Stock_price_indicator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Price Indicator\n",
    "## I. ETL Pipeline Preparation\n",
    "### 1. Import libraries and load datasets.\n",
    "- Import Python libraries\n",
    "- Load data from Yahoo Finance (Accessing data from the Yahoo finance plattform via the corresponing API you are able to download data since 1950 (if the stock existed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from alpha_vantage.timeseries import TimeSeries\n",
    "import os.path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download data from Alpha-Vantage\n",
    "\n",
    "def download_stock_data(symbol, API_key='AG0F6BKQTUPYSY99'):\n",
    "    try:\n",
    "        ts = TimeSeries(key = API_key, output_format = 'pandas')\n",
    "        data = ts.get_daily_adjusted(symbol, outputsize='full') \n",
    "        symbol = symbol.upper()\n",
    "        df = data[0]['5. adjusted close'].reset_index().rename(columns={\"5. adjusted close\": symbol}).sort_values('date', ascending =True)\n",
    "        df.to_csv('app/data_wrangling/'+symbol+'.csv', index = False)\n",
    "        return df\n",
    "    except Exception as e:\n",
    "                print('Execption occurred: {}'.format(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5428</th>\n",
       "      <td>1999-11-01</td>\n",
       "      <td>0.595872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5427</th>\n",
       "      <td>1999-11-02</td>\n",
       "      <td>0.616062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5426</th>\n",
       "      <td>1999-11-03</td>\n",
       "      <td>0.625658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5425</th>\n",
       "      <td>1999-11-04</td>\n",
       "      <td>0.641933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5424</th>\n",
       "      <td>1999-11-05</td>\n",
       "      <td>0.677937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>127.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>126.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-26</td>\n",
       "      <td>126.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>125.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>124.610000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5429 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date        AAPL\n",
       "5428 1999-11-01    0.595872\n",
       "5427 1999-11-02    0.616062\n",
       "5426 1999-11-03    0.625658\n",
       "5425 1999-11-04    0.641933\n",
       "5424 1999-11-05    0.677937\n",
       "...         ...         ...\n",
       "4    2021-05-24  127.100000\n",
       "3    2021-05-25  126.900000\n",
       "2    2021-05-26  126.850000\n",
       "1    2021-05-27  125.280000\n",
       "0    2021-05-28  124.610000\n",
       "\n",
       "[5429 rows x 2 columns]"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_stock_data('AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data from csv file\n",
    "\n",
    "def get_data(symbol='AAPL', start_date='2020-01-01', end_date='2020-12-31'):\n",
    "    '''\n",
    "    Setup an empty dataframe with the given timeperiod as index to be used as instance for further gathered data.\n",
    "    Then loads data from a .csv file for the selected symbol and selects the Adj Close column\n",
    "    INPUT: \n",
    "    symbols - list - symbols of listed stocks\n",
    "    start_date - datetime - Beginning of the period to analyze\n",
    "    end_date - datetime - End of the period to analyze\n",
    "    \n",
    "    OUTPUT\n",
    "    df - dataframe - Dataframe containing the Adj Close for the symbol with the time period as index (ordered ascending)\n",
    "    '''\n",
    "    try:\n",
    "        dates= pd.date_range(start_date, end_date)\n",
    "        df = pd.DataFrame(index=dates)\n",
    "    \n",
    "        if os.path.isfile('app/data_wrangling/'+symbol+'.csv') == False:\n",
    "            print(\"No such file exists; will be downloaded\")\n",
    "            download_stock_data(symbol)\n",
    "\n",
    "        df_tmp = pd.read_csv('app/data_wrangling/'+symbol+'.csv', index_col = 'date')\n",
    "        \n",
    "        df = df.join(df_tmp)\n",
    "        df = df.dropna()\n",
    "    \n",
    "        return df\n",
    "    except Exception as e:\n",
    "                print('Execption occurred: {}'.format(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 874,
   "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>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>136.255198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>134.440987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>133.294645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>132.267922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>128.998355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL\n",
       "2020-12-28  136.255198\n",
       "2020-12-29  134.440987\n",
       "2020-12-30  133.294645\n",
       "2020-12-31  132.267922\n",
       "2021-01-04  128.998355"
      ]
     },
     "execution_count": 874,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_analysis_df = get_data(symbol='AAPL', start_date='2020-12-25', end_date='2021-05-25')\n",
    "data_analysis_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Normalize data and plot to check for anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 875,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Noramlize the stock price data\n",
    "\n",
    "def normalize_stock_data(df):\n",
    "    df = df/df.iloc[0,:]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 876,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_analysis_norm = normalize_stock_data(data_analysis_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 877,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot stock price data and check for anomalies\n",
    "\n",
    "def plot_stock_data(df):\n",
    "    if isinstance(df, pd.Series):\n",
    "        plt.figure(figsize=(12,8))\n",
    "        ax1 = df.plot()\n",
    "        ax1.set_xlabel('time')\n",
    "        ax1.set_ylabel('price')\n",
    "        ax1.set_title('Stock price development')\n",
    "        plt.legend(loc='upper right')\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.figure(figsize=(12,18))\n",
    "        ax2 = plt.subplot(2,1,1)\n",
    "        ax2.set_xlabel('time')\n",
    "        ax2.set_ylabel('price')\n",
    "        ax2.set_title('Stock price development')\n",
    "        for col in df.columns:\n",
    "            df[col].plot()\n",
    "    \n",
    "        plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 878,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stock_data(data_analysis_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 879,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stock_data(data_analysis_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 880,
   "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>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-05-19</th>\n",
       "      <td>0.016654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-20</th>\n",
       "      <td>0.014823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-21</th>\n",
       "      <td>0.010452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-24</th>\n",
       "      <td>0.010638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>0.011011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL\n",
       "2021-05-19  0.016654\n",
       "2021-05-20  0.014823\n",
       "2021-05-21  0.010452\n",
       "2021-05-24  0.010638\n",
       "2021-05-25  0.011011"
      ]
