stefan4.ipynb

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      "Using TensorFlow backend.\n"
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true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "from matplotlib import pyplot\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout\n",
    "from keras.layers import LSTM\n",
    "from keras.optimizers import RMSprop\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "py.init_notebook_mode(connected=True)\n",
    "%matplotlib inline\n",
    "import quandl\n",
    "data = quandl.get('BCHARTS/KRAKENUSD', returns='pandas')\n",
    "data['Weighted Price'].replace(0, np.nan, inplace=True)\n",
    "data['Weighted Price'].fillna(method='ffill', inplace=True)\n",
    "btc_trace = go.Scatter(x=data.index, y=data['Weighted Price'], name= 'Price')\n",
    "py.iplot([btc_trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "981\n",
      "420\n"
     ]
    }
   ],
   "source": [
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back):\n",
    "        a = dataset[i:(i + look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    print(len(dataY))\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    "values = data[['Weighted Price'] + ['Volume (BTC)'] + ['Volume (Currency)']].values\n",
    "values = values.astype('float32')\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(values)\n",
    "reframed = series_to_supervised(scaled, 1, 1)\n",
    "reframed.head()\n",
    "reframed.drop(reframed.columns[[4,5]], axis=1, inplace=True)\n",
    "values = reframed.values\n",
    "n_train_hours = int(len(values) * 0.7)\n",
    "train = values[:n_train_hours, :]\n",
    "test = values[n_train_hours:, :]\n",
    "# split into input and outputs\n",
    "train_X, train_y = train[:, :-1], train[:, -1]\n",
    "test_X, test_y = test[:, :-1], test[:, -1]\n",
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
    "test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n",
    "look_back = 1\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)\n",
    "predictDates = data.tail(len(testX)).index\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(150, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "\n",
    "model.add(Dense(64, activation='linear'))\n",
    "model.add(Dense(1))\n",
    "\n",
    "\n",
    "model.compile(loss='mae', optimizer='RMSprop')\n",
    "multi_history = model.fit(train_X, train_y, epochs=300, batch_size=100, validation_data=(test_X, test_y), verbose=0, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ef4Bcuk5Cp5RKPHGN3DXGLMcK7pHblkY8NsD1PZzjbeDtoy7hIBG9GEpDt3X8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\nfS4i4haRD0TkI/tafmZvP7GfiTFm0P8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122eaea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1243ce748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pyplot.plot(multi_history.history['val_loss'], label='multi_test')\n",
    "pyplot.legend()\n",
    "pyplot.show()\n",
    "yhat = model.predict(test_X)\n",
    "pyplot.plot(yhat, label='predict')\n",
    "pyplot.plot(test_y, label='true')\n",
    "pyplot.legend()\n",
    "pyplot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 186.917\n"
     ]
    }
   ],
   "source": [
    "test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))\n",
    "# invert scaling for forecast\n",
    "inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,0]\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,0]\n",
    "rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "Multi Predict Price",
         "type": "scatter",
         "x": [
          "2016-09-17",
          "2016-09-18",
          "2016-09-19",
          "2016-09-20",
          "2016-09-21",
          "2016-09-22",
          "2016-09-23",
          "2016-09-24",
          "2016-09-25",
          "2016-09-26",
          "2016-09-27",
          "2016-09-28",
          "2016-09-29",
          "2016-09-30",
          "2016-10-01",
          "2016-10-02",
          "2016-10-03",
          "2016-10-04",
          "2016-10-05",
          "2016-10-06",
          "2016-10-07",
          "2016-10-08",
          "2016-10-09",
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          "2016-10-11",
          "2016-10-12",
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          "2016-10-14",
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          "2016-10-16",
          "2016-10-17",
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          "2016-10-19",
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   ],
   "source": [
    "\n",
    "actual_chart = go.Scatter(x=predictDates, y=inv_y, name= 'Actual Price')\n",
    "multi_predict_chart = go.Scatter(x=predictDates, y=inv_yhat, name= 'Multi Predict Price')\n",
    "\n",
    "py.iplot([ multi_predict_chart, actual_chart])"
   ]
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   "outputs": [],
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  {
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    "collapsed": true
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   "outputs": [],
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