rnn-lstm.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9e989304-ca31-4ed6-af3b-75e59c6a0999",
    "_uuid": "adbb914cc1466a3652d51972988e26cf2b309a90"
   },
   "source": [
    "# Bitcoin Time Series Prediction with LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "660f58b8-64df-4932-8866-2a7468cecbd1",
    "_uuid": "998bc1be9e33074e35114ff479c91a8fdba35d0f"
   },
   "source": [
    "#### Import necessary library needed for the model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "1a872b10-351e-453b-8e3e-2e59ce70b53c",
    "_uuid": "124a1f6d7d906ede16ee8c759bd70d924d25beba"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "from matplotlib import pyplot\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "py.init_notebook_mode(connected=True)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c1475f28-8326-4e08-b89e-6d2ba1a3a20d",
    "_uuid": "40cc3e9db110c4de4eff941912212060ea97da05"
   },
   "source": [
    "#### Use Quandl api to get BTC/USD data from kraken exchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "45e392f6-5787-408b-b22a-5bf9fc834be0",
    "_uuid": "79c4ff6143a459e824dc025e2068bf18c5eedc35",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import quandl\n",
    "data = quandl.get('BCHARTS/KRAKENUSD', returns='pandas')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7ef19ece-0947-4006-97f4-052aa6f62f52",
    "_uuid": "910ebe956b1740ec5831482ab179538df6d32b49"
   },
   "source": [
    "#### Read data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "a18cd3ab-5312-4f8d-b6f9-3ba3c24a6f2a",
    "_uuid": "37738a19100d498fa63ad2401e66edc3d917b121"
   },
   "outputs": [],
   "source": [
    "#data = pd.read_csv(filepath_or_buffer=\"../input/btcusdkraken/BTCUSDKRAKEN\", index_col=\"Date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ca232c72-88f3-4a81-9693-a715655a46a9",
    "_uuid": "8f7fc9a995d3f5f71e2702ff052ae48e45b9f621"
   },
   "source": [
    "#### View data info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "c302db3f-be94-4b1d-9af9-39c0a20430f0",
    "_uuid": "32b9cc8ae0a4b7c136923f91bf24c7ea8880a45b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1399 entries, 2014-01-07 to 2017-11-05\n",
      "Data columns (total 7 columns):\n",
      "Open                 1399 non-null float64\n",
      "High                 1399 non-null float64\n",
      "Low                  1399 non-null float64\n",
      "Close                1399 non-null float64\n",
      "Volume (BTC)         1399 non-null float64\n",
      "Volume (Currency)    1399 non-null float64\n",
      "Weighted Price       1399 non-null float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 87.4 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "139fc78a-7e77-417e-a8cc-02e9728245a3",
    "_uuid": "1a226a0d76f4e0c09b32d53da222d71a56397ccf"
   },
   "source": [
    "#### View data rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "ebc65085-628d-4b41-82c0-a86d46fbee02",
    "_uuid": "69b388872884bc1f09877b325ba5d8d09767decb"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume (BTC)</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>874.67040</td>\n",
       "      <td>892.06753</td>\n",
       "      <td>810.00000</td>\n",
       "      <td>810.00000</td>\n",
       "      <td>15.622378</td>\n",
       "      <td>13151.472844</td>\n",
       "      <td>841.835522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>810.00000</td>\n",
       "      <td>899.84281</td>\n",
       "      <td>788.00000</td>\n",
       "      <td>824.98287</td>\n",
       "      <td>19.182756</td>\n",
       "      <td>16097.329584</td>\n",
       "      <td>839.156269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>825.56345</td>\n",
       "      <td>870.00000</td>\n",
       "      <td>807.42084</td>\n",
       "      <td>841.86934</td>\n",
       "      <td>8.158335</td>\n",
       "      <td>6784.249982</td>\n",
       "      <td>831.572913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>839.99000</td>\n",
       "      <td>857.34056</td>\n",
