz43/oLs/M2TthVR7rsjMXvtk7WnfSJKs+/a3MrPnitSeK7Y+cHYmGy4876rNjZdenA0XbVrhuuKhj80l7/jrZOPGJMmG886ZzOSn3MyKvTK7z75ZfcrXkyRrvnVKZlbslZkVe211XM3MZP35P/3dbbj44qy/YNP2zF57Z+13v50kWX/B+bn8P/5f9jjq7hP6BNPNvbe4uf8AAAAAdp6hV/5vpar2TXLTJN/q7jWTrD1JS25waNafc+ZWY+t/dHaW3ODQrPvuaVeNrf7y/5dltz8y+x79J8lMZf15P8raU/83WbpbevWV2f3O98iKBzwsWbpbrvj4P+bKf//UhD/J9Fl6yA2z9swfbjW27uyzsvSQG2bN6d+6auyKz382y3/lTjnwOc9PZmay7pyzs/obmwLLXrM6y464bdZ88xtZepObZa/7PCCXn/Tpcfe52kW49xY39x8AAADAzjNo+F9Vr+nu5869/rUkr03yxSS/VFVP7e7/HbL+gqlK0lcf763Hdrv17VOzS3LZR96bXrUqez/6Cdnjfr+R1V/8bJbe7JaZ3WufXHDsM1K7L88BL3tj1n7n1Kw/64zJfIZpVbna72mTrcd2v90dUkuW5Cfvf1c2XnllrveEo7P3Qx6WSz/2kZz38pfkwGOem5kVK7Lmu9/OZZ/6eDauWT2R6U89997i5v4DAAAA2GkGbfuT5Fe2eP2iJL/e3c9M8ogkx13Tm6rq6Ko6uapOXrly5cBT3Pk2nHtOlhx8o63Glhx8aNZv0z5knyc8Mxe/4w3ZcN6PsvHSi3Px2/5PVjzot7Pxskuy/ryzc/nH/jHpTq+6Mqu/+B9ZetPDJvkxptK6H52T3W50463Glh56w6w75+ytxg54xnNywRtfl/U/OicbL7k4F77h1dnntx656Rxnn5lznvesnPX0P8wFr3tllv/SkVlz6jcn9hmmmXtvcXP/AQAAAD+Lmqld/mchDR3+b/np1nf3eUnS3T9Ksuya3tTdK7v7yO4+8uijjx54ijvfxssvTa9ZnaWH3SpJsvRmt8jGSy9JLVmafZ/xgquO69WrsuxWt79qe/k97p91Z/4gSbL6q1/Mng/67U07Zpdk2S/eKWu/c+rEPsO02njpJdm4elWWHX7rJMluh90yGy+5OLV0aQ563ot/etyqVVl+u5/+7lbc74FZ+4PvJ0mWHHJoMrPp1trjLndPZmey5rRvheG59xY39x8AAADAzjN4z/+qWppNXwKcUlW36O5vV1Ul2TB07YV08fGvzX7P+fPM7L48G6+4PD9+/V+k9tgzS25w6FXH/OSNr8i+T39e9n78U5PurD/7h7n4ra9Oklz6vrdl3yf/cQ58zdtTVbnsXz+QDeeefU3l2IkueP1xuf6L/zIze+yRjZdfnnNfdmxm9tgzSw/96Yry8497aQ7602Oz/1P+KOnO2h+ekfNf+4okydJDb5TrH/uyzCzfI+vPPSfnvfwlC/VRppJ7b3Fz/wEAAADsHNXb7a+8k05e9f4k189P/wLgld19YlXdKcltuvvvd+A0feYDf3mwOTKsG33yqzn9jkcs9DS4Dm75X9+Me2/xcu8tbrf8L62KAAAARmph+8CMzNo3PmK4cHsn2e1Z/7Rgv/NBV/5392OvYfzLSb48ZG0AAAAAAJhWk2j7c70kj01yWJJO8p0k7+/unwxdGwAAAAAAptGg4X9V/XaSpydZmeQLc8O3TPLeqvqb7v7YkPUBAAAAABipmZmFnsEubeiV/y9KctfuXrPF2Fer6p+T/HsS4T8AAAAAAOxkQ3810tsE/5sH106gNgAAAAAATKWhV/5/tKrekOS47j43SarqoCTPS/KZgWsDAAAAADBSNVsLPYVd2qDhf3e/rKoemOTNVXX9ueELkryruz8yZG0AAAAAAJhWQ6/8T3d/Msknh64DAAAAAABsMnj4X1VHJHlCksOSdJLvJPm77j516NoAAAAAAIzUjLY/8xk0/K+qpyX5tSSvT/LtueFbJnlxVf17d799yPoAAAAAADCNhl75/8TuPnKbsS9W1ZeSfD6J8B8AAAAAAHaywdv+bE93d1UtSG0AAAAAAEZgVtuf+cwMfP7jq+pDVXXnqtqvqvatqjtV1QeTvHvg2gAAAAAAMJUGXX3f3W+rqv8vyROz6YG/SfK9JK/s7q8NWRsAAAAAAKbV4K13uvtbSf506DoAAAAAAMAmE++7X1U36e4zJl0XAAAAAIDxqBk9/+czdM//7fn7BagJAAAAAABTYyHCf1/HAAAAAADAgCbe9gcAAAAAAH5uswuxtn3xmFj4X1VHJDk0ycqqun+SdPeJk6oPAAAAAADTYiLhf1W9Psmtknw5Sc8NdxLhPwAAAAAA7GSTWvl/5+6+y4RqAQAAAAAwdrMeLzufSTVFOruqbjChWgAAAAAAMNUGW/lfVa/MT1v8nJ/kq1X10SQXJakk3d0vGqo+AAAAAABMqyHb/nxyi9ed5IP56ZcBAAAAAABwndWMtj/zGSz87+7/2Py6qj6SZO9sWvGfbPoS4Mqqum+S13b3pUPNAwAAAAAAps2kHvi7Lslzk3wjyYokT5wbOyPJ25M8akLzAAAAAACA0ZtU+L9/d39l7vWaJK+uqn/r7vtW1TMmNAcAAAAAAMZiVtuf+cxMqE5V1a232LhpfvrFw8YJzQEAAAAAAKbCpFb+/2GSN1XVkmwK+5cneVpV3SvJmyc0BwAAAAAAmAqDhv9VdVB3n59kaZI/nqu3IsnFSdLdJw1ZHwAAAAAAptFg4X9VPT7JPlV1SZKXJuntHHbzoeoDAAAAADBiM3r+z2fIlf/Lk6yd+/dVSZ6T5PUD1gMAAAAAADJg+N/dx2+5XVWP3nYMAAAAAADY+Sb1wN9k+21/AAAAAADgZ1az2v7MZ2ahJwAAAAAAAOxcQz7w9/5bbibZf5uxdPeJQ9UHAAAAAIBpNWTbn6O22f7nbcY6ifAfAAAAAICf3YzGNvMZ8oG/Lx3q3AAAAAAAwDXz1QgAAAAAAIzMkG1/AAAAAABgGNr+zMvVAQAAAACAkRH+AwAAAADAyGj7AwAAAADA4qPtz7xcHQAAAAAAGBnhPwAAAAAAjIzwHwAAAAAARkbPfwAAAAAAFp+ZWugZ7NKs/AcAAAAAgJER/gMAAAAAwMho+wMAAAAAwOIzY237fFwdAAAAAAAYGeE/AAAAAACMjLY/AAAAAAAsPtr+zMvVAQAAAACAkRH+AwAAAADAyGj7AwAAAADA4qPtz7xcHQAAAAAAGBnhPwAAAAAAjIy2PwAAAAAALD7a/szL1QEAAAAAgJER/gMAAAAAwMho+wMAAAAAwOKj7c+8qrsXeg7XZpefIAAAAADADqiFnsCYbPz3P97ls+OZX3v9gv3OF8XK/3Vvf+xCT4HraOmT3p8TDzh8oafBdXD/C09z7y1i7r3F7f4XnpZv/+ptFnoaXAe3+NIpCz0FAAAASKLnPwAAAAAAjM6iWPkPAAAAAABb0fN/Xq4OAAAAAACMjPAfAAAAAABGRtsfAAAAAAAWn5la6Bns0qz8BwAAAACAkRH+AwAAAADAyGj7AwAAAADA4jNjbft8XB0AAAAAABgZ4T8AAAAAAIyMtj8AAAAAACw+2v7My9UBAAAAAICREf4DAAAAAMDIaPsDAAAAAMCiU2Vt+3xcHQAAAAAAGBnhPwAAAAAAjIzwHwAAAAAARkbPfwAAAAAAFp8Za9vn4+oAAAAAAMDICP8BAAAAAGBktP0BAAAAAGDx0fZnXq4OAAAAAACMjPAfAAAAAABGRtsfAAAAAAAWn5la6Bns0qz8BwAAAACAkRH+AwAAAADAyGj7AwAAAADA4jNjbft8XB0AAAAAABgZ4T8AAAAAAIyMtj8AAAAAACw+