     },
     "execution_count": 880,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple moving average (SMA)\n",
    "\n",
    "win = 10\n",
    "sma = data_analysis_norm.rolling(window=win).mean()\n",
    "std_df = data_analysis_norm.rolling(window=win).std()\n",
    "sma.tail()\n",
    "std_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 881,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bollinger(R) Bands\n",
    "\n",
    "def get_bollinger_bands(rm=sma, rstd=std_df):\n",
    "    upper_band = rm + rstd*2\n",
    "    lower_band = rm - rstd*2\n",
    "    return upper_band, lower_band\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 882,
   "metadata": {
    "scrolled": true
   },
   "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>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-05-19</th>\n",
       "      <td>0.960605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-20</th>\n",
       "      <td>0.955321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-21</th>\n",
       "      <td>0.943071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-24</th>\n",
       "      <td>0.943627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>0.945099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL\n",
       "2021-05-19  0.960605\n",
       "2021-05-20  0.955321\n",
       "2021-05-21  0.943071\n",
       "2021-05-24  0.943627\n",
       "2021-05-25  0.945099"
      ]
     },
     "execution_count": 882,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_upper_band, b_lower_band = get_bollinger_bands()\n",
    "b_upper_band.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 883,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,12))\n",
    "ax = plt.subplot(1,1,1)\n",
    "ax.set_xlabel('time')\n",
    "ax.set_ylabel('price')\n",
    "ax.set_title('Stock price development & indicators')\n",
    "ax.plot(data_analysis_norm, color='lightblue', linewidth=3, label='Stock price')\n",
    "ax.plot(sma, color='darkblue', linewidth=3, label='SMA')\n",
    "ax.plot(b_upper_band, color='orange', linewidth=3, label='Bollinger upper band')\n",
    "ax.plot(b_lower_band, color='orange', linewidth=3, label='Bollinger lower band')\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 884,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Daily returns\n",
    "\n",
    "def get_daily_returns(df=data_analysis_df):\n",
    "    daily_returns = df.copy()\n",
    "    daily_returns[1:] = (df[1:]/df[:-1].values) - 1\n",
    "    daily_returns.iloc[0,:] = 0\n",
    "    return daily_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 885,
   "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>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>-0.013315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>-0.008527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>-0.007703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>-0.024719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL\n",
       "2020-12-28  0.000000\n",
       "2020-12-29 -0.013315\n",
       "2020-12-30 -0.008527\n",
       "2020-12-31 -0.007703\n",
       "2021-01-04 -0.024719"
      ]
     },
     "execution_count": 885,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_returns = get_daily_returns()\n",
    "daily_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 886,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_returns.hist(bins= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cumulative returns\n",
    "\n",
    "def get_cumulative_returns(df=data_analysis_df):\n",
    "    cumulative_returns = df.copy\n",
    "    cumulative_returns= (df/df.iloc[0]) - 1\n",
    "    return cumulative_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "metadata": {
    "scrolled": true
   },
   "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>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>-0.013315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>-0.021728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>-0.029263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>-0.053259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL\n",
       "2020-12-28  0.000000\n",
       "2020-12-29 -0.013315\n",
       "2020-12-30 -0.021728\n",
       "2020-12-31 -0.029263\n",
       "2021-01-04 -0.053259"
      ]
     },
     "execution_count": 888,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_returns = get_cumulative_returns()\n",
    "cumulative_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_returns.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Momentum\n",
    "\n",
    "def get_momentum(df=data_analysis_df, win=5):\n",
    "    df_momentum = df.copy()\n",
    "    df_momentum[win:] = (df[win:]/df[:-(win)].values) - 1\n",
    "    df_momentum.iloc[0:(win),:] = 0\n",
    "    return df_momentum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "metadata": {
    "scrolled": true
   },
   "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>AAPL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-05-19</th>\n",
       "      <td>0.015639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-20</th>\n",
       "      <td>0.018724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-21</th>\n",
       "      <td>-0.015849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-24</th>\n",
       "      <td>0.006573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>0.016420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL\n",
       "2021-05-19  0.015639\n",
       "2021-05-20  0.018724\n",
       "2021-05-21 -0.015849\n",
       "2021-05-24  0.006573\n",
       "2021-05-25  0.016420"
      ]
     },
     "execution_count": 891,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momentum = get_momentum()\n",
    "momentum.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "momentum.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 894,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                   SPY\n",
       " 2020-12-28  370.952995\n",
       " 2020-12-29  370.245316\n",
       " 2020-12-30  370.773583\n",
       " 2020-12-31  372.657403\n",
       " 2021-01-04  367.584047,\n",
       "                SPY\n",
       " 2021-05-19  410.86\n",
       " 2021-05-20  415.28\n",
       " 2021-05-21  414.94\n",
       " 2021-05-24  419.17\n",
       " 2021-05-25  418.24)"
      ]
     },
     "execution_count": 894,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get market index\n",
    "\n",
    "market_index = get_data(symbol='SPY', start_date='2020-12-25', end_date='2021-05-25')\n",
    "market_index.head(), market_index.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 895,
   "metadata": {},
   "outputs": [],
   "source": [
    "market_index_norm = normalize_stock_data(market_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Merge all indicators and symbols into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 896,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_indicator_dataframe(df):\n",
    "    ''' Function which which takes the Adj Close and corresponding dates per symbol, adds a new column containing the symbol \n",
    "        and joins all indicators to one dataframe \n",
    "        INPUT: \n",
    "        df - dataframe - contains the orginal data to analyse \n",
    "        OUTPUT:\n",
    "        indicator_df - dataframe - contains the Adj Close and all indicators as features tagged by the symbol '''\n",
    "    indicator_df = pd.DataFrame(columns=['Date','Symbol', 'Adj Close','Daily Returns','Cumulative Returns','SMA', 'Momentum', 'Upper Band','Lower Band','Market Index'])\n",
    "    \n",
    "    for symbol in df.columns:\n",
