charlesDADI/ipython_btc_part1

## ipython_btc_part1
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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Art of trading Bitcoin and Measuring Risk part 1"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1>  </h1>\n",
      "<h2>Aims:</h2>\n",
      "<p style=\"color:#FF0000\"><b> Disclamer: Examples given in this notebook are provided for illustrative purposes only and should not be construed as investment advice or strategy.</b></p>\n",
      "<h2> <u>Organisation:</u> </h2>\n",
      "<ul>\n",
      "    <h3>0-Tools skills used in this notebook</h3>\n",
      "        <li>a-Ipython Notebook</li>\n",
      "        <li>b-Python including database management, pandas, numpy, matplotlib </li>\n",
      "        <li>c-Datavisualisation (d3.js)</li>         \n",
      "        <li>d-Trading strategy and backtest of several methods</li>\n",
      "    <h3>I-Data</h3>\n",
      "        <li>a-Where could we obtain historical and intraday data for many plateforms?</li>\n",
      "        <li>b- What kind of data is available for BTC? and more generally for crypto-currency? </li>\n",
      "        <li>c- How could we clean and store BTC data ?</li>\n",
      "    <h3><li>II- Focus on two trading strategies to exploit BTC market characteristics.</h3>\n",
      "        <li>a-What is scalping in forex and why it can be interesting for dealing BTC?</li>\n",
      "        <li>b-How build up a systematic trading strategy based on TA (technical analysis)?</li>\n",
      "        <li>c-Some ideas to go further and using Machine Learning</li>\n",
      "    <h3>III- Backtest and Results</h3>\n",
      "          <li>a-Scalping opportunities could disappear with the increasing of fees in next months</li>\n",
      "        <li>b-Technical analysis strategy Vs Buy/Hold</li>\n",
      "        <li>c-In comparaison with USDEUR</li>\n",
      "   <h3>IV- Crypto currencies call for strong risk management </h3>\n",
      "        <li>a-Some word about risk management measures</li>\n",
      "        <li>b-Trying to compare Value-At-Risk methods for BTC</li>\n",
      "        <li>c-Bootstrap</li>\n",
      "        <h3>IV- Conclusions</h3> \n",
      "   <h3>V-Digital analysis of bitcoin</h3>\n",
      "        <li>a-Twitter analysis</li>\n",
      "        <li>b-Datavizualisation</li>\n",
      "        <li>c-Further analysis..</li>\n",
      "        <h3>IV- Conclusions</h3>         \n",
      "</ul>\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This ipython notebook can be useful for student in applied mathematics and computer science, data journalist, bitcoin enthusiast and anyone who love play with tricky data issues ..enjoy  it and send me an email for any comment..<A HREF=\"mailto:charles-abner.dadi@graduates.centraliens.net\">charles-abner.dadi@graduates.centraliens.net</A>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a href=\"mailto:charlesabner.dadi@gmail.com?Subject=Hello%20again\" target=\"_top\">"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "I- Data"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "I-a-Where could we obtain historical and intraday data for many plateforms?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When you deal with BTC you should understand that BTC must not been considered as a currency. Why? First, it is not created by a central bank. Worst, it doesnt fit with the traditional definition of a currency which says that a currency should be a store of values, an unit of count and a medium of change. Despite, the incredible high volatilty avoids converting BTC into a store of value. But there are more obvious reasons to explain why BTC is not a currency. For instance, it is new and its is knowns that a currency should call confidence of a large part of people for becoming a currency."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can fetch the complete history at http://api.bitcoincharts.com/v1/csv/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To run this notebook we need download history data for every plateform dealing with USD currency and especially with btceUSD and MTgoxUSD markets\n",
      "http://api.bitcoincharts.com/v1/csv/btceUSD.csv.gz\n",
      "http://api.bitcoincharts.com/v1/csv/mtgoxUSD.csv.gz\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There exist a lot of plateform to exchange national currency to crypto currencies. We find out very famous plateform such as Mtgox, BTCe..which are one of the first plateforms and also very recently plateforms. Nevertheless, for a given currency each plateforms have specific characteristics such as liquidity, volatility, delay between order and execution.. It is easily understanding that discrepencies betweens theses 'markets' create trading opportunities. All the more, fee cost to make transaction are very low in the most of these markets due to the competitiveness."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is possible to find BTC data almost everywhere in internet but to perform interesting comparaison and statistical analysis we have prefered only one source of data. The website http://bitcoincharts.com/about/markets-api/ purpose historical data in .csv file updated each 15 min. We can fetch also complete historical data for each plateform and each currency. Theses files are the best source to build up analysis and backtest trading strategies."
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "We are starting by download all these files automatically through a python script and convert in a well handle format. In fact, in the raw bitcoin history format is Date(unixtime), price and amount. Each date figures out a transaction and we could convert this time series into a most common financial format called OHLC (Open, High, Low, Close) for a given time frequency."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "\n",
      "os.getcwd()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "'/home/cerveau2charles'"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.dates import num2date\n",
      "from matplotlib.dates import date2num\n",
      "from matplotlib.finance import candlestick\n",
      "import json\n",
      "from pprint import pprint\n",
      "from PyQt4 import QtGui, QtCore\n",
      "import urllib\n",
      "import MySQLdb\n",
      "import requests\n",
      "import sys\n",
      "import gzip\n",
      "import urllib\n",
      "import datetime\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import time\n",
      "import re"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The following function allows to read each raw bitcoin history for a given frequency and a given curreny, then it convert data into the OHLC format and store OHLC history into a CSV file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ConvertAllData(directory, freq):\n",
      "    \"\"\"Make it possible to convert all data file in ohlc data file for a given frequency\"\"\"\n",
      "    for root, dirs, files in os.walk(directory):\n",
      "        for file in files:\n",
      "                bitcoin_csv_file_name = directory + '/USD/'  + file\n",
      "                try:\n",
      "                    # Read raw bitcoin price history from a csv file\n",
      "                    bitstamp_raw = load_btc_csv(bitcoin_csv_file_name)\n",
      "                    # Get bitcoin OHLC data frame with 1h frequency\n",
      "                    bitstamp = convert_to_ohlc(bitstamp_raw, freq)\n",
      "                    # Select a specific time interval\n",
      "                    from_date=bitstamp['open'].index[0]\n",
      "                    to_date=bitstamp['open'].index[len(bitstamp['open'])-1]\n",
      "                    #    from_date = '2011-08-14'\n",
      "                    #    to_date = '2014-04-26'\n",
      "                    bitstamp = bitstamp[\n",
      "                        (bitstamp.index >= from_date) &\n",
      "                        (bitstamp.index < to_date)]\n",
      "                    fname= directory +'/ohlc/' +freq+'_'+file\n",
      "                    if os.path.isfile(fname)==False:\n",
      "                    #export ohlc to csv\n",
      "                        ExportToOHLC(bitstamp,freq,file)\n",
      "                        print file + ' has been created'\n",
      "                except:\n",
      "                    print file + 'has crached'\n",
      "        print str(file) +' has been stored at ' + str(directory)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The previous function calls three function. One to read the .csv file an another to convert raw history to ohlc and finally a third to export the OHLC time series in a csv file."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Firstly, we would convert raw history into a pandas dataframe which is a very convenient format to handle financial data. I recommand you a fantastic ebook to understand and practice the library pandas:\n",
      "http://shop.oreilly.com/product/0636920023784.do dealing with some application to finance.\n",
      "This function, load directly the raw history to a dataframe and putting date as index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_btc_csv(bitcoin_csv_file_name):\n",
      "    \"\"\"\n",
      "    Read raw bitcoin price history from a csv file with the following format:\n",
      "    unixtime, price, amount\n",
      "    Bitcoin price history source:\n",
      "    http://www.bitcoincharts.com/\n",
      "    http://api.bitcoincharts.com/v1/csv/\n",
      "    \"\"\"\n",
      "    # Read bitcoin csv file\n",
      "    \n",
      "    frame = pd.read_csv(bitcoin_csv_file_name, header=None)\n",
      "    frame.columns = ['date', 'price', 'amount']\n",
      "    frame['date'] = pd.to_datetime(frame['date'], unit='s')\n",
      "    frame.set_index(frame['date'], inplace=True)\n",
      "    frame = frame[['price', 'amount']]\n",
      "    return frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Please, indicate the path to the file mtgoxUSD.csv uncompressed\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mtgoxUSD=load_btc_csv('...mtgoxUSD.csv')\n",
      "mtgoxUSD['price']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "date\n",
        "2010-07-17 23:09:17    0.04951\n",
        "2010-07-18 03:43:06    0.05941\n",
        "2010-07-18 17:48:56    0.08080\n",
        "2010-07-18 21:44:11    0.08585\n",
        "2010-07-18 22:00:26    0.08584\n",
        "2010-07-18 22:00:36    0.08584\n",
        "2010-07-19 03:53:04    0.09090\n",
        "2010-07-19 16:24:13    0.09307\n",
        "2010-07-19 17:03:33    0.08911\n",
        "2010-07-19 17:07:58    0.08752\n",
        "2010-07-19 17:12:27    0.09109\n",
        "2010-07-19 20:44:16    0.08416\n",
        "2010-07-19 20:55:25    0.07921\n",
        "2010-07-19 20:56:29    0.07921\n",
        "2010-07-19 20:57:00    0.07723\n",
        "...\n",
        "2014-02-25 01:58:37    134.27701\n",
        "2014-02-25 01:58:37    134.27701\n",
        "2014-02-25 01:58:46    134.27701\n",
        "2014-02-25 01:58:46    134.27701\n",
        "2014-02-25 01:58:49    134.27702\n",
        "2014-02-25 01:58:49    134.27701\n",
        "2014-02-25 01:58:49    133.30001\n",
        "2014-02-25 01:58:49    134.27701\n",
        "2014-02-25 01:58:49    133.30000\n",
        "2014-02-25 01:58:52    133.30000\n",
        "2014-02-25 01:58:58    133.30001\n",
        "2014-02-25 01:59:00    133.30001\n",
        "2014-02-25 01:59:00    133.30000\n",
        "2014-02-25 01:59:01    133.16044\n",
        "2014-02-25 01:59:06    135.00000\n",
        "Name: price, Length: 8295809, dtype: float64"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The raw history should be cleaned espacially to handle identical date:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def remove_bitcoin_date_duplicates(frame):\n",
      "    \"\"\"\n",
      "    Remove bitcoin date duplicates by using (weighted) aggregation.\n",
      "    \"\"\"\n",
      "    # Aggregate duplicate dates\n",
      "    frame['price'] = frame['price'] * frame['amount']\n",
      "    frame = frame.groupby(level=0).sum()\n",
      "    frame['price'] = np.round(frame['price'] / frame['amount'], 5)\n",
      "    return frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame=load_btc_csv('...mtgoxUSD.csv')\n",
      "remove_bitcoin_date_duplicates(frame)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 3933621 entries, 2010-07-17 23:09:17 to 2014-02-25 01:59:06\n",
        "Columns: 2 entries, price to amount\n",
        "dtypes: float64(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 3933621 entries, 2010-07-17 23:09:17 to 2014-02-25 01:59:06\n",
        "Columns: 2 entries, price to amount\n",
        "dtypes: float64(2)"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This function is particulary insteresting because it convert the raw history into the most common financial format (OHLC) for a given frequency parameter. In order to perform this task we have to use the resampling method dedicated for numpy objects."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def convert_to_ohlc(frame, freq='1D'):\n",