       "      <td>817.00000</td>\n",
       "      <td>857.33056</td>\n",
       "      <td>8.024510</td>\n",
       "      <td>6780.220188</td>\n",
       "      <td>844.938794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>858.20000</td>\n",
       "      <td>918.05471</td>\n",
       "      <td>857.16554</td>\n",
       "      <td>899.84105</td>\n",
       "      <td>18.748285</td>\n",
       "      <td>16698.566929</td>\n",
       "      <td>890.671709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                 Open       High        Low      Close  Volume (BTC)  \\\n",
       "Date                                                                   \n",
       "2014-01-07  874.67040  892.06753  810.00000  810.00000     15.622378   \n",
       "2014-01-08  810.00000  899.84281  788.00000  824.98287     19.182756   \n",
       "2014-01-09  825.56345  870.00000  807.42084  841.86934      8.158335   \n",
       "2014-01-10  839.99000  857.34056  817.00000  857.33056      8.024510   \n",
       "2014-01-11  858.20000  918.05471  857.16554  899.84105     18.748285   \n",
       "\n",
       "            Volume (Currency)  Weighted Price  \n",
       "Date                                           \n",
       "2014-01-07       13151.472844      841.835522  \n",
       "2014-01-08       16097.329584      839.156269  \n",
       "2014-01-09        6784.249982      831.572913  \n",
       "2014-01-10        6780.220188      844.938794  \n",
       "2014-01-11       16698.566929      890.671709  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6a41c5d5-7eb7-4e91-9bea-5c87c8eb16d3",
    "_uuid": "6cbbeb2df12a04da539508663f080d647180081e"
   },
   "source": [
    "#### Check latest date of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "e5d3d0ae-5d5e-4953-b7df-4ee3bd77d822",
    "_uuid": "e2aaf99d1bcb8989a2205e194f228d0160101309"
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume (BTC)</th>\n",
       "      <th>Volume (Currency)</th>\n",
       "      <th>Weighted Price</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>6442.3</td>\n",
       "      <td>6749.0</td>\n",
       "      <td>6362.0</td>\n",
       "      <td>6741.9</td>\n",
       "      <td>5055.978944</td>\n",
       "      <td>3.309668e+07</td>\n",
       "      <td>6546.048195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02</th>\n",
       "      <td>6741.8</td>\n",
       "      <td>7289.0</td>\n",
       "      <td>6710.8</td>\n",
       "      <td>7007.3</td>\n",
       "      <td>7346.374226</td>\n",
       "      <td>5.118178e+07</td>\n",
       "      <td>6966.943635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-03</th>\n",
       "      <td>7020.3</td>\n",
       "      <td>7449.0</td>\n",
       "      <td>6894.1</td>\n",
       "      <td>7160.0</td>\n",
       "      <td>4840.042536</td>\n",
       "      <td>3.507105e+07</td>\n",
       "      <td>7246.020896</td>\n",
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       "    <tr>\n",
       "      <th>2017-11-04</th>\n",
       "      <td>7160.0</td>\n",
       "      <td>7480.0</td>\n",
       "      <td>6934.1</td>\n",
       "      <td>7369.9</td>\n",
       "      <td>2705.035039</td>\n",
       "      <td>1.958950e+07</td>\n",
       "      <td>7241.866100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-05</th>\n",
       "      <td>7366.5</td>\n",
       "      <td>7575.0</td>\n",
       "      <td>7297.0</td>\n",
       "      <td>7342.0</td>\n",
       "      <td>2431.355756</td>\n",
       "      <td>1.813717e+07</td>\n",
       "      <td>7459.694613</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "              Open    High     Low   Close  Volume (BTC)  Volume (Currency)  \\\n",
       "Date                                                                          \n",
       "2017-11-01  6442.3  6749.0  6362.0  6741.9   5055.978944       3.309668e+07   \n",
       "2017-11-02  6741.8  7289.0  6710.8  7007.3   7346.374226       5.118178e+07   \n",
       "2017-11-03  7020.3  7449.0  6894.1  7160.0   4840.042536       3.507105e+07   \n",
