TimeseriesGenerator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras TimeseriesGenerator tutorial\n",
    "Blog - [How to use Keras TimeseriesGenerator for time series data](https://www.dlology.com/blog/how-to-use-keras-timeseriesgenerator-for-time-series-data/)\n",
    "\n",
    "\n",
    "Keras [TimeseriesGenerator](https://keras.io/preprocessing/sequence/#timeseriesgenerator) API doc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`mpld3` enables zooming of the matplotlib and interactive data visualizations on Jupyter notebook.\n",
    "\n",
    "Install it with `pip3 install mpld3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import mpld3\n",
    "mpld3.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hasee\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import read_csv\n",
    "import math\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load DJI, AAPL, AMAZN close index/price\n",
    "During 2015/09/19-2018/09/18\n",
    "\n",
    "Downloaded from [YAHOO finance](https://finance.yahoo.com/quote/AMZN/history?period1=1442592000&period2=1537286400&interval=1d&filter=history&frequency=1d)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_DJI = read_csv('./data/DJI_20150919-20180918.csv', usecols=[4]).values.astype('float32')\n",
    "dataset_AAPL = read_csv('./data/AAPL_20150919-20180918.csv', usecols=[4]).values.astype('float32')\n",
    "dataset_AMAZN = read_csv('./data/AMZN_20150919-20180918.csv', usecols=[4]).values.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def delta_time_series(data):\n",
    "    return data[1:]- data[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_delta_DJI = delta_time_series(dataset_DJI)\n",
    "dataset_delta_APPL = delta_time_series(dataset_AAPL)\n",
    "dataset_delta_AMAZN = delta_time_series(dataset_AMAZN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(756, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_DJI.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_delta(data):\n",
    "    plt.plot(delta_time_series(data))\n",
    "    plt.ylabel('close')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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DmCiE+AcR/R7ATwFUmrveKoSY0pBzZYrAYhRBVY9tIoqPyS3SjT9kp+d35H/F07OT3jsN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\nzYde9te/PLVfyuh89m2n4s/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25Yx8DfnjmdkMwzCMK83Z9cQwDMN4gBUFwzAM\n4worCoZhGMYVVhQMwzCMK6woGIZhGFdYUTAMwzCusKJgGIZhXGFFwTAMw7jy/zsUlR9l6CdRAAAA\nAElFTkSuQmCC\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el1703617890341609282625701189\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el1703617890341609282625701189\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data01\": [[0.0, 8.1300048828125], [1.0, -9.989990234375], [2.0, -2.33001708984375], [3.0, -2.32000732421875], [4.0, -9.5], [5.0, -20.19000244140625], [6.0, -7.989990234375], [7.0, 15.82000732421875], [8.0, 8.8299560546875], [9.0, 11.82000732421875], [10.0, 11.1400146484375], [11.0, -6.20001220703125], [12.0, 4.46002197265625], [13.0, -8.780029296875], [14.0, 6.6400146484375], [15.0, 10.3900146484375], [16.0, -1.28997802734375], [17.0, -4.07000732421875], [18.0, 17.6099853515625], [19.0, 8.32000732421875], [20.0, 2.3900146484375], [21.0, -12.27001953125], [22.0, -5.1099853515625], [23.0, 8.13995361328125], [24.0, 35.12005615234375], [25.0, 9.5799560546875], [26.0, 2.4000244140625], [27.0, 6.0899658203125], [28.0, 9.45001220703125], [29.0, -0.64996337890625], [30.0, 2.449951171875], [31.0, -3.03997802734375], [32.0, 15.6400146484375], [33.0, 14.70001220703125], [34.0, 3.719970703125], [35.0, -3.8800048828125], [36.0, 4.19000244140625], [37.0, 13.57000732421875], [38.0, -7.6500244140625], [39.0, -23.25], [40.0, 5.46002197265625], [41.0, -4.510009765625], [42.0, 20.239990234375], [43.0, -2.26995849609375], [44.0, 7.17999267578125], [45.0, 10.53997802734375], [46.0, -7.8399658203125], [47.0, 4.19000244140625], [48.0, -2.08001708984375], [49.0, -8.46002197265625], [50.0, 14.260009765625], [51.0, -3.04998779296875], [52.0, -9.760009765625], [53.0, 6.3900146484375], [54.0, -2.80999755859375], [55.0, 7.5], [56.0, -12.5400390625], [57.0, -2.469970703125], [58.0, -22.16998291015625], [59.0, 17.75994873046875], [60.0, 0.73004150390625], [61.0, 17.1300048828125], [62.0, -5.1199951171875], [63.0, -6.510009765625], [64.0, 0.3699951171875], [65.0, -1.3599853515625], [66.0, 0.54998779296875], [67.0, -0.9100341796875], [68.0, 12.4100341796875], [69.0, 18.76995849609375], [70.0, -4.89996337890625], [71.0, -13.17999267578125], [72.0, -38.9000244140625], [73.0, 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\"id\": \"el170361789034160928\", \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"enabled\": false, \"type\": \"zoom\"}, {\"button\": true, \"enabled\": false, \"type\": \"boxzoom\"}], \"axes\": [{\"collections\": [], \"yscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.775, 0.755], \"ylim\": [-85.54347229003906, 138.7134552001953], \"texts\": [{\"text\": \"close\", \"position\": [-0.09595280764635604, 0.49999999999999994], \"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"alpha\": 1, \"fontsize\": 10.0, \"rotation\": -90.0, \"coordinates\": \"axes\", \"id\": \"el170361788922570120\", \"zorder\": 3}], \"sharex\": [], \"id\": \"el170361789034162776\", \"ydomain\": [-85.54347229003906, 138.7134552001953], \"axes\": [{\"nticks\": 10, \"tickformat\": null, \"position\": \"bottom\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}, {\"nticks\": 11, \"tickformat\": null, \"position\": \"left\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}], \"xlim\": [-37.7, 791.7], \"xdomain\": [-37.7, 791.7], \"paths\": [], \"zoomable\": true, \"xscale\": \"linear\", \"markers\": [], \"axesbgalpha\": null, \"lines\": [{\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361788922716736\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"yindex\": 1, \"dasharray\": \"none\", \"zorder\": 2}], \"sharey\": [], \"images\": [], \"axesbg\": \"#FFFFFF\"}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
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\"left\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}], \"xlim\": [-37.7, 791.7], \"xdomain\": [-37.7, 791.7], \"paths\": [], \"zoomable\": true, \"xscale\": \"linear\", \"markers\": [], \"axesbgalpha\": null, \"lines\": [{\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361788922716736\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"yindex\": 1, \"dasharray\": \"none\", \"zorder\": 2}], \"sharey\": [], \"images\": [], \"axesbg\": \"#FFFFFF\"}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el1703617890341609282625701189\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data01\": [[0.0, 8.1300048828125], [1.0, -9.989990234375], [2.0, -2.33001708984375], [3.0, -2.32000732421875], [4.0, -9.5], [5.0, -20.19000244140625], [6.0, -7.989990234375], [7.0, 15.82000732421875], [8.0, 8.8299560546875], [9.0, 11.82000732421875], [10.0, 11.1400146484375], [11.0, -6.20001220703125], [12.0, 4.46002197265625], [13.0, -8.780029296875], [14.0, 6.6400146484375], [15.0, 10.3900146484375], [16.0, -1.28997802734375], [17.0, -4.07000732421875], [18.0, 17.6099853515625], [19.0, 8.32000732421875], [20.0, 2.3900146484375], [21.0, -12.27001953125], [22.0, -5.1099853515625], [23.0, 8.13995361328125], [24.0, 35.12005615234375], [25.0, 9.5799560546875], [26.0, 2.4000244140625], [27.0, 6.0899658203125], [28.0, 9.45001220703125], [29.0, -0.64996337890625], [30.0, 2.449951171875], [31.0, -3.03997802734375], [32.0, 15.6400146484375], [33.0, 14.70001220703125], [34.0, 3.719970703125], [35.0, -3.8800048828125], [36.0, 4.19000244140625], [37.0, 13.57000732421875], [38.0, -7.6500244140625], [39.0, -23.25], [40.0, 5.46002197265625], [41.0, -4.510009765625], [42.0, 20.239990234375], [43.0, -2.26995849609375], [44.0, 7.17999267578125], [45.0, 10.53997802734375], [46.0, -7.8399658203125], [47.0, 4.19000244140625], [48.0, -2.08001708984375], [49.0, -8.46002197265625], [50.0, 14.260009765625], [51.0, -3.04998779296875], [52.0, -9.760009765625], [53.0, 6.3900146484375], [54.0, -2.80999755859375], [55.0, 7.5], [56.0, -12.5400390625], [57.0, -2.469970703125], [58.0, -22.16998291015625], [59.0, 17.75994873046875], [60.0, 0.73004150390625], [61.0, 17.1300048828125], [62.0, -5.1199951171875], [63.0, -6.510009765625], [64.0, 0.3699951171875], [65.0, -1.3599853515625], [66.0, 0.54998779296875], [67.0, -0.9100341796875], [68.0, 12.4100341796875], [69.0, 18.76995849609375], [70.0, -4.89996337890625], [71.0, -13.17999267578125], [72.0, 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a08abee320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_delta(dataset_AMAZN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper function to get y as a numpy array from the TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_y_from_generator(gen):\n",
    "    '''\n",
    "    Get all targets y from a TimeseriesGenerator instance.\n",
    "    '''\n",
    "    y = None\n",
    "    for i in range(len(gen)):\n",
    "        batch_y = gen[i][1]\n",
    "        if y is None:\n",
    "            y = batch_y\n",
    "        else:\n",
    "            y = np.append(y, batch_y)\n",
    "    y = y.reshape((-1,1))\n",
    "    print(y.shape)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_accuracy(a, b):\n",
    "    '''\n",
    "    Helper function to compute the match score of two \n",
    "    binary numpy arrays.\n",
    "    '''\n",
    "    assert len(a) == len(b)\n",
    "    return (a == b).sum() / len(a)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Single time series as input\n",
    "### Normalize datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_delta_DJI\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.67)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "look_back = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_gen = TimeseriesGenerator(train, train,\n",
    "                               length=look_back, sampling_rate=1,stride=1,\n",
    "                               batch_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_gen = TimeseriesGenerator(test, test,\n",
    "                               length=look_back, sampling_rate=1,stride=1,\n",
    "                               batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(4, input_shape=(look_back, 1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(model, to_file='model.png', show_shapes=True)\n",
    "from IPython.display import Image\n",
    "Image(filename='model.png') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "168/168 [==============================] - 2s 13ms/step - loss: 0.2233\n",
      "Epoch 2/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0058\n",
      "Epoch 3/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 4/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 5/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 6/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0048\n",
