LSTM_FINAL.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "c:\\python35\\lib\\site-packages\\h5py\\__init__.py:34: 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"
     ]
    }
   ],
   "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 plotly\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import csv as csv\n",
    "\n",
    "import talib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.16\n"
     ]
    }
   ],
   "source": [
    "print(talib.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dateParser(x):\n",
    "\treturn datetime.strptime(x,'%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Time</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2018-02-23</td>\n",
       "      <td>0.296954</td>\n",
       "      <td>10370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-02-22</td>\n",
       "      <td>0.296954</td>\n",
       "      <td>9847.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>0.296954</td>\n",
       "      <td>10595.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       Time  Sentiment    Price\n",
       "0           0 2018-02-23   0.296954  10370.0\n",
       "1           1 2018-02-22   0.296954   9847.6\n",
       "2           2 2018-02-21   0.296954  10595.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc = pd.read_csv('bitcoinprices.txt', names=['Time', 'Price'])\n",
    "btc.columns = [\"Time\",\"Price\"]\n",
    "sent = pd.read_csv('sentiment6.txt', names=['Time', 'Sentiment'])\n",
    "sent.columns = [\"Time\",\"Sentiment\"]\n",
    "merged = sent.merge(btc, left_index=False, right_index=False, how=\"inner\")\n",
    "merged.to_csv('merged_data.csv')\n",
    "\n",
    "data = pd.read_csv(\"merged_data.csv\", index_col=False)\n",
    "\n",
    "with open('merged_data.csv', 'r') as f_input, open('output.csv', 'w') as f_output:\n",
    "    csv_output = csv.writer(f_output, delimiter = ',')\n",
    "\n",
    "    for row in csv.reader(f_input, delimiter = ','):\n",
    "        if row[2] != 'None' and row[3] != 'None':\n",
    "            csv_output.writerow(row)\n",
    "            \n",
    "            \n",
    "data = pd.read_csv(\"output.csv\", index_col=False, parse_dates=['Time'], date_parser=dateParser)\n",
    "\n",
    "data[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.3.0'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotly.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#talib.SMA(numpy.asarray(f['Close']), 20)\n",
    "data['Sma20'] = talib.SMA(np.asarray(data['Price']), 20)\n",
    "data['Sma5'] = talib.SMA(np.asarray(data['Price']), 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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      "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>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plotly work with Jupyter notebook using the plotly.js CDN\n",
    "py.init_notebook_mode(connected=True)\n",
    "\n",
    "#btc_trace = go.Scatter(x=data['Time'], y=data['Price'], name= 'Price')\n",
    "btc_trace = go.Scatter(x=data['Time'], y=data['Price'], name= 'Price')\n",
    "btc_trace_sma20 = go.Scatter(x=data['Time'], y=data['Sma20'], name= 'Price_sma20')\n",
    "btc_trace_sma5 = go.Scatter(x=data['Time'], y=data['Sma5'], name= 'Price_sma5')\n",
    "py.iplot([btc_trace, btc_trace_sma20, btc_trace_sma5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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0.21937938733783946, 0.21937938733783946, 0.21937938733783946, 0.13911557163792745, 0.13911557163792745, 0.13911557163792745, 0.13911557163792745, 0.3155478707786948, 0.3155478707786948, 0.3155478707786948, 0.3155478707786948, 0.202256286348018, 0.26168307936717905, 0.26168307936717905, 0.26168307936717905, 0.15935586977899302, 0.15935586977899302, 0.15935586977899302, 0.19902807275293424, 0.19902807275293424, 0.19902807275293424, 0.2922590459921108, 0.2922590459921108, 0.2922590459921108, 0.2922590459921108, 0.3967377901168179, 0.29820160282947983, 0.29820160282947983, 0.29820160282947983, 0.29820160282947983, 0.29820160282947983], \"name\": \"Sentiment\"}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sent_trace = go.Scatter(x=data['Time'], y=data['Sentiment'], name= 'Sentiment')\n",
    "py.iplot([sent_trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Time</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Price</th>\n",
       "      <th>Sma20</th>\n",
       "      <th>Sma5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2018-02-23</td>\n",
       "      <td>0.296954</td>\n",
       "      <td>10370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-02-22</td>\n",
       "      <td>0.296954</td>\n",
       "      <td>9847.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>0.296954</td>\n",
       "      <td>10595.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       Time  Sentiment    Price  Sma20  Sma5\n",
       "0           0 2018-02-23   0.296954  10370.0    NaN   NaN\n",
       "1           1 2018-02-22   0.296954   9847.6    NaN   NaN\n",
       "2           2 2018-02-21   0.296954  10595.5    NaN   NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-08</th>\n",
       "      <td>459.8</td>\n",
       "      <td>0.298202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>462.0</td>\n",
       "      <td>0.298202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-10</th>\n",
       "      <td>455.2</td>\n",
