predicting_tesla_stock_price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "needed-studio",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pystock'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-dc7e556d399d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpystock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstock_scanner\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mStock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mStockScanner\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrecall_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecision_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmake_scorer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroc_auc_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclassification_report\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pystock'"
     ]
    }
   ],
   "source": [
    "from pystock.stock_scanner import Stock, StockScanner\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import f1_score, recall_score, precision_score, make_scorer, accuracy_score, roc_auc_score, classification_report\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization, Bidirectional, Reshape, TimeDistributed, LayerNormalization, Attention\n",
    "from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint\n",
    "tf.random.set_seed(11)\n",
    "np.random.seed(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "scenic-friday",
   "metadata": {},
   "source": [
    "# Get stock data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "dying-washer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(802, 5)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = Stock('TSLA')\n",
    "s.get_technical_data(start_date=datetime(2018,1,1))\n",
    "TIME = s.technical.index\n",
    "T = s.technical[['open','high','low','volume','close',]].copy()\n",
    "T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "persistent-amino",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ancient-transition",
   "metadata": {},
   "source": [
    "# Preprocess\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weird-draft",
   "metadata": {},
   "source": [
    "## Min-max Scaling and Train/Test Split\n",
    "Below, we apply a min-max scaler to the dataset, and then convert it to a supervised learning problem. Basically, we have \"sliding window\", that divides the dataset into many different windows of length 20 days, that allow us to predict the 21st day. As the window slides from left to right, we are able to use a LSTM model to predict a \"future\" day from 20 preceding days."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "derived-florence",
   "metadata": {},
   "source": [
    "### Create features for predicting next day close:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "aware-tracy",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_ohlv = StandardScaler() #MinMaxScaler(feature_range=(0,1))\n",
    "scaler_close = StandardScaler()\n",
    "T = T.values\n",
    "T[:,0:4] = scaler_ohlv.fit_transform(T[:,0:4])\n",
    "T[:,4:] = scaler_close.fit_transform(T[:,4:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "recovered-score",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((641, 5), (161, 5))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size = int(len(T) * 0.8)\n",
    "test_size = int(len(T) - train_size)\n",
    "train, test = T[0:train_size, :], T[train_size:, :]\n",
    "assert T.shape[0] == train.shape[0] + test.shape[0], \"Train and test samples must add up to dataset length. Check for leaking of train into test.\"\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "structural-increase",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method for create features from the time series data\n",
    "def create_features(data, window_size):\n",
    "    \"\"\" Create timeseries dataset based on window_size with label being next days close price. \"\"\"\n",
    "    X, Y = [], []\n",
    "    for i in range(len(data) - window_size):\n",
    "        window = data[i:(i + window_size), :]\n",
    "        X.append(window)\n",
    "        Y.append(data[i + window_size, 4])\n",
    "    return np.array(X), np.array(Y)\n",
    "\n",
    "\n",
    "# Roughly one month of trading assuming 5 trading days per week\n",
    "window_size = 20\n",
    "n_features = train.shape[1]\n",
    "X_train, y_train = create_features(train, window_size)\n",
    "\n",
    "X_test, y_test = create_features(test, window_size)\n",
    "\n",
    "# Reshape to the format of [batch, timesteps, features]\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], window_size, n_features))\n",
    "\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], window_size, n_features))\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "realistic-rachel",
   "metadata": {},
   "source": [
    "### Create feature for predicting next day up/down:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "military-influence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data for \n",
    "T_binary = np.log(T).diff().copy()\n",
    "T_binary.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "sexual-collect",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_ohlv = StandardScaler() #MinMaxScaler(feature_range=(0,1))\n",
    "scaler_close = StandardScaler()\n",
    "T_binary = T_binary.values[1:] # from 1 becuase first timestep is nan\n",
    "T_binary[:,0:4] = scaler_ohlv.fit_transform(T_binary[:,0:4])\n",
    "T_binary[:,4:] = scaler_close.fit_transform(T_binary[:,4:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "representative-immigration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((640, 5), (161, 5))"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size = int(len(T_binary) * 0.8)\n",
    "test_size = int(len(T_binary) - train_size)\n",
    "train, test = T_binary[0:train_size, :], T_binary[train_size:, :]\n",
    "assert T_binary.shape[0] == train.shape[0] + test.shape[0], \"Train and test samples must add up to dataset length. Check for leaking of train into test.\"\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "approximate-college",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((620, 20, 5), (141, 20, 5), (620,), (141,))"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_features_binary_from_close_price(data, window_size):\n",