2v7My9UBAAAAAICREf4DAAAAAMDIaPsDAAAAAMDio+3PvFwdAAAAAAAYGeE/AAAAAACMjPAfAAAAAABGRs9/AAAAAAAWHz3/5+XqAAAAAADAyAj/AQAAAABggVTV9avqE1X1par6dFUdcg3H3a+qPldV/6+q/m9V7TnfebX9AQAAAABg8RlP25/XJfmL7v5SVR2Z5LVJHrflAVV1syTHJvmN7r64qm6QZON8Jx3N1QEAAAAAgMWkqvZNsn93fylJuvvkJPvMjW/puUn+rLsvnjvu3O5eNd+5hf8AAAAAADCAqjq6qk7e4ufobQ65WZLvbDP2vbnxLd02yZqq+peq+mxVvaKq5s33tf0BAAAAAGDxmamFnsG16u6VSVbOc0gl6e29dZvtPZP8TpLfS3JZkjck+cMkb7+mEw8a/m/nW4ytzH1wAAAAAACYRj9Ictg2YzefG9/SD5O8ursvTZKqeneSP5jvxEO3/blekr9MsjrJmu38AAAAAADAVOruHye5sqp+OUmq6vZJLkqyW1W9ZYtD35LklVU1O7f90CRfmu/cg6787+7jquqh3f2uIesAAAAAADBlZkbzSNtnJ3lHVa1IckmSJyTZK5v+AiBJ0t3/VlWHJflsVa1P8p9J3jPfSSfR8//hE6gBAAAAAACLTnefleQB2wyfl+RB2xz3t0n+dkfPO/hXI919ztA1AAAAAACAn5rEyn8AAAAAANi5xtP2ZxCuDgAAAAAAjMygK/+r6pVJ+pr2d/eLhqwPAAAAAADTaOi2P19P8pokj0tSA9cCAAAAAAAycPjf3e+vqmd292eHrAMAAAAAwJTR839ek7g6d59ADQAAAAAAYM7g4X93bxy6BgAAAAAA8FODtf2pqj2S7NPdP5rbriTPTPILSf5fd390qNoAAAAAAIxcafsznyGvzhuT3HOL7ZcmeVCSTyd5VFUdM2BtAAAAAACYWkM+8Pd23f2kJKmqmSSPS/Ir3X1JVX0yyb8n+esB6wMAAAAAwFQaMvy/YovX90tycndfkiTdvaGq1g5YGwAAAACAMdP2Z15DXp3vVdVjquqQJH+ZZOXmHVV1kyTCfwAAAAAAGMC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",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# corr 시각화\n",
    "def corr_heatmap(dataset, method_option = 'pearson'):\n",
    "    \"\"\"[상관관계 그래프]\n",
    "\n",
    "    Args:\n",
    "        dataset ([DataFrame]): [데이터프래임을 입력해주세요]\n",
    "        method_option (str, optional): [correlation을 도출하는 방식 선택]. Defaults to 'pearson' in addition, there are other options ('spearman', 'kendall').\n",
    "        \n",
    "    \"\"\"\n",
    "    # 라이브러리 호출\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "    import seaborn as sns\n",
    "    # 한글폰트 출력 가능 코드\n",
    "    from matplotlib import rc\n",
    "    rc('font', family='AppleGothic') # Mac Os\n",
    "    #rc('font', family='NanumGothic') # Windows Os\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    \n",
    "    # heatmap 그리기\n",
    "    mask_ut=np.triu(np.ones(dataset.corr().shape)).astype(bool) \n",
    "    plt.figure(figsize=(30,30))\n",
    "    sns.heatmap(dataset.corr(method=method_option), annot = True, cmap = 'RdYlBu_r', cbar_kws={\"shrink\": .5}, \n",
    "                annot_kws={\"size\": 10}, linewidths=.5, mask=mask_ut)\n",
    "    plt.show()\n",
    "\n",
    "# 함수 사용\n",
    "corr_heatmap(stock_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_heatmap(stock_price, method_option='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_heatmap(stock_price, method_option='kendall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 참고2) ACF, PACF (자기상관계수, 편자기상관계수)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def acf_plot(data, N_LAGS, alpha): \n",
    "    from statsmodels.graphics.tsaplots import plot_acf \n",
    "    fig = plot_acf(data, lags=N_LAGS, alpha=alpha) \n",
    "    plt.xlabel(f'Lag at 0 to {N_LAGS}') \n",
    "    plt.ylabel(\"lag at k's autocorrelation\") \n",
    "    plt.show()\n",
    "    \n",
    "acf_plot(stock_price['Adj Close']['005930.KS'], 8, 0.05)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lag at 0's autocorrelation =  1.0\n",
      "lag at 1's autocorrelation =  0.58\n",
      "lag at 2's autocorrelation =  0.18\n",
      "lag at 3's autocorrelation =  0.07\n",
      "lag at 4's autocorrelation =  -0.29\n",
      "lag at 5's autocorrelation =  -0.37\n",
      "lag at 6's autocorrelation =  -0.21\n",
      "lag at 7's autocorrelation =  -0.24\n",
      "lag at 8's autocorrelation =  -0.24\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def acf_plot(data, N_LAGS, pval): \n",
    "    from statsmodels.graphics.tsaplots import plot_acf \n",
    "    auto = pd.Series(data.values) \n",
    "    for i in range(0, N_LAGS+1): \n",
    "        print(f\"lag at {i}'s autocorrelation = \", round(auto.autocorr(lag=i),2)) \n",
    "        scatter = pd.DataFrame() \n",
