{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "# specify to ignore warning messages\n",
    "warnings.filterwarnings(\"ignore\") \n",
    "\n",
    "from lstm_utils import get_raw_data\n",
    "from lstm_utils import get_seq_model\n",
    "from lstm_utils import get_seq_train_test\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context('talk')\n",
    "\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "plt.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TRAIN_PERCENT = 0.7\n",
    "STOCK_INDEX = '^GSPC'\n",
    "VERBOSE=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1635619bdd8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LEdPXF88vHo9zORV4d/MJlFXpMLeV4CG6rw/cJIDRBEwfHYHE/gGIi/RHXX3z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1635018e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp_df = get_raw_data(STOCK_INDEX)\n",
    "sp_close_series = sp_df.Close \n",
    "\n",
    "sp_close_series.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Split Complete\n",
      "train_x shape=(1, 1965, 1)\n",
      "train_y shape=(1, 1965, 1)\n",
      "test_x shape=(1, 842, 1)\n",
      "test_y shape=(1, 842, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# split train and test datasets\n",
    "train,test,scaler = get_seq_train_test(sp_close_series,\n",
    "                                   scaling=True,\n",
    "                                   train_size=TRAIN_PERCENT)\n",
    "\n",
    "train = np.reshape(train,(1,train.shape[0],1))\n",
    "test = np.reshape(test,(1,test.shape[0],1))\n",
    "\n",
    "train_x = train[:,:-1,:]\n",
    "train_y = train[:,1:,:]\n",
    "\n",
    "test_x = test[:,:-1,:]\n",
    "test_y = test[:,1:,:]\n",
    "\n",
    "print(\"Data Split Complete\")\n",
    "\n",
    "print(\"train_x shape={}\".format(train_x.shape))\n",
    "print(\"train_y shape={}\".format(train_y.shape))\n",
    "print(\"test_x shape={}\".format(test_x.shape))\n",
    "print(\"test_y shape={}\".format(test_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Build Failed. Trying Again\n",
      "> Compilation Time :  0.008561372756958008\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 1965, 4)           96        \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 1965, 1)           5         \n",
      "=================================================================\n",
      "Total params: 101\n",
      "Trainable params: 101\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# build RNN model\n",
    "seq_lstm_model=None\n",
    "try:\n",
    "    seq_lstm_model = get_seq_model(input_shape=(train_x.shape[1],1),\n",
    "                                                verbose=VERBOSE)   \n",
    "except:\n",
    "    print(\"Model Build Failed. Trying Again\")\n",
    "    seq_lstm_model = get_seq_model(input_shape=(train_x.shape[1],1),\n",
    "                                                verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "0s - loss: 0.1923\n",
      "Epoch 2/150\n",
      "0s - loss: 0.1838\n",
      "Epoch 3/150\n",
      "0s - loss: 0.1781\n",
      "Epoch 4/150\n",
      "0s - loss: 0.1734\n",
      "Epoch 5/150\n",
      "0s - loss: 0.1694\n",
      "Epoch 6/150\n",
      "0s - loss: 0.1658\n",
      "Epoch 7/150\n",
      "0s - loss: 0.1625\n",
      "Epoch 8/150\n",
      "0s - loss: 0.1593\n",
      "Epoch 9/150\n",
      "0s - loss: 0.1564\n",
