stock_price_classical_algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QI4PhD_bi9dN"
   },
   "source": [
    "## **Importing Libraries**\n",
    "\n",
    "A library was built with the name tfqml.py to store all the dependencies, classes and methods that are used in this work, these classes are\n",
    "- ClassicalPreprocessing,\n",
    "\n",
    "together with the methods \n",
    "- visualization and \n",
    "- save_data\n",
    "\n",
    "\n",
    "the dependencies are:\n",
    "- numpy \n",
    "- pandas \n",
    "- matplotlib\n",
    "- sklearn\n",
    "- tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-25 10:27:19.873239: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-02-25 10:27:19.873275: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2022-02-25 10:27:21.219740: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-02-25 10:27:21.219765: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (toto): /proc/driver/nvidia/version does not exist\n",
      "2022-02-25 10:27:21.220027: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from tfqml import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3J64HGEcjKho"
   },
   "source": [
    "## **Choose the 8 stocks**\n",
    "\n",
    "we select 8 stocks from 8 conglomerates based on the dataset of Xu et al. [[2]](https://aclanthology.org/P18-1183/): \n",
    "\n",
    "- Basic Materials: TOTAL S.A. \"TOT\"\n",
    "\n",
    "- Consumer Goods: Appel Inc. \"AAPL\"\n",
    "\n",
    "- Healthcare: AbbVie Inc. \"ABBV\"\n",
    "\n",
    "- Services: Wall-Mart Stores Inc. \"WMT\"\n",
    "\n",
    "- Utilites: Duke energy corporation \"DUK\"\n",
    "\n",
    "- Financial: HSBS Holding pcl \"HSBC\"\n",
    "\n",
    "- Industrial Goods: ABB Ltd. \"ABB\"\n",
    "\n",
    "- Technology: China Mobile Limited \"CHL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_stocks = ['AAPL','ABB','ABBV','TOT','WMT','DUK','CHL','HSBC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classical Preprocessing\n",
    "\n",
    "The methods of our library are used to preprocess the csv files of the 8 stocks,\n",
    "We consider 30 days por each instance and we obtain the shape of the train and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train data: (880, 1)\n",
      "Shape of test data: (378, 1)\n"
     ]
    }
   ],
   "source": [
    "cp = ClassicalPreprocessing(filename=name_stocks[0])\n",
    "days = 30\n",
    "x_train,y_train,x_test,y_test = cp.preprocessing(days=days,flag_pca=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we can check the dataset for trai nand test and confirm is the same dimention of the days and using a normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.50210573, 0.5198373 , 0.5154993 , 0.52431991, 0.52865792,\n",
       "       0.50093085, 0.50407582, 0.52309078, 0.54584719, 0.56281965,\n",
       "       0.56377762, 0.56446444, 0.56247619, 0.56835058, 0.54024395,\n",
       "       0.54277451, 0.50749205, 0.49944871, 0.52558515, 0.51186616,\n",
       "       0.49496611, 0.50047898, 0.51202891, 0.5010935 , 0.46798009,\n",
       "       0.45310439, 0.45507448, 0.46729319, 0.43667413, 0.44171711])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eyHweGaykp-o"
   },
   "source": [
    "Creating our LSTM  model.The first layer is our proposal which input is 30 parameters per instance, followed by LSTM and Dropout layers, at the end a Dense layer to have an output value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the Keras model.\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.LSTM(units=100, return_sequences=True, input_shape=(x_train.shape[1], 1)),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.LSTM(units=100, return_sequences=True),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.LSTM(units=100, return_sequences=True),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.LSTM(units=5),\n",
    "    tf.keras.layers.Dense(units=1),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is important consier the prpeorcessing the initial dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the as loss the MSE and optimizer ADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_4 (LSTM)               (None, 30, 100)           40800     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 30, 100)           0         \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 30, 100)           80400     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 30, 100)           0         \n",
      "                                                                 \n",
      " lstm_6 (LSTM)               (None, 30, 100)           80400     \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 30, 100)           0         \n",
      "                                                                 \n",