       "2017-11-04  7160.0  7480.0  6934.1  7369.9   2705.035039       1.958950e+07   \n",
       "2017-11-05  7366.5  7575.0  7297.0  7342.0   2431.355756       1.813717e+07   \n",
       "\n",
       "            Weighted Price  \n",
       "Date                        \n",
       "2017-11-01     6546.048195  \n",
       "2017-11-02     6966.943635  \n",
       "2017-11-03     7246.020896  \n",
       "2017-11-04     7241.866100  \n",
       "2017-11-05     7459.694613  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "41e3ec1b-2d82-407e-b0f8-7a1f62ccb0d9",
    "_uuid": "653f159c1880f0c22b7877430000c4e81f1285c3"
   },
   "source": [
    "#### Plot line graph base on `Weighted Price`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "46292551-6d2c-4e48-b5a6-4ab6cd55bea7",
    "_uuid": "35c98b25a65f47f7851d4b2729b77ec612146435"
   },
   "outputs": [
    {
     "data": {
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        {
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         "type": "scatter",
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5674.17262985, 5624.91971389, 5394.45177479, 5646.48953065, 5882.59501961, 6071.99625007, 5909.1405485, 5856.93578092, 5671.33268554, 5569.82058951, 5863.65151928, 5824.91186537, 5751.87192163, 5982.50245698, 6117.285003, 6299.34695841, 6546.0481951, 6966.94363478, 7246.02089563, 7241.86609991, 7459.69461253], \"name\": \"Price\"}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_trace = go.Scatter(x=data.index, y=data['Weighted Price'], name= 'Price')\n",
    "py.iplot([btc_trace])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "246ce6ce-c93b-405a-87d8-0b590cb95adb",
    "_uuid": "d846d35a894a4b8f7f514708364a44e72aea382a"
   },
   "source": [
    "#### Fill value 0 data points on `Weighted Price` with NAN and then use ffill method to fill values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "3a513b31-fb03-430c-b477-04a2954e8fdf",
    "_uuid": "9f3be8345c5888078ee1bf14fe96f0e02dc0206f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Weighted Price'].replace(0, np.nan, inplace=True)\n",
    "data['Weighted Price'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "99f168ee-aca2-4940-90f6-9b6edbedb5b2",
    "_uuid": "30c6169b68d6a626834598465480d15aeba05fdc"
   },
   "source": [
    "#### Plot new line graph again on `Weighted Price` with newly filled values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "33b93385-590d-48d2-a9ff-2039b49d02a3",
    "_uuid": "c91bd913b17fbf6e3c197a22bbc2325ec154ce8b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "Price",
         "type": "scatter",
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3522.2916099, 3403.44533776, 3771.14357968, 3678.99726003, 3984.57617189, 3967.6036018, 3941.69775409, 3741.24683096, 3624.34977741, 3745.29811205, 3704.38212411, 3856.03000002, 3917.25882266, 4074.45610506, 4195.60276489, 4132.64186709, 4290.11751372, 4314.30529539, 4409.72165846, 4311.75708706, 4250.84298229, 4272.96926154, 4373.80097435, 4363.22972981, 4509.19825617, 4699.68160799, 4818.28699361, 4802.2444099, 5161.42414693, 5615.51155477, 5692.83113515, 5610.77765763, 5674.17262985, 5624.91971389, 5394.45177479, 5646.48953065, 5882.59501961, 6071.99625007, 5909.1405485, 5856.93578092, 5671.33268554, 5569.82058951, 5863.65151928, 5824.91186537, 5751.87192163, 5982.50245698, 6117.285003, 6299.34695841, 6546.0481951, 6966.94363478, 7246.02089563, 7241.86609991, 7459.69461253], \"name\": \"Price\"}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_trace = go.Scatter(x=data.index, y=data['Weighted Price'], name= 'Price')\n",
    "py.iplot([btc_trace])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "40133528-c852-4ebe-b262-ddc553394ad3",
    "_uuid": "d986032a8c0d87513bd0ec3d5cb26c91906695a5"
   },
   "source": [
    "### Using `Weighted Price` as a feature to train the LSTM model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "25360a06-b917-4c5b-9af1-e989668193c9",
    "_uuid": "12c5f6367744affa3469b9a67ba4aef88cc0bbab"
   },