      "Epoch 10/150\n",
      "0s - loss: 0.1535\n",
      "Epoch 11/150\n",
      "0s - loss: 0.1507\n",
      "Epoch 12/150\n",
      "0s - loss: 0.1479\n",
      "Epoch 13/150\n",
      "0s - loss: 0.1452\n",
      "Epoch 14/150\n",
      "0s - loss: 0.1426\n",
      "Epoch 15/150\n",
      "0s - loss: 0.1399\n",
      "Epoch 16/150\n",
      "0s - loss: 0.1373\n",
      "Epoch 17/150\n",
      "0s - loss: 0.1346\n",
      "Epoch 18/150\n",
      "0s - loss: 0.1320\n",
      "Epoch 19/150\n",
      "0s - loss: 0.1294\n",
      "Epoch 20/150\n",
      "0s - loss: 0.1267\n",
      "Epoch 21/150\n",
      "0s - loss: 0.1241\n",
      "Epoch 22/150\n",
      "0s - loss: 0.1214\n",
      "Epoch 23/150\n",
      "0s - loss: 0.1188\n",
      "Epoch 24/150\n",
      "0s - loss: 0.1162\n",
      "Epoch 25/150\n",
      "0s - loss: 0.1135\n",
      "Epoch 26/150\n",
      "0s - loss: 0.1109\n",
      "Epoch 27/150\n",
      "0s - loss: 0.1083\n",
      "Epoch 28/150\n",
      "0s - loss: 0.1056\n",
      "Epoch 29/150\n",
      "0s - loss: 0.1030\n",
      "Epoch 30/150\n",
      "0s - loss: 0.1004\n",
      "Epoch 31/150\n",
      "0s - loss: 0.0978\n",
      "Epoch 32/150\n",
      "0s - loss: 0.0952\n",
      "Epoch 33/150\n",
      "0s - loss: 0.0926\n",
      "Epoch 34/150\n",
      "0s - loss: 0.0900\n",
      "Epoch 35/150\n",
      "0s - loss: 0.0875\n",
      "Epoch 36/150\n",
      "0s - loss: 0.0849\n",
      "Epoch 37/150\n",
      "0s - loss: 0.0824\n",
      "Epoch 38/150\n",
      "0s - loss: 0.0798\n",
      "Epoch 39/150\n",
      "0s - loss: 0.0774\n",
      "Epoch 40/150\n",
      "0s - loss: 0.0749\n",
      "Epoch 41/150\n",
      "0s - loss: 0.0724\n",
      "Epoch 42/150\n",
      "0s - loss: 0.0700\n",
      "Epoch 43/150\n",
      "0s - loss: 0.0676\n",
      "Epoch 44/150\n",
      "0s - loss: 0.0653\n",
      "Epoch 45/150\n",
      "0s - loss: 0.0630\n",
      "Epoch 46/150\n",
      "0s - loss: 0.0607\n",
      "Epoch 47/150\n",
      "0s - loss: 0.0584\n",
      "Epoch 48/150\n",
      "0s - loss: 0.0562\n",
      "Epoch 49/150\n",
      "0s - loss: 0.0541\n",
      "Epoch 50/150\n",
      "0s - loss: 0.0520\n",
      "Epoch 51/150\n",
      "0s - loss: 0.0500\n",
      "Epoch 52/150\n",
      "0s - loss: 0.0480\n",
      "Epoch 53/150\n",
      "0s - loss: 0.0460\n",
      "Epoch 54/150\n",
      "0s - loss: 0.0442\n",
      "Epoch 55/150\n",
      "0s - loss: 0.0424\n",
      "Epoch 56/150\n",
      "0s - loss: 0.0406\n",
      "Epoch 57/150\n",
      "0s - loss: 0.0390\n",
      "Epoch 58/150\n",
      "0s - loss: 0.0374\n",
      "Epoch 59/150\n",
      "0s - loss: 0.0359\n",
      "Epoch 60/150\n",
      "0s - loss: 0.0344\n",
      "Epoch 61/150\n",
      "0s - loss: 0.0331\n",
      "Epoch 62/150\n",
      "0s - loss: 0.0318\n",
      "Epoch 63/150\n",
      "0s - loss: 0.0305\n",
      "Epoch 64/150\n",
      "0s - loss: 0.0294\n",
      "Epoch 65/150\n",
      "0s - loss: 0.0283\n",
      "Epoch 66/150\n",
      "0s - loss: 0.0273\n",
      "Epoch 67/150\n",
      "0s - loss: 0.0264\n",
      "Epoch 68/150\n",
      "0s - loss: 0.0255\n",
      "Epoch 69/150\n",
      "0s - loss: 0.0247\n",
      "Epoch 70/150\n",
      "0s - loss: 0.0240\n",
      "Epoch 71/150\n",
      "0s - loss: 0.0233\n",
      "Epoch 72/150\n",
      "0s - loss: 0.0227\n",
      "Epoch 73/150\n",
      "0s - loss: 0.0221\n",
      "Epoch 74/150\n",