      " lstm_7 (LSTM)               (None, 5)                 2120      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 203,726\n",
      "Trainable params: 203,726\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss='mean_squared_error',\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider only 60 epoch with a batch size of 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "27/27 [==============================] - 7s 74ms/step - loss: 0.0328 - root_mean_squared_error: 0.1811\n",
      "Epoch 2/60\n",
      "27/27 [==============================] - 2s 69ms/step - loss: 0.0047 - root_mean_squared_error: 0.0686\n",
      "Epoch 3/60\n",
      "27/27 [==============================] - 2s 63ms/step - loss: 0.0039 - root_mean_squared_error: 0.0622\n",
      "Epoch 4/60\n",
      "27/27 [==============================] - 2s 58ms/step - loss: 0.0034 - root_mean_squared_error: 0.0586\n",
      "Epoch 5/60\n",
      "27/27 [==============================] - 2s 65ms/step - loss: 0.0033 - root_mean_squared_error: 0.0576\n",
      "Epoch 6/60\n",
      "27/27 [==============================] - 2s 70ms/step - loss: 0.0034 - root_mean_squared_error: 0.0583\n",
      "Epoch 7/60\n",
      "27/27 [==============================] - 2s 59ms/step - loss: 0.0030 - root_mean_squared_error: 0.0544\n",
      "Epoch 8/60\n",
      "27/27 [==============================] - 2s 60ms/step - loss: 0.0029 - root_mean_squared_error: 0.0539\n",
      "Epoch 9/60\n",
      "27/27 [==============================] - 2s 56ms/step - loss: 0.0030 - root_mean_squared_error: 0.0547\n",
      "Epoch 10/60\n",
      "27/27 [==============================] - 2s 74ms/step - loss: 0.0030 - root_mean_squared_error: 0.0552\n",
      "Epoch 11/60\n",
      "27/27 [==============================] - 2s 57ms/step - loss: 0.0028 - root_mean_squared_error: 0.0526\n",
      "Epoch 12/60\n",
      "27/27 [==============================] - 2s 60ms/step - loss: 0.0030 - root_mean_squared_error: 0.0548\n",
      "Epoch 13/60\n",
      "27/27 [==============================] - 2s 68ms/step - loss: 0.0025 - root_mean_squared_error: 0.0503\n",
      "Epoch 14/60\n",
      "27/27 [==============================] - 2s 60ms/step - loss: 0.0024 - root_mean_squared_error: 0.0491\n",
      "Epoch 15/60\n",
      "27/27 [==============================] - 2s 63ms/step - loss: 0.0026 - root_mean_squared_error: 0.0509\n",
      "Epoch 16/60\n",
      "27/27 [==============================] - 2s 62ms/step - loss: 0.0024 - root_mean_squared_error: 0.0493\n",
      "Epoch 17/60\n",
      "27/27 [==============================] - 2s 80ms/step - loss: 0.0024 - root_mean_squared_error: 0.0488\n",
      "Epoch 18/60\n",
      "27/27 [==============================] - 2s 62ms/step - loss: 0.0024 - root_mean_squared_error: 0.0493\n",
      "Epoch 19/60\n",
      "27/27 [==============================] - 2s 71ms/step - loss: 0.0023 - root_mean_squared_error: 0.0475\n",
      "Epoch 20/60\n",
      "27/27 [==============================] - 2s 61ms/step - loss: 0.0023 - root_mean_squared_error: 0.0484\n",
      "Epoch 21/60\n",
      "27/27 [==============================] - 2s 60ms/step - loss: 0.0022 - root_mean_squared_error: 0.0464\n",
      "Epoch 22/60\n",
      "27/27 [==============================] - 2s 64ms/step - loss: 0.0021 - root_mean_squared_error: 0.0454\n",
      "Epoch 23/60\n",
      "27/27 [==============================] - 2s 68ms/step - loss: 0.0021 - root_mean_squared_error: 0.0461\n",
      "Epoch 24/60\n",
      "27/27 [==============================] - 2s 57ms/step - loss: 0.0022 - root_mean_squared_error: 0.0471\n",
      "Epoch 25/60\n",
      "27/27 [==============================] - 2s 62ms/step - loss: 0.0020 - root_mean_squared_error: 0.0443\n",
      "Epoch 26/60\n",
      "27/27 [==============================] - 2s 66ms/step - loss: 0.0022 - root_mean_squared_error: 0.0465\n",
      "Epoch 27/60\n",
      "27/27 [==============================] - 2s 70ms/step - loss: 0.0022 - root_mean_squared_error: 0.0474\n",
      "Epoch 28/60\n",
      "27/27 [==============================] - 2s 64ms/step - loss: 0.0023 - root_mean_squared_error: 0.0475\n",
      "Epoch 29/60\n",
      "27/27 [==============================] - 2s 64ms/step - loss: 0.0023 - root_mean_squared_error: 0.0477\n",
      "Epoch 30/60\n",
      "27/27 [==============================] - 2s 71ms/step - loss: 0.0021 - root_mean_squared_error: 0.0453\n",
      "Epoch 31/60\n",
      "27/27 [==============================] - 2s 59ms/step - loss: 0.0019 - root_mean_squared_error: 0.0435\n",
      "Epoch 32/60\n",
      "27/27 [==============================] - 2s 59ms/step - loss: 0.0018 - root_mean_squared_error: 0.0420\n",
      "Epoch 33/60\n",
      "27/27 [==============================] - 2s 58ms/step - loss: 0.0018 - root_mean_squared_error: 0.0420\n",