   "source": [
    "#### Use MinMaxScaler to normalize `Weighted Price` to range from 0 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "23dad7d9-c9dd-422d-b9be-3fdf17631277",
    "_uuid": "f284c809e79eaaa8d231415f90316bb9fe6cb504",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "values = data['Weighted Price'].values.reshape(-1,1)\n",
    "values = values.astype('float32')\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8ec96ba5-4005-444a-ac65-a6211417d370",
    "_uuid": "28ff135df254c60c16f201664f70095366b7c876"
   },
   "source": [
    "#### Split 70% of data for training and 30% for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "52fe887e-e707-48f5-afb3-27a0cedf38fa",
    "_uuid": "9ee003e139d523e695c07370ecc057e61be183dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "979 420\n"
     ]
    }
   ],
   "source": [
    "train_size = int(len(scaled) * 0.7)\n",
    "test_size = len(scaled) - train_size\n",
    "train, test = scaled[0:train_size,:], scaled[train_size:len(scaled),:]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c0b1a2fe-93e1-4dad-a121-2e132aa20f83",
    "_uuid": "79bcf4ebd0b148eb04625b1bb8167aab61e1892e"
   },
   "source": [
    "#### Create function for creating dataset with look back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "2f95fc03-1418-455c-b5ed-a1a6e22e97a7",
    "_uuid": "1c2d33296f3cbf17939a0f56b01e0bea007f8614",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back):\n",
    "        a = dataset[i:(i + look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    print(len(dataY))\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "da2d7810-5ddd-4f66-a288-a7ad4276bca3",
    "_uuid": "2f54261c39e80a64371754ae35f4d043dbb16974"
   },
   "source": [
    "#### Generate dataset for trainX, trainY, testX, testY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "d414263e-260e-4dc0-88b4-a5c5052c2551",
    "_uuid": "74236b094646cd0fa1d9b56ca31ff26b3c1ef79a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "978\n",
      "419\n"
     ]
    }
   ],
   "source": [
    "look_back = 1\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d5553396-ea30-42fd-8754-b7f2da6d0e57",
    "_uuid": "86d330b8044e090cbc71316d4f33a78907742c79"
   },
   "source": [
    "#### Reshape X for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "c35247b2-058e-4f48-bf1d-93a4c1c2d6d5",
    "_uuid": "b19a891be9692af149ca50ca8472700110287adc",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "670f6dc3-18cd-46c7-a0e9-f6c32c0e7cbb",
    "_uuid": "e385ad7589183e6bbedd32df0bcb457cba860ece"
   },
   "source": [
    "#### Running the LSTM model with 300 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "bf2d6bd2-4490-4fac-bfb3-77c9727a3082",
    "_uuid": "f8398f9b7d19f8bd3da30cd855a463399019d654",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(100, input_shape=(trainX.shape[1], trainX.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')\n",
    "history = model.fit(trainX, trainY, epochs=300, batch_size=100, validation_data=(testX, testY), verbose=0, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "346dc856-2beb-4409-a7dd-2f5ea4abe55c",
    "_uuid": "4748a9eabd2a3f71d3b3491a862b977e1da038b7"
   },
   "source": [
    "#### Plot line graph to show amount loss according the the epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "0b1ba0ae-d20b-4fb2-af53-28c013c13dd1",
    "_uuid": "b0d148c334d28a6728a1d1437ff79c3faed01e47"
   },
   "outputs": [],
   "source": [
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e7b0f4f0-558d-4fa4-a49a-54f47d7fbe5a",
    "_uuid": "ffaee208ae1088d9c3c83a8cb433eb811209bd5a"
   },
   "source": [
    "#### Make prediction using textX and plotting line graph against testY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "953f11ca-a00e-44ef-ad66-889007b1faf7",
    "_uuid": "99c245ddefc779f24fa6ef077309d3fb5ad222eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TDDsA+LX5KczKQ27ZuUGrVwRGkrsQ0UZrLB01vOqZTseoCxl0xvchq4R4r51r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\nQogB6P8B6r/pJPpr2xQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14b239080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = model.predict(testX)\n",