      "Epoch 7/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 8/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 9/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 10/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 11/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 12/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 13/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 14/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 15/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 16/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 17/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 18/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 19/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 20/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 21/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 22/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 23/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 24/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 25/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 26/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 27/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 28/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0047\n",
      "Epoch 29/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 30/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 31/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 32/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 33/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 34/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 35/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 36/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 37/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 38/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 39/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 40/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 41/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 42/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 43/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 44/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 45/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 46/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 47/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 48/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 49/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 50/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 51/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 52/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 53/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 54/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 55/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 56/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 57/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 58/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 59/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 60/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 61/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0049\n",
      "Epoch 62/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 63/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 64/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 65/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 66/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 67/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 68/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 69/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 70/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 71/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 72/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 73/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 74/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 75/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 76/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 77/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 78/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 79/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 80/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 81/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 82/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 83/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 84/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 85/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 86/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 87/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 88/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 89/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 90/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 91/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 92/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 93/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0048\n",
      "Epoch 94/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 95/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 96/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 97/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 98/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 99/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 100/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_data_gen, epochs=100).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013424654420067732"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(test_data_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(502, 1)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainPredict = model.predict_generator(train_data_gen)\n",
    "trainPredict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(247, 1)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict = model.predict_generator(test_data_gen)\n",
    "testPredict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invert predictions, scale values back to real index/price range.\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "testPredict = scaler.inverse_transform(testPredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(502, 1)\n",
      "(247, 1)\n"
     ]
    }
   ],
   "source": [
    "trainY = get_y_from_generator(train_data_gen)\n",
    "testY = get_y_from_generator(test_data_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainY = scaler.inverse_transform(trainY)\n",
    "testY = scaler.inverse_transform(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 125.89 RMSE\n",
      "Test Score: 213.73 RMSE\n"
     ]
    }
   ],
   "source": [
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(trainY[:,0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY[:, 0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(755, 1)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = scaler.inverse_transform(dataset)\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(dataset)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "# Delta + previous close\n",
    "trainPredictPlot = trainPredictPlot + dataset_DJI[1:]\n",
    "# set empty values\n",
    "# trainPredictPlot[0:look_back, :] = np.nan\n",
    "# trainPredictPlot[len(trainPredict)+look_back:, :] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(dataset)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2):len(dataset), :] = testPredict\n",
    "\n",
    "# Delta + previous close\n",
    "testPredictPlot = testPredictPlot + dataset_DJI[1:]\n",
    "# set empty values\n",
    "# testPredictPlot[0:len(trainPredict)+(look_back*2), :] = np.nan\n",
    "# testPredictPlot[len(dataset):, :] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k5fs1eyvkK/YGuN78HyWORNqcPAHDUFw0qD0xsgdCs1NjcNBa79FaLw29LgDW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\nnH0vA+BIKJteEhth5w9nnMITaR/T6bw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[719.0, 25162.599609375, NaN, 25320.904296875], [720.0, 25523.548828125, NaN, 25427.53125], [721.0, 25252.451171875, NaN, 25335.8203125], [722.0, 25318.5, NaN, 25332.744140625], [723.0, 25599.0, NaN, 25471.974609375], [724.0, 25541.779296875, NaN, 25504.640625], [725.0, 25755.640625, NaN, 25636.416015625], [726.0, 25538.58984375, NaN, 25591.880859375], [727.0, 25434.7109375, NaN, 25519.58984375], [728.0, 25117.05078125, NaN, 25324.689453125], [729.0, 25062.2578125, NaN, 25197.07421875], [730.0, 25412.140625, NaN, 25306.001953125], [731.0, 25024.900390625, NaN, 25161.63671875], [732.0, 25955.05078125, NaN, 25566.927734375], [733.0, 25779.91015625, NaN, 25669.52734375], [734.0, 25848.05859375, NaN, 25764.03515625], [735.0, 25885.888671875, NaN, 25837.9375], [736.0, 25644.91015625, NaN, 25742.931640625], [737.0, 25580.361328125, NaN, 25668.884765625], [738.0, 25923.71875, NaN, 25799.98828125], [739.0, 26308.931640625, NaN, 26051.40625], [740.0, 26078.3984375, NaN, 26065.037109375], [741.0, 26185.12109375, NaN, 26136.958984375], [742.0, 25849.26953125, NaN, 25999.263671875], [743.0, 25942.720703125, NaN, 25975.8828125], [744.0, 25940.140625, NaN, 25960.5078125], [745.0, 25997.5, NaN, 25979.0625], [746.0, 26016.748046875, NaN, 26002.08203125], [747.0, 25837.208984375, NaN, 25923.328125], [748.0, 25797.6015625, NaN, 25866.763671875], [749.0, 26085.05078125, NaN, 25979.384765625], [750.0, 26026.779296875, NaN, 26001.45703125], [751.0, 26293.060546875, NaN, 26152.345703125], [752.0, 26163.349609375, NaN, 26161.728515625], [753.0, 25969.568359375, NaN, 26071.21875], [754.0, 26431.802734375, NaN, 26259.86328125]]}, \"id\": \"el27161914675893248\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el271619146758932485769613106\", {\"axes\": [{\"zoomable\": true, \"ydomain\": [14833.86845703125, 27412.38349609375], \"lines\": [{\"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el27161914676200112\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"data\": \"data01\", \"dasharray\": \"none\", \"linewidth\": 1.5, \"zorder\": 2, \"xindex\": 0}, {\"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el27161914676201400\", \"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"data\": \"data01\", \"dasharray\": \"none\", \"linewidth\": 1.5, \"zorder\": 2, \"xindex\": 0}, {\"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el27161914676200448\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"data\": \"data01\", \"dasharray\": \"none\", \"linewidth\": 1.5, \"zorder\": 2, \"xindex\": 0}], \"xdomain\": [-37.7, 791.7], \"paths\": [], \"sharey\": [], \"axesbgalpha\": null, \"markers\": [], \"yscale\": \"linear\", \"images\": [], \"texts\": [], \"bbox\": [0.125, 0.125, 0.775, 0.755], \"axes\": [{\"position\": \"bottom\", \"grid\": {\"gridOn\": false}, \"scale\": \"linear\", \"tickvalues\": null, \"tickformat\": null, \"fontsize\": 10.0, \"visible\": true, \"nticks\": 10}, {\"position\": \"left\", \"grid\": {\"gridOn\": false}, \"scale\": \"linear\", \"tickvalues\": null, \"tickformat\": null, \"fontsize\": 10.0, \"visible\": true, \"nticks\": 8}], \"axesbg\": \"#FFFFFF\", \"xscale\": \"linear\", \"sharex\": [], \"ylim\": [14833.86845703125, 27412.38349609375], \"xlim\": [-37.7, 791.7], \"id\": \"el27161914675923264\", \"collections\": []}], \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"enabled\": false, \"button\": true}, {\"type\": \"boxzoom\", \"enabled\": false, \"button\": true}], \"height\": 288.0, \"width\": 432.0, \"data\": {\"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 16122.7509765625, 16211.1767578125, NaN], [4.0, 16428.01953125, 16318.736328125, NaN], [5.0, 15689.109375, 16004.9560546875, NaN], [6.0, 16096.3701171875, 16061.873046875, NaN], [7.0, 16520.271484375, 16291.1689453125, NaN], [8.0, 16259.3193359375, 16267.595703125, NaN], [9.0, 16672.728515625, 16483.177734375, NaN], [10.0, 17080.490234375, 16784.826171875, NaN], [11.0, 16803.94921875, 16792.080078125, NaN], [12.0, 17034.388671875, 16926.609375, NaN], [13.0, 17189.2109375, 17062.7578125, NaN], [14.0, 17118.23046875, 