Support Vector Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>index</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>gdp</th>\n",
       "      <th>effr</th>\n",
       "      <th>acwi_open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.345435</td>\n",
       "      <td>-1.342970</td>\n",
       "      <td>-1.356437</td>\n",
       "      <td>-0.762869</td>\n",
       "      <td>-1.196648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.329387</td>\n",
       "      <td>-1.348735</td>\n",
       "      <td>-1.356437</td>\n",
       "      <td>-0.762869</td>\n",
       "      <td>-1.221651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.319719</td>\n",
       "      <td>-1.328121</td>\n",
       "      <td>-1.356437</td>\n",
       "      <td>-0.756076</td>\n",
       "      <td>-1.182173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.311241</td>\n",
       "      <td>-1.319250</td>\n",
       "      <td>-1.356437</td>\n",
       "      <td>-0.756076</td>\n",
       "      <td>-1.149933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-1.318699</td>\n",
       "      <td>-1.310099</td>\n",
       "      <td>-1.356437</td>\n",
       "      <td>-0.756076</td>\n",
       "      <td>-1.123615</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2493</th>\n",
       "      <td>2493</td>\n",
       "      <td>1.559943</td>\n",
       "      <td>1.533400</td>\n",
       "      <td>2.088713</td>\n",
       "      <td>2.803442</td>\n",
       "      <td>1.271356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2494</th>\n",
       "      <td>2494</td>\n",
       "      <td>1.568512</td>\n",
       "      <td>1.567091</td>\n",
       "      <td>2.088713</td>\n",
       "      <td>2.803442</td>\n",
       "      <td>1.310175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2495</th>\n",
       "      <td>2495</td>\n",
       "      <td>1.582418</td>\n",
       "      <td>1.569266</td>\n",
       "      <td>2.088713</td>\n",
       "      <td>2.803442</td>\n",
       "      <td>1.285831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>2496</td>\n",
       "      <td>1.587353</td>\n",
       "      <td>1.589633</td>\n",
       "      <td>2.088713</td>\n",
       "      <td>2.803442</td>\n",
       "      <td>1.301622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>2497</td>\n",
       "      <td>1.547618</td>\n",
       "      <td>1.597528</td>\n",
       "      <td>2.088713</td>\n",
       "      <td>2.803442</td>\n",
       "      <td>1.320045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2498 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index     close      open       gdp      effr  acwi_open\n",
       "0         0 -1.345435 -1.342970 -1.356437 -0.762869  -1.196648\n",
       "1         1 -1.329387 -1.348735 -1.356437 -0.762869  -1.221651\n",
       "2         2 -1.319719 -1.328121 -1.356437 -0.756076  -1.182173\n",
       "3         3 -1.311241 -1.319250 -1.356437 -0.756076  -1.149933\n",
       "4         4 -1.318699 -1.310099 -1.356437 -0.756076  -1.123615\n",
       "...     ...       ...       ...       ...       ...        ...\n",
       "2493   2493  1.559943  1.533400  2.088713  2.803442   1.271356\n",
       "2494   2494  1.568512  1.567091  2.088713  2.803442   1.310175\n",
       "2495   2495  1.582418  1.569266  2.088713  2.803442   1.285831\n",
       "2496   2496  1.587353  1.589633  2.088713  2.803442   1.301622\n",
       "2497   2497  1.547618  1.597528  2.088713  2.803442   1.320045\n",
       "\n",
       "[2498 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_main = os.getcwd()\n",
    "dir_prepared_data = os.path.join(dir_main, \"dataset\", \"prepared\")\n",
    "df = pd.read_csv(os.path.join(dir_prepared_data, \"dataset.csv\"),\n",
    "                      sep=\",\",\n",
    "                      header=0,\n",
    "                      dtype={\"date\": object, \"close\": float, \"open\": float,\n",
    "                             \"high\": float, \"low\": float,\n",
    "                             \"gdp\": float, \"effr\": float,\n",
    "                             \"acwi_open\": float})\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.drop(['date','high','low'], axis=1, inplace=True)\n",
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = df[:-30]\n",
    "test_data = df[-30:]\n",
    "\n",
    "X_train = train_data.drop('close', axis=1).to_numpy()\n",
    "y_train = train_data['close'].to_numpy()\n",
    "X_test = test_data.drop('close', axis=1).to_numpy()\n",
    "y_test = test_data['close'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "SVR(kernel='linear')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model = SVR(kernel='linear')\n",
    "svm_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = svm_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_temp = np.arange(len(y_test)) + 1\n",
    "plt.plot(x_temp, y_test, color = 'black', label = 'Stock Price')\n",
    "plt.plot(x_temp, y_hat, color = 'green', label = 'Predicted Stock Price')\n",
    "plt.title('S&P500 Stock Price Prediction using SVM')\n",
    "plt.xlabel('# of prediction date')\n",
    "plt.ylabel('S&P500 Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir = os.path.join(os.getcwd(), \"dataset\", \"Prediction\")\n",
    "df = pd.DataFrame(y_hat, columns=[\"prediction\"])\n",
    "df.to_csv(os.path.join(dir, \"svm.csv\"), index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python310",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}