Stock Market Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Stock Market with Linear Regression\n",
    "\n",
    "In this notebook I'll be using historical data on the price of the S&P500 Index to make a predictive model and predictions about future prices. The S&P500 is a stock market index that aggregates the stock prices of 500 large companies, more information on the S&P500 can be found [here](https://en.wikipedia.org/wiki/S%26P_500_Index). Predicting whether the index will go up or down will help forecast how the stock market as a whole will perform. \n",
    "\n",
    "Note: You shouldn't make trades with any models developed in this notebook. Trading stocks has risks, and nothing in this notebook constitutes stock trading advice. \n",
    "\n",
    "First I'll import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.style as style\n",
    "\n",
    "\n",
    "from datetime import datetime\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Dataset\n",
    "\n",
    "I'll be working with a csv file containing index prices. Each row in the file contains a daily record of the price of the S&P500 Index from 1950 to 2015. The dataset is stored in `sphist.csv`.\n",
    "\n",
    "The columns of the dataset are:\n",
    "\n",
    "- `Date` -- The date of the record.\n",
    "- `Open` -- The opening price of the day (when trading starts).\n",
    "- `High` -- The highest trade price during the day.\n",
    "- `Low` -- The lowest trade price during the day.\n",
    "- `Close` -- The closing price for the day (when trading is finished).\n",
    "- `Volume` -- The number of shares traded.\n",
    "- `Adj Close` -- The daily closing price, adjusted retroactively to include any corporate actions. Read more here.\n",
    "\n",
    "\n",
    "I'll be using this dataset to develop the predictive model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "  </tbody>\n",
       "</table>\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",
       "         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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in data\n",
    "data = pd.read_csv('sphist.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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  object \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: float64(6), object(1)\n",
      "memory usage: 907.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#Get Info\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fromat Date Column as datetime\n",
    "data['Date'] = pd.to_datetime(data['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "#Sanity Check date column\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16589</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>16588</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>16587</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>16586</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>16585</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",
       "16589 1950-01-03  16.66  16.66  16.66  16.66  1260000.0      16.66\n",
       "16588 1950-01-04  16.85  16.85  16.85  16.85  1890000.0      16.85\n",
       "16587 1950-01-05  16.93  16.93  16.93  16.93  2550000.0      16.93\n",
       "16586 1950-01-06  16.98  16.98  16.98  16.98  2010000.0      16.98\n",
       "16585 1950-01-09  17.08  17.08  17.08  17.08  2520000.0      17.08"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort in oldest to newest\n",
    "data.sort_values('Date', inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Closing Value\n",
    "plt.figure(figsize=(40,15))\n",
    "plt.scatter(data['Date'], data['Close'], c='b')\n",
    "plt.title('S&P Closing Value', fontsize=60)\n",
    "plt.xlabel('Date', fontsize=40)\n",
    "plt.ylabel('Price', fontsize=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Features\n",
    "\n",
    "In a normal machine learning exercise, each row is treated independently. Stock market data is sequential, and each observation comes a day after the previous observation. Thus, the observations are not all independent, and can't be treated as such.\n",
    "\n",
    "This means I'll have to be extra careful to not inject \"future\" knowledge into past rows when I do training and prediction. Injecting future knowledge will make our model look good when you're training and testing it, but will make it fail in the real world.\n",
    "\n",
    "The time series nature of the data means that can I generate indicators to make our model more accurate. This will incorporate information from multiple prior rows into one, and will make predictions much more accurate.\n",
