stock_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# S&P500 Stock Price Prediction\n",
    "\n",
    "In this project we are going to predict S&P500 Stock price using linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "import pandas as pd\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-07</td>\n",
       "      <td>2090.419922</td>\n",
       "      <td>2090.419922</td>\n",
       "      <td>2066.780029</td>\n",
       "      <td>2077.070068</td>\n",
       "      <td>4.043820e+09</td>\n",
       "      <td>2077.070068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>2051.239990</td>\n",
       "      <td>2093.840088</td>\n",
       "      <td>2051.239990</td>\n",
       "      <td>2091.689941</td>\n",
       "      <td>4.214910e+09</td>\n",
       "      <td>2091.689941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>2080.709961</td>\n",
       "      <td>2085.000000</td>\n",
       "      <td>2042.349976</td>\n",
       "      <td>2049.620117</td>\n",
       "      <td>4.306490e+09</td>\n",
       "      <td>2049.620117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>2101.709961</td>\n",
       "      <td>2104.270020</td>\n",
       "      <td>2077.110107</td>\n",
       "      <td>2079.510010</td>\n",
       "      <td>3.950640e+09</td>\n",
       "      <td>2079.510010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2082.929932</td>\n",
       "      <td>2103.370117</td>\n",
       "      <td>2082.929932</td>\n",
       "      <td>2102.629883</td>\n",
       "      <td>3.712120e+09</td>\n",
       "      <td>2102.629883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16585</th>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>2.520000e+06</td>\n",
       "      <td>17.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16586</th>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>16.980000</td>\n",
       "      <td>16.980000</td>\n",
       "      <td>16.980000</td>\n",
       "      <td>16.980000</td>\n",
       "      <td>2.010000e+06</td>\n",
       "      <td>16.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16587</th>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>16.930000</td>\n",
       "      <td>16.930000</td>\n",
       "      <td>16.930000</td>\n",
       "      <td>16.930000</td>\n",
       "      <td>2.550000e+06</td>\n",
       "      <td>16.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16588</th>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>1.890000e+06</td>\n",
       "      <td>16.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16589</th>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>1.260000e+06</td>\n",
       "      <td>16.660000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16590 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date         Open         High          Low        Close  \\\n",
       "0      2015-12-07  2090.419922  2090.419922  2066.780029  2077.070068   \n",
       "1      2015-12-04  2051.239990  2093.840088  2051.239990  2091.689941   \n",
       "2      2015-12-03  2080.709961  2085.000000  2042.349976  2049.620117   \n",
       "3      2015-12-02  2101.709961  2104.270020  2077.110107  2079.510010   \n",
       "4      2015-12-01  2082.929932  2103.370117  2082.929932  2102.629883   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "16585  1950-01-09    17.080000    17.080000    17.080000    17.080000   \n",
       "16586  1950-01-06    16.980000    16.980000    16.980000    16.980000   \n",
       "16587  1950-01-05    16.930000    16.930000    16.930000    16.930000   \n",
       "16588  1950-01-04    16.850000    16.850000    16.850000    16.850000   \n",
       "16589  1950-01-03    16.660000    16.660000    16.660000    16.660000   \n",
       "\n",
       "             Volume    Adj Close  \n",
       "0      4.043820e+09  2077.070068  \n",
       "1      4.214910e+09  2091.689941  \n",
       "2      4.306490e+09  2049.620117  \n",
       "3      3.950640e+09  2079.510010  \n",
       "4      3.712120e+09  2102.629883  \n",
       "...             ...          ...  \n",
       "16585  2.520000e+06    17.080000  \n",
       "16586  2.010000e+06    16.980000  \n",
       "16587  2.550000e+06    16.930000  \n",
       "16588  1.890000e+06    16.850000  \n",
       "16589  1.260000e+06    16.660000  \n",
       "\n",
       "[16590 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading data\n",
    "df = pd.read_csv(\"sphist.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting date to datetime type\n",
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16590 entries, 0 to 16589\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       16590 non-null  datetime64[ns]\n",
      " 1   Open       16590 non-null  float64       \n",
      " 2   High       16590 non-null  float64       \n",
      " 3   Low        16590 non-null  float64       \n",
      " 4   Close      16590 non-null  float64       \n",
      " 5   Volume     16590 non-null  float64       \n",
      " 6   Adj Close  16590 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(6)\n",
      "memory usage: 907.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True\n",
       "         ...  \n",
       "16585    False\n",
       "16586    False\n",
       "16587    False\n",
       "16588    False\n",
       "16589    False\n",
       "Name: Date, Length: 16590, dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] > datetime(year=2015, month=4, day=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>1260000.0</td>\n",
       "      <td>16.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>2550000.0</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>2010000.0</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>2520000.0</td>\n",