    "        df_temp = df[symbol].reset_index().rename(columns={'index':'Date', symbol:'Adj Close'})\n",
    "        df_temp['Symbol']= symbol\n",
    "\n",
    "        df_temp = df_temp.join(daily_returns[symbol], on='Date').rename(columns={symbol:'Daily Returns'})\n",
    "        df_temp = df_temp.join(cumulative_returns[symbol], on='Date').rename(columns={symbol:'Cumulative Returns'})\n",
    "        df_temp = df_temp.join(sma[symbol], on='Date').rename(columns={symbol:'SMA'})\n",
    "        df_temp = df_temp.join(momentum[symbol], on='Date').rename(columns={symbol:'Momentum'})\n",
    "        df_temp = df_temp.join(b_upper_band[symbol], on='Date').rename(columns={symbol:'Upper Band'})\n",
    "        df_temp = df_temp.join(b_lower_band[symbol], on='Date').rename(columns={symbol:'Lower Band'})\n",
    "        df_temp = df_temp.join(market_index['SPY'], on='Date').rename(columns={'SPY':'Market Index'})\n",
    "        indicator_df = pd.concat([indicator_df, df_temp])\n",
    "        \n",
    "        indicator_df.fillna(method='ffill', inplace=True)\n",
    "        indicator_df.fillna(method='bfill', inplace=True)\n",
    "        indicator_df.dropna()\n",
    "    \n",
    "    return indicator_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 897,
   "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>Symbol</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Daily Returns</th>\n",
       "      <th>Cumulative Returns</th>\n",
       "      <th>SMA</th>\n",
       "      <th>Momentum</th>\n",
       "      <th>Upper Band</th>\n",
       "      <th>Lower Band</th>\n",
       "      <th>Market Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>136.255198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>370.952995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>134.440987</td>\n",
       "      <td>-0.013315</td>\n",
       "      <td>-0.013315</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>370.245316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>133.294645</td>\n",
       "      <td>-0.008527</td>\n",
       "      <td>-0.021728</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>370.773583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>132.267922</td>\n",
       "      <td>-0.007703</td>\n",
       "      <td>-0.029263</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>372.657403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>128.998355</td>\n",
       "      <td>-0.024719</td>\n",
       "      <td>-0.053259</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>367.584047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2021-05-19</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>124.690000</td>\n",
       "      <td>-0.001282</td>\n",
       "      <td>-0.084879</td>\n",
       "      <td>0.927298</td>\n",
       "      <td>0.015639</td>\n",
       "      <td>0.960605</td>\n",
       "      <td>0.893990</td>\n",
       "      <td>410.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2021-05-20</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>127.310000</td>\n",
       "      <td>0.021012</td>\n",
       "      <td>-0.065650</td>\n",
       "      <td>0.925675</td>\n",
       "      <td>0.018724</td>\n",
       "      <td>0.955321</td>\n",
       "      <td>0.896028</td>\n",
       "      <td>415.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>125.430000</td>\n",
       "      <td>-0.014767</td>\n",
       "      <td>-0.079448</td>\n",
       "      <td>0.922167</td>\n",
       "      <td>-0.015849</td>\n",
       "      <td>0.943071</td>\n",
       "      <td>0.901262</td>\n",
       "      <td>414.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>127.100000</td>\n",
       "      <td>0.013314</td>\n",
       "      <td>-0.067192</td>\n",
       "      <td>0.922350</td>\n",
       "      <td>0.006573</td>\n",
       "      <td>0.943627</td>\n",
       "      <td>0.901073</td>\n",
       "      <td>419.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>126.900000</td>\n",
       "      <td>-0.001574</td>\n",
       "      <td>-0.068659</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.016420</td>\n",
       "      <td>0.945099</td>\n",
       "      <td>0.901055</td>\n",
       "      <td>418.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Symbol   Adj Close  Daily Returns  Cumulative Returns  \\\n",
       "0   2020-12-28   AAPL  136.255198       0.000000            0.000000   \n",
       "1   2020-12-29   AAPL  134.440987      -0.013315           -0.013315   \n",
       "2   2020-12-30   AAPL  133.294645      -0.008527           -0.021728   \n",
       "3   2020-12-31   AAPL  132.267922      -0.007703           -0.029263   \n",
       "4   2021-01-04   AAPL  128.998355      -0.024719           -0.053259   \n",
       "..         ...    ...         ...            ...                 ...   \n",
       "98  2021-05-19   AAPL  124.690000      -0.001282           -0.084879   \n",
       "99  2021-05-20   AAPL  127.310000       0.021012           -0.065650   \n",
       "100 2021-05-21   AAPL  125.430000      -0.014767           -0.079448   \n",
       "101 2021-05-24   AAPL  127.100000       0.013314           -0.067192   \n",
       "102 2021-05-25   AAPL  126.900000      -0.001574           -0.068659   \n",
       "\n",
       "          SMA  Momentum  Upper Band  Lower Band  Market Index  \n",
       "0    0.963450  0.000000    1.007037    0.919863    370.952995  \n",
       "1    0.963450  0.000000    1.007037    0.919863    370.245316  \n",
       "2    0.963450  0.000000    1.007037    0.919863    370.773583  \n",
       "3    0.963450  0.000000    1.007037    0.919863    372.657403  \n",
       "4    0.963450  0.000000    1.007037    0.919863    367.584047  \n",
       "..        ...       ...         ...         ...           ...  \n",
       "98   0.927298  0.015639    0.960605    0.893990    410.860000  \n",
       "99   0.925675  0.018724    0.955321    0.896028    415.280000  \n",
       "100  0.922167 -0.015849    0.943071    0.901262    414.940000  \n",
       "101  0.922350  0.006573    0.943627    0.901073    419.170000  \n",
       "102  0.923077  0.016420    0.945099    0.901055    418.240000  \n",
       "\n",
       "[103 rows x 10 columns]"
      ]
     },
     "execution_count": 897,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicator_model_df = create_indicator_dataframe(data_analysis_df)\n",
    "indicator_model_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. MACHINE LEARNING PIPELINE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Create train and test data and standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 967,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "def create_train_test_data(indicator_df, symbol, start_pred, end_pred, train_size=0.8):\n",
    "    ''' Splits the indicator dataframe into a train and test dataset and standardizes the data of the indipendent variable\n",
    "        INPUT:\n",
    "        indicator_df - dataframe object - dataframe which contains the Adj Close and different indicators for each symbol\n",
    "        symbol - str - symbol of the listed company for which you want to predict stock price\n",
    "        train_size - float - size of train dataset\n",
    "        start_pred - str - start date of prediction\n",
    "        end_pred - str - end date of prediction\n",
    "        OUTPUT:\n",
    "        pred_days - int - days to be predicted\n",
    "        Y_train - 1d array - contains the training dataset of the dependent variable (stock price)\n",