17090.177734375, NaN], [15.0, 17179.228515625, 17142.533203125, NaN], [16.0, 17031.921875, 17091.7890625, NaN], [17.0, 16767.609375, 16934.349609375, NaN], [18.0, 17358.75, 17152.947265625, NaN], [19.0, 17290.19140625, 17216.228515625, NaN], [20.0, 17245.107421875, 17234.611328125, NaN], [21.0, 17203.6796875, 17229.935546875, NaN], [22.0, 17120.109375, 17178.12109375, NaN], [23.0, 17809.7109375, 17497.712890625, NaN], [24.0, 17804.23828125, 17646.3671875, NaN], [25.0, 17599.40234375, 17628.56640625, NaN], [26.0, 17539.80859375, 17598.265625, NaN], [27.0, 17977.609375, 17791.32421875, NaN], [28.0, 17732.08203125, 17757.994140625, NaN], [29.0, 17571.27734375, 17672.19140625, NaN], [30.0, 17993.98046875, 17842.6328125, NaN], [31.0, 18007.541015625, 17920.642578125, NaN], [32.0, 17817.009765625, 17872.3125, NaN], [33.0, 17859.279296875, 17876.578125, NaN], [34.0, 17957.23046875, 17919.451171875, NaN], [35.0, 17550.630859375, 17735.58203125, NaN], [36.0, 17785.94140625, 17769.53125, NaN], [37.0, 17646.23046875, 17708.537109375, NaN], [38.0, 17193.919921875, 17452.490234375, NaN], [39.0, 17042.41015625, 17257.744140625, NaN], [40.0, 17720.779296875, 17490.353515625, NaN], [41.0, 17495.990234375, 17484.15625, NaN], [42.0, 17984.8203125, 17741.861328125, NaN], [43.0, 17728.33984375, 17740.2265625, NaN], [44.0, 17914.87109375, 17833.60546875, NaN], [45.0, 17761.548828125, 17803.91796875, NaN], [46.0, 17831.69921875, 17820.8828125, NaN], [47.0, 17814.591796875, 17822.21875, NaN], [48.0, 17813.390625, 17820.119140625, NaN], [49.0, 17626.44921875, 17727.71875, NaN], [50.0, 18056.779296875, 17897.615234375, NaN], [51.0, 17571.009765625, 17732.65234375, NaN], [52.0, 17225.66015625, 17487.63671875, NaN], [53.0, 18217.591796875, 17862.703125, NaN], [54.0, 17613.388671875, 17724.26953125, NaN], [55.0, 17405.490234375, 17575.568359375, NaN], [56.0, 17416.6015625, 17511.21484375, NaN], [57.0, 17657.19921875, 17579.173828125, NaN], [58.0, 16955.671875, 17266.287109375, NaN], [59.0, 17471.7890625, 17380.908203125, NaN], [60.0, 17681.3203125, 17529.5078125, NaN], [61.0, 17973.26953125, 17746.931640625, NaN], [62.0, 17242.58984375, 17501.84765625, NaN], [63.0, 16761.26171875, 17147.08203125, NaN], [64.0, 17374.6875, 17269.3359375, NaN], [65.0, 17582.919921875, 17412.6328125, NaN], [66.0, 17787.94921875, 17599.162109375, NaN], [67.0, 17501.73046875, 17559.603515625, NaN], [68.0, 17504.369140625, 17542.25, NaN], [69.0, 17913.69140625, 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NaN, 24093.8671875], [654.0, 24560.849609375, NaN, 24324.3671875], [655.0, 24300.0390625, NaN, 24304.357421875], [656.0, 24015.111328125, NaN, 24174.23828125], [657.0, 24034.951171875, NaN, 24115.212890625], [658.0, 23750.91015625, NaN, 23931.96484375], [659.0, 23935.3203125, NaN, 23936.64453125], [660.0, 24594.869140625, NaN, 24266.58984375], [661.0, 24452.130859375, NaN, 24353.533203125], [662.0, 24363.1015625, NaN, 24368.46875], [663.0, 24724.8671875, NaN, 24558.5625], [664.0, 24936.51953125, NaN, 24745.162109375], [665.0, 24922.810546875, NaN, 24835.765625], [666.0, 24967.650390625, NaN, 24910.626953125], [667.0, 24513.41015625, NaN, 24717.6875], [668.0, 24831.44921875, NaN, 24783.08984375], [669.0, 24659.03125, NaN, 24720.00390625], [670.0, 24716.19921875, NaN, 24719.52734375], [671.0, 25311.48828125, NaN, 25021.609375], [672.0, 24655.53125, NaN, 24834.009765625], [673.0, 24939.2109375, NaN, 24900.767578125], [674.0, 24736.708984375, NaN, 24823.8046875], [675.0, 24694.419921875, NaN, 24758.158203125], [676.0, 23969.80859375, NaN, 24370.572265625], [677.0, 24974.109375, NaN, 24680.048828125], [678.0, 24163.900390625, NaN, 24412.220703125], [679.0, 24854.58203125, NaN, 24641.505859375], [680.0, 24992.16796875, NaN, 24821.775390625], [681.0, 24786.271484375, NaN, 24799.697265625], [682.0, 25492.80078125, NaN, 25160.755859375], [683.0, 25336.4296875, NaN, 25245.0703125], [684.0, 25391.6484375, NaN, 25324.486328125], [685.0, 25328.091796875, NaN, 25336.99609375], [686.0, 25319.150390625, NaN, 25330.849609375], [687.0, 25081.66796875, NaN, 25210.40625], [688.0, 25149.421875, NaN, 25185.197265625], [689.0, 25005.650390625, NaN, 25097.212890625], [690.0, 24884.4609375, NaN, 24993.37109375], [691.0, 24412.951171875, NaN, 24708.1875], [692.0, 24615.390625, NaN, 24667.509765625], [693.0, 24265.59765625, NaN, 24464.630859375], [694.0, 24700.08203125, NaN, 24584.556640625], [695.0, 23924.7109375, NaN, 24254.9453125], [696.0, 24313.41796875, NaN, 24293.271484375], [697.0, 23952.0703125, NaN, 24124.416015625], [698.0, 24314.51171875, NaN, 24219.0], [699.0, 24326.76953125, NaN, 24275.99609375], [700.0, 24342.94921875, NaN, 24310.359375], [701.0, 24042.4609375, NaN, 24184.201171875], [702.0, 24538.66015625, NaN, 24368.4609375], [703.0, 24556.220703125, NaN, 24459.705078125], [704.0, 25096.69921875, NaN, 24780.33984375], [705.0, 25062.73046875, NaN, 24924.75390625], [706.0, 24481.23828125, NaN, 24709.560546875], [707.0, 25149.33203125, NaN, 24945.982421875], [708.0, 25113.9296875, NaN, 25023.638671875], [709.0, 25109.30859375, NaN, 25066.67578125], [710.0, 25175.421875, NaN, 25132.369140625], [711.0, 25278.6875, NaN, 25208.056640625], [712.0, 24929.7109375, NaN, 25071.66796875], [713.0, 25051.73828125, NaN, 25070.306640625], [714.0, 25030.458984375, NaN, 25052.48828125], [715.0, 25439.58984375, NaN, 25246.26171875], [716.0, 25586.259765625, NaN, 25416.921875], [717.0, 25640.041015625, NaN, 25531.98828125], [718.0, 25375.05078125, NaN, 25461.94140625], [719.0, 25162.599609375, NaN, 25320.904296875], [720.0, 25523.548828125, NaN, 25427.53125], [721.0, 25252.451171875, NaN, 25335.8203125], [722.0, 25318.5, NaN, 25332.744140625], [723.0, 25599.0, NaN, 25471.974609375], [724.0, 25541.779296875, NaN, 25504.640625], [725.0, 25755.640625, NaN, 25636.416015625], [726.0, 25538.58984375, NaN, 25591.880859375], [727.0, 25434.7109375, NaN, 25519.58984375], [728.0, 25117.05078125, NaN, 25324.689453125], [729.0, 25062.2578125, NaN, 25197.07421875], [730.0, 25412.140625, NaN, 25306.001953125], [731.0, 25024.900390625, NaN, 25161.63671875], [732.0, 25955.05078125, NaN, 25566.927734375], [733.0, 25779.91015625, NaN, 25669.52734375], [734.0, 25848.05859375, NaN, 25764.03515625], [735.0, 25885.888671875, NaN, 25837.9375], [736.0, 25644.91015625, NaN, 25742.931640625], [737.0, 25580.361328125, NaN, 25668.884765625], [738.0, 25923.71875, NaN, 25799.98828125], [739.0, 26308.931640625, NaN, 26051.40625], [740.0, 26078.3984375, NaN, 26065.037109375], [741.0, 26185.12109375, NaN, 26136.958984375], [742.0, 25849.26953125, NaN, 25999.263671875], [743.0, 25942.720703125, NaN, 25975.8828125], [744.0, 25940.140625, NaN, 25960.5078125], [745.0, 25997.5, NaN, 25979.0625], [746.0, 26016.748046875, NaN, 26002.08203125], [747.0, 25837.208984375, NaN, 25923.328125], [748.0, 25797.6015625, NaN, 25866.763671875], [749.0, 26085.05078125, NaN, 25979.384765625], [750.0, 26026.779296875, NaN, 26001.45703125], [751.0, 26293.060546875, NaN, 26152.345703125], [752.0, 26163.349609375, NaN, 26161.728515625], [753.0, 25969.568359375, NaN, 26071.21875], [754.0, 26431.802734375, NaN, 26259.86328125]]}, \"id\": \"el27161914675893248\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el271619146758932485769613106\", {\"axes\": [{\"zoomable\": true, \"ydomain\": [14833.86845703125, 27412.38349609375], \"lines\": [{\"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el27161914676200112\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"data\": \"data01\", \"dasharray\": \"none\", \"linewidth\": 1.5, \"zorder\": 2, \"xindex\": 0}, {\"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el27161914676201400\", \"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"data\": \"data01\", \"dasharray\": \"none\", \"linewidth\": 1.5, \"zorder\": 2, \"xindex\": 0}, {\"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el27161914676200448\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"data\": \"data01\", \"dasharray\": \"none\", \"linewidth\": 1.5, \"zorder\": 2, \"xindex\": 0}], \"xdomain\": [-37.7, 791.7], \"paths\": [], \"sharey\": [], \"axesbgalpha\": null, \"markers\": [], \"yscale\": \"linear\", \"images\": [], \"texts\": [], \"bbox\": [0.125, 0.125, 0.775, 0.755], \"axes\": [{\"position\": \"bottom\", \"grid\": {\"gridOn\": false}, \"scale\": \"linear\", \"tickvalues\": null, \"tickformat\": null, \"fontsize\": 10.0, \"visible\": true, \"nticks\": 10}, {\"position\": \"left\", \"grid\": {\"gridOn\": false}, \"scale\": \"linear\", \"tickvalues\": null, \"tickformat\": null, \"fontsize\": 10.0, \"visible\": true, \"nticks\": 8}], \"axesbg\": \"#FFFFFF\", \"xscale\": \"linear\", \"sharex\": [], \"ylim\": [14833.86845703125, 27412.38349609375], \"xlim\": [-37.7, 791.7], \"id\": \"el27161914675923264\", \"collections\": []}], \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"enabled\": false, \"button\": true}, {\"type\": \"boxzoom\", \"enabled\": false, \"button\": true}], \"height\": 288.0, \"width\": 432.0, \"data\": {\"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 16122.7509765625, 16211.1767578125, NaN], [4.0, 16428.01953125, 16318.736328125, NaN], [5.0, 15689.109375, 16004.9560546875, NaN], [6.0, 16096.3701171875, 16061.873046875, NaN], [7.0, 16520.271484375, 16291.1689453125, NaN], [8.0, 16259.3193359375, 16267.595703125, NaN], [9.0, 16672.728515625, 16483.177734375, NaN], [10.0, 17080.490234375, 16784.826171875, NaN], [11.0, 16803.94921875, 16792.080078125, NaN], [12.0, 17034.388671875, 16926.609375, NaN], [13.0, 17189.2109375, 17062.7578125, NaN], [14.0, 17118.23046875, 17090.177734375, NaN], [15.0, 17179.228515625, 17142.533203125, NaN], [16.0, 17031.921875, 17091.7890625, NaN], [17.0, 16767.609375, 16934.349609375, NaN], [18.0, 17358.75, 17152.947265625, NaN], [19.0, 17290.19140625, 17216.228515625, NaN], [20.0, 17245.107421875, 17234.611328125, NaN], [21.0, 17203.6796875, 17229.935546875, NaN], [22.0, 17120.109375, 17178.12109375, NaN], [23.0, 17809.7109375, 17497.712890625, NaN], [24.0, 17804.23828125, 17646.3671875, NaN], [25.0, 17599.40234375, 17628.56640625, NaN], [26.0, 17539.80859375, 17598.265625, NaN], [27.0, 17977.609375, 17791.32421875, NaN], [28.0, 17732.08203125, 17757.994140625, NaN], [29.0, 17571.27734375, 17672.19140625, NaN], [30.0, 17993.98046875, 17842.6328125, NaN], [31.0, 18007.541015625, 17920.642578125, NaN], [32.0, 17817.009765625, 17872.3125, NaN], [33.0, 17859.279296875, 17876.578125, NaN], [34.0, 17957.23046875, 17919.451171875, NaN], [35.0, 17550.630859375, 17735.58203125, NaN], [36.0, 17785.94140625, 17769.53125, NaN], [37.0, 17646.23046875, 17708.537109375, NaN], [38.0, 17193.919921875, 17452.490234375, NaN], [39.0, 17042.41015625, 17257.744140625, NaN], [40.0, 17720.779296875, 17490.353515625, NaN], [41.0, 17495.990234375, 17484.15625, NaN], [42.0, 17984.8203125, 17741.861328125, NaN], [43.0, 17728.33984375, 17740.2265625, NaN], [44.0, 17914.87109375, 17833.60546875, NaN], [45.0, 17761.548828125, 17803.91796875, NaN], [46.0, 17831.69921875, 17820.8828125, NaN], [47.0, 17814.591796875, 17822.21875, NaN], [48.0, 17813.390625, 17820.119140625, NaN], [49.0, 17626.44921875, 17727.71875, NaN], [50.0, 18056.779296875, 17897.615234375, NaN], [51.0, 17571.009765625, 17732.65234375, NaN], [52.0, 17225.66015625, 17487.63671875, NaN], [53.0, 18217.591796875, 17862.703125, NaN], [54.0, 17613.388671875, 17724.26953125, NaN], [55.0, 17405.490234375, 17575.568359375, NaN], [56.0, 17416.6015625, 17511.21484375, NaN], [57.0, 17657.19921875, 17579.173828125, NaN], [58.0, 