       "      <td>17.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Open   High    Low  Close     Volume  Adj Close\n",
       "0 1950-01-03  16.66  16.66  16.66  16.66  1260000.0      16.66\n",
       "1 1950-01-04  16.85  16.85  16.85  16.85  1890000.0      16.85\n",
       "2 1950-01-05  16.93  16.93  16.93  16.93  2550000.0      16.93\n",
       "3 1950-01-06  16.98  16.98  16.98  16.98  2010000.0      16.98\n",
       "4 1950-01-09  17.08  17.08  17.08  17.08  2520000.0      17.08"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sorting Values and resetting index\n",
    "df.sort_values('Date', inplace = True)\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculatin rolling average for different time periods\n",
    "df['5d_avg'] = df['Close'].rolling(window = 5).mean().shift(1)\n",
    "df['30d_avg'] = df['Close'].rolling(window = 30).mean().shift(1)\n",
    "df['365d_avg'] = df['Close'].rolling(window = 365).mean().shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>5d_avg</th>\n",
       "      <th>30d_avg</th>\n",
       "      <th>365d_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>1260000.0</td>\n",
       "      <td>16.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>16.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>2550000.0</td>\n",
       "      <td>16.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>2010000.0</td>\n",
       "      <td>16.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>2520000.0</td>\n",
       "      <td>17.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Open   High    Low  Close     Volume  Adj Close  5d_avg  \\\n",
       "0 1950-01-03  16.66  16.66  16.66  16.66  1260000.0      16.66     NaN   \n",
       "1 1950-01-04  16.85  16.85  16.85  16.85  1890000.0      16.85     NaN   \n",
       "2 1950-01-05  16.93  16.93  16.93  16.93  2550000.0      16.93     NaN   \n",
       "3 1950-01-06  16.98  16.98  16.98  16.98  2010000.0      16.98     NaN   \n",
       "4 1950-01-09  17.08  17.08  17.08  17.08  2520000.0      17.08     NaN   \n",
       "\n",
       "   30d_avg  365d_avg  \n",
       "0      NaN       NaN  \n",
       "1      NaN       NaN  \n",
       "2      NaN       NaN  \n",
       "3      NaN       NaN  \n",
       "4      NaN       NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating rolling standard deviation for different time period\n",
    "df['5d_std'] =  df['Close'].rolling(window = 5).std().shift(1)\n",
    "df['365d_std'] = df['Close'].rolling(window = 5).std().shift(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['5d_365d_avg_rat'] = df['5d_avg'] / df['365d_avg']\n",
    "df['5d_365d_std_rat'] = df['5d_std'] / df['365d_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>5d_avg</th>\n",
       "      <th>30d_avg</th>\n",
       "      <th>365d_avg</th>\n",
       "      <th>5d_std</th>\n",
       "      <th>365d_std</th>\n",
       "      <th>5d_365d_avg_rat</th>\n",
       "      <th>5d_365d_std_rat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>1260000.0</td>\n",
       "      <td>16.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>16.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>2550000.0</td>\n",
       "      <td>16.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>2010000.0</td>\n",
       "      <td>16.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>2520000.0</td>\n",
       "      <td>17.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Open   High    Low  Close     Volume  Adj Close  5d_avg  \\\n",
       "0 1950-01-03  16.66  16.66  16.66  16.66  1260000.0      16.66     NaN   \n",
       "1 1950-01-04  16.85  16.85  16.85  16.85  1890000.0      16.85     NaN   \n",
       "2 1950-01-05  16.93  16.93  16.93  16.93  2550000.0      16.93     NaN   \n",
       "3 1950-01-06  16.98  16.98  16.98  16.98  2010000.0      16.98     NaN   \n",
       "4 1950-01-09  17.08  17.08  17.08  17.08  2520000.0      17.08     NaN   \n",
       "\n",
       "   30d_avg  365d_avg  5d_std  365d_std  5d_365d_avg_rat  5d_365d_std_rat  \n",
       "0      NaN       NaN     NaN       NaN              NaN              NaN  \n",
       "1      NaN       NaN     NaN       NaN              NaN              NaN  \n",
       "2      NaN       NaN     NaN       NaN              NaN              NaN  \n",
       "3      NaN       NaN     NaN       NaN              NaN              NaN  \n",
       "4      NaN       NaN     NaN       NaN              NaN              NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)\n",
    "df.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[df['Date'] < datetime(year =2013, month = 1, day = 1)]\n",
    "test = df[df['Date'] >= datetime(year =2013, month = 1, day = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting close price using linear regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import numpy as np\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(train[['5d_avg','30d_avg', '365d_avg', '5d_std', '5d_365d_avg_rat', '365d_std','5d_365d_std_rat']], train['Close'])\n",
    "predict = lr.predict(test[['5d_avg','30d_avg', '365d_avg', '5d_std', '5d_365d_avg_rat', '365d_std','5d_365d_std_rat']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating mse, mae and rmse\n",
    "mse = mean_squared_error(predict, test['Close'])\n",
    "mae = mean_absolute_error(predict, test['Close'])\n",
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492.81988051238216, 22.199546853762175, 16.129098066308355)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse,rmse,mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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