      "    \"\"\"\n",
      "    Compute bitcoin OHLC data frame with the given frequency.\n",
      "    @param frame: raw bitcoin price history.\n",
      "    @param freq: target OHLC frequency.\n",
      "    \"\"\"\n",
      "    ohlc = frame['price'].resample(freq, how='ohlc')\n",
      "    close = frame['price'].resample(freq, how='last', fill_method='ffill')\n",
      "    for column in ['open', 'high', 'low', 'close']:\n",
      "        ohlc[column] = np.where(np.isnan(ohlc[column]), close, ohlc[column])\n",
      "    ohlc['amount'] = frame['amount'].resample(freq, how='last')\n",
      "    ohlc['amount'].fillna(0.0, inplace=True)\n",
      "    return ohlc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame=load_btc_csv('...mtgoxUSD.csv')\n",
      "frame=remove_bitcoin_date_duplicates(frame)\n",
      "ohlc=convert_to_ohlc(frame, freq='1D')\n",
      "ohlc[len(ohlc)-20:len(ohlc)-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>amount</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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-02-06</th>\n",
        "      <td> 904.70001</td>\n",
        "      <td> 909.00000</td>\n",
        "      <td> 801.00000</td>\n",
        "      <td> 828.99424</td>\n",
        "      <td> 4.473700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-07</th>\n",
        "      <td> 827.00000</td>\n",
        "      <td> 832.29629</td>\n",
        "      <td> 651.71264</td>\n",
        "      <td> 695.61818</td>\n",
        "      <td> 0.010000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-08</th>\n",
        "      <td> 685.22000</td>\n",
        "      <td> 718.76447</td>\n",
        "      <td> 632.01101</td>\n",
        "      <td> 648.72940</td>\n",
        "      <td> 2.636822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-09</th>\n",
        "      <td> 658.81324</td>\n",
        "      <td> 693.99999</td>\n",
        "      <td> 622.75332</td>\n",
        "      <td> 659.49776</td>\n",
        "      <td> 0.010000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-10</th>\n",
        "      <td> 663.29499</td>\n",
        "      <td> 700.00000</td>\n",
        "      <td> 500.00000</td>\n",
        "      <td> 582.51412</td>\n",
        "      <td> 0.086535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-11</th>\n",
        "      <td> 582.54000</td>\n",
        "      <td> 609.49490</td>\n",
        "      <td> 550.00000</td>\n",
        "      <td> 578.77200</td>\n",
        "      <td> 1.332180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-12</th>\n",
        "      <td> 567.70774</td>\n",
        "      <td> 585.00000</td>\n",
        "      <td> 511.77002</td>\n",
        "      <td> 531.00000</td>\n",
        "      <td> 0.040000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-13</th>\n",
        "      <td> 540.03697</td>\n",
        "      <td> 548.89967</td>\n",
        "      <td> 451.10001</td>\n",
        "      <td> 451.17000</td>\n",
        "      <td> 4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-14</th>\n",
        "      <td> 451.12100</td>\n",
        "      <td> 499.52539</td>\n",
        "      <td> 302.00000</td>\n",
        "      <td> 427.52000</td>\n",
        "      <td> 1.991000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-15</th>\n",
        "      <td> 427.52000</td>\n",
        "      <td> 447.87999</td>\n",
        "      <td> 310.00000</td>\n",
        "      <td> 371.00000</td>\n",
        "      <td> 0.240882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-16</th>\n",
        "      <td> 370.61794</td>\n",
        "      <td> 540.00000</td>\n",
        "      <td> 220.29327</td>\n",
        "      <td> 299.71758</td>\n",
        "      <td> 0.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-17</th>\n",
        "      <td> 299.72763</td>\n",
        "      <td> 411.00000</td>\n",
        "      <td> 263.10100</td>\n",
        "      <td> 272.19851</td>\n",
        "      <td> 2.553478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-18</th>\n",
        "      <td> 280.00000</td>\n",
        "      <td> 369.98030</td>\n",
        "      <td> 248.14826</td>\n",
        "      <td> 293.80000</td>\n",
        "      <td> 1.006243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-19</th>\n",
        "      <td> 293.60000</td>\n",
        "      <td> 308.47145</td>\n",
        "      <td> 257.20891</td>\n",
        "      <td> 261.69702</td>\n",
        "      <td> 5.020000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-20</th>\n",
        "      <td> 264.32800</td>\n",
        "      <td> 270.81807</td>\n",
        "      <td> 109.00000</td>\n",
        "      <td> 111.69700</td>\n",
        "      <td> 0.179719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-21</th>\n",
        "      <td> 111.61995</td>\n",
        "      <td> 159.99632</td>\n",
        "      <td>  91.50000</td>\n",
        "      <td> 111.40000</td>\n",
        "      <td> 0.030000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-22</th>\n",
        "      <td> 110.52860</td>\n",
        "      <td> 290.14435</td>\n",
        "      <td>  96.63450</td>\n",
        "      <td> 255.53000</td>\n",
        "      <td> 0.010888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-23</th>\n",
        "      <td> 269.17377</td>\n",
        "      <td> 348.96826</td>\n",
        "      <td> 220.17537</td>\n",
        "      <td> 309.99989</td>\n",
        "      <td> 3.017863</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-24</th>\n",
        "      <td> 314.99996</td>\n",
        "      <td> 316.78999</td>\n",
        "      <td> 131.72093</td>\n",
        "      <td> 173.84638</td>\n",
        "      <td> 0.012060</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "                 open       high        low      close    amount\n",
        "date                                                            \n",
        "2014-02-06  904.70001  909.00000  801.00000  828.99424  4.473700\n",
        "2014-02-07  827.00000  832.29629  651.71264  695.61818  0.010000\n",
        "2014-02-08  685.22000  718.76447  632.01101  648.72940  2.636822\n",
        "2014-02-09  658.81324  693.99999  622.75332  659.49776  0.010000\n",
        "2014-02-10  663.29499  700.00000  500.00000  582.51412  0.086535\n",
        "2014-02-11  582.54000  609.49490  550.00000  578.77200  1.332180\n",
        "2014-02-12  567.70774  585.00000  511.77002  531.00000  0.040000\n",
        "2014-02-13  540.03697  548.89967  451.10001  451.17000  4.000000\n",
        "2014-02-14  451.12100  499.52539  302.00000  427.52000  1.991000\n",
        "2014-02-15  427.52000  447.87999  310.00000  371.00000  0.240882\n",
        "2014-02-16  370.61794  540.00000  220.29327  299.71758  0.100000\n",
        "2014-02-17  299.72763  411.00000  263.10100  272.19851  2.553478\n",
        "2014-02-18  280.00000  369.98030  248.14826  293.80000  1.006243\n",
        "2014-02-19  293.60000  308.47145  257.20891  261.69702  5.020000\n",
        "2014-02-20  264.32800  270.81807  109.00000  111.69700  0.179719\n",
        "2014-02-21  111.61995  159.99632   91.50000  111.40000  0.030000\n",
        "2014-02-22  110.52860  290.14435   96.63450  255.53000  0.010888\n",
        "2014-02-23  269.17377  348.96826  220.17537  309.99989  3.017863\n",
        "2014-02-24  314.99996  316.78999  131.72093  173.84638  0.012060"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ohlc['close'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xba1226c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V1dV47bXXcPDgQRw+fBgdHR3YtGkTcnJykJKSgoqKCiQnJyMnJwcAUF5ejs2bN6O8vBwl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\n"
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, after having cleaned and converted data into an OHLC time series we could export data to a .CSV file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ExportToOHLC(bitstamp,freq,File):\n",
      "    path_to_store='/home/cerveau2charles/Documents/bitcoin/data/ohlc/' +freq+'_'\n",
      "    dataframe_to_export=pd.DataFrame(index=bitstamp.index,columns=['open','high','low','close'])\n",
      "    dataframe_to_export['open']=bitstamp['open']\n",
      "    dataframe_to_export['high']=bitstamp['high']\n",
      "    dataframe_to_export['low']=bitstamp['low']\n",
      "    dataframe_to_export['close']=bitstamp['close']\n",
      "    dataframe_to_export.to_csv(path_to_store+File,sep='|')\n",
      "    print 'csv file has been created'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we could plot the OHLC time series in the candlestick foramt and also compute some Technical indicators."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_candlestick(frame, ylabel='BTC/USD', candle_width=1.0, freq='1M'):\n",
      "    \"\"\"\n",
      "    Plot candlestick graph.\n",
      "    @param frame: bitcoin OHLC data frame to be plotted.\n",
      "    @param ylabel: label on the y axis.\n",
      "    @param candle_width: width of the candles in days.\n",
      "    @param freq: frequency of the plotted x labels.\n",
      "    \"\"\"\n",
      "    candlesticks = zip(\n",
      "        date2num(frame.index),\n",
      "        frame['open'],\n",
      "        frame['close'],\n",
      "        frame['high'],\n",
      "        frame['low'],\n",
      "        frame['amount'])\n",
      "    # Figure\n",
      "    ax0 = plt.subplot2grid((3,1), (0,0), rowspan=2)\n",
      "    ax1 = plt.subplot2grid((3,1), (2,0), rowspan=1, sharex=ax0)\n",
      "    plt.subplots_adjust(bottom=0.15)\n",
      "    plt.setp(ax0.get_xticklabels(), visible=False)\n",
      "    ax0.grid(True)\n",
      "    ax0.set_ylabel(ylabel, size=20)\n",
      "    # Candlestick\n",
      "    candlestick(ax0, candlesticks,\n",
      "        width=0.5*candle_width,\n",
      "        colorup='g', colordown='r')\n",
      "    # Get data from candlesticks for a bar plot\n",
      "    dates = np.asarray([x[0] for x in candlesticks])\n",
      "    volume = np.asarray([x[5] for x in candlesticks])\n",
      "    # Make bar plots and color differently depending on up/down for the day\n",
      "    pos = frame['open'] - frame['close'] < 0\n",
      "    neg = frame['open'] - frame['close'] > 0\n",
      "    ax1.grid(True)\n",
      "    ax1.bar(dates[pos], volume[pos], color='g', width=candle_width, align='center')\n",
      "    ax1.bar(dates[neg], volume[neg], color='r', width=candle_width, align='center')\n",
      "    # Scale the x-axis tight\n",
      "    ax1.set_xlim(min(dates),max(dates))\n",
      "    ax1.set_ylabel('VOLUME', size=20)\n",
      "    # Format the x-ticks with a human-readable date.\n",
      "    xt = [date2num(date) for date in pd.date_range(\n",
      "            start=min(frame.index),\n",
      "            end=max(frame.index),\n",
      "            freq=freq)]\n",
      "    ax1.set_xticks(xt)\n",
      "    xt_labels = [num2date(d).strftime('%Y-%m-%d\\n%H:%M:%S') for d in xt]\n",
      "    ax1.set_xticklabels(xt_labels,rotation=45, horizontalalignment='right')\n",
      "    # Plot\n",
      "    plt.ion()\n",
      "    plt.show()\n",
      "    return (ax0, ax1)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n=len(ohlc)\n",
