Bitcoin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import gdax\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plotly.tools.set_credentials_file(username='gasia4444', api_key='rC8FB3pveGzyME366dL7')\n",
    "\n",
    "# plotly.tools.set_credentials_file(username='gasia44', api_key='WquddqqfPKg5KJfPJFuk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GDAX to get BTC historical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mod = gdax.PublicClient()\n",
    "data = mod.get_product_historic_rates('BTC-USD')\n",
    "df = pd.DataFrame(data, columns = ['time', 'low', 'high', 'open', 'close', 'volume'])\n",
    "df = df.sort_values('time', ascending=True).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1522592100</td>\n",
       "      <td>6521.43</td>\n",
       "      <td>6538.52</td>\n",
       "      <td>6538.52</td>\n",
       "      <td>6534.81</td>\n",
       "      <td>7.970211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1522592160</td>\n",
       "      <td>6534.80</td>\n",
       "      <td>6534.81</td>\n",
       "      <td>6534.81</td>\n",
       "      <td>6534.80</td>\n",
       "      <td>3.942811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1522592220</td>\n",
       "      <td>6534.12</td>\n",
       "      <td>6534.81</td>\n",
       "      <td>6534.81</td>\n",
       "      <td>6534.81</td>\n",
       "      <td>6.619137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1522592280</td>\n",
       "      <td>6511.03</td>\n",
       "      <td>6528.19</td>\n",
       "      <td>6527.20</td>\n",
       "      <td>6511.03</td>\n",
       "      <td>12.328278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1522592340</td>\n",
       "      <td>6511.04</td>\n",
       "      <td>6513.36</td>\n",
       "      <td>6511.04</td>\n",
       "      <td>6513.36</td>\n",
       "      <td>9.755722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time      low     high     open    close     volume\n",
       "0  1522592100  6521.43  6538.52  6538.52  6534.81   7.970211\n",
       "1  1522592160  6534.80  6534.81  6534.81  6534.80   3.942811\n",
       "2  1522592220  6534.12  6534.81  6534.81  6534.81   6.619137\n",
       "3  1522592280  6511.03  6528.19  6527.20  6511.03  12.328278\n",
       "4  1522592340  6511.04  6513.36  6511.04  6513.36   9.755722"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "utc_time = datetime.datetime.utcfromtimestamp(np.min(df.time.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_time = []\n",
    "for i in df.time.values:\n",
    "    utc_time = datetime.datetime.utcfromtimestamp(i)\n",
    "    utc_time.strftime(\"%Y-%m-%d %H:%M:%S (UTC)\")\n",
    "    date_time.append(utc_time.strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['timestamp'] = pd.to_datetime(date_time)\n",
    "dd = df.set_index('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 300 entries, 2018-04-01 14:15:00 to 2018-04-01 19:14:00\n",
      "Data columns (total 6 columns):\n",
      "time      300 non-null int64\n",
      "low       300 non-null float64\n",
      "high      300 non-null float64\n",
      "open      300 non-null float64\n",
      "close     300 non-null float64\n",
      "volume    300 non-null float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 16.4 KB\n"
     ]
    }
   ],
   "source": [
    "dd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dd = dd.drop('time', axis = 1)\n",
    "dd = dd.drop('volume', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f9d61cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,7))\n",
    "plt.plot(dd)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['low', 'high', 'open', 'close'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~gasia4444/0 or inside your plot.ly account where it is named 'gasia'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia4444/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "       go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.low,\n",
    "          mode='lines',\n",
    "          name='low'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.high,\n",
    "          mode='lines',\n",
    "          name='high'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.open,\n",
    "          mode='lines',\n",
    "          name='open'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.close,\n",
    "          mode='lines',\n",
    "          name='close'),\n",
    "    \n",
    "    ]\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get a lot of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class GDAX(object):\n",
    "  \"\"\"Class for fetching candle data for a given currency pair.\"\"\"\n",
    "\n",