    "pyplot.plot(yhat, label='predict')\n",
    "pyplot.plot(testY, label='true')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0efdd9bb-1367-48cf-8f25-7af211c7a34c",
    "_uuid": "099a70f2b58c7af5432dbaf88e89392a5a0ad6d3"
   },
   "source": [
    "#### Scaler Inverse Y back to normal value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "a4ccceee-0d5a-4e39-a75c-e856e215362e",
    "_uuid": "c97f1a6826f7286277a37a7993f72bcdbc8a0229"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "non-broadcastable output operand with shape (419,1) doesn't match the broadcast shape (419,3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-989b621ea994>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myhat_inverse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myhat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtestY_inverse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFLOAT_DTYPES\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: non-broadcastable output operand with shape (419,1) doesn't match the broadcast shape (419,3)"
     ]
    }
   ],
   "source": [
    "yhat_inverse = scaler.inverse_transform(yhat.reshape(-1, 1))\n",
    "testY_inverse = scaler.inverse_transform(testY.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b51c1c6c-cfeb-4683-9965-eaffb9045ddc",
    "_uuid": "2972db934026a61a6d11411443dff95dd734600b"
   },
   "source": [
    "#### RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "3bdde533-1d5c-4d9f-8ee2-8867a7f9434f",
    "_uuid": "d4ec1bc2342f2ec215c83a817eb0e1980cadc023"
   },
   "outputs": [],
   "source": [
    "rmse = sqrt(mean_squared_error(testY_inverse, yhat_inverse))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "144efbdc-9c05-434a-a46d-2e5ca3625ce1",
    "_uuid": "3d93e1c74042684723566bc3a5bde324ffd3159b"
   },
   "source": [
    "#### Plot line graph with Y as USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "912fe352-6aab-4703-ad18-79de70e0d19b",
    "_uuid": "ab4797992d846a0bb03fcf057b206bf59d49a5fa"
   },
   "outputs": [],
   "source": [
    "pyplot.plot(yhat_inverse, label='predict')\n",
    "pyplot.plot(testY_inverse, label='actual', alpha=0.5)\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e7fc0104-7422-42f1-a03a-28311e5c8294",
    "_uuid": "d48aa3332b570c27f701e4879139d2e92c670b45"
   },
   "source": [
    "#### Convert X to dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "15223447-e632-4c25-8c3c-464f7bd70945",
    "_uuid": "b5662c373113d24394cdb8927fa7f078419bb2eb",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictDates = data.tail(len(testX)).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8123d3a6-2f8f-4f15-95d4-06251039863f",
    "_uuid": "bef479f621dba50a5405511f29e55cac8e8897f9"
   },
   "source": [
    "#### Reshape testY and yhat for plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "faf4111f-0fe1-414b-89d8-3f12d029a992",
    "_uuid": "be096523ce37675beac3b1c10bc8fa80f00a4fe1",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testY_reshape = testY_inverse.reshape(len(testY_inverse))\n",
    "yhat_reshape = yhat_inverse.reshape(len(yhat_inverse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a72a41ea-8c62-4f2b-9c28-a40b3e76b3b9",
    "_uuid": "681ffe9db364a1271cabcbd215365349242dd92f"
   },
   "source": [
    "#### Plot predicted and actual line graph with X=dates, Y=USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "be934be3-bc30-4bc0-bbed-4a4932f264cd",
    "_uuid": "2bafae13424578cf12b75d4532c849d6b305963b"
   },
   "outputs": [],
   "source": [
    "actual_chart = go.Scatter(x=predictDates, y=testY_reshape, name= 'Actual Price')\n",
    "predict_chart = go.Scatter(x=predictDates, y=yhat_reshape, name= 'Predict Price')\n",
    "py.iplot([predict_chart, actual_chart])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9ba600c2-a75c-4c0f-b429-e58915c9dd08",
    "_uuid": "cfb796ce963c9e27eb9c21528cbab2334bf7f16b"
   },
   "source": [
