RNN Over Bitcoin Historical Data.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "aa8cb822-fd88-45a0-8abe-31736de0fdce",
    "_uuid": "53472747b896842ed25212bfec61cbb1e3b43a1f"
   },
   "source": [
    "# Bitcoin Historical Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bitcoin data at 1-min intervals from select exchanges, Jan 2012 to Jan 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bitcoin is the longest running and most well known cryptocurrency, first released as open source in 2009 by \n",
    "the anonymous Satoshi Nakamoto. Bitcoin serves as a decentralized medium of digital exchange, with transactions\n",
    "verified and recorded in a public distributed ledger (the blockchain) without the need for a trusted record \n",
    "keeping authority or central intermediary. Transaction blocks contain a SHA-256 cryptographic hash of previous\n",
    "transaction blocks, and are thus \"chained\" together, serving as an immutable record of all transactions that \n",
    "have ever occurred. As with any currency/commodity on the market, bitcoin trading and financial instruments \n",
    "soon followed public adoption of bitcoin and continue to grow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference Material: \n",
    "\n",
    "* RNN and LSTM : http://colah.github.io/posts/2015-08-Understanding-LSTMs/\n",
    "* LSTM on time series using Keras : https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/\n",
    "\n",
    "\n",
    "## An unrolled recurrent neural network.\n",
    "\n",
    "\n",
    "<img src='http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. \n",
    "They’re the natural architecture of neural network to use for such data. In the last few years, there have been incredible success applying RNNs to a variety of problems: speech recognition, language modeling, translation, image captioning. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long Short-Term Memory Network\n",
    "The Long Short-Term Memory network, or LSTM network, is a recurrent neural network that is trained using \n",
    "Backpropagation Through Time and overcomes the vanishing gradient problem.\n",
    "As such, it can be used to create large recurrent networks that in turn can be used to address difficult \n",
    "sequence problems in machine learning and achieve state-of-the-art results.\n",
    "Instead of neurons, LSTM networks have memory blocks that are connected through layers.\n",
    "A block has components that make it smarter than a classical neuron and a memory for recent sequences. \n",
    "A block contains gates that manage the block’s state and output. A block operates upon an input sequence\n",
    "and each gate within a block uses the sigmoid activation units to control whether they are triggered or not, \n",
    "making the change of state and addition of information flowing through the block conditional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following are the datsets which are avaialble for use. \n",
    "\n",
    "\n",
    "* coincheckJPY_1-min_data_2014-10-31_to_2018-01-08.csv\n",
    "\n",
    "* bitflyerJPY_1-min_data_2017-07-04_to_2018-01-08.csv\n",
    "\n",
    "* coinbaseUSD_1-min_data_2014-12-01_to_2018-01-08.csv\n",
    "\n",
    "* bitstampUSD_1-min_data_2012-01-01_to_2018-01-08.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "_cell_guid": "17a589ea-bd46-4042-8ae6-dffdb1844e5b",
    "_uuid": "1ffb6ce66752e41790e28bffbf7f89ee4fc172a6",
    "collapsed": true
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   "outputs": [],
   "source": [
    "# First step, import libraries.\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import os\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "import plotly.offline as py\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8683904c-e696-45e9-a520-ce0a1ce94381",
    "_uuid": "13cb799840f26f73e2543db4614bf463b61aa5ec"
   },
   "source": [
    "** Dataset Imported = coinbaseUSD_1-min_data_2014-12-01_to_2018-01-08.csv.**\n",