    "    \"\"\" Create timeseries dataset based on window_size of ohlc data with label being up (1) or down (0) the next day.\"\"\"\n",
    "    X, Y = [], []\n",
    "    for i in range(len(data) - window_size):\n",
    "        window = data[i:(i + window_size), :]\n",
    "        if window[-1,4] > data[i+window_size, 4]:\n",
    "            y = 0 # down next day\n",
    "        else: \n",
    "            y = 1 # up next day\n",
    "        X.append(window)\n",
    "        Y.append(y)\n",
    "    return np.array(X), np.array(Y)\n",
    "\n",
    "def create_features_binary(data, window_size):\n",
    "    \"\"\" Create timeseries dataset based on window_size of differenced ohlc data with label being up (1) or down (0) the next day.\"\"\"\n",
    "    X, Y = [], []\n",
    "    for i in range(len(data) - window_size):\n",
    "        window = data[i:(i + window_size), :]\n",
    "        if data[i+window_size, 4] < 0:\n",
    "            y = 0 # down next day\n",
    "        else: \n",
    "            y = 1 # up next day\n",
    "        X.append(window)\n",
    "        Y.append(y)\n",
    "    return np.array(X), np.array(Y)\n",
    "\n",
    "# Roughly one month of trading assuming 5 trading days per week\n",
    "window_size = 20\n",
    "n_features = train.shape[1]\n",
    "X_train, y_train = create_features_binary(train, window_size)\n",
    "\n",
    "X_test, y_test = create_features_binary(test, window_size)\n",
    "\n",
    "# Reshape to the format of [batch, timesteps, features]\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], window_size, n_features))\n",
    "\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], window_size, n_features))\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "signed-spiritual",
   "metadata": {},
   "source": [
    "# Building and training the LSTM RNN Model\n",
    "Below, we build a LSTM model that consists of one LSTM layer with a density of 50 neurons and a RELU activation function, as well as a Dropout regularization layer. We add a model checkpoint that is looking to minimize the validation set's loss. We save each model that consists of a lower validation loss compared to any model that came before it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "dated-sellers",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building model\n",
    "model = Sequential()\n",
    "\n",
    "# model.add(Bidirectional(LSTM(units = 50, activation = 'relu', return_sequences = False), \n",
    "#                         input_shape = (None, X_train.shape[2])\n",
    "#                        )\n",
    "#          )\n",
    "model.add(LSTM(units = 100, activation = 'relu', return_sequences = False, input_shape = (None, X_train.shape[2])))\n",
    "model.add(LayerNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation = 'sigmoid')) # use activation='linear' for predicting price instead of binary label\n",
    "model.compile(loss = 'binary_crossentropy', optimizer = 'adam') # use loss = 'mean_squared_error' for predicting price instead of binary label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "fatty-inflation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_23\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_23 (LSTM)               (None, 100)               42400     \n",
      "_________________________________________________________________\n",
      "layer_normalization_17 (Laye (None, 100)               200       \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 42,701\n",
      "Trainable params: 42,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "unique-cornwall",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "31/31 [==============================] - 1s 14ms/step - loss: 0.7865 - val_loss: 0.7356\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.73555, saving model to nn_snapshots\\model_epoch_01.hdf5\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.7599 - val_loss: 0.7377\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.73555\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.7546 - val_loss: 0.7430\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.73555\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.7509 - val_loss: 0.7458\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.73555\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.7207 - val_loss: 0.7325\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.73555 to 0.73248, saving model to nn_snapshots\\model_epoch_05.hdf5\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.7331 - val_loss: 0.7085\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.73248 to 0.70845, saving model to nn_snapshots\\model_epoch_06.hdf5\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.7148 - val_loss: 0.6996\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.70845 to 0.69965, saving model to nn_snapshots\\model_epoch_07.hdf5\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.7118 - val_loss: 0.6941\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.69965 to 0.69411, saving model to nn_snapshots\\model_epoch_08.hdf5\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6962 - val_loss: 0.6889\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.69411 to 0.68891, saving model to nn_snapshots\\model_epoch_09.hdf5\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6793 - val_loss: 0.6909\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.68891\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6960 - val_loss: 0.6934\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.68891\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6943 - val_loss: 0.6969\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.68891\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6808 - val_loss: 0.6961\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.68891\n",
      "Epoch 14/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6772 - val_loss: 0.6998\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.68891\n",
      "Epoch 15/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.6928 - val_loss: 0.7003\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.68891\n",
      "Epoch 16/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6795 - val_loss: 0.7041\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.68891\n",
      "Epoch 17/100\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.686 - 0s 8ms/step - loss: 0.6826 - val_loss: 0.6992\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.68891\n",
