part 04.ipynb

{
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   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import quandl\n",
    "\n",
    "from sklearn import preprocessing, cross_validation, svm\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>100.01</td>\n",
       "      <td>104.06</td>\n",
       "      <td>95.96</td>\n",
       "      <td>100.335</td>\n",
       "      <td>44659000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.159839</td>\n",
       "      <td>52.191109</td>\n",
       "      <td>48.128568</td>\n",
       "      <td>50.322842</td>\n",
       "      <td>44659000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>101.01</td>\n",
       "      <td>109.08</td>\n",
       "      <td>100.50</td>\n",
       "      <td>108.310</td>\n",
       "      <td>22834300</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.661387</td>\n",
       "      <td>54.708881</td>\n",
       "      <td>50.405597</td>\n",
       "      <td>54.322689</td>\n",
       "      <td>22834300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>110.76</td>\n",
       "      <td>113.48</td>\n",
       "      <td>109.05</td>\n",
       "      <td>109.400</td>\n",
       "      <td>18256100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55.551482</td>\n",
       "      <td>56.915693</td>\n",
       "      <td>54.693835</td>\n",
       "      <td>54.869377</td>\n",
       "      <td>18256100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>111.24</td>\n",
       "      <td>111.60</td>\n",
       "      <td>103.57</td>\n",
       "      <td>104.870</td>\n",
       "      <td>15247300</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55.792225</td>\n",
       "      <td>55.972783</td>\n",
       "      <td>51.945350</td>\n",
       "      <td>52.597363</td>\n",
       "      <td>15247300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>104.76</td>\n",
       "      <td>108.00</td>\n",
       "      <td>103.88</td>\n",
       "      <td>106.000</td>\n",
       "      <td>9188600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52.542193</td>\n",
       "      <td>54.167209</td>\n",
       "      <td>52.100830</td>\n",
       "      <td>53.164113</td>\n",
       "      <td>9188600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Close    Volume  Ex-Dividend  \\\n",
       "Date                                                                 \n",
       "2004-08-19  100.01  104.06   95.96  100.335  44659000            0   \n",
       "2004-08-20  101.01  109.08  100.50  108.310  22834300            0   \n",
       "2004-08-23  110.76  113.48  109.05  109.400  18256100            0   \n",
       "2004-08-24  111.24  111.60  103.57  104.870  15247300            0   \n",
       "2004-08-25  104.76  108.00  103.88  106.000   9188600            0   \n",
       "\n",
       "            Split Ratio  Adj. Open  Adj. High   Adj. Low  Adj. Close  \\\n",
       "Date                                                                   \n",
       "2004-08-19            1  50.159839  52.191109  48.128568   50.322842   \n",
       "2004-08-20            1  50.661387  54.708881  50.405597   54.322689   \n",
       "2004-08-23            1  55.551482  56.915693  54.693835   54.869377   \n",
       "2004-08-24            1  55.792225  55.972783  51.945350   52.597363   \n",
       "2004-08-25            1  52.542193  54.167209  52.100830   53.164113   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
       "2004-08-19     44659000  \n",
       "2004-08-20     22834300  \n",
       "2004-08-23     18256100  \n",
       "2004-08-24     15247300  \n",
       "2004-08-25      9188600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quandl.ApiConfig.api_key = ''\n",
    "\n",
    "# load Google Stock data\n",
    "df = quandl.get('WIKI/GOOGL')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "      <th>HL_PCT</th>\n",
       "      <th>PCT_change</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>2004-08-19</th>\n",
       "      <td>50.322842</td>\n",
       "      <td>44659000</td>\n",
       "      <td>3.712563</td>\n",
       "      <td>0.324968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>54.322689</td>\n",
       "      <td>22834300</td>\n",
       "      <td>0.710922</td>\n",
       "      <td>7.227007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>54.869377</td>\n",
       "      <td>18256100</td>\n",
       "      <td>3.729433</td>\n",
       "      <td>-1.227880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>52.597363</td>\n",