      "plot_candlestick(ohlc[n-100:n-50], ylabel='BTC/USD', candle_width=1.0, freq='1D')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Z2s6ZtWnThl577TWN93/ttdeobdu22jZjUKBHn4nO865VIJc50lpLXfSZPMGY\n+eHlfB5X91ibgo9D37CGDK2nufz8/LB3714cOnSoyn2PHDmCvXv3iql2ORxO7YPHMeMAOvhMJk2a\nBEEQMHToUGRkZEjud/LkSQwdOhRmZmaYOHFitYw0JlkMTdd5V118HKy5WlOfo5aLjVmSCmq3z0SH\neGWGaEsu/ajrsdbFrymX64KlGc1n4unpidmzZyMiIgLt27fHG2+8gd69e8PZ2RlA6bqO7du34/ff\nf0dRUREiIyPh6empk3F1GZ7TnMMxXeqkX1PXubMvvviCLCwsSBAEta/69etTVFSUrtUbFJiAz4Rj\nYLjPRBUdfSaS8aY0iG2lb7RdI2NIn0lthTVkaD3NdenSJdy5cwezZs3CmTNn8Omnn8LPzw+enp7w\n9PSEn58fPvvsM5w5cwYzZ86s9mB3+vRp+Pj4iC8bGxt88803BgtBz+FwNEcyL0/FJz71mM1SykfD\n13rVLFoPJi4uLli0aJH4f2RkJLZv346MjAxkZGRg+/btiIiIQIsWLfRioIeHB44ePYqjR4/iyJEj\naNiwIQYNGmSwEPRZDM2YPhO5zJ+yNLnYwdK4z6Qyav1xTx5xzSr3v4i1NWYIgnSYIda5Ly3p97NZ\nWyMrMlLSRkP4THTxa8rlumBpuvpM9JbPxBhs27YNrq6uaN68ORITExEaGgoACA0NRXx8PAAgISEB\nI0aMgIWFBVxcXODq6oqDBw/WpNkcjqxQ648rKCidkAoNrbzGRKnEtXL/1xSsuFcqdlWw0TraGn6f\n+uk9Hwj3a1ZA2zkzQRAoMjKyWvNuuhIeHk7fffcdEZFK1OCSkhLxvbqowRs2bFCpB9xnwqnDPhMW\nUv4UrfPrVPTB6GHtCjPulZWVpH+mLh9PfcMaMgyez0RfFBUVYePGjZg3b14lTRAEZvwttVo8AFsg\ngiKe5jN5Iime/BXfVxH/v3z+C3W6yea/qM3vLS2B+/dL31taAuWOT1XHs869h475dcr2f/JXfK9B\n+0lJSeLUdaVpl7L2yteXmAgoFEBEROn7csfTMtsS9/NLj7VVfaua708Teq9QGDCfSU39MomPj6fA\nwEDxvYeHB+Xk5BAR0dWrV8VMi9HR0RQdHS3uFxgYSPv371epC4xfJupyXJSh15wIjDpZ2+WkycUO\nlsYqo+64iJntjJgzRi59RSSdv0PqupA894Eqr6eySN+a2lHxWtPIdg00uZyPctF0zWei0y+ThQsX\nar3q9cLiUBazAAAgAElEQVSFC7o0JbJmzRqMGDFCfD9gwADExcVh+vTpiIuLw8CBA8XtI0eOxIcf\nfogrV67g7Nmz6Ny5c7Xari518plzE8VQ8dQ4lWHlQ+eYIJLDjARS60qqelWHu3fvUtOmTamgoEDc\nduvWLerduze5ublRQEAA3b59W9S++OILeumll8jDw4OSkpIq1Qcj+0w48qQuxlaqCrVrNQzkM9G2\n/6vKFcKPp+FhDRk6/TKZMmUKPvjgA5TWbXgaNWpUKYx9WQh6dcyaNQuzZs0yhmkcTq2ituYeMYV8\nIKaOTo8G29nZoUWLFnBxcdH4ZSpkMbRKjsDy6Hldgi5ljK3JxQ6Wxiojl5wxcukrlpYlWQI6rzPR\ntf+l2mOt+zBmPhCWJpfjydKY9zkGJrXOhMPhcKTg6z5qGG3nzGpynYm+gI4+E33neefULKx59LqU\nC71KdPGZaBCbS98+E47hYQ0Z/JdJecqHkSj/v7U1IAjICg+XDinBqVXUVt+B0ShbUT/7yXBSflX9\nk+sJjPAnHNND68Fkx44dYhiTWkdBAbJCQyuf/E/IYpXlPhNZarr6TORio1y0LMkS0DkunTqlLGRK\nZEJkpZAp1cmvLhdNLnawNKP5THx9fZlBHLOzs5GYmIiEhATcuHFDJ6NkR/m4RRUGmoontbbxfTic\nOgfjemIWm1kAmk2YHjhdXA/EkRG6zJulp6dTWFgYhYSEUEREBN29e5eIiD7++GOVHCfPPPMMffnl\nl7pNzhkQSPlMSPdn1flz7KYHP2YaoovP5An8eqpdsIYMrX+ZnDp1Ct26dUNcXBz++usvREZGYuTI\nkVizZg2ioqLwzDPPwMfHBy4uLigqKsLUqVOxffv2ag14+fn5GDJkCFq1aoXWrVvjwIEDPJ8Jh2MC\nmNKyAE710HowiYmJwb179zB58mQkJCTgvffew6ZNm/DFF1/Az88P2dnZOHLkCC5cuIA//vgDRIRv\nv/22WkZOmTIFffv2xcmTJ/HPP//A09PTcPlMdHz2Xd/z73KZP2VpcrGDpXGfiX60LMkSYPpMWIMJ\nq//rYq4QuWhG85mkpqaia9eu+Oabb9C/f38sWrQIXbt2RUZGBmJiYmBjYyPuO3DgQAQHB1crn8id\nO3ewa9cuvPXWWwAAc3Nz2NjY8HwmHI6Jw4pZx9eMmB5ah1PJycnB4MGDVbZ16tQJe/bsQdu2bSvt\n37p162pNNWVmZqJZs2YIDw/HsWPH0KFDByxcuBC5ublwcHAAADg4OCA3NxcAcPXqVXTp0kUs7+zs\njCtXrlSuWF0Iel9fuLi4iCNzxZDMZd+ypPQyKupl29SFeC4L82yM+qp6r0t7+q5P3/az6jP28ZR6\nr+/69G4/VFFXn9oQ9FXYXzHEfF0/H+Vif/n6FAojh6CfPXu2ZDBHlqYJhw4dInNzczp48CAREU2Z\nMoU++eQTleRYRER2dnZEpD451u+//66yLwzggOeL3EwP7uTVkGo44Dm1C9aQIftFi87OznB2dkan\nTp0AAEOGDEFaWhocHR3FENY5OTmwt7cHADg5OeHy5cti+ezsbDg5OWncnq5+EX3H/tGljLE1udjB\n0lhluM9Ecy1LsgT0vsZKV00ufcXS5GIHS2OVYaGXwUTrLIda4OjoiObNm+PMmTMASvPAt2nTBv37\n90dcXBwAVMpnsnbtWhQVFSEzM1MW+Uw4HA6n1qPtzxxBEMjMzEzlVbaupOL2Ms3MzKxaP63S09Op\nY8eO9PLLL9OgQYMoPz/fMPlMiD/7XpfgU5Mawqe5OE9gDRk65TMhiTwm2m7XFC8vLxw6dKjSdp7P\nhFMdePwtDkd/aD3NVVJSotPLVOA5LjTX5GIHS5OLHSxNLnawtCzJEuA+Ey00udjB0mrUZ8LhcDic\nuo1A1Z2DMkEEQQAiSv+n2aofPyIiAhEREWrLsTQOp9YiCIiAeMmUek7KpEihdCGK7xNpdp27ndQp\nBEGQdFvwXyYcDoeNVJ4fqEbJ5hGz6zZ8MKkA95lorsnFDpYmFztYmlzskNQYeX4KZhYgtEUoaDap\nDQvP+1GedrA07jPRAf5NisPhcPSEUR5OriYtWrSgdu3akbe3N3Xq1ImIiG7dukX+/v5q15lERUWR\nq6sreXh40NatWyvVx/rYfJ0Jh1MZHkqIQ2Ti4VSAUqePQqHA0aNHxQjAhgpBz+FwtIOv1+EAJjTN\nRRWeIDBUCHruM9Fck4sdLE0udrA0udjB0uQSx4ylycUOliYXO1harfaZCIIAf39/dOzYEcuWLQMA\nZgh6Z2dnsaxkCHoJWDkWOBwOh6MencKpGJs9e/bgueeew40bNxAQEABPT08VXRAErYNNhoWFiZF+\ny+cziYmJEUfmms5/oe/6DJEvQd/11fX8EXK2X9/18fNRnvaXr0+h0DyficktWoyMjETjxo2xbNky\nKBQKODo6IicnB35+fjh16pToO5kxYwaA0oxtkZGReOWVV8Q6WAtvWPBFi5y6Cj/3OYCJL1osLCyE\nUqkEANy7dw/Jyclo164dBgwYYJAQ9BW/BZSH+0zkaQdLk4sdLE0udrA07jPRjyYXO1gaqwwL2U9z\n5ebmYtCgQQCAx48fY9SoUejTpw86duyIYcOGYfny5XBxccFvv/0GoDRN8LBhw9C6dWuYm5tjyZIl\n1c6pwuFwOBw2JjfNpQ/4NBeHox383OcAJj7NxeFwOBz5wweTCnCfieaaXOxgaXKxg6XJxQ6Wxn0m\n+tHkYgdL09VnwgcTLeBrUDh1FX7uc6qC+0w4HA6HoxHcZ8LhcDgcg8IHkwqYwrylXDS52MHS5GIH\nS5OLHSxNLnawNLnYwdLkYgdL4z4TPZGeni4LTS52sDS52MHS5GIHS5OLHSxNLnawNLnYwdLkYgdL\nY5VhYTKDSXFxMXx8fNC/f38AQF5eHgICAuDu7o4+ffogPz9f3Dc6Ohpubm7w9PREcnKyVu2Ur6cm\nNbnYwdLkYgdLk4sdLE0udrA0udjB0uRiB0uTix0sjVWGhckMJosWLULr1q3F1ew8nwmHw+HIB5MY\nTLKzs7F582aMGzdOfJJAbvlM9K3JxQ6WJhc7WJpc7GBpcrGDpcnFDpYmFztYmlzsYGmsMkwMmuNR\nTwwZMoTS0tJIoVBQSEgIERHZ2tqKeklJifh+8uTJtHr1alEbO3YsbdiwQaU+APzFX/zFX/ylw0sK\n2Qd63LRpE+zt7eHj4yP5lIG2+UyIrzHhcDgcvSL7wWTv3r1ITEzE5s2b8eDBAxQUFGD06NFwcHDA\ntWvXxHwm9vb2AAAnJydcvnxZLJ+dnQ0nJ6eaMp/D4XDqBLL3mURFReHy5cvIzMzE2rVr0atXL6xa\ntcpg+Uw4HA6Hoz2y/2VSkbIpqxkzZvB8JhwOhyMT6mRsLg6Hw+HoF6NNc8XFxeGff/5R2fbw4UMU\nFBSo3T81NRWff/65MUzjcDgcTjUx2mASHh4urgUpIyYmBnZ2dmr3T0lJQWRkpDFM43A4HE41qVEH\nPBExH9PlM3AcDodjGsj+aS4Oh8PhyB+Te5qrPC4uLrC2tka9evVgYWGBgwcPIi8vD8OHD8fFixfF\np7xsbW1r2lQOh8Op1Zj0LxNBEKBQKHD06FEx/pZUAEgOh8PhGA6THkyAyn4VqQCQHA6HwzEcRp3m\nys/Px6VLlwCUDgJ37twBAHFbGeU1FoIgwN/fH/Xq1cPbb7+N8ePHIzc3Fw4ODgAABwcH5Obmqi3H\n4XA4HO2RfDCq+jF9NUMQBBIEgczMzMSXum3lNTMzM2adV69eJSKi69evk5eXF+3cuVMlmjARkZ2d\nXaVyrI89e/ZsWWiGbsvKxkoyKqiVjZUsbNSHJhc7WJpc7GBpcrGDpcnFDpYmFztYGqsM695ptF8m\nL7zwgtZlqvoF8dxzzwEAmjVrhkGDBuHgwYOSASA1xRRyCuhDU95RAhHlxHgAA59oEUpZ2KgPTS52\nsDS52MHS5GIHS5OLHSxNLnawNF3zmRhtMNE54YoEhYWFKC4uhpWVFe7du4fk5GTMnj1bDAA5ffp0\nlQCQHA6HwzEc9SIiIiJq2ghdyM7ORmBgIH744QcsXboUQ4YMQVhYGDp06IB58+Zh7ty5uH37NhYt\nWoQGDRqolI2MjITUx7a1tYWLi0uNa4ZuKzIyEvAtJzYAUPYEtQIq/VNTNupDk4sd3EZuo5zs0NVG\n1r2zTgZ6FAShzq+uFwRBdZqrPBE8+gCHw6kM695ptEeDL126pNPL2EhlczS2Zmw7kCUtycVGU+hH\nbmPNaXKxg6XJxQ6WxrxPMDCaz8TFxUXjXwRl+wmCgOLiYiNYx+FwOJzqYLRpLjMzM5ibm6NDhw6o\nV6+eRmUEQcCuXbv0bguf5uLTXBwOR3tY906j/TJp3Lgx7t69i0uXLuGtt97C2LFjJZ082lBcXIyO\nHTvC2dkZGzdu5LG5OBwOpwYwms/k6tWrWLp0KZydnfHFF1/A1dUVgYGB2LBhAx4/fqxzvYsWLULr\n1q3FNSnVjc1lCvOW3GeiuSYXO1iaXOxgaXKxg6XJxQ6WJhc7WJquPhOjDSaNGzfGuHHjcODAAaSn\np+Pdd9/FwYMHMWzYMDg5OWHatGk4e/asVnVmZ2dj8+bNGDdunPjTi8fm4nA4HONTo48G379/Hxs2\nbMDSpUuxZ88eCIKAHj16YOnSpXBzc6uy/NChQzFr1iwUFBRgwYIF2LhxI+zs7HD79m0ApfP+TZo0\nEd+XwX0m3GfC4XC0RxY+E3VYWlpi9OjRGD16NPbt24dhw4YhNTUVJ0+erHIw2bRpE+zt7eHj4yP5\ns0wQBMmQLGFhYaLPxtbWFt7e3vD19QXw9GdebX8vkvXkr8vTTQqFosbt4+/5e/6+Zt8rFArExsYC\nQNU+bsmoXUZi586dNHr0aGrYsCEJgkAuLi6UlpZWZbmZM2eSs7Mzubi4kKOjIzVs2JDefPNN8vDw\noJycHCIqDQTp4eFRqSzrY6ekpMhCM3RbAAgR5V5h5f6v0D81ZaM+NLnYwdLkYgdLk4sdLE0udrA0\nudjB0lhlWPfOGslncvPmTXz55Zdo1aoVevbsiXXr1iE4OBhJSUm4cOECfHx8qqwjKioKly9fRmZm\nJtauXYtevXph1apVYmwuADw2F4fD4RgJo/pM/v77byxbtgwJCQl49OgRXF1dMW7cOISGhoo5SHQh\nNTUVX375JRITE5GXl4dhw4bh0qVLko8Gc58J95lwOBztYd07jTaYtGzZEllZWXjmmWcwePBgjB8/\nXpyjMzZ8MOGDCYfD0R5ZxObKysqCubk5/Pz8YGZmhp9//hljxoyp8mVsypxPNa0Z2w6+zqTmNLnY\nwdLkYgdLk4sdLE0udrA05n2CgVGf5nr8+DGSkpK0KrNy5UoDWcPhcDgcfWG0aS5dRztDTIXxaS4+\nzcXhcLRHFutMaso/wuFwOBzDUyOPBuuDBw8e4JVXXoG3tzdat26NmTNnAgDy8vIQEBAAd3d39OnT\nB/n5+VrVawrzltxnorkmFztYmlzsYGlysYOlycUOliYXO1iarrNIJjuYNGjQACkpKUhPT8c///yD\nlJQU7N69u9qBHjkcDoejPUbNZyIV2gQonYuztbWFl5cXwsLCMHr0aI3rLiwsRM+ePREbG4s33ngD\nqampcHBwwLVr1+Dr64tTp05Vaquu+wS4z4TD4WiLLHwmAPsGRUTIy8tDSkoKUlJSsHXrVqxevZpZ\nX0lJCdq3b4/z58/j3XffRZs2bZCbmysugHRwcEBubq7asjw2Vzmynvx1ebpJoeCxufh7/r6uv1co\nTCg2VxnFxcWUm5tL8fHx5OXlRYIg0K+//qpR2fz8fHrllVdox44dZGtrq6LZ2dlV2p/1sU0hPg6P\nzaW5Jhc7WJpc7GBpcrGDpcnFDpYmFztYmknF5lKHmZkZ7O3t8frrr0OhUKBp06ZYsWKFRmVtbGzQ\nr18/HDlyRJzeAoCcnBzY29sb0mwOh8PhoIbzmbAYN24cEhMTcf36dbX6zZs3YW5uDltbW9y/fx+B\ngYGYPXs2tm7diqZNm2L69OmIiYlBfn5+JSc895lwnwmHw9Ee2fhMtMHR0ZH5WG9OTg5CQ0NRUlKC\nkpISjB49Gr1794aPjw+GDRuG5cuXi4EeORwOh2NYZDPNVZFbt26hcePGknq7du2QlpYmPho8depU\nAECTJk2wbds2nDlzBsnJyZUiBldFmfOppjVj28HXmdScJhc7WJpc7GBpcrGDpcnFDpbGvE8wkOVg\nUlRUhC1btqBNmzY1bQqHw+FwNEB2PpPjx49j2rRp2Lp1KxYvXoxJkybpvQ3uM+E+Ew6Hoz2y8Jm8\n+OKLzEWLJSUlyMvLw927dwEAPXr0wNtvv20s8zgcDodTDYw2zXXx4kVkZWVJvi5duoS7d+/ihRde\nwOzZs5GcnAxzc+M/H2AK85bcZ6K5Jhc7WJpc7GBpcrGDpcnFDpYmFztYmq4+E6PdrS9cuMDUzczM\nYGNjAxsbGwDA/fv3UVBQAGtra7X7X758GWPGjMH169chCAImTJiA999/H3l5eRg+fDguXrwombaX\nw+FwOPpFdj6TMsLDw7Fy5UoUFxer1a9du4Zr167B29sbd+/eRYcOHRAfH48VK1bg2WefxbRp0zBv\n3jzcvn2brzNRA/eZcDgcbZFF2l5tISLmDc3R0RHe3t4AgMaNG6NVq1a4cuUKEhMTERoaCgAIDQ1F\nfHy8UezlcDicuoxsFy0CYDrsy5OVlYWjR4/ilVdeqXagx/LzhRUDn5VtK3tfXk9PT8cHH3ygsn+Z\nvnDhQslAklLt6bs+dfaLZAG4BqDL000KhaLK9ozZH7q2J5fjyWrPmP2ha3tyOZ6s9ozZH7q2J5fj\nyWqvfH0KhQkGeqxIaGgoCYJQ5X5KpZLat29Pf/75JxERD/TIAz3K0g6WJhc7WJpc7GBpcrGDpcnF\nDpama6BH2fpMwsLCsHLlSpSUlEju8+jRI4SEhCA4OFgctT09PaFQKODo6IicnBz4+fnxfCZq4D4T\nDoejLSbpM6kKIsLYsWPRunVrcSABgAEDBiAuLg4AEBcXh4EDB9aUiRwOh1NnMNnBZM+ePVi9ejVS\nUlLg4+MDHx8fJCUlYcaMGfj777/h7u6OHTt2YMaMGVrVW8mfUEOase3g60xqTpOLHSxNLnawNLnY\nwdLkYgdLY94nGBjNAV9V2t6KEBFz/27duklOgW3btk1r+zgcDoejO0bNAa8LLJ+JrnCfCfeZcDgc\n7ZFFbC5DDAocDofDkQcm6zMxFKYwb8l9JpprcrGDpcnFDpYmFztYmlzsYGlysYOl6eozMenB5K23\n3oKDgwPatWsnbsvLy0NAQADc3d3Rp08fZrZGDqc2YW1rDUEQIAgC/Pz8xP+tbdXHt+Nw9EmNrDO5\ndOkSfv75Z+zduxdXr14FADz//PN47bXX8NZbb6F58+Ya1bNr1y40btwYY8aMwfHjxwEA06ZN47G5\nNID7TGofksc0gh9Pjn5g3TuNPpjMnTsXn3/+OR4/fqxWt7CwwKeffopPPvlEo/qysrLQv39/cTDx\n9PREamoqHBwccO3aNfj6+vJFi2rgg0ntgw8mHEMjm0WLM2bMwGeffQZBEDB06FAsXboUmzdvxubN\nm/Hjjz9i6NChICJ89tlnWq8PKUPT2FxSyHXesvwURsVX+WkM7jORpx0sTS4+MJYml75iaXKxg6XJ\nxQ6WpqvPxGhPcx0+fBjz58+Hi4sLEhMT0bZt20r7jB8/HidOnMCAAQPwv//9D0OGDEHHjh11brPs\nZqsOqUCPwNPOVBcYTUpPT0+XLJ+enq62Ptb7ivUp7yiffuvcD8ARQKn5UEYooVAotLZfJAulgR5d\nVPepyv6K9RmyP6rTnqb1DRg4oLSf1WDZyBKFdwsNYr++6hPJgk7HU5vz0RD2V7c9fddX0+djTdlf\nvj6FQvNAj0ab5goPD8evv/6Ko0ePonXr1sx9T548CS8vL4waNQorVqxg7qtumkuhqH2xufQ9LcWn\nuSpj6n3Cp7k4hkYW01w7d+5Enz59qhxIAKBVq1YIDAxEamqq1u3w2FwcDodjfIw2mOTk5Kid2pKi\nTZs2yMnJYe4zYsQIdO3aFadPn0bz5s2xYsWKOhGbS9/z4brWKZe+YmnG7A9dNUP0B/eZyFOTix0s\njXleMTCaz8TCwgJFRUUa719UVAQLCwvmPmvWrFG7ncfm4nA4HONiNJ+Jl5cXLC0tsX//fo3279Kl\nCwoLC/HPP//o3RbuMzF9/4AhMPU+4T4TjqGRhc8kMDAQBw8exB9//FHlvvHx8Th48CCCgoKMYBmH\nw+HUbTRdesDCaIPJlClTYGlpidGjR2PZsmVqR7eSkhL89NNPGDVqFCwtLTFlyhRjmSdiEvOWWdJS\nxXJSJ0l16tTERm00KRsrnsTcZ6K5Vpt8JoZeYyWX67omNXHpQQSAMDz9PwKSj8tXxGiDiZOTE+Li\n4vDw4UO8/fbbeOGFFzB69GjMmjULs2bNwptvvokWLVpgwoQJePjwIX7++Wc4OTkZyzyRsmesa1pj\nlcE1aaliOZWTJAhP/69GnZrYqI0mZWPFk1jf/aivPq6uZojzSt/21+R1oXJ+REDyHJHLtcvS5GIH\nU2OcOyyM5oAHgCFDhiA5ORnvvvsuzp49i19++aXSPq6urliyZAn8/f11bicpKQkffPABiouLMW7c\nOEyfPl3jsqzAkIbWmlhb47by6cXxf//3f+L/5gDEADQPJKtjB7ZklNOlTkP0h66fTd826tsOllab\n+9GYn01ONppCP0pqrPsEA6MNJgkJCejfvz969eqFkydPIjU1Fbt37xYf/33uuefQrVs39OzZU+dE\nWgBQXFyMyZMnY9u2bXByckKnTp0wYMAAtGrVSl8fRcTa1lrlm1FkZCQAwMrGCgX5BVrXd1upRNnk\nXwRUf0BonqOSw9EPFb/clJ3fAGBnZYW8Au3P8dpKxXsBUP37galhtMFk0KBBeP755xEeHo5x48bB\nz88Pfn5+em/n4MGDcHV1FZf+/+c//0FCQoLGg0lWVpbGmkqIk3gAT9ZHKiNUTypt6hS3M2xExS8U\nZlDxg5S/6JnlNNQkbdThc1Wl6WIHS5OLHSxNrv1Y/stNGIDYcvsJ5QaZivVpOgjp+7NVLCP1ZQ9Q\nvcHro/9V7gWA5P3AEMda75quWTvISAQEBJAgCCQIAtWrV48CAwPp999/p8ePH+u1nfXr19O4cePE\n96tWraLJkyer7AOAv/iLv/iLv3R4SWG0XybJycnIysrC8uXLsWLFCiQnJyM5ORkODg4ICwvD+PHj\n0bJly2q3IxXYsTzEn7nncDgcvWLUEPQuLi6YM2cOLl68iMTERPTv3x83b97EvHnz4O7ujoCAAKxf\nv14y14kmODk54fLly+L7y5cvw9nZWR/mczgcDkeCGsm0WJ6cnBysWLECy5cvR2ZmJgCgWbNmCA0N\nxfjx4+Hm5qZVfY8fP4aHhwe2b9+O559/Hp07d8aaNWsM4oDncDgcTik1PpiUQUTYsWMHfvrpJ8TH\nx+Phw4cQBAHFxcVa17Vlyxbx0eCxY8di5syZBrCYw+FwOGXIZjABgEePHiE+Ph5ffvklDh48CKB0\nVbyuEJGkD0WdxtpfE10f5YqKilC/fn21Wm5uLuzs7CT16tghpRmiT7QtY6g+4f2hnS38mtFM06WM\npro+yrD6qzoY1WcixalTp/Df//4Xzs7OGD58OA4ePAgXFxfMnTtX7f6HDh3Cr7/+ilOnTlUabC5c\nuCAmyqrYwefOnUNaWlolTVnuMcaKY2tmZqZkfRkZGVAoFCrly9izZw82bNgglitf79atW7Fw4cJK\nZeLj4/H++++joKCgkh2bN29G//79xbbK6/ruD0C3PtF3fwD67xNj9gcg3Sdy6Q+AXzPGuGZM4Xqq\nNho8bWsQCgsLKTY2lrp16yY+MmxhYUFDhgyhrVu3SpZLSEggT09PGjZsGPXo0YOuXbumorVq1YqG\nDh1Ko0ePpvXr19OdO3eIiOj333+nF198kfr06UP9+/enNWvW0M2bN2nTpk3Uq1cvSklJEespKSkh\nIqJNmzZRmzZtqHfv3tSrVy+xrZKSEtq8eTN5enpS3759qWPHjvTw4UNRu3fvHrm4uJCvry8tX75c\nrLe4uJi2bt1KPj4+tH37dpXPtW3bNmrdujVt3Lix0mfeunUrdejQgV588UWaMGGCQfuj7HNr2yd/\n/fWXXvvDEH1izP6QOkfk1B+69gm/ZrQ/R+R+PekDow8maWlp9O6775KdnZ04iLi6ulJMTAzl5uYy\ny16+fJm6detG6enpREQ0atQoSkhIoNu3b9OFCxeod+/e9M8//xAR0YIFC6ht27b07bffUnZ2Ng0b\nNoz27dtHREQ//PADffDBBzR16lR69tln6e2336bXX39d5WDv3r2b3N3dxTKhoaE0fPhwIiJKTU0l\nV1dX2rNnDxERDRo0iHbv3i3eLIiIpk6dSpGRkfTRRx/RDz/8QEREO3fupHr16tGJEyeIiCgvL4+u\nXr1KRUVFtGDBAvrpp5+IiOjq1au0bds2+vfff+nPP/+kl156iY4cOUIFBQU0cuRI8TNeunRJr/2x\naNEiUigU1KxZM636xBD9UWazvvrk5MmT5OfnZ5T+YJ0jDx48kEV/8GvGONdMWlqa7K+nsgGxutSL\niIiI0O9vHfV8//33eOeddxAZGYnDhw+juLgYQ4YMwaJFi/D111+jW7duaNSoEbMOQRCQmJgIJycn\nODk54cMPP8StW7fw559/4t69e7hw4QLat2+PF198EV27dsXGjRsBANbW1khJSYGtrS06deqEjh07\norCwEKdOncLLL7+MiIgIFBUVITY2Fk5OTmjRogXOnz+Pjh07Ijg4GADQuXNnbN26FUOGDMHt27cR\nHByMbt26ITs7G9OmTUN2djZWrVoFOzs7uLq64uzZs7h69So6duyIY8eOYdeuXTh16hSOHz8Od3d3\neCaXOKUAACAASURBVHl5oX///khKSsLq1atx69YtWFpaolu3bggODsbp06eRmpqKQ4cO4dNPP0WP\nHj1w584dxMfHg4jQpUsXmJmZISEhQW/9cezYMTRs2BD+/v4YP348Hj9+jBUrVlTZJ71799Zbf2zd\nuhUrV65E06ZNcerUKTx8+BCvvfZatftky5YtyM7ORufOneHu7q7SHzY2NpL90ahRI2Z/dOrUSUyV\noM05QkRq++Pff/+V7I/Tp09L9sdnn30m2R/8mqmZa8bFxQWWlpZo3rw5JkyYgEePHsnuetq1axfS\n09Ph4+NT5f23SvQyJGlA2a8QDw8PWrBgAd24cUPrOh48eEBr1qyh4OBgeu2112ju3LlERPTbb7/R\n4MGDafr06fThhx9SXFwcffzxxxQaGkrR0dE0ePBg2rJlC7399tviaF9cXEyLFy+moKAgIiK6desW\n/fjjj9S3b1/x28OxY8eoqKiISkpKKDs7m7y8vCgvL4+IiG7fvk1FRUU0b9480Y6vvvqK+vbtS3fv\n3qX9+/fTnDlziIjo008/pfr169NHH31Ely5dombNmlG9evXEbxQLFiyg7t27U69evWjo0KH0888/\nExHRvn37aOzYsbRv3z7x24VCoSAXFxc6dOgQERGtXr2aAgMDVfpj3bp1NHToUJo6dSr93//9n9gf\no0ePprlz54r98c4774j9QUT0zTffUHBwMD169EiyT9LT0yX75MaNGxQTEyPZH59//nml/rh48aLa\n/ggKCqIjR45QQECAxn1y+PBhyT757bffqF27dvTWW2+pPT+Sk5Pp3XffVemPxYsXU3BwsPhtsGJ/\n5Ofn0/Hjx9X2R3FxMd2+fZsePXqkco4sWLCA+vbtSwqFQu35kZ2dTQ4ODmr74+jRo9SnT59K/REe\nHk779u0To0mU7w9+zRjumnn8+DF99dVXktfMjh07iIgoKyuLioqKZH89VRej/TI5c+YMFi9ejAUL\nFqBr165o2LChRuWOHTuGmzdvwt7eHubm5mjbti0GDx6MI0eO4Nlnn4WtrS38/Pzw66+/okOHDnB0\ndMSuXbuQmZmJZs2awczMDOfOncN///tfZGRk4K+//sKaNWvw77//onv37khJSUGnTp3QvHlzZGZm\nYs+ePfjtt9+wfft2pKSkYMCAATAzM8P27duxbt06PHz4ECkpKUhISEBQUBBeffVVPH78GF9//TWI\nCKdOnULPnj3h4uKC+Ph47Nq1C4sXL8bLL7+M+/fvw9LSEvPnz8e9e/dw48YN7N27Fz179sSBAwfQ\nu3dvpKSk4N69e9ixYwcuX76MkydPonnz5vDy8gIR4dy5c9izZw/S0tLw0ksvISAgAG+88QbS0tJg\nYWGBP//8E3fu3MHJkycREhICa2tr7N69G+fPn0eDBg2gVCqRk5OD9957D2fOnEFGRgaOHDmCFStW\noF69ekhPT4evry+effZZWFpa4tSpU9i1axf++OMP/P3330hNTcWAAQOwZcsWfPPNNzhx4gS6deuG\nnTt3IjY2Fh9//DEePHiA+fPnw8LCAhkZGfD19YWLiwsSEhKQkpKCb7/9Fl5eXrh37x4aNWqE+fPn\nw9nZGS4uLpg/fz7q16+P48eP44033oCjoyMSEhJQUFCALVu2IDc3FydOnFDpkxMnTmDPnj1IT09H\n8+bNERQUhDfeeAN//vknzp07h/Pnz6Nz585IS0uDg4MDzp49i2vXrmHmzJkYPHgw/vjjDwwcOBCX\nL19GRkYGsrOzYW5ujpCQEMTFxaFjx45o1qwZLC0toVQq8fDhQyQlJWHPnj3YuHGjeI4cO3YMmzZt\nwkcffYRVq1Zh+fLlCAoKQqNGjdC2bVvY29uja9euWLt2Lfz9/bF7924cO3YMS5cuxdixY6FUKvHo\