       "      <td>0.298202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Sentiment\n",
       "Time                        \n",
       "2016-05-08  459.8   0.298202\n",
       "2016-05-09  462.0   0.298202\n",
       "2016-05-10  455.2   0.298202"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datag = data[['Price','Sentiment']].groupby(data['Time']).mean()\n",
    "datag[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a simple random model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "values = datag['Price'].values.reshape(-1,1)\n",
    "sentiment = datag['Sentiment'].values.reshape(-1,1)\n",
    "values = values.astype('float32')\n",
    "sentiment = sentiment.astype('float32')\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.29820159],\n",
       "       [ 0.29820159],\n",
       "       [ 0.29820159]], dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sentiment = sentiment.astype('float32')\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "459 197\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))\n",
    "split = train_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back, sentiment, sent=False):\n",
    "    Tracer()()\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back):\n",
    "        if i >= look_back:\n",
    "            a = dataset[i-look_back:i+1, 0]\n",
    "            a = a.tolist()\n",
    "            if(sent==True):\n",
    "                a.append(sentiment[i].tolist()[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": {},
   "source": [
    "### with lookback = 1 (kind of unigram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python35\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning:\n",
      "\n",
      "`Tracer` is deprecated since version 5.1, directly use `IPython.core.debugger.Pdb.set_trace()`\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[1;32m<ipython-input-77-1bef0fd8f3da>\u001b[0m(3)\u001b[0;36mcreate_dataset\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m      1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mcreate_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlook_back\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msentiment\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      2 \u001b[1;33m    \u001b[0mTracer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m----> 3 \u001b[1;33m    \u001b[0mdataX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      4 \u001b[1;33m    \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlook_back\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      5 \u001b[1;33m        \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlook_back\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> n\n",
      "> \u001b[1;32m<ipython-input-77-1bef0fd8f3da>\u001b[0m(4)\u001b[0;36mcreate_dataset\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m      2 \u001b[1;33m    \u001b[0mTracer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      3 \u001b[1;33m    \u001b[0mdataX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m----> 4 \u001b[1;33m    \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlook_back\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      5 \u001b[1;33m        \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlook_back\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      6 \u001b[1;33m            \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlook_back\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> look_back\n",
      "1\n",
      "ipdb> n\n",
      "> \u001b[1;32m<ipython-input-77-1bef0fd8f3da>\u001b[0m(5)\u001b[0;36mcreate_dataset\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m      3 \u001b[1;33m    \u001b[0mdataX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      4 \u001b[1;33m    \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlook_back\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m----> 5 \u001b[1;33m        \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlook_back\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      6 \u001b[1;33m            \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlook_back\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m      7 \u001b[1;33m            \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> len(dataset) - look_back\n",
      "458\n",
      "ipdb> len(dataset)\n",
      "459\n"
     ]
    }
   ],
   "source": [
    "look_back = 1\n",
    "trainX, trainY = create_dataset(train, look_back, sentiment[0:train_size])\n",
    "testX, testY = create_dataset(test, look_back, sentiment[train_size:len(scaled)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "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": "code",
   "execution_count": 22,
   "metadata": {
    "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": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"822.3833333333333\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yhat_inverse_1 = scaler.inverse_transform(yhat.reshape(-1, 1))\n",
    "testY_inverse_1 = scaler.inverse_transform(testY.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 1030.664\n"
     ]
    }
   ],
   "source": [
    "rmse_1 = sqrt(mean_squared_error(testY_inverse_1, yhat_inverse_1))\n",
    "print('Test RMSE: %.3f' % rmse_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_1 = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### with lookback = 2(kind of biram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "look_back = 2\n",