16955.671875, 17266.287109375, NaN], [59.0, 17471.7890625, 17380.908203125, NaN], [60.0, 17681.3203125, 17529.5078125, NaN], [61.0, 17973.26953125, 17746.931640625, NaN], [62.0, 17242.58984375, 17501.84765625, NaN], [63.0, 16761.26171875, 17147.08203125, NaN], [64.0, 17374.6875, 17269.3359375, NaN], [65.0, 17582.919921875, 17412.6328125, NaN], [66.0, 17787.94921875, 17599.162109375, NaN], [67.0, 17501.73046875, 17559.603515625, NaN], [68.0, 17504.369140625, 17542.25, NaN], [69.0, 17913.69140625, 17732.8671875, NaN], [70.0, 17486.7578125, 17605.701171875, NaN], [71.0, 17246.189453125, 17436.037109375, NaN], [72.0, 16872.849609375, 17163.6171875, NaN], [73.0, 17168.380859375, 17166.490234375, NaN], [74.0, 16654.359375, 16906.6875, NaN], [75.0, 16121.689453125, 16519.630859375, NaN], [76.0, 16178.80078125, 16358.7490234375, NaN], [77.0, 16450.69140625, 16398.64453125, NaN], [78.0, 16633.87109375, 16511.490234375, NaN], [79.0, 15786.599609375, 16152.8037109375, NaN], [80.0, 16606.689453125, 16396.115234375, NaN], [81.0, 15597.1103515625, 15990.583984375, NaN], [82.0, 16043.958984375, 16024.4619140625, NaN], [83.0, 15517.4609375, 15775.888671875, NaN], [84.0, 15998.619140625, 15885.529296875, NaN], [85.0, 16304.33984375, 16096.9912109375, NaN], [86.0, 15676.9296875, 15883.3564453125, NaN], [87.0, 16449.240234375, 16183.6904296875, NaN], [88.0, 15721.689453125, 15949.1162109375, NaN], [89.0, 16194.8193359375, 16079.3935546875, NaN], [90.0, 16862.9609375, 16476.052734375, NaN], [91.0, 16432.05859375, 16443.83203125, NaN], [92.0, 15857.900390625, 16167.6640625, NaN], [93.0, 16519.78125, 16359.89453125, NaN], [94.0, 16496.5, 16417.220703125, NaN], [95.0, 15993.359375, 16205.4951171875, NaN], [96.0, 15849.1298828125, 16043.978515625, NaN], [97.0, 16001.7099609375, 16025.240234375, NaN], [98.0, 15815.1005859375, 15915.4951171875, NaN], [99.0, 15405.619140625, 15665.08203125, NaN], [100.0, 16287.5, 15984.8583984375, NaN], [101.0, 16418.98046875, 16192.708984375, NaN], [102.0, 16711.25, 16453.359375, NaN], [103.0, 16373.029296875, 16423.8125, NaN], [104.0, 16370.55078125, 16407.69921875, NaN], [105.0, 16849.330078125, 16634.349609375, NaN], [106.0, 16242.8984375, 16433.078125, NaN], [107.0, 16538.201171875, 16497.9140625, NaN], [108.0, 16909.587890625, 16707.509765625, NaN], [109.0, 16582.65234375, 16638.873046875, NaN], [110.0, 16393.029296875, 16528.22265625, NaN], [111.0, 17213.66015625, 16879.66796875, NaN], [112.0, 16933.560546875, 16896.82421875, NaN], [113.0, 16988.48046875, 16950.841796875, NaN], [114.0, 17069.638671875, 17021.779296875, NaN], [115.0, 17141.12890625, 17081.1328125, NaN], [116.0, 16854.25, 16971.587890625, NaN], [117.0, 17036.619140625, 17012.095703125, NaN], [118.0, 16989.90234375, 17002.365234375, NaN], [119.0, 17431.490234375, 17218.45703125, NaN], [120.0, 17244.951171875, 17232.546875, NaN], [121.0, 17273.927734375, 17260.33984375, NaN], [122.0, 17399.990234375, 17337.9765625, NaN], [123.0, 17637.220703125, 17487.80859375, NaN], [124.0, 17723.111328125, 17607.412109375, NaN], [125.0, 17645.4375, 17631.607421875, NaN], [126.0, 17541.271484375, 17594.16015625, NaN], [127.0, 17422.609375, 17513.880859375, NaN], [128.0, 17528.87109375, 17524.8359375, NaN], [129.0, 17555.05078125, 17540.728515625, NaN], [130.0, 17730.828125, 17638.236328125, NaN], [131.0, 17800.2109375, 17722.38671875, NaN], [132.0, 17653.51953125, 17691.931640625, NaN], [133.0, 17900.41015625, 17803.814453125, NaN], [134.0, 17681.25, 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23952.0703125, NaN, 24124.416015625], [698.0, 24314.51171875, NaN, 24219.0], [699.0, 24326.76953125, NaN, 24275.99609375], [700.0, 24342.94921875, NaN, 24310.359375], [701.0, 24042.4609375, NaN, 24184.201171875], [702.0, 24538.66015625, NaN, 24368.4609375], [703.0, 24556.220703125, NaN, 24459.705078125], [704.0, 25096.69921875, NaN, 24780.33984375], [705.0, 25062.73046875, NaN, 24924.75390625], [706.0, 24481.23828125, NaN, 24709.560546875], [707.0, 25149.33203125, NaN, 24945.982421875], [708.0, 25113.9296875, NaN, 25023.638671875], [709.0, 25109.30859375, NaN, 25066.67578125], [710.0, 25175.421875, NaN, 25132.369140625], [711.0, 25278.6875, NaN, 25208.056640625], [712.0, 24929.7109375, NaN, 25071.66796875], [713.0, 25051.73828125, NaN, 25070.306640625], [714.0, 25030.458984375, NaN, 25052.48828125], [715.0, 25439.58984375, NaN, 25246.26171875], [716.0, 25586.259765625, NaN, 25416.921875], [717.0, 25640.041015625, NaN, 25531.98828125], [718.0, 25375.05078125, NaN, 25461.94140625], [719.0, 25162.599609375, NaN, 25320.904296875], [720.0, 25523.548828125, NaN, 25427.53125], [721.0, 25252.451171875, NaN, 25335.8203125], [722.0, 25318.5, NaN, 25332.744140625], [723.0, 25599.0, NaN, 25471.974609375], [724.0, 25541.779296875, NaN, 25504.640625], [725.0, 25755.640625, NaN, 25636.416015625], [726.0, 25538.58984375, NaN, 25591.880859375], [727.0, 25434.7109375, NaN, 25519.58984375], [728.0, 25117.05078125, NaN, 25324.689453125], [729.0, 25062.2578125, NaN, 25197.07421875], [730.0, 25412.140625, NaN, 25306.001953125], [731.0, 25024.900390625, NaN, 25161.63671875], [732.0, 25955.05078125, NaN, 25566.927734375], [733.0, 25779.91015625, NaN, 25669.52734375], [734.0, 25848.05859375, NaN, 25764.03515625], [735.0, 25885.888671875, NaN, 25837.9375], [736.0, 25644.91015625, NaN, 25742.931640625], [737.0, 25580.361328125, NaN, 25668.884765625], [738.0, 25923.71875, NaN, 25799.98828125], [739.0, 26308.931640625, NaN, 26051.40625], [740.0, 26078.3984375, NaN, 26065.037109375], [741.0, 26185.12109375, NaN, 26136.958984375], [742.0, 25849.26953125, NaN, 25999.263671875], [743.0, 25942.720703125, NaN, 25975.8828125], [744.0, 25940.140625, NaN, 25960.5078125], [745.0, 25997.5, NaN, 25979.0625], [746.0, 26016.748046875, NaN, 26002.08203125], [747.0, 25837.208984375, NaN, 25923.328125], [748.0, 25797.6015625, NaN, 25866.763671875], [749.0, 26085.05078125, NaN, 25979.384765625], [750.0, 26026.779296875, NaN, 26001.45703125], [751.0, 26293.060546875, NaN, 26152.345703125], [752.0, 26163.349609375, NaN, 26161.728515625], [753.0, 25969.568359375, NaN, 26071.21875], [754.0, 26431.802734375, NaN, 26259.86328125]]}, \"id\": \"el27161914675893248\"});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bdcb939400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot baseline and predictions\n",
    "plt.plot(dataset + dataset_DJI[1:])\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot baseline and predictions\n",
    "plt.plot(dataset + dataset_DJI[:-1])\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train with multi-series inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_x = np.concatenate((dataset_delta_DJI, dataset_delta_APPL, dataset_delta_AMAZN), axis = 1)\n",
    "dataset_y = dataset_delta_DJI\n",
    "# normalize the dataset\n",
    "scaler_multi = MinMaxScaler(feature_range=(0, 1))\n",
    "scaler_multi.fit_transform(dataset_x.reshape(-1, 1))\n",
    "dataset_x = scaler_multi.transform(dataset_x)\n",
    "dataset_y = scaler_multi.transform(dataset_y)\n",
    "# split into train and test sets\n",
    "train_size = int(len(dataset_x) * 0.67)\n",
    "test_size = len(dataset_x) - train_size\n",
    "train_x, test_x = dataset_x[0:train_size,:], dataset_x[train_size:len(dataset_x),:]\n",
    "train_y, test_y = dataset_y[0:train_size,:], dataset_y[train_size:len(dataset_y),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(505, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(505, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "look_back = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_gen = TimeseriesGenerator(train_x, train_y,\n",
    "                               length=look_back, sampling_rate=1,stride=1,\n",
    "                               batch_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_gen = TimeseriesGenerator(test_x, test_y,\n",
    "                               length=look_back, sampling_rate=1,stride=1,\n",
    "                               batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(4, input_shape=(look_back, train_x.shape[1])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(model, to_file='model.png', show_shapes=True)\n",
    "from IPython.display import Image\n",
    "Image(filename='model.png') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "168/168 [==============================] - 3s 16ms/step - loss: 0.1261\n",
      "Epoch 2/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 3/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 4/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 5/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 6/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 7/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 8/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 9/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 10/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 11/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 12/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 13/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 14/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 15/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 16/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 17/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 18/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 19/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 20/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 21/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 22/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 23/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 24/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 25/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 26/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 27/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 28/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 29/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 30/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 31/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 32/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0049\n",
      "Epoch 33/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 34/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 35/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 36/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 37/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 38/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 39/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 40/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 41/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 42/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 43/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 44/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 45/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0048\n",
      "Epoch 46/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 47/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 48/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 49/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 50/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 51/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 52/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 53/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 54/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 55/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 56/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 57/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 58/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 59/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 60/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 61/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 62/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 63/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0048\n",