    "\n",
    "I will create the following features:\n",
    "- `5_day_avg` - The average price from the past 5 days.\n",
    "- `30_day_avg` - The average price for the past 30 days.\n",
    "- `year_avg` - The average price for the past 365 days.\n",
    "- `avg_ratio`- The ratio between the average price for the past 5 days, and the average price for the past 365 days.\n",
    "- `5_day_std` - The standard deviation of the price over the past 5 days.\n",
    "- `year_std`- The standard deviation of the price over the past 365 days.\n",
    "- `std_ratio`- The ratio between the standard deviation for the past 5 days, and the standard deviation for the past 365 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The rolling mean and standard deviation will use the current day's price. \n",
    "# I use .he shift method to reindex the resulting series to shift all the values \"forward\" one day. \n",
    "# For example, the rolling mean calculated for 1950-01-03 will need to be assigned to 1950-01-04, and so on. \n",
    "data['5_day_avg'] = data['Close'].rolling(window=5).mean().shift(1)\n",
    "data['30_day_avg']= data['Close'].rolling(window=5).mean().shift(1)\n",
    "data['year_avg'] = data['Close'].rolling(window=365).mean().shift(1)\n",
    "data['avg_ratio'] = data['5_day_avg']/data['year_avg']\n",
    "\n",
    "data['5_day_std'] = data['Close'].rolling(window=5).std().shift(1)\n",
    "data['year_std'] = data['Close'].rolling(window=365).mean().shift(1)\n",
    "data['std_ratio'] = data['5_day_std']/data['year_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>5_day_avg</th>\n",
       "      <th>30_day_avg</th>\n",
       "      <th>year_avg</th>\n",
       "      <th>avg_ratio</th>\n",
       "      <th>5_day_std</th>\n",
       "      <th>year_std</th>\n",
       "      <th>std_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>1260000.0</td>\n",
       "      <td>16.660000</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>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>1890000.0</td>\n",
       "      <td>16.850000</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>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>2550000.0</td>\n",
       "      <td>16.930000</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>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>2010000.0</td>\n",
       "      <td>16.980000</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>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>2520000.0</td>\n",
       "      <td>17.080000</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>16584</th>\n",
       "      <td>1950-01-10</td>\n",
       "      <td>17.030001</td>\n",
       "      <td>17.030001</td>\n",
       "      <td>17.030001</td>\n",
       "      <td>17.030001</td>\n",
       "      <td>2160000.0</td>\n",
       "      <td>17.030001</td>\n",
       "      <td>16.900</td>\n",
       "      <td>16.900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.157956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16583</th>\n",
       "      <td>1950-01-11</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>2630000.0</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>16.974</td>\n",
       "      <td>16.974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16582</th>\n",
       "      <td>1950-01-12</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>2970000.0</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>17.022</td>\n",
       "      <td>17.022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.067602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16581</th>\n",
       "      <td>1950-01-13</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>3330000.0</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>16.988</td>\n",
       "      <td>16.988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.134796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16580</th>\n",
       "      <td>1950-01-16</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>1460000.0</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>16.926</td>\n",
       "      <td>16.926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.196545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16579</th>\n",
       "      <td>1950-01-17</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>1790000.0</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.854</td>\n",
