Feature_Importance_Continuous.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Stock's Data Feature Importance (Continuous)"
      ],
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          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
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    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2020-01-01'\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 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "            Adj Close  Close  High   Low  Open    Volume\n",
              "Date                                                    \n",
              "2014-01-02       3.95   3.95  3.98  3.84  3.85  20548400\n",
              "2014-01-03       4.00   4.00  4.00  3.88  3.98  22887200\n",
              "2014-01-06       4.13   4.13  4.18  3.99  4.01  42398300\n",
              "2014-01-07       4.18   4.18  4.25  4.11  4.19  42932100\n",
              "2014-01-08       4.18   4.18  4.26  4.14  4.23  30678700"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</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>2014-01-02</th>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.85</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.98</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.01</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.19</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.23</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
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    },
    {
      "cell_type": "code",
      "source": [
        "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()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": [
              "            Adj Close  Close  High   Low  Open    Volume  Increase_Decrease  \\\n",
              "Date                                                                          \n",
              "2014-01-03       4.00   4.00  4.00  3.88  3.98  22887200                  1   \n",
              "2014-01-06       4.13   4.13  4.18  3.99  4.01  42398300                  1   \n",
              "2014-01-07       4.18   4.18  4.25  4.11  4.19  42932100                  0   \n",
              "2014-01-08       4.18   4.18  4.26  4.14  4.23  30678700                  0   \n",
              "2014-01-09       4.09   4.09  4.23  4.05  4.20  30667600                  0   \n",
              "\n",
              "            Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "Date                                              \n",
              "2014-01-03                 1         1  0.012658  \n",
              "2014-01-06                 1         1  0.032500  \n",
              "2014-01-07                 1         0  0.012106  \n",
              "2014-01-08                 0         0  0.000000  \n",
              "2014-01-09                 0         1 -0.021531  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.98</td>\n",
              "      <td>22887200</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.012658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.01</td>\n",
              "      <td>42398300</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.032500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.19</td>\n",
              "      <td>42932100</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.012106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.23</td>\n",
              "      <td>30678700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-09</th>\n",
              "      <td>4.09</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.23</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.20</td>\n",
              "      <td>30667600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.021531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "(1509, 10)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
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    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low', 'Volume', 'Increase_Decrease','Buy_Sell_on_Open', 'Buy_Sell', 'Returns']]\n",
        "y = dataset['Adj Close']"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
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        "jupyter": {
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        "execution": {
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          "shell.execute_reply": "2020-06-28T18:20:20.991Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Create decision tree classifer object\n",
        "clf = RandomForestRegressor(random_state=0, n_jobs=-1)\n",
        "\n",
        "# Train model\n",
        "model = clf.fit(X, y)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
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        "execution": {
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          "shell.execute_reply": "2020-06-28T18:20:20.993Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate feature importances\n",
        "importances = model.feature_importances_"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Sort feature importances in descending order\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Rearrange feature names so they match the sorted feature importances\n",
        "names = [dataset.columns[i] for i in indices]\n",
        "\n",
        "# Create plot\n",
        "plt.figure()\n",
        "\n",
        "# Create plot title\n",
        "plt.title(\"Feature Importance\")\n",
        "\n",
        "# Add bars\n",
        "plt.bar(range(X.shape[1]), importances[indices])\n",
        "\n",
        "# Add feature names as x-axis labels\n",
        "plt.xticks(range(X.shape[1]), names, rotation=90)\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:20:21.099Z",
          "iopub.execute_input": "2020-06-28T18:20:21.104Z",
          "iopub.status.idle": "2020-06-28T18:20:21.267Z",
          "shell.execute_reply": "2020-06-28T18:20:21.275Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "file_extension": ".py",
      "name": "python",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "version": "3.5.5",
      "pygments_lexer": "ipython3"
    },
    "kernelspec": {
      "argv": [
        "C:\\Users\\Tin Hang\\Anaconda3\\envs\\py35\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.23.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}