    "trainX, trainY = create_dataset(train, look_back, sentiment[0:train_size])\n",
    "testX, testY = create_dataset(test, look_back, sentiment[train_size:len(scaled)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "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": "code",
   "execution_count": 37,
   "metadata": {
    "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": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yhat_inverse_2 = scaler.inverse_transform(yhat.reshape(-1, 1))\n",
    "testY_inverse_2 = scaler.inverse_transform(testY.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 1017.205\n"
     ]
    }
   ],
   "source": [
    "rmse_2 = sqrt(mean_squared_error(testY_inverse_2, yhat_inverse_2))\n",
    "print('Test RMSE: %.3f' % rmse_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model2 = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With lookback = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "look_back = 3\n",
    "trainX, trainY = create_dataset(train, look_back, sentiment[0:train_size])\n",
    "testX, testY = create_dataset(test, look_back, sentiment[train_size:len(scaled)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "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": "code",
   "execution_count": 44,
   "metadata": {
    "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": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yhat_inverse_3 = scaler.inverse_transform(yhat.reshape(-1, 1))\n",
    "testY_inverse_3 = scaler.inverse_transform(testY.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 1263.808\n"
     ]
    }
   ],
   "source": [
    "rmse_3 = sqrt(mean_squared_error(testY_inverse_3, yhat_inverse_3))\n",
    "print('Test RMSE: %.3f' % rmse_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model3 = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting different lookups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pyplot.plot(yhat_inverse_1, label='predict_lookup_1')\n",
    "pyplot.plot(yhat_inverse_2, label='predict_lookup_2')\n",
    "pyplot.plot(yhat_inverse_3, label='predict_lookup_3')\n",
    "pyplot.plot(testY_inverse_3, label='true')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "656"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datag.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "459 197\n"
     ]
    }
   ],
   "source": [
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_1_trace = go.Scatter(x=datag.index.values[len(train)-len(yhat_inverse_1):], y=yhat_inverse_1.reshape(len(yhat_inverse_1)), name= 'predict_lookup_1')\n",
    "btc_2_trace = go.Scatter(x=datag.index.values[len(train)-len(yhat_inverse_2):], y=yhat_inverse_2.reshape(len(yhat_inverse_2)), name= 'predict_lookup_2')\n",
    "btc_3_trace = go.Scatter(x=datag.index.values[len(train)-len(yhat_inverse_3):], y=yhat_inverse_3.reshape(len(yhat_inverse_3)), name= 'predict_lookup_3')\n",
    "btc_t_trace = go.Scatter(x=datag.index.values[len(train)-len(yhat_inverse_1):], y=testY_inverse_1.reshape(len(yhat_inverse_1)), name= 'True')\n",
    "py.iplot([btc_1_trace,btc_2_trace, btc_3_trace, btc_t_trace])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# With sentiment and lookup2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "look_back = 2\n",
    "trainX, trainY = create_dataset(train, look_back, sentiment[0:train_size],sent=True)\n",
    "testX, testY = create_dataset(test, look_back, sentiment[train_size:len(scaled)], sent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "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": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(100, input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))\n",
    "model.add(LSTM(100))\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": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yhat_inverse_sent = scaler.inverse_transform(yhat.reshape(-1, 1))\n",
    "testY_inverse_sent = scaler.inverse_transform(testY.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 1015.796\n"
     ]
    }
   ],
   "source": [
    "rmse_sent = sqrt(mean_squared_error(testY_inverse_sent, yhat_inverse_sent))\n",
    "print('Test RMSE: %.3f' % rmse_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "455 193\n"
     ]
    }
   ],
   "source": [
    "print(len(trainX), len(testX))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_1_trace = go.Scatter(x=datag.index.values[len(trainX)-len(yhat_inverse_sent):][0:500], y=yhat_inverse_sent.reshape(len(yhat_inverse_sent))[0:500], name= 'With_Sentiment')\n",
    "btc_2_trace = go.Scatter(x=datag.index.values[len(trainX)-len(yhat_inverse_1):][0:500], y=yhat_inverse_1.reshape(len(yhat_inverse_1))[0:500], name= 'No_Sentiment')\n",
    "btc_3_trace = go.Scatter(x=datag.index.values[len(trainX)-len(testY_inverse_sent):][0:500], y=testY_inverse_sent.reshape(len(testY_inverse_sent))[0:500], name= 'True')\n",
    "py.iplot([btc_1_trace,btc_2_trace,btc_3_trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_sent = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now go live with the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import MySQLdb\n",
    "#Enter the values for you database connection\n",
    "dsn_database = \"bitcoin\"         # e.g. \"MySQLdbtest\"\n",