       "      <td>16.854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.191912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16578</th>\n",
       "      <td>1950-01-18</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>1570000.0</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>16.820</td>\n",
       "      <td>16.820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16577</th>\n",
       "      <td>1950-01-19</td>\n",
       "      <td>16.870001</td>\n",
       "      <td>16.870001</td>\n",
       "      <td>16.870001</td>\n",
       "      <td>16.870001</td>\n",
       "      <td>1170000.0</td>\n",
       "      <td>16.870001</td>\n",
       "      <td>16.772</td>\n",
       "      <td>16.772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16576</th>\n",
       "      <td>1950-01-20</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>1440000.0</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>16.794</td>\n",
       "      <td>16.794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.092359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16575</th>\n",
       "      <td>1950-01-23</td>\n",
       "      <td>16.920000</td>\n",
       "      <td>16.920000</td>\n",
       "      <td>16.920000</td>\n",
       "      <td>16.920000</td>\n",
       "      <td>1340000.0</td>\n",
       "      <td>16.920000</td>\n",
       "      <td>16.840</td>\n",
       "      <td>16.840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.069643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16574</th>\n",
       "      <td>1950-01-24</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.880</td>\n",
       "      <td>16.880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16573</th>\n",
       "      <td>1950-01-25</td>\n",
       "      <td>16.740000</td>\n",
       "      <td>16.740000</td>\n",
       "      <td>16.740000</td>\n",
       "      <td>16.740000</td>\n",
       "      <td>1700000.0</td>\n",
       "      <td>16.740000</td>\n",
       "      <td>16.880</td>\n",
       "      <td>16.880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16572</th>\n",
       "      <td>1950-01-26</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1150000.0</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.858</td>\n",
       "      <td>16.858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16571</th>\n",
       "      <td>1950-01-27</td>\n",
       "      <td>16.820000</td>\n",
       "      <td>16.820000</td>\n",
       "      <td>16.820000</td>\n",
       "      <td>16.820000</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>16.820000</td>\n",
       "      <td>16.830</td>\n",
       "      <td>16.830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089443</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16570</th>\n",
       "      <td>1950-01-30</td>\n",
       "      <td>17.020000</td>\n",
       "      <td>17.020000</td>\n",
       "      <td>17.020000</td>\n",
       "      <td>17.020000</td>\n",
       "      <td>1640000.0</td>\n",
       "      <td>17.020000</td>\n",
       "      <td>16.814</td>\n",
       "      <td>16.814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.080499</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  \\\n",
       "16589 1950-01-03  16.660000  16.660000  16.660000  16.660000  1260000.0   \n",
       "16588 1950-01-04  16.850000  16.850000  16.850000  16.850000  1890000.0   \n",
       "16587 1950-01-05  16.930000  16.930000  16.930000  16.930000  2550000.0   \n",
       "16586 1950-01-06  16.980000  16.980000  16.980000  16.980000  2010000.0   \n",
       "16585 1950-01-09  17.080000  17.080000  17.080000  17.080000  2520000.0   \n",
       "16584 1950-01-10  17.030001  17.030001  17.030001  17.030001  2160000.0   \n",
       "16583 1950-01-11  17.090000  17.090000  17.090000  17.090000  2630000.0   \n",
       "16582 1950-01-12  16.760000  16.760000  16.760000  16.760000  2970000.0   \n",
       "16581 1950-01-13  16.670000  16.670000  16.670000  16.670000  3330000.0   \n",
       "16580 1950-01-16  16.719999  16.719999  16.719999  16.719999  1460000.0   \n",
       "16579 1950-01-17  16.860001  16.860001  16.860001  16.860001  1790000.0   \n",
       "16578 1950-01-18  16.850000  16.850000  16.850000  16.850000  1570000.0   \n",
       "16577 1950-01-19  16.870001  16.870001  16.870001  16.870001  1170000.0   \n",
       "16576 1950-01-20  16.900000  16.900000  16.900000  16.900000  1440000.0   \n",
       "16575 1950-01-23  16.920000  16.920000  16.920000  16.920000  1340000.0   \n",