      "Epoch 64/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0046\n",
      "Epoch 65/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 66/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 67/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 68/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 69/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 70/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 71/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 72/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 73/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 74/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 75/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0048\n",
      "Epoch 76/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 77/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 78/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 79/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 80/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0047\n",
      "Epoch 81/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 82/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 83/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0047\n",
      "Epoch 84/100\n",
      "168/168 [==============================] - 2s 10ms/step - loss: 0.0047\n",
      "Epoch 85/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 86/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0047\n",
      "Epoch 87/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 88/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0048\n",
      "Epoch 89/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 90/100\n",
      "168/168 [==============================] - 1s 8ms/step - loss: 0.0047\n",
      "Epoch 91/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0048\n",
      "Epoch 92/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 93/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 94/100\n",
      "168/168 [==============================] - 2s 9ms/step - loss: 0.0047\n",
      "Epoch 95/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 96/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 97/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 98/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 99/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n",
      "Epoch 100/100\n",
      "168/168 [==============================] - 1s 9ms/step - loss: 0.0047\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_data_gen, epochs=100).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('dji_model.h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "model = load_model('dji_model.h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013383093938651303"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(test_data_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(502, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainPredict = model.predict_generator(train_data_gen)\n",
    "trainPredict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(247, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict = model.predict_generator(test_data_gen)\n",
    "testPredict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invert predictions, scale values back to real index/price range.\n",
    "trainPredict = scaler_multi.inverse_transform(trainPredict)\n",
    "testPredict = scaler_multi.inverse_transform(testPredict)\n",
    "dataset_y = scaler_multi.inverse_transform(dataset_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(502, 1)\n",
      "(247, 1)\n"
     ]
    }
   ],
   "source": [
    "trainY = get_y_from_generator(train_data_gen)\n",
    "testY = get_y_from_generator(test_data_gen)\n",
    "trainY = scaler_multi.inverse_transform(trainY)\n",
    "testY = scaler_multi.inverse_transform(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 125.83 RMSE\n",
      "Test Score: 213.39 RMSE\n"
     ]
    }
   ],
   "source": [
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(trainY[:,0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY[:, 0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TENWRjiMmYBiKC4Z1JE72QGhxagwOWutdWusl4deFwGqgvdb6O611IJxtPlC6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\nYeO/vV9gxtjedE6OZmDHRBa+8XEkjz/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       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el1703617907131323687841185863\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el1703617907131323687841185863\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 16122.7509765625, 16223.8662109375, NaN], [4.0, 16428.01953125, 16334.9677734375, NaN], [5.0, 15689.109375, 16013.923828125, NaN], [6.0, 16096.3701171875, 16075.8466796875, NaN], [7.0, 16520.271484375, 16301.451171875, NaN], [8.0, 16259.3193359375, 16276.0283203125, NaN], [9.0, 16672.728515625, 16488.138671875, NaN], [10.0, 17080.490234375, 16788.341796875, NaN], [11.0, 16803.94921875, 16793.109375, NaN], [12.0, 17034.388671875, 16925.154296875, NaN], [13.0, 17189.2109375, 17063.271484375, NaN], [14.0, 17118.23046875, 17092.50390625, NaN], [15.0, 17179.228515625, 17145.9296875, NaN], [16.0, 17031.921875, 17097.9140625, NaN], [17.0, 16767.609375, 16945.236328125, NaN], [18.0, 17358.75, 17167.7265625, NaN], [19.0, 17290.19140625, 17227.224609375, NaN], 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el1703617907131323687841185863\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 16122.7509765625, 16223.8662109375, NaN], [4.0, 16428.01953125, 16334.9677734375, NaN], [5.0, 15689.109375, 16013.923828125, NaN], [6.0, 16096.3701171875, 16075.8466796875, NaN], [7.0, 16520.271484375, 16301.451171875, NaN], [8.0, 16259.3193359375, 16276.0283203125, NaN], [9.0, 16672.728515625, 16488.138671875, NaN], [10.0, 17080.490234375, 16788.341796875, NaN], [11.0, 16803.94921875, 16793.109375, NaN], [12.0, 17034.388671875, 16925.154296875, NaN], [13.0, 17189.2109375, 17063.271484375, NaN], [14.0, 17118.23046875, 17092.50390625, NaN], [15.0, 17179.228515625, 17145.9296875, NaN], [16.0, 17031.921875, 17097.9140625, NaN], [17.0, 16767.609375, 16945.236328125, NaN], [18.0, 17358.75, 17167.7265625, NaN], [19.0, 17290.19140625, 17227.224609375, NaN], [20.0, 17245.107421875, 17242.5625, NaN], [21.0, 17203.6796875, 17235.24609375, NaN], [22.0, 17120.109375, 17186.392578125, NaN], [23.0, 17809.7109375, 17507.74609375, NaN], [24.0, 17804.23828125, 17649.783203125, NaN], [25.0, 17599.40234375, 17631.701171875, NaN], [26.0, 17539.80859375, 17602.025390625, NaN], [27.0, 17977.609375, 17803.220703125, NaN], [28.0, 17732.08203125, 17766.609375, NaN], [29.0, 17571.27734375, 17680.458984375, NaN], [30.0, 17993.98046875, 17852.537109375, NaN], [31.0, 18007.541015625, 17930.431640625, NaN], [32.0, 17817.009765625, 17877.8515625, NaN], [33.0, 17859.279296875, 17883.470703125, NaN], [34.0, 17957.23046875, 17931.76171875, NaN], [35.0, 17550.630859375, 17748.673828125, NaN], [36.0, 17785.94140625, 17784.365234375, NaN], [37.0, 17646.23046875, 17721.546875, NaN], [38.0, 17193.919921875, 17468.572265625, NaN], [39.0, 17042.41015625, 17276.525390625, NaN], [40.0, 17720.779296875, 17511.634765625, NaN], [41.0, 17495.990234375, 17495.970703125, NaN], [42.0, 17984.8203125, 17747.908203125, NaN], [43.0, 17728.33984375, 17741.2734375, NaN], [44.0, 17914.87109375, 17838.654296875, NaN], [45.0, 17761.548828125, 17808.357421875, NaN], [46.0, 17831.69921875, 17831.09375, NaN], [47.0, 17814.591796875, 17831.482421875, NaN], [48.0, 17813.390625, 17830.8671875, NaN], [49.0, 17626.44921875, 17737.07421875, NaN], [50.0, 18056.779296875, 17909.5390625, NaN], [51.0, 17571.009765625, 17741.365234375, NaN], [52.0, 17225.66015625, 17499.763671875, NaN], [53.0, 18217.591796875, 17879.640625, NaN], [54.0, 17613.388671875, 17733.984375, NaN], [55.0, 17405.490234375, 17583.07421875, NaN], [56.0, 17416.6015625, 17520.6875, NaN], [57.0, 17657.19921875, 17597.140625, NaN], [58.0, 16955.671875, 17278.70703125, NaN], [59.0, 17471.7890625, 17395.287109375, NaN], [60.0, 17681.3203125, 17542.087890625, NaN], [61.0, 17973.26953125, 17756.408203125, NaN], [62.0, 17242.58984375, 17503.548828125, NaN], [63.0, 16761.26171875, 17155.087890625, NaN], [64.0, 17374.6875, 17290.91015625, NaN], [65.0, 17582.919921875, 17432.544921875, NaN], [66.0, 17787.94921875, 17608.29296875, NaN], [67.0, 17501.73046875, 17558.48828125, NaN], [68.0, 17504.369140625, 17544.927734375, NaN], [69.0, 17913.69140625, 17741.8984375, NaN], [70.0, 17486.7578125, 17615.859375, NaN], [71.0, 17246.189453125, 17446.673828125, NaN], [72.0, 16872.849609375, 17176.546875, NaN], [73.0, 17168.380859375, 17186.01953125, NaN], [74.0, 16654.359375, 16921.90234375, NaN], [75.0, 16121.689453125, 16538.654296875, NaN], [76.0, 16178.80078125, 16383.42578125, NaN], [77.0, 16450.69140625, 16426.18359375, NaN], [78.0, 16633.87109375, 16531.345703125, NaN], [79.0, 15786.599609375, 16163.1728515625, NaN], [80.0, 16606.689453125, 16408.740234375, NaN], [81.0, 15597.1103515625, 15998.705078125, NaN], [82.0, 16043.958984375, 16042.197265625, NaN], [83.0, 15517.4609375, 15788.4404296875, NaN], [84.0, 15998.619140625, 15908.0712890625, NaN], [85.0, 16304.33984375, 16109.9990234375, NaN], [86.0, 15676.9296875, 15892.8310546875, NaN], [87.0, 16449.240234375, 16193.1005859375, NaN], [88.0, 15721.689453125, 15954.5947265625, NaN], [89.0, 16194.8193359375, 16090.0556640625, NaN], [90.0, 16862.9609375, 16485.841796875, NaN], [91.0, 16432.05859375, 16444.322265625, NaN], [92.0, 15857.900390625, 16162.5556640625, NaN], [93.0, 16519.78125, 16361.5458984375, NaN], [94.0, 16496.5, 16424.9296875, NaN], [95.0, 15993.359375, 16211.4140625, NaN], [96.0, 15849.1298828125, 16048.6845703125, NaN], [97.0, 16001.7099609375, 16039.5888671875, NaN], [98.0, 15815.1005859375, 15930.3466796875, NaN], [99.0, 15405.619140625, 15681.1943359375, NaN], [100.0, 16287.5, 16006.6630859375, NaN], [101.0, 16418.98046875, 16205.2255859375, NaN], [102.0, 16711.25, 16457.650390625, NaN], [103.0, 16373.029296875, 16418.306640625, NaN], [104.0, 16370.55078125, 16408.15234375, NaN], [105.0, 16849.330078125, 16641.2265625, NaN], [106.0, 16242.8984375, 16441.798828125, NaN], [107.0, 16538.201171875, 16509.466796875, NaN], [108.0, 16909.587890625, 16717.076171875, NaN], [109.0, 16582.65234375, 16645.96484375, NaN], [110.0, 16393.029296875, 16533.25, NaN], [111.0, 17213.66015625, 16889.482421875, NaN], [112.0, 16933.560546875, 16905.30078125, NaN], [113.0, 16988.48046875, 16955.744140625, NaN], [114.0, 17069.638671875, 17023.619140625, NaN], [115.0, 17141.12890625, 17087.578125, NaN], [116.0, 16854.25, 16975.7890625, NaN], [117.0, 17036.619140625, 17020.328125, NaN], [118.0, 16989.90234375, 17011.697265625, NaN], [119.0, 17431.490234375, 17229.849609375, NaN], [120.0, 17244.951171875, 17237.779296875, NaN], [121.0, 17273.927734375, 17265.84375, NaN], [122.0, 17399.990234375, 17343.890625, NaN], [123.0, 17637.220703125, 17495.69140625, NaN], [124.0, 17723.111328125, 17609.888671875, NaN], [125.0, 17645.4375, 17630.8984375, NaN], [126.0, 