Regression/Tesla-Stock-Price-Forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tesla Stock Price Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import quandl\n",
    "import math, datetime\n",
    "import numpy as np\n",
    "from sklearn import preprocessing, model_selection, svm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import pickle\n",
    "\n",
    "style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This will NOT make you rich! The value of a company is not based on the features used below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collecting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>HL_PCT</th>\n",
       "      <th>PCT_change</th>\n",
       "      <th>Adj. Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>316.05</td>\n",
       "      <td>0.598007</td>\n",
       "      <td>1.006711</td>\n",
       "      <td>13702080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-11</th>\n",
       "      <td>327.22</td>\n",
       "      <td>0.018336</td>\n",
       "      <td>3.550633</td>\n",
       "      <td>11400054.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-12</th>\n",
       "      <td>329.52</td>\n",
       "      <td>1.086429</td>\n",
       "      <td>-0.266344</td>\n",
       "      <td>10297839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13</th>\n",
       "      <td>323.41</td>\n",
       "      <td>2.532389</td>\n",
       "      <td>-2.029626</td>\n",
       "      <td>8540442.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-14</th>\n",
       "      <td>327.78</td>\n",
       "      <td>0.195253</td>\n",
       "      <td>1.420217</td>\n",
       "      <td>5590393.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close    HL_PCT  PCT_change  Adj. Volume\n",
       "Date                                                     \n",
       "2017-07-10      316.05  0.598007    1.006711   13702080.0\n",
       "2017-07-11      327.22  0.018336    3.550633   11400054.0\n",
       "2017-07-12      329.52  1.086429   -0.266344   10297839.0\n",
       "2017-07-13      323.41  2.532389   -2.029626    8540442.0\n",
       "2017-07-14      327.78  0.195253    1.420217    5590393.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = quandl.get(\"WIKI/TSLA\")\n",
    "\n",
    "df = df[[\"Adj. Open\", \"Adj. High\", \"Adj. Low\", \"Adj. Close\", \"Adj. Volume\"]]\n",
    "df[\"HL_PCT\"] = (df[\"Adj. High\"] - df[\"Adj. Close\"]) / df[\"Adj. Close\"] * 100.0  # PCT - percent\n",
    "df[\"PCT_change\"] = (df[\"Adj. Close\"] - df[\"Adj. Open\"]) / df[\"Adj. Open\"] * 100.0\n",
    "df = df[[\"Adj. Close\", \"HL_PCT\", \"PCT_change\", \"Adj. Volume\"]]\n",
    "\n",
    "df.fillna(-9999, inplace=True)  # Fills the not a number with -99999, resulting an outlier\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast for 178 days\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>HL_PCT</th>\n",
       "      <th>PCT_change</th>\n",
       "      <th>Adj. Volume</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>316.05</td>\n",
       "      <td>0.598007</td>\n",
       "      <td>1.006711</td>\n",
       "      <td>13702080.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-11</th>\n",
       "      <td>327.22</td>\n",
       "      <td>0.018336</td>\n",
       "      <td>3.550633</td>\n",
       "      <td>11400054.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-12</th>\n",
       "      <td>329.52</td>\n",
       "      <td>1.086429</td>\n",
       "      <td>-0.266344</td>\n",
       "      <td>10297839.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13</th>\n",
       "      <td>323.41</td>\n",
       "      <td>2.532389</td>\n",
       "      <td>-2.029626</td>\n",
       "      <td>8540442.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-14</th>\n",
       "      <td>327.78</td>\n",
       "      <td>0.195253</td>\n",
       "      <td>1.420217</td>\n",
       "      <td>5590393.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close    HL_PCT  PCT_change  Adj. Volume  label\n",
       "Date                                                            \n",
       "2017-07-10      316.05  0.598007    1.006711   13702080.0    NaN\n",
       "2017-07-11      327.22  0.018336    3.550633   11400054.0    NaN\n",
       "2017-07-12      329.52  1.086429   -0.266344   10297839.0    NaN\n",
       "2017-07-13      323.41  2.532389   -2.029626    8540442.0    NaN\n",
       "2017-07-14      327.78  0.195253    1.420217    5590393.0    NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_col = \"Adj. Close\"\n",
    "forecast_out = int(math.ceil(0.1 * len(df)))  # 10% of the frame.\n",
    "print(\"Forecast for %d days\" %forecast_out)\n",
    "\n",
    "df[\"label\"] = df[forecast_col].shift(-forecast_out)\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop([\"label\"], 1))\n",
    "X = preprocessing.scale(X)  # Adds processing time\n",
    "X_lately = X[-forecast_out:]\n",
    "X = X[:-forecast_out]\n",
    "df.dropna(inplace=True)\n",
