prj1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import FinanceDataReader as fdr\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def rjitt(arr):\n",
    "    stdev = .01*(max(arr)-min(arr))\n",
    "    return arr + np.random.randn(len(arr)) * stdev\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa20f2af908>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k200 = fdr.DataReader('KS200', '2015')\n",
    "k200['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>2019-10-31</th>\n",
       "      <td>275.82</td>\n",
       "      <td>276.84</td>\n",
       "      <td>279.04</td>\n",
       "      <td>275.82</td>\n",
       "      <td>77180.0</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>278.43</td>\n",
       "      <td>276.17</td>\n",
       "      <td>278.55</td>\n",
       "      <td>275.59</td>\n",
       "      <td>56880.0</td>\n",
       "      <td>0.0095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>282.54</td>\n",
       "      <td>279.90</td>\n",
       "      <td>282.77</td>\n",
       "      <td>279.90</td>\n",
       "      <td>75200.0</td>\n",
       "      <td>0.0148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>284.38</td>\n",
       "      <td>282.99</td>\n",
       "      <td>284.43</td>\n",
       "      <td>282.31</td>\n",
       "      <td>74550.0</td>\n",
       "      <td>0.0065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-06</th>\n",
       "      <td>284.52</td>\n",
       "      <td>284.31</td>\n",
       "      <td>286.43</td>\n",
       "      <td>283.70</td>\n",
       "      <td>74800.0</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close    Open    High     Low   Volume  Change\n",
       "Date                                                       \n",
       "2019-10-31  275.82  276.84  279.04  275.82  77180.0  0.0011\n",
       "2019-11-01  278.43  276.17  278.55  275.59  56880.0  0.0095\n",
       "2019-11-04  282.54  279.90  282.77  279.90  75200.0  0.0148\n",
       "2019-11-05  284.38  282.99  284.43  282.31  74550.0  0.0065\n",
       "2019-11-06  284.52  284.31  286.43  283.70  74800.0  0.0005"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k200.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa21c4f70b8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s500 = fdr.DataReader('US500', '2015')\n",
    "s500['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
       "      <th>Close_shifted</th>\n",
       "      <th>Change_shifted</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>3046.77</td>\n",
       "      <td>3039.74</td>\n",
       "      <td>3050.10</td>\n",
       "      <td>3025.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>3036.89</td>\n",
       "      <td>-0.0008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>3037.56</td>\n",
       "      <td>3046.90</td>\n",
       "      <td>3046.90</td>\n",
       "      <td>3023.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>3046.77</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>3066.91</td>\n",
       "      <td>3050.72</td>\n",
       "      <td>3066.95</td>\n",
       "      <td>3050.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>3037.56</td>\n",
       "      <td>-0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>3078.27</td>\n",
       "      <td>3078.96</td>\n",
       "      <td>3085.20</td>\n",
       "      <td>3074.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>3066.91</td>\n",
       "      <td>0.0097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>3074.62</td>\n",
       "      <td>3080.80</td>\n",
       "      <td>3083.95</td>\n",
       "      <td>3072.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>3078.27</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close     Open     High      Low  Volume  Change  Close_shifted  \\\n",
       "Date                                                                            \n",
       "2019-10-30  3046.77  3039.74  3050.10  3025.96     0.0  0.0033        3036.89   \n",
       "2019-10-31  3037.56  3046.90  3046.90  3023.19     0.0 -0.0030        3046.77   \n",
       "2019-11-01  3066.91  3050.72  3066.95  3050.72     0.0  0.0097        3037.56   \n",
       "2019-11-04  3078.27  3078.96  3085.20  3074.87     0.0  0.0037        3066.91   \n",
       "2019-11-05  3074.62  3080.80  3083.95  3072.15     0.0 -0.0012        3078.27   \n",
       "\n",
       "            Change_shifted  \n",
       "Date                        \n",
       "2019-10-30         -0.0008  \n",
       "2019-10-31          0.0033  \n",
       "2019-11-01         -0.0030  \n",
       "2019-11-04          0.0097  \n",
       "2019-11-05          0.0037  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s500['Close_shifted'] = s500['Close'].shift(1)\n",
    "s500['Change_shifted'] = s500['Change'].shift(1)\n",
    "s500.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s500.columns=['SNP500_Close'\n",
    "#             ,'SNP500_Open'\n",
    "#             ,'SNP500_High'\n",
    "#             ,'SNP500_Low'\n",
    "#             ,'SNP500_Volume'\n",
    "#             ,'SNP500_Change']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrg1 = pd.merge(k200, s500, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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_x</th>\n",
       "      <th>Open_x</th>\n",
       "      <th>High_x</th>\n",
       "      <th>Low_x</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Change_x</th>\n",
       "      <th>Close_y</th>\n",
       "      <th>Open_y</th>\n",
       "      <th>High_y</th>\n",
       "      <th>Low_y</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>Change_y</th>\n",
       "      <th>Close_shifted</th>\n",
       "      <th>Change_shifted</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>244.79</td>\n",
       "      <td>243.96</td>\n",
       "      <td>245.32</td>\n",
       "      <td>243.25</td>\n",
       "      <td>54150.0</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>2058.20</td>\n",
       "      <td>2058.90</td>\n",
       "      <td>2072.36</td>\n",