17541.271484375, 17595.66796875, NaN], [127.0, 17422.609375, 17522.265625, NaN], [128.0, 17528.87109375, 17539.423828125, NaN], [129.0, 17555.05078125, 17556.205078125, NaN], [130.0, 17730.828125, 17650.869140625, NaN], [131.0, 17800.2109375, 17731.611328125, NaN], [132.0, 17653.51953125, 17698.853515625, NaN], [133.0, 17900.41015625, 17811.431640625, NaN], [134.0, 17681.25, 17750.658203125, NaN], [135.0, 17469.640625, 17621.166015625, NaN], [136.0, 17828.78125, 17740.0703125, NaN], [137.0, 17367.87109375, 17556.900390625, NaN], [138.0, 17611.9609375, 17600.0703125, NaN], [139.0, 17535.859375, 17575.8203125, NaN], [140.0, 17886.08984375, 17738.265625, NaN], [141.0, 18095.30859375, 17919.775390625, NaN], [142.0, 17944.580078125, 17934.505859375, NaN], [143.0, 17868.4921875, 17913.2890625, NaN], [144.0, 18110.859375, 18024.763671875, NaN], [145.0, 18103.0390625, 18068.373046875, NaN], [146.0, 18138.939453125, 18110.013671875, NaN], [147.0, 17868.76953125, 17997.8984375, NaN], [148.0, 18024.98046875, 18025.638671875, NaN], [149.0, 17950.73046875, 17994.822265625, NaN], [150.0, 18003.400390625, 18007.787109375, NaN], [151.0, 18092.78125, 18057.728515625, NaN], [152.0, 17619.96875, 17843.619140625, NaN], [153.0, 17716.521484375, 17797.75390625, NaN], [154.0, 18008.6796875, 17919.390625, NaN], [155.0, 17610.66015625, 17770.74609375, NaN], [156.0, 17551.609375, 17678.484375, NaN], [157.0, 17670.162109375, 17685.9609375, NaN], [158.0, 17820.55078125, 17756.64453125, NaN], [159.0, 17671.189453125, 17719.595703125, NaN], [160.0, 18150.7890625, 17947.28125, NaN], [161.0, 17493.888671875, 17723.091796875, NaN], [162.0, 17729.880859375, 17747.751953125, NaN], [163.0, 17350.140625, 17559.2265625, NaN], [164.0, 17886.1015625, 17736.3984375, NaN], [165.0, 17349.25, 17542.21875, NaN], [166.0, 17523.2578125, 17546.884765625, NaN], [167.0, 17344.181640625, 17455.259765625, NaN], [168.0, 17566.478515625, 17521.369140625, NaN], [169.0, 17484.919921875, 17509.677734375, NaN], [170.0, 17919.171875, 17722.5625, NaN], [171.0, 17996.96875, 17860.056640625, NaN], [172.0, 17805.068359375, 17836.171875, NaN], [173.0, 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25311.48828125, NaN, 25031.298828125], [672.0, 24655.53125, NaN, 24838.12109375], [673.0, 24939.2109375, NaN, 24907.373046875], [674.0, 24736.708984375, NaN, 24832.6953125], [675.0, 24694.419921875, NaN, 24774.46484375], [676.0, 23969.80859375, NaN, 24385.466796875], [677.0, 24974.109375, NaN, 24705.578125], [678.0, 24163.900390625, NaN, 24426.7734375], [679.0, 24854.58203125, NaN, 24659.240234375], [680.0, 24992.16796875, NaN, 24828.115234375], [681.0, 24786.271484375, NaN, 24808.67578125], [682.0, 25492.80078125, NaN, 25167.9296875], [683.0, 25336.4296875, NaN, 25247.931640625], [684.0, 25391.6484375, NaN, 25324.8515625], [685.0, 25328.091796875, NaN, 25333.599609375], [686.0, 25319.150390625, NaN, 25336.369140625], [687.0, 25081.66796875, NaN, 25220.0], [688.0, 25149.421875, NaN, 25200.310546875], [689.0, 25005.650390625, NaN, 25114.416015625], [690.0, 24884.4609375, NaN, 25010.384765625], [691.0, 24412.951171875, NaN, 24725.33203125], [692.0, 24615.390625, NaN, 24691.396484375], [693.0, 24265.59765625, NaN, 24488.634765625], [694.0, 24700.08203125, NaN, 24607.42578125], [695.0, 23924.7109375, NaN, 24265.44921875], [696.0, 24313.41796875, NaN, 24304.775390625], [697.0, 23952.0703125, NaN, 24135.96875], [698.0, 24314.51171875, NaN, 24235.01953125], [699.0, 24326.76953125, NaN, 24290.501953125], [700.0, 24342.94921875, NaN, 24321.3984375], [701.0, 24042.4609375, NaN, 24194.240234375], [702.0, 24538.66015625, NaN, 24378.9609375], [703.0, 24556.220703125, NaN, 24467.578125], [704.0, 25096.69921875, NaN, 24786.373046875], [705.0, 25062.73046875, NaN, 24925.484375], [706.0, 24481.23828125, NaN, 24709.580078125], [707.0, 25149.33203125, NaN, 24953.763671875], [708.0, 25113.9296875, NaN, 25036.103515625], [709.0, 25109.30859375, NaN, 25079.42578125], [710.0, 25175.421875, NaN, 25139.32421875], [711.0, 25278.6875, NaN, 25217.91015625], [712.0, 24929.7109375, NaN, 25079.998046875], [713.0, 25051.73828125, NaN, 25079.18359375], [714.0, 25030.458984375, NaN, 25061.64453125], [715.0, 25439.58984375, NaN, 25256.470703125], [716.0, 25586.259765625, NaN, 25424.515625], [717.0, 25640.041015625, NaN, 25538.48828125], [718.0, 25375.05078125, NaN, 25459.884765625], [719.0, 25162.599609375, NaN, 25324.0546875], [720.0, 25523.548828125, NaN, 25433.71875], [721.0, 25252.451171875, NaN, 25345.24609375], [722.0, 25318.5, NaN, 25344.9921875], [723.0, 25599.0, NaN, 25488.1171875], [724.0, 25541.779296875, NaN, 25517.25390625], [725.0, 25755.640625, NaN, 25646.4296875], [726.0, 25538.58984375, NaN, 25597.80078125], [727.0, 25434.7109375, NaN, 25532.298828125], [728.0, 25117.05078125, NaN, 25339.806640625], [729.0, 25062.2578125, NaN, 25217.203125], [730.0, 25412.140625, NaN, 25327.11328125], [731.0, 25024.900390625, NaN, 25178.578125], [732.0, 25955.05078125, NaN, 25579.630859375], [733.0, 25779.91015625, NaN, 25669.818359375], [734.0, 25848.05859375, NaN, 25761.203125], [735.0, 25885.888671875, NaN, 25833.126953125], [736.0, 25644.91015625, NaN, 25746.642578125], [737.0, 25580.361328125, NaN, 25680.119140625], [738.0, 25923.71875, NaN, 25815.447265625], [739.0, 26308.931640625, NaN, 26063.65625], [740.0, 26078.3984375, NaN, 26069.611328125], [741.0, 26185.12109375, NaN, 26139.890625], [742.0, 25849.26953125, NaN, 26010.515625], [743.0, 25942.720703125, NaN, 25994.947265625], [744.0, 25940.140625, NaN, 25979.85546875], [745.0, 25997.5, NaN, 25997.197265625], [746.0, 26016.748046875, NaN, 26011.091796875], [747.0, 25837.208984375, NaN, 25926.296875], [748.0, 25797.6015625, NaN, 25870.388671875], [749.0, 26085.05078125, NaN, 25987.861328125], [750.0, 26026.779296875, NaN, 26015.4453125], [751.0, 26293.060546875, NaN, 26163.849609375], [752.0, 26163.349609375, NaN, 26169.10546875], [753.0, 25969.568359375, NaN, 26074.8046875], [754.0, 26431.802734375, NaN, 26260.1875]]}, \"id\": \"el170361790713132368\", \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"enabled\": false, \"type\": \"zoom\"}, {\"button\": true, \"enabled\": false, \"type\": \"boxzoom\"}], \"axes\": [{\"collections\": [], \"yscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.775, 0.755], \"ylim\": [14833.86845703125, 27412.38349609375], \"texts\": [], \"sharex\": [], \"id\": \"el170361790713133152\", \"ydomain\": [14833.86845703125, 27412.38349609375], \"axes\": [{\"nticks\": 10, \"tickformat\": null, \"position\": \"bottom\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}, {\"nticks\": 8, \"tickformat\": null, \"position\": \"left\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}], \"xlim\": [-37.7, 791.7], \"xdomain\": [-37.7, 791.7], \"paths\": [], \"zoomable\": true, \"xscale\": \"linear\", \"markers\": [], \"axesbgalpha\": null, \"lines\": [{\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361790713304624\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"yindex\": 1, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361790713305912\", \"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"yindex\": 2, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361790713304960\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"yindex\": 3, \"dasharray\": \"none\", \"zorder\": 2}], \"sharey\": [], \"images\": [], \"axesbg\": \"#FFFFFF\"}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el1703617907131323687841185863\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 16122.7509765625, 16223.8662109375, NaN], [4.0, 16428.01953125, 16334.9677734375, NaN], [5.0, 15689.109375, 16013.923828125, NaN], [6.0, 16096.3701171875, 16075.8466796875, NaN], [7.0, 16520.271484375, 16301.451171875, NaN], [8.0, 16259.3193359375, 16276.0283203125, NaN], [9.0, 16672.728515625, 16488.138671875, NaN], [10.0, 17080.490234375, 16788.341796875, NaN], [11.0, 16803.94921875, 16793.109375, NaN], [12.0, 17034.388671875, 16925.154296875, NaN], [13.0, 17189.2109375, 17063.271484375, NaN], [14.0, 17118.23046875, 17092.50390625, NaN], [15.0, 17179.228515625, 17145.9296875, NaN], [16.0, 17031.921875, 17097.9140625, NaN], [17.0, 16767.609375, 16945.236328125, NaN], [18.0, 17358.75, 17167.7265625, NaN], [19.0, 17290.19140625, 17227.224609375, NaN], [20.0, 17245.107421875, 17242.5625, NaN], [21.0, 17203.6796875, 17235.24609375, NaN], [22.0, 17120.109375, 17186.392578125, NaN], [23.0, 17809.7109375, 17507.74609375, NaN], [24.0, 17804.23828125, 17649.783203125, NaN], [25.0, 17599.40234375, 17631.701171875, NaN], [26.0, 17539.80859375, 17602.025390625, NaN], [27.0, 17977.609375, 17803.220703125, NaN], [28.0, 17732.08203125, 17766.609375, NaN], [29.0, 17571.27734375, 17680.458984375, NaN], [30.0, 17993.98046875, 17852.537109375, NaN], [31.0, 18007.541015625, 17930.431640625, NaN], [32.0, 17817.009765625, 17877.8515625, NaN], [33.0, 17859.279296875, 17883.470703125, NaN], [34.0, 17957.23046875, 17931.76171875, NaN], [35.0, 17550.630859375, 17748.673828125, NaN], [36.0, 17785.94140625, 17784.365234375, NaN], [37.0, 17646.23046875, 17721.546875, NaN], [38.0, 17193.919921875, 17468.572265625, NaN], [39.0, 17042.41015625, 17276.525390625, NaN], [40.0, 17720.779296875, 17511.634765625, NaN], [41.0, 17495.990234375, 17495.970703125, NaN], [42.0, 17984.8203125, 17747.908203125, NaN], [43.0, 17728.33984375, 17741.2734375, NaN], [44.0, 17914.87109375, 17838.654296875, NaN], [45.0, 17761.548828125, 17808.357421875, NaN], [46.0, 17831.69921875, 17831.09375, NaN], [47.0, 17814.591796875, 17831.482421875, NaN], [48.0, 17813.390625, 17830.8671875, NaN], [49.0, 17626.44921875, 17737.07421875, NaN], [50.0, 18056.779296875, 17909.5390625, NaN], [51.0, 17571.009765625, 17741.365234375, NaN], [52.0, 17225.66015625, 17499.763671875, NaN], [53.0, 18217.591796875, 17879.640625, NaN], [54.0, 17613.388671875, 17733.984375, NaN], [55.0, 17405.490234375, 17583.07421875, NaN], [56.0, 17416.6015625, 17520.6875, NaN], [57.0, 17657.19921875, 17597.140625, NaN], [58.0, 16955.671875, 17278.70703125, NaN], [59.0, 17471.7890625, 17395.287109375, NaN], [60.0, 17681.3203125, 17542.087890625, NaN], [61.0, 17973.26953125, 17756.408203125, NaN], [62.0, 17242.58984375, 17503.548828125, NaN], [63.0, 16761.26171875, 17155.087890625, NaN], [64.0, 17374.6875, 17290.91015625, NaN], [65.0, 17582.919921875, 17432.544921875, NaN], [66.0, 17787.94921875, 17608.29296875, NaN], [67.0, 17501.73046875, 17558.48828125, NaN], [68.0, 17504.369140625, 17544.927734375, NaN], [69.0, 17913.69140625, 17741.8984375, NaN], [70.0, 17486.7578125, 17615.859375, NaN], [71.0, 17246.189453125, 17446.673828125, NaN], [72.0, 16872.849609375, 17176.546875, NaN], [73.0, 17168.380859375, 17186.01953125, NaN], [74.0, 16654.359375, 16921.90234375, NaN], [75.0, 16121.689453125, 16538.654296875, NaN], [76.0, 16178.80078125, 16383.42578125, NaN], [77.0, 16450.69140625, 16426.18359375, NaN], [78.0, 16633.87109375, 16531.345703125, NaN], [79.0, 15786.599609375, 16163.1728515625, NaN], [80.0, 16606.689453125, 16408.740234375, NaN], [81.0, 15597.1103515625, 15998.705078125, NaN], [82.0, 16043.958984375, 16042.197265625, NaN], [83.0, 15517.4609375, 15788.4404296875, NaN], [84.0, 15998.619140625, 15908.0712890625, NaN], [85.0, 16304.33984375, 16109.9990234375, NaN], [86.0, 15676.9296875, 15892.8310546875, NaN], [87.0, 16449.240234375, 16193.1005859375, NaN], [88.0, 15721.689453125, 15954.5947265625, NaN], [89.0, 16194.8193359375, 16090.0556640625, NaN], [90.0, 16862.9609375, 16485.841796875, NaN], [91.0, 16432.05859375, 