      "0s - loss: 0.0215\n",
      "Epoch 75/150\n",
      "0s - loss: 0.0210\n",
      "Epoch 76/150\n",
      "0s - loss: 0.0205\n",
      "Epoch 77/150\n",
      "0s - loss: 0.0201\n",
      "Epoch 78/150\n",
      "0s - loss: 0.0196\n",
      "Epoch 79/150\n",
      "0s - loss: 0.0192\n",
      "Epoch 80/150\n",
      "0s - loss: 0.0188\n",
      "Epoch 81/150\n",
      "0s - loss: 0.0184\n",
      "Epoch 82/150\n",
      "0s - loss: 0.0180\n",
      "Epoch 83/150\n",
      "0s - loss: 0.0177\n",
      "Epoch 84/150\n",
      "0s - loss: 0.0173\n",
      "Epoch 85/150\n",
      "0s - loss: 0.0169\n",
      "Epoch 86/150\n",
      "0s - loss: 0.0165\n",
      "Epoch 87/150\n",
      "0s - loss: 0.0161\n",
      "Epoch 88/150\n",
      "0s - loss: 0.0157\n",
      "Epoch 89/150\n",
      "0s - loss: 0.0153\n",
      "Epoch 90/150\n",
      "0s - loss: 0.0149\n",
      "Epoch 91/150\n",
      "0s - loss: 0.0145\n",
      "Epoch 92/150\n",
      "0s - loss: 0.0141\n",
      "Epoch 93/150\n",
      "0s - loss: 0.0137\n",
      "Epoch 94/150\n",
      "0s - loss: 0.0133\n",
      "Epoch 95/150\n",
      "0s - loss: 0.0129\n",
      "Epoch 96/150\n",
      "0s - loss: 0.0124\n",
      "Epoch 97/150\n",
      "0s - loss: 0.0120\n",
      "Epoch 98/150\n",
      "0s - loss: 0.0116\n",
      "Epoch 99/150\n",
      "0s - loss: 0.0111\n",
      "Epoch 100/150\n",
      "0s - loss: 0.0107\n",
      "Epoch 101/150\n",
      "0s - loss: 0.0103\n",
      "Epoch 102/150\n",
      "0s - loss: 0.0098\n",
      "Epoch 103/150\n",
      "0s - loss: 0.0094\n",
      "Epoch 104/150\n",
      "0s - loss: 0.0090\n",
      "Epoch 105/150\n",
      "0s - loss: 0.0086\n",
      "Epoch 106/150\n",
      "0s - loss: 0.0082\n",
      "Epoch 107/150\n",
      "0s - loss: 0.0078\n",
      "Epoch 108/150\n",
      "0s - loss: 0.0073\n",
      "Epoch 109/150\n",
      "0s - loss: 0.0070\n",
      "Epoch 110/150\n",
      "0s - loss: 0.0066\n",
      "Epoch 111/150\n",
      "0s - loss: 0.0062\n",
      "Epoch 112/150\n",
      "0s - loss: 0.0058\n",
      "Epoch 113/150\n",
      "0s - loss: 0.0055\n",
      "Epoch 114/150\n",
      "0s - loss: 0.0051\n",
      "Epoch 115/150\n",
      "0s - loss: 0.0048\n",
      "Epoch 116/150\n",
      "0s - loss: 0.0045\n",
      "Epoch 117/150\n",
      "0s - loss: 0.0042\n",
      "Epoch 118/150\n",
      "0s - loss: 0.0039\n",
      "Epoch 119/150\n",
      "0s - loss: 0.0036\n",
      "Epoch 120/150\n",
      "0s - loss: 0.0033\n",
      "Epoch 121/150\n",
      "0s - loss: 0.0031\n",
      "Epoch 122/150\n",
      "0s - loss: 0.0028\n",
      "Epoch 123/150\n",
      "0s - loss: 0.0026\n",
      "Epoch 124/150\n",
      "0s - loss: 0.0024\n",
      "Epoch 125/150\n",
      "0s - loss: 0.0022\n",
      "Epoch 126/150\n",
      "0s - loss: 0.0020\n",
      "Epoch 127/150\n",
      "0s - loss: 0.0018\n",
      "Epoch 128/150\n",
      "0s - loss: 0.0017\n",
      "Epoch 129/150\n",
      "0s - loss: 0.0015\n",
      "Epoch 130/150\n",
      "0s - loss: 0.0014\n",
      "Epoch 131/150\n",
      "0s - loss: 0.0013\n",
      "Epoch 132/150\n",
      "0s - loss: 0.0012\n",
      "Epoch 133/150\n",
      "0s - loss: 0.0011\n",
      "Epoch 134/150\n",
      "0s - loss: 0.0010\n",
      "Epoch 135/150\n",
      "0s - loss: 9.3632e-04\n",
      "Epoch 136/150\n",
      "0s - loss: 8.7300e-04\n",
      "Epoch 137/150\n",
      "0s - loss: 8.1800e-04\n",