    "  def __init__(self, pair):\n",
    "    \"\"\"Create the exchange object.\n",
    "    Args:\n",
    "      pair (str): Examples: 'BTC-USD', 'ETH-USD'...\n",
    "    \"\"\"\n",
    "    self.pair = pair\n",
    "    self.uri = 'https://api.gdax.com/products/{pair}/candles'.format(pair=self.pair)\n",
    "\n",
    "  @staticmethod\n",
    "  def __date_to_iso8601(date):\n",
    "    \"\"\"Convert a datetime object to the ISO-8601 date format (expected by the GDAX API).\n",
    "    Args:\n",
    "      date (datetime): The date to be converted.\n",
    "    Returns:\n",
    "      str: The ISO-8601 formatted date.\n",
    "    \"\"\"\n",
    "    return '{year}-{month:02d}-{day:02d}T{hour:02d}:{minute:02d}:{second:02d}'.format(\n",
    "        year=date.year,\n",
    "        month=date.month,\n",
    "        day=date.day,\n",
    "        hour=date.hour,\n",
    "        minute=date.minute,\n",
    "        second=date.second)\n",
    "\n",
    "  def request_slice(self, start, end, granularity):\n",
    "    # Allow 3 retries (we might get rate limited).\n",
    "    retries = 3\n",
    "    for retry_count in xrange(0, retries):\n",
    "      # From https://docs.gdax.com/#get-historic-rates the response is in the format:\n",
    "      # [[time, low, high, open, close, volume], ...]\n",
    "      response = requests.get(self.uri, {\n",
    "        'start': GDAX.__date_to_iso8601(start),\n",
    "        'end': GDAX.__date_to_iso8601(end),\n",
    "        'granularity': granularity * 60  # GDAX API granularity is in seconds.\n",
    "      })\n",
    "\n",
    "      if response.status_code != 200 or not len(response.json()):\n",
    "        if retry_count + 1 == retries:\n",
    "          raise Exception('Failed to get exchange data for ({}, {})!'.format(start, end))\n",
    "        else:\n",
    "          # Exponential back-off.\n",
    "          sleep(1.5 ** retry_count)\n",
    "      else:\n",
    "        # Sort the historic rates (in ascending order) based on the timestamp.\n",
    "        result = sorted(response.json(), key=lambda x: x[0])\n",
    "        return result\n",
    "\n",
    "  def fetch(self, start, end, granularity):\n",
    "    \"\"\"Fetch the candle data for a given range and granularity.\n",
    "    Args:\n",
    "      start (datetime): The start of the date range.\n",
    "      end (datetime): The end of the date range (excluded).\n",
    "      granularity (int): The granularity of the candles data (in minutes).\n",
    "    Returns:\n",
    "      (pandas.DataFrame): A data frame of the OHLC and volume information, indexed by their unix timestamp.\n",
    "    \"\"\"\n",
    "    data = []\n",
    "\n",
    "    # We will fetch the candle data in windows of maximum 100 items.\n",
    "    # GDAX has a limit of returning maximum of 200, per request.\n",
    "    delta = timedelta(minutes=granularity * 100)\n",
    "\n",
    "    slice_start = start\n",
    "    while slice_start != end:\n",
    "      slice_end = min(slice_start + delta, end)\n",
    "      data += self.request_slice(slice_start, slice_end, granularity)\n",
    "      slice_start = slice_end\n",
    "\n",
    "    data_frame = pandas.DataFrame(data=data, columns=['time', 'low', 'high', 'open', 'close', 'volume'])\n",
    "    data_frame.set_index('time', inplace=True)\n",
    "    return data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-115-bd831373db53>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGDAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'LTC-EUR'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2017\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2017\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1440\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'module' object is not callable"
     ]
    }
   ],
   "source": [
    "data_frame = GDAX('LTC-EUR').fetch(datetime(2017, 6, 1), datetime(2017, 8, 1), 1440)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm, tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8dc8efb460a44029b6163b7917107291",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=546), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "end = pd.to_datetime('2017-01-01 00:00:00')\n",
    "for_until = pd.to_datetime('2018-04-01 00:00:00')\n",
    "all_data =[]\n",
    "mod = gdax.PublicClient()\n",
    "# for_until_unix = calendar.timegm(for_until.utctimetuple())\n",
    "# tq = for_until_unix - calendar.timegm(end.utctimetuple())\n",
    "\n",
    "ee = (for_until - end) \n",
    "pbar = tqdm_notebook(total = (ee.days*12)//10 )\n",
    "\n",
    "while end + timedelta(hours = 10) < for_until:\n",
    "    pbar.update(1)\n",