    "        scatter['lags'] = [i for i in range (1, N_LAGS +1)] \n",
    "        scatter['autocorrelation'] = [ auto.autocorr(lag=i) for i in range(1, N_LAGS +1)] \n",
    "    fig = plot_acf(data, lags=N_LAGS, alpha=pval) \n",
    "    plt.xlabel(f'Lag at k (0 to {N_LAGS})') \n",
    "    plt.ylabel(\"lag at k's autocorrelation\") \n",
    "    plt.scatter(x=scatter['lags'], y=scatter['autocorrelation'], edgecolors='red',linewidth=1, s=200, alpha = .5) \n",
    "    plt.show() \n",
    "\n",
    "acf_plot(stock_price['Adj Close']['005930.KS'], 8, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pacf_plot(data, N_LAGS, pval): \n",
    "    from statsmodels.graphics.tsaplots import plot_pacf \n",
    "    plot_pacf(data, lags=N_LAGS, alpha=pval, method='ywm') \n",
    "    plt.xlabel(f'Lag at k (0 to {N_LAGS})') \n",
    "    plt.ylabel(\"lag at k's Partial autocorrelation\") \n",
    "    plt.show() \n",
    "    \n",
    "pacf_plot(stock_price['Adj Close']['005930.KS'], 5, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       pacf  upper confidence interval  lower confidence interval\n",
      "0  1.000000                   1.000000                   1.000000\n",
      "1  0.527680                   1.071276                  -0.015916\n",
      "2 -0.201784                   0.341813                  -0.745380\n",
      "3  0.109346                   0.652942                  -0.434251\n",
      "4 -0.499792                   0.043805                  -1.043388\n",
      "5  0.048122                   0.591719                  -0.495474\n"
     ]
    }
   ],
   "source": [
    "def pacf(data, alpha, N_LAGS): \n",
    "    from statsmodels.tsa.stattools import pacf \n",
    "    result = pd.DataFrame() \n",
    "    result['pacf'] = pacf(data, alpha=alpha)[0][:N_LAGS+1] \n",
    "    result['upper confidence interval'] = [pacf(data, alpha=alpha)[1][i][1] for i in range(N_LAGS + 1)] \n",
    "    result['lower confidence interval'] = [pacf(data, alpha=alpha)[1][i][0] for i in range(N_LAGS + 1)] \n",
    "    \n",
    "    return result \n",
    "\n",
    "result = pacf(stock_price['Adj Close']['005930.KS'], 0.05, 5) \n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lag at 0's Partial autocorrelation =  1.0\n",
      "lag at 1's Partial autocorrelation =  0.53\n",
      "lag at 2's Partial autocorrelation =  -0.2\n",
      "lag at 3's Partial autocorrelation =  0.11\n",
      "lag at 4's Partial autocorrelation =  -0.5\n",
      "lag at 5's Partial autocorrelation =  0.05\n",
      "1번째 lag애서 파란 음영의 값 범위는 -0.5657928670380858, +0.5657928670380858입니다.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pacf_plot(data, N_LAGS, alpha): \n",
    "    # 라이브러리 호출 \n",
    "    from statsmodels.graphics.tsaplots import plot_pacf \n",
    "    from statsmodels.tsa.stattools import pacf \n",
    "    import scipy.stats\n",
    "    \n",
    "    # 편자기상관계수를 구하는 부분 \n",
    "    auto = pd.Series(data.values) \n",
    "    \n",
    "    for i in range(0, N_LAGS+1): \n",
    "        # lag 별 pacf 추정 계수를 출력하는 부분 \n",
    "        print(f\"lag at {i}'s Partial autocorrelation = \", round(pacf(data, alpha=.05)[0][i],2)) \n",
    "        \n",
    "        scatter = pd.DataFrame() \n",
    "        scatter['lags'] = [i for i in range (1, N_LAGS +1)] \n",
    "        scatter['Partial autocorrelation'] = [pacf(data, alpha=.05)[0][i] for i in range(1, N_LAGS +1)] \n",
    "    print(f\"1번째 lag애서 파란 음영의 값 범위는 -{scipy.stats.norm.ppf(1-(alpha)/2) * np.sqrt(1/(data.shape[0]-1))}, +{scipy.stats.norm.ppf(1-(alpha)/2) * np.sqrt(1/(data.shape[0]-1))}입니다.\") \n",
    "    # 표 그리는 부분 \n",
    "    plot_pacf(data, lags=N_LAGS, alpha=alpha, method='ywm')\n",
    "    plt.xlabel(f'Lag at k (0 to {N_LAGS})') \n",
    "    plt.ylabel(\"lag at k's Partial autocorrelation\") \n",
    "    # lag 별로 PACF 추정 계수를 점으로 찍는 부분 \n",