    "        Y_test - 1d array - contains the test dataset of the dependent variable (stock price)\n",
    "        X_train - nd array - contains the training dataset of the independent variables\n",
    "        X_test - nd array - contains the test dataset of the independent variables\n",
    "        X_predict = nd array - contains the data of the independent variable for the prediction period\n",
    "        time_series_train - 1d array - selected time period of training data\n",
    "        time_series_test - 1d array - selected time period of test data\n",
    "    '''\n",
    "    sd = dt.datetime.strptime(start_pred, '%Y-%m-%d')\n",
    "    ed = dt.datetime.strptime(end_pred, '%Y-%m-%d')\n",
    "    \n",
    "    try:\n",
    "        if sd >= ed:\n",
    "            raise ValueError('Start date beyound end date')\n",
    "        else:\n",
    "            pred_days = (ed-sd).days\n",
    "\n",
    "            indicator_df = indicator_df[indicator_df['Date'] <= start_pred] \n",
    "            \n",
    "            df = indicator_df.copy().drop(['Symbol','Date'], axis=1)\n",
    "            \n",
    "            for i in range(1,pred_days):\n",
    "                indicator_df=indicator_df.join(df.shift(i), rsuffix=\"[{} day before]\".format(i))\n",
    "\n",
    "        \n",
    "            train_df = indicator_df.copy().iloc[pred_days:] # Training data starts from the date where data for all indicators is available\n",
    "            \n",
    "            if pred_days > 0:\n",
    "                X = train_df.iloc[:-pred_days,3:] # Reduces the X Date by the number of pred_days at the end of the dataframe\n",
    "                X_predict = train_df.iloc[-pred_days:, 3:]\n",
    "                Y = train_df.drop('Symbol', axis=1).iloc[pred_days:,:2] # Starts at pred_days and takes all data until the end of the dataframe\n",
    "                \n",
    "                X.fillna(method='ffill', inplace=True)\n",
    "                X.fillna(method='bfill', inplace=True)\n",
    "\n",
    "                Y.fillna(method='ffill', inplace=True)\n",
    "                Y.fillna(method='bfill', inplace=True)\n",
    "                \n",
    "            \n",
    "            train_ct = int(X.shape[0] * train_size)\n",
    "            test_ct = X.shape[0] - train_size\n",
    "\n",
    "            X_train, X_test = X.iloc[:train_ct], X.iloc[train_ct:]  \n",
    "            Y_train, Y_test = Y.iloc[:train_ct].copy().drop('Date', axis=1), Y.iloc[train_ct:].copy().drop('Date', axis=1)\n",
    "\n",
    "            time_series_train = Y.iloc[:train_ct].Date\n",
    "            time_series_test = Y.iloc[train_ct:].Date   \n",
    "\n",
    "\n",
    "            return pred_days, preprocessing.scale(X_train), Y_train['Adj Close'].tolist(), preprocessing.scale(X_test), Y_test['Adj Close'].tolist(), time_series_train, time_series_test, preprocessing.scale(X_predict)\n",
    "        \n",
    "    \n",
    "    except ValueError:\n",
    "        raise\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pred_days, X_train, Y_train, X_test, Y_test, time_series_train, time_series_test, X_predict   = create_train_test_data(indicator_model_df, symbol='AAPL', start_pred='2021-05-15', end_pred='2021-05-20', train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 68, 68, 68)"
      ]
     },
     "execution_count": 993,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_days, len(X_train), len(Y_train),  len(time_series_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 18, 18)"
      ]
     },
     "execution_count": 994,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test), len(Y_test),len(time_series_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Setup and validate different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate model performance and plot data\n",
    "def evaluate_model_performance(Y_test, Y_predict, Y_future, time_series_test):\n",
    "    ''' Function that generates different performance indicators comparing test data and the predicted data\n",
    "        and plots the data\n",
    "        INPUT:\n",
    "        Y_test - 1d array - contains the test dataset of the dependent variable (stock price)\n",
    "        Y_predict - 1d array - contains the predicted dataset of the dependent variable (stock price) for the test period\n",
    "        Y_future - 1d array - contains the predicted dataset of the dependent variable (stock price) for a future period\n",
    "        time_series_test - 1d array - selected time period of test data\n",
    "    '''\n",
    "    \n",
    "    corr = np.corrcoef(Y_test, Y_predict)\n",
    "    corrcoef = corr[0,1]\n",
    "    mae = mean_absolute_error(Y_test, Y_predict)\n",
    "    mape = mean_absolute_percentage_error(Y_test, Y_predict)\n",
    "    r2 = r2_score(Y_test, Y_predict)\n",
    "    \n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    \n",
    "    value_days = len(Y_future)\n",
    "    end_date = (time_series_test.iloc[-1] + dt.timedelta(days=value_days))\n",
    "\n",
    "    time_series_future = pd.date_range(time_series_test.iloc[-1] + dt.timedelta(days=1) , end_date).tolist()\n",
    "    \n",
    "\n",
    "    plt.plot(time_series_test, Y_test, color='lightblue', linewidth=2, label='test data')\n",
    "    plt.plot(time_series_test, Y_predict.reshape(-1,1), color='red',  linewidth=2, label='predicted data')\n",
    "    \n",
    "    plt.plot(time_series_future , Y_future.reshape(-1,1), color='green',  linewidth=2, label='future predicted data')\n",
    "    \n",
    "    plt.legend()\n",
    "    \n",
    "    return print(('MAE {} \\n MAPE {} \\n r2 {} \\n CORRCOEF {} \\n').format(mae, mape, r2 , corrcoef))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lasso LARS\n",
    "\n",
    "lassolars = linear_model.LassoLars(alpha = 0.1)\n",
    "lassolars.fit(X_train, Y_train)\n",
    "Y_lassolars_predict = lassolars.predict(X_test)\n",
    "Y_lassolars_future = lassolars.predict(X_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 2.342397719541426 \n",
      " MAPE 0.01794216385715601 \n",
      " r2 0.37334522906248424 \n",
      " CORRCOEF 0.7914236081144906 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model_performance(Y_test, Y_lassolars_predict, Y_lassolars_future, time_series_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([130.01303755, 128.99012632, 124.20411223, 127.00275109,\n",
       "       131.9486494 ])"
      ]
     },
     "execution_count": 999,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_lassolars_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [],
   "source": [
    "# knn\n",
    "knn = KNeighborsRegressor(n_neighbors=15)  #weights='distance'\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_knn_predict = knn.predict(X_test)\n",
    "Y_knn_future = knn.predict(X_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 2.9003871107925994 \n",
      " MAPE 0.022178743829784263 \n",
      " r2 -0.2708068569315625 \n",
      " CORRCOEF 0.4157665285885926 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model_performance(Y_test, Y_knn_predict, Y_knn_future, time_series_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AdaBoost\n",
    "AdaBoost = AdaBoostRegressor(base_estimator=knn, random_state=42)\n",
    "AdaBoost.fit(X_train, Y_train)\n",
    "Y_AdaBoost_predict = AdaBoost.predict(X_test)\n",
    "Y_AdaBoost_future = AdaBoost.predict(X_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 3.3421564260037067 \n",
      " MAPE 0.025661134726348386 \n",
      " r2 -0.4595010066485703 \n",