    "    \n",
    "    start = end \n",
    "    end  = start + timedelta(hours = 10)\n",
    "    mod = gdax.PublicClient()\n",
    "    data = mod.get_product_historic_rates('BTC-USD', start = start, end = end, granularity='300' )\n",
    "    all_data.extend(data)\n",
    "    \n",
    "    \n",
    "pbar.close()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ?mod.get_product_historic_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qq = [i for i in all_data if len(i) == 6]\n",
    "df = pd.DataFrame(qq, columns = ['time', 'low', 'high', 'open', 'close', 'volume'])\n",
    "# df = df.sort_values('time', ascending=True).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_time = []\n",
    "for i in df.time.values:\n",
    "    utc_time = datetime.datetime.utcfromtimestamp(i)\n",
    "#     utc_time.strftime(\"%Y-%m-%d %H:%M:%S (UTC)\")\n",
    "    date_time.append(utc_time.strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['timestamp'] = pd.to_datetime(date_time)\n",
    "dd = df.set_index('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 09:55:00</th>\n",
       "      <td>1483264500</td>\n",
       "      <td>970.08</td>\n",
       "      <td>970.38</td>\n",
       "      <td>970.09</td>\n",
       "      <td>970.38</td>\n",
       "      <td>1.873499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 09:50:00</th>\n",
       "      <td>1483264200</td>\n",
       "      <td>970.08</td>\n",
       "      <td>970.52</td>\n",
       "      <td>970.52</td>\n",
       "      <td>970.10</td>\n",
       "      <td>3.055400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 09:45:00</th>\n",
       "      <td>1483263900</td>\n",
       "      <td>969.93</td>\n",
       "      <td>970.62</td>\n",
       "      <td>969.93</td>\n",
       "      <td>970.30</td>\n",
       "      <td>2.865889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 09:40:00</th>\n",
       "      <td>1483263600</td>\n",
       "      <td>969.70</td>\n",
       "      <td>970.68</td>\n",
       "      <td>970.37</td>\n",
       "      <td>969.95</td>\n",
       "      <td>14.419408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 09:35:00</th>\n",
       "      <td>1483263300</td>\n",
       "      <td>970.23</td>\n",
       "      <td>970.38</td>\n",
       "      <td>970.23</td>\n",
       "      <td>970.38</td>\n",
       "      <td>1.430990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time     low    high    open   close     volume\n",
       "timestamp                                                                 \n",
       "2017-01-01 09:55:00  1483264500  970.08  970.38  970.09  970.38   1.873499\n",
       "2017-01-01 09:50:00  1483264200  970.08  970.52  970.52  970.10   3.055400\n",
       "2017-01-01 09:45:00  1483263900  969.93  970.62  969.93  970.30   2.865889\n",
       "2017-01-01 09:40:00  1483263600  969.70  970.68  970.37  969.95  14.419408\n",
       "2017-01-01 09:35:00  1483263300  970.23  970.38  970.23  970.38   1.430990"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-31 04:20:00</th>\n",
       "      <td>1522470000</td>\n",
       "      <td>7001.00</td>\n",
       "      <td>7027.01</td>\n",
       "      <td>7027.01</td>\n",
       "      <td>7016.78</td>\n",
       "      <td>36.620171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 04:15:00</th>\n",
       "      <td>1522469700</td>\n",
       "      <td>7027.00</td>\n",
       "      <td>7027.69</td>\n",
       "      <td>7027.69</td>\n",
       "      <td>7027.01</td>\n",
       "      <td>13.486690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 04:10:00</th>\n",
       "      <td>1522469400</td>\n",
       "      <td>7014.99</td>\n",
       "      <td>7030.00</td>\n",
       "      <td>7015.00</td>\n",
       "      <td>7027.69</td>\n",
       "      <td>29.912234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 04:05:00</th>\n",
       "      <td>1522469100</td>\n",
       "      <td>6985.00</td>\n",
       "      <td>7017.14</td>\n",
       "      <td>6990.00</td>\n",
       "      <td>7015.00</td>\n",
       "      <td>33.018283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 04:00:00</th>\n",
       "      <td>1522468800</td>\n",
       "      <td>6968.00</td>\n",
       "      <td>6995.39</td>\n",
       "      <td>6969.00</td>\n",
       "      <td>6990.01</td>\n",
       "      <td>30.824588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time      low     high     open    close     volume\n",
       "timestamp                                                                     \n",
       "2018-03-31 04:20:00  1522470000  7001.00  7027.01  7027.01  7016.78  36.620171\n",