      "Epoch 138/150\n",
      "0s - loss: 7.7047e-04\n",
      "Epoch 139/150\n",
      "0s - loss: 7.2956e-04\n",
      "Epoch 140/150\n",
      "0s - loss: 6.9441e-04\n",
      "Epoch 141/150\n",
      "0s - loss: 6.6422e-04\n",
      "Epoch 142/150\n",
      "0s - loss: 6.3824e-04\n",
      "Epoch 143/150\n",
      "0s - loss: 6.1577e-04\n",
      "Epoch 144/150\n",
      "0s - loss: 5.9622e-04\n",
      "Epoch 145/150\n",
      "0s - loss: 5.7904e-04\n",
      "Epoch 146/150\n",
      "0s - loss: 5.6379e-04\n",
      "Epoch 147/150\n",
      "0s - loss: 5.5009e-04\n",
      "Epoch 148/150\n",
      "0s - loss: 5.3764e-04\n",
      "Epoch 149/150\n",
      "0s - loss: 5.2620e-04\n",
      "Epoch 150/150\n",
      "0s - loss: 5.1558e-04\n",
      "Model Fit Complete\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "seq_lstm_model.fit(train_x, train_y, \n",
    "               epochs=150, batch_size=1, \n",
    "               verbose=2)\n",
    "print(\"Model Fit Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Prediction Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.02 RMSE\n"
     ]
    }
   ],
   "source": [
    "# train fit performance\n",
    "trainPredict = seq_lstm_model.predict(train_x)\n",
    "trainScore = math.sqrt(mean_squared_error(train_y[0], trainPredict[0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Prediction Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pad input sequence\n",
    "testPredict = pad_sequences(test_x,\n",
    "                                maxlen=train_x.shape[1],\n",
    "                                padding='post',\n",
    "                                dtype='float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.07 RMSE\n"
     ]
    }
   ],
   "source": [
    "# forecast values\n",
    "testPredict = seq_lstm_model.predict(testPredict)\n",
    "\n",
    "# evaluate performances\n",
    "testScore = math.sqrt(mean_squared_error(test_y[0], \n",
    "                                         testPredict[0][:test_x.shape[1]]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Test Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# inverse transformation\n",
    "trainPredict = scaler.inverse_transform(trainPredict.\\\n",
    "                                        reshape(trainPredict.shape[1]))\n",
    "testPredict = scaler.inverse_transform(testPredict.\\\n",
    "                                       reshape(testPredict.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8PIvf/fz8aN++PQkJCWqZk5PTdeuvqE8C0OvAysqKqKioZluCe+LECXr27Hnd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hXnX0ZGRtLVtysLYxeiUqgoqSvh1fRXeebYM5gtZnQ6XYPgU6FQcNtttxHYO5A3Ct5oEHwCvDb2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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x163779a9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the true and forecasted values\n",
    "train_size = len(trainPredict)+1\n",
    "\n",
    "plt.plot(sp_close_series.index,\n",
    "         sp_close_series.values,c='black',\n",
    "         alpha=0.3,label='True Data')\n",
    "plt.plot(sp_close_series.index[1:train_size],\n",
    "         trainPredict,label='Training Fit',c='g')\n",
    "plt.plot(sp_close_series.index[train_size+1:],\n",
    "         testPredict[:test_x.shape[1]],label='Testing Forecast')\n",
    "plt.title('Forecast Plot')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}