    "y = np.array(df[\"label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.765029921769\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3)\n",
    "clf = LinearRegression(n_jobs=-1)  # Classifier\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Saving the Classifier\n",
    "# with open(\"linearregression.pickle\", \"wb\") as f:\n",
    "#     pickle.dump(clf, f)\n",
    "\n",
    "# pickle_in = open(\"linearregression.pickle\", \"rb\")\n",
    "# clf = pickle.load(pickle_in)\n",
    "\n",
    "accuracy = clf.score(X_test, y_test)\n",
    "print(\"Accuracy: %s\" %accuracy)  # square error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting and Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast for the next 178 days:\n",
      " [ 215.6128732   219.21574919  218.11846178  203.95986179  197.41828594\n",
      "  209.81806568  216.88659993  206.86320153  223.92150979  214.59587355\n",
      "  206.73890354  209.85370707  200.27948265  204.96696212  207.2385882\n",
      "  200.13665884  199.51856284  214.44993326  210.33200897  207.57570009\n",
      "  212.29822885  204.40928032  209.36340484  203.74766592  201.10218723\n",
      "  198.80773437  208.61526884  212.05999578  215.00902312  205.02466436\n",
      "  206.04748984  216.44780281  210.09491915  210.52388095  218.09758368\n",
      "  219.21347857  224.52904096  223.35673092  222.7533497   229.37786751\n",
      "  233.0093669   230.25951434  229.4162715   231.59371944  233.43136777\n",
      "  255.8423577   253.20316667  248.37150252  244.61746739  244.77234668\n",
      "  245.88570055  243.92114468  252.16556571  245.22604502  248.17946722\n",
      "  268.81272972  259.54502066  264.52136799  265.7779993   267.11110722\n",
      "  256.71049773  261.14886278  254.26708706  251.73201729  258.82754104\n",
      "  251.92912424  256.27239699  258.72378897  266.29239433  263.77551599\n",
      "  288.71756155  274.77673074  288.93123748  286.03467943  287.01122301\n",
      "  276.87182983  282.21663542  281.43160825  289.12461964  308.59002754\n",
      "  280.24916959  294.27872044  262.92681701  261.45608401  253.30834301\n",
      "  259.28030362  256.64480641  251.28661649  252.35852298  253.86935813\n",
      "  251.56978433  253.56949067  269.41211096  262.99909185  279.77049512\n",
      "  279.13359503  263.57307345  256.5032774   264.03510378  258.53710764\n",
      "  270.03422995  276.38341829  296.56010573  280.79023622  277.39337076\n",
      "  281.19208403  333.03490247  324.75526909  303.56566505  300.39340625\n",
      "  302.23053784  324.31950899  314.06213535  295.28944171  314.8683019\n",
      "  301.75716389  298.0512525   301.82776796  305.71728231  304.78737352\n",
      "  311.45345515  320.16057972  305.95067386  301.8647699   310.53756275\n",
      "  323.74054813  311.30017286  306.92770694  333.08093547  316.33771735\n",
      "  312.83875919  331.15210671  327.1833665   319.74170794  320.42319773\n",
      "  326.89896082  312.15191548  311.9052784   312.54256461  308.51345476\n",
      "  307.58358499  300.79972838  310.80397846  314.00601617  331.28704432\n",
      "  335.88893578  356.48219985  347.52029761  336.59909799  342.05112007\n",
      "  355.80296333  370.89598485  372.44070063  389.39151294  369.07511152\n",
      "  391.43115249  402.49796375  390.4446622   364.97642014  362.34420001\n",
      "  368.7671764   365.42019853  376.43142605  372.48633003  368.43317831\n",
      "  353.32506957  365.16050106  361.52124554  352.994144    336.42858869\n",
      "  364.12914378  365.27179095  353.8354174   354.56842792  346.7972825\n",
      "  350.48514784  333.73460035  326.86790769] \n",
      "Accuracy: 0.765029921769\n"
     ]
    }
   ],
   "source": [
    "forecast_set = clf.predict(X_lately)\n",
    "print(\"Forecast for the next %s days:\\n\" %forecast_out, forecast_set, \"\\nAccuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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Ly6VSpN/4SnWX9MkfRTryXONxA+uW5toBOkyuLhm494lT8Vt3PBaP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S6iw2zS5MgOozwqsswkj2iaBsxdaC1IQYTBnEOkg6avB/qPvwv9/p9FbtPWAqjr1k5DU/RjGiOx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x27261065dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Forecast\"] = np.nan  # Specify that the column is not a number\n",
    "last_date = df.iloc[-1].name \n",
    "last_unix = last_date.timestamp()\n",
    "one_day = 86400  # Seconds in a day\n",
    "next_unix = last_unix + one_day\n",
    "for i in forecast_set:\n",
    "    # Making the future features NaN\n",
    "    next_date = datetime.datetime.fromtimestamp(next_unix)\n",
    "    next_unix += one_day\n",
    "    df.loc[next_date] = [np.nan for _ in range(len(df.columns) - 1)] + [i]  # The index of the dataframe\n",
    "    \n",
    "plt.figure(figsize=(20, 10), dpi= 100)\n",
    "df[\"Adj. Close\"].plot()\n",
    "df[\"Forecast\"].plot()\n",
    "plt.legend(loc=4)  # Putting it on the bottom right\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}