      "Epoch 18/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6784 - val_loss: 0.7046\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.68891\n",
      "Epoch 19/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6666 - val_loss: 0.7177\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.68891\n",
      "Epoch 20/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6806 - val_loss: 0.7094\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.68891\n",
      "Epoch 21/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6660 - val_loss: 0.7145\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.68891\n",
      "Epoch 22/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6636 - val_loss: 0.7279\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.68891\n",
      "Epoch 23/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6569 - val_loss: 0.7284\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.68891\n",
      "Epoch 24/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6691 - val_loss: 0.7376\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.68891\n",
      "Epoch 25/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6459 - val_loss: 0.7500\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.68891\n",
      "Epoch 26/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6726 - val_loss: 0.7502\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.68891\n",
      "Epoch 27/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.6433 - val_loss: 0.7752\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.68891\n",
      "Epoch 28/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6592 - val_loss: 0.7717\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.68891\n",
      "Epoch 29/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6693 - val_loss: 0.7682\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.68891\n",
      "Epoch 30/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6442 - val_loss: 0.7692\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.68891\n",
      "Epoch 31/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6408 - val_loss: 0.7826\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.68891\n",
      "Epoch 32/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6264 - val_loss: 0.7857\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.68891\n",
      "Epoch 33/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6362 - val_loss: 0.7812\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.68891\n",
      "Epoch 34/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.6293 - val_loss: 0.7993\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.68891\n",
      "Epoch 35/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.6270 - val_loss: 0.8173\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.68891\n",
      "Epoch 36/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6149 - val_loss: 0.8026\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.68891\n",
      "Epoch 37/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6197 - val_loss: 0.8664\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.68891\n",
      "Epoch 38/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6217 - val_loss: 0.8539\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.68891\n",
      "Epoch 39/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.6046 - val_loss: 0.9515\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.68891\n",
      "Epoch 40/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.6172 - val_loss: 0.8794\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.68891\n",
      "Epoch 41/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.6040 - val_loss: 0.8680\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.68891\n",
      "Epoch 42/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5810 - val_loss: 0.9214\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.68891\n",
      "Epoch 43/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5803 - val_loss: 0.9258\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.68891\n",
      "Epoch 44/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5843 - val_loss: 0.9616\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.68891\n",
      "Epoch 45/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5857 - val_loss: 0.9728\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.68891\n",
      "Epoch 46/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5854 - val_loss: 0.9838\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.68891\n",
      "Epoch 47/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5726 - val_loss: 0.9507\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.68891\n",
      "Epoch 48/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5607 - val_loss: 1.0119\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.68891\n",
      "Epoch 49/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5556 - val_loss: 1.0411\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.68891\n",
      "Epoch 50/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5582 - val_loss: 1.1680\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.68891\n",
      "Epoch 51/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5696 - val_loss: 1.1290\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.68891\n",
      "Epoch 52/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.5783 - val_loss: 1.1299\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.68891\n",
      "Epoch 53/100\n",
      "31/31 [==============================] - ETA: 0s - loss: 0.550 - 0s 8ms/step - loss: 0.5461 - val_loss: 1.1288\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.68891\n",
      "Epoch 54/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5687 - val_loss: 1.0867\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.68891\n",
      "Epoch 55/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5526 - val_loss: 1.0821\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.68891\n",
      "Epoch 56/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5516 - val_loss: 0.9471\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.68891\n",
      "Epoch 57/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5634 - val_loss: 1.0152\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.68891\n",