       "2018-03-31 04:15:00  1522469700  7027.00  7027.69  7027.69  7027.01  13.486690\n",
       "2018-03-31 04:10:00  1522469400  7014.99  7030.00  7015.00  7027.69  29.912234\n",
       "2018-03-31 04:05:00  1522469100  6985.00  7017.14  6990.00  7015.00  33.018283\n",
       "2018-03-31 04:00:00  1522468800  6968.00  6995.39  6969.00  6990.01  30.824588"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dd= dd.drop_duplicates()\n",
    "dd.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dd.to_csv('bitcoin_data_2.csv')\n",
    "df = pd.read_csv('bitcoin_data_2.csv', parse_dates = ['timestamp']).set_index('timestamp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### append the new scraped data to the old one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dd_new = dd.copy()\n",
    "# merged_data = pd.concat([df, dd_new])\n",
    "# merged_data.drop_duplicates(inplace=True)\n",
    "# merged_data = merged_data.sort_index()\n",
    "# merged_data.to_csv('bitcoin_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('bitcoin_data_1.csv', parse_dates = ['timestamp']).set_index('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'low'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-200-f726d6b39c44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m        go.Scatter(\n\u001b[1;32m      4\u001b[0m           \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m           \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m           \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lines'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m           name='low'),\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   3612\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3613\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3614\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3616\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'low'"
     ]
    }
   ],
   "source": [
    "dd = df\n",
    "data = [\n",
    "       go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.low,\n",
    "          mode='lines',\n",
    "          name='low'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.high,\n",
    "          mode='lines',\n",
    "          name='high'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.open,\n",
    "          mode='lines',\n",
    "          name='open'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.close,\n",
    "          mode='lines',\n",
    "          name='close'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.volume,\n",
    "          mode='lines',\n",
    "          name='volume'),\n",
    "    \n",
    "    ]\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dd = dd.drop('time', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:20:00</th>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:25:00</th>\n",
       "      <td>271.60</td>\n",
       "      <td>344.67</td>\n",
       "      <td>271.84</td>\n",
       "      <td>344.67</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:30:00</th>\n",
       "      <td>270.35</td>\n",
       "      <td>346.41</td>\n",
       "      <td>346.41</td>\n",
       "      <td>321.98</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:35:00</th>\n",
       "      <td>281.36</td>\n",
       "      <td>349.67</td>\n",
       "      <td>302.25</td>\n",
       "      <td>341.35</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:40:00</th>\n",
       "      <td>273.20</td>\n",
       "      <td>351.38</td>\n",
       "      <td>348.77</td>\n",
       "      <td>351.38</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        low    high    open   close  volume\n",
       "timestamp                                                  \n",
       "2015-01-08 01:20:00  360.00  360.00  360.00  360.00    0.01\n",
       "2015-01-08 01:25:00  271.60  344.67  271.84  344.67    0.06\n",
       "2015-01-08 01:30:00  270.35  346.41  346.41  321.98    0.13\n",
       "2015-01-08 01:35:00  281.36  349.67  302.25  341.35    0.11\n",
       "2015-01-08 01:40:00  273.20  351.38  348.77  351.38    0.11"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "take the mean of low, high, open, close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dd[['low', 'high', 'open', 'close']].mean(axis=1), columns=['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['volume'] = dd['volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df = pd.DataFrame(dd[['close', 'volume']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "percentage change, to detect interesting events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "change = pd.DataFrame(df.data.pct_change(1))\n",