n0SP873//g5OTE7p3746bN29i4MCB+OWXX+Dv7w9nZ2fEx8cDKF0E3KFDB2zYsAF2dnbw9vZGSUkJ\nCgoKcOPGDdSvXx+dOnWCp6cnhgwZwq8ZPV4z6enp+OGHH5CXl4cLFy6oXDOnT5/G7t278ccffyA5\nORlbt27FgAEDYGNjI5vrycHBAb169YKzszPWr1+PJk2a4OWXX67eTb7aw5EB+euvv0gQBJo0aZL4\nrbOM9957jwBQ//79xbnNixcvEhHRxo0bqV27drR48WIaOnQoWVtb07lz5+i7774jBwcHevXVV2nA\ngAHUqlUratGiBV2/fl2lzMsvv0wWFhaUkJCgUt8rr7xCQUFBZGVlRYmJiZXaGjVqFDVu3Jj27t1L\nDx48oODgYLKwsKB33nmH5s+fT23atKHU1FSVMvPnz6fWrVtTq1at6NSpUzRx4kRq1KgRDRo0iEaM\nGEGNGjUSv+GUlRs1ahQ1a9aMevToQadPnyYiosmTJ1OzZs1o4sSJNGbMGGrUqJH4bWPTpk3k5eVF\nq1evpuHDh5OjoyMplUo6f/48jR8/nqytrWnUqFEUFhZGZmZmtGzZMiIi2r9/P7m7u9PKlSupffv2\nJAgCRUdHq2x/9dVXqWHDhtSyZUtKT09X0caOHUv16tWjJUuWEBHRwIEDydzcnD766CP6/vvvycrK\nir755hsiKv2GVLHcihUr6N69ezR16lSytLSkESNGUHh4ONWrV69SufHjx5ODgwM1bdqUfv31V9q/\nfz85ODhQcHAwTZo0iaysrOiFF16gixcvqpxXn332GXXt2pWUSiUdO3aMRo8eTQCoW7duNGfOHHJz\nc6Pr169TcXGxSjl/f39ycHCgtLQ0KikpETVPT0+aNGkSderUidLT02nTpk1imSNHjtDatWupffv2\nlJmZScHBwQSAQkJC6MiRI/Tbb7/RlStXKp37UVFR5OXlRbm5uZSbm0uTJ08mAPTaa6/RnDlzyMvL\ni86fP69iR+/evalFixZ0//59fs3o+ZoZNWoUNWnShN58800aOXIk2djY0PXr1yudx2XXTExMjKyu\npyVLllDPnj3pyy+/pNWrV4vnT3Uxaj4TbSgsLMThw4cRFRWFO3fu4LfffoMgCGjfvj0KCwtRXFyM\n4OBgZGRk4N9//0VUVBReeOEF5ObmIi4uDosWLUJmZib27t2LgIAAXLhwASkpKfjmm2/g6OiIBQsW\noLi4GFFRUSgpKRHLFBUV4ebNm3j99dfh7Ows1vf1119j9OjRuHz5MoKDg+Hk5CRq8+bNw/Xr13Ho\n0CEEBQXhmWeeQX5+Pu7fv4+vv/4akyZNglKpxNmzZ/Hw4UOxrR49emD58uW4cuUKli5dCltbW1y7\ndg3fffcdDh8+DEEQEBwcDBsbG5XP5efnh7CwMBQVFeHu3bvIzc3FmTNnMGjQIFy8eBEPHjxAUFAQ\n6tevj9zcXKxduxYLFy7EhQsXsH37drRt2xarVq2Cj48PQkJCkJWVhfv378Pc3ByLFy/GV199BWtr\nazRo0ACdO3eGvb09rl+/jh9++AELFy7E7du30blzZwwfPhzTp09Hw4YNIQgCTp8+jQYNGsDHxwcl\nJSXYu3cvlixZgoULF6Jly5bo168f7t+/jwULFgAo/Wb94YcfwsHBAQ0aNEDHjh0BALt378Z3332H\n+fPnw8bGBt26dcOpU6fQtGlTPPPMM/j222/xzTffiOU6d+6MpUuXAgC6d++OOXPmYNCgQfD19YW3\ntzd2794Nd3d33L59GykpKbh48SLmzp2LPXv2YOXKlVi2bBkaN24MV1dXNGvWDBMmTIBCocC6desQ\nERGBZs2aobCwEIcOHUJUVBSOHj2KAwcO4KeffoKPjw8KCwtx8OBBzJ07F1FRUVi5ciViY2Ph5uaG\nhIQEREVF4ebNm/j000+RkZGBjRs3wt7eHs899xwmTpwIa2trrFu3DkOGDMHzzz8vtjV37lzs27cP\na9aswZIlS2Bvb4/CwkLY2triww8/RGpqKhITEzFz5ky0bNlStKPsmrlx4wa2bduGkJAQfs3o6Zpp\n0aIFBEHAJ598gt27d+POnTvw9vbGhg0b4O7ujnv37qm9Zl588UXZXE/vvvsuevTogR9++AFXr17F\nypUr9RIX0WjTXNpiYWGBFi1aICQkBO3atcO2bdtw9uxZ2NjYwMXFBW3atMGkSZMwYMAAZGdno6Cg\nALa2tnB1dYWNjQ26du2K559/Hm+++SbS0tJw9+5dhISEwN/fH66urhgyZAiOHz+OevXqoU+fPmIZ\ne3t7jBw5EocPH0ZeXp6ovfbaa7C0tMScOXOQlpamor3yyivIzs7GBx98gLS0NNy6dQuBgYF47rnn\n8Prrr8PMzAwNGjTAxo0bce/ePfTr1w9du3aFhYUFrl+/DqVSifr16yMwMBC2trYIDg5Gv3790Ldv\nX/z9998qbZWVe/3117F161axLXt7e0yYMAHDhg3DG2+8gV27donlOnbsCG9vb1hYWGDbtm3w9PSE\nra0tJkyYgC5dukCpVCIqKgojR45Ely5d4ObmhrFjx6J79+7Izs5GUFAQxo0bh4CAALi5uWHmzJlo\n2rQpevXqhaZNm+KLL75At27dMHbsWPTo0QNXrlzB888/j2nTpiEwMBCurq5488030a9fP9y6dQvt\n27eHlZUV3NzcxLZ69OiB7OxsvPjii/joo48QFBQEV1dXhIeHo2vXrrh16xY++eQTvPHGG+jUqVMl\nGzt06ID//Oc/8Pb2hqurK6ZPnw4nJyfMnDkTYWFhGDlyJHx8fPDOO+8gLCwM77zzDtq2bYtbt26h\nsLAQ1tbWcHFxgYeHB0aPHo2BAwfi0qVLyM/Ph62tLVq0aAEXFxfxfLxx4wbu3r2rovXv3x8lJSVo\n0qQJ7t69i2bNmuHVV19FSEgIPD09sXHjRvj4+KBdu3ZwcXGBl5cXRo0ahVatWmHHjh04f/682voE\nQUBhYaF47rds2RLDhw9Hv379kJmZiby8PLU2ZmZmIjc3l18zerxm3n//fUyZMgVvv/02/Pz8xC8N\ndnZ2mDRpErp06YJr165VumbkdD298v/tnXdYVNf6tp+hDdMoIzgIMygCgiCIWCB2wjkKalQEjEos\nCRJ7jcZjjl7Bg8ZEE1tiIRoS7AWxoCiigoJiDUgTEKSI9CpV2vv9kY99ifQiMT/381fkvtba937X\nLpld1raywtChQ2Fvb49x48ZBS0urS47Z7+3JBADU1NRARBAKhbC0tERgYCCSkpJgYmKC4OBglJeX\nw8zMDMOGDUNgYCASExNhZmaGY8eO4dChQ6isrASXy8WrV6+grKyMOXPm4LvvvsPevXuRl5eHjIwM\n9OzZEyNHjsSRI0fw66+/ori4GEpKSigpKYGioiLDvLy8IJVKmevmbzJvb2+oqakx11ffZAcPHkR2\ndjbKysrw6tUrcLlcxmP//v0QiUTIz8+HRCLByJEjmev/hw4dQmpqKl6+fNnA8eDBg8jMzER5eXmj\nZf3666/Iy8tDbW1tA7Zr1y788ssvyMnJwbBhw7Bt2zaMGDECampquHHjBh48eIDAwEDo6uqib9++\nMDAwAJfLxcOHDxESEoLg4GAMGDCAYUpKSrh69SpOnDiBgQMHgsfjYdy4ceByuXjw4AFCQkKQmJiI\ngQMHom/fvjA0NASXy8X9+/cREhKCgIAASKXSBsuqbxcfH9+o3aNHj5ptV+/4JjM0NISioiL8/f1x\n4sQJEBGqq6sZx7CwMFy6dAmVlZUYMmQIEhIS8Pz5c5iYmGDPnj3Yt28fqqqqYGZmhoSEBCQlJcHU\n1BR79uzB3r17AQCDBw9uxDw9PWFsbMz0mZSUhBEjRmDTpk04efIkrKyskJCQgLy8PGYbDg4Oho+P\nD6RSKZ48eYLc3FymvwMHDkBLS6tBf/Vs3759eP36NQYMGNAkq6ioaMDq123v3r2oqanBwIEDkZCQ\nwO4z7dhnhg8fDnV1dQQEBOD69euIjY3F4MGDGfbm/vT2PtPS/lT/66f+/om1tXWj/aIr96fr16/j\n8uXLSElJQWlpaYNfJVevXsX27dubZK3lvXgDvqVwOBzU1NRAS0sLGzduhIKCAlxcXLB69WqIRKIG\nTFFRERMnTsTOnTthZmYGIsKXX36JlJQUaGlpYfv27di6dStMTU1BRLh8+TISEhKwbds2fP/99w3a\nJCcno1evXgyrnyiyKWZiYtJsuwEDBjTr0b9//wYely5dwtq1a7F161bm5uHbjm/29/aymvPfunUr\nzMzMoKGhgX379uG3334D8NfDDTdu3MCyZcuQm5sLZ2dnbNu2DQAQHR2Ns2fPNmJ1dXWIi4tDbm4u\n6urqcO7cObi6usLLy6tBm7y8vDb19y5YXV0d4uPjGUcfHx/GMSYmBmfOnMGgQYNQU1OD//znPzAw\nMGBuuO/YsQNmZmaN2MSJE7Fjxw6Ym5s3y0xMTJpkO3fuhLGxMUQiESIiIhATEwMXFxcsW7YMP/30\nE8zMzBqw+v5MTU2bXVZLjk2x+nV725/dZ9q+z3A4HERGRiIwMBAWFhZdsj/dvn0bZ8+eRf/+/ZGb\nm4uZM2c22aar9qf67d/CwoJZby8vLwB/XQ5btmxZk6xN6fRdly5O/WOYRH89ivg227JlC2loaDAv\nFr3NVFRUaMaMGczf7969SwKBgLhcLvXt25dmzpzJsPXr1xMA0tTUbNAmLCyM+Hw+cblc0tfX7xLW\nFo/evXvTjh07aMmSJe/UUSwWk6urK/Xt25emT59ORESvX78mR0dHUlJSIgsLC5JKpeTs7NyAKSoq\nMmzGjBn0+vVrWrJkCU2YMIEUFBRITU2tUZuW+nsXrC2O9S/SvVkPBwcHEolETdaqI0xdXZ1hb451\nWFgYKSsrk0AgaDTW9YzL5XaZR0cZn88nJSWlRtvqN998QwBIQ0OjURuBQMC06SrWmoeurm6jOr4L\nxzf3mabG2s3NjfT19RvtT4qKijRo0CCSyWQNttVp06aRkpIS6enpkZqaGkVGRrbapjNMSUmJYU2t\n22+//Ua+vr6Ntsd61pa8F5e54uLiUFJSAnV19QbzzMjJycHb2xv79u1j5ti5ceMGtm7dCgsLiyaZ\nk5MTIiIiAACHDx+Gq6sr5OXlcePGDbi5ueHx48e4f/8+Jk+ejNLSUty5cwe1tbXg8XggIuzbtw+L\nFy9mrpXOnz8fBQUFKC0tbTer/yxmWz3k5ORgZWWFgICALndMSUnBmjVrwOVyYW5ujkOHDmH27Nko\nLi5GUlIS3N3dMXLkSEyaNAnXr1/H7Nmz8fTpUwQGBuLQoUPYsWMHhg8fjnPnzmHevHkIDQ2Fj48P\nLCws4OTkBEtLS4SFhYHP5yMrK6vJ/goKCppdVkdZRxx5PB5yc3OZekyfPh2enp74/PPPUVxc3KBW\nbWUJCQm4ceMGdu3ahVmzZmHcuHHYvn07XF1dcf/+fRw+fBi5ubkwMTHBq1evkJOTA2tra1y8eLER\ne/HiBfh8fpc7tsV/586dcHFxQc+ePZlt9e7du/D29kZubi4kEgmioqKY7fFNR3Nzc4SFhcHV1bXJ\nba4t7OnTp+3yqN9nLl68+M4d6/eZoqIilJaWYv/+/Vi0aBGUlJSwa9cuzJkzB0VFRSgqKsKpU6dw\n8OBBGBsbw9fXF3PmzEFKSgpqampw9epV7NmzB0ZGRjh37hz69euHnj17NtmmoKCg2f5aYyKRCD4+\nPtizZw+MjY1x7tw5zJ07F3l5eXB0dASHw4FUKsWYMWOwdu1aGBgYID4+vklWf5+vpfztJxM/Pz+M\nGjUKfD4f2tra0NTUBIfDAYfDwS+//IIFCxagd+/eMDc3h1QqxdixY6Gjo9OAicViEBGcnZ2RnZ2N\nzZs3w9/fHw4ODtDS0oJUKoW6ujrS0tJw9epVZGZmYuDAgdDW1sayZcsgEolw7do1HD58mOnP3Nwc\n6urqMDIygr+/Pzw9PdvFKioqcOzYsVY9Jk6ciJ49e8LGxgY6OjqIiIh4J47Hjh3D6NGjUVFRgYcP\nH2LatGkYMGAAc1/ho48+QmVlJYKDgzFt2jSUlpbC398fsbGxMDY2xpkzZzBlyhRoa2sjISEBQUFB\nqK6uhkQiwZkzZ7BlyxYoKyvj2rVrOH/+fKP+DAwMml1WR1lnHH19fTFmzBiUl5fj6tWrcHJygomJ\nCVOr9rCqqir4+/sjJiYGZmZmOH36NMaNGwepVIqUlBTcvHkTRARDQ0McOnQIq1atgo6ODp48ecKw\nnj17wtvbG7/++isEAkGXO7bXf9asWejVqxdevHjRwPH69es4duwYevbsyTjWb1ePHz/G1KlTme3q\nzW2uLaympqZNHoaGhrhy5Qrc3d2hr6/fqI7vwvHtfebNfc3MzAzq6uro168f/P39cfDgQUgkEmho\naEBfXx9isRgFBQU4duwYrl69iqFDh6JXr16wsbEBANy8eRNHjx5t1EZXV7fZ/lpjDx8+xJ49ezBn\nzhwIhUKoq6tDXV0dMpkMCQkJOHHiBGbMmIHk5GTIyclBKBRCQ0OjAQMAqVSK8vJyVFdXw8LCosVj\n+d86BX1JSQm+/fZb8Hg8lJWVQUNDA05OTjA2NkZJSQlcXV3B5/OhpqaGHj16wNnZuRHLzc3F48eP\noaWlhcGDByM3NxeqqqoIDAyEgoIC+Hw+evTogYqKCuTk5GDcuHG4fPkyXr16BSMjI4wYMQKZmZmI\njY1FaWkp6urqQETo1asX5OXlIZFIIBQKERoa2mZWv27Dhw9v0SMoKAg1NTUQCATvzFEsFqOkpATF\nxcUQiUQoKSlBfn4+NDU1oa2tjdLSUhQVFTVgampqKC4uxsaNG3H48GFUV1cjJSUFvXv3Rm1tLdLT\n07F161bs378fSUlJ4PF4+Ne//oWSkhI8fvwYampqbV5WR9n74thWD19fX+Tk5CAtLQ26urpsHf+h\ndezduzdEIhFCQkLatB9qaWmBw+EwxwM/Pz9UVlbC0NAQI0aMQFZWVqP9WktLCwoKCs321xrT0dHB\nmjVrsGbNGgBARUUFBAIBqquroaOjg4sXL2LcuHEoKipCZWUlBAIBqqqqGjA+n4+TJ09CWVkZGzdu\nRFlZGXbs2NHi8fxvvQEvEAiwcOFCbNmyBQsWLMDz58/h4+ODmJgYCAQCbN68GX/88QdcXV2RnJzc\niO3btw8FBQUYOnQoHBwc0KdPH/D5fIwZMwb+/v7Iz89HbW0t1NXVkZ2dDQsLCwwdOhQGBgbQ0NBg\nnt0ODg6GnZ0dEhMTUVNTA3l5edTV1SEtLQ1CoRDm5ubtYunp6TAyMmrRY+TIkfDx8UFBQQHq6ure\nmaOKigoGDx6Me/fuIS0tDTo6OuByuXj58iVUVFRgaWnZiGVnZ8PQ0BAKCgoIDg5GZmYmFBUVoaio\niLy8PJiamqKqqop59LX+yZLQ0FCMHTu2XcvqKHtfHNviAQA+Pj7IysqCgoICW8d/cB0FAkGz+5pA\nIMDAgQMbMCJijgfW1tYwMTFhrqSEhYUhODgY9vb2jdq01F9rjMfjISEhAcHBwUhLS4NEIoGenh4y\nMzPB4/GYDxC+ePECEokEffr0acCuXbsGHo+HefPmwcXFhXmApbX87Ze5xGIxOBwONDU1YWhoiEuX\nLiE7OxsDBw7EnTt3IBQK0a9fvyYZl8vFlStX4OLigoiICAgEAowePRpHjx6FiooK0tPToaqqCplM\nhu+//x7FxcWIjIzEy5cvsXHjRmRmZkImk8HFxQV3795FdXU1srOz4ejoiKKiIowZMwZ2dnY4d+5c\nu9jo0aMxefLkVj20tbURHx8PFRWVd+Y4ceJEXLt2DXV1dcjIyMCnn36KmpoaODo6wt7eHgEBAcwG\nX8+cnJzg4OCA48ePo6qqChkZGZDJZLC2tsb27dtRWVmJyMhIJCUl4YcffkBRUREGDRoEFxcXhIeH\nN+qvpWV1lL0vjm3xqL+2//z5c7aO/9A6FhUVwc7ODhMnTsSFCxdQU1ODzMxMODo6IiMjA8OGDcMn\nn3yCCxcuoKqqCtnZ2bCxsUFRURFsbGzg4OCAo0ePIjs7G5s2bUJiYiIkEgnmzJmDe/fuMW1a6q81\nVr/P29vb48iRI6iqqkJmZiY+/fRTjB07FosWLYKenh5u3bqFuro6JCYmYvr06Y0Yh8PB+vXroaur\ni379+mHp0qUwNjZu9Vje7SeTc+fOYcOGDfj000+Zz/LKycmhrq4Od+7cwc2bNyEUCuHp6QlPT0/M\nnTsXYrG4STZ//nwkJydj7969WLduHeLj43H16lWEhYVBSUkJhoaGCA0NhaGhIa5evYqLFy9i4sSJ\nyMnJQVhYGF6/fo1+/frh7t27uHv3LpSVlTFlyhScPn0atbW10NXVRUBAQJuYrq4ujh8/js2bNzP3\nRFrysLe3R1RUFGQyWbc5Tp06FV5eXkhJScHs2bNRWlqKo0ePAgBkMhk8PT0xatQoDBs2DK9evcLx\n48cBALq6urh8+TKcnZ1RU1OD/fv3Y+zYscjKysKVK1eQmJiIhQsXgsPhMP1Nnjy52WV1lL0vju31\nGD16NCIjIyGRSNg6/kPrGBcXh6ysLCQlJSEsLAyKioqYOnUqTp06hdzcXCQmJiIoKAjR0dHg8/no\n3bs3jh49Ch6Ph8zMTNy8eRMxMTHM/YysrCykp6fj4cOHTBsHB4dm+2uNhYSEYNGiRUhNTWX2eS6X\nCwcHB1y5cgVOTk4wNTWFtrY2cnJycO/ePdjb2zfJQkNDMXHiROjo6MDAwABisbhtB/c2PfPVRXn8\n+DEZGBiQnp4ejRo1ivl7dXV1AyaVSkkikVB0dHSzLCIigu7fv888WterVy+aP38+SSQSCgsLI3Nz\nc5JKpaSurk4CgYA0NDQoIiKCSktLycTEhFRVVUlFRYW0tLRIIpHQw4cP6aOPPiJvb28Si8XtYmZm\nZiSTydrsER0dTUVFRd3q+CbjcrmkqKhIQ4YMaeCvrq5OIpGINDU1aciQIYy/TCYjHo9HcnJyNGDA\ngEaOb/fX0rI6yt4Xx/Z6vDnWbB3/eXW8efMmSaVSGjBgAInFYtLX1ydbW1vq06cPwxwcHJg5l117\nwQAAG75JREFUumxtbUlXV5dMTU3J1dWV5s6dS7a2tsTlcumrr74iS0tL0tLSatSmpf5aY/fv3yd3\nd3davHgxc+x5+PAhDR8+vAGLjY0lIqLS0tJmWWVlJQ0fPpwSExPbfXzv1nsmVVVV+O677/D8+XOI\nRCKMHDkSAKCgoICKigps2bIF4eHh4HA46NWrF0xNTZtlAwcOREFBAb799ls8fPgQAoEAhw8fhru7\nO5KSklBWVob169fj5s2bkJeXx+vXr5Gbm8v8/Fu3bh1WrFiBoqIiCIVCxMfHM7Nuuru7t4uVl5fj\n66+/brNHZmYmLl261K2OZWVl4PP5cHd3xyeffILa2loUFxcz/mvXrmX88/PzsWTJkgb+Hh4ekJeX\nR1paWiPHt/traVkdZe+LY3s93h5rto7/rDpqamrCxMQEMpkMlZWVWLNmDaytrVFYWIhHjx7BxMQE\nOjo6CAkJgbu7O6ytrVFUVAR5eXnMnj0bMpkMcXFxWLx4MYKCgpCTkwNjY+NGbVrqrzUWFhaGzz77\nDGKxGAEBAXB3d0d8fDxKS0uhpaWFzz77jLkpf/36dZw/f75Z5uPjg8rKSqiqqrb7+N7tT3MVFBQw\nP5smTZqEwsJC3LlzBwAQFRUFMzMzVFVVYdq0aQ3YkSNHoKWlBS6Xi23btqGwsBATJkyAVCqFqqoq\namtrsW7dOhQUFGDkyJGQyWSQl5fHJ598gsrKSmzcuJG5GeXq6gqJRAI+n49Hjx7By8sL1dXVmDlz\nJmxtbZkNrD1s2LBhzXoYGBjA2toaqamp2LhxI/Lz8/8WR1tbW1RVVaGmpgb379/H6dOnG/hraGig\nsLAQGzduRHFxMePI5XIxduxYFBYWYufOnQ0cpVJpk/21tKyOsvfFsSUPY2NjmJiY4NmzZ/jpp58a\njDVbx39mHU+cOIGYmBiMGjUKGRkZqK2tRU1NDYRCIfh8PiIiIvDvf/8biYmJDIuLi4O6ujo2btyI\nwsJC+Pr6IjIyEkZGRtDW1m6yTUv9tcZWr16NzMxMPH78GDExMeBwOJDJZLC1tUWvXr2QkZGB8PBw\nPHnypAGbNm0a8vPz4efnh7Nnz4LD4cDd3R2WlpbtPrZ3+z0THo/H3CeZNWsWfH19cfjwYdTW1uLk\nyZOwsbGBQCBowMLDw7F582bU1NTg1q1bEAqFAIDk5GTU1dVhw4YNUFRUhKqqKrKysiAWi6GtrQ0v\nLy9mA9HW1oZYLIaKigp4PB42b94MeXl5ZGRkQF9fH5qamsjPz0dlZWWHWHMednZ28PX1xcGDByEv\nLw+hUPi3OVZWVsLd3Z15kett//Xr10MgEDAzsdY7Hjx4kPlex9uOzfXX0rI6yt4Xx+Y87OzscPr0\naXh7e0MgEDQaa7aO/6w65uXlobKyEqdOnYK6ujpiY2MhlUrh4+MDGxsb7Nu3DwkJCeDxeIiIiIBU\nKsXp06chLy+PU6dOQSQS4fLly3j9+jWOHz8OR0dHeHt7N2rTUn+tMVtbW+zduxcnTpyAnJwcXrx4\nAWVlZZw8eRKTJ09GSkoK3NzcQERITU1twOqnu+Hz+bCwsMCUKVMwc+ZMSKXSjh3cu+BWSIt5c3qU\nN/9dV1fH/LdMJiOxWEzh4eFNMiUlJfr222+JiKikpITGjh1LEydOJJlMRoqKiuTu7k6XL1+mjIwM\nGjt2LCkrKzdoc/bsWRo5ciTTpp75+/tTRkYGjRkzpt3s5cuXbfIQi8Vkb29PXl5e3e7YFf6sIzvW\nH3IdFRUV6dtvvyVvb29atGgRiUQiGjNmDP3xxx/Uo0cP2rVrF3l7e9OCBQtIRUWFRo8eTdbW1tS3\nb18KDw+nKVOmNNumpf5aYyoqKgzT0NCg3bt3k7+/P2VmZjI1rq2tJRcXF/Ly8mrE3oy/vz8VFxd3\n9lD/7u6ZJCYmIisrC/n5+czf6qfTDgoKQlxcHPLz83Hr1i1wuVwEBQXBwsKiSbZkyRJmJszMzEwE\nBQUhMzMThYWFWLZsGUpLSxEYGIiXL1/C3d0dHA4H+vr6kMlkCAoKQkhICHbu3InMzEwUFRVhyZIl\nkMlkOHv2LKKiouDj49NmxuVy4efnh6dPn7bJY8SIEbCxsUFsbGy3OXal/4fsyI71h1XHY8eOITIy\nEsHBwcjIyGjArl27hsmTJ2P16tWIi4vDunXrMGvWLOTm5iIsLAxubm5YsWIFEhISEBUVhc8++wyp\nqano06cPPv/88wZthEJhs/21xqKiouDq6opVq1YhPj4e69atg4uLCwQCASIiIlBeXo6goCBUVlbi\niy++wMCBA1FXV9eIzZs3jzkuR0REIC8vr9PH/Hdymevy5ctYunQpQkJC8PLlS1haWkJBQQHy8vLY\nunUrli5dyky33atXLyxZsgQmJiYNWHp6OmQyGZYsWYL4+Hh4eHjgzJkzKC8vh4WFBUxMTJCVlYXC\nwkIcPnwYRISqqiqmza1bt3Ds2DEcPXoUSkpKKC0txYwZM5gppTds2ICIiAikpqYiPT29TUwgEGDn\nzp149uwZFBUVm/XgcDhQU1PDihUrcO/ePSQlJXWbY1f4s47sWH9odXR3d8ejR48QHh6OsWPHYujQ\noXj58iWsrKwYFhERAXd3d6xduxbh4eHQ09PDli1b8PTpU9jb2+PLL7/E7NmzERwcjICAABw5cgSl\npaX4/vvvmTZDhgxptr/W2ObNm1FaWgpnZ2eMHz8eX375JSIiImBpaYkdO3bAw8MDhoaGKCkpgbOz\nM86ePQt9ff0GDPjrceezZ8/C2NgYEokEo0aNgrq6eqeP+13+pcWAgAD897//xcGDB6GkpAR3d3e8\nfv0aXC4X165dw+bNm7Fx40bY29vD3d0dixYtgoqKCq5cucIwZWVlbN++HVFRURg0aBDOnz8PTU1N\nFBcXIyQkBGlpaVBUVER6ejqePXuGyZMn4/r163j+/DmioqLwxRdfQE5ODqWlpeByuRg2bBh+//13\nREVFoaysrMF3DdrK0tPTkZiY2KrHrVu3UFFRgUePHkFRURHx8fHd5tgV/qwjO9YfWh1NTU3B4/EA\n/HX/YMSIEbC2tkZFRQUMDQ3B5/NRUFAAHo/HsLKyMlhZWUEoFKKsrAwzZ87EiBEjsHTpUvTq1YuZ\n5PXtNi311xrjcrmIiorCjRs3EBwcDADIz8+HmZkZ1qxZg9GjR+Pbb79lWE5OTgN2+/ZtGBkZQU1N\nDQoKCnj16lXXHvw7faHsrezevZsuX75MRETZ2dmkr69Pjo6OtGbNGlq7di3zHeisrKwmWXx8PGlq\napKOjg6Zm5uTrq4ujR49moqLi2ndunUEgHnnhMvl0scff0wREREkFApJQUGBtLW1SUdHh7hcLv38\n88/k4eFBcnJypKqqShKJhJSVlcnU1JTOnDnTLqagoEDDhg1r0aOsrIy+/vrrv82xs/6sIzvWH2Id\nJ06cSHl5eWRlZUVycnI0cuRIUldXJ6FQSHZ2dvTixYtGTCAQ0Pjx48nR0ZEEAgGpqalRjx49yNjY\nmIRCId25c6dd/bXG7O3tKSsrizw8PEheXp5WrFhB9vb2JBaLae7cuVRWVkYeHh6koKBAK1eubMQO\nHjxI/fv3pz179tD//vc/MjY27pLvvr+ZLr/MZWVlBUNDQ1RUVGDu3LmYOnUq5syZg5KSEuTl5WHe\nvHmora3FvHnzmmQvXrzAmTNnsHz5cqxcuRIcDgfPnz/HkydPEBERAQ6Hg+nTp6OgoAD9+/eHpaUl\n/Pz8kJWVBScnJwwfPhwvX76EhoYGeDweJk+ejHPnzmHs2LHQ1dXF0KFDUVxcjMrKynYxKysr5OXl\ntehx+fJl5l2Yv8Oxs/6sIzvWH1odhwwZgrS0NMTFxcHKygrJyclYuHAhnjx5wrwTEhcXh8GDBzOs\n/h6DsbEx8vLyIBKJsH//fkRGRuLJkycYMWIE8vPzG7Rpqb/WWG5uLoyMjBAbG4tp06YhJiYGH3/8\nMcrLy7Fs2TJUV1fD398fzs7OiI6Oho2NTSO2bt069OvXD69evUJCQgJ++uknGBkZdeWhv2smekxI\nSEBWVhaAv26yA3/9vDtw4AAcHBwglUphY2OD1NRUyMvLQyAQNMsGDx4MFRUVEBGGDBmCzz77DHw+\nH1wuFwsXLoRQKISqqioOHDgALpeLCRMmoFevXuDz+ZBKpVixYgWOHj0KsVgMeXl5lJWVQSqVwtzc\nHHv37sXq1avB4/HaxDQ1NWFlZYWzZ89i5cqVrXpwuVwsWLCgWx270v9DdmTH+sOs41dffcWw6upq\nlJWV4eHDh6iqqsLMmTMRExODkpIS1NTUMPPmERGmT5+O2NhYKCsrIy0tDWfOnEFWVhZcXFyQmprK\ntGmpv9ZYUlIS6urqMGPGDMTGxqKkpAQFBQWorKxEXV0dDhw4gOnTp2PSpEmQk5NDbm4uKioqmmS3\nbt3C+PHjsXDhQhw4cICZOLMr0+lfJhcuXMDMmTORlZUFMzMzqKmpoba2FhwOBzdu3GBYVVUVnjx5\ngilTpoDH4zXJJkyYgKCgIJw+fRpxcXGQk5NDdXU1Hj9+jL59++L8+fNITk5m5snJzc2Fq6srEhMT\nmU94/vnnn8xb33p6erhz5w6sra3x448/toudOHGC+T+WtngsXLgQMTEx8PHx6TbHrvT/kB3ZsWbr\nqK+vj+fPn6OgoACBgYHo06cPDh06BC6Xi9raWty5cwdJSUnMd028vb1RWloKkUiEP//8E/fu3WvU\nJi0trdn+WmMcDof50JWnpycqKipQW1uLBw8eYMWKFbhy5Qqqq6sxYMAAaGtr49GjR7h161aTLDw8\nHDdu3MDUqVMB/PUp9HeRTr0BX1xcjFmzZqF///7o2bMnsrKysHLlSujq6qKoqAguLi7o378/4uPj\n8eDBAxw5coSZR/9t5u3tjY8++ggzZsyAqakpXrx4wcxgefXqVdy9exdbtmyBk5MTgoODER8fj7lz\n5+LAgQP4+eef8cMPP8DU1BSxsbEoKirCmTNnkJOTw3wXBPjr28htYSkpKfDw8MD06dORkZHRqsfn\nn3+OX375BZ6ent3m2JX+H7IjO9ZsHeuZn58fnj17Bh6Ph6qqKqioqMDLywsBAQHw8PCAubk5MxOv\nk5MTNm3ahD179mDTpk0wNDRETU0N0yYkJKTZ/lpjAQEBGD9+PJ49e4a0tDRYWlpi6dKlOHbsGK5f\nv46DBw/i1KlTuHHjBgYPHoylS5fi6NGjTbLly5fj8OHDuHnzJjw9PcHlcjt6uG89nb3pkpSURMXF\nxXTv3j1av349rVq1ip4/f86wjIwM+uKLL2j+/PmN2MOHD2nevHk0d+5cWrVqFUVGRtL48eMpJiaG\nDh06RCYmJqSmpkZ2dnako6NDw4cPJz8/P5oxYwaNGzeONDU1ydbWlng8Hv3+++90/fp1GjduHKmr\nq5OBgQFJJBKytbWlkJCQdrPhw4e36PHo0SOaOnUqjR8/njQ0NP4Wx874s47sWLN1bMxu3bpFoaGh\ntGPHDtLU1GywbgEBAeTn50fOzs4NHA8dOtRkm5b6a40lJydTREQE+fv7k5mZGTk6OpKOjg7Z2tpS\nWloaRURE0JUrV8jc3LwRy8vLoxs3btCgQYPI0dGRZDIZRUREdPZQ32o69Ghw/Vw41dXV6Nu3L4C/\nbrwDf33vfOfOnfDw8EBBQQH09fVx6NAhPHjwoAG7cOECM/10bGws+vfvD09PT/z0008ICAiAp6cn\njIyMUFlZCSUlJQQFBcHLywtr1qzBqFGjkJOTA1tbWygpKcHX1xdPnz7F999/j1GjRqG6uhoCgQBa\nWlr48ssvERoayiyrJVZSUgIdHR2oqKhg9erVzXo8efIEz549Q3x8PPr16wcVFZVuc+wKf9aRHWu2\njg2ZRCLBggULEBISAk9PT4wZMwYGBgbQ0dGBSCTCkCFDsHDhQowcORL5+fmwtbWFoqIifH19ERsb\niy+++AJjxozBgAEDmu2vNdazZ0+oqqpi+fLl8PX1ZVi/fv3g5uaGvXv3orCwEE+ePMG6deswYsQI\nGBoaNmAymQwA8PHHH+P+/ftITU2FSCSCRCLpop8fLaS9Zx8/Pz8yMTEhNzc3cnZ2pqdPnzZgenp6\nNGzYMBKLxSQQCCg1NbURU1NTIw6HQydPnqQjR46QRCIhLpdLhoaGZGJiQnJycnTy5En68ccfSSwW\nk4KCAolEIuJwOHT8+HHKysoiJycnkpeXJzMzswb93bt3jz7//PN2sx9//LFNHqqqqsTn86lfv34U\nFBTUrY5d4c86smPN1rF961b/uLJAIKBHjx69E8ft27eTWCymVatWkaWlJVPHeiaRSCgqKorS0tJo\nwIABFBQU1IgREdXU1BARdWgK+c6mXSeTN1ckKyuLtm/fTlpaWo1Wcv78+aSmpkYaGhrNMmVlZTp2\n7Bjz9//973+kpKREcnJy9K9//YvOnz9PIpGILl26RFZWVsThcEhJSYkCAwMpNDSURCIRubm5EZ/P\nJxUVFVJWVqajR4+Sra0tmZiY0OLFi9vMdu/ezSxr06ZNrXqoq6uTo6Njtzp2pf+H7MiONVvH9q6b\noqIiAaBff/31nThOmjSJiouLaceOHSQQCEhbW5scHR0pLCysAdPW1qaYmBhyc3NrksXFxRERUUpK\nCn3yySdUXFzcaG7Ed5l2nUyqq6vJzc2NXrx4QbW1tUREtHPnTtLW1qbo6Ghyc3OjqKgoMjMzoz//\n/LMR8/Pzo759+9KtW7fI3NycVFRUaPr06fTixQsKCwujHj16kJGRESkrK9N//vMfmjdvHtNmwYIF\npKamRioqKhQdHc0wsVhM48ePJzMzM1JVVaX79+8zy2orU1FRIQcHhxY9oqKiqF+/fuTk5ESDBg3q\ndsfO+rOO7FizdWzfugUHB9OlS5dIIpGQSCR6Z46PHz8mIqL79+9Tjx49aPz48TR16lTatWsXvXr1\nioiI4uLiaNGiRTRjxoxGjIjohx9+oDlz5lB5eTkRUQPWXWnT01yJiYnM/Y/FixfD0tIS69atY9ju\n3buRm5uLmpoaDB06FCtWrICysnIDlpqaisTERJiYmEBPTw8VFRXw9fWFiooKxo0bhz///BNqamoo\nLCyEsrIyYmNjIZPJUFpaChMTE+jr6yMkJATJyckYOHAglJSUkJiYiL59+6K8vByDBg3C8ePH28UK\nCwsxaNCgVj3MzMwwYMAA3Lt3DxoaGsjPz+82x67wZx3ZsWbr2L51U1ZWBpfLhZmZGWQyGcLCwrrc\n0cbGBgcOHIClpSWWL1+OTZs2QU1NjWFeXl7Yvn07evbsiW+++QZisRilpaUN2KJFiwD89TmOrVu3\nwtPT8509+ttaWn3PxM/PDwsWLMDt27cRGxuLqVOnwsPDA+Xl5SgqKsKCBQuQnZ2N9PR0rFy5Eps3\nb0ZVVVUD9uLFC/D5fBQXF2PQoEG4desWsrKyIBAIkJOTg7i4ONjY2KB///4IDAxkvgmfl5cHOTk5\nTJo0CcnJyQgPD4dQKERKSgrzkpG5uTni4uKQlJQEBQWFNjNNTU3Exsbi1atXEIlELXpkZWUhOjoa\nH3/8MYyNjbvNsav8P3RHdqzZOrZn3YRCIfLy8qCgoICJEyciNTW1yx3T0tIwatQoKCgo4M6dO0hI\nSMCGDRswaNAgXL9+HaNGjYJQKMT+/fsRHR2NDRs2wMLCogE7cOAAlJWV0aNHD4SGhuLChQtwdnZm\n5hnr9rT0s+XOnTtkbGzM/AybP38+/fe//6WXL1+SpqYmaWho0Pnz5+n3338nDQ0NWr16dZOsR48e\n1Lt3b1q2bBkpKCjQggUL6I8//iB9fX3i8/mkqalJioqKpKCgQDt27KA1a9aQQCAgFRUVGjJkCCkr\nK5OCggItXryYZs6cScrKyiQQCMjKyork5eVJTU2N3N3d28xMTEwIADk5ObXJw9LSkiQSSbc6dqX/\nh+zIjjVbx/aum56eHsnJydHAgQPfiaOioiItWrSI/vjjD5o5cyb17NmTtLS0aPTo0TR48GAKDw+n\nwMBA2rlzJ61atYq0tbWbZGvWrKE5c+aQm5sbDR48mCIjI7vhYlbzafGXSVpaGoyNjTFhwgQAgLW1\nNQ4cOICFCxeid+/eeP78OV69eoVz587h0KFDuHjxYpPst99+Q1hYGLKzs2FnZ4eqqipkZGQgLi4O\nXC4XBw8exNOnT6GqqgqhUIjQ0FDMnDkT0dHRMDAwwIwZM1BeXg5NTU0EBwdj9OjRUFJSgkQigYOD\nAyorK6GsrNxmJhAIYGtrCy6X2yaPhw8fYv/+/d3q2JX+H7IjO9ZsHdu7bvHx8ZCXl0f//v3fieOa\nNWuQnJyMjIwMBAcHY8yYMfjmm28QHR2Nr7/+GqNGjYKuri6ePXuG9PR0rFixokmWlpaGXbt2Ydq0\naXB2dkbv3r3f+Y+PltLi3FzW1taYNm0aAKCmpgZVVVV4+fIlMjIy4OjoiH379uHnn39GQEAADAwM\nWmSVlZWws7NDRUUF7OzssHv3bly9ehXFxcUgIsydOxdGRkZwdnZGQEAAli5dirKyMujp6SEtLQ0L\nFizA3r178ejRI2RkZKC4uBjW1tbIycmBq6tru5idnR3q6ura7JGXl9ftjl3p/yE7smPN1rEj6/b6\n9et35njv3j3Y2dlh7969iIiIYCawHD9+PM6dO4fbt29DQUEBc+fORWRkZIssLi4OALrkeySdTYsv\nLcrLy0NVVZX5t6qqKnr06AFtbW0cOXIEoaGh2LVrF9TU1FBRUdEi09DQwNKlS7Fy5Up89dVXyMzM\nhJKSEogIVlZWyMjIQE5ODrZu3cqcwIgIy5cvx3fffYeNGzeitLQUPB4PRUVFkEgkWLx4MVauXNlu\n1hGP7nbsav8P1ZEda7aOHV23d+n41VdfIScnh7k/07dvXxgbG4PD4eC7775DXFwclJSUWmU6Ojpd\ncBromrR5okc5OTlwuVzcvHkTDx48wLFjx7BlyxZIpdI2s8jISNy9exdr167F7du3kZ2dDS0tLTx/\n/hxnzpzBvn37MGzYMJw9e5ZhGRkZTJvg//85zW3btiEhIaFBfx1lbfX4Ox27wp91ZMearWP71q07\nHNPT0/HDDz/A0NAQPB4PFhYW0NXVhY+PT5vZe5O23lypq6ujyspK0tPTI5lMRvHx8Z1iVVVVVFZW\n1mSb5lh1dTVVV1c32V9HWUc8utuxq/0/VEd2rNk6dnTd3pVH/Rvrb6ej7O9Mu6dT8fLyoujo6C5j\nXd3fP92jo+x98fgnOL4vHh1lrMe7Z93t8X8h7Z6CnoiafSmmI6yr+/une3SUvS8e/wTH98Wjo4z1\nePesuz3+L6RT3zNhw4YNGzZsgC76bC8bNmzYsPmww55M2LBhw4ZNp8OeTNiwYcOGTafDnkzYsGHD\nhk2nw55M2LBhw4ZNp8OeTNiwYcOGTafDnkzYsGHDhk2nw55M2LBhw4ZNp8OeTNiwYcOGTafDnkzY\nsGHDhk2nw55M2LBhw4ZNp8OeTNiwYcOGTafDnkzYsGHDhk2n8/8Ac68TsO1tIe4AAAAASUVORK5C\nYII=\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "(<matplotlib.axes.AxesSubplot at 0x135079ac>,\n",
        " <matplotlib.axes.AxesSubplot at 0x1367216c>)"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Moving Average : this is the most used tehcnical indicator . This statistic\n",
      "indicator allows to observe trend in a price times series on a time interval. A\n",
      "moving average (SMA) makes it possible to avoid short time pertubations in\n",
      "price. The main parameters of a SMA is the time horizon that will be noted\n",
      "N. Hence, for each time n we have the following formula:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Latex\n",
      "Latex(r\"\"\"\\begin{equation}\n",
      "SMA_n^N=\\frac{1}{N}\\displaystyle\\sum_{k=0}^{N-1}x_{n-k}\n",
      "\\end{equation}\"\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "\\begin{equation}\n",
        "SMA_n^N=\\frac{1}{N}\\displaystyle\\sum_{k=0}^{N-1}x_{n-k}\n",
        "\\end{equation}"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "<IPython.core.display.Latex at 0xbba736c>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def PlotMA(price)    :\n",
      "    mavg_2 = pd.rolling_mean(price, 2)\n",
      "    mavg_5 = pd.rolling_mean(price, 5)\n",
      "    mavg_10 = pd.rolling_mean(price, 10)\n",
      "    mavg_20= pd.rolling_mean(price, 20)\n",
      "    mavg_50= pd.rolling_mean(price, 50)\n",
      "    price.plot(label='price')\n",
      "\n",
      "    mavg_2.plot(label='mavg_2')\n",
      "    mavg_5.plot(label='mavg_5')\n",
      "    mavg_10.plot(label='mavg_10')\n",
      "    mavg_20.plot(label='mavg_20')\n",
      "    mavg_50.plot(label='mavg_50')\n",
      "    plt.legend(loc=3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "We compute SMA2, SMA5, SMA10, SMA20 and SMA50 on the BTC Time series for the plateform MtGOX USD:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n=len(ohlc['close'])\n",
      "PlotMA(ohlc['close'][n-300:n-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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X2z1hMIC8IY3SwXKmWyvYWF9K9M1Kql4/+XnwOxAG3w2hEF8NBQ0dCC2BDEQd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\n"
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The next script make it possible to update a SQL database with the latest trade information for each plateform and each currency."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We establish connection with the SQL database\n",
      "sql_params=dict({\"host\":\"localhost\",\"user\":\"root\",\"passwd\":\"xxxx\",\"db\":\"historical_btc_trade\"})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "market_list_path='/home/cerveau2charles/Dropbox/Dan-Charles/Bitcoin/historical_trade/market.txt'\n",
      "with open(market_list_path) as f:\n",
      "    marketlist = f.readlines()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marketlist[0:20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "['anxhkCNY\\r\\n',\n",
        " 'anxhkHKD\\r\\n',\n",
        " 'anxhkUSD\\r\\n',\n",
        " 'aqoinEUR\\r\\n',\n",
        " 'b2cUSD\\r\\n',\n",
        " 'b7BGN\\r\\n',\n",
        " 'b7EUR\\r\\n',\n",
        " 'b7PLN\\r\\n',\n",
        " 'b7SAR\\r\\n',\n",
        " 'b7USD\\r\\n',\n",
        " 'bbmBRL\\r\\n',\n",
        " 'bcEUR\\r\\n',\n",
        " 'bcGBP\\r\\n',\n",
        " 'bcLREUR\\r\\n',\n",
        " 'bcLRUSD\\r\\n',\n",
        " 'bcPGAU\\r\\n',\n",
        " 'bcmBMAUD\\r\\n',\n",
        " 'bcmBMGAU\\r\\n',\n",
        " 'bcmBMUSD\\r\\n',\n",
        " 'bcmLRUSD\\r\\n']"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#This function make\n",
      "markets = []\n",
      "for line in marketlist:\n",
      "\tmarkets.append({\"market\":re.search(r'([a-z0-9]+)([A-Z]+)',line).group(1),\"curncy\":re.search(r'([a-z0-9]+)([A-Z]+)',line).group(2)})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "markets[0:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "[{'curncy': 'CNY', 'market': 'anxhk'},\n",
        " {'curncy': 'HKD', 'market': 'anxhk'},\n",
        " {'curncy': 'USD', 'market': 'anxhk'},\n",
        " {'curncy': 'EUR', 'market': 'aqoin'},\n",
        " {'curncy': 'USD', 'market': 'b2c'},\n",
        " {'curncy': 'BGN', 'market': 'b7'},\n",
        " {'curncy': 'EUR', 'market': 'b7'},\n",
        " {'curncy': 'PLN', 'market': 'b7'},\n",
        " {'curncy': 'SAR', 'market': 'b7'},\n",
        " {'curncy': 'USD', 'market': 'b7'}]"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The following function makes it possible to update the historical database of data which include history data for each available plateform and each currency.\n",
      "It works easily by download a .CSV and store each dataline in a temporary database. Then, we store  in the specific database (for a given plateform/curncy) the temporary row after having checked this row does'nt exist yet.\n",
      "Finally, we remove the temporary database and process the next file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def UpdateSQLDb(markets):\n",
      "    for l in markets:\n",
      "        symbol = \"\".join([l['market'],l['curncy']])\n",
      "        data=[]\n",
      "        # Download the file from `url` and save it locally under `file_name`:\n",
      "        url = \"http://api.bitcoincharts.com/v1/csv/\" + symbol + format_file\n",
      "        destination= \"/home/cerveau2charles/github/Bitcoin/historical_trade/{}{}\".format(symbol,format_file)\n",
      "        urllib.urlretrieve(url, destination)\n",
      "        time.sleep(5) # delays for 5 seconds\n",
      "        try:\n",
      "            f = gzip.open(destination, 'rb')\n",
      "            data = f.read(\".\"+symbol+\".csv\").splitlines()\n",
      "            f.close()\n",
      "        except: print('File : ' + destination+format_file + ' not available')\n",
      "        finally:\n",
      "            temp=[]\n",
      "            for line in data:\n",
      "                 temp.append(map(float, line.split(\",\")))\n",
      "            temp.reverse() # on inverse l'odre pour obtenir un ordre croissant (en fonction du temps)\n",
      "            try:\n",
      "                db=MySQLdb.connect(host=sql_params[\"host\"],user=sql_params[\"user\"], passwd=sql_params[\"passwd\"], db=sql_params[\"db\"])\n",
      "                for line in temp:\n",
      "                    cur=db.cursor()\n",
      "                    try:\n",
      "                        #cur.execute('insert into localbtcILS (Date,Trade,Amount) values (%s,%s,%s)' ,(datetime.datetime.fromtimestamp(line[0]).strftime(\"%Y-%m-%d %H:%M:%S\"),line[1],line[2]) 'where datetime.datetime.fromtimestamp(line[0]).strftime(\"%Y-%m-%d %H:%M:%S\") NOT in (select distinct Date from 'localbtcILS')')\n",
      "                        cur.execute(\"\"\"insert into Temp (Date,Trade,Amount) values (%s,%s,%s)\"\"\" ,(datetime.datetime.fromtimestamp(line[0]).strftime(\"%Y-%m-%d %H:%M:%S\"),line[1],line[2]))\n",