      " CORRCOEF 0.31638970494451985 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model_performance(Y_test, Y_AdaBoost_predict, Y_AdaBoost_future, time_series_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1004,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Regression\n",
    "randomforest = RandomForestRegressor(random_state=42, criterion='mse', n_estimators=10, min_samples_split=10)\n",
    "randomforest.fit(X_train, Y_train)\n",
    "Y_randomforest_predict = randomforest.predict(X_test)\n",
    "Y_randomforest_future = randomforest.predict(X_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1005,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 3.0285611715061185 \n",
      " MAPE 0.023346266266300895 \n",
      " r2 -0.0011986899151184005 \n",
      " CORRCOEF 0.5908766010043889 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model_performance(Y_test, Y_randomforest_predict, Y_randomforest_future, time_series_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Put all the above into one class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1006,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StockDataAnalysis():\n",
    "    ''' Creates a StockDataAnalysis object which is able to take one or mutiple stock symbols and a timeframe and then computes\n",
    "        a range of indicators on the stock data and plots the results'''\n",
    "    \n",
    "    def __init__(self, symbol='AAPL', start_date='2020-01-01', end_date='2021-04-16'):\n",
    "        ''' Create an instance of StockDataAnalysis'''\n",
    "        self.symbol = symbol\n",
    "        self.start_date = start_date\n",
    "        self.end_date = end_date\n",
    "        \n",
    "        self.data = get_data(self.symbol, self.start_date, self.end_date)\n",
    "        self.data_norm = normalize_stock_data(self.data)\n",
    "        \n",
    "    # Plot stock price data and check for anomalies\n",
    "\n",
    "    def plot_stock_data(self, normalized=True):\n",
    "        if normalized:\n",
    "            df = self.data_norm\n",
    "            title_str = 'Relative stock price development'\n",
    "        else:\n",
    "            df = self.data\n",
    "            title_str = 'Absolute stock price development'\n",
    "        if isinstance(df, pd.Series):\n",
    "            plt.figure(figsize=(12,8))\n",
    "            ax1 = df.plot()\n",
    "            ax1.set_xlabel('time')\n",
    "            ax1.set_ylabel('price')\n",
    "            ax1.set_title(title_str)\n",
    "            plt.legend(loc='upper right')\n",
    "            plt.show()\n",
    "        else:\n",
    "            plt.figure(figsize=(12,18))\n",
    "            ax2 = plt.subplot(2,1,1)\n",
    "            ax2.set_xlabel('time')\n",
    "            ax2.set_ylabel('price development')\n",
    "            ax2.set_title(title_str)\n",
    "            for col in df.columns:\n",
    "                df[col].plot()\n",
    "        \n",
    "            plt.legend(loc='upper right')\n",
    "           \n",
    "    def calculate_rolling_stats(self, win=10):\n",
    "        rm = self.data_norm.rolling(window=win).mean()\n",
    "        rstd = self.data_norm.rolling(window=win).std()\n",
    "        self.sma = rm.dropna()\n",
    "        self.rstd = rstd.dropna()\n",
    "        \n",
    "    def calculate_bollinger_bands(self):\n",
    "        self.b_upper_band = self.sma + self.rstd*2\n",
    "        self.b_lower_band = self.sma - self.rstd*2\n",
    "            \n",
    "    def calculate_daily_returns(self):\n",
    "        daily_returns = self.data.copy()\n",
    "        daily_returns[1:] = (self.data[1:]/self.data[:-1].values) - 1\n",
    "        daily_returns.iloc[0,:] = 0\n",
    "        self.daily_returns = daily_returns\n",
    "        \n",
    "\n",
    "    def calculate_cumulative_returns(self):\n",
    "        cumulative_returns = self.data.copy\n",
    "        cumulative_returns= (self.data/self.data.iloc[0]) - 1\n",
    "        self.cumulative_returns = cumulative_returns\n",
    "        \n",
    "        \n",
    "    def calculate_momentum(self, win=5):\n",
    "        self.momentum = self.data.copy()\n",
    "        self.momentum[win:] = (self.data[win:]/self.data[:-(win)].values) - 1\n",
    "        self.momentum.iloc[0:(win),:] = 0\n",
    "        \n",
    "    \n",
    "    def get_market_index(self, market_ix='SPY'):\n",
    "        self.market_ix = market_ix\n",
    "        self.market_index = get_data(symbol=market_ix, start_date=self.start_date, end_date=self.end_date)\n",
    "        \n",
    "    def setup_features(self, market_ix='SPY'):\n",
    "        self.calculate_rolling_stats()\n",
    "        self.calculate_bollinger_bands()\n",
    "        self.calculate_daily_returns()\n",
    "        self.calculate_cumulative_returns()\n",
    "        self.calculate_momentum()\n",
    "        self.get_market_index(market_ix=market_ix)\n",
    "        \n",
    "            \n",
    "    def create_indicator_dataframe(self):\n",
    "        ''' Function which which takes the Adj Close and corresponding dates per symbol, adds a new column containing the symbol \n",
    "            and joins all indicators to one dataframe \n",
    "            INPUT: \n",
    "            object \n",
    "            OUTPUT:\n",
    "            indicator_df - dataframe - contains the Adj Close and all indicators as features tagged by the symbol '''\n",
    "        \n",
    "        self.indicator_df = pd.DataFrame(columns=['Date','Symbol', 'Adj Close','Daily Returns','Cumulative Returns','SMA', 'Momentum', 'Upper Band','Lower Band','Market Index'])\n",
    "        \n",
    "        for symbol in self.data.columns:\n",
    "            df_temp = self.data[symbol].reset_index().rename(columns={'index':'Date', symbol:'Adj Close'})\n",
    "            df_temp['Symbol'] = symbol\n",
    "            \n",
    "            df_temp = df_temp.join(self.daily_returns[symbol], on='Date').rename(columns={symbol:'Daily Returns'})\n",
    "            df_temp = df_temp.join(self.cumulative_returns[symbol], on='Date').rename(columns={symbol:'Cumulative Returns'})\n",
    "            df_temp = df_temp.join(self.sma[symbol], on='Date').rename(columns={symbol:'SMA'})\n",
    "            df_temp = df_temp.join(self.momentum[symbol], on='Date').rename(columns={symbol:'Momentum'})\n",
    "            df_temp = df_temp.join(self.b_upper_band[symbol], on='Date').rename(columns={symbol:'Upper Band'})\n",
    "            df_temp = df_temp.join(self.b_lower_band[symbol], on='Date').rename(columns={symbol:'Lower Band'})\n",
    "            df_temp = df_temp.join(self.market_index[self.market_ix], on='Date').rename(columns={self.market_ix:'Market Index'})                   \n",
    "\n",
    "            self.indicator_df = pd.concat([self.indicator_df, df_temp])\n",
    "            \n",
    "            self.indicator_df.fillna(method='ffill', inplace=True)\n",
    "            self.indicator_df.fillna(method='bfill', inplace=True)\n",
    "            self.indicator_df.dropna()\n",
    "\n",
    "        return self.indicator_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_data = StockDataAnalysis('AAPL','2020-12-25','2021-05-25')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1008,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st_data.plot_stock_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "st_data.setup_features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1010,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_data.calculate_rolling_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1011,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_data.calculate_bollinger_bands()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_data.calculate_daily_returns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_data.calculate_momentum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_data.calculate_cumulative_returns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_data.get_market_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "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>Symbol</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Daily Returns</th>\n",