    "change.columns = ['percent_change']\n",
    "change.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py:224: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia4444/4.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [go.Histogram(x=change.percent_change.values)]\n",
    "py.iplot(data, filename='histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py:224: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~gasia4444/0 or inside your plot.ly account where it is named 'gasia'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/api/v1/clientresp.py:40: UserWarning:\n",
      "\n",
      "Estimated Draw Time Too Long\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia4444/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd = df\n",
    "data = [\n",
    "       go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.data,\n",
    "          mode='lines',\n",
    "          name='data'),\n",
    "    \n",
    "    ]\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spikes = change[np.abs(change.percent_change)>=0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp = pd.DataFrame(df['data'].rolling(5000).mean().dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc0d576f2b0>]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0d63abc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.plot(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.signal import find_peaks_cwt\n",
    "indexes = find_peaks_cwt(df.data.values, np.arange(1, 550))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "find_peaks_df = df.iloc[indexes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py:224: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~gasia4444/0 or inside your plot.ly account where it is named 'gasia'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/api/v1/clientresp.py:40: UserWarning:\n",
      "\n",
      "Estimated Draw Time Too Long\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia4444/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "\n",
    "        go.Scatter(\n",
    "          x = df.index,\n",
    "          y = df.data,\n",
    "          mode='lines',\n",
    "          name='data'),\n",
    "        go.Scatter(\n",
    "          x = find_peaks_df.index,\n",
    "          y = find_peaks_df.data,\n",
    "          mode='markers',\n",
    "          name='min_max'),\n",
    "    \n",
    "    ]\n",
    "\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "from numpy import NaN, Inf, arange, isscalar, asarray, array\n",
    "\n",
    "def peakdet(v, delta, x = None):\n",
    "    \"\"\"\n",
    "    Converted from MATLAB script at http://billauer.co.il/peakdet.html\n",
    "    \n",
    "    Returns two arrays\n",
    "    \n",
    "    function [maxtab, mintab]=peakdet(v, delta, x)\n",
    "    %PEAKDET Detect peaks in a vector\n",
    "    %        [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local\n",
    "    %        maxima and minima (\"peaks\") in the vector V.\n",
    "    %        MAXTAB and MINTAB consists of two columns. Column 1\n",
    "    %        contains indices in V, and column 2 the found values.\n",
    "    %      \n",
    "    %        With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices\n",
    "    %        in MAXTAB and MINTAB are replaced with the corresponding\n",
    "    %        X-values.\n",
    "    %\n",
    "    %        A point is considered a maximum peak if it has the maximal\n",
    "    %        value, and was preceded (to the left) by a value lower by\n",
    "    %        DELTA.\n",
    "    \n",
    "    % Eli Billauer, 3.4.05 (Explicitly not copyrighted).\n",
    "    % This function is released to the public domain; Any use is allowed.\n",
    "    \n",
    "    \"\"\"\n",
    "    maxtab = []\n",
    "    mintab = []\n",
    "       \n",
    "    if x is None:\n",
    "        x = arange(len(v))\n",
    "    \n",
    "    v = asarray(v)\n",
    "    \n",
    "    if len(v) != len(x):\n",
    "        sys.exit('Input vectors v and x must have same length')\n",
    "    \n",
    "    if not isscalar(delta):\n",
    "        sys.exit('Input argument delta must be a scalar')\n",
    "    \n",
    "    if delta <= 0:\n",
    "        sys.exit('Input argument delta must be positive')\n",
    "    \n",
    "    mn, mx = Inf, -Inf\n",
    "    mnpos, mxpos = NaN, NaN\n",
    "    \n",
    "    lookformax = True\n",
    "    \n",
    "    for i in arange(len(v)):\n",
    "        this = v[i]\n",
    "        if this > mx:\n",
    "            mx = this\n",
    "            mxpos = x[i]\n",
    "        if this < mn:\n",
    "            mn = this\n",
    "            mnpos = x[i]\n",