    "\n",
    "* Recoding the datetime and sorted the data by date. \n",
    "* Grouping the dataset by date and take the average price of all minutes in the day as the price of the day to minimize the fluctuation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "_cell_guid": "ce3103c4-8888-4561-be6e-652c946c8324",
    "_uuid": "2adcf509da7887dc5f8b611a426a9855def8e41f",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('coinbaseUSD_1-min_data_2014-12-01_to_2018-01-08.csv')\n",
    "df['date'] = pd.to_datetime(df['Timestamp'],unit='s').dt.date\n",
    "group = df.groupby('date')\n",
    "Real_Price = group['Weighted_Price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</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",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1417411980</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1417412040</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1417412100</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1417412160</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1417412220</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Timestamp   Open   High    Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1417411980  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "1  1417412040  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "2  1417412100  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "3  1417412160  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "4  1417412220  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "\n",
       "   Weighted_Price        date  \n",
       "0           300.0  2014-12-01  \n",
       "1           300.0  2014-12-01  \n",
       "2           300.0  2014-12-01  \n",
       "3           300.0  2014-12-01  \n",
       "4           300.0  2014-12-01  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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CY8zdIvI/wAtYa6b2AZOALyqQvSLHsxb4HtiAVWC9g3UfL6/p1bID6x56GeP+NWvUJAAW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\nETEF/h+dpKZDTT7QvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa08c42828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True, cmap='RdYlGn', linewidths=0.1, vmin=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation: Volume is corrlerated to Weighted Price. \n",
    "    Open, High, Low, Close are directly related to Weighted Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-12-01    366.775068\n",
       "2014-12-02    375.943001\n",
       "2014-12-03    377.882701\n",
       "2014-12-04    377.991743\n",
       "2014-12-06    378.000000\n",
       "Name: Weighted_Price, dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Real_Price.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4366c7d2-a937-45ce-96a8-6e432076a585",
    "_uuid": "72edb8232d568a3bbe6746eb84fa8b993610aeac"
   },
   "source": [
    "Extrating the data of last 30 days as the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "_cell_guid": "c53ad7b4-d787-4b92-b5c6-4c2da940f7e3",
    "_uuid": "e9fb9fc322b5fa706fd60ee7d9f53e52c1dc7b46",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split data\n",
    "prediction_days = 30\n",
    "df_train= Real_Price[:len(Real_Price)-prediction_days]\n",
    "df_test= Real_Price[len(Real_Price)-prediction_days:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1071"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-12-01    366.775068\n",
       "2014-12-02    375.943001\n",
       "2014-12-03    377.882701\n",
       "2014-12-04    377.991743\n",
       "2014-12-06    378.000000\n",
       "Name: Weighted_Price, dtype: float64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8c44bf60-9d49-4b77-834a-09e8459416e5",
    "_uuid": "7d8da26d7398a6c95444e9ec07a5ff2ed912e488"
   },
   "source": [
    "\n",
    "\n",
    "Feature scaling data to prepare input for Keras ( I always believe its name should be lasagna, cause it has got layers)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For an LSTM layer, you need the shape like (NumberOfExamples, TimeSteps, FeaturesPerStep).\n",