       "16574 1950-01-24  16.860001  16.860001  16.860001  16.860001  1250000.0   \n",
       "16573 1950-01-25  16.740000  16.740000  16.740000  16.740000  1700000.0   \n",
       "16572 1950-01-26  16.730000  16.730000  16.730000  16.730000  1150000.0   \n",
       "16571 1950-01-27  16.820000  16.820000  16.820000  16.820000  1250000.0   \n",
       "16570 1950-01-30  17.020000  17.020000  17.020000  17.020000  1640000.0   \n",
       "\n",
       "       Adj Close  5_day_avg  30_day_avg  year_avg  avg_ratio  5_day_std  \\\n",
       "16589  16.660000        NaN         NaN       NaN        NaN        NaN   \n",
       "16588  16.850000        NaN         NaN       NaN        NaN        NaN   \n",
       "16587  16.930000        NaN         NaN       NaN        NaN        NaN   \n",
       "16586  16.980000        NaN         NaN       NaN        NaN        NaN   \n",
       "16585  17.080000        NaN         NaN       NaN        NaN        NaN   \n",
       "16584  17.030001     16.900      16.900       NaN        NaN   0.157956   \n",
       "16583  17.090000     16.974      16.974       NaN        NaN   0.089051   \n",
       "16582  16.760000     17.022      17.022       NaN        NaN   0.067602   \n",
       "16581  16.670000     16.988      16.988       NaN        NaN   0.134796   \n",
       "16580  16.719999     16.926      16.926       NaN        NaN   0.196545   \n",
       "16579  16.860001     16.854      16.854       NaN        NaN   0.191912   \n",
       "16578  16.850000     16.820      16.820       NaN        NaN   0.166283   \n",
       "16577  16.870001     16.772      16.772       NaN        NaN   0.082280   \n",
       "16576  16.900000     16.794      16.794       NaN        NaN   0.092359   \n",
       "16575  16.920000     16.840      16.840       NaN        NaN   0.069643   \n",
       "16574  16.860001     16.880      16.880       NaN        NaN   0.029155   \n",
       "16573  16.740000     16.880      16.880       NaN        NaN   0.029155   \n",
       "16572  16.730000     16.858      16.858       NaN        NaN   0.070143   \n",
       "16571  16.820000     16.830      16.830       NaN        NaN   0.089443   \n",
       "16570  17.020000     16.814      16.814       NaN        NaN   0.080499   \n",
       "\n",
       "       year_std  std_ratio  \n",
       "16589       NaN        NaN  \n",
       "16588       NaN        NaN  \n",
       "16587       NaN        NaN  \n",
       "16586       NaN        NaN  \n",
       "16585       NaN        NaN  \n",
       "16584       NaN        NaN  \n",
       "16583       NaN        NaN  \n",
       "16582       NaN        NaN  \n",
       "16581       NaN        NaN  \n",
       "16580       NaN        NaN  \n",
       "16579       NaN        NaN  \n",
       "16578       NaN        NaN  \n",
       "16577       NaN        NaN  \n",
       "16576       NaN        NaN  \n",
       "16575       NaN        NaN  \n",
       "16574       NaN        NaN  \n",
       "16573       NaN        NaN  \n",
       "16572       NaN        NaN  \n",
       "16571       NaN        NaN  \n",
       "16570       NaN        NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Data\n",
    "\n",
    "Some of the indicators use 365 days of historical data, and the dataset starts on 1950-01-03. Thus, the first 365 rows  don't have enough historical data to compute all the indicators. I'll remove these rows before I split the data into a training and testing set. \n",
    "\n",
    "I'll train the model with data from 1950-2012, and try to make predictions from 2013-2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          0\n",
       "Open          0\n",
       "High          0\n",
       "Low           0\n",
       "Close         0\n",
       "Volume        0\n",
       "Adj Close     0\n",
       "5_day_avg     0\n",
       "30_day_avg    0\n",
       "year_avg      0\n",
       "avg_ratio     0\n",
       "5_day_std     0\n",
       "year_std      0\n",
       "std_ratio     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna(axis=0)\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>5_day_avg</th>\n",
       "      <th>30_day_avg</th>\n",
       "      <th>year_avg</th>\n",
       "      <th>avg_ratio</th>\n",
       "      <th>5_day_std</th>\n",
       "      <th>year_std</th>\n",
       "      <th>std_ratio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16224</th>\n",
       "      <td>1951-06-19</td>\n",
       "      <td>22.020000</td>\n",
       "      <td>22.020000</td>\n",
       "      <td>22.020000</td>\n",