    "        \n",
    "        if lookformax:\n",
    "            if this < mx-delta:\n",
    "                maxtab.append((mxpos, mx))\n",
    "                mn = this\n",
    "                mnpos = x[i]\n",
    "                lookformax = False\n",
    "        else:\n",
    "            if this > mn+delta:\n",
    "                mintab.append((mnpos, mn))\n",
    "                mx = this\n",
    "                mxpos = x[i]\n",
    "                lookformax = True\n",
    "\n",
    "    return array(maxtab), array(mintab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0cf196c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import plot, scatter, show, figure\n",
    "# series = [0,0,0,2,0,0,0,-2,0,0,0,2,0,0,0,-2,0]\n",
    "series = df.data.values.tolist()\n",
    "maxtab, mintab = peakdet(series,1000)\n",
    "figure(figsize = (15,7))\n",
    "plot(series)\n",
    "scatter(array(maxtab)[:,0], array(maxtab)[:,1], color='black')\n",
    "scatter(array(mintab)[:,0], array(mintab)[:,1], color='red')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pd.DataFrame(df.iloc[array(maxtab)[:,0]].data , index=df.iloc[array(maxtab)[:,0]].index).to_csv('max_btc.csv')\n",
    "# pd.DataFrame(df.iloc[array(mintab)[:,0]].data , index=df.iloc[array(mintab)[:,0]].index).to_csv('min_btc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py:224: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~gasia4444/0 or inside your plot.ly account where it is named 'gasia'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/api/v1/clientresp.py:40: UserWarning:\n",
      "\n",
      "Estimated Draw Time Too Long\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia4444/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "\n",
    "        go.Scatter(\n",
    "          x = df.index,\n",
    "          y = df.data,\n",
    "          mode='lines',\n",
    "          name='data'),\n",
    "        go.Scatter(\n",
    "          x = df.iloc[array(maxtab)[:,0]].index,\n",
    "          y = df.iloc[array(maxtab)[:,0]].data,\n",
    "          mode='markers',\n",
    "          name='max'),\n",
    "    \n",
    "            go.Scatter(\n",
    "          x = df.iloc[array(mintab)[:,0]].index,\n",
    "          y = df.iloc[array(mintab)[:,0]].data,\n",
    "          mode='markers',\n",
    "          name='min'),\n",
    "    \n",
    "    ]\n",
    "\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.corrcoef(df.volume.values[1:], change.percent_change.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [\n",
    "       go.Scatter(\n",
    "          x = df.index,\n",
    "          y = df.close,\n",
    "          mode='lines',\n",
    "          name='low'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = spikes.index,\n",
    "          y = df.loc[spikes.index].close,\n",
    "          mode='markers',\n",
    "          name='close'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.volume*10,\n",
    "          mode='lines',\n",
    "          name='volume'),\n",
    "    \n",
    "    ]\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# spikes.to_csv(\"spike_price_change.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [\n",
    "       go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.low,\n",
    "          mode='lines',\n",
    "          name='low'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.high,\n",
    "          mode='lines',\n",
    "          name='high'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.open,\n",
    "          mode='lines',\n",
    "          name='open'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.close,\n",
    "          mode='lines',\n",
    "          name='close'),\n",
    "    \n",
    "        go.Scatter(\n",
    "          x = dd.index,\n",
    "          y = dd.volume*10,\n",
    "          mode='lines',\n",
    "          name='volume'),\n",
    "    \n",
    "    ]\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dif = df.diff(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dif.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# take ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [\n",
    "       go.Scatter(\n",
    "          x = df.index,\n",
    "          y = np.log(df.close),\n",
    "          mode='lines',\n",
    "          name='dif'),\n",
    "    \n",
    "    \n",
    "    ]\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py:224: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The draw time for this plot will be slow for all clients.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/api/v1/clientresp.py:40: UserWarning:\n",
      "\n",