      "Epoch 58/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5140 - val_loss: 1.0908\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.68891\n",
      "Epoch 59/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5175 - val_loss: 1.0728\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.68891\n",
      "Epoch 60/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5082 - val_loss: 1.1361\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.68891\n",
      "Epoch 61/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4950 - val_loss: 1.1500\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.68891\n",
      "Epoch 62/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4913 - val_loss: 1.0275\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.68891\n",
      "Epoch 63/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.5071 - val_loss: 1.1897\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.68891\n",
      "Epoch 64/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4781 - val_loss: 1.2207\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.68891\n",
      "Epoch 65/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4772 - val_loss: 1.3060\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.68891\n",
      "Epoch 66/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4540 - val_loss: 1.2308\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.68891\n",
      "Epoch 67/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.4674 - val_loss: 1.3455\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.68891\n",
      "Epoch 68/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4970 - val_loss: 1.3912\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.68891\n",
      "Epoch 69/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4584 - val_loss: 1.3544\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.68891\n",
      "Epoch 70/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4497 - val_loss: 1.4401\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.68891\n",
      "Epoch 71/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4149 - val_loss: 1.3284\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.68891\n",
      "Epoch 72/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4334 - val_loss: 1.5518\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.68891\n",
      "Epoch 73/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4303 - val_loss: 1.4368\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.68891\n",
      "Epoch 74/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4040 - val_loss: 1.4951\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.68891\n",
      "Epoch 75/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4141 - val_loss: 1.3532\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.68891\n",
      "Epoch 76/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.4113 - val_loss: 1.5225\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.68891\n",
      "Epoch 77/100\n",
      "31/31 [==============================] - 1s 23ms/step - loss: 0.4053 - val_loss: 1.4654\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.68891\n",
      "Epoch 78/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.3763 - val_loss: 1.4257\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.68891\n",
      "Epoch 79/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4047 - val_loss: 1.5498\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.68891\n",
      "Epoch 80/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.4146 - val_loss: 1.4678\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.68891\n",
      "Epoch 81/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4224 - val_loss: 1.6120\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.68891\n",
      "Epoch 82/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.4100 - val_loss: 1.5314\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.68891\n",
      "Epoch 83/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.3980 - val_loss: 1.7191\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.68891\n",
      "Epoch 84/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.3460 - val_loss: 1.6735\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.68891\n",
      "Epoch 85/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.3471 - val_loss: 1.6827\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.68891\n",
      "Epoch 86/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.3346 - val_loss: 1.7806\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.68891\n",
      "Epoch 87/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.3050 - val_loss: 1.8240\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.68891\n",
      "Epoch 88/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.3106 - val_loss: 1.6729\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.68891\n",
      "Epoch 89/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.3671 - val_loss: 1.8607\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.68891\n",
      "Epoch 90/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.3420 - val_loss: 1.8085\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.68891\n",
      "Epoch 91/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.3139 - val_loss: 1.8069\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.68891\n",
      "Epoch 92/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.2679 - val_loss: 2.0655\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.68891\n",
      "Epoch 93/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.2964 - val_loss: 1.9068\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.68891\n",
      "Epoch 94/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.2763 - val_loss: 1.7747\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.68891\n",
      "Epoch 95/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.2688 - val_loss: 1.9735\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.68891\n",
      "Epoch 96/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.2311 - val_loss: 2.0180\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.68891\n",
      "Epoch 97/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.2725 - val_loss: 2.1940\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.68891\n",
      "Epoch 98/100\n",
      "31/31 [==============================] - 0s 9ms/step - loss: 0.2782 - val_loss: 2.1147\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.68891\n",
