quandl_hist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import Quandl\n",
    "#btc_exc = ['BITSTAMPUSD', 'BTCNCNY', 'BTCEEUR', 'BTCEUSD']\n",
    "mydata = Quandl.get(\"BITCOIN/BTCEUSD\", returns=\"pandas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume (BTC)</th>\n",
       "      <th>Volume (Currency)</th>\n",
       "      <th>Weighted Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2015-06-19</th>\n",
       "      <td>248.069</td>\n",
       "      <td>250.000</td>\n",
       "      <td>242.000</td>\n",
       "      <td>243.815</td>\n",
       "      <td>7225.006807</td>\n",
       "      <td>1776618.653628</td>\n",
       "      <td>245.898544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-20</th>\n",
       "      <td>243.737</td>\n",
       "      <td>250.888</td>\n",
       "      <td>240.000</td>\n",
       "      <td>245.142</td>\n",
       "      <td>6671.914345</td>\n",
       "      <td>1627779.640027</td>\n",
       "      <td>243.974901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-21</th>\n",
       "      <td>245.157</td>\n",
       "      <td>246.987</td>\n",
       "      <td>240.567</td>\n",
       "      <td>244.298</td>\n",
       "      <td>4350.056361</td>\n",
       "      <td>1062794.393855</td>\n",
       "      <td>244.317385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-22</th>\n",
       "      <td>244.412</td>\n",
       "      <td>249.400</td>\n",
       "      <td>243.300</td>\n",
       "      <td>247.700</td>\n",
       "      <td>6415.995175</td>\n",
       "      <td>1577634.398755</td>\n",
       "      <td>245.890833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-23</th>\n",
       "      <td>247.699</td>\n",
       "      <td>247.845</td>\n",
       "      <td>247.000</td>\n",
       "      <td>247.500</td>\n",
       "      <td>132.731252</td>\n",
       "      <td>32803.156678</td>\n",
       "      <td>247.139662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Volume (BTC)  \\\n",
       "Date                                                           \n",
       "2015-06-19  248.069  250.000  242.000  243.815   7225.006807   \n",
       "2015-06-20  243.737  250.888  240.000  245.142   6671.914345   \n",
       "2015-06-21  245.157  246.987  240.567  244.298   4350.056361   \n",
       "2015-06-22  244.412  249.400  243.300  247.700   6415.995175   \n",
       "2015-06-23  247.699  247.845  247.000  247.500    132.731252   \n",
       "\n",
       "            Volume (Currency)  Weighted Price  \n",
       "Date                                           \n",
       "2015-06-19     1776618.653628      245.898544  \n",
       "2015-06-20     1627779.640027      243.974901  \n",
       "2015-06-21     1062794.393855      244.317385  \n",
       "2015-06-22     1577634.398755      245.890833  \n",
       "2015-06-23       32803.156678      247.139662  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.tail()\n",
    "#mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116c1d1d0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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nChg7VtfMHDw4fvcgIopEXBK5VZ1iJVxr65RDh+xFGiynnKJba+rY9u2BwsLo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f3cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.log(mydata['Weighted Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116f803d0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yq/m5ktqgFgU+6PxeHvzitYE4aZLjAbwEYFsRmS8ipwC4FUB3AJNEZIqI3JGY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bd30d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.log(mydata['Volume (Currency)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1178bc9d0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lEfe06W477eSf36wU3Kjnvttu5cuMHOmPY7uhDRVx/Z132cXW1dHvGx320KWj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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116fb8410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.log(mydata['Volume (BTC)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  71.,  240.,  203.,   37.,   32.,   36.,  177.,  195.,  225.,  191.]),\n",
       " array([ 0.74809716,  1.36928172,  1.99046628,  2.61165084,  3.23283541,\n",
       "         3.85401997,  4.47520453,  5.09638909,  5.71757366,  6.33875822,\n",
       "         6.95994278]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1178d1450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log(mydata['Weighted Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117b853d0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U7kuTiNgJ5MCB0XIWzyBu+6mnRstnnOE/zg2P995bfd0dd9TLaZtYfvrTtduuu07/1xOD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a96490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transform import stock2return\n",
    "returns = stock2return(mydata['Weighted Price'])\n",
    "plt.plot(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-01-01    312.443\n",
       "2015-01-02    312.870\n",
       "2015-01-03    276.110\n",
       "2015-01-04    266.000\n",
       "2015-01-05    271.861\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.loc['2015-01-01':'2015-06-26']['Close'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DjqE9B789370khvYsmjwIWMnQHtg8hvYAAAAAbCKFFAAAAICJFFIAAAAAJlJI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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118f35d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (19.0, 12.0)\n",
    "for i in range(1,5):\n",
    "    plt.subplot(2, 2, i)\n",
    "    plt.hist(stock2return(mydata.loc[str(2010+i)+'-01-01':str(2010+i)+'-12-31']['Close']),bins=20)\n",
    "    plt.title(\"Histogram of BTC returns for year \"+str(2010+i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011: \n",
      "    mean = -0.00551185664349\n",
      "    std = 0.080100208453\n",
      "2012: \n",
      "    mean = 0.0028118154391\n",
      "    std = 0.0400848200432\n",
      "2013: \n",
      "    mean = 0.0110259248541\n",
      "    std = 0.0779277073114\n",
      "2014: \n",
      "    mean = -0.00243137429928\n",
      "    std = 0.0404414966477\n"
     ]
    }
   ],
   "source": [
    "for year in range(2011,2015):\n",
    "    tmp = stock2return(mydata.loc[str(year)+'-01-01':str(year)+'-12-31']['Close'])\n",
    "    print(str(year)+\": \\n    mean = \"+str(np.mean(tmp))+\"\\n    std = \"+str(np.std(tmp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-e3a952c972ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Price_USD\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m5000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "data[data[\"Price_USD\"] > 5000]\n"
   ]
  },
  {
   "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",
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