       "      <th>Cumulative Returns</th>\n",
       "      <th>SMA</th>\n",
       "      <th>Momentum</th>\n",
       "      <th>Upper Band</th>\n",
       "      <th>Lower Band</th>\n",
       "      <th>Market Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>136.255198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>370.952995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>134.440987</td>\n",
       "      <td>-0.013315</td>\n",
       "      <td>-0.013315</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>370.245316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>133.294645</td>\n",
       "      <td>-0.008527</td>\n",
       "      <td>-0.021728</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>370.773583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>132.267922</td>\n",
       "      <td>-0.007703</td>\n",
       "      <td>-0.029263</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>372.657403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>128.998355</td>\n",
       "      <td>-0.024719</td>\n",
       "      <td>-0.053259</td>\n",
       "      <td>0.963450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007037</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>367.584047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2021-05-19</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>124.690000</td>\n",
       "      <td>-0.001282</td>\n",
       "      <td>-0.084879</td>\n",
       "      <td>0.927298</td>\n",
       "      <td>0.015639</td>\n",
       "      <td>0.960605</td>\n",
       "      <td>0.893990</td>\n",
       "      <td>410.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2021-05-20</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>127.310000</td>\n",
       "      <td>0.021012</td>\n",
       "      <td>-0.065650</td>\n",
       "      <td>0.925675</td>\n",
       "      <td>0.018724</td>\n",
       "      <td>0.955321</td>\n",
       "      <td>0.896028</td>\n",
       "      <td>415.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>125.430000</td>\n",
       "      <td>-0.014767</td>\n",
       "      <td>-0.079448</td>\n",
       "      <td>0.922167</td>\n",
       "      <td>-0.015849</td>\n",
       "      <td>0.943071</td>\n",
       "      <td>0.901262</td>\n",
       "      <td>414.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>127.100000</td>\n",
       "      <td>0.013314</td>\n",
       "      <td>-0.067192</td>\n",
       "      <td>0.922350</td>\n",
       "      <td>0.006573</td>\n",
       "      <td>0.943627</td>\n",
       "      <td>0.901073</td>\n",
       "      <td>419.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>126.900000</td>\n",
       "      <td>-0.001574</td>\n",
       "      <td>-0.068659</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.016420</td>\n",
       "      <td>0.945099</td>\n",
       "      <td>0.901055</td>\n",
       "      <td>418.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Symbol   Adj Close  Daily Returns  Cumulative Returns  \\\n",
       "0   2020-12-28   AAPL  136.255198       0.000000            0.000000   \n",
       "1   2020-12-29   AAPL  134.440987      -0.013315           -0.013315   \n",
       "2   2020-12-30   AAPL  133.294645      -0.008527           -0.021728   \n",
       "3   2020-12-31   AAPL  132.267922      -0.007703           -0.029263   \n",
       "4   2021-01-04   AAPL  128.998355      -0.024719           -0.053259   \n",
       "..         ...    ...         ...            ...                 ...   \n",
       "98  2021-05-19   AAPL  124.690000      -0.001282           -0.084879   \n",
       "99  2021-05-20   AAPL  127.310000       0.021012           -0.065650   \n",
       "100 2021-05-21   AAPL  125.430000      -0.014767           -0.079448   \n",
       "101 2021-05-24   AAPL  127.100000       0.013314           -0.067192   \n",
       "102 2021-05-25   AAPL  126.900000      -0.001574           -0.068659   \n",
       "\n",
       "          SMA  Momentum  Upper Band  Lower Band  Market Index  \n",
       "0    0.963450  0.000000    1.007037    0.919863    370.952995  \n",
       "1    0.963450  0.000000    1.007037    0.919863    370.245316  \n",
       "2    0.963450  0.000000    1.007037    0.919863    370.773583  \n",
       "3    0.963450  0.000000    1.007037    0.919863    372.657403  \n",
       "4    0.963450  0.000000    1.007037    0.919863    367.584047  \n",
       "..        ...       ...         ...         ...           ...  \n",
       "98   0.927298  0.015639    0.960605    0.893990    410.860000  \n",
       "99   0.925675  0.018724    0.955321    0.896028    415.280000  \n",
       "100  0.922167 -0.015849    0.943071    0.901262    414.940000  \n",
       "101  0.922350  0.006573    0.943627    0.901073    419.170000  \n",
       "102  0.923077  0.016420    0.945099    0.901055    418.240000  \n",
       "\n",
       "[103 rows x 10 columns]"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_data.create_indicator_dataframe()\n",
    "st_data.indicator_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelStockPrice():\n",
    "    def __init__(self, start_predict=None, end_predict=None):\n",
    "        '''Create an instance of the model to predict stockprice'''\n",
    "        \n",
    "        self.start_predict = start_predict\n",
    "        self.end_predict = end_predict\n",
    "        \n",
    "        self.model = RandomForestRegressor(random_state=42, criterion='mse', n_estimators=10, min_samples_split=10)\n",
    "\n",
    "\n",
    "    def create_train_test_data(self, stockdata, train_size=0.8, start_pred='2019-05-10', end_pred='2019-05-17'):\n",
    "        ''' Splits the indicator dataframe into a train and test dataset and standardizes the data of the indipendent variable\n",
    "            INPUT:\n",
    "            indicator_df - dataframe object - dataframe which contains the Adj Close and different indicators for each symbol\n",
    "            symbol - str - symbol of the listed company for which you want to predict stock price\n",
    "            train_size - float - size of train dataset\n",
    "            start_pred - str - start date of prediction\n",
    "            end_pred - str - end date of prediction\n",
    "            OUTPUT:\n",
    "            pred_days - int - days to be predicted\n",
    "            Y_train - 1d array - contains the training dataset of the dependent variable (stock price)\n",
    "            Y_test - 1d array - contains the test dataset of the dependent variable (stock price)\n",
    "            X_train - nd array - contains the training dataset of the independent variables\n",
    "            X_test - nd array - contains the test dataset of the independent variables\n",
    "            X_predict = nd array - contains the data of the independent variable for the prediction period\n",
    "            time_series_train - 1d array - selected time period of training data\n",
    "            time_series_test - 1d array - selected time period of test data\n",
    "            time_series_test - 1d array - predicted time period\n",