       "      <td>22.020000</td>\n",
       "      <td>1.100000e+06</td>\n",
       "      <td>22.020000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>19.447726</td>\n",
       "      <td>1.120954</td>\n",
       "      <td>0.256223</td>\n",
       "      <td>19.447726</td>\n",
       "      <td>0.013175</td>\n",
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       "    <tr>\n",
       "      <th>16223</th>\n",
       "      <td>1951-06-20</td>\n",
       "      <td>21.910000</td>\n",
       "      <td>21.910000</td>\n",
       "      <td>21.910000</td>\n",
       "      <td>21.910000</td>\n",
       "      <td>1.120000e+06</td>\n",
       "      <td>21.910000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>19.462411</td>\n",
       "      <td>1.125246</td>\n",
       "      <td>0.213659</td>\n",
       "      <td>19.462411</td>\n",
       "      <td>0.010978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16222</th>\n",
       "      <td>1951-06-21</td>\n",
       "      <td>21.780001</td>\n",
       "      <td>21.780001</td>\n",
       "      <td>21.780001</td>\n",
       "      <td>21.780001</td>\n",
       "      <td>1.100000e+06</td>\n",
       "      <td>21.780001</td>\n",
       "      <td>21.972000</td>\n",
       "      <td>21.972000</td>\n",
       "      <td>19.476274</td>\n",
       "      <td>1.128142</td>\n",
       "      <td>0.092574</td>\n",
       "      <td>19.476274</td>\n",
       "      <td>0.004753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16221</th>\n",
       "      <td>1951-06-22</td>\n",
       "      <td>21.549999</td>\n",
       "      <td>21.549999</td>\n",
       "      <td>21.549999</td>\n",
       "      <td>21.549999</td>\n",
       "      <td>1.340000e+06</td>\n",
       "      <td>21.549999</td>\n",
       "      <td>21.960000</td>\n",
       "      <td>21.960000</td>\n",
       "      <td>19.489562</td>\n",
       "      <td>1.126757</td>\n",
       "      <td>0.115108</td>\n",
       "      <td>19.489562</td>\n",
       "      <td>0.005906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16220</th>\n",
       "      <td>1951-06-25</td>\n",
       "      <td>21.290001</td>\n",
       "      <td>21.290001</td>\n",
       "      <td>21.290001</td>\n",
       "      <td>21.290001</td>\n",
       "      <td>2.440000e+06</td>\n",
       "      <td>21.290001</td>\n",
       "      <td>21.862000</td>\n",
       "      <td>21.862000</td>\n",
       "      <td>19.502082</td>\n",
       "      <td>1.121008</td>\n",
       "      <td>0.204132</td>\n",
       "      <td>19.502082</td>\n",
       "      <td>0.010467</td>\n",
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       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2012-12-24</td>\n",
       "      <td>1430.150024</td>\n",
       "      <td>1430.150024</td>\n",
       "      <td>1424.660034</td>\n",
       "      <td>1426.660034</td>\n",
       "      <td>1.248960e+09</td>\n",
       "      <td>1426.660034</td>\n",
       "      <td>1437.360010</td>\n",
       "      <td>1437.360010</td>\n",
       "      <td>1326.114028</td>\n",
       "      <td>1.083889</td>\n",
       "      <td>7.622009</td>\n",
       "      <td>1326.114028</td>\n",
       "      <td>0.005748</td>\n",
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       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>2012-12-26</td>\n",
       "      <td>1426.660034</td>\n",
       "      <td>1429.420044</td>\n",
       "      <td>1416.430054</td>\n",
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       "      <td>1436.620019</td>\n",
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       "      <td>1.083087</td>\n",
       "      <td>8.589693</td>\n",
       "      <td>1326.412494</td>\n",
       "      <td>0.006476</td>\n",
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       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1419.829956</td>\n",
       "      <td>1422.800049</td>\n",
       "      <td>1401.800049</td>\n",
       "      <td>1418.099976</td>\n",
       "      <td>2.830180e+09</td>\n",
       "      <td>1418.099976</td>\n",
       "      <td>1431.228003</td>\n",
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       "      <td>1.078775</td>\n",
       "      <td>9.058684</td>\n",
       "      <td>1326.716494</td>\n",
       "      <td>0.006828</td>\n",
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       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1418.099976</td>\n",
       "      <td>1418.099976</td>\n",