       "      <td>2046.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>244.26</td>\n",
       "      <td>243.83</td>\n",
       "      <td>244.56</td>\n",
       "      <td>241.76</td>\n",
       "      <td>62850.0</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>2020.58</td>\n",
       "      <td>2054.44</td>\n",
       "      <td>2054.44</td>\n",
       "      <td>2017.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0183</td>\n",
       "      <td>2058.20</td>\n",
       "      <td>-0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>239.93</td>\n",
       "      <td>241.36</td>\n",
       "      <td>242.06</td>\n",
       "      <td>239.32</td>\n",
       "      <td>66250.0</td>\n",
       "      <td>-0.0177</td>\n",
       "      <td>2002.61</td>\n",
       "      <td>2022.15</td>\n",
       "      <td>2030.25</td>\n",
       "      <td>1992.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0089</td>\n",
       "      <td>2020.58</td>\n",
       "      <td>-0.0183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>240.53</td>\n",
       "      <td>239.41</td>\n",
       "      <td>240.91</td>\n",
       "      <td>239.09</td>\n",
       "      <td>54600.0</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>2025.90</td>\n",
       "      <td>2005.55</td>\n",
       "      <td>2029.61</td>\n",
       "      <td>2005.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>2002.61</td>\n",
       "      <td>-0.0089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>243.94</td>\n",
       "      <td>242.28</td>\n",
       "      <td>244.13</td>\n",
       "      <td>241.68</td>\n",
       "      <td>64020.0</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>2062.14</td>\n",
       "      <td>2030.61</td>\n",
       "      <td>2064.08</td>\n",
       "      <td>2030.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>2025.90</td>\n",
       "      <td>0.0116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close_x  Open_x  High_x   Low_x  Volume_x  Change_x  Close_y  \\\n",
       "Date                                                                       \n",
       "2015-01-02   244.79  243.96  245.32  243.25   54150.0    0.0030  2058.20   \n",
       "2015-01-05   244.26  243.83  244.56  241.76   62850.0   -0.0022  2020.58   \n",
       "2015-01-06   239.93  241.36  242.06  239.32   66250.0   -0.0177  2002.61   \n",
       "2015-01-07   240.53  239.41  240.91  239.09   54600.0    0.0025  2025.90   \n",
       "2015-01-08   243.94  242.28  244.13  241.68   64020.0    0.0142  2062.14   \n",
       "\n",
       "             Open_y   High_y    Low_y  Volume_y  Change_y  Close_shifted  \\\n",
       "Date                                                                       \n",
       "2015-01-02  2058.90  2072.36  2046.04       0.0   -0.0003            NaN   \n",
       "2015-01-05  2054.44  2054.44  2017.34       0.0   -0.0183        2058.20   \n",
       "2015-01-06  2022.15  2030.25  1992.44       0.0   -0.0089        2020.58   \n",
       "2015-01-07  2005.55  2029.61  2005.55       0.0    0.0116        2002.61   \n",
       "2015-01-08  2030.61  2064.08  2030.61       0.0    0.0179        2025.90   \n",
       "\n",
       "            Change_shifted  \n",
       "Date                        \n",
       "2015-01-02             NaN  \n",
       "2015-01-05         -0.0003  \n",
       "2015-01-06         -0.0183  \n",
       "2015-01-07         -0.0089  \n",
       "2015-01-08          0.0116  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrg1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa20edd5c18>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mrg1['Change_x'], mrg1['Change_shifted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         Change_shifted   R-squared:                       0.124\n",
      "Model:                            OLS   Adj. R-squared:                  0.124\n",
      "Method:                 Least Squares   F-statistic:                     163.2\n",
      "Date:                Wed, 06 Nov 2019   Prob (F-statistic):           4.73e-35\n",
      "Time:                        16:26:20   Log-Likelihood:                 3925.1\n",
      "No. Observations:                1152   AIC:                            -7846.\n",
      "Df Residuals:                    1150   BIC:                            -7836.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0004      0.000      1.590      0.112    -8.8e-05       0.001\n",
      "Change_x       0.3656      0.029     12.777      0.000       0.309       0.422\n",
      "==============================================================================\n",
      "Omnibus:                      124.801   Durbin-Watson:                   2.230\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              752.618\n",
      "Skew:                          -0.281   Prob(JB):                    3.73e-164\n",
      "Kurtosis:                       6.920   Cond. No.                         121.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 선형회귀\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "mrg2 = mrg1.dropna()\n",
    "\n",
    "X = mrg2['Change_x']\n",
    "Y = mrg2['Change_shifted']\n",
    "\n",
    "# with statsmodels\n",
    "X = sm.add_constant(X) # adding a constant\n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "\n",
    "# 회귀분석 결과의 출력\n",
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p < 0.000 이므로 회귀모형은 유의함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const       0.000376\n",
       "Change_x    0.365589\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모형계수 확인\n",
    "\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 산점도 및 회귀선\n",
    "\n",
    "plt.scatter(rjitt(mrg2['Change_x']), rjitt(mrg2['Change_shifted']), s=20, alpha=0.5)\n",
    "plt.plot(mrg2['Change_x'], predictions, color='red', linestyle=':')\n",
    "plt.xlabel('KOSPI200 Daily Change')\n",
    "plt.ylabel(\"S&P500 Daily Chane\")\n",
    "plt.title('Simple Linear Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myjupyterenv",
   "language": "python",
   "name": "myjupyterenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}