    "#dsn_hostname = \"173.194.231.244\"      # e.g.: \"mydbinstance.xyz.us-east-1.rds.amazonaws.com\"\n",
    "dsn_hostname = \"localhost\"\n",
    "dsn_port = 3306                  # e.g. 3306 \n",
    "dsn_uid = \"zhivko\"             # e.g. \"user1\"\n",
    "dsn_pwd = \"Doitman1\"              # e.g. \"Password123\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = MySQLdb.connect(host=dsn_hostname, port=dsn_port, user=dsn_uid, passwd=dsn_pwd, db=dsn_database)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, datetime.datetime(2018, 2, 18, 0, 0), 234.34, 2342.23)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cursor=conn.cursor()\n",
    "cursor.execute(\"\"\"SELECT * FROM live_data\"\"\")\n",
    "cursor.fetchone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Show me the records:\n",
      "\n",
      "((2, datetime.datetime(2018, 2, 18, 0, 0), 234.34, 2342.23),\n",
      " (3, datetime.datetime(2018, 2, 18, 0, 0), 234.34, 2342.23))\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nShow me the records:\\n\")\n",
    "rows = cursor.fetchall()\n",
    "import pprint\n",
    "pprint.pprint(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cursor.execute(\"\"\"\n",
    "    INSERT INTO live_data\n",
    "    (time, \n",
    "    price,\n",
    "    sentiment) \n",
    "     values\n",
    "        (STR_TO_DATE(\\'18.2.2018 00:00:00\\',\\'%d.%m.%Y %H:%i:%s\\'),\n",
    "        2342.23,\n",
    "        234.34);\n",
    "    \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cursor.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# develop a real time pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import queue \n",
    "import time\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.2'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "type object 'datetime.datetime' has no attribute 'datetime'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-105-3732bf601469>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[0mdecision\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[0mprev\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0myhat_inverse\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m     \u001b[0minput_string\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"INSERT INTO live_data values ({},{},{},'{}','{}');\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myhat_inverse\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbit_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msent_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m-%d %H:%M:%S '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdecision\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m     \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_string\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m     \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: type object 'datetime.datetime' has no attribute 'datetime'"
     ]
    }
   ],
   "source": [
    "import queue\n",
    "import matplotlib.pyplot as plt\n",
    "true_q = queue.Queue()\n",
    "pred_q = queue.Queue()\n",
    "'''\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fig.show()\n",
    "fig.canvas.draw()\n",
    "plt.ion()\n",
    "'''\n",
    "\n",
    "def process_data(in_data):\n",
    "    out_data = []\n",
    "    for line in in_data:\n",
    "        out_data.append(float(line.split(',')[0]))\n",
    "    return np.array(out_data).reshape(-1,1)\n",
    "prev = 15000\n",
    "threshold = 0.5\n",
    "while True:\n",
    "    btc = open('live_bitcoin.csv','r')\n",
    "    sent = open('live_tweet.csv','r')\n",
    "    bit_data = btc.readlines()\n",
    "    sent_data = sent.readlines()\n",
    "    bit_data = process_data(bit_data[len(bit_data)-5:])\n",
    "    sent_data = process_data(sent_data[len(sent_data)-5:])\n",
    "    live = scaler.transform(bit_data)\n",
    "    testX, testY = create_dataset(live, 2, sent_data, sent=True)\n",
    "    testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
    "    yhat = model.predict(testX)\n",
    "    yhat_inverse = scaler.inverse_transform(yhat.reshape(-1, 1))\n",
    "    true_q.put(bit_data[4])\n",
    "    pred_q.put(yhat_inverse[0])\n",
    "    val = 100*((yhat_inverse[0][0] - prev)/prev)\n",
    "    if val > threshold:\n",
    "        decision = 'buy'\n",
    "    elif val <-threshold:\n",
    "        decision = 'sell'\n",
    "    else:\n",
    "        decision = ''\n",
    "    prev = yhat_inverse[0][0]\n",
    "    input_string = \"INSERT INTO live_data values ({},{},{},'{}','{}');\".format(yhat_inverse[0][0],bit_data[0][0],sent_data[4][0],datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S '),decision)\n",
    "    cursor.execute(input_string)\n",
    "    conn.commit()\n",
    "    time.sleep(60)\n",
    "    '''\n",
    "    if true_q.qsize() > 9:\n",
    "        true_q.get()\n",
    "        pred_q.get()\n",
    "        ax.clear()\n",
    "        ax.plot()\n",
    "        ax.plot(list(true_q.queue),'-',label='True')\n",
    "        ax.plot(list(pred_q.queue),'--',label='Predict')\n",
    "        ax.legend()\n",
    "        fig.canvas.draw()\n",
    "        time.sleep(60)\n",
    "    ''' \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 12, 13, 21, 49, 54, 337601)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "datetime.datetime.now()"
   ]
  }
 ],
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