       "      <td>1401.579956</td>\n",
       "      <td>1402.430054</td>\n",
       "      <td>2.426680e+09</td>\n",
       "      <td>1402.430054</td>\n",
       "      <td>1427.685986</td>\n",
       "      <td>1427.685986</td>\n",
       "      <td>1326.995836</td>\n",
       "      <td>1.075878</td>\n",
       "      <td>10.208568</td>\n",
       "      <td>1326.995836</td>\n",
       "      <td>0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1402.430054</td>\n",
       "      <td>1426.739990</td>\n",
       "      <td>1398.109985</td>\n",
       "      <td>1426.189941</td>\n",
       "      <td>3.204330e+09</td>\n",
       "      <td>1426.189941</td>\n",
       "      <td>1419.434009</td>\n",
       "      <td>1419.434009</td>\n",
       "      <td>1327.261562</td>\n",
       "      <td>1.069446</td>\n",
       "      <td>10.701861</td>\n",
       "      <td>1327.261562</td>\n",
       "      <td>0.008063</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>15486 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "            Date         Open         High          Low        Close  \\\n",
       "16224 1951-06-19    22.020000    22.020000    22.020000    22.020000   \n",
       "16223 1951-06-20    21.910000    21.910000    21.910000    21.910000   \n",
       "16222 1951-06-21    21.780001    21.780001    21.780001    21.780001   \n",
       "16221 1951-06-22    21.549999    21.549999    21.549999    21.549999   \n",
       "16220 1951-06-25    21.290001    21.290001    21.290001    21.290001   \n",
       "...          ...          ...          ...          ...          ...   \n",
       "743   2012-12-24  1430.150024  1430.150024  1424.660034  1426.660034   \n",
       "742   2012-12-26  1426.660034  1429.420044  1416.430054  1419.829956   \n",
       "741   2012-12-27  1419.829956  1422.800049  1401.800049  1418.099976   \n",
       "740   2012-12-28  1418.099976  1418.099976  1401.579956  1402.430054   \n",
       "739   2012-12-31  1402.430054  1426.739990  1398.109985  1426.189941   \n",
       "\n",
       "             Volume    Adj Close    5_day_avg   30_day_avg     year_avg  \\\n",
       "16224  1.100000e+06    22.020000    21.800000    21.800000    19.447726   \n",
       "16223  1.120000e+06    21.910000    21.900000    21.900000    19.462411   \n",
       "16222  1.100000e+06    21.780001    21.972000    21.972000    19.476274   \n",
       "16221  1.340000e+06    21.549999    21.960000    21.960000    19.489562   \n",
       "16220  2.440000e+06    21.290001    21.862000    21.862000    19.502082   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "743    1.248960e+09  1426.660034  1437.360010  1437.360010  1326.114028   \n",
       "742    2.285030e+09  1419.829956  1436.620019  1436.620019  1326.412494   \n",
       "741    2.830180e+09  1418.099976  1431.228003  1431.228003  1326.716494   \n",
       "740    2.426680e+09  1402.430054  1427.685986  1427.685986  1326.995836   \n",
       "739    3.204330e+09  1426.189941  1419.434009  1419.434009  1327.261562   \n",
       "\n",
       "       avg_ratio  5_day_std     year_std  std_ratio  \n",
       "16224   1.120954   0.256223    19.447726   0.013175  \n",
       "16223   1.125246   0.213659    19.462411   0.010978  \n",
       "16222   1.128142   0.092574    19.476274   0.004753  \n",
       "16221   1.126757   0.115108    19.489562   0.005906  \n",
       "16220   1.121008   0.204132    19.502082   0.010467  \n",
       "...          ...        ...          ...        ...  \n",
       "743     1.083889   7.622009  1326.114028   0.005748  \n",
       "742     1.083087   8.589693  1326.412494   0.006476  \n",
       "741     1.078775   9.058684  1326.716494   0.006828  \n",
       "740     1.075878  10.208568  1326.995836   0.007693  \n",
       "739     1.069446  10.701861  1327.261562   0.008063  \n",
       "\n",
       "[15486 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = data[data['Date']< datetime(year=2013, month=1, day=1)] \n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Low</th>\n",
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       "      <th>738</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1426.189941</td>\n",
       "      <td>1462.430054</td>\n",
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       "      <th>737</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>1462.420044</td>\n",
       "      <td>1465.469971</td>\n",
       "      <td>1455.530029</td>\n",
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       "      <td>1425.793994</td>\n",