      "Estimated Draw Time Too Long\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia44/107.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "       go.Scatter(\n",
    "          x = dif.index,\n",
    "          y = dif.close,\n",
    "          mode='lines',\n",
    "          name='dif'),\n",
    "    \n",
    "    ]\n",
    "py.iplot(data, filename = 'gasia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py:236: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia44/109.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = dif.close.values\n",
    "data = [go.Histogram(x=x)]\n",
    "\n",
    "py.iplot(data, filename='basic histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gasia/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py:236: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~gasia44/111.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [go.Histogram(x=x,\n",
    "                     cumulative=dict(enabled=True))]\n",
    "\n",
    "py.iplot(data, filename='cumulative histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   1.00000000e+00,   1.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          3.00000000e+00,   3.00000000e+00,   3.00000000e+00,\n",
       "          9.00000000e+00,   8.00000000e+00,   1.10000000e+01,\n",
       "          2.60000000e+01,   4.20000000e+01,   8.70000000e+01,\n",
       "          1.95000000e+02,   6.10000000e+02,   2.37500000e+03,\n",
       "          1.42042000e+05,   1.72190000e+04,   1.97100000e+03,\n",
       "          4.97000000e+02,   1.64000000e+02,   8.60000000e+01,\n",
       "          3.80000000e+01,   1.20000000e+01,   1.10000000e+01,\n",
       "          1.10000000e+01,   5.00000000e+00,   3.00000000e+00,\n",
       "          5.00000000e+00,   1.00000000e+00,   1.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   3.00000000e+00,\n",
       "          0.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00]),\n",
       " array([ -3.30201000e+03,  -3.25268000e+03,  -3.20335000e+03,\n",
       "         -3.15402000e+03,  -3.10469000e+03,  -3.05536000e+03,\n",
       "         -3.00603000e+03,  -2.95670000e+03,  -2.90737000e+03,\n",
       "         -2.85804000e+03,  -2.80871000e+03,  -2.75938000e+03,\n",
       "         -2.71005000e+03,  -2.66072000e+03,  -2.61139000e+03,\n",
       "         -2.56206000e+03,  -2.51273000e+03,  -2.46340000e+03,\n",
       "         -2.41407000e+03,  -2.36474000e+03,  -2.31541000e+03,\n",
       "         -2.26608000e+03,  -2.21675000e+03,  -2.16742000e+03,\n",
       "         -2.11809000e+03,  -2.06876000e+03,  -2.01943000e+03,\n",
       "         -1.97010000e+03,  -1.92077000e+03,  -1.87144000e+03,\n",
       "         -1.82211000e+03,  -1.77278000e+03,  -1.72345000e+03,\n",
       "         -1.67412000e+03,  -1.62479000e+03,  -1.57546000e+03,\n",
       "         -1.52613000e+03,  -1.47680000e+03,  -1.42747000e+03,\n",
       "         -1.37814000e+03,  -1.32881000e+03,  -1.27948000e+03,\n",
       "         -1.23015000e+03,  -1.18082000e+03,  -1.13149000e+03,\n",
       "         -1.08216000e+03,  -1.03283000e+03,  -9.83500000e+02,\n",
       "         -9.34170000e+02,  -8.84840000e+02,  -8.35510000e+02,\n",
       "         -7.86180000e+02,  -7.36850000e+02,  -6.87520000e+02,\n",
       "         -6.38190000e+02,  -5.88860000e+02,  -5.39530000e+02,\n",
       "         -4.90200000e+02,  -4.40870000e+02,  -3.91540000e+02,\n",
       "         -3.42210000e+02,  -2.92880000e+02,  -2.43550000e+02,\n",
       "         -1.94220000e+02,  -1.44890000e+02,  -9.55600000e+01,\n",
       "         -4.62300000e+01,   3.10000000e+00,   5.24300000e+01,\n",
       "          1.01760000e+02,   1.51090000e+02,   2.00420000e+02,\n",
       "          2.49750000e+02,   2.99080000e+02,   3.48410000e+02,\n",
       "          3.97740000e+02,   4.47070000e+02,   4.96400000e+02,\n",
       "          5.45730000e+02,   5.95060000e+02,   6.44390000e+02,\n",
       "          6.93720000e+02,   7.43050000e+02,   7.92380000e+02,\n",
       "          8.41710000e+02,   8.91040000e+02,   9.40370000e+02,\n",
       "          9.89700000e+02,   1.03903000e+03,   1.08836000e+03,\n",
       "          1.13769000e+03,   1.18702000e+03,   1.23635000e+03,\n",
       "          1.28568000e+03,   1.33501000e+03,   1.38434000e+03,\n",
       "          1.43367000e+03,   1.48300000e+03,   1.53233000e+03,\n",
       "          1.58166000e+03,   1.63099000e+03]),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4MAAAGfCAYAAADlHAczAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3d9f29978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(dif.close.values, bins =100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}