Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 2,
     "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "RandomForestRegressor(random_state=7777)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest_model = RandomForestRegressor(n_estimators=100, random_state=7777)\n",
    "random_forest_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = random_forest_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 Random Forest(n=100)')\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": 8,
   "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, \"random_forest.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
}