    "    plt.scatter(x=scatter['lags'], y=scatter['Partial autocorrelation'], edgecolors='red',linewidth=1, s=200, alpha = .5) \n",
    "    # lag = 1 에서 신뢰구간의 upper 부분을 점으로 찍는 부분 \n",
    "    plt.scatter(x=1, y=[scipy.stats.norm.ppf(1-(alpha)/2) * (1/np.sqrt(data.shape[0]))]) \n",
    "    plt.show() \n",
    "\n",
    "pacf_plot(stock_price['Adj Close']['005930.KS'], 5, 0.05)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 참고3) Cross correlation 시차상관계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "마케팅 비용과 수익의 0번째 기간 후 상관계수 값 :  0.77109\n",
      "마케팅 비용과 수익의 1번째 기간 후 상관계수 값 :  0.46239\n",
      "마케팅 비용과 수익의 2번째 기간 후 상관계수 값 :  0.19352\n",
      "마케팅 비용과 수익의 3번째 기간 후 상관계수 값 : -0.06066\n",
      "마케팅 비용과 수익의 4번째 기간 후 상관계수 값 : -0.28160\n",
      "마케팅 비용과 수익의 5번째 기간 후 상관계수 값 : -0.44531\n",
      "마케팅 비용과 수익의 6번째 기간 후 상관계수 값 : -0.49159\n",
      "마케팅 비용과 수익의 7번째 기간 후 상관계수 값 : -0.35784\n",
      "마케팅 비용과 수익의 8번째 기간 후 상관계수 값 : -0.15697\n",
      "마케팅 비용과 수익의 9번째 기간 후 상관계수 값 : -0.03430\n",
      "마케팅 비용과 수익의 10번째 기간 후 상관계수 값 :  0.01588\n",
      "마케팅 비용과 수익의 11번째 기간 후 상관계수 값 :  0.00704\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 예시 : 마케팅 비용과 수익간의 상관성 분석\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "marketing = np.array([3, 4, 5, 5, 7, 9, 13, 15, 12, 10, 8, 8])\n",
    "revenue = np.array([21, 19, 22, 24, 25, 29, 30, 34, 37, 40, 35, 30]) \n",
    "\n",
    "\n",
    "cc = sm.tsa.stattools.ccf(marketing, revenue, adjusted=False)\n",
    "\n",
    "for i in range(len(cc)):\n",
    "    print(f'마케팅 비용과 수익의 {i}번째 기간 후 상관계수 값 : {cc[i] : .5f}')\n",
    "\n",
    "for i in range(len(cc)):\n",
    "    plt.scatter(i, cc[i])\n",
    "plt.axvline(x=0, color='k' ,linestyle = ':') # draw x =0 axes \n",
    "plt.axhline(y=0, color='k' ,linestyle = ':') # draw y =0 axes\n",
    "plt.title('Cross correlation (marketing, revenue)')\n",
    "plt.xlabel('after k period')\n",
    "plt.ylabel('cross corr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x =2\n",
    "y =3\n",
    "np.dot(x, x) * np.dot(y, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5]),\n",
       " array([0.48499974, 0.63799254, 0.76590456, 0.86183857, 0.91858386,\n",
       "        0.96216172, 0.90510293, 0.82954706, 0.73329954, 0.64771135,\n",
       "        0.55679349]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def xcorr(x, y, normed=True, detrend=False, maxlags=10):\n",
    "    # Cross correlation of two signals of equal length\n",
    "    # Returns the coefficients when normed=True\n",
    "    # Returns inner products when normed=False\n",
    "    # Usage: lags, c = xcorr(x,y,maxlags=len(x)-1)\n",
    "    # Optional detrending e.g. mlab.detrend_mean\n",
    "\n",
    "    Nx = len(x)\n",
    "    if Nx != len(y):\n",
    "        raise ValueError('x and y must be equal length')\n",
    "    \n",
    "    if detrend:\n",
    "        import matplotlib.mlab as mlab\n",
    "        x = mlab.detrend_mean(np.asarray(x)) # can set your preferences here\n",
    "        y = mlab.detrend_mean(np.asarray(y))\n",
    "    \n",
    "    c = np.correlate(x, y, mode='full')\n",
    "\n",
    "    if normed:\n",
    "        n = np.sqrt(np.dot(x, x) * np.dot(y, y)) # this is the transformation function\n",
    "        c = np.true_divide(c,n)\n",
    "\n",
    "    if maxlags is None:\n",
    "        maxlags = Nx - 1\n",
    "\n",
    "    if maxlags >= Nx or maxlags < 1:\n",
    "        raise ValueError('maglags must be None or strictly '\n",
    "                         'positive < %d' % Nx)\n",
    "\n",
    "    lags = np.arange(-maxlags, maxlags + 1)\n",
    "    c = c[Nx - 1 - maxlags:Nx + maxlags]\n",
    "    \n",
    "    return lags, c\n",
    "\n",
    "xcorr(marketing, revenue, maxlags=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 참고 3 실습 : 한국국채 10년물, KOSPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import FinanceDataReader as fdr\n",