      "                        cur.execute(\"insert into \" + symbol +\" (Date,Trade,Amount) select Date,Trade,Amount from Temp where Date NOT in (select Date from \" + symbol +\" )\")\n",
      "                        cur.execute('delete from Temp')\n",
      "                        cur.close ()\n",
      "                    except:\n",
      "                        print ('Error to copy value into the SQL database'+ sql_params[\"db\"] )\n",
      "                    finally:\n",
      "                        db.commit ()\n",
      "                db.close ()\n",
      "            except: print ('failed SQL connexion to ' + sql_params[\"db\"])\n",
      "    print(symbol + ' updated correctly')\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 108
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "In order to process the previous function, we had to created a SQL database for each market/curncy. In order to precess this SQL query automatically we have written a python script which create a database for each of the 199 plateform/curncy mix. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# -*- coding: utf-8 -*-\n",
      "\"\"\"\n",
      "Created on Mon Feb 17 16:54:36 2014\n",
      "\n",
      "@author: charles-abner.dadi@graduates.centraliens.net\n",
      "\"\"\"\n",
      "import MySQLdb\n",
      "import re\n",
      "\n",
      "sql_params=dict({\"host\":\"localhost\",\"user\":\"root\",\"passwd\":\"*****\",\"db\":\"BTC\"})\n",
      "db=MySQLdb.connect(host=sql_params['host'],user=sql_params['user'], passwd=sql_params['passwd'], db=sql_params['db'])\n",
      "cur=db.cursor()\n",
      "\n",
      "for l in markets:\n",
      "    m = \"\".join([l['market'],l['curncy']])\n",
      "    #print m\n",
      "    try:\n",
      "        cur.execute(\"create table \"+ m + \" (id bigint not null AUTO_INCREMENT,Date DATETIME null,Trade bigint null,Amount bigint null,PRIMARY KEY (id))\")\n",
      "    except:\n",
      "        print (m + \"already exists!\")\n",
      "cur.close ()\n",
      "db.commit ()\n",
      "db.close ()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "199"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "II- Focus on two trading strategies to take profits from BTC market characteristics."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "a-What is Scalping and why it can be interesting for BTC?"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Usually, when we talk about scalping in finance we think about high frequency trader who take profits by making arbitrages into FX markets.  Nevertheless, there are no only high frequency traders who could make scalping. Actually, it is suffisent to take one position a day to be a scalper trader. In the BTC are high frequency trading is until now very difficult due to the lack of liquidity. Nevertheless, it is interesting to benefit from BTC inneficiencies to make profit. Actually, for a given currency, let say USD we could find very different Bid/Ask on plateforms. In other words, there is a wide difference in available prices and fragmented liquidity from one market. Hence, this discrepency make it possible obvious trading strategy which consists to take profit from the largest available spread from a market to another. Hence, for a given time t and a given currency, let say USD, we could BUY BTC at the smallest ASK and SELL BTC at the biggest BID. This strategy makes it possible to take profit from the spread minus fees for buying and selling. "
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "In order to build up a backtest for the scalping strategy we had to store in database the spread matrix for different trading plateforms for a given currency and then compute the largest spread which indicates what is the best plateform for BID and ASK in a given time period. Concerning the technical environnement we have decided to work with a noSQL database, using MongoDB, due to the flexibility to store a large variety of object (matrix, data, string..). We have written a script to donwload the lastest transfaction information for each plateform and each currency and then finding out the two platforms which maximize the spread BID/ASK."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "from pymongo import Connection\n",
      "from pymongo.errors import ConnectionFailure\n",
      "from datetime import date,timedelta\n",
      "from time import mktime\n",
      "import datetime\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "from bson.son import SON\n",
      "import json\n",
      "from PyQt4 import QtGui, QtCore\n",
      "import time\n",
      "import urllib2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\" Connect to MongoDB \"\"\"\n",
      "try:\n",
      "    c = Connection(host=\"localhost\", port=27017)\n",
      "    print \"Connected successfully\"\n",
      "except ConnectionFailure, e:\n",
      "    sys.stderr.write(\"Could not connect to MongoDB: %s\" % e)\n",
      "    sys.exit(1)\n",
      "\n",
      "# Get a Database handle to a database named \"bitcoin\"\n",
      "dbh = c[\"bitcoin\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Connected successfully\n"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The following function will download the last transaction for each plateform/curncy and store these data in a MongoDB database. It is important to launch this function at regular time interval in order to catch all transaction. We add this script to the crontab process in a AWS instance and it is launched every 30 minutes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "date_now=datetime.datetime.now()\n",
      "\n",
      "\n",
      "\n",
      "#Connecting to MongoDB with Python\n",
      "def UpdateLatestTransactionData():\n",
      "    path='http://api.bitcoincharts.com/v1/markets.json'\n",
      "    \"\"\"collect data\"\"\"\n",
      "    #we create an exception to handle a missing JSON file\n",
      "    try:\n",
      "        json_data=urllib2.urlopen(path,timeout=60)\n",
      "        data = json.load(json_data)\n",
      "        json_data.close()\n",
      "\n",
      "        \"\"\" ajout de la date d'update (date courante')\"\"\"\n",
      "        for t in data :\n",
      "            t['Update']=date_now\n",
      "\n",
      "        \"\"\" Connect to MongoDB \"\"\"\n",
      "        try:\n",
      "            c = Connection(host=\"localhost\", port=27017)\n",
      "            print \"Connected successfully\"\n",
      "        except ConnectionFailure, e:\n",
      "            sys.stderr.write(\"Could not connect to MongoDB: %s\" % e)\n",
      "            sys.exit(1)\n",
      "\n",
      "        # Get a Database handle to a database named \"mydb\"\n",
      "        dbh = c[\"bitcoin\"]\n",
      "        #dbh.last_bid_ask.remove()\n",
      "        #we export each JSON dictionary\n",
      "        [dbh.last_bid_ask.insert(t, safe=True) for t in data]\n",
      "\n",
      "    except:\n",
      "        print(\"The JSON file is missing or corrupted\")\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "UpdateLatestTransactionData()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Connected successfully\n"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The following function is to compute the intra market arbitrages matrix. That means, for a given currency it will compute all spread between bid of one BTC plateform and ASK from another and return finally the largest spread BID-ASK for each currency. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "date_now=datetime.datetime.now()\n",
      "currency=list(set(Curr))\n",
      "Plateforme=[]\n",
      "#Using the aggregate method to get the most recent transaction from the data. Actually, the latest BID/ASK may have different date between plateforms,\n",
      "#so we have to get the most recent transaction for each plateform.\n",
      "dico_cur=dict()\n",
      "for cur in currency:\n",
      "    doc_cur=None\n",
      "    doc_cur= dbh.last_bid_ask.aggregate([{\"$match\":{'latest_trade': { \"$gt\" :  int(mktime((date_now - timedelta(hours=12)).timetuple()))}, 'currency':cur}},{\"$sort\":SON([(\"latest_trade\",-1)])},{\"$group\": {\"_id\": \"$symbol\",'Bid':{\"$first\":\"$bid\"},'Ask':{\"$first\":\"$ask\"},'Date':{\"$first\":\"$latest_trade\"}}}])\n",
      "    data_cur=[]\n",
      "    for dc in doc_cur['result']:\n",
      "            data_cur.append({'Market':dc.get('_id') ,'Date':dc.get('Date'),'Bid':dc.get('Bid'),'Ask':dc.get('Ask')})\n",
      "    #we have built up the spread matrix which gathers spreads (Bid/Ask) for the whole possible combination\n",
      "    Max=-1\n",
      "    mat=np.zeros(shape = (len(data_cur),len(data_cur)))\n",
      "    #now, we compute the maximal possible spread.\n",
      "    for i in range(0,len(data_cur)):\n",
      "        for j in range(0,len(data_cur)):\n",
      "            mat[i,j]=float((data_cur[i]['Bid']/data_cur[j]['Ask'])-1) \n",
      "            if mat[i,j]>Max:\n",
      "                BidOpti=data_cur[j]['Bid']\n",
      "                AskOpti=data_cur[i]['Ask']\n",
      "                MarketAsk=data_cur[i]['Market']\n",
      "                MarketBid=data_cur[j]['Market']\n",
      "                Max=mat[i,j]\n",
      "    #we are updating the MongoDB collection:\n",
      "    mat=pd.DataFrame(mat,index=[mark['Market'] for mark in data_cur],columns=[mark['Market'] for mark in data_cur])\n",
      "    mat_to_dico=mat.set_index(mat.index).to_dict()\n",
      "    dico_cur[cur]=dict({'Bid':BidOpti,'Ask':AskOpti,'Date':date_now,'Matrix':mat_to_dico,'MarketAsk':MarketAsk,'MarketBid':MarketBid, 'SpreadMax':Max})\n",
      "\n",
      "\"\"\" Connect to MongoDB \"\"\"\n",
      "try:\n",
      "    c = Connection(host=\"localhost\", port=27017)\n",
      "    print \"Connected successfully\"\n",
      "except ConnectionFailure, e:\n",
      "    sys.stderr.write(\"Could not connect to MongoDB: %s\" % e)\n",
      "    sys.exit(1)\n",
      "\n",
      "# Get a Database handle to a database named \"mydb\"\n",
      "dbh = c[\"Scalping\"]\n",
      "\n",
      "\"\"\"we store data in the collection: 'Scalping' \"\"\"\n",
      "dbh.matrix.insert(dico_cur, safe=True)\n",
      "print(\"Matrix updated at \" + str(date_now))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Connected successfully\n",
        "Matrix updated at 2014-05-10 13:07:02.273618\n"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The previous script should be launched at regulary time intervals by paying attention to the database and internet pipeline. In order to make this process as independant as possible we have decided to launched this script by crontab in an Amazon EC2 instance."
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "In order to compute the backtest we need to extract the data stored onto the the Amazon EC2 hard disk partition. So, we have written the convenient script to perform this task and to compute the P&L of this strategy."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ExtractScalpint(currency,start_date,end_date,database,collection):\n",
      "    #start_date=datetime(2014, 1, 2, 13, 15, 14)\n",
      "    #end_date=datetime(2014, 5, 2, 16, 15, 14)\n",
      "    #database=HandleMongo()\n",
      "    #currency='USD'\n",
      "    #collection='matrix'\n",
      "    #database[collection].aggregate([{'$match':{'USD':{'Date':{'$gt':start_date, '$lt':end_date}}}}])\n",
      "    matrixs=database[collection].find({'USD.Date':{'$gt':start_date, '$lt':end_date}})\n",
      "    spread=pd.DataFrame(index=[t[currency]['Date'].strftime(\"%d/%m/%y %H:%M\") for t in matrixs],columns=['spread','Ask','Bid','marketAsk','marketBid'])\n",
      "    matrixs=database[collection].find({'USD.Date':{'$gt':start_date, '$lt':end_date}})\n",
      "    for (i,t) in enumerate(matrixs):\n",
      "        try:\n",
      "            spread['spread'][i]=t[currency]['SpreadMax']\n",
      "            spread['Ask'][i]=t[currency]['Ask']\n",
      "            spread['Bid'][i]=t[currency]['Bid']\n",
      "            spread['marketBid'][i]=t[currency]['MarketBid']\n",
      "            spread['marketAsk'][i]=t[currency]['MarketAsk']\n",
      "        except:\n",
      "            print 'issue'\n",
      "    #we delete aberant values\n",
      "    SpreadMax=spread['spread'][spread['spread']<1]\n",
      "    Ask=spread['Ask'][spread['spread']<1]\n",
      "    Bid=spread['Bid'][spread['spread']<1]\n",
      "    marketBid=spread['marketBid'][spread['spread']<1]\n",
      "    marketAsk=spread['marketAsk'][spread['spread']<1]\n",
      "\n",
      "    #count markets for bid and ask\n",
      "    Counter(marketBid)\n",
      "    Counter(marketAsk)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Computing this matrix at each available time interval matchs with backtest a scalping strategy. Actually, it will determine the profit to make buy at the lowest ASK and sell to the largest BID price. To compute the P&L for a such trading strategy we have to substract fees for each transaction."