    "\n",
    "Passing the tuple through np.reshape where np.reshape(a, newshape, order='C')\n",
    "a-array to be reshaped\n",
    "newshape= length of final set\n",
    "order='C'-  C-like index ordering, in this case its 1 array\n",
    "\n",
    "The order keyword gives the index ordering both for fetching the values from a,\n",
    "and then placing the values into the output array. \n",
    "For example, let’s say you have an array:\n",
    "* a = np.arange(6).reshape((3, 2))\n",
    "* a\n",
    "* Result will be :\n",
    "\n",
    "array([[0, 1],\n",
    "       [2, 3],\n",
    "       [4, 5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "_cell_guid": "e8c46eda-81f7-4c8d-a30b-061f1e953459",
    "_uuid": "b226a99e16ba031cbc8ba47ef649e4f726e65acc",
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  366.77506775],\n",
       "       [  375.9430013 ],\n",
       "       [  377.88270139],\n",
       "       ...,\n",
       "       [15487.71739279],\n",
       "       [16144.05431135],\n",
       "       [15574.30671093]])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocess\n",
    "training_set = df_train.values\n",
    "training_set = np.reshape(training_set, (len(training_set), 1))\n",
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As numpy has floting point value, no need of conversion. Othervise, ints are converted to floats because \n",
    "NN work better with them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling feature to a range: MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTMs are sensitive to the scale of the input data, specifically when the sigmoid (default) or tanh activation functions are used. It can be a good practice to rescale the data to the range of 0-to-1, also called normalizing. We can easily normalize the dataset using the MinMaxScaler preprocessing class from the scikit-learn library.\n",
    "\n",
    "As we have a numpy.array as an input data, we will be transforming features by scaling each feature to a \n",
    "given range. This estimator scales and translates each feature individually such that it is in the given \n",
    "range on the training set, i.e. between zero and one.\n",
    "\n",
    "The transformation is given by:\n",
    "\n",
    "X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
    "\n",
    "X_scaled = X_std * (max - min) + min\n",
    "\n",
    "where min, max = feature_range.\n",
    "\n",
    "\n",
    "This transformation is often used as an alternative to zero mean, unit variance scaling.\n",
    "\n",
    "* fit_transform= It gets fits- x_min and x_max, and then transform\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feed Forward Net Vs RNN\n",
    "\n",
    "In feed forward nets the hidden layers weights are adjusted based on the series of input values. But in RNN, recurrent neural net, the hidden layers weights are determined by Value at Time T , and Value at Time (T-1).\n",
    "\n",
    "Here the preicted value is T+1 so we have taken value at T to be input which is building model by getting trained on Y_Train( which is T+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "training_set = sc.fit_transform(training_set)\n",
    "X_train = training_set[0:len(training_set)-1]\n",
    "y_train = training_set[1:len(training_set)]\n",
    "X_train = np.reshape(X_train, (len(X_train), 1, 1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X_train=t and y_train=t+1 pattern in the numbers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01335148],\n",
       "       [0.01347285],\n",
       "       [0.01347968],\n",
       "       ...,\n",
       "       [0.95893143],\n",
       "       [1.        ],\n",
       "       [0.96434953]])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.01277782]],\n",
       "\n",
       "       [[0.01335148]],\n",
       "\n",
       "       [[0.01347285]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.78642872]],\n",