    "\n",
    "kr10YT = fdr.DataReader('KR10YT=RR','2010-01-01', '2020-01-01')\n",
    "ks11 = fdr.DataReader('KS11', '2010-01-01', '2020-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>5.34</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.34</td>\n",
       "      <td>-0.0056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-26</th>\n",
       "      <td>5.13</td>\n",
       "      <td>5.13</td>\n",
       "      <td>5.13</td>\n",
       "      <td>5.13</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>4.94</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.94</td>\n",
       "      <td>-0.0120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>4.83</td>\n",
       "      <td>4.83</td>\n",
       "      <td>4.83</td>\n",
       "      <td>4.83</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>4.94</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.94</td>\n",
       "      <td>0.0041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Open  High   Low  Change\n",
       "Date                                       \n",
       "2010-01-29   5.34  5.34  5.34  5.34 -0.0056\n",
       "2010-02-26   5.13  5.13  5.13  5.13  0.0000\n",
       "2010-03-31   4.94  4.94  4.94  4.94 -0.0120\n",
       "2010-04-30   4.83  4.83  4.83  4.83  0.0021\n",
       "2010-05-31   4.94  4.94  4.94  4.94  0.0041"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 월말 영업일 데이터만 추출 시행\n",
    "\n",
    "# 데이터 인덱스(날짜)를 컬럼으로 형성 후 처음과 끝을 추출\n",
    "kr10YT = kr10YT.reset_index()\n",
    "start_date = kr10YT['Date'].astype(str).iloc[0]\n",
    "end_date = kr10YT['Date'].astype(str).iloc[-1]\n",
    "\n",
    "# 날짜 처음과 끝에서 영업일 기준 freq 적용 후 데이터 프레임 형성\n",
    "date_all = pd.date_range(start_date, end_date, freq='B').to_frame().rename(columns={0:'Date'}).reset_index(drop=True)\n",
    "\n",
    "# 데이터 병합 (영업일 date와 10년물 국체 병합)\n",
    "df_all = pd.merge(date_all,kr10YT,how='left')\n",
    "\n",
    "# 직전 값으로 NaN 값 채우기\n",
    "df_all = df_all.fillna(method='ffill')\n",
    "\n",
    "# 마지막 영업일 제외후 나머지 drop\n",
    "df_KR10 = df_all.set_index(df_all['Date'])\n",
    "\n",
    "# 각 월당 마지막 영업일 도출\n",
    "month_end = pd.date_range('2010-01-01', '2020-01-01', freq='BM')\n",
    "\n",
    "# 마지막 영업일 제외후 나머지 drop\n",
    "kr10yt_BM = df_KR10[df_KR10['Date'].isin(month_end)].reset_index(drop=True)\n",
    "kr10yt_BM = kr10yt_BM.set_index(kr10yt_BM['Date'])\n",
    "kr10yt_BM = kr10yt_BM.drop(columns='Date')\n",
    "kr10yt_BM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-26</th>\n",
       "      <td>-3.932584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>-3.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>-2.226721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>2.277433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "Date                \n",
       "2010-02-26 -3.932584\n",
       "2010-03-31 -3.703704\n",
       "2010-04-30 -2.226721\n",
       "2010-05-31  2.277433\n",
       "2010-06-30  0.000000"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전월대비 수익률 도출 (pct_change 사용, %로 표현하기 위해 100을 곱해준다)\n",
    "\n",
    "kr10yt_bm_return = kr10yt_BM['Close'].pct_change().dropna()\n",
    "kr10yt_bm_return = pd.DataFrame(kr10yt_bm_return) * 100\n",
    "kr10yt_bm_return.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>1602.43</td>\n",
       "      <td>1621.00</td>\n",
       "      <td>1626.57</td>\n",
       "      <td>1595.39</td>\n",
       "      <td>473940000.0</td>\n",
       "      <td>-0.0244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-26</th>\n",
       "      <td>1594.58</td>\n",
       "      <td>1593.73</td>\n",
       "      <td>1596.55</td>\n",
       "      <td>1580.70</td>\n",
       "      <td>331760000.0</td>\n",
       "      <td>0.0045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>1692.85</td>\n",