16444.322265625, NaN], [92.0, 15857.900390625, 16162.5556640625, NaN], [93.0, 16519.78125, 16361.5458984375, NaN], [94.0, 16496.5, 16424.9296875, NaN], [95.0, 15993.359375, 16211.4140625, NaN], [96.0, 15849.1298828125, 16048.6845703125, NaN], [97.0, 16001.7099609375, 16039.5888671875, NaN], [98.0, 15815.1005859375, 15930.3466796875, NaN], [99.0, 15405.619140625, 15681.1943359375, NaN], [100.0, 16287.5, 16006.6630859375, NaN], [101.0, 16418.98046875, 16205.2255859375, NaN], [102.0, 16711.25, 16457.650390625, NaN], [103.0, 16373.029296875, 16418.306640625, NaN], [104.0, 16370.55078125, 16408.15234375, NaN], [105.0, 16849.330078125, 16641.2265625, NaN], [106.0, 16242.8984375, 16441.798828125, NaN], [107.0, 16538.201171875, 16509.466796875, NaN], [108.0, 16909.587890625, 16717.076171875, NaN], [109.0, 16582.65234375, 16645.96484375, NaN], [110.0, 16393.029296875, 16533.25, NaN], [111.0, 17213.66015625, 16889.482421875, NaN], [112.0, 16933.560546875, 16905.30078125, NaN], [113.0, 16988.48046875, 16955.744140625, NaN], [114.0, 17069.638671875, 17023.619140625, NaN], [115.0, 17141.12890625, 17087.578125, NaN], [116.0, 16854.25, 16975.7890625, NaN], [117.0, 17036.619140625, 17020.328125, NaN], [118.0, 16989.90234375, 17011.697265625, NaN], [119.0, 17431.490234375, 17229.849609375, NaN], [120.0, 17244.951171875, 17237.779296875, NaN], [121.0, 17273.927734375, 17265.84375, NaN], [122.0, 17399.990234375, 17343.890625, NaN], [123.0, 17637.220703125, 17495.69140625, NaN], [124.0, 17723.111328125, 17609.888671875, NaN], [125.0, 17645.4375, 17630.8984375, NaN], [126.0, 17541.271484375, 17595.66796875, NaN], [127.0, 17422.609375, 17522.265625, NaN], [128.0, 17528.87109375, 17539.423828125, NaN], [129.0, 17555.05078125, 17556.205078125, NaN], [130.0, 17730.828125, 17650.869140625, NaN], [131.0, 17800.2109375, 17731.611328125, NaN], [132.0, 17653.51953125, 17698.853515625, NaN], [133.0, 17900.41015625, 17811.431640625, NaN], [134.0, 17681.25, 17750.658203125, NaN], [135.0, 17469.640625, 17621.166015625, NaN], [136.0, 17828.78125, 17740.0703125, NaN], [137.0, 17367.87109375, 17556.900390625, NaN], [138.0, 17611.9609375, 17600.0703125, NaN], [139.0, 17535.859375, 17575.8203125, NaN], [140.0, 17886.08984375, 17738.265625, NaN], [141.0, 18095.30859375, 17919.775390625, NaN], [142.0, 17944.580078125, 17934.505859375, NaN], [143.0, 17868.4921875, 17913.2890625, NaN], [144.0, 18110.859375, 18024.763671875, NaN], [145.0, 18103.0390625, 18068.373046875, NaN], [146.0, 18138.939453125, 18110.013671875, NaN], [147.0, 17868.76953125, 17997.8984375, NaN], [148.0, 18024.98046875, 18025.638671875, NaN], [149.0, 17950.73046875, 17994.822265625, NaN], [150.0, 18003.400390625, 18007.787109375, 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0eed1f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(dataset_y)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "# Delta + previous close\n",
    "trainPredictPlot = trainPredictPlot + dataset_DJI[1:]\n",
    "\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(dataset_y)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2):len(dataset_y), :] = testPredict\n",
    "\n",
    "# Delta + previous close\n",
    "testPredictPlot = testPredictPlot + dataset_DJI[1:]\n",
    "\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.plot(dataset_y + dataset_DJI[1:])\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58t0+rrbOJCeoJS0HjsNiUVzQvyVh8gyEY06VwUFrnaq1XhZ4nQusBRK11j9r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\nTYFhUbTo3J+f1u7FGVidPKp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PziKWbLIJKZe3rhS4fXzw9TfMcDzHLN/xTF02Fhxw6rlX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\nqoB8z7k/3rm/9+ue6Eaa8sOS6e4c5nx5AIDL2QX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      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el1703617970211875442812994365\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el1703617970211875442812994365\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data02\": [[0.5761499402628437, 0.2915746872700514, 0.1940765268579837, 0.09657836644591604], [0.6621714456391878, 0.2915746872700514, 0.1940765268579837, 0.09657836644591604]], \"data03\": [[0.5675477897252094, 0.03311258278145693], [0.9698924731182795, 0.03311258278145693], [0.9784946236559139, 0.03311258278145693], [0.9784946236559139, 0.04635761589403972], [0.9784946236559139, 0.33222958057395136], [0.9784946236559139, 0.3454746136865341], [0.9698924731182795, 0.3454746136865341], [0.5675477897252094, 0.3454746136865341], [0.5589456391875749, 0.3454746136865341], [0.5589456391875749, 0.33222958057395136], [0.5589456391875749, 0.04635761589403972], [0.5589456391875749, 0.03311258278145693], [0.5675477897252094, 0.03311258278145693]], \"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 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[0.6965800477897254, 0.26839587932303155], \"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"alpha\": 1, \"fontsize\": 14.399999999999999, \"rotation\": -0.0, \"coordinates\": \"axes\", \"id\": \"el170361797021443464\", \"zorder\": 1000003.0}, {\"text\": \"train predict\", \"position\": [0.6965800477897254, 0.17089771891096386], \"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"alpha\": 1, \"fontsize\": 14.399999999999999, \"rotation\": -0.0, \"coordinates\": \"axes\", \"id\": \"el170361797021445032\", \"zorder\": 1000003.0}, {\"text\": \"test predict\", \"position\": [0.6965800477897254, 0.07339955849889618], \"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"alpha\": 1, \"fontsize\": 14.399999999999999, \"rotation\": -0.0, \"coordinates\": \"axes\", \"id\": \"el170361797021462992\", \"zorder\": 1000003.0}], \"sharex\": [], \"id\": \"el170361797021259760\", \"ydomain\": [14833.86845703125, 27412.38349609375], \"axes\": [{\"nticks\": 10, \"tickformat\": null, \"position\": \"bottom\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}, {\"nticks\": 8, \"tickformat\": null, \"position\": \"left\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}], \"xlim\": [-37.7, 791.7], \"xdomain\": [-37.7, 791.7], \"paths\": [{\"facecolor\": \"#FFFFFF\", \"data\": \"data03\", \"xindex\": 0, \"edgecolor\": \"#CCCCCC\", \"alpha\": 1, \"yindex\": 1, \"zorder\": 1000000.0, \"coordinates\": \"axes\", \"id\": \"el170361797021443016\", \"edgewidth\": 1.0, \"dasharray\": \"none\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"]}], \"zoomable\": true, \"xscale\": \"linear\", \"markers\": [], \"axesbgalpha\": null, \"lines\": [{\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361797021184464\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"yindex\": 1, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361797021428424\", \"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"yindex\": 2, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361797021427472\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"yindex\": 3, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data02\", \"xindex\": 0, \"coordinates\": \"axes\", \"id\": \"el170361797021443968\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"yindex\": 1, \"dasharray\": \"none\", \"zorder\": 1000002.0}, {\"linewidth\": 1.5, \"data\": \"data02\", \"xindex\": 0, \"coordinates\": \"axes\", \"id\": \"el170361797021445480\", \"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"yindex\": 2, \"dasharray\": \"none\", \"zorder\": 1000002.0}, {\"linewidth\": 1.5, \"data\": \"data02\", \"xindex\": 0, \"coordinates\": \"axes\", \"id\": \"el170361797021463440\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"yindex\": 3, \"dasharray\": \"none\", \"zorder\": 1000002.0}], \"sharey\": [], \"images\": [], \"axesbg\": \"#FFFFFF\"}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el1703617970211875442812994365\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data02\": [[0.5761499402628437, 0.2915746872700514, 0.1940765268579837, 0.09657836644591604], [0.6621714456391878, 0.2915746872700514, 0.1940765268579837, 0.09657836644591604]], \"data03\": [[0.5675477897252094, 0.03311258278145693], [0.9698924731182795, 0.03311258278145693], [0.9784946236559139, 0.03311258278145693], [0.9784946236559139, 0.04635761589403972], [0.9784946236559139, 0.33222958057395136], [0.9784946236559139, 0.3454746136865341], [0.9698924731182795, 0.3454746136865341], [0.5675477897252094, 0.3454746136865341], [0.5589456391875749, 0.3454746136865341], [0.5589456391875749, 0.33222958057395136], [0.5589456391875749, 0.04635761589403972], [0.5589456391875749, 0.03311258278145693], [0.5675477897252094, 0.03311258278145693]], \"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 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24765.66796875], [568.0, 24698.55078125, NaN, 24744.685546875], [569.0, 24837.927734375, NaN, 24801.5078125], [570.0, 24725.83203125, NaN, 24767.955078125], [571.0, 24738.361328125, NaN, 24762.41796875], [572.0, 24802.390625, NaN, 24792.537109375], [573.0, 24900.71875, NaN, 24854.626953125], [574.0, 24600.931640625, NaN, 24734.849609375], [575.0, 24928.798828125, NaN, 24845.0], [576.0, 25021.349609375, NaN, 24938.30078125], [577.0, 25227.58203125, NaN, 25088.177734375], [578.0, 25516.607421875, NaN, 25308.21875], [579.0, 25270.130859375, NaN, 25291.552734375], [580.0, 25488.6015625, NaN, 25404.171875], [581.0, 25352.4609375, NaN, 25385.990234375], [582.0, 25780.330078125, NaN, 25593.232421875], [583.0, 26031.6484375, NaN, 25814.5703125], [584.0, 25782.529296875, NaN, 25801.453125], [585.0, 26438.44140625, NaN, 26133.93359375], [586.0, 25919.970703125, NaN, 26022.015625], [587.0, 26125.630859375, NaN, 26086.849609375], [588.0, 26357.478515625, NaN, 26230.7890625], [589.0, 26207.021484375, NaN, 26223.75390625], [590.0, 26293.427734375, NaN, 26274.111328125], [591.0, 26533.458984375, NaN, 26413.580078125], [592.0, 26840.6328125, NaN, 26630.875], [593.0, 26262.25, NaN, 26448.26953125], [594.0, 25714.30078125, NaN, 26103.8984375], [595.0, 26221.890625, NaN, 26192.513671875], [596.0, 26224.03125, NaN, 26210.16796875], [597.0, 24855.2109375, NaN, 25530.474609375], [598.0, 23170.5390625, NaN, 24392.6171875], [599.0, 25479.7890625, NaN, 24990.58984375], [600.0, 24873.9296875, NaN, 24895.798828125], [601.0, 22827.572265625, NaN, 23858.056640625], [602.0, 24521.33984375, NaN, 24252.07421875], [603.0, 25011.638671875, NaN, 24605.556640625], [604.0, 24679.62890625, NaN, 24624.642578125], [605.0, 25146.53125, NaN, 24908.2265625], [606.0, 25507.248046875, NaN, 25215.34765625], [607.0, 25238.392578125, NaN, 25225.275390625], [608.0, 24710.119140625, NaN, 24978.275390625], [609.0, 24630.80859375, NaN, 24828.703125], [610.0, 25127.181640625, NaN, 24994.35546875], [611.0, 25657.5, NaN, 25324.71875], [612.0, 26108.548828125, NaN, 25710.517578125], [613.0, 25110.7890625, NaN, 25407.306640625], [614.0, 24648.369140625, NaN, 25055.3828125], [615.0, 24188.76171875, NaN, 24650.46484375], [616.0, 24467.140625, NaN, 24577.00390625], [617.0, 25211.458984375, NaN, 24898.802734375], [618.0, 24893.478515625, NaN, 24886.955078125], [619.0, 24718.599609375, NaN, 24816.32421875], [620.0, 24989.0625, NaN, 24920.283203125], [621.0, 25776.26953125, NaN, 25352.814453125], [622.0, 25021.478515625, NaN, 25178.294921875], [623.0, 24835.44921875, NaN, 25028.904296875], [624.0, 24509.208984375, NaN, 24789.18359375], [625.0, 24989.201171875, NaN, 24905.822265625], [626.0, 25019.359375, NaN, 24960.509765625], [627.0, 24275.310546875, NaN, 24620.455078125], [628.0, 24843.62890625, NaN, 24754.931640625], [629.0, 24637.3515625, NaN, 