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to compute his backtest and ploting P&L we need to export results from the MongoDB database for a given currency:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ExtractScalpint(currency,start_date,end_date,database,collection):\n",
      "    start_date=datetime(2014, 1, 2, 13, 15, 14)\n",
      "    end_date=datetime(2014, 5, 2, 16, 15, 14)\n",
      "    database=HandleMongo()\n",
      "    currency='USD'\n",
      "    collection='matrix'\n",
      "    #database[collection].aggregate([{'$match':{'USD':{'Date':{'$gt':start_date, '$lt':end_date}}}}])\n",
      "    matrixs=database[collection].find({'USD.Date':{'$gt':start_date, '$lt':end_date}})\n",
      "    spread_matrix=pd.DataFrame(index=[t[currency]['Date'].strftime(\"%d/%m/%y %H:%M\") for t in matrixs],columns=['spread','Ask','Bid','marketAsk','marketBid'])\n",
      "    matrixs=database[collection].find({'USD.Date':{'$gt':start_date, '$lt':end_date}})\n",
      "    for (i,t) in enumerate(matrixs):\n",
      "        try:\n",
      "            spread_matrix['spread'][i]=t[currency]['SpreadMax']\n",
      "            spread_matrix['Ask'][i]=t[currency]['Ask']\n",
      "            spread_matrix['Bid'][i]=t[currency]['Bid']\n",
      "            spread_matrix['marketBid'][i]=t[currency]['MarketBid']\n",
      "            spread_matrix['marketAsk'][i]=t[currency]['MarketAsk']\n",
      "        except:\n",
      "            print 'issue'\n",
      "    return(spread_matrix)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ComputePL(spread_matrix,fees):\n",
      "    CumulativeReturn=0\n",
      "    for s in spread_matrix:\n",
      "        CumulativeReturn=CumulativeReturn+(spread_matrix['SpreadMax']-2*fees)\n",
      "    return(CumulativeReturn)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "We will show up heare results for a scalping strategy for USD and EUR by considering two time horizons (1D, 1H). In order to illustrate this strategy we could plot the close price on several plateform and notice spread between plateforms. For instance during the 'Mtgox krach' at the end of 2013 there was a lag about few days before every plateform price decreased."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename=folder + 'close_intramarket_1D_EUR.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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GRga9evWqlXulp6cH/B2YpsmAAQNYs2YNs2fPZsmSJbXSDrn4zZ07l8ceeyzc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4RPfxrmzvX+XD/DonwphDqilMzr7L7u/J4Vfq07WNFLk9BcgykDMc0tRKPzBnSsoijEuoMVHzOF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\n9iC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63xpGRLzE4OFHyWzsydmg1ehZTaJDxUqHBsW1BUoVFElB1dqKozVsKujDZ9IIui/W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\nWfHLjevIkSNIS0tDaGgo9uzZg5S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       "prompt_number": 14,
       "text": [
        "<IPython.core.display.Image at 0xa99d60c>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "We underline that this strategy suffer from several severe approximations due to the discrepancy in the liquidity level between different plateform. Moreover, each within the last months each plateform has been faced to global issues which hurted the bitcoin community (China decision to enforce restrictions for buying BTC, the Mtgox krach..)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "We have extracted output between for USD and EUR. We have exclude markets which have significative low liquidity.. After having add fees we could plot P&L and obtain:"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "folder='/home/cerveau2charles/Documents/bitcoin/results/'\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "We plot now the return obtain whether we had bought at the lowest Bid price and sell at the highest minus fees of 2%."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename=folder + 'scalping_return_1D_EUR.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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Rj35UcpvjOFq4cKEOHjyoNWvWaOLEia0cOgAAaIBZE9Le7p6G9803pcOH4x0TkEYEkAou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\nPVf+6+YFP009IMi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NGzZWI0YQQFolyiVYlZrQvT0gNuzf9IAgKQggVWzfvl1TpkwZUuWY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\ngIAAbNiwAdnZ2Yr6pLNxbd26FV9++SVWrVpl1zPYa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       "prompt_number": 8,
       "text": [
        "<IPython.core.display.Image at 0xa82630c>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename=folder + 'scalping_return_1D_USD.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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02t0O3g8wdNudSej5QH8H9BFAYDXTAaS1tdXMhpFptLsdvMcP3XbnSuj5QH8H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\nAACgGAECAAAUI0AAAIBiBAgAAFDMPyVh2y1x/H8mAAAAAElFTkSuQmCC\n",
       "prompt_number": 10,
       "text": [
        "<IPython.core.display.Image at 0xa99d78c>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename=folder + 'scalping_return_1H_EUR.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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sV/cBOqMxDlBTc+fOTR0CJCL3ac33vhfxwx+mjqJz6j656u+X+1VXl0Z10dR9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\nnOL9+DBklpdeAli/HqBLl/BzmjcHGD26eN9//qNXrqSgx3j28NfPmjX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XWgp3LlxWP8++/jz6F1UKtW8X5WHUVdL4uItw7Vw+KXMicqyT/uc0963pYtAJddlihLLpLWK+se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       "prompt_number": 11,
       "text": [
        "<IPython.core.display.Image at 0xa99d88c>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename=folder + 'scalping_return_1H_USD.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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BOmQJkpMjgGz5cCjqXr3cqNmHQwEAAECx1EvnQH1yxikAAAAAQAXFKQAAAABABcUpAAAA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       "prompt_number": 12,
       "text": [
        "<IPython.core.display.Image at 0xa99d7cc>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Nevertheless, this strategy suggests that transaction can be done at the same velocity on each plateform that is wrong. Actually, velocity on a given plateform depends on technical issues, IT solutions choosen by a plateform' owners, liquidity level..\n",
      "Despite in the very few months, fees will globally increases on plateforms and rogner profit of a scalping strategy.\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "We would now show off another trading strategy based on technical analysis. This strategy is based on an automatic analysis of several technical indicators. After having compute differents technical indicators for "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "We would now show off another trading strategy based on technical analysis. This strategy is based on an automatic analysis of several technical indicators. After having compute differents technical indicators on several time horizon we compute a trading signal by computing a weighted sum of signals given by each technical indicators. These strategy should be tuned at several levels. For instance, we have to select the optimal set of technical indicators in order to contain noise and to increase the time computation. Morevover, we have to tuned the decision rules for each technical indicators. Finally, we have tuned a time windows in order to handle with market regime shifts.\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "To summarize this strategy working could be described as following:\n",
      "1/ Computation of a battery of technical indicator \n",
      "2/ For each technnical indicator we convert the time series in trading signals according to a specific trading rules.\n",
      "3/ Computation of a weight sum to extract a future trading signal for one step ahead\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "We notice out a backtest for USD/BTC on the btceUSD and we will make comparaison with EUR/USD."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To plot directly P&L you need dowload directly results I have obtained:\n",
      "   \n",
      "http://www.weebly.com/uploads/1/8/5/4/18547470/0eurusd.csv\n",
      "and\n",
      "http://www.weebly.com/uploads/1/8/5/4/18547470/2btceusd.csv"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Please, specify path to the file dowloaded\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "path_EUR_USD=../0eurusd.csv\n",
      "path_BTCE_USD=../2btceusd.csv"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "from numpy import genfromtxt\n",
      "import pandas as pd\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "from matplotlib import cm\n",
      "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plotPL(path_data):\n",
      "    my_data = pd.read_csv(path_data, delimiter='|')\n",
      "    X=my_data.index.values\n",
      "    # Remember that Python does not slice inclusive of the ending index.\n",
      "    Y=my_data.columns.values\n",
      "    Y=[int(y) for y in Y[1:] ]\n",
      "    X=[int(y) for y in X ]\n",
      "    len(X)\n",
      "    len(Y)\n",
      "    Z=my_data.ix[:,1:].values\n",
      "    Z.shape\n",
      "    Z=np.matrix(Z)\n",
      "    fig = plt.figure()\n",
      "    ax = fig.gca(projection='3d')\n",
      "    X, Y = np.meshgrid(X, Y)\n",
      "    surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,\n",
      "            linewidth=0, antialiased=False)\n",
      "    ax.set_zlim(Z.min()-10,Z.max()+10)\n",
      "    ax.zaxis.set_major_locator(LinearLocator(10))\n",
      "    ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
      "    fig.colorbar(surf, shrink=0.5, aspect=5)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Strategy applied to EURUSD"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotPL(path_EUR_USD)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rK4XVbDYjPz9fcw4pQT0vw+GwJotw7/IDKYmrlDimw0qVco88vJBHNDZpwoPX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa7d02cc>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Strategy applied to Btce / USD"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotPL(path_BTCE_USD)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3HDx4EPPmzQsp7br33ntx2WWXoV+/fnj44YeDvBfIBIhZs2bB4/GgqKgIJSUl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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa8e988c>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We plot the P&L for several parameters."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "V- Digital analysis of bitcoin"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "a-Twitter analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We decided to go into the depth by making a twitter analysis. First, we had to stream in continuous tweets and store it into a MongoDB database."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to realized the streaming we have used the Twitter API in the streaming version. The followwing script makes it possible to store in database each tweet which is posted on the social network containing a word into Q=['BTC','BITCOIN','bitcoinarbitrage','MtGox','BitStamp','cryptocurrency']\n",
      ". Please, note that we API search function could be used with operator (AND, OR..) that can be very useful in order realize robust streaming. In order to not broke the pipeline we launch this script into a Amazon micro instance in nohup mode. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import json\n",
      "#Connecting to MongoDB with Python\n",
      "\"\"\" Connect to MongoDB \"\"\"\n",
      "try:\n",
      "    c = Connection(host=\"localhost\", port=27017)\n",
      "    print \"Connected successfully\"\n",
      "except ConnectionFailure, e:\n",
      "    sys.stderr.write(\"Could not connect to MongoDB: %s\" % e)\n",
      "    sys.exit(1)\n",
      "# Get a Database handle to a database named \"mydb\"\n",
      "dbh = c[\"BTC_TEXT\"]\n",
      "\n",
      "# Query terms\n",
      "Q = sys.argv[1:]\n",
      "Q=['BTC','BITCOIN','bitcoinarbitrage','MtGox','BitStamp','cryptocurrency']\n",
      "\n",
      "API_code={\n",
      "\"consumer_key\":'XXXXXXXXXXXXXXXXXXXXXXXX',\n",
      "\"consumer_secret\":'XXXXXXXXXXXXXXXXXXXXXXXX',\n",
      "\"access_token_key\":'XXXXXXXXXXXXXXXXXXXXXXXX',\n",
      "\"access_token_secret\": 'XXXXXXXXXXXXXXXXXXXXXXXX'\n",
      "}\n",
      "\n",
      "\n",
      "auth = tweepy.OAuthHandler(API_code[\"consumer_key\"],API_code[\"consumer_secret\"])\n",
      "auth.set_access_token(API_code[\"access_token_key\"],API_code[\"access_token_secret\"])\n",
      "api = tweepy.API(auth)\n",
      "\n",
      "class CustomStreamListener(tweepy.StreamListener):\n",
      "\n",
      "    def __init__(self, api):\n",
      "        self.api = api\n",
      "        super(tweepy.StreamListener, self).__init__()\n",
      "        self.db = pymongo.MongoClient().BTC_TEXT\n",
      "\n",
      "    def on_data(self, tweet):\n",
      "        a=json.loads(tweet)\n",
      "        #        a['created_at']=datetime.strptime(str(a['created_at']),'%a %b %d %H:%M:%S +0000 %Y').strftime('%Y-%m-%d %H:%M:%S')\n",
      "        #        self.db.tweet_test.insert(a)\n",
      "        #        print json.loads(tweet)['id_str'] +str(datetime.now())\n",
      "        a['created_at']=datetime.strptime(str(a['created_at']),'%a %b %d %H:%M:%S +0000 %Y')\n",
      "        a['user']['created_at']=datetime.strptime(str(a['user']['created_at']),'%a %b %d %H:%M:%S +0000 %Y')\n",
      "        self.db.tweet_test.insert(a)\n",
      "        print json.loads(tweet)['id_str'] +str(datetime.now())\n",
      "\n",
      "    def on_error(self, status_code):\n",
      "        print >> sys.stderr, 'Encountered error with status code:', status_code\n",
      "        return True # Don't kill the stream\n",
      "\n",
      "    def on_timeout(self):\n",
      "        print >> sys.stderr, 'Timeout...'\n",
      "        return True # Don't kill the stream\n",
      "\n",
      "# Create a streaming API and set a timeout value of 60 seconds.\n",
      "\n",
      "streaming_api = tweepy.streaming.Stream(auth, CustomStreamListener(api), timeout=60)\n",
      "\n",
      "# Optionally filter the statuses you want to track by providing a list\n",
      "# of users to \"follow\".\n",
      "\n",
      "print >> sys.stderr, 'Filtering the public timeline for \"%s\"' % (' '.join(sys.argv[1:]),)\n",
      "\n",
      "streaming_api.filter(follow=None, track=Q)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Geolocation of tweets dealing with BTC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After about one week we have stream more than 130K of tweets. We could try first to plot a google map to show up areas where we tweet the most about BTC. A very few proportion of tweets contain a geolocation because user should manually activate this functionnality. Hence, we have generally 2% of tweets which contain a geocode."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ReturnAmazonGeocode(path_to_file):\n",
      "    f = urllib2.urlopen(path_to_geocodes)\n",
      "    #f=open(path_to_file,'r+')\n",
      "    \n",
      "    data=f.readlines()\n",
      "    data=[d.split('|') for d in data]\n",
      "    geocode=[]\n",
      "    for d in data:\n",
      "        try:\n",
      "            geocode.append(d[2][1:-1])\n",
      "        except:\n",
      "            pass\n",
      "    geocode=[g.split(',') for g in geocode[1:]]\n",
      "    for i in range(1,len(geocode)):\n",
      "        geocode[i][0]=float(geocode[i][0])\n",
      "        geocode[i][1]=float(geocode[i][1])\n",
      "    return(geocode)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import urllib2\n",
      "path_to_geocodes='http://www.weebly.com/uploads/1/8/5/4/18547470/tweetgeocoded.csv'\n",
      "geocode=ReturnAmazonGeocode(path_to_geocodes)\n",
      "geocode[0:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "[['39.38833542', ' -76.73633924'],\n",
        " [51.5035456, -0.2671622],\n",
        " [45.61731472, -122.57354498],\n",
        " [37.35075025, -122.05815151],\n",
        " [42.37766753, -71.09636144],\n",
        " [30.20112801, -85.65639825],\n",
        " [36.63301062, -79.85273073],\n",
        " [42.3745335, -83.1891809],\n",
        " [27.695604, -97.4052562],\n",
        " [37.78526536, -122.39849137]]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core import display\n",
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "js_loader = \"\"\"\n",
      "function verifyJSLoaded(){\n",
      "    var jsapiLoaded = (typeof google === 'object' && typeof google.maps === 'object');\n",
      "    console.log(\"Google API Loaded: \" + jsapiLoaded);\n",
      "    return jsapiLoaded;\n",
      "}\n",
      "\n",
      "function loadScript() {\n",
      "  if (!verifyJSLoaded()) {\n",
      "    console.log('Loading Google API.');\n",
      "    var script = document.createElement(\"script\");\n",
      "    script.type = \"text/javascript\";\n",
      "    script.src = \"https://maps.googleapis.com/maps/api/js?sensor=false&libraries=visualization&callback=console.log\";\n",
      "    document.body.appendChild(script);\n",
      "  }\n",
      "}\n",
      "\n",
      "loadScript();\n",
      "\"\"\"\n",
      "display.Javascript(js_loader)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "\n",
        "function verifyJSLoaded(){\n",
        "    var jsapiLoaded = (typeof google === 'object' && typeof google.maps === 'object');\n",
        "    console.log(\"Google API Loaded: \" + jsapiLoaded);\n",
        "    return jsapiLoaded;\n",
        "}\n",
        "\n",
        "function loadScript() {\n",
        "  if (!verifyJSLoaded()) {\n",
        "    console.log('Loading Google API.');\n",
        "    var script = document.createElement(\"script\");\n",
        "    script.type = \"text/javascript\";\n",
        "    script.src = \"https://maps.googleapis.com/maps/api/js?sensor=false&libraries=visualization&callback=console.log\";\n",
        "    document.body.appendChild(script);\n",
        "  }\n",
        "}\n",
        "\n",
        "loadScript();\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<IPython.core.display.Javascript at 0xb15e26c>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "html_template = '<div id=\"%s\" style=\"width: 600px; height: 400px\"></div>'\n",
      "#This is to make sure the JS gets loaded before we try to load the maps\n",
      "time.sleep(1) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "def gen_javascript(pairs, div_id=None, N=100):\n",
      "    \"\"\"\n",
      "    Generates javascript to draw a heatmap with Google Maps API.\n",
      "    ARGS:\n",
      "    key: The name of the Redis key storing coordinate pairs to map.\n",
      "    div_id: The id of the HTML div to place the map in. A value \n",
      "            of None will draw the map in a div with id = key.\n",
      "    N: The number of coordinate pairs to plot. The N most recent\n",
      "       pairs will be drawn.\n",
      "    \"\"\"\n",
      "\n",
      "    if div_id == None:\n",
      "        div_id = \"python\"\n",
      "    # Gets the N most *recent* coordinates \n",
      "    #pairs = [json.loads(x) for x in r.lrange(key,-N,-1)]\n",
      "    # Creates Javascript objects which will comprise geoData.\n",
      "    coords = ',\\n  '.join([\"new google.maps.LatLng(%s, %s)\" % tuple(pair) for pair in pairs])    \n",
      "    template_jscript = \"\"\"\n",
      "    var geoData = [\n",
      "    %s\n",
      "    ];\n",
      "        \n",
      "    var map, heatmap;\n",
      "        \n",
      "    function hmap_initialize() {\n",
      "        var mapOptions = {\n",
      "        zoom: 1,\n",
      "        center: new google.maps.LatLng(30.5171, 0.1062),\n",