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       "      <th>736</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>1459.369995</td>\n",
       "      <td>1467.939941</td>\n",
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       "      <td>1.079412</td>\n",
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       "      <td>1328.224877</td>\n",
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       "      <th>735</th>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>1466.469971</td>\n",
       "      <td>1466.469971</td>\n",
       "      <td>1456.619995</td>\n",
       "      <td>1461.890015</td>\n",
       "      <td>3.304970e+09</td>\n",
       "      <td>1461.890015</td>\n",
       "      <td>1443.376001</td>\n",
       "      <td>1443.376001</td>\n",
       "      <td>1328.557617</td>\n",
       "      <td>1.086423</td>\n",
       "      <td>27.945242</td>\n",
       "      <td>1328.557617</td>\n",
       "      <td>0.021034</td>\n",
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       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>1461.890015</td>\n",
       "      <td>1461.890015</td>\n",
       "      <td>1451.640015</td>\n",
       "      <td>1457.150024</td>\n",
       "      <td>3.601600e+09</td>\n",
       "      <td>1457.150024</td>\n",
       "      <td>1455.267993</td>\n",
       "      <td>1455.267993</td>\n",
       "      <td>1328.898603</td>\n",
       "      <td>1.095093</td>\n",
       "      <td>16.453319</td>\n",
       "      <td>1328.898603</td>\n",
       "      <td>0.012381</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <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",
       "      <td>2087.024023</td>\n",
       "      <td>2087.024023</td>\n",
       "      <td>2035.531178</td>\n",
       "      <td>1.025297</td>\n",
       "      <td>3.916109</td>\n",
       "      <td>2035.531178</td>\n",
       "      <td>0.001924</td>\n",
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       "    <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",
       "      <td>2090.231982</td>\n",
       "      <td>2090.231982</td>\n",
       "      <td>2035.914082</td>\n",
       "      <td>1.026680</td>\n",
       "      <td>7.956808</td>\n",
       "      <td>2035.914082</td>\n",
       "      <td>0.003908</td>\n",
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       "    <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",
       "      <td>2088.306006</td>\n",
       "      <td>2088.306006</td>\n",
       "      <td>2036.234356</td>\n",
       "      <td>1.025573</td>\n",
       "      <td>9.333599</td>\n",
       "      <td>2036.234356</td>\n",
       "      <td>0.004584</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",
       "      <td>2080.456006</td>\n",
       "      <td>2080.456006</td>\n",
       "      <td>2036.507343</td>\n",
       "      <td>1.021580</td>\n",
       "      <td>19.599946</td>\n",
       "      <td>2036.507343</td>\n",
       "      <td>0.009624</td>\n",
       "    </tr>\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",
       "      <td>2080.771973</td>\n",
       "      <td>2080.771973</td>\n",
       "      <td>2036.869425</td>\n",
       "      <td>1.021554</td>\n",
       "      <td>19.806136</td>\n",
       "      <td>2036.869425</td>\n",
       "      <td>0.009724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>739 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date         Open         High          Low        Close  \\\n",
       "738 2013-01-02  1426.189941  1462.430054  1426.189941  1462.420044   \n",
       "737 2013-01-03  1462.420044  1465.469971  1455.530029  1459.369995   \n",
       "736 2013-01-04  1459.369995  1467.939941  1458.989990  1466.469971   \n",
       "735 2013-01-07  1466.469971  1466.469971  1456.619995  1461.890015   \n",
       "734 2013-01-08  1461.890015  1461.890015  1451.640015  1457.150024   \n",
       "..         ...          ...          ...          ...          ...   \n",
       "4   2015-12-01  2082.929932  2103.370117  2082.929932  2102.629883   \n",
       "3   2015-12-02  2101.709961  2104.270020  2077.110107  2079.510010   \n",
       "2   2015-12-03  2080.709961  2085.000000  2042.349976  2049.620117   \n",
       "1   2015-12-04  2051.239990  2093.840088  2051.239990  2091.689941   \n",
       "0   2015-12-07  2090.419922  2090.419922  2066.780029  2077.070068   \n",
       "\n",
       "           Volume    Adj Close    5_day_avg   30_day_avg     year_avg  \\\n",