      "Epoch 99/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.2486 - val_loss: 2.3086\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.68891\n",
      "Epoch 100/100\n",
      "31/31 [==============================] - 0s 8ms/step - loss: 0.2580 - val_loss: 2.2210\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.68891\n"
     ]
    }
   ],
   "source": [
    "filepath = 'nn_snapshots/model_epoch_{epoch:02d}.hdf5'\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath = filepath,\n",
    "                             monitor = 'val_loss',\n",
    "                             verbose = 1,\n",
    "                             save_best_only = True,\n",
    "                             mode ='min'\n",
    "                            )\n",
    "\n",
    "history = model.fit(X_train, y_train, \n",
    "                    epochs = 100, \n",
    "                    batch_size = 20, \n",
    "                    validation_data = (X_test, y_test), \n",
    "                    callbacks = [checkpoint], \n",
    "                    verbose = 1, \n",
    "                    shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "heard-jackson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Training Loss vs. Testing Loss by Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'testing'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "acting-intelligence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "northern-highland",
   "metadata": {},
   "source": [
    "# Evaluate closing price model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "exterior-blackjack",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = load_model('nn_snapshots/model_epoch_01.hdf5')\n",
    "\n",
    "# Predicting and inverse transforming the predictions\n",
    "\n",
    "y_pred_train = best_model.predict(X_train)\n",
    "\n",
    "y_pred_train = scaler_close.inverse_transform(y_pred_train).reshape(-1,)\n",
    "\n",
    "y_pred_test = best_model.predict(X_test)\n",
    "\n",
    "y_pred_test = scaler_close.inverse_transform(y_pred_test).reshape(-1,)\n",
    "\n",
    "# Inverse transforming the actual values, to return them to their original values\n",
    "y_test = scaler_close.inverse_transform([y_test]).reshape(-1,)\n",
    "y_train = scaler_close.inverse_transform([y_train]).reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "sapphire-cabinet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE is: \n",
      "8.63390538404823 \n",
      "\n",
      "Test RMSE is: \n",
      "71.79325975143182\n"
     ]
    }
   ],
   "source": [
    "train_RMSE = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
    "\n",
    "test_RMSE = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "\n",
    "print('Train RMSE is: ')\n",
    "print(train_RMSE, '\\n')\n",
    "print('Test RMSE is: ')\n",
    "print(test_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "certain-rugby",
   "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_true</th>\n",
       "      <th>close_pred</th>\n",
       "      <th>close_true_up</th>\n",
       "      <th>close_pred_up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>653.200012</td>\n",
       "      <td>804.729858</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>621.440002</td>\n",
       "      <td>788.487305</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>597.950012</td>\n",
       "      <td>792.461975</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>563.000000</td>\n",
       "      <td>767.686279</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>673.580017</td>\n",
       "      <td>763.257263</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     close_true  close_pred  close_true_up  close_pred_up\n",
       "756  653.200012  804.729858          False           True\n",
       "757  621.440002  788.487305          False          False\n",
       "758  597.950012  792.461975          False           True\n",
       "759  563.000000  767.686279          False          False\n",
       "760  673.580017  763.257263           True          False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df = pd.DataFrame()\n",
    "\n",
    "result_df['close_true'] = np.concatenate([y_train, y_test])\n",
    "result_df['close_pred'] = np.concatenate([y_pred_train, y_pred_test])\n",
    "result_df['close_true_up'] = result_df['close_true'].diff()>0\n",
    "result_df['close_pred_up'] = result_df['close_pred'].diff()>0\n",
    "\n",
    "result_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "alternative-scene",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5269382391590013"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(result_df['close_true_up'], result_df['close_pred_up'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "inside-spencer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southern-distinction",
   "metadata": {},
   "source": [
    "# Evaluate binary model (up/down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "oriented-participant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.614516129032258, 0.5602836879432624)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = load_model('nn_snapshots/model_epoch_09.hdf5')\n",
    "y_pred_train = (best_model.predict(X_train)>0.5).astype(int)\n",
    "y_pred_test = (best_model.predict(X_test)>0.5).astype(int)\n",
    "accuracy_score(y_train, y_pred_train), accuracy_score(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "flying-academy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b297e8c348>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.concatenate([y_train,y_test]), label='true')\n",
    "plt.plot(np.concatenate([best_model.predict(X_train),best_model.predict(X_test)]),label='pred')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chief-polymer",
   "metadata": {},
   "source": [
    "# Predict close tomorrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "joint-chemical",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[773.33075]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_today = T[-20:,:]\n",
    "y_tmr = best_model.predict(X_today.reshape(-1,X_today.shape[0], n_features))\n",
    "y_tmr = scaler_close.inverse_transform(y_tmr)\n",
    "y_tmr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hundred-louis",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}