{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tia.analysis.ta as ta\n",
    "import tia.analysis.talib_wrapper as talib\n",
    "import pandas as pd\n",
    "from pandas.io.data import get_data_yahoo\n",
    "from tia.analysis.model import SingleAssetPortfolio, PortfolioPricer, load_yahoo_stock, PortfolioSummary\n",
    "from tia.analysis.model.ret import RoiiRetCalculator\n",
    "from tia.util.fmt import DynamicColumnFormatter, DynamicRowFormatter, new_dynamic_formatter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pandas\\tseries\\base.py:363: FutureWarning: using '-' to provide set differences with datetimelike Indexes is deprecated, use .difference()\n",
      "  \"use .difference()\",FutureWarning)\n",
      "C:\\Anaconda\\lib\\site-packages\\pandas\\core\\index.py:1196: FutureWarning: using '-' to provide set differences with Indexes is deprecated, use .difference()\n",
      "  \"use .difference()\",FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# drop adj close & volume for example sake \n",
    "msft = load_yahoo_stock('MSFT', start='1/1/2010')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IH3RA+LCDoPHYF7i2UGMNYPsOUjrPCyUdBYtjrq8HrmrLWGf8EhgIsTpMNQiD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\nREoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc08eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build signal when 50d crosses 200d\n",
    "moving_avgs = pd.DataFrame({'50': ta.sma(msft.pxs.close, 50), '200': ta.sma(msft.pxs.close, 200)})\n",
    "signal = ta.cross_signal(moving_avgs['50'], moving_avgs['200']).dropna()\n",
    "# keep only entry\n",
    "entry_signal = signal.copy()\n",
    "entry_signal[signal.shift(1) == signal]  = 0 \n",
    "entry_signal = entry_signal[entry_signal != 0]\n",
    "# show when the signal triggers\n",
    "moving_avgs.plot(color=['b', 'k'], title='MSFT moving averages')\n",
    "for i, v in entry_signal.iteritems():\n",
    "    if v == -1:\n",
    "        plt.plot(i, moving_avgs['50'][i], 'rv')\n",
    "    else:\n",
    "        plt.plot(i, moving_avgs['50'][i], 'k^')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Trade(1, qty=1.0, px=28.07, ts=2010-12-27 00:00:00)>,\n",
       " <Trade(2, qty=-1.0, px=25.15, ts=2011-04-19 00:00:00)>,\n",
       " <Trade(3, qty=-1.0, px=25.15, ts=2011-04-19 00:00:00)>,\n",
       " <Trade(4, qty=1.0, px=26.59, ts=2011-10-26 00:00:00)>,\n",
       " <Trade(5, qty=1.0, px=26.59, ts=2011-10-26 00:00:00)>,\n",
       " <Trade(6, qty=-1.0, px=29.49, ts=2012-10-16 00:00:00)>,\n",
       " <Trade(7, qty=-1.0, px=29.49, ts=2012-10-16 00:00:00)>,\n",
       " <Trade(8, qty=1.0, px=32.61, ts=2013-04-29 00:00:00)>,\n",
       " <Trade(9, qty=1.0, px=32.61, ts=2013-04-29 00:00:00)>,\n",
       " <Trade(10, qty=-1.0, px=43.11, ts=2015-03-05 00:00:00)>,\n",
       " <Trade(11, qty=-1.0, px=43.11, ts=2015-03-05 00:00:00)>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can get the set of trades created by this signal\n",
    "trades = ta.Signal(signal).close_to_close(msft.pxs.close)\n",
    "trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# construct a portfolio from the trades\n",
    "port = SingleAssetPortfolio(msft, trades, ret_calc=RoiiRetCalculator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>date</th>\n",
       "      <th>txn_ts</th>\n",
       "      <th>pid</th>\n",
       "      <th>tid</th>\n",
       "      <th>txn_qty</th>\n",
       "      <th>txn_px</th>\n",
       "      <th>txn_fees</th>\n",
       "      <th>txn_premium</th>\n",
       "      <th>open_val</th>\n",
       "      <th>pos</th>\n",
       "      <th>txn_intent</th>\n",
       "      <th>txn_action</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-10-16</td>\n",
       "      <td>2012-10-16</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "      <td>29.49</td>\n",
       "      <td>0</td>\n",
       "      <td>29.49</td>\n",
       "      <td>29.49</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>32.61</td>\n",
       "      <td>0</td>\n",
       "      <td>-32.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>32.61</td>\n",
       "      <td>0</td>\n",
       "      <td>-32.61</td>\n",
       "      <td>-32.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-03-05</td>\n",
       "      <td>2015-03-05</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>43.11</td>\n",
       "      <td>0</td>\n",
       "      <td>43.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-03-05</td>\n",
       "      <td>2015-03-05</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>-1</td>\n",
       "      <td>43.11</td>\n",
       "      <td>0</td>\n",
       "      <td>43.11</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date     txn_ts  pid  tid  txn_qty  txn_px  txn_fees  txn_premium  \\\n",