    "### Using additional features for model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2434fb95-6a2a-42a3-a05c-61dedae9bbf6",
    "_uuid": "9d61365cbfe5088e84c040399ca899c4374c7b2b"
   },
   "source": [
    "#### Find corrleration in features to `Weighted Price`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "b68facb3-0dd1-4357-a677-b2f8f0ade7e3",
    "_uuid": "580c582495eec72a74e47a19def10a873a205de8"
   },
   "outputs": [],
   "source": [
    "sns.heatmap(data.corr(), annot=True, cmap='RdYlGn', linewidths=0.1, vmin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "270379bd-8266-4380-94a7-23d822e7551e",
    "_uuid": "aef9bf9d2cfcc6865e6e273b97fefd0cd97b2d76"
   },
   "source": [
    "Observation: `Volume` is corrlerated to `Weighted Price`. \n",
    "`Open`, `High`, `Low`, `Close` are directly related to `Weighted Price`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c9e82c53-2b11-4292-ab75-f4ad2ac9d7b3",
    "_uuid": "1c570a4d8dd3cff00c33917b5b6b647dd3fe424f"
   },
   "source": [
    "#### Function to convert series to supervised learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "eb787912-5c49-4eff-9e1a-710c1ada2417",
    "_uuid": "3975d18827952f89ffca2444704bbe91026aaef8",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "41816928-fdea-4a78-a86f-86915dada48e",
    "_uuid": "d84865f796a5eb22b562f30ad24cce8e6ab14184"
   },
   "source": [
    "#### Get all data values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "0c080f0f-6d65-4cb8-949f-9bfca949bfc0",
    "_uuid": "c6fe0e5b2edb63afacf6824095bf8cc1f10f59bd",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values = data[['Weighted Price'] + ['Volume (BTC)'] + ['Volume (Currency)']].values\n",
    "values = values.astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "17c5c659-84fb-441f-92bf-b752fd6a264b",
    "_uuid": "440faceb02d9350f2a17e368daf669c32eb076e5"
   },
   "source": [
    "#### Normalize features to range from 0 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "8253e5dc-6091-4fba-b10f-064a6f93c553",
    "_uuid": "50f0c9f96d9b785cf0df20499980b85dd915a327",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d2f9600c-056e-4ff1-8c1f-9a2ec43352e2",
    "_uuid": "88bdb07cd9cc695c410c6739a0880d8efb8d8720"
   },
   "source": [
    "#### Frame as supervised learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "e12b64fd-1230-486a-977a-e1d6c3fbe9dc",
    "_uuid": "a885b48a939d66b2a24bea7b5a56d3a4e4645628"
   },
   "outputs": [],
   "source": [
    "reframed = series_to_supervised(scaled, 1, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dabfece4-6507-4ff4-a4d9-73fc06923fae",
    "_uuid": "b8b5249b6487130c5955b672c101789054c3de48"
   },
   "source": [
    "#### Drop unncessary columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "271b5d20-4088-4afe-8667-9d4da86abff6",
    "_uuid": "3800f9bc16aa5dd687e8443467a4ee8f838754e5"
   },
   "outputs": [],
   "source": [
    "reframed.drop(reframed.columns[[4,5]], axis=1, inplace=True)\n",
    "print(reframed.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c805b901-cea2-4bda-a8ed-1d70e885a422",
    "_uuid": "1972851254c74d6f468cefe14af0814a146927f4"
   },
   "source": [
    "#### Split data to 70% training, 30% testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "25395950-10a3-4f95-95cd-09e3723d3045",
    "_uuid": "ad6b4cd4274ac0f916e6a604e6ab42b318f43827"
   },
   "outputs": [],
   "source": [
    "values = reframed.values\n",
    "n_train_hours = int(len(values) * 0.7)\n",
    "train = values[:n_train_hours, :]\n",
    "test = values[n_train_hours:, :]\n",
    "# split into input and outputs\n",
    "train_X, train_y = train[:, :-1], train[:, -1]\n",
    "test_X, test_y = test[:, :-1], test[:, -1]\n",
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
    "test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c6e27b43-76ea-454e-99b6-ef7927e74a9c",
    "_uuid": "a9522ef4744d6bc7cbf71bb5b041fbd908c3aac9"
   },
   "source": [
    "#### Training the LSTM model with 300 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "7efdad54-d362-4492-892d-c454eb098712",
