Stock_Algorithms/Linear_Regression_Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression Classification"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2007-01-01'\n",
        "end = '2018-12-31'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume\n",
              "Date                                                                       \n",
              "2007-01-03  20.080000  20.400000  19.350000  19.520000  19.520000  28350300\n",
              "2007-01-04  19.660000  19.860001  19.320000  19.790001  19.790001  23652500\n",
              "2007-01-05  19.540001  19.910000  19.540001  19.709999  19.709999  15902400\n",
              "2007-01-08  19.709999  19.860001  19.370001  19.469999  19.469999  15814800\n",
              "2007-01-09  19.450001  19.709999  19.370001  19.650000  19.650000  14494200"
            ],
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              "    <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>Adj Close</th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-03</th>\n",
              "      <td>20.080000</td>\n",
              "      <td>20.400000</td>\n",
              "      <td>19.350000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>28350300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-04</th>\n",
              "      <td>19.660000</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.320000</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>23652500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-05</th>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.910000</td>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>15902400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-08</th>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>15814800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-09</th>\n",
              "      <td>19.450001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>14494200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Open_Close'] = (dataset['Open'] - dataset['Adj Close'])/dataset['Open']\n",
        "dataset['High_Low'] = (dataset['High'] - dataset['Low'])/dataset['Low']\n",
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
              "Date                                                                          \n",
              "2007-01-04  19.660000  19.860001  19.320000  19.790001  19.790001  23652500   \n",
              "2007-01-05  19.540001  19.910000  19.540001  19.709999  19.709999  15902400   \n",
              "2007-01-08  19.709999  19.860001  19.370001  19.469999  19.469999  15814800   \n",
              "2007-01-09  19.450001  19.709999  19.370001  19.650000  19.650000  14494200   \n",
              "2007-01-10  19.639999  20.020000  19.500000  20.010000  20.010000  19783200   \n",
              "\n",
              "            Open_Close  High_Low  Increase_Decrease  Buy_Sell_on_Open  \\\n",
              "Date                                                                    \n",
              "2007-01-04   -0.006612  0.027950                  0                 0   \n",
              "2007-01-05   -0.008700  0.018935                  0                 1   \n",
              "2007-01-08    0.012177  0.025297                  0                 0   \n",
              "2007-01-09   -0.010283  0.017553                  1                 1   \n",
              "2007-01-10   -0.018839  0.026667                  1                 1   \n",
              "\n",
              "            Buy_Sell   Returns  \n",
              "Date                            \n",
              "2007-01-04         0  0.013832  \n",
              "2007-01-05         0 -0.004043  \n",
              "2007-01-08         1 -0.012177  \n",
              "2007-01-09         1  0.009245  \n",
              "2007-01-10         1  0.018321  "
            ],
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              "<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>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open_Close</th>\n",
              "      <th>High_Low</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</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",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-04</th>\n",
              "      <td>19.660000</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.320000</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>23652500</td>\n",
              "      <td>-0.006612</td>\n",
              "      <td>0.027950</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.013832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-05</th>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.910000</td>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>15902400</td>\n",
              "      <td>-0.008700</td>\n",
              "      <td>0.018935</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.004043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-08</th>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>15814800</td>\n",
              "      <td>0.012177</td>\n",
              "      <td>0.025297</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.012177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-09</th>\n",
              "      <td>19.450001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>14494200</td>\n",
              "      <td>-0.010283</td>\n",
              "      <td>0.017553</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.009245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-10</th>\n",
              "      <td>19.639999</td>\n",
              "      <td>20.020000</td>\n",
              "      <td>19.500000</td>\n",
              "      <td>20.010000</td>\n",
              "      <td>20.010000</td>\n",
              "      <td>19783200</td>\n",
              "      <td>-0.018839</td>\n",
              "      <td>0.026667</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.018321</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "(3019, 12)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 3019 entries, 2007-01-04 to 2018-12-31\n",
            "Data columns (total 12 columns):\n",
            "Open                 3019 non-null float64\n",
            "High                 3019 non-null float64\n",
            "Low                  3019 non-null float64\n",
            "Close                3019 non-null float64\n",
            "Adj Close            3019 non-null float64\n",
            "Volume               3019 non-null int32\n",
            "Open_Close           3019 non-null float64\n",
            "High_Low             3019 non-null float64\n",
            "Increase_Decrease    3019 non-null int32\n",
            "Buy_Sell_on_Open     3019 non-null int32\n",
            "Buy_Sell             3019 non-null int32\n",
            "Returns              3019 non-null float64\n",
            "dtypes: float64(8), int32(4)\n",
            "memory usage: 259.4 KB\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(dataset['Adj Close'])"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x165bc842860>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(dataset.corr())"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x165bc914048>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low','Adj Close','Volume', 'Open_Close', 'High_Low', 'Returns']]\n",
        "y = dataset['Buy_Sell']"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                   test_size=0.4, random_state=101)"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lm = LinearRegression()\n",
        "lm.fit(X_train,y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(lm.intercept_)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.505640629758\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coeff_df = pd.DataFrame(lm.coef_,X.columns,columns={'Coefficient'})\n",
        "coeff_df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": [
              "             Coefficient\n",
              "Open        1.412059e-03\n",
              "High        5.360175e-02\n",
              "Low        -1.917319e-02\n",
              "Adj Close  -4.230593e-02\n",
              "Volume      1.314511e-10\n",
              "Open_Close  5.664673e-01\n",
              "High_Low    8.127310e-02\n",
              "Returns     7.507877e-01"
            ],
            "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>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Open</th>\n",
              "      <td>1.412059e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>5.360175e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low</th>\n",
              "      <td>-1.917319e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adj Close</th>\n",
              "      <td>-4.230593e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Volume</th>\n",
              "      <td>1.314511e-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Open_Close</th>\n",
              "      <td>5.664673e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High_Low</th>\n",
              "      <td>8.127310e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Returns</th>\n",
              "      <td>7.507877e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lm.predict(X_test)\n",
        "plt.scatter(y_test,y_pred)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x165be3e7908>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot((y_test-y_pred),bins=50)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x165bca20358>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.499526063141\n",
            "MSE: 0.250861187586\n",
            "RMSE: 0.500860447216\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "file_extension": ".py",
      "version": "3.5.5",
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.14.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}