       "seq                                                                           \n",
       "6   2012-10-16 2012-10-16    4    7       -1   29.49         0        29.49   \n",
       "7   2013-04-29 2013-04-29    4    8        1   32.61         0       -32.61   \n",
       "8   2013-04-29 2013-04-29    5    9        1   32.61         0       -32.61   \n",
       "9   2015-03-05 2015-03-05    5   10       -1   43.11         0        43.11   \n",
       "10  2015-03-05 2015-03-05    6   11       -1   43.11         0        43.11   \n",
       "\n",
       "     open_val  pos  txn_intent  txn_action  \n",
       "seq                                         \n",
       "6       29.49   -1           1           3  \n",
       "7        0.00    0           2           4  \n",
       "8      -32.61    1           1           1  \n",
       "9        0.00    0           2           2  \n",
       "10      43.11   -1           1           3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show each of the individial transactions\n",
    "port.txns.frame.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>side</th>\n",
       "      <th>open_dt</th>\n",
       "      <th>close_dt</th>\n",
       "      <th>open_qty</th>\n",
       "      <th>open_px</th>\n",
       "      <th>close_px</th>\n",
       "      <th>open_premium</th>\n",
       "      <th>pl</th>\n",
       "      <th>ret</th>\n",
       "      <th>duration</th>\n",
       "      <th>ntxns</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Long</td>\n",
       "      <td>2010-12-27</td>\n",
       "      <td>2011-04-19</td>\n",
       "      <td>1</td>\n",
       "      <td>28.07</td>\n",
       "      <td>25.15</td>\n",
       "      <td>-28.07</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-0.098326</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Short</td>\n",
       "      <td>2011-04-19</td>\n",
       "      <td>2011-10-26</td>\n",
       "      <td>-1</td>\n",
       "      <td>25.15</td>\n",
       "      <td>26.59</td>\n",
       "      <td>25.15</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-0.069980</td>\n",
       "      <td>133</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Long</td>\n",
       "      <td>2011-10-26</td>\n",
       "      <td>2012-10-16</td>\n",
       "      <td>1</td>\n",
       "      <td>26.59</td>\n",
       "      <td>29.49</td>\n",
       "      <td>-26.59</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.139150</td>\n",
       "      <td>246</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Short</td>\n",
       "      <td>2012-10-16</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>-1</td>\n",
       "      <td>29.49</td>\n",
       "      <td>32.61</td>\n",
       "      <td>29.49</td>\n",
       "      <td>-3.58</td>\n",
       "      <td>-0.121397</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Long</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2015-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>32.61</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-32.61</td>\n",
       "      <td>12.70</td>\n",
       "      <td>0.389451</td>\n",
       "      <td>467</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Short</td>\n",
       "      <td>2015-03-05</td>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>-1</td>\n",
       "      <td>43.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>-0.110415</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      side    open_dt   close_dt  open_qty  open_px  close_px  open_premium  \\\n",
       "pid                                                                           \n",
       "1     Long 2010-12-27 2011-04-19         1    28.07     25.15        -28.07   \n",
       "2    Short 2011-04-19 2011-10-26        -1    25.15     26.59         25.15   \n",
       "3     Long 2011-10-26 2012-10-16         1    26.59     29.49        -26.59   \n",
       "4    Short 2012-10-16 2013-04-29        -1    29.49     32.61         29.49   \n",
       "5     Long 2013-04-29 2015-03-05         1    32.61     43.11        -32.61   \n",
       "6    Short 2015-03-05 2015-04-24        -1    43.11       NaN         43.11   \n",
       "\n",
       "        pl       ret  duration  ntxns   state  \n",
       "pid                                            \n",
       "1    -2.76 -0.098326        80      2  Closed  \n",
       "2    -1.76 -0.069980       133      2  Closed  \n",
       "3     3.70  0.139150       246      2  Closed  \n",
       "4    -3.58 -0.121397       132      2  Closed  \n",
       "5    12.70  0.389451       467      2  Closed  \n",