    "_uuid": "702f2dfd9e07355620bcad02de830052471c431f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "multi_model = Sequential()\n",
    "multi_model.add(LSTM(800, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "multi_model.add(Dense(1))\n",
    "multi_model.compile(loss='mae', optimizer='adam')\n",
    "multi_history = multi_model.fit(train_X, train_y, epochs=300, batch_size=100, validation_data=(test_X, test_y), verbose=0, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "398acc81-7905-4543-a86f-d742533223e8",
    "_uuid": "cedb18926575c3eb4f45bb28d5ed25330cc90dbb"
   },
   "source": [
    "#### Plot line graph to show amount loss according the the epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "e6d16091-921a-41ae-99df-f749433ec078",
    "_uuid": "a4ce67e2d990ed36b02afbf0a9f1d82608a524ec",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pyplot.plot(multi_history.history['loss'], label='multi_train')\n",
    "pyplot.plot(multi_history.history['val_loss'], label='multi_test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d57bcae5-faf4-4627-aed6-196c79b45501",
    "_uuid": "539968ae29f8f3e04e9ab9c13475328195e4b67c"
   },
   "source": [
    "#### Make prediction using textX and plotting line graph against testY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "3675ba21-d893-4810-b750-be56f174d3b1",
    "_uuid": "935e2453284414dc00d48c995374838cd45d3878"
   },
   "outputs": [],
   "source": [
    "yhat = multi_model.predict(test_X)\n",
    "pyplot.plot(yhat, label='predict')\n",
    "pyplot.plot(test_y, label='true')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2693f259-62e6-4618-ba35-e8c61796ca85",
    "_uuid": "dae0a33db1cc2b8895571b5daf28079a7bfc1d5d"
   },
   "source": [
    "#### Scaler Inverse Y back to normal value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "95d5c947-bae5-463c-90d6-aa5ecbdc6ed6",
    "_uuid": "854d9c93dd568a111cf9a14335dbbf54d279be31",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))\n",
    "# invert scaling for forecast\n",
    "inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,0]\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a78efc96-a474-4e7a-9710-1acc5ec5f3af",
    "_uuid": "db764d6976a5ba5efb33547b7dc48e9fdc9698ab"
   },
   "source": [
    "#### RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "5aed0b53-1b40-42fe-8472-bb0998ccf1b7",
    "_uuid": "148769aa228dc1501e2c49584e62576076bea186"
   },
   "outputs": [],
   "source": [
    "rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6ae77413-adb0-4957-8c40-38a624341cb1",
    "_uuid": "c5164ca1c50b217786cc5be52e7354121265d302"
   },
   "source": [
    "#### Plot line graph with actual price, predicted price with feature `Weighted Price`, predicted price with features `Volume` and `Weighted Price`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "2fb1ffb0-439c-498f-8a20-ab2446443259",
    "_uuid": "941a79e76ab478a4b24cf14a584b35808a7c06b9"
   },
   "outputs": [],
   "source": [
    "actual_chart = go.Scatter(x=predictDates, y=inv_y, name= 'Actual Price')\n",
    "multi_predict_chart = go.Scatter(x=predictDates, y=inv_yhat, name= 'Multi Predict Price')\n",
    "predict_chart = go.Scatter(x=predictDates, y=yhat_reshape, name= 'Predict Price')\n",
    "py.iplot([predict_chart, multi_predict_chart, actual_chart])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4f084906-ab05-4521-828f-0065071da284",
    "_uuid": "b16bb454b6fb5b223364d5c37b6d525350262ee9"
   },
   "source": [
    "- LSTM with single feature of `Weighted Price` have RMSE of 159.194\n",
    "- LSTM with features of `Volume(BTC)`, `Volume(Currency)` and `Weighted Price` have RMSE of 96.184\n",
    "- LSTM with multi features shows more accurate results as show in line chart above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "2ad528d7-09e1-475f-a15b-c13c4afee39d",
    "_uuid": "1053194c8ce05328bb7a54a176adb9c538724801",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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