       "      <td>1702.58</td>\n",
       "      <td>1704.81</td>\n",
       "      <td>1689.92</td>\n",
       "      <td>328320000.0</td>\n",
       "      <td>-0.0043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>1741.56</td>\n",
       "      <td>1742.32</td>\n",
       "      <td>1749.69</td>\n",
       "      <td>1739.81</td>\n",
       "      <td>412070000.0</td>\n",
       "      <td>0.0076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>1641.25</td>\n",
       "      <td>1625.08</td>\n",
       "      <td>1641.25</td>\n",
       "      <td>1621.09</td>\n",
       "      <td>299380000.0</td>\n",
       "      <td>0.0114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close     Open     High      Low       Volume  Change\n",
       "Date                                                               \n",
       "2010-01-29  1602.43  1621.00  1626.57  1595.39  473940000.0 -0.0244\n",
       "2010-02-26  1594.58  1593.73  1596.55  1580.70  331760000.0  0.0045\n",
       "2010-03-31  1692.85  1702.58  1704.81  1689.92  328320000.0 -0.0043\n",
       "2010-04-30  1741.56  1742.32  1749.69  1739.81  412070000.0  0.0076\n",
       "2010-05-31  1641.25  1625.08  1641.25  1621.09  299380000.0  0.0114"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 월말 영업일 데이터만 추출 시행\n",
    "\n",
    "# 데이터 인덱스(날짜)를 컬럼으로 형성 후 처음과 끝을 추출\n",
    "ks11 = ks11.reset_index()\n",
    "start_date = ks11['Date'].astype(str).iloc[0]\n",
    "end_date = ks11['Date'].astype(str).iloc[-1]\n",
    "\n",
    "# 날짜 처음과 끝에서 영업일 기준 freq 적용 후 데이터 프레임 형성\n",
    "date_all = pd.date_range(start_date, end_date, freq='B').to_frame().rename(columns={0:'Date'}).reset_index(drop=True)\n",
    "\n",
    "# 데이터 병합 (영업일 date와 10년물 국체 병합)\n",
    "df_all = pd.merge(date_all,ks11,how='left')\n",
    "\n",
    "# 직전 값으로 NaN 값 채우기\n",
    "df_all = df_all.fillna(method='ffill')\n",
    "\n",
    "\n",
    "# 마지막 영업일 제외후 나머지 drop\n",
    "df_ks11 = df_all.set_index(df_all['Date'])\n",
    "\n",
    "# 각 월당 마지막 영업일 도출\n",
    "month_end = pd.date_range('2010-01-01', '2020-01-01', freq='BM')\n",
    "\n",
    "# 마지막 영업일 제외후 나머지 drop\n",
    "ks11_BM = df_ks11[df_ks11['Date'].isin(month_end)].reset_index(drop=True)\n",
    "ks11_BM = ks11_BM.set_index(ks11_BM['Date'])\n",
    "ks11_BM = ks11_BM.drop(columns='Date')\n",
    "ks11_BM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-26</th>\n",
       "      <td>-0.489881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>6.162751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>2.877396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>-5.759779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>3.475400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "Date                \n",
       "2010-02-26 -0.489881\n",
       "2010-03-31  6.162751\n",
       "2010-04-30  2.877396\n",
       "2010-05-31 -5.759779\n",
       "2010-06-30  3.475400"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전월대비 수익률 도출 (pct_change 사용, %로 표현하기 위해 100을 곱해준다)\n",
    "\n",
    "kospi_bm_return = ks11_BM['Close'].pct_change().dropna()\n",
    "kospi_bm_return = pd.DataFrame(kospi_bm_return) * 100\n",
    "kospi_bm_return.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10년물 국체수익률과 코스피지수의 0개월 후 상관계수 값 : -0.43303\n",
      "10년물 국체수익률과 코스피지수의 1개월 후 상관계수 값 : -0.45656\n",
      "10년물 국체수익률과 코스피지수의 2개월 후 상관계수 값 : -0.44144\n",
      "10년물 국체수익률과 코스피지수의 3개월 후 상관계수 값 : -0.41604\n",
      "10년물 국체수익률과 코스피지수의 4개월 후 상관계수 값 : -0.40058\n",
      "10년물 국체수익률과 코스피지수의 5개월 후 상관계수 값 : -0.39650\n",
      "10년물 국체수익률과 코스피지수의 6개월 후 상관계수 값 : -0.38512\n",
      "10년물 국체수익률과 코스피지수의 7개월 후 상관계수 값 : -0.37847\n",
      "10년물 국체수익률과 코스피지수의 8개월 후 상관계수 값 : -0.37944\n",
      "10년물 국체수익률과 코스피지수의 9개월 후 상관계수 값 : -0.37080\n",
      "10년물 국체수익률과 코스피지수의 10개월 후 상관계수 값 : -0.36968\n",
      "10년물 국체수익률과 코스피지수의 11개월 후 상관계수 값 : -0.36963\n",