    "\n",
    "        '''\n",
    "        sd = dt.datetime.strptime(start_pred, '%Y-%m-%d')\n",
    "        ed = dt.datetime.strptime(end_pred, '%Y-%m-%d')\n",
    "\n",
    "        try:\n",
    "            if sd >= ed:\n",
    "                raise ValueError('Start date beyound end date')\n",
    "            else:\n",
    "                self.pred_days = (ed-sd).days\n",
    "\n",
    "                indicator_df = stockdata.indicator_df[stockdata.indicator_df['Date'] <= start_pred] \n",
    "\n",
    "                df = stockdata.indicator_df.copy().drop(['Symbol','Date'], axis=1)\n",
    "\n",
    "                for i in range(1, self.pred_days):\n",
    "                    indicator_df=indicator_df.join(df.shift(i), rsuffix=\"[{} day before]\".format(i))\n",
    "\n",
    "\n",
    "                train_df = indicator_df.copy().iloc[self.pred_days:] # Training data starts from the date where data for all indicators is available\n",
    "\n",
    "                if self.pred_days > 0:\n",
    "                    X = train_df.iloc[:-pred_days,3:] # Reduces the X Date by the number of pred_days at the end of the dataframe\n",
    "                    self.X_predict = preprocessing.scale(train_df.iloc[-pred_days:, 3:])\n",
    "                    Y = train_df.drop('Symbol', axis=1).iloc[pred_days:,:2] # Starts at pred_days and takes all data until the end of the dataframe\n",
    "\n",
    "                    X.fillna(method='ffill', inplace=True)\n",
    "                    X.fillna(method='bfill', inplace=True)\n",
    "\n",
    "                    Y.fillna(method='ffill', inplace=True)\n",
    "                    Y.fillna(method='bfill', inplace=True)\n",
    "\n",
    "\n",
    "                train_ct = int(X.shape[0] * train_size)\n",
    "                test_ct = X.shape[0] - train_size\n",
    "\n",
    "                self.X_train, self.X_test = preprocessing.scale(X.iloc[:train_ct]), preprocessing.scale(X.iloc[train_ct:])  \n",
    "                self.Y_train, self.Y_test = Y.iloc[:train_ct, 1].copy().tolist(), Y.iloc[train_ct:, 1].copy().tolist()\n",
    "\n",
    "                self.time_series_train = Y.iloc[:train_ct].Date\n",
    "                self.time_series_test = Y.iloc[train_ct:].Date   \n",
    "                \n",
    "\n",
    "                return self.pred_days, self.X_train, self.Y_train, self.X_test, self.Y_test, self.time_series_train, self.time_series_test, X_predict\n",
    "            \n",
    "        except ValueError:\n",
    "            raise\n",
    "\n",
    "            \n",
    "    def fit(self):\n",
    "        '''Fit the model with training data '''\n",
    "        self.model.fit(self.X_train, self.Y_train)\n",
    "\n",
    "    def predict(self):\n",
    "        '''Predict stockprice '''\n",
    "        self.Y_predict = self.model.predict(self.X_test)\n",
    "        self.Y_future = self.model.predict(self.X_predict)\n",
    "        return self.Y_predict, self.Y_future\n",
    "    \n",
    "    def evaluate_model_performance(self):\n",
    "        ''' Function that generates different performance indicators comparing test data and the predicted data\n",
    "            and plots the data\n",
    "            INPUT:\n",
    "            Y_test - 1d array - contains the test dataset of the dependent variable (stock price)\n",
    "            Y_predict - 1d array - contains the predicted dataset of the dependent variable (stock price) for the test period\n",
    "            Y_future - 1d array - contains the predicted dataset of the dependent variable (stock price) for a future period\n",
    "            time_series_test - 1d array - selected time period of test data\n",
    "        '''\n",
    "\n",
    "        corr = np.corrcoef(self.Y_test, self.Y_predict)\n",
    "        corrcoef = corr[0,1]\n",
    "        mae = mean_absolute_error(self.Y_test, self.Y_predict)\n",
    "        mape = mean_absolute_percentage_error(self.Y_test, self.Y_predict)\n",
    "        r2 = r2_score(self.Y_test, self.Y_predict)\n",
    "\n",
    "        fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "        value_days = len(self.Y_future)\n",
    "        end_date = (self.time_series_test.iloc[-1] + dt.timedelta(days=value_days))\n",
    "\n",
    "        time_series_future = pd.date_range(self.time_series_test.iloc[-1]+ dt.timedelta(days=1) , end_date).tolist()\n",
    "\n",
    "        plt.plot(self.time_series_test, self.Y_test, color='lightblue', linewidth=2, label='test data')\n",
    "        plt.plot(self.time_series_test, self.Y_predict.reshape(-1,1), color='red',  linewidth=2, label='predicted data')\n",
    "\n",
    "        plt.plot(time_series_future , self.Y_future.reshape(-1,1), color='green',  linewidth=2, label='future predicted data')\n",
    "\n",
    "        plt.legend()\n",
    "\n",
    "        return print(('MAE {} \\n MAPE {} \\n r2 {} \\n CORRCOEF {} \\n').format(mae, mape, r2 , corrcoef))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_reg = ModelStockPrice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1047,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "          0.54669512, -1.66371733],\n",
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       "        [-3.38812120e-01,  5.13945497e-01,  1.01511478e+00,\n",
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       "         -9.60486715e-02,  7.87202711e-01, -8.86069992e-01],\n",
       "        [ 6.42767700e-02,  4.67104895e-01,  9.43209428e-01,\n",
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       "         -1.06728152e+00,  9.88983637e-01,  5.79725394e-01,\n",
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       "          8.59007685e-01,  7.78237121e-01,  9.89029236e-01,\n",
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       "        [-1.03417873e+00, -4.79075271e-01,  7.52871735e-01,\n",
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       "         -6.19219738e-02, -3.28021632e-01,  8.73967471e-01,\n",
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       "          1.16820042e+00,  8.41438151e-01, -6.36869853e-01,\n",
       "          4.64246849e-03,  9.22285503e-01,  8.59912944e-01],\n",
       "        [ 7.48793987e-01,  2.68032336e-02,  5.90732219e-01,\n",
       "         -5.75035143e-01, -1.36767526e+00,  7.90865468e-01,\n",
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       "          9.50099879e-01,  1.81377266e+00,  3.09702711e-01,\n",
       "         -3.35670007e-01,  3.09702711e-01,  8.63516341e-01,\n",
       "         -3.55044579e-01, -5.30070408e-01,  9.67281622e-01,\n",
       "          7.70956170e-01,  9.14624298e-01, -3.41530012e-01,\n",
       "          9.14624298e-01,  8.40211490e-01, -2.81881237e-01,\n",
       "         -1.36904951e-03,  9.22529842e-01,  8.30486265e-01],\n",
       "        [-2.58068794e+00, -2.17002101e+00,  2.19926196e-01,\n",
       "         -2.01824083e+00,  4.24735909e-01,  8.36997065e-02,\n",
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       "         -1.31610344e-01,  1.00916135e+00,  7.91250694e-01],\n",
       "        [ 2.70763553e-01, -2.05291951e+00, -1.60749189e-01,\n",
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       "         -1.22510982e-01,  7.17476778e-01, -1.22510982e-01,\n",
       "          6.07712702e-01, -7.47085594e-01, -1.16085738e+00,\n",
       "          7.69208402e-01,  1.05546711e+00, -9.53606264e-01,\n",
       "         -9.39541608e-01, -9.53606264e-01,  7.29640318e-01,\n",
       "         -1.06670597e+00, -4.23482176e-01,  8.12833981e-01,\n",
       "          7.32894703e-01,  2.15368942e-01, -2.12965737e-01,\n",