       "738  4.202600e+09  1462.420044  1418.641992  1418.641992  1327.534055   \n",
       "737  3.829730e+09  1459.369995  1425.793994  1425.793994  1327.908247   \n",
       "736  3.424290e+09  1466.469971  1433.702002  1433.702002  1328.224877   \n",
       "735  3.304970e+09  1461.890015  1443.376001  1443.376001  1328.557617   \n",
       "734  3.601600e+09  1457.150024  1455.267993  1455.267993  1328.898603   \n",
       "..            ...          ...          ...          ...          ...   \n",
       "4    3.712120e+09  2102.629883  2087.024023  2087.024023  2035.531178   \n",
       "3    3.950640e+09  2079.510010  2090.231982  2090.231982  2035.914082   \n",
       "2    4.306490e+09  2049.620117  2088.306006  2088.306006  2036.234356   \n",
       "1    4.214910e+09  2091.689941  2080.456006  2080.456006  2036.507343   \n",
       "0    4.043820e+09  2077.070068  2080.771973  2080.771973  2036.869425   \n",
       "\n",
       "     avg_ratio  5_day_std     year_std  std_ratio  \n",
       "738   1.068629   9.820801  1327.534055   0.007398  \n",
       "737   1.073714  22.261321  1327.908247   0.016764  \n",
       "736   1.079412  26.274326  1328.224877   0.019782  \n",
       "735   1.086423  27.945242  1328.557617   0.021034  \n",
       "734   1.095093  16.453319  1328.898603   0.012381  \n",
       "..         ...        ...          ...        ...  \n",
       "4     1.025297   3.916109  2035.531178   0.001924  \n",
       "3     1.026680   7.956808  2035.914082   0.003908  \n",
       "2     1.025573   9.333599  2036.234356   0.004584  \n",
       "1     1.021580  19.599946  2036.507343   0.009624  \n",
       "0     1.021554  19.806136  2036.869425   0.009724  \n",
       "\n",
       "[739 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = data[data[\"Date\"] > datetime(year=2013, month=1, day=1)]\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Predictions\n",
    "\n",
    "Now I will build a model and make predictions. I will use the [Mean Absolute Error](https://en.wikipedia.org/wiki/Mean_absolute_error), also called MAE, as an error metric, because it will show how \"close\" the predictions are to the price in intuitive terms.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close',\n",
       "       '5_day_avg', '30_day_avg', 'year_avg', 'avg_ratio', '5_day_std',\n",
       "       'year_std', 'std_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.09914408011438"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['5_day_avg', '30_day_avg', 'year_avg', 'avg_ratio', '5_day_std',\n",
    "       'year_std', 'std_ratio']\n",
    "y_train = train['Close']\n",
    "y_test = test['Close']\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(train[features], y_train) \n",
    "predictions = model.predict(test[features]) \n",
    "mae = mean_absolute_error(test['Close'], predictions)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Stock Price')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40,15))\n",
    "plt.scatter(test['Date'], test['Close'], c='b')\n",
    "plt.scatter(test['Date'], predictions, c='r')\n",
    "plt.title('Stock Predictions', fontsize=60)\n",
    "plt.xlabel('Date', fontsize=40)\n",
    "plt.ylabel('Stock Price', fontsize=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is off by 16 points, not too bad for a first pass. But, there is definitely room for improvement both indicator and structural wise. Below are some ideas\n",
    "\n",
    "### Improvement Ideas\n",
    "\n",
    "- Accuracy would improve greatly by making predictions only one day ahead. For example, train a model using data from 1951-01-03 to 2013-01-02, make predictions for 2013-01-03, and then train another model using data from 1951-01-03 to 2013-01-03, make predictions for 2013-01-04, and so on. This more closely simulates what to do if you want to trade using the algorithm.\n",
    "\n",
    "- Try other techniques, like a random forest, and see if they perform better.\n",
    "\n",
    "- Incorporate outside data, such as the weather in New York City (where most trading happens) the day before, and the amount of Twitter activity around certain stocks.\n",
    "\n",
    "- Make the system real-time by writing an automated script to download the latest data when the market closes, and make predictions for the next day.\n",
    "\n",
    "- Make the system \"higher-resolution\". Make hourly, minute-by-minute, or second by second predictions. This will require obtaining more data, though."
   ]
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