      "10년물 국체수익률과 코스피지수의 12개월 후 상관계수 값 : -0.36676\n",
      "10년물 국체수익률과 코스피지수의 13개월 후 상관계수 값 : -0.38482\n",
      "10년물 국체수익률과 코스피지수의 14개월 후 상관계수 값 : -0.41424\n",
      "10년물 국체수익률과 코스피지수의 15개월 후 상관계수 값 : -0.42477\n",
      "10년물 국체수익률과 코스피지수의 16개월 후 상관계수 값 : -0.45561\n",
      "10년물 국체수익률과 코스피지수의 17개월 후 상관계수 값 : -0.49250\n",
      "10년물 국체수익률과 코스피지수의 18개월 후 상관계수 값 : -0.51110\n",
      "10년물 국체수익률과 코스피지수의 19개월 후 상관계수 값 : -0.52868\n",
      "10년물 국체수익률과 코스피지수의 20개월 후 상관계수 값 : -0.54698\n",
      "10년물 국체수익률과 코스피지수의 21개월 후 상관계수 값 : -0.53097\n",
      "10년물 국체수익률과 코스피지수의 22개월 후 상관계수 값 : -0.51061\n",
      "10년물 국체수익률과 코스피지수의 23개월 후 상관계수 값 : -0.50377\n",
      "10년물 국체수익률과 코스피지수의 24개월 후 상관계수 값 : -0.48936\n",
      "10년물 국체수익률과 코스피지수의 25개월 후 상관계수 값 : -0.46923\n",
      "10년물 국체수익률과 코스피지수의 26개월 후 상관계수 값 : -0.45415\n",
      "10년물 국체수익률과 코스피지수의 27개월 후 상관계수 값 : -0.44599\n",
      "10년물 국체수익률과 코스피지수의 28개월 후 상관계수 값 : -0.43527\n",
      "10년물 국체수익률과 코스피지수의 29개월 후 상관계수 값 : -0.42443\n",
      "10년물 국체수익률과 코스피지수의 30개월 후 상관계수 값 : -0.40159\n",
      "10년물 국체수익률과 코스피지수의 31개월 후 상관계수 값 : -0.38237\n",
      "10년물 국체수익률과 코스피지수의 32개월 후 상관계수 값 : -0.35833\n",
      "10년물 국체수익률과 코스피지수의 33개월 후 상관계수 값 : -0.32649\n",
      "10년물 국체수익률과 코스피지수의 34개월 후 상관계수 값 : -0.30301\n",
      "10년물 국체수익률과 코스피지수의 35개월 후 상관계수 값 : -0.26859\n",
      "10년물 국체수익률과 코스피지수의 36개월 후 상관계수 값 : -0.24305\n",
      "10년물 국체수익률과 코스피지수의 37개월 후 상관계수 값 : -0.22263\n",
      "10년물 국체수익률과 코스피지수의 38개월 후 상관계수 값 : -0.18918\n",
      "10년물 국체수익률과 코스피지수의 39개월 후 상관계수 값 : -0.16819\n",
      "10년물 국체수익률과 코스피지수의 40개월 후 상관계수 값 : -0.14778\n",
      "10년물 국체수익률과 코스피지수의 41개월 후 상관계수 값 : -0.12006\n",
      "10년물 국체수익률과 코스피지수의 42개월 후 상관계수 값 : -0.09733\n",
      "10년물 국체수익률과 코스피지수의 43개월 후 상관계수 값 : -0.06325\n",
      "10년물 국체수익률과 코스피지수의 44개월 후 상관계수 값 : -0.03213\n",
      "10년물 국체수익률과 코스피지수의 45개월 후 상관계수 값 : -0.00924\n",
      "10년물 국체수익률과 코스피지수의 46개월 후 상관계수 값 :  0.01031\n",
      "10년물 국체수익률과 코스피지수의 47개월 후 상관계수 값 :  0.01729\n",
      "10년물 국체수익률과 코스피지수의 48개월 후 상관계수 값 :  0.02366\n",
      "10년물 국체수익률과 코스피지수의 49개월 후 상관계수 값 :  0.03159\n",
      "10년물 국체수익률과 코스피지수의 50개월 후 상관계수 값 :  0.04084\n",
      "10년물 국체수익률과 코스피지수의 51개월 후 상관계수 값 :  0.04655\n",
      "10년물 국체수익률과 코스피지수의 52개월 후 상관계수 값 :  0.04794\n",
      "10년물 국체수익률과 코스피지수의 53개월 후 상관계수 값 :  0.05286\n",
      "10년물 국체수익률과 코스피지수의 54개월 후 상관계수 값 :  0.04675\n",
      "10년물 국체수익률과 코스피지수의 55개월 후 상관계수 값 :  0.03534\n",
      "10년물 국체수익률과 코스피지수의 56개월 후 상관계수 값 :  0.01795\n",
      "10년물 국체수익률과 코스피지수의 57개월 후 상관계수 값 :  0.00751\n",
      "10년물 국체수익률과 코스피지수의 58개월 후 상관계수 값 :  0.00799\n",
      "10년물 국체수익률과 코스피지수의 59개월 후 상관계수 값 :  0.01581\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "kr10yt_data = np.array(kr10yt_BM['Close'])\n",
    "ks11_data = np.array(ks11_BM['Close'])\n",
    "\n",
    "\n",
    "cc = sm.tsa.stattools.ccf(ks11_data, kr10yt_data, adjusted=False, fft=True)\n",
    "\n",
    "for i in range(60):\n",
    "    print(f'10년물 국체수익률과 코스피지수의 {i}개월 후 상관계수 값 : {cc[i] : .5f}')\n",
    "\n",
    "for i in range(60):\n",
    "    plt.scatter(i, cc[i])\n",
    "plt.axvline(x=0, color='k' ,linestyle = ':') \n",
    "plt.axhline(y=0, color='k' ,linestyle = ':')\n",
    "plt.title('Cross correlation (KS11, KR10YT)')\n",
    "plt.xlabel('after k period')\n",
    "plt.ylabel('cross corr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10년물 국체수익률과 코스피지수 수익률의 0개월 후 상관계수 값 :  0.12547\n",
      "10년물 국체수익률과 코스피지수 수익률의 1개월 후 상관계수 값 :  0.14507\n",
      "10년물 국체수익률과 코스피지수 수익률의 2개월 후 상관계수 값 : -0.13085\n",
      "10년물 국체수익률과 코스피지수 수익률의 3개월 후 상관계수 값 :  0.02708\n",
      "10년물 국체수익률과 코스피지수 수익률의 4개월 후 상관계수 값 :  0.07631\n",
      "10년물 국체수익률과 코스피지수 수익률의 5개월 후 상관계수 값 :  0.09027\n",
      "10년물 국체수익률과 코스피지수 수익률의 6개월 후 상관계수 값 :  0.01290\n",
      "10년물 국체수익률과 코스피지수 수익률의 7개월 후 상관계수 값 :  0.01621\n",
      "10년물 국체수익률과 코스피지수 수익률의 8개월 후 상관계수 값 : -0.04429\n",