       "\n",
       "       [[0.95893143]],\n",
       "\n",
       "       [[1.        ]]])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1070"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1070"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "efdfac61-2231-40b1-ba7a-e229a1e65de1",
    "_uuid": "d10ba4778d5c917ff7dca3744c3be4e50dd1b49c"
   },
   "source": [
    "# Implementing Keras: Building the model\n",
    "\n",
    "Why Keras?\n",
    "\n",
    "* First, Keras is a wrapper that allows you to use either the Theano or the TensorFlow backend! That means you can easily switch between the two, depending on your application.\n",
    "\n",
    "* Second, it has beautiful guiding principles: modularity, minimalism, extensibility, and Python-nativeness. In practice, this makes working in Keras simple and enjoyable.\n",
    "\n",
    "* Finally, Keras has out-of-the-box implementations of common network structures. It’s fast and easy to get a convolutional neural network up and running."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network has a visible layer with 1 input, a hidden layer with 4 LSTM blocks or neurons,\n",
    "and an output layer that makes a single value prediction. The default sigmoid activation function is\n",
    "used for the LSTM blocks. The network is trained for 100 epochs and a batch size of 1 is used.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "_cell_guid": "8c3cab32-a40c-4d78-8261-3deb0e7ecf72",
    "_uuid": "fd2b05885cdc720f5970e16606403a287613209a",
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1070/1070 [==============================] - 2s 2ms/step - loss: 0.1293\n",
      "Epoch 2/100\n",
      "1070/1070 [==============================] - 0s 321us/step - loss: 0.0213\n",
      "Epoch 3/100\n",
      "1070/1070 [==============================] - 0s 327us/step - loss: 0.0149\n",
      "Epoch 4/100\n",
      "1070/1070 [==============================] - 0s 325us/step - loss: 0.0142\n",
      "Epoch 5/100\n",
      "1070/1070 [==============================] - 0s 327us/step - loss: 0.0136\n",
      "Epoch 6/100\n",
      "1070/1070 [==============================] - 0s 399us/step - loss: 0.0130\n",
      "Epoch 7/100\n",
      "1070/1070 [==============================] - 0s 414us/step - loss: 0.0123\n",
      "Epoch 8/100\n",
      "1070/1070 [==============================] - 0s 424us/step - loss: 0.0116\n",
      "Epoch 9/100\n",
      "1070/1070 [==============================] - 0s 384us/step - loss: 0.0109\n",
      "Epoch 10/100\n",
      "1070/1070 [==============================] - 0s 320us/step - loss: 0.0101\n",
      "Epoch 11/100\n",
      "1070/1070 [==============================] - 0s 378us/step - loss: 0.0094\n",
      "Epoch 12/100\n",
      "1070/1070 [==============================] - 0s 341us/step - loss: 0.0086\n",
      "Epoch 13/100\n",
      "1070/1070 [==============================] - 0s 343us/step - loss: 0.0078\n",
      "Epoch 14/100\n",
      "1070/1070 [==============================] - 0s 350us/step - loss: 0.0070\n",
      "Epoch 15/100\n",
      "1070/1070 [==============================] - 0s 324us/step - loss: 0.0061\n",
      "Epoch 16/100\n",
      "1070/1070 [==============================] - 0s 325us/step - loss: 0.0053\n",
      "Epoch 17/100\n",
      "1070/1070 [==============================] - 0s 350us/step - loss: 0.0045\n",
      "Epoch 18/100\n",
      "1070/1070 [==============================] - 0s 341us/step - loss: 0.0037\n",
      "Epoch 19/100\n",
      "1070/1070 [==============================] - 0s 334us/step - loss: 0.0030\n",
      "Epoch 20/100\n",
      "1070/1070 [==============================] - 0s 331us/step - loss: 0.0023\n",
      "Epoch 21/100\n",
      "1070/1070 [==============================] - 0s 323us/step - loss: 0.0017\n",
      "Epoch 22/100\n",
      "1070/1070 [==============================] - 0s 331us/step - loss: 0.0012\n",
      "Epoch 23/100\n",