      "        mapTypeId: google.maps.MapTypeId.SATELLITE\n",
      "        };\n",
      "        \n",
      "        map = new google.maps.Map(document.getElementById('%s'),\n",
      "              mapOptions);\n",
      "        \n",
      "        var pointArray = new google.maps.MVCArray(geoData);\n",
      "        \n",
      "        heatmap = new google.maps.visualization.HeatmapLayer({\n",
      "        data: pointArray\n",
      "        });\n",
      "        \n",
      "      heatmap.setMap(map);\n",
      "    }\n",
      "    \n",
      "    hmap_initialize();\n",
      "    \"\"\"\n",
      "    return template_jscript % (coords, div_id)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jscript=gen_javascript(geocode)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display.HTML(html_template % 'python')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div id=\"python\" style=\"width: 600px; height: 400px\"></div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<IPython.core.display.HTML at 0xb15e28c>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display.Javascript(jscript)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "\n",
        "    var geoData = [\n",
        "    new google.maps.LatLng(39.38833542,  -76.73633924),\n",
        "  new google.maps.LatLng(51.5035456, -0.2671622),\n",
        "  new google.maps.LatLng(45.61731472, -122.57354498),\n",
        "  new google.maps.LatLng(37.35075025, -122.05815151),\n",
        "  new google.maps.LatLng(42.37766753, -71.09636144),\n",
        "  new google.maps.LatLng(30.20112801, -85.65639825),\n",
        "  new google.maps.LatLng(36.63301062, -79.85273073),\n",
        "  new google.maps.LatLng(42.3745335, -83.1891809),\n",
        "  new google.maps.LatLng(27.695604, -97.4052562),\n",
        "  new google.maps.LatLng(37.78526536, -122.39849137),\n",
        "  new google.maps.LatLng(31.54624422, -97.11832266),\n",
        "  new google.maps.LatLng(27.6956275, -97.4051148),\n",
        "  new google.maps.LatLng(-6.89286, 107.5847),\n",
        "  new google.maps.LatLng(30.26960519, -97.74361687),\n",
        "  new google.maps.LatLng(30.3133396, -97.711433),\n",
        "  new google.maps.LatLng(39.985388, -83.052707),\n",
        "  new google.maps.LatLng(42.69372022, -84.58455211),\n",
        "  new google.maps.LatLng(37.33692256, -122.04683114),\n",
        "  new google.maps.LatLng(42.69371441, -84.58461583),\n",
        "  new google.maps.LatLng(27.96717134, -81.9011228),\n",
        "  new google.maps.LatLng(35.4408835, -82.4744193),\n",
        "  new google.maps.LatLng(-6.89387, 107.58488),\n",
        "  new google.maps.LatLng(38.61901277, -75.33584936),\n",
        "  new google.maps.LatLng(33.87520063, -117.41461579),\n",
        "  new google.maps.LatLng(37.78754722, -122.38749147),\n",
        "  new google.maps.LatLng(37.70348795, -121.88511187),\n",
        "  new google.maps.LatLng(-6.89399, 107.58472),\n",
        "  new google.maps.LatLng(-6.894, 107.58476),\n",
        "  new google.maps.LatLng(41.40273285, 2.15401411),\n",
        "  new google.maps.LatLng(35.92268686, -86.88272689),\n",
        "  new google.maps.LatLng(47.62252963, -122.1324933),\n",
        "  new google.maps.LatLng(43.10488734, 131.92764494),\n",
        "  new google.maps.LatLng(32.84728866, -117.24884033),\n",
        "  new google.maps.LatLng(42.43993875, -83.21822168),\n",
        "  new google.maps.LatLng(14.573683, 120.989737),\n",
        "  new google.maps.LatLng(-6.27971, 106.72124),\n",
        "  new google.maps.LatLng(37.77967502, -122.40679074),\n",
        "  new google.maps.LatLng(30.3739851, -97.7064126),\n",
        "  new google.maps.LatLng(45.52806275, -122.89265259),\n",
        "  new google.maps.LatLng(-6.89359, 107.58487),\n",
        "  new google.maps.LatLng(37.7922448, -122.4057954),\n",
        "  new google.maps.LatLng(40.0007133, -75.2505591),\n",
        "  new google.maps.LatLng(-6.89261, 107.58339),\n",
        "  new google.maps.LatLng(-36.85838776, 174.76041039),\n",
        "  new google.maps.LatLng(-6.86806, 107.57374),\n",
        "  new google.maps.LatLng(-6.89621, 107.59109),\n",
        "  new google.maps.LatLng(41.7950491, -123.3792185),\n",
        "  new google.maps.LatLng(-6.89333, 107.58569),\n",
        "  new google.maps.LatLng(-6.89367, 107.58646),\n",
        "  new google.maps.LatLng(-6.8965, 107.58859),\n",
        "  new google.maps.LatLng(-6.89281, 107.58357),\n",
        "  new google.maps.LatLng(-6.89275, 107.58347),\n",
        "  new google.maps.LatLng(37.77710276, -122.41818108),\n",
        "  new google.maps.LatLng(-6.8902311, 107.556095),\n",
        "  new google.maps.LatLng(41.7923326, -123.3782027),\n",
        "  new google.maps.LatLng(47.16667, 9.53333),\n",
        "  new google.maps.LatLng(-36.85836786, 174.76052218),\n",
        "  new google.maps.LatLng(47.61989866, -122.11968228),\n",
        "  new google.maps.LatLng(-6.89326, 107.5857),\n",
        "  new google.maps.LatLng(37.77953207, -122.40683688),\n",
        "  new google.maps.LatLng(-6.8916, 107.58226),\n",
        "  new google.maps.LatLng(26.0506462, 50.5149525),\n",
        "  new google.maps.LatLng(-6.89474, 107.58841),\n",
        "  new google.maps.LatLng(1.12865, 104.05444),\n",
        "  new google.maps.LatLng(-6.89342, 107.58529),\n",
        "  new google.maps.LatLng(-6.90162, 107.6149),\n",
        "  new google.maps.LatLng(-27.47157034, 152.99654257),\n",
        "  new google.maps.LatLng(32.84728866, -117.24884033),\n",
        "  new google.maps.LatLng(-6.893191, 107.585469),\n",
        "  new google.maps.LatLng(-6.89351, 107.58408),\n",
        "  new google.maps.LatLng(-6.89303, 107.58528),\n",
        "  new google.maps.LatLng(-6.89368, 107.58468),\n",
        "  new google.maps.LatLng(-6.89519, 107.58719),\n",
        "  new google.maps.LatLng(-1.27745, 116.83957),\n",
        "  new google.maps.LatLng(51.89844611, -8.47540369),\n",
        "  new google.maps.LatLng(-6.89254, 107.58378),\n",
        "  new google.maps.LatLng(-6.8929, 107.58534),\n",
        "  new google.maps.LatLng(55.6569779, 12.47440794),\n",
        "  new google.maps.LatLng(-6.25893, 107.01874),\n",
        "  new google.maps.LatLng(-6.90601, 107.58405),\n",
        "  new google.maps.LatLng(39.96378925, -82.99527267),\n",
        "  new google.maps.LatLng(35.85175287, -86.94479696),\n",
        "  new google.maps.LatLng(-6.25773, 107.0208),\n",
        "  new google.maps.LatLng(40.8551664, -73.9386269),\n",
        "  new google.maps.LatLng(31.96246606, 35.90117718),\n",
        "  new google.maps.LatLng(-6.89324, 107.58562),\n",
        "  new google.maps.LatLng(-1.27512, 116.84215),\n",
        "  new google.maps.LatLng(29.82982229, -95.72541728),\n",
        "  new google.maps.LatLng(-6.89084, 107.58228),\n",
        "  new google.maps.LatLng(-6.89286, 107.58459),\n",
        "  new google.maps.LatLng(-6.89274, 107.58461),\n",
        "  new google.maps.LatLng(53.3080486, -2.1467234),\n",
        "  new google.maps.LatLng(-6.88526, 107.59852),\n",
        "  new google.maps.LatLng(55.68746121, 12.57978168),\n",
        "  new google.maps.LatLng(-6.89659, 107.5918),\n",
        "  new google.maps.LatLng(-6.89327, 107.58356),\n",
        "  new google.maps.LatLng(48.87148279, 2.29968808),\n",
        "  new google.maps.LatLng(-6.89253, 107.5836),\n",
        "  new google.maps.LatLng(32.15198189, -81.22847859),\n",
        "  new google.maps.LatLng(38.897579, -77.036712),\n",
        "  new google.maps.LatLng(33.72611997, -116.36199468),\n",
        "  new google.maps.LatLng(44.7277536, 11.2893812),\n",
        "  new google.maps.LatLng(-1.115391, 35.9909424),\n",
        "  new google.maps.LatLng(33.72610102, -116.36199748),\n",
        "  new google.maps.LatLng(51.51504569, -0.12879123),\n",
        "  new google.maps.LatLng(-6.94475, 107.56264),\n",
        "  new google.maps.LatLng(42.10322748, -72.59070503),\n",
        "  new google.maps.LatLng(-6.89233, 107.59064),\n",
        "  new google.maps.LatLng(1.5131116, 103.8679456),\n",
        "  new google.maps.LatLng(29.5957085, -95.3861979),\n",
        "  new google.maps.LatLng(10.34301452, 123.91366188),\n",
        "  new google.maps.LatLng(39.9964962, -105.23083035),\n",
        "  new google.maps.LatLng(-6.8898, 107.58658),\n",
        "  new google.maps.LatLng(37.79015777, -122.39323941),\n",
        "  new google.maps.LatLng(25.06077957, -77.37750292),\n",
        "  new google.maps.LatLng(1.33527072, 103.84554186),\n",
        "  new google.maps.LatLng(34.2672495, -119.2109798),\n",
        "  new google.maps.LatLng(38.73201972, -9.14168126),\n",
        "  new google.maps.LatLng(51.16035, 3.73118),\n",
        "  new google.maps.LatLng(34.10153781, -118.33984612),\n",
        "  new google.maps.LatLng(42.351075, -71.117179),\n",
        "  new google.maps.LatLng(34.24016871, -118.41089381),\n",
        "  new google.maps.LatLng(39.9815248, -75.16082553),\n",
        "  new google.maps.LatLng(40.7063967, -74.008459),\n",
        "  new google.maps.LatLng(25.04245654, -77.3283519),\n",
        "  new google.maps.LatLng(40.7416723, -74.0028383),\n",
        "  new google.maps.LatLng(-24.66295904, 25.91089549),\n",
        "  new google.maps.LatLng(25.0765009, -77.3319758),\n",
        "  new google.maps.LatLng(51.507404, -0.0737377),\n",
        "  new google.maps.LatLng(42.238161, -83.6240143),\n",
        "  new google.maps.LatLng(51.50704428, -0.0739467),\n",
        "  new google.maps.LatLng(51.50706232, -0.07392219),\n",
        "  new google.maps.LatLng(51.50696115, -0.074054),\n",
        "  new google.maps.LatLng(42.70287595, -73.87717849),\n",
        "  new google.maps.LatLng(36.98518112, -86.45101492),\n",
        "  new google.maps.LatLng(40.0746942, -74.80200554),\n",
        "  new google.maps.LatLng(40.73917913, -74.00813945),\n",
        "  new google.maps.LatLng(20.77826619, -103.45111409),\n",
        "  new google.maps.LatLng(34.42159936, -119.64081321),\n",
        "  new google.maps.LatLng(42.27583377, -83.73444425),\n",
        "  new google.maps.LatLng(26.2523523, -80.1531265),\n",
        "  new google.maps.LatLng(40.7633781, -111.8911494),\n",
        "  new google.maps.LatLng(40.98403209, -74.96469588),\n",
        "  new google.maps.LatLng(39.94819559, -75.16378455),\n",
        "  new google.maps.LatLng(33.5657527, -117.6486745),\n",
        "  new google.maps.LatLng(33.5660266, -117.6494773),\n",
        "  new google.maps.LatLng(32.7919195, -96.8007946),\n",
        "  new google.maps.LatLng(33.5654788, -117.6478717),\n",
        "  new google.maps.LatLng(40.71434539, -74.03613789),\n",
        "  new google.maps.LatLng(42.3671609, -83.1256758),\n",
        "  new google.maps.LatLng(48.87506926, 2.30103486),\n",
        "  new google.maps.LatLng(53.38271683, -6.5948459),\n",
        "  new google.maps.LatLng(38.7099606, -9.15214693),\n",
        "  new google.maps.LatLng(38.21200883, -85.64605698),\n",
        "  new google.maps.LatLng(30.22377537, -97.767193),\n",
        "  new google.maps.LatLng(41.30285621, -72.46545842),\n",
        "  new google.maps.LatLng(-33.87787437, 151.21204766),\n",
        "  new google.maps.LatLng(45.16259402, -84.92907705),\n",
        "  new google.maps.LatLng(53.38278001, -6.59486048),\n",
        "  new google.maps.LatLng(41.3027988, -72.46563599),\n",
        "  new google.maps.LatLng(45.74917077, -108.57443903),\n",
        "  new google.maps.LatLng(26.30171215, -81.81665649),\n",
        "  new google.maps.LatLng(43.6743018, -79.3982333),\n",
        "  new google.maps.LatLng(40.7960668, -77.8640619),\n",
        "  new google.maps.LatLng(-27.45891178, 153.03256737),\n",
        "  new google.maps.LatLng(38.94402368, -92.32192181),\n",
        "  new google.maps.LatLng(-27.45124985, 153.04074011),\n",
        "  new google.maps.LatLng(33.78375411, -118.01605755),\n",
        "  new google.maps.LatLng(55.7741209, -4.3000314),\n",
        "  new google.maps.LatLng(37.40293957, -122.04984422),\n",
        "  new google.maps.LatLng(41.09420406, -74.0121355),\n",
        "  new google.maps.LatLng(37.77493632, -122.41783868),\n",
        "  new google.maps.LatLng(37.53100178, -122.00008084),\n",
        "  new google.maps.LatLng(37.8650538, -122.2694231),\n",
        "  new google.maps.LatLng(43.67003944, -79.40416748),\n",
        "  new google.maps.LatLng(37.4851363, -122.14806459),\n",
        "  new google.maps.LatLng(40.70600101, -74.01137804),\n",
        "  new google.maps.LatLng(51.750789, -0.33948975),\n",
        "  new google.maps.LatLng(38.59106138, -90.27488493),\n",
        "  new google.maps.LatLng(37.7430765, -122.4753463),\n",
        "  new google.maps.LatLng(38.62852972, -90.18674131),\n",
        "  new google.maps.LatLng(37.7430718, -122.47534),\n",
        "  new google.maps.LatLng(38.62885083, -90.1873123),\n",
        "  new google.maps.LatLng(41.40753573, 2.16710696),\n",
        "  new google.maps.LatLng(38.6287754, -90.1871513),\n",
        "  new google.maps.LatLng(42.43994333, -83.21835632),\n",
        "  new google.maps.LatLng(42.44009909, -83.21802109),\n",
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        "  new google.maps.LatLng(42.37224579, -83.22100847),\n",
        "  new google.maps.LatLng(33.77159716, -118.14273465),\n",
        "  new google.maps.LatLng(42.32063522, -83.3983797),\n",
        "  new google.maps.LatLng(42.32059832, -83.39822554),\n",
        "  new google.maps.LatLng(35.0, 38.0),\n",
        "  new google.maps.LatLng(-6.89338, 107.5851),\n",
        "  new google.maps.LatLng(37.79149729, -122.40407755),\n",
        "  new google.maps.LatLng(54.0633, -2.88424),\n",
        "  new google.maps.LatLng(-27.6493326, 153.1144392),\n",
        "  new google.maps.LatLng(41.22892548, -85.85991047),\n",
        "  new google.maps.LatLng(37.75882142, -122.41953046),\n",
        "  new google.maps.LatLng(0.0, 0.0),\n",
        "  new google.maps.LatLng(54.0633, -2.88424),\n",
        "  new google.maps.LatLng(-6.89276, 107.58485),\n",
        "  new google.maps.LatLng(42.36948017, -82.94874198),\n",
        "  new google.maps.LatLng(-6.87841, 107.59647),\n",
        "  new google.maps.LatLng(-6.8932, 107.58483),\n",
        "  new google.maps.LatLng(5.9941138, 116.120626),\n",
        "  new google.maps.LatLng(42.36959274, -82.94868683),\n",
        "  new google.maps.LatLng(-1.27708, 116.83378),\n",
        "  new google.maps.LatLng(0.0, 0.0),\n",
        "  new google.maps.LatLng(-6.89428, 107.5859),\n",
        "  new google.maps.LatLng(36.0316832, -115.0545713),\n",
        "  new google.maps.LatLng(-6.89382, 107.5845),\n",
        "  new google.maps.LatLng(-6.89423, 107.58627),\n",
        "  new google.maps.LatLng(30.26960519, -97.74361687),\n",
        "  new google.maps.LatLng(-6.89153, 107.58255),\n",
        "  new google.maps.LatLng(-6.89319, 107.58459),\n",
        "  new google.maps.LatLng(52.61977841, 1.30153206),\n",
        "  new google.maps.LatLng(-6.89391, 107.58627),\n",
        "  new google.maps.LatLng(1.13087, 104.05554),\n",
        "  new google.maps.LatLng(40.70607613, -74.01145337),\n",
        "  new google.maps.LatLng(-6.88759, 107.57857),\n",
        "  new google.maps.LatLng(-1.2749, 116.83845),\n",
        "  new google.maps.LatLng(13.0191729, 77.6617641),\n",
        "  new google.maps.LatLng(-1.2369769, 36.7724879),\n",
        "  new google.maps.LatLng(-6.893191, 107.585469),\n",
        "  new google.maps.LatLng(40.67652128, -74.18050908),\n",
        "  new google.maps.LatLng(41.6182578, -0.9102207),\n",
        "  new google.maps.LatLng(0.0, 0.0),\n",
        "  new google.maps.LatLng(0.0, 0.0),\n",
        "  new google.maps.LatLng(53.51996558, -1.13796387),\n",
        "  new google.maps.LatLng(42.8094945, 23.2110905)\n",
        "    ];\n",
        "        \n",
        "    var map, heatmap;\n",
        "        \n",
        "    function hmap_initialize() {\n",
        "        var mapOptions = {\n",
        "        zoom: 1,\n",
        "        center: new google.maps.LatLng(30.5171, 0.1062),\n",
        "        mapTypeId: google.maps.MapTypeId.SATELLITE\n",
        "        };\n",
        "        \n",
        "        map = new google.maps.Map(document.getElementById('python'),\n",
        "              mapOptions);\n",
        "        \n",
        "        var pointArray = new google.maps.MVCArray(geoData);\n",
        "        \n",
        "        heatmap = new google.maps.visualization.HeatmapLayer({\n",
        "        data: pointArray\n",
        "        });\n",
        "        \n",
        "      heatmap.setMap(map);\n",
        "    }\n",
        "    \n",
        "    hmap_initialize();\n",
        "    "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<IPython.core.display.Javascript at 0xb15e1ac>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we would try to make a segmention of users who tweet about BTC. In order to make this unsupervised clustering it can be useful to study what kind of information will be convenient. Among possible feature we keep the count of followers, the count of tweets, its description..\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will publish in a next notebook an application of several algorithm (K-mean, n-grams, TD-ID, random Forest) to perform with this objective."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "c-Data vizualisation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For instance we detailled the D3.js code for plotting the time series of BTC prices:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://dadi5.free.fr/www/dataviz/bitcoin_d3/ width=600 height=450></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://dadi5.free.fr/www/dataviz/bitcoin_d3/ width=600 height=450></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<IPython.core.display.HTML at 0xaa1a72c>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://dadi5.free.fr/www/dataviz/volumeLangue/index_v2.html width=1000 height=550></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://dadi5.free.fr/www/dataviz/volumeLangue/index_v2.html width=1000 height=550></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<IPython.core.display.HTML at 0xaa1a0ac>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://dadi5.free.fr/www/dataviz/word_cloud/simple.html width=1000 height=550></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://dadi5.free.fr/www/dataviz/word_cloud/simple.html width=1000 height=550></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<IPython.core.display.HTML at 0xb15e10c>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://dadi5.free.fr/www/dataviz/Bubble%20Chart/ width=600 height=550></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://dadi5.free.fr/www/dataviz/Bubble%20Chart/ width=600 height=550></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<IPython.core.display.HTML at 0xb1d018c>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Conclusion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Before concluding this notebook I would remind you that this notebook and in particular financials parts doesn't meet the rigour that we should expected by reading a financial paper. Therefore this notebook doesn't include invesment advices or suggests. The main concern of this notebook is to highlight many data and statistical skills that could be re used in other studies. To summarize, we have insisted through this study in python ( numpy, panda, pymongo, sql in python, matplotlib), ipython notebook and some magic function such as %R, %html, google map.., d3.js tools.."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The next ipython notebook will treat how compute efficiently Value-At-Risk on BTC data... \n",
      "Feel free to contact me at <A HREF=\"mailto:charles-abner.dadi@graduates.centraliens.net\">charles-abner.dadi@graduates.centraliens.net</A>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}