      "Epoch 34/60\n",
      "27/27 [==============================] - 2s 67ms/step - loss: 0.0017 - root_mean_squared_error: 0.0416\n",
      "Epoch 35/60\n",
      "27/27 [==============================] - 2s 59ms/step - loss: 0.0018 - root_mean_squared_error: 0.0427\n",
      "Epoch 36/60\n",
      "27/27 [==============================] - 1s 56ms/step - loss: 0.0018 - root_mean_squared_error: 0.0424\n",
      "Epoch 37/60\n",
      "27/27 [==============================] - 2s 61ms/step - loss: 0.0018 - root_mean_squared_error: 0.0423\n",
      "Epoch 38/60\n",
      "27/27 [==============================] - 2s 62ms/step - loss: 0.0017 - root_mean_squared_error: 0.0412\n",
      "Epoch 39/60\n",
      "27/27 [==============================] - 2s 62ms/step - loss: 0.0018 - root_mean_squared_error: 0.0422\n",
      "Epoch 40/60\n",
      "27/27 [==============================] - 1s 56ms/step - loss: 0.0017 - root_mean_squared_error: 0.0418\n",
      "Epoch 41/60\n",
      "27/27 [==============================] - 2s 74ms/step - loss: 0.0017 - root_mean_squared_error: 0.0418\n",
      "Epoch 42/60\n",
      "27/27 [==============================] - ETA: 0s - loss: 0.0018 - root_mean_squared_error: 0.042 - 2s 61ms/step - loss: 0.0018 - root_mean_squared_error: 0.0423\n",
      "Epoch 43/60\n",
      "27/27 [==============================] - 2s 57ms/step - loss: 0.0017 - root_mean_squared_error: 0.0412\n",
      "Epoch 44/60\n",
      "27/27 [==============================] - 2s 59ms/step - loss: 0.0016 - root_mean_squared_error: 0.0398\n",
      "Epoch 45/60\n",
      "27/27 [==============================] - 2s 69ms/step - loss: 0.0018 - root_mean_squared_error: 0.0424\n",
      "Epoch 46/60\n",
      "27/27 [==============================] - 2s 66ms/step - loss: 0.0016 - root_mean_squared_error: 0.0399\n",
      "Epoch 47/60\n",
      "27/27 [==============================] - 2s 61ms/step - loss: 0.0014 - root_mean_squared_error: 0.0376\n",
      "Epoch 48/60\n",
      "27/27 [==============================] - 2s 65ms/step - loss: 0.0014 - root_mean_squared_error: 0.0380\n",
      "Epoch 49/60\n",
      "27/27 [==============================] - 2s 74ms/step - loss: 0.0015 - root_mean_squared_error: 0.0382\n",
      "Epoch 50/60\n",
      "27/27 [==============================] - 2s 61ms/step - loss: 0.0014 - root_mean_squared_error: 0.0374\n",
      "Epoch 51/60\n",
      "27/27 [==============================] - 2s 60ms/step - loss: 0.0015 - root_mean_squared_error: 0.0391\n",
      "Epoch 52/60\n",
      "27/27 [==============================] - 2s 67ms/step - loss: 0.0014 - root_mean_squared_error: 0.0374\n",
      "Epoch 53/60\n",
      "27/27 [==============================] - 2s 65ms/step - loss: 0.0015 - root_mean_squared_error: 0.0383\n",
      "Epoch 54/60\n",
      "27/27 [==============================] - 2s 61ms/step - loss: 0.0014 - root_mean_squared_error: 0.0370\n",
      "Epoch 55/60\n",
      "27/27 [==============================] - 2s 63ms/step - loss: 0.0014 - root_mean_squared_error: 0.0377\n",
      "Epoch 56/60\n",
      "27/27 [==============================] - 2s 83ms/step - loss: 0.0014 - root_mean_squared_error: 0.0379\n",
      "Epoch 57/60\n",
      "27/27 [==============================] - 2s 58ms/step - loss: 0.0013 - root_mean_squared_error: 0.0357\n",
      "Epoch 58/60\n",
      "27/27 [==============================] - 1s 55ms/step - loss: 0.0012 - root_mean_squared_error: 0.0353\n",
      "Epoch 59/60\n",
      "27/27 [==============================] - 2s 57ms/step - loss: 0.0014 - root_mean_squared_error: 0.0370\n",
      "Epoch 60/60\n",
      "27/27 [==============================] - 2s 64ms/step - loss: 0.0014 - root_mean_squared_error: 0.0370\n",
      "11/11 [==============================] - 1s 21ms/step - loss: 0.0081 - root_mean_squared_error: 0.0900\n"
     ]
    }
   ],
   "source": [
    "qnn_history = model.fit(\n",
    "      x_train, y_train,\n",
    "      batch_size=32,\n",
    "      epochs=60)\n",
    "\n",
    "qnn_results = model.evaluate(x_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the result between the  real and predict with both data set, train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(model,x_train,y_train)\n",
    "visualization(model,x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we save the model and outputs, the proposal save this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_data(model,x_train,y_train,name_stocks[1]+\"_train\")\n",
    "save_data(model,x_test,y_test,name_stocks[1]+\"_test\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPVe0xJHFIRM8xqWAthKDQp",
   "include_colab_link": true,
   "name": "stock price predection.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}