       "      <td>15247300</td>\n",
       "      <td>6.417469</td>\n",
       "      <td>-5.726357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>53.164113</td>\n",
       "      <td>9188600</td>\n",
       "      <td>1.886792</td>\n",
       "      <td>1.183658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close  Adj. Volume    HL_PCT  PCT_change\n",
       "Date                                                     \n",
       "2004-08-19   50.322842     44659000  3.712563    0.324968\n",
       "2004-08-20   54.322689     22834300  0.710922    7.227007\n",
       "2004-08-23   54.869377     18256100  3.729433   -1.227880\n",
       "2004-08-24   52.597363     15247300  6.417469   -5.726357\n",
       "2004-08-25   53.164113      9188600  1.886792    1.183658"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# High-low percentage\n",
    "df['HL_PCT'] = (df['Adj. High'] - df['Adj. Close']) / df['Adj. Close'] * 100\n",
    "\n",
    "# Percentage change ((new - old) / old * 100)\n",
    "df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100\n",
    "\n",
    "# Define useful columns\n",
    "columns = [\n",
    "    'Adj. Close',\n",
    "    'Adj. Volume',\n",
    "    'HL_PCT',\n",
    "    'PCT_change',\n",
    "]\n",
    "\n",
    "df = df[columns]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_col = 'Adj. Close'\n",
    "\n",
    "# fill column to prevent missing data\n",
    "df.fillna(-99999, inplace=True)\n",
    "\n",
    "import math\n",
    "\n",
    "# amount of days in advance\n",
    "forecast_out = int(math.ceil(0.01*len(df)))\n",
    "forecast_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "      <th>HL_PCT</th>\n",
       "      <th>PCT_change</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>2004-08-19</th>\n",
       "      <td>50.322842</td>\n",
       "      <td>44659000</td>\n",
       "      <td>3.712563</td>\n",
       "      <td>0.324968</td>\n",
       "      <td>69.399229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>54.322689</td>\n",
       "      <td>22834300</td>\n",
       "      <td>0.710922</td>\n",
       "      <td>7.227007</td>\n",
       "      <td>68.752232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>54.869377</td>\n",
       "      <td>18256100</td>\n",
       "      <td>3.729433</td>\n",
       "      <td>-1.227880</td>\n",
       "      <td>69.639972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>52.597363</td>\n",
       "      <td>15247300</td>\n",
       "      <td>6.417469</td>\n",
       "      <td>-5.726357</td>\n",
       "      <td>69.078238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>53.164113</td>\n",
       "      <td>9188600</td>\n",
       "      <td>1.886792</td>\n",
       "      <td>1.183658</td>\n",
       "      <td>67.839414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close  Adj. Volume    HL_PCT  PCT_change      label\n",
       "Date                                                                \n",
       "2004-08-19   50.322842     44659000  3.712563    0.324968  69.399229\n",
       "2004-08-20   54.322689     22834300  0.710922    7.227007  68.752232\n",
       "2004-08-23   54.869377     18256100  3.729433   -1.227880  69.639972\n",
       "2004-08-24   52.597363     15247300  6.417469   -5.726357  69.078238\n",
       "2004-08-25   53.164113      9188600  1.886792    1.183658  67.839414"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the label to the shift of the forecast col\n",
    "df['label'] = df[forecast_col].shift(-forecast_out)\n",
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/preprocessing/data.py:167: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
      "  warnings.warn(\"Numerical issues were encountered \"\n"
     ]
    }
   ],
   "source": [
    "# X feature\n",
    "X = np.array(df.drop(['label'], 1))\n",
    "\n",
    "# y labels\n",
    "y = np.array(df['label'])\n",
    "\n",
    "# Scale up X\n",
    "X = preprocessing.scale(X)\n",
    "y = np.array(df['label'])\n",
    "\n",
    "# Create the train and test set\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9705800137716929"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LinearRegression()\n",
    "# Train the model\n",
    "clf.fit(X_train, y_train)\n",
    "# Test the accuracy\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81936128205999459"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVR()\n",
    "# Train the model\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  }
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