       "6    -4.76 -0.110415        36      1    Open  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the position summary\n",
    "port.positions.frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>date</th>\n",
       "      <th>pos</th>\n",
       "      <th>pid</th>\n",
       "      <th>tid</th>\n",
       "      <th>txn_qty</th>\n",
       "      <th>txn_px</th>\n",
       "      <th>txn_fees</th>\n",
       "      <th>txn_premium</th>\n",
       "      <th>txn_intent</th>\n",
       "      <th>txn_action</th>\n",
       "      <th>close</th>\n",
       "      <th>open_val</th>\n",
       "      <th>mkt_val</th>\n",
       "      <th>total_val</th>\n",
       "      <th>dvds</th>\n",
       "      <th>fees</th>\n",
       "      <th>rpl_gross</th>\n",
       "      <th>rpl</th>\n",
       "      <th>upl</th>\n",
       "      <th>pl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.91</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-42.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.64</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-42.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.99</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-42.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.34</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-43.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.87</td>\n",
       "      <td>43.11</td>\n",
       "      <td>-47.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.53</td>\n",
       "      <td>-4.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  pos  pid  tid  txn_qty  txn_px  txn_fees  txn_premium  \\\n",
       "1336 2015-04-20   -1    6    0      NaN     NaN         0            0   \n",
       "1337 2015-04-21   -1    6    0      NaN     NaN         0            0   \n",
       "1338 2015-04-22   -1    6    0      NaN     NaN         0            0   \n",
       "1339 2015-04-23   -1    6    0      NaN     NaN         0            0   \n",
       "1340 2015-04-24   -1    6    0      NaN     NaN         0            0   \n",
       "\n",
       "      txn_intent  txn_action  close  open_val  mkt_val  total_val  dvds  fees  \\\n",
       "1336           0           0  42.91     43.11   -42.91          0     0     0   \n",
       "1337           0           0  42.64     43.11   -42.64          0     0     0   \n",
       "1338           0           0  42.99     43.11   -42.99          0     0     0   \n",
       "1339           0           0  43.34     43.11   -43.34          0     0     0   \n",
       "1340           0           0  47.87     43.11   -47.87          0     0     0   \n",
       "\n",
       "      rpl_gross  rpl   upl    pl  \n",
       "1336          0    0 -1.29 -1.29  \n",
       "1337          0    0  0.27  0.27  \n",
       "1338          0    0 -0.35 -0.35  \n",
       "1339          0    0 -0.35 -0.35  \n",
       "1340          0    0 -4.53 -4.53  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transaction Level Profit & Loss\n",
    "port.pl.txn_frame.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>dvds</th>\n",
       "      <th>fees</th>\n",
       "      <th>rpl_gross</th>\n",
       "      <th>rpl</th>\n",
       "      <th>upl</th>\n",
       "      <th>pl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.53</td>\n",
       "      <td>-4.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dvds  fees  rpl_gross  rpl   upl    pl\n",
       "2015-04-20     0     0          0    0 -1.29 -1.29\n",
       "2015-04-21     0     0          0    0  0.27  0.27\n",
       "2015-04-22     0     0          0    0 -0.35 -0.35\n",
       "2015-04-23     0     0          0    0 -0.35 -0.35\n",
       "2015-04-24     0     0          0    0 -4.53 -4.53"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Daily Profit & Loss Frame\n",
    "port.pl.dly_frame.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>mpl avg</th>\n",
       "      <th>mpl std ann</th>\n",
       "      <th>maxdd</th>\n",
       "      <th>maxdd dt</th>\n",
       "      <th>avg dd</th>\n",
       "      <th>best month</th>\n",
       "      <th>worst month</th>\n",
       "      <th>best day</th>\n",
       "      <th>worst day</th>\n",
       "      <th>nmonths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>(0)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>(0.2)</td>\n",
       "      <td>30-Dec-10</td>\n",
       "      <td>(0.2)</td>\n",
       "      <td>0</td>\n",
       "      <td>(0.2)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(0.1)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>(0.4)</td>\n",
       "      <td>3.6</td>\n",
       "      <td>(7.4)</td>\n",
       "      <td>25-Nov-11</td>\n",
       "      <td>(2.8)</td>\n",