      "1070/1070 [==============================] - 0s 335us/step - loss: 8.6314e-04\n",
      "Epoch 24/100\n",
      "1070/1070 [==============================] - 0s 325us/step - loss: 5.7612e-04\n",
      "Epoch 25/100\n",
      "1070/1070 [==============================] - 0s 395us/step - loss: 3.7097e-04\n",
      "Epoch 26/100\n",
      "1070/1070 [==============================] - 0s 403us/step - loss: 2.3615e-04\n",
      "Epoch 27/100\n",
      "1070/1070 [==============================] - 0s 446us/step - loss: 1.5274e-04\n",
      "Epoch 28/100\n",
      "1070/1070 [==============================] - 0s 394us/step - loss: 1.0832e-04\n",
      "Epoch 29/100\n",
      "1070/1070 [==============================] - 0s 407us/step - loss: 8.5046e-05\n",
      "Epoch 30/100\n",
      "1070/1070 [==============================] - 0s 357us/step - loss: 7.2974e-05\n",
      "Epoch 31/100\n",
      "1070/1070 [==============================] - 0s 343us/step - loss: 6.8202e-05\n",
      "Epoch 32/100\n",
      "1070/1070 [==============================] - 0s 360us/step - loss: 6.6552e-05\n",
      "Epoch 33/100\n",
      "1070/1070 [==============================] - 0s 320us/step - loss: 6.5005e-05\n",
      "Epoch 34/100\n",
      "1070/1070 [==============================] - 0s 333us/step - loss: 6.3913e-05\n",
      "Epoch 35/100\n",
      "1070/1070 [==============================] - 0s 343us/step - loss: 6.3751e-05\n",
      "Epoch 36/100\n",
      "1070/1070 [==============================] - 0s 317us/step - loss: 6.3043e-05\n",
      "Epoch 37/100\n",
      "1070/1070 [==============================] - 0s 325us/step - loss: 6.3375e-05\n",
      "Epoch 38/100\n",
      "1070/1070 [==============================] - 0s 341us/step - loss: 6.2503e-05\n",
      "Epoch 39/100\n",
      "1070/1070 [==============================] - 0s 325us/step - loss: 6.2186e-05\n",
      "Epoch 40/100\n",
      "1070/1070 [==============================] - 0s 340us/step - loss: 6.0840e-05\n",
      "Epoch 41/100\n",
      "1070/1070 [==============================] - 0s 341us/step - loss: 6.1324e-05\n",
      "Epoch 42/100\n",
      "1070/1070 [==============================] - 0s 326us/step - loss: 5.9223e-05\n",
      "Epoch 43/100\n",
      "1070/1070 [==============================] - 0s 350us/step - loss: 5.9956e-05\n",
      "Epoch 44/100\n",
      "1070/1070 [==============================] - 0s 357us/step - loss: 5.8635e-05\n",
      "Epoch 45/100\n",
      "1070/1070 [==============================] - 0s 360us/step - loss: 5.9023e-05\n",
      "Epoch 46/100\n",
      "1070/1070 [==============================] - 0s 387us/step - loss: 5.7795e-05\n",
      "Epoch 47/100\n",
      "1070/1070 [==============================] - 0s 347us/step - loss: 5.7324e-05\n",
      "Epoch 48/100\n",
      "1070/1070 [==============================] - 0s 359us/step - loss: 5.8096e-05\n",
      "Epoch 49/100\n",
      "1070/1070 [==============================] - 0s 427us/step - loss: 5.7525e-05\n",
      "Epoch 50/100\n",
      "1070/1070 [==============================] - 0s 417us/step - loss: 5.7536e-05\n",
      "Epoch 51/100\n",
      "1070/1070 [==============================] - 0s 422us/step - loss: 5.6730e-05\n",
      "Epoch 52/100\n",
      "1070/1070 [==============================] - 0s 375us/step - loss: 5.7047e-05\n",
      "Epoch 53/100\n",
      "1070/1070 [==============================] - 0s 422us/step - loss: 5.6049e-05\n",
      "Epoch 54/100\n",
      "1070/1070 [==============================] - 0s 375us/step - loss: 5.9862e-05\n",
      "Epoch 55/100\n",
      "1070/1070 [==============================] - 0s 364us/step - loss: 5.4006e-05\n",
      "Epoch 56/100\n",
      "1070/1070 [==============================] - 0s 361us/step - loss: 5.4761e-05\n",
      "Epoch 57/100\n",
      "1070/1070 [==============================] - 0s 323us/step - loss: 5.4923e-05\n",
      "Epoch 58/100\n",
      "1070/1070 [==============================] - 0s 363us/step - loss: 5.4066e-05\n",
