SimpleRNN_Model_Stock_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "aboriginal-vitamin",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import modules and packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "\n",
    "from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, TensorBoard\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "constant-wallpaper",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape == (1260, 7)\n",
      "All timestamps == 1260\n",
      "Featured selected: ['Open', 'High', 'Low', 'Close', 'Adj Close']\n"
     ]
    }
   ],
   "source": [
    "dataset_train = pd.read_csv('AMD_Dataset/AMD2010-2021.csv')\n",
    "\n",
    "cols = list(dataset_train)[1:6]\n",
    "# Select features (columns) to be involved intro training and predictions\n",
    "cols = list(dataset_train)[1:6]\n",
    "\n",
    "# Extract dates (will be used in visualization)\n",
    "datelist_train = list(dataset_train['Date'])\n",
    "datelist_train = [dt.datetime.strptime(date, '%Y-%m-%d').date() for date in datelist_train]\n",
    "\n",
    "print('Training set shape == {}'.format(dataset_train.shape))\n",
    "print('All timestamps == {}'.format(len(datelist_train)))\n",
    "print('Featured selected: {}'.format(cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "nonprofit-japan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set == (1260, 5).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 2.77    ,  2.82    ,  2.63    ,  2.77    ,  2.77    ],\n",
       "       [ 2.77    ,  2.8     ,  2.64    ,  2.75    ,  2.75    ],\n",
       "       [ 2.66    ,  2.71    ,  2.47    ,  2.51    ,  2.51    ],\n",
       "       ...,\n",
       "       [90.779999, 92.849998, 90.190002, 92.290001, 92.290001],\n",
       "       [92.099998, 92.300003, 90.870003, 91.709999, 91.709999],\n",
       "       [92.110001, 96.059998, 90.919998, 92.300003, 92.300003]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train = dataset_train[cols].astype(str)\n",
    "for i in cols:\n",
    "    for j in range(0, len(dataset_train)):\n",
    "        dataset_train[i][j] = dataset_train[i][j].replace(',', '')\n",
    "\n",
    "dataset_train = dataset_train.astype(float)\n",
    "\n",
    "# Using multiple features (predictors)\n",
    "training_set = dataset_train.values\n",
    "\n",
    "print('Shape of training set == {}.'.format(training_set.shape))\n",
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "through-dublin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.01233131],\n",
       "       [-1.01233131],\n",
       "       [-1.01708769],\n",
       "       ...,\n",
       "       [ 2.79320761],\n",
       "       [ 2.85028417],\n",
       "       [ 2.8507167 ]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "\n",
    "sc_predict = StandardScaler()\n",
    "sc_predict.fit_transform(training_set[:, 0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "mexican-reduction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape == (1166, 90, 4).\n",
      "y_train shape == (1166, 1).\n"
     ]
    }
   ],
   "source": [
    "# Creating a data structure with 90 timestamps and 1 output\n",
    "X_train = []\n",
    "y_train = []\n",
    "\n",
    "n_future = 5   # Number of days we want top predict into the future\n",
    "n_past = 90     # Number of past days we want to use to predict the future\n",
    "\n",
    "for i in range(n_past, len(training_set_scaled) - n_future +1):\n",
    "    X_train.append(training_set_scaled[i - n_past:i, 0:dataset_train.shape[1] - 1])\n",
    "    y_train.append(training_set_scaled[i + n_future - 1:i + n_future, 0])\n",
    "\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "\n",
    "print('X_train shape == {}.'.format(X_train.shape))\n",
    "print('y_train shape == {}.'.format(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "higher-bidder",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import Libraries and packages from Keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import SimpleRNN\n",
    "from keras.layers import Dropout\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "efficient-dimension",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Initializing the Neural Network based on LSTM\n",
    "model = Sequential()\n",
    "\n",
    "# Adding 1st LSTM layer\n",
    "model.add(SimpleRNN(units=50, return_sequences=True, input_shape=(n_past, dataset_train.shape[1]-1)))\n",
    "\n",
    "# Adding 2nd LSTM layer\n",
    "model.add(SimpleRNN(units=50, return_sequences=True))\n",
    "\n",
    "model.add(SimpleRNN(units=50, return_sequences=True))\n",
    "\n",
    "\n",
    "model.add(SimpleRNN(units=50, return_sequences=False))\n",
    "\n",
    "\n",
    "# Adding Dropout\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# Output layer\n",
    "model.add(Dense(units=1, activation='linear'))\n",
    "\n",
    "# Compiling the Neural Network\n",
    "model.compile(optimizer = Adam(learning_rate=0.001), loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "weird-wales",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "2/4 [==============>...............] - ETA: 1s - loss: 0.9178WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2724s vs `on_train_batch_end` time: 1.2646s). Check your callbacks.\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.7039\n",
      "Epoch 00001: val_loss improved from inf to 0.99761, saving model to weights.h5\n",
      "4/4 [==============================] - 3s 754ms/step - loss: 0.7039 - val_loss: 0.9976\n",
      "Epoch 2/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.3191\n",
      "Epoch 00002: val_loss did not improve from 0.99761\n",
      "4/4 [==============================] - 1s 209ms/step - loss: 0.3191 - val_loss: 1.3141\n",
      "Epoch 3/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.2830\n",
      "Epoch 00003: val_loss improved from 0.99761 to 0.97574, saving model to weights.h5\n",
      "4/4 [==============================] - 1s 210ms/step - loss: 0.2830 - val_loss: 0.9757\n",
      "Epoch 4/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.2426\n",
      "Epoch 00004: val_loss improved from 0.97574 to 0.73997, saving model to weights.h5\n",
      "4/4 [==============================] - 1s 214ms/step - loss: 0.2426 - val_loss: 0.7400\n",
      "Epoch 5/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.2098\n",
      "Epoch 00005: val_loss improved from 0.73997 to 0.73978, saving model to weights.h5\n",
      "4/4 [==============================] - 1s 209ms/step - loss: 0.2098 - val_loss: 0.7398\n",
      "Epoch 6/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1961\n",
      "Epoch 00006: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 202ms/step - loss: 0.1961 - val_loss: 0.7811\n",
      "Epoch 7/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1671\n",
      "Epoch 00007: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 198ms/step - loss: 0.1671 - val_loss: 1.1990\n",
      "Epoch 8/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1152\n",
      "Epoch 00008: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 193ms/step - loss: 0.1152 - val_loss: 1.7927\n",
      "Epoch 9/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0988\n",
      "Epoch 00009: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 194ms/step - loss: 0.0988 - val_loss: 1.3540\n",
      "Epoch 10/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0752\n",
      "Epoch 00010: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 183ms/step - loss: 0.0752 - val_loss: 1.1212\n",
      "Epoch 11/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0683\n",
      "Epoch 00011: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 181ms/step - loss: 0.0683 - val_loss: 0.9855\n",
      "Epoch 12/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0667\n",
      "Epoch 00012: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 181ms/step - loss: 0.0667 - val_loss: 1.0000\n",
      "Epoch 13/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0539\n",
      "Epoch 00013: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 177ms/step - loss: 0.0539 - val_loss: 1.0680\n",
      "Epoch 14/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0452\n",
      "Epoch 00014: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 179ms/step - loss: 0.0452 - val_loss: 1.1480\n",
      "Epoch 15/50\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0379\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.73978\n",
      "4/4 [==============================] - 1s 181ms/step - loss: 0.0379 - val_loss: 1.1386\n",
      "Epoch 00015: early stopping\n",
      "CPU times: user 43.9 s, sys: 28.7 s, total: 1min 12s\n",
      "Wall time: 21.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "es = EarlyStopping(monitor='val_loss', min_delta=1e-10, patience=10, verbose=1)\n",
    "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, verbose=1)\n",
    "mcp = ModelCheckpoint(filepath='weights.h5', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True)\n",
    "\n",
    "tb = TensorBoard('logs')\n",
    "\n",
    "history = model.fit(X_train, y_train, shuffle=True, epochs=50, callbacks=[es, rlr, mcp, tb], validation_split=0.2, verbose=1, batch_size=256)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "continuous-customer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate list of sequence of days for predictions\n",
    "datelist_future = pd.date_range(datelist_train[-1], periods=n_future, freq='1d').tolist()\n",
    "\n",
    "'''\n",
    "Remeber, we have datelist_train from begining.\n",
    "'''\n",
    "\n",
    "# Convert Pandas Timestamp to Datetime object (for transformation) --> FUTURE\n",
    "datelist_future_ = []\n",
    "for this_timestamp in datelist_future:\n",
    "    datelist_future_.append(this_timestamp.date())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "whole-clock",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Perform predictions\n",
    "predictions_future = model.predict(X_train[-n_future:])\n",
    "\n",
    "predictions_train = model.predict(X_train[n_past:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "employed-martial",
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>5.631549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-27</th>\n",
       "      <td>6.109632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-28</th>\n",
       "      <td>5.990811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-29</th>\n",
       "      <td>5.896537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>5.817658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>48.997963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>49.536217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>49.129772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>49.764805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>48.113560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1076 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close\n",
       "2016-09-26   5.631549\n",
       "2016-09-27   6.109632\n",
       "2016-09-28   5.990811\n",
       "2016-09-29   5.896537\n",
       "2016-09-30   5.817658\n",
       "...               ...\n",
       "2020-12-28  48.997963\n",
       "2020-12-29  49.536217\n",
       "2020-12-30  49.129772\n",
       "2020-12-31  49.764805\n",
       "2021-01-04  48.113560\n",
       "\n",
       "[1076 rows x 1 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inverse the predictions to original measurements\n",
    "\n",
    "# ---> Special function: convert <datetime.date> to <Timestamp>\n",
    "def datetime_to_timestamp(x):\n",
    "    '''\n",
    "        x : a given datetime value (datetime.date)\n",
    "    '''\n",
    "    return datetime.strptime(x.strftime('%Y%m%d'), '%Y%m%d')\n",
    "\n",
    "\n",
    "y_pred_future = sc_predict.inverse_transform(predictions_future)\n",
    "y_pred_train = sc_predict.inverse_transform(predictions_train)\n",
    "\n",
    "PREDICTIONS_FUTURE = pd.DataFrame(y_pred_future, columns=['Close']).set_index(pd.Series(datelist_future))\n",
    "PREDICTION_TRAIN = pd.DataFrame(y_pred_train, columns=['Close']).set_index(pd.Series(datelist_train[2 * n_past + n_future -1:]))\n",
    "\n",
    "# Convert <datetime.date> to <Timestamp> for PREDCITION_TRAIN\n",
    "PREDICTION_TRAIN.index = PREDICTION_TRAIN.index.to_series().apply(datetime_to_timestamp)\n",
    "\n",
    "PREDICTION_TRAIN.head(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "sticky-framing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parse training set timestamp for better visualization\n",
    "dataset_train = pd.DataFrame(dataset_train, columns=cols)\n",
    "dataset_train.index = datelist_train\n",
    "dataset_train.index = pd.to_datetime(dataset_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "vocational-implementation",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set plot size \n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 5\n",
    "\n",
    "# Plot parameters\n",
    "START_DATE_FOR_PLOTTING =  '2021-01-01'\n",
    "\n",
    "plt.plot(PREDICTIONS_FUTURE.index, PREDICTIONS_FUTURE['Close'], color='r', label='Predicted Stock Price')\n",
    "plt.plot(PREDICTION_TRAIN.loc[START_DATE_FOR_PLOTTING:].index, PREDICTION_TRAIN.loc[START_DATE_FOR_PLOTTING:]['Close'], color='orange', label='Training predictions')\n",
    "plt.plot(dataset_train.loc[START_DATE_FOR_PLOTTING:].index, dataset_train.loc[START_DATE_FOR_PLOTTING:]['Close'], color='b', label='Actual Stock Price')\n",
    "\n",
    "plt.axvline(x = min(PREDICTIONS_FUTURE.index), color='green', linewidth=2, linestyle='--')\n",
    "\n",
    "plt.grid(which='major', color='#cccccc', alpha=0.5)\n",
    "\n",
    "plt.legend(shadow=True)\n",
    "plt.title('Predcitions and Acutal Stock Prices', family='Arial', fontsize=12)\n",
    "plt.xlabel('Timeline', family='Arial', fontsize=10)\n",
    "plt.ylabel('Stock Price Value', family='Arial', fontsize=10)\n",
    "plt.xticks(rotation=45, fontsize=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "alpine-federation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "\n",
    "PREDICTIONS_FUTURE.to_csv('Prediction_SimpleRNN_AMD_5Year.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "southeast-renewal",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "turkish-testimony",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "present-seafood",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "young-royal",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "awful-apple",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}