      "10년물 국체수익률과 코스피지수 수익률의 9개월 후 상관계수 값 : -0.04753\n",
      "10년물 국체수익률과 코스피지수 수익률의 10개월 후 상관계수 값 : -0.00915\n",
      "10년물 국체수익률과 코스피지수 수익률의 11개월 후 상관계수 값 : -0.06412\n",
      "10년물 국체수익률과 코스피지수 수익률의 12개월 후 상관계수 값 :  0.06611\n",
      "10년물 국체수익률과 코스피지수 수익률의 13개월 후 상관계수 값 :  0.08057\n",
      "10년물 국체수익률과 코스피지수 수익률의 14개월 후 상관계수 값 : -0.07865\n",
      "10년물 국체수익률과 코스피지수 수익률의 15개월 후 상관계수 값 :  0.04310\n",
      "10년물 국체수익률과 코스피지수 수익률의 16개월 후 상관계수 값 : -0.05456\n",
      "10년물 국체수익률과 코스피지수 수익률의 17개월 후 상관계수 값 : -0.19848\n",
      "10년물 국체수익률과 코스피지수 수익률의 18개월 후 상관계수 값 :  0.05902\n",
      "10년물 국체수익률과 코스피지수 수익률의 19개월 후 상관계수 값 : -0.05833\n",
      "10년물 국체수익률과 코스피지수 수익률의 20개월 후 상관계수 값 : -0.13140\n",
      "10년물 국체수익률과 코스피지수 수익률의 21개월 후 상관계수 값 :  0.07690\n",
      "10년물 국체수익률과 코스피지수 수익률의 22개월 후 상관계수 값 : -0.03310\n",
      "10년물 국체수익률과 코스피지수 수익률의 23개월 후 상관계수 값 : -0.01028\n",
      "10년물 국체수익률과 코스피지수 수익률의 24개월 후 상관계수 값 : -0.05733\n",
      "10년물 국체수익률과 코스피지수 수익률의 25개월 후 상관계수 값 : -0.10359\n",
      "10년물 국체수익률과 코스피지수 수익률의 26개월 후 상관계수 값 : -0.00635\n",
      "10년물 국체수익률과 코스피지수 수익률의 27개월 후 상관계수 값 : -0.01628\n",
      "10년물 국체수익률과 코스피지수 수익률의 28개월 후 상관계수 값 :  0.08657\n",
      "10년물 국체수익률과 코스피지수 수익률의 29개월 후 상관계수 값 :  0.04188\n",
      "10년물 국체수익률과 코스피지수 수익률의 30개월 후 상관계수 값 :  0.05887\n",
      "10년물 국체수익률과 코스피지수 수익률의 31개월 후 상관계수 값 : -0.10078\n",
      "10년물 국체수익률과 코스피지수 수익률의 32개월 후 상관계수 값 : -0.16525\n",
      "10년물 국체수익률과 코스피지수 수익률의 33개월 후 상관계수 값 :  0.00741\n",
      "10년물 국체수익률과 코스피지수 수익률의 34개월 후 상관계수 값 : -0.05262\n",
      "10년물 국체수익률과 코스피지수 수익률의 35개월 후 상관계수 값 :  0.11029\n",
      "10년물 국체수익률과 코스피지수 수익률의 36개월 후 상관계수 값 :  0.00799\n",
      "10년물 국체수익률과 코스피지수 수익률의 37개월 후 상관계수 값 : -0.11735\n",
      "10년물 국체수익률과 코스피지수 수익률의 38개월 후 상관계수 값 :  0.10892\n",
      "10년물 국체수익률과 코스피지수 수익률의 39개월 후 상관계수 값 :  0.05667\n",
      "10년물 국체수익률과 코스피지수 수익률의 40개월 후 상관계수 값 : -0.02817\n",
      "10년물 국체수익률과 코스피지수 수익률의 41개월 후 상관계수 값 : -0.00744\n",
      "10년물 국체수익률과 코스피지수 수익률의 42개월 후 상관계수 값 :  0.05447\n",
      "10년물 국체수익률과 코스피지수 수익률의 43개월 후 상관계수 값 : -0.04247\n",
      "10년물 국체수익률과 코스피지수 수익률의 44개월 후 상관계수 값 :  0.10626\n",
      "10년물 국체수익률과 코스피지수 수익률의 45개월 후 상관계수 값 : -0.04575\n",
      "10년물 국체수익률과 코스피지수 수익률의 46개월 후 상관계수 값 :  0.11849\n",
      "10년물 국체수익률과 코스피지수 수익률의 47개월 후 상관계수 값 : -0.01288\n",
      "10년물 국체수익률과 코스피지수 수익률의 48개월 후 상관계수 값 :  0.04041\n",
      "10년물 국체수익률과 코스피지수 수익률의 49개월 후 상관계수 값 :  0.06638\n",
      "10년물 국체수익률과 코스피지수 수익률의 50개월 후 상관계수 값 : -0.02307\n",
      "10년물 국체수익률과 코스피지수 수익률의 51개월 후 상관계수 값 :  0.05367\n",
      "10년물 국체수익률과 코스피지수 수익률의 52개월 후 상관계수 값 : -0.03267\n",
      "10년물 국체수익률과 코스피지수 수익률의 53개월 후 상관계수 값 :  0.08665\n",
      "10년물 국체수익률과 코스피지수 수익률의 54개월 후 상관계수 값 :  0.05277\n",
      "10년물 국체수익률과 코스피지수 수익률의 55개월 후 상관계수 값 : -0.01266\n",
      "10년물 국체수익률과 코스피지수 수익률의 56개월 후 상관계수 값 : -0.04976\n",
      "10년물 국체수익률과 코스피지수 수익률의 57개월 후 상관계수 값 : -0.05664\n",
      "10년물 국체수익률과 코스피지수 수익률의 58개월 후 상관계수 값 : -0.01772\n",
      "10년물 국체수익률과 코스피지수 수익률의 59개월 후 상관계수 값 :  0.03169\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 예시 : 마케팅 비용과 수익간의 상관성 분석\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "kr10yt_data = np.array(kr10yt_bm_return['Close'])\n",
    "ks11_data = np.array(kospi_bm_return['Close'])\n",
    "\n",
    "\n",
    "cc = sm.tsa.stattools.ccf(ks11_data, kr10yt_data, adjusted=False, fft=True)\n",
    "\n",
    "for i in range(60):\n",
    "    print(f'10년물 국체수익률과 코스피지수 수익률의 {i}개월 후 상관계수 값 : {cc[i] : .5f}')\n",
    "\n",
    "for i in range(60):\n",
    "    plt.scatter(i, cc[i])\n",
    "plt.axvline(x=0, color='k' ,linestyle = ':') \n",
    "plt.axhline(y=0, color='k' ,linestyle = ':')\n",
    "plt.title('Cross correlation (KS11, KR10YT)')\n",
    "plt.xlabel('after k period')\n",
    "plt.ylabel('cross corr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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