       "      <td>1.7</td>\n",
       "      <td>(1.7)</td>\n",
       "      <td>1.4</td>\n",
       "      <td>(1.1)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>(4.2)</td>\n",
       "      <td>01-Jun-12</td>\n",
       "      <td>(0.6)</td>\n",
       "      <td>3.6</td>\n",
       "      <td>(2.6)</td>\n",
       "      <td>1.6</td>\n",
       "      <td>(1)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>(0)</td>\n",
       "      <td>6.8</td>\n",
       "      <td>(7.4)</td>\n",
       "      <td>06-Sep-13</td>\n",
       "      <td>(2.3)</td>\n",
       "      <td>3</td>\n",
       "      <td>(3.5)</td>\n",
       "      <td>2.4</td>\n",
       "      <td>(4)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>(4.8)</td>\n",
       "      <td>16-Oct-14</td>\n",
       "      <td>(1.1)</td>\n",
       "      <td>2.7</td>\n",
       "      <td>(1.4)</td>\n",
       "      <td>1.8</td>\n",
       "      <td>(1.8)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>(1.9)</td>\n",
       "      <td>19</td>\n",
       "      <td>(8.9)</td>\n",
       "      <td>24-Apr-15</td>\n",
       "      <td>(5)</td>\n",
       "      <td>3.8</td>\n",
       "      <td>(7.2)</td>\n",
       "      <td>1.4</td>\n",
       "      <td>(4.5)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltd</th>\n",
       "      <td>0.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>(10.6)</td>\n",
       "      <td>24-Apr-15</td>\n",
       "      <td>(1.7)</td>\n",
       "      <td>3.8</td>\n",
       "      <td>(7.2)</td>\n",
       "      <td>2.4</td>\n",
       "      <td>(4.5)</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mpl avg mpl std ann   maxdd   maxdd dt avg dd best month worst month  \\\n",
       "2010     (0)         0.2   (0.2)  30-Dec-10  (0.2)          0       (0.2)   \n",
       "2011   (0.4)         3.6   (7.4)  25-Nov-11  (2.8)        1.7       (1.7)   \n",
       "2012     0.6         5.9   (4.2)  01-Jun-12  (0.6)        3.6       (2.6)   \n",
       "2013     (0)         6.8   (7.4)  06-Sep-13  (2.3)          3       (3.5)   \n",
       "2014     0.8         3.9   (4.8)  16-Oct-14  (1.1)        2.7       (1.4)   \n",
       "2015   (1.9)          19   (8.9)  24-Apr-15    (5)        3.8       (7.2)   \n",
       "ltd      0.1         6.5  (10.6)  24-Apr-15  (1.7)        3.8       (7.2)   \n",
       "\n",
       "     best day worst day nmonths  \n",
       "2010      0.1     (0.1)      12  \n",
       "2011      1.4     (1.1)      12  \n",
       "2012      1.6       (1)      12  \n",
       "2013      2.4       (4)      12  \n",
       "2014      1.8     (1.8)      12  \n",
       "2015      1.4     (4.5)       4  \n",
       "ltd       2.4     (4.5)      64  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show Profit & Loss Report\n",
    "DynamicColumnFormatter(port.pl.report_by_year())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>positions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win_pct</th>\n",
       "      <td>33.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_avg</th>\n",
       "      <td>2.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_std</th>\n",
       "      <td>20.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_min</th>\n",
       "      <td>(12.14%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_max</th>\n",
       "      <td>38.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_avg</th>\n",
       "      <td>59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_std</th>\n",
       "      <td>6.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_min</th>\n",
       "      <td>(4.76)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_max</th>\n",
       "      <td>12.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_avg</th>\n",
       "      <td>182.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_std</th>\n",
       "      <td>156.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consecutive_win_cnt_max</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consecutive_loss_cnt_max</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<tia.analysis.model.pos.PositionsStats at 0xcbe4cf8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show Position Information\n",
    "port.positions.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\projects\\good_to_see_you\\tia\\tia\\analysis\\perf.py:93: UserWarning: frequency not set. guessed it to be B\n",
      "  warnings.warn('frequency not set. guessed it to be %s' % freq)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.text.Text at 0xcfba390>, <matplotlib.text.Text at 0xcfdd198>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yBcCdqlrRMCne9m8DH6nq2yKyHDi8pt4HInIScJuqnhigbAQmXMttGO1cfYwW\ncKmqDpRSU7Y9QEