RNN_TSLA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Close\n",
      "Date                  \n",
      "2014-01-14   32.254002\n",
      "2014-01-15   32.826000\n",
      "2014-01-16   34.194000\n",
      "2014-01-17   34.001999\n",
      "2014-01-21   35.335999\n",
      "...                ...\n",
      "2020-11-09  421.260010\n",
      "2020-11-10  410.359985\n",
      "2020-11-11  417.130005\n",
      "2020-11-12  411.760010\n",
      "2020-11-13  408.500000\n",
      "\n",
      "[1723 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('TSLA.csv', header=0, usecols=['Date', 'Close'], parse_dates=True, index_col='Date')\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Close'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1723"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00749597]\n",
      " [0.00871406]\n",
      " [0.01162727]\n",
      " ...\n",
      " [0.82710302]\n",
      " [0.81566743]\n",
      " [0.80872512]]\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "print(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_length = int(len(scaled_data) * 0.7)\n",
    "test_length = len(scaled_data) - train_length\n",
    "train_data = scaled_data[0:train_length,:]\n",
    "test_data = scaled_data[train_length:len(scaled_data),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, timestep=1):\n",
    "    data_x, data_y = [], []\n",
    "    for i in range(len(dataset) - timestep - 1):\n",
    "        data_x.append(dataset[i:(i+timestep),0])\n",
    "        data_y.append(dataset[i+timestep,0])\n",
    "    return np.array(data_x), np.array(data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00749597]\n",
      " [0.00871406]\n",
      " [0.01162727]\n",
      " ...\n",
      " [0.04954577]\n",
      " [0.04995038]\n",
      " [0.06408624]]\n"
     ]
    }
   ],
   "source": [
    "timestep = 1\n",
    "train_x, train_y = create_dataset(train_data, timestep)\n",
    "test_x, test_y = create_dataset(test_data, timestep)\n",
    "\n",
    "print(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1204, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.00749597]]\n",
      "\n",
      " [[0.00871406]]\n",
      "\n",
      " [[0.01162727]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.04954577]]\n",
      "\n",
      " [[0.04995038]]\n",
      "\n",
      " [[0.06408624]]]\n"
     ]
    }
   ],
   "source": [
    "train_x = np.reshape(train_x, (train_x.shape[0], 1, train_x.shape[1]))\n",
    "test_x = np.reshape(test_x, (test_x.shape[0], 1, test_x.shape[1]))\n",
    "\n",
    "print(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "515"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 256)               264192    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 264,449\n",
      "Trainable params: 264,449\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(256, input_shape=(1,1)))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1204/1204 [==============================] - 2s 2ms/step - loss: 0.0102 - accuracy: 8.3056e-04\n",
      "Keras model loss =  0.000595724384766072\n",
      "Keras model accuracy =  0.000830564764328301\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_x, train_y, epochs=1, batch_size=1, verbose=1)\n",
    "score = model.evaluate(train_x, train_y, verbose=0)\n",
    "print('Keras model loss = ', score[0])\n",
    "print('Keras model accuracy = ', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.04796395]\n",
      " [0.04787573]\n",
      " [0.04766533]\n",
      " ...\n",
      " [0.0450035 ]\n",
      " [0.04497588]\n",
      " [0.04401949]]\n"
     ]
    }
   ],
   "source": [
    "train_predictions = model.predict(train_x)\n",
    "test_predictions = model.predict(test_x)\n",
    "\n",
    "print(train_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[51.2572  ]\n",
      " [51.215775]\n",
      " [51.11697 ]\n",
      " ...\n",
      " [49.867016]\n",
      " [49.854042]\n",
      " [49.404938]]\n"
     ]
    }
   ],
   "source": [
    "train_predictions = scaler.inverse_transform(train_predictions)\n",
    "train_y = scaler.inverse_transform([train_y])\n",
    "\n",
    "test_predictions = scaler.inverse_transform(test_predictions)\n",
    "test_y = scaler.inverse_transform([test_y])\n",
    "\n",
    "print(train_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "515"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1204"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predict_plot = np.empty_like(scaled_data)\n",
    "train_predict_plot[:,:] = np.nan\n",
    "train_predict_plot[1:len(train_predictions)+1, :] = train_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predict_plot = np.empty_like(scaled_data)\n",
    "test_predict_plot[:,:] = np.nan\n",
    "test_predict_plot[\n",
    "    len(train_predictions)+2+1:len(scaled_data)-1, :] = test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scaler.inverse_transform(scaled_data))\n",
    "plt.plot(train_predict_plot)\n",
    "plt.plot(test_predict_plot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}