       "          2.15368942e-01,  8.56464745e-01, -2.12415674e-01,\n",
       "         -2.18567926e-01,  9.97839418e-01,  1.66097253e+00],\n",
       "        [ 1.09907451e+00, -1.28473363e+00, -3.15839161e-01,\n",
       "         -1.10097485e+00,  9.27488561e-01, -4.67125370e-01,\n",
       "          9.65300924e-01, -2.17445502e+00,  1.73975846e-01,\n",
       "         -2.17445502e+00,  3.52657906e-03, -1.52909608e+00,\n",
       "          8.49969380e-01, -1.56210732e-01, -1.15383375e-01,\n",
       "         -2.43624188e+00, -2.35088834e+00, -2.43624188e+00,\n",
       "          3.79544808e-01, -1.90769780e+00,  5.09414007e-01,\n",
       "          2.68952521e-01,  3.62392140e-03, -3.10033767e-01,\n",
       "          6.08830971e-01, -3.10033767e-01,  6.46867509e-01,\n",
       "         -9.00564993e-01, -7.74835805e-01,  7.93999964e-01,\n",
       "          1.07544791e+00, -1.33682427e+00, -1.30024464e+00,\n",
       "         -1.33682427e+00,  7.73665145e-01, -1.32624903e+00,\n",
       "         -6.29032253e-02,  8.65774396e-01,  7.58554384e-01],\n",
       "        [ 5.27564172e-01, -9.61533475e-01, -5.90066429e-01,\n",
       "         -2.04193807e-01,  1.94252983e-01, -5.25542522e-01,\n",
       "          2.12823752e+00, -1.40005627e+00,  9.31598317e-01,\n",
       "         -1.40005627e+00, -1.16863532e-01, -1.05379698e+00,\n",
       "          8.84084171e-01, -2.66269400e-01,  1.28325972e+00,\n",
       "         -2.31290868e+00,  2.76936195e-01, -2.31290868e+00,\n",
       "          1.45304005e-01, -1.63471051e+00,  9.45670389e-01,\n",
       "         -2.56190620e-02,  5.66237719e-02, -3.10480692e+00,\n",
       "         -2.28256410e+00, -3.10480692e+00,  4.57569692e-01,\n",
       "         -1.95651971e+00,  6.11997712e-01,  3.48515164e-01,\n",
       "          9.34620481e-02, -5.06938788e-01,  4.64586638e-01,\n",
       "         -5.06938788e-01,  7.03132153e-01, -1.15356288e+00,\n",
       "         -2.89386910e-01,  8.48289167e-01,  1.05282117e+00]]),\n",
       " [133.274821743,\n",
       "  131.71745304,\n",
       "  134.093438626,\n",
       "  134.492763935,\n",
       "  134.163320555,\n",
       "  133.354686805,\n",
       "  133.254855478,\n",
       "  131.23826267,\n",
       "  132.316441003,\n",
       "  127.63435176,\n",
       "  127.883930077,\n",
       "  129.52116384299998,\n",
       "  130.21,\n",
       "  126.85,\n",
       "  125.91,\n",
       "  122.77,\n",
       "  124.97,\n",
       "  127.45],\n",
       " 10   2021-01-12\n",
       " 11   2021-01-13\n",
       " 12   2021-01-14\n",
       " 13   2021-01-15\n",
       " 14   2021-01-19\n",
       "         ...    \n",
       " 73   2021-04-14\n",
       " 74   2021-04-15\n",
       " 75   2021-04-16\n",
       " 76   2021-04-19\n",
       " 77   2021-04-20\n",
       " Name: Date, Length: 68, dtype: datetime64[ns],\n",
       " 78   2021-04-21\n",
       " 79   2021-04-22\n",
       " 80   2021-04-23\n",
       " 81   2021-04-26\n",
       " 82   2021-04-27\n",
       " 83   2021-04-28\n",
       " 84   2021-04-29\n",
       " 85   2021-04-30\n",
       " 86   2021-05-03\n",
       " 87   2021-05-04\n",
       " 88   2021-05-05\n",
       " 89   2021-05-06\n",
       " 90   2021-05-07\n",
       " 91   2021-05-10\n",
       " 92   2021-05-11\n",
       " 93   2021-05-12\n",
       " 94   2021-05-13\n",
       " 95   2021-05-14\n",
       " Name: Date, dtype: datetime64[ns],\n",
       " array([[-1.09332814,  0.76719812,  1.47640309, -1.00902694,  1.22498885,\n",
       "          1.53786965,  1.11061444,  1.66498627,  0.72278668,  1.66498627,\n",
       "          1.35481375,  1.40333429,  1.29411707,  1.34124334,  1.3975282 ,\n",
       "          0.92332495,  1.3352378 ,  0.92332495,  1.19514323, -0.13901716,\n",
       "          1.49307721,  1.01362156,  0.57674606, -0.11908683,  0.3450212 ,\n",
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       "         -1.06229722,  0.06481211,  1.68906187, -1.03333692],\n",
       "        [-0.16770097,  0.19484397,  0.76068017,  1.52204641,  0.56619616,\n",
       "          0.84189421,  0.2912727 ,  0.28977637, -1.10588245,  0.28977637,\n",
       "          0.74418147, -0.97883192,  0.68565632,  0.7497514 ,  0.67881906,\n",
       "          1.18093426,  0.85500548,  1.18093426,  0.84113663,  1.34886265,\n",
       "          0.69221024,  0.88125414,  1.10658807,  0.90124023,  1.16159751,\n",
       "          0.90124023,  0.8168434 , -0.12181388,  1.0271817 ,  0.65673653,\n",
       "          0.45787522, -0.42949144,  0.54005652, -0.42949144,  0.52001399,\n",
       "         -0.5315882 ,  0.96753805,  0.26720356, -0.97917007],\n",
       "        [-1.04974833, -1.71706246, -0.15721741, -0.88844639,  0.49185081,\n",
       "         -0.63846717, -1.64175877, -0.09495497, -0.02510559, -0.09495497,\n",
       "          0.08476768,  0.99952616, -0.10777163,  0.18152204,  0.03748292,\n",
       "         -0.07563053, -1.14203301, -0.07563053,  0.14443736, -1.11900612,\n",
       "         -0.04833086,  0.22959295,  0.38044394,  1.33052418,  0.59818373,\n",
       "          1.33052418,  0.33610912,  1.34273828,  0.18554646,  0.44176237,\n",
       "          1.57330608,  0.88250782,  1.11560564,  0.88250782,  0.17616541,\n",
       "          0.37176577,  1.03424533, -0.19177078,  0.40416807],\n",
       "        [ 1.10695205, -0.37751019, -0.87567501, -0.44688472, -0.73483576,\n",
       "         -0.905817  , -0.57200158, -1.38012136, -1.05499799, -1.38012136,\n",
       "         -0.76091479, -0.88458277, -0.19731068, -1.02711947, -1.47558896,\n",
       "         -0.42716949,  0.03825322, -0.42716949, -0.60791904,  0.93052682,\n",
       "         -1.01399052, -0.39643931, -0.26752679, -0.76343393, -1.74476354,\n",
       "         -0.76343393, -0.60999534, -1.08643813, -0.59269201, -0.61658203,\n",
       "          0.04461723,  1.43450751,  0.71849358,  1.43450751, -0.4318214 ,\n",
       "          1.7738024 , -0.37961678, -0.39124043,  1.67500582],\n",
       "        [ 1.20382539,  1.13253056, -1.20419084,  0.82231163, -1.54820006,\n",
       "         -0.83547969,  0.81187321, -0.47968631,  1.46319936, -0.47968631,\n",
       "         -1.42284812, -0.53944576, -1.67469107, -1.24539732, -0.63824123,\n",
       "         -1.6014592 , -1.0864635 , -1.6014592 , -1.57279818, -1.0213662 ,\n",
       "         -1.12296607, -1.72802935, -1.79625128, -1.34924364, -0.3600389 ,\n",
       "         -1.34924364, -1.63168114,  0.93096131, -1.60750874, -1.63330319,\n",
       "         -1.31949985, -1.25803256, -0.93289065, -1.25803256, -1.62836397,\n",
       "         -0.55168275, -1.68697872, -1.37325422, -0.0666669 ]]))"
      ]
     },
     "execution_count": 1047,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_reg.create_train_test_data(st_data, start_pred='2021-05-15', end_pred='2021-05-20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1048,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_reg.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([130.96332036, 133.63803463, 128.41882388, 128.16798734,\n",
       "       129.79235665])"
      ]
     },
     "execution_count": 1049,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_predict, Y_future_predict = price_reg.predict()\n",
    "Y_future_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 3.0285611715061185 \n",
      " MAPE 0.023346266266300895 \n",
      " r2 -0.0011986899151184005 \n",
      " CORRCOEF 0.5908766010043889 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_reg.evaluate_model_performance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}