24700.666015625], [630.0, 23233.470703125, NaN, 23975.4296875], [631.0, 23108.5078125, NaN, 23584.72265625], [632.0, 24872.0, NaN, 24240.578125], [633.0, 23512.822265625, NaN, 23844.259765625], [634.0, 23839.12890625, NaN, 23863.4296875], [635.0, 24357.798828125, NaN, 24118.181640625], [636.0, 23185.26953125, NaN, 23639.50390625], [637.0, 24422.529296875, NaN, 24056.626953125], [638.0, 24495.2421875, NaN, 24267.251953125], [639.0, 24746.140625, NaN, 24501.7265625], [640.0, 23360.298828125, NaN, 23942.484375], [641.0, 24025.439453125, NaN, 24019.693359375], [642.0, 24836.900390625, NaN, 24430.88671875], [643.0, 23970.8984375, NaN, 24183.361328125], [644.0, 24776.65234375, NaN, 24504.818359375], [645.0, 24237.23046875, NaN, 24371.3828125], [646.0, 24785.9375, NaN, 24590.001953125], [647.0, 25000.22265625, NaN, 24797.66796875], [648.0, 24709.509765625, NaN, 24757.9296875], [649.0, 24581.7109375, NaN, 24689.5234375], [650.0, 24260.98828125, NaN, 24494.55859375], [651.0, 24434.439453125, NaN, 24477.990234375], [652.0, 23599.572265625, NaN, 24043.4609375], [653.0, 24143.529296875, NaN, 24111.49609375], [654.0, 24560.849609375, NaN, 24337.0859375], [655.0, 24300.0390625, NaN, 24316.390625], [656.0, 24015.111328125, NaN, 24187.75390625], [657.0, 24034.951171875, NaN, 24132.71875], [658.0, 23750.91015625, NaN, 23953.248046875], [659.0, 23935.3203125, NaN, 23956.83984375], [660.0, 24594.869140625, NaN, 24282.82421875], [661.0, 24452.130859375, NaN, 24359.625], [662.0, 24363.1015625, NaN, 24370.51953125], [663.0, 24724.8671875, NaN, 24560.21875], [664.0, 24936.51953125, NaN, 24751.07421875], [665.0, 24922.810546875, NaN, 24837.65234375], [666.0, 24967.650390625, NaN, 24909.9453125], [667.0, 24513.41015625, NaN, 24718.759765625], [668.0, 24831.44921875, NaN, 24791.189453125], [669.0, 24659.03125, NaN, 24730.583984375], [670.0, 24716.19921875, NaN, 24732.044921875], [671.0, 25311.48828125, NaN, 25031.298828125], [672.0, 24655.53125, NaN, 24838.12109375], [673.0, 24939.2109375, NaN, 24907.373046875], [674.0, 24736.708984375, NaN, 24832.6953125], [675.0, 24694.419921875, NaN, 24774.46484375], [676.0, 23969.80859375, NaN, 24385.466796875], [677.0, 24974.109375, NaN, 24705.578125], [678.0, 24163.900390625, NaN, 24426.7734375], [679.0, 24854.58203125, NaN, 24659.240234375], [680.0, 24992.16796875, NaN, 24828.115234375], [681.0, 24786.271484375, NaN, 24808.67578125], [682.0, 25492.80078125, NaN, 25167.9296875], [683.0, 25336.4296875, NaN, 25247.931640625], [684.0, 25391.6484375, NaN, 25324.8515625], [685.0, 25328.091796875, NaN, 25333.599609375], [686.0, 25319.150390625, NaN, 25336.369140625], [687.0, 25081.66796875, NaN, 25220.0], [688.0, 25149.421875, NaN, 25200.310546875], [689.0, 25005.650390625, NaN, 25114.416015625], [690.0, 24884.4609375, NaN, 25010.384765625], [691.0, 24412.951171875, NaN, 24725.33203125], [692.0, 24615.390625, NaN, 24691.396484375], [693.0, 24265.59765625, NaN, 24488.634765625], [694.0, 24700.08203125, NaN, 24607.42578125], [695.0, 23924.7109375, NaN, 24265.44921875], [696.0, 24313.41796875, NaN, 24304.775390625], [697.0, 23952.0703125, NaN, 24135.96875], [698.0, 24314.51171875, NaN, 24235.01953125], [699.0, 24326.76953125, NaN, 24290.501953125], [700.0, 24342.94921875, NaN, 24321.3984375], [701.0, 24042.4609375, NaN, 24194.240234375], [702.0, 24538.66015625, NaN, 24378.9609375], [703.0, 24556.220703125, NaN, 24467.578125], [704.0, 25096.69921875, NaN, 24786.373046875], [705.0, 25062.73046875, NaN, 24925.484375], [706.0, 24481.23828125, NaN, 24709.580078125], [707.0, 25149.33203125, NaN, 24953.763671875], [708.0, 25113.9296875, NaN, 25036.103515625], [709.0, 25109.30859375, NaN, 25079.42578125], [710.0, 25175.421875, NaN, 25139.32421875], [711.0, 25278.6875, NaN, 25217.91015625], [712.0, 24929.7109375, NaN, 25079.998046875], [713.0, 25051.73828125, NaN, 25079.18359375], [714.0, 25030.458984375, NaN, 25061.64453125], [715.0, 25439.58984375, NaN, 25256.470703125], [716.0, 25586.259765625, NaN, 25424.515625], [717.0, 25640.041015625, NaN, 25538.48828125], [718.0, 25375.05078125, NaN, 25459.884765625], [719.0, 25162.599609375, NaN, 25324.0546875], [720.0, 25523.548828125, NaN, 25433.71875], [721.0, 25252.451171875, NaN, 25345.24609375], [722.0, 25318.5, NaN, 25344.9921875], [723.0, 25599.0, NaN, 25488.1171875], [724.0, 25541.779296875, NaN, 25517.25390625], [725.0, 25755.640625, NaN, 25646.4296875], [726.0, 25538.58984375, NaN, 25597.80078125], [727.0, 25434.7109375, NaN, 25532.298828125], [728.0, 25117.05078125, NaN, 25339.806640625], [729.0, 25062.2578125, NaN, 25217.203125], [730.0, 25412.140625, NaN, 25327.11328125], [731.0, 25024.900390625, NaN, 25178.578125], [732.0, 25955.05078125, NaN, 25579.630859375], [733.0, 25779.91015625, NaN, 25669.818359375], [734.0, 25848.05859375, NaN, 25761.203125], [735.0, 25885.888671875, NaN, 25833.126953125], [736.0, 25644.91015625, NaN, 25746.642578125], [737.0, 25580.361328125, NaN, 25680.119140625], [738.0, 25923.71875, NaN, 25815.447265625], [739.0, 26308.931640625, NaN, 26063.65625], [740.0, 26078.3984375, NaN, 26069.611328125], [741.0, 26185.12109375, NaN, 26139.890625], [742.0, 25849.26953125, NaN, 26010.515625], [743.0, 25942.720703125, NaN, 25994.947265625], [744.0, 25940.140625, NaN, 25979.85546875], [745.0, 25997.5, NaN, 25997.197265625], [746.0, 26016.748046875, NaN, 26011.091796875], [747.0, 25837.208984375, NaN, 25926.296875], [748.0, 25797.6015625, NaN, 25870.388671875], [749.0, 26085.05078125, NaN, 25987.861328125], [750.0, 26026.779296875, NaN, 26015.4453125], [751.0, 26293.060546875, NaN, 26163.849609375], [752.0, 26163.349609375, NaN, 26169.10546875], [753.0, 25969.568359375, NaN, 26074.8046875], [754.0, 26431.802734375, NaN, 26260.1875]]}, \"id\": \"el170361797021187544\", \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"enabled\": false, \"type\": \"zoom\"}, {\"button\": true, \"enabled\": false, \"type\": \"boxzoom\"}], \"axes\": [{\"collections\": [], \"yscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.775, 0.755], \"ylim\": [14833.86845703125, 27412.38349609375], \"texts\": [{\"text\": \"REAL\", \"position\": [0.6965800477897254, 0.26839587932303155], \"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"alpha\": 1, \"fontsize\": 14.399999999999999, \"rotation\": -0.0, \"coordinates\": \"axes\", \"id\": \"el170361797021443464\", \"zorder\": 1000003.0}, {\"text\": \"train predict\", \"position\": [0.6965800477897254, 0.17089771891096386], \"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"alpha\": 1, \"fontsize\": 14.399999999999999, \"rotation\": -0.0, \"coordinates\": \"axes\", \"id\": \"el170361797021445032\", \"zorder\": 1000003.0}, {\"text\": \"test predict\", \"position\": [0.6965800477897254, 0.07339955849889618], \"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"alpha\": 1, \"fontsize\": 14.399999999999999, \"rotation\": -0.0, \"coordinates\": \"axes\", \"id\": \"el170361797021462992\", \"zorder\": 1000003.0}], \"sharex\": [], \"id\": \"el170361797021259760\", \"ydomain\": [14833.86845703125, 27412.38349609375], \"axes\": [{\"nticks\": 10, \"tickformat\": null, \"position\": \"bottom\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}, {\"nticks\": 8, \"tickformat\": null, \"position\": \"left\", \"fontsize\": 10.0, \"scale\": \"linear\", \"grid\": {\"gridOn\": false}, \"visible\": true, \"tickvalues\": null}], \"xlim\": [-37.7, 791.7], \"xdomain\": [-37.7, 791.7], \"paths\": [{\"facecolor\": \"#FFFFFF\", \"data\": \"data03\", \"xindex\": 0, \"edgecolor\": \"#CCCCCC\", \"alpha\": 1, \"yindex\": 1, \"zorder\": 1000000.0, \"coordinates\": \"axes\", \"id\": \"el170361797021443016\", \"edgewidth\": 1.0, \"dasharray\": \"none\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"]}], \"zoomable\": true, \"xscale\": \"linear\", \"markers\": [], \"axesbgalpha\": null, \"lines\": [{\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361797021184464\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"yindex\": 1, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361797021428424\", \"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"yindex\": 2, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data01\", \"xindex\": 0, \"coordinates\": \"data\", \"id\": \"el170361797021427472\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"yindex\": 3, \"dasharray\": \"none\", \"zorder\": 2}, {\"linewidth\": 1.5, \"data\": \"data02\", \"xindex\": 0, \"coordinates\": \"axes\", \"id\": \"el170361797021443968\", \"drawstyle\": \"default\", \"color\": \"#1F77B4\", \"alpha\": 1, \"yindex\": 1, \"dasharray\": \"none\", \"zorder\": 1000002.0}, {\"linewidth\": 1.5, \"data\": \"data02\", \"xindex\": 0, \"coordinates\": \"axes\", \"id\": \"el170361797021445480\", \"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"yindex\": 2, \"dasharray\": \"none\", \"zorder\": 1000002.0}, {\"linewidth\": 1.5, \"data\": \"data02\", \"xindex\": 0, \"coordinates\": \"axes\", \"id\": \"el170361797021463440\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"yindex\": 3, \"dasharray\": \"none\", \"zorder\": 1000002.0}], \"sharey\": [], \"images\": [], \"axesbg\": \"#FFFFFF\"}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el1703617970211875442812994365\", {\"height\": 288.0, \"width\": 432.0, \"data\": {\"data02\": [[0.5761499402628437, 0.2915746872700514, 0.1940765268579837, 0.09657836644591604], [0.6621714456391878, 0.2915746872700514, 0.1940765268579837, 0.09657836644591604]], \"data03\": [[0.5675477897252094, 0.03311258278145693], [0.9698924731182795, 0.03311258278145693], [0.9784946236559139, 0.03311258278145693], [0.9784946236559139, 0.04635761589403972], [0.9784946236559139, 0.33222958057395136], [0.9784946236559139, 0.3454746136865341], [0.9698924731182795, 0.3454746136865341], [0.5675477897252094, 0.3454746136865341], [0.5589456391875749, 0.3454746136865341], [0.5589456391875749, 0.33222958057395136], [0.5589456391875749, 0.04635761589403972], [0.5589456391875749, 0.03311258278145693], [0.5675477897252094, 0.03311258278145693]], \"data01\": [[0.0, 16635.798828125, NaN, NaN], [1.0, 16150.75, NaN, NaN], [2.0, 16229.3095703125, NaN, NaN], [3.0, 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\"drawstyle\": \"default\", \"color\": \"#FF7F0E\", \"alpha\": 1, \"yindex\": 2, \"dasharray\": \"none\", \"zorder\": 1000002.0}, {\"linewidth\": 1.5, \"data\": \"data02\", \"xindex\": 0, \"coordinates\": \"axes\", \"id\": \"el170361797021463440\", \"drawstyle\": \"default\", \"color\": \"#2CA02C\", \"alpha\": 1, \"yindex\": 3, \"dasharray\": \"none\", \"zorder\": 1000002.0}], \"sharey\": [], \"images\": [], \"axesbg\": \"#FFFFFF\"}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a266cf3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(dataset_y + dataset_DJI[1:], label='REAL')\n",
    "ax.plot(trainPredictPlot, label='train predict')\n",
    "ax.plot(testPredictPlot, label='test predict')\n",
    "\n",
    "legend = ax.legend(loc='lower right', shadow=True, fontsize='x-large')\n",
    "\n",
    "# Put a nicer background color on the legend.\n",
    "legend.get_frame().set_facecolor('1')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5398406374501992"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_accuracy(trainY[:,0]>0,  trainPredict[:,0] >0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6072874493927125"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_accuracy(testY[:,0]>0,  testPredict[:,0] >0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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