      "Epoch 59/100\n",
      "1070/1070 [==============================] - 0s 345us/step - loss: 5.4703e-05\n",
      "Epoch 60/100\n",
      "1070/1070 [==============================] - 0s 327us/step - loss: 5.3618e-05\n",
      "Epoch 61/100\n",
      "1070/1070 [==============================] - 0s 349us/step - loss: 5.3881e-05\n",
      "Epoch 62/100\n",
      "1070/1070 [==============================] - 0s 318us/step - loss: 5.2474e-05\n",
      "Epoch 63/100\n",
      "1070/1070 [==============================] - 0s 319us/step - loss: 5.3759e-05\n",
      "Epoch 64/100\n",
      "1070/1070 [==============================] - 0s 346us/step - loss: 5.1504e-05\n",
      "Epoch 65/100\n",
      "1070/1070 [==============================] - 0s 346us/step - loss: 5.1895e-05\n",
      "Epoch 66/100\n",
      "1070/1070 [==============================] - 0s 343us/step - loss: 5.2045e-05\n",
      "Epoch 67/100\n",
      "1070/1070 [==============================] - 0s 409us/step - loss: 5.2085e-05\n",
      "Epoch 68/100\n",
      "1070/1070 [==============================] - 0s 347us/step - loss: 5.2141e-05\n",
      "Epoch 69/100\n",
      "1070/1070 [==============================] - 0s 340us/step - loss: 5.1812e-05\n",
      "Epoch 70/100\n",
      "1070/1070 [==============================] - 0s 323us/step - loss: 5.1508e-05\n",
      "Epoch 71/100\n",
      "1070/1070 [==============================] - 0s 333us/step - loss: 5.1172e-05\n",
      "Epoch 72/100\n",
      "1070/1070 [==============================] - 0s 361us/step - loss: 5.4289e-05\n",
      "Epoch 73/100\n",
      "1070/1070 [==============================] - 0s 320us/step - loss: 5.3218e-05\n",
      "Epoch 74/100\n",
      "1070/1070 [==============================] - 0s 323us/step - loss: 5.1878e-05\n",
      "Epoch 75/100\n",
      "1070/1070 [==============================] - 0s 364us/step - loss: 5.1174e-05\n",
      "Epoch 76/100\n",
      "1070/1070 [==============================] - 0s 318us/step - loss: 5.4207e-05\n",
      "Epoch 77/100\n",
      "1070/1070 [==============================] - 0s 342us/step - loss: 4.9778e-05\n",
      "Epoch 78/100\n",
      "1070/1070 [==============================] - 0s 321us/step - loss: 5.0988e-05\n",
      "Epoch 79/100\n",
      "1070/1070 [==============================] - 0s 331us/step - loss: 4.9973e-05\n",
      "Epoch 80/100\n",
      "1070/1070 [==============================] - 0s 347us/step - loss: 4.9943e-05\n",
      "Epoch 81/100\n",
      "1070/1070 [==============================] - 0s 325us/step - loss: 4.9604e-05\n",
      "Epoch 82/100\n",
      "1070/1070 [==============================] - 0s 325us/step - loss: 5.2760e-05\n",
      "Epoch 83/100\n",
      "1070/1070 [==============================] - 0s 413us/step - loss: 5.0934e-05\n",
      "Epoch 84/100\n",
      "1070/1070 [==============================] - 0s 371us/step - loss: 4.9653e-05\n",
      "Epoch 85/100\n",
      "1070/1070 [==============================] - 0s 423us/step - loss: 5.0192e-05\n",
      "Epoch 86/100\n",
      "1070/1070 [==============================] - 0s 339us/step - loss: 5.0551e-05\n",
      "Epoch 87/100\n",
      "1070/1070 [==============================] - 0s 333us/step - loss: 4.7988e-05\n",
      "Epoch 88/100\n",
      "1070/1070 [==============================] - 1s 513us/step - loss: 4.9746e-05\n",
      "Epoch 89/100\n",
      "1070/1070 [==============================] - 0s 390us/step - loss: 5.0574e-05\n",
      "Epoch 90/100\n",
      "1070/1070 [==============================] - 0s 378us/step - loss: 4.8939e-05\n",
      "Epoch 91/100\n",
      "1070/1070 [==============================] - 0s 327us/step - loss: 5.0970e-05\n",
      "Epoch 92/100\n",
      "1070/1070 [==============================] - 0s 326us/step - loss: 5.0926e-05\n",
      "Epoch 93/100\n",
      "1070/1070 [==============================] - 0s 343us/step - loss: 4.8851e-05\n",
      "Epoch 94/100\n",
      "1070/1070 [==============================] - 0s 350us/step - loss: 5.1015e-05\n",
      "Epoch 95/100\n",
      "1070/1070 [==============================] - 0s 326us/step - loss: 5.0451e-05\n",
      "Epoch 96/100\n",
      "1070/1070 [==============================] - 0s 320us/step - loss: 5.0432e-05\n",
      "Epoch 97/100\n",
      "1070/1070 [==============================] - 0s 321us/step - loss: 4.7751e-05\n",
      "Epoch 98/100\n",
      "1070/1070 [==============================] - 0s 347us/step - loss: 5.0251e-05\n",
      "Epoch 99/100\n",
      "1070/1070 [==============================] - 0s 318us/step - loss: 4.8843e-05\n",
      "Epoch 100/100\n",
      "1070/1070 [==============================] - 0s 340us/step - loss: 4.8436e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1fa036a6d30>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "\n",
    "# Regressor is basically model we have built. Here we have assigned sequential class to it because it \n",
    "#will hold sequece of layers\n",
    "regressor = Sequential()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Adding the input layer and the LSTM layer\n",
    "# expected input data shape: (batch_size, timesteps, data_dim)\n",
    "#the first layer holds the number of inputs the regressor holds, that is 1 over here, given in input_shape=(None,1)\n",
    "#sigmoid is default activation function used with LSTM\n",
    "\n",
    "regressor.add(LSTM(units = 4, activation = 'sigmoid', input_shape = (None, 1)))\n",
    "# returns a sequence of vectors of dimension 4\n",
    "\n",
    "# Adding the output layer\n",
    "regressor.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "\n",
    "# Fitting the RNN to the Training set\n",
    "regressor.fit(X_train, y_train, batch_size = 5, epochs = 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "history = regressor.fit(X_train, y_train, epochs=300, batch_size=100, validation_data=(X_train, y_train), verbose=0, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot line graph to show amount loss according the the epoch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RIMl2GHLO3Qy8DDwNXAgsAWY458pkUF8JmArMBuoDw4H3nHMt/GouBcYA7wIN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\n9aEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa07c22470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": "4c933dc9-2622-489a-8d1b-90b6accab53c",
    "_uuid": "2d1380f3fcf755245941b05a533cbccc8cab3f17"
   },
   "source": [
    "* **Prediction**\n",
    "\n",
    "Notice that I only predict the price of the next day using the price today. Since there must be a lot of influence factors and it must have a lot of error when you predict a longer time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming the test doing Reshape and then normalization.\n",
    "\n",
    "Just the way input ata is transformed, output is tansformed to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "_cell_guid": "2a958a6e-7dca-41d4-9c12-1be4c6e38b2b",
    "_uuid": "b6ab45bc630e8cb4df8bea4aeafbe58a586061a2",
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Making the predictions\n",
    "test_set = df_test.values\n",
    "inputs = np.reshape(test_set, (len(test_set), 1))\n",
    "inputs = sc.transform(inputs)\n",
    "inputs = np.reshape(inputs, (len(inputs), 1, 1))\n",
    "predicted_BTC_price = regressor.predict(inputs)\n",
    "predicted_BTC_price = sc.inverse_transform(predicted_BTC_price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14778.08775822, 16692.39851465, 17129.40795339, 17140.07518942,\n",
       "       16791.15996964, 17548.21443229, 18466.78384939, 19537.27694058,\n",
       "       18883.3263529 , 18400.15138622, 16865.04266576, 16345.10873036,\n",
       "       13914.68688142, 15102.20616022, 14165.75384436, 14188.09605382,\n",
       "       15364.19577808, 15654.23175858, 14292.86003112, 14556.45351606,\n",
       "       13584.14207723, 13386.49771138, 13425.05417801, 13897.40957171,\n",
       "       14989.9393498 , 14851.47788038, 15796.59696844, 16661.98824725,\n",
       "       16526.9735835 , 16174.220219  ])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15484.228 ],\n",
       "       [16145.044 ],\n",
       "       [16276.962 ],\n",
       "       [16280.17  ],\n",
       "       [16174.934 ],\n",
       "       [16402.555 ],\n",
       "       [16675.275 ],\n",
       "       [16988.564 ],\n",
       "       [16797.744 ],\n",
       "       [16655.615 ],\n",
       "       [16197.265 ],\n",
       "       [16039.566 ],\n",
       "       [14608.265 ],\n",
       "       [15657.215 ],\n",
       "       [14862.985 ],\n",
       "       [14885.653 ],\n",
       "       [15738.456 ],\n",
       "       [15827.987 ],\n",
       "       [14991.941 ],\n",
       "       [15259.371 ],\n",
       "       [14272.912 ],\n",
       "       [14072.3955],\n",
       "       [14111.512 ],\n",
       "       [14590.735 ],\n",
       "       [15622.291 ],\n",
       "       [15544.83  ],\n",
       "       [15871.779 ],\n",
       "       [16135.829 ],\n",
       "       [16094.874 ],\n",
       "       [15987.449 ]], dtype=float32)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_BTC_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.01277782]],\n",
       "\n",
       "       [[0.01335148]],\n",
       "\n",
       "       [[0.01347285]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.78642872]],\n",
       "\n",
       "       [[0.95893143]],\n",
       "\n",
       "       [[1.        ]]])"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising\n",
    "Plot the predicted price and the real price. Compare the diference. The difference is larger when the time is further to the training set. That is why I only want to predict the price of one month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g1TLtgiBALpfjs88+w4kTJ0rMCg5WVlYaj8fHx8PR0bGIoyl62mxLkn0SslWr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\nIiIiIiIiIiIiIiIiIiLKLyY35MPTp0/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6Ykt91WdBJgdZTLSUtLcu+9ySGHFJ0GAAAAAGC9MrlhDWy22Wb53Oc+l+9+97u57rrr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Hz/B+rNmzcf/HlcXBxu3Ljx4JgtW7agdevWWLhwIf75558Hf56YmIj169ejWbNm2LVrF0wm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\nvN7QlxsCAXvjtkKF0D4uytaIETa5YepU6dpri1dE+OHPD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Bz8US2fhYkIvLRi4mF1c1ZOPwYLgBAFDhlixZovXr10uSfv/73/tcDQAAAPxCLgQAAADI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\nX03TZNy4cbnnnns6ffwtVn1/YoPq+xM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+d9ttt/V65wiAk046abPOsK6jjjqq1ztJrFix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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa04a558d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the results\n",
    "plt.figure(figsize=(25,15), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.gca()  \n",
    "plt.plot(test_set, color = 'red', label = 'Real BTC Price')\n",
    "plt.plot(predicted_BTC_price, color = 'blue', label = 'Predicted BTC Price')\n",
    "plt.title('BTC Price Prediction', fontsize=40)\n",
    "df_test = df_test.reset_index()\n",
    "x=df_test.index\n",
    "labels = df_test['date']\n",
    "plt.xticks(x, labels, rotation = 'vertical')\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(18)\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(18)\n",
    "plt.xlabel('Time', fontsize=40)\n",
    "plt.ylabel('BTC Price(USD)', fontsize=40)\n",
    "plt.legend(loc=2, prop={'size': 25})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "24306beb-9b46-4a6f-a8e6-fef53313918a",
    "_uuid": "9e38e597a657c7a25442ddf26a6ef8a6b41bf6c1"
   },
   "source": [
    "**Visualising**\n",
    "\n",
    "Plot the predicted price and the real price. Compare the diference. The difference is larger when the time is further to the training set. So the prediction is done for only one month."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}