LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 70,
   "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": 70,
     "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": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units=50,return_sequences=True,input_shape=(X_train.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50,return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50,return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "78/78 [==============================] - 10s 11ms/step - loss: 0.2879\n",
      "Epoch 2/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0344\n",
      "Epoch 3/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0311\n",
      "Epoch 4/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0280\n",
      "Epoch 5/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0286\n",
      "Epoch 6/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0259\n",
      "Epoch 7/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0211\n",
      "Epoch 8/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0215\n",
      "Epoch 9/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0205\n",
      "Epoch 10/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0205\n",
      "Epoch 11/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0183\n",
      "Epoch 12/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0177\n",
      "Epoch 13/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0169\n",
      "Epoch 14/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0180\n",
      "Epoch 15/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0174\n",
      "Epoch 16/50\n",
      "78/78 [==============================] - 1s 14ms/step - loss: 0.0170\n",
      "Epoch 17/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0152\n",
      "Epoch 18/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0154\n",
      "Epoch 19/50\n",
      "78/78 [==============================] - 1s 14ms/step - loss: 0.0172\n",
      "Epoch 20/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0169\n",
      "Epoch 21/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0156\n",
      "Epoch 22/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0149\n",
      "Epoch 23/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0161\n",
      "Epoch 24/50\n",
      "78/78 [==============================] - 1s 14ms/step - loss: 0.0145\n",
      "Epoch 25/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0151\n",
      "Epoch 26/50\n",
      "78/78 [==============================] - 1s 13ms/step - loss: 0.0128\n",
      "Epoch 27/50\n",
      "78/78 [==============================] - 1s 16ms/step - loss: 0.0121\n",
      "Epoch 28/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0131\n",
      "Epoch 29/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0128\n",
      "Epoch 30/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0152\n",
      "Epoch 31/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0122\n",
      "Epoch 32/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0123\n",
      "Epoch 33/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0115\n",
      "Epoch 34/50\n",
      "78/78 [==============================] - 1s 12ms/step - loss: 0.0118\n",
      "Epoch 35/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0119\n",
      "Epoch 36/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0116\n",
      "Epoch 37/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0121\n",
      "Epoch 38/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0119\n",
      "Epoch 39/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0113\n",
      "Epoch 40/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0118\n",
      "Epoch 41/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0112\n",
      "Epoch 42/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0117\n",
      "Epoch 43/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0106\n",
      "Epoch 44/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0103\n",
      "Epoch 45/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0104\n",
      "Epoch 46/50\n",
      "78/78 [==============================] - 1s 9ms/step - loss: 0.0104\n",
      "Epoch 47/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0096\n",
      "Epoch 48/50\n",
      "78/78 [==============================] - 1s 11ms/step - loss: 0.0102\n",
      "Epoch 49/50\n",
      "78/78 [==============================] - 1s 10ms/step - loss: 0.0092\n",
      "Epoch 50/50\n",
      "78/78 [==============================] - 1s 9ms/step - loss: 0.0095\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x176631db490>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='mean_squared_error')\n",
    "model.fit(X_train,y_train,epochs=20,batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n"
     ]
    }
   ],
   "source": [
    "y_hat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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 4-layers of LSTM')\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": 78,
   "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, \"LSTM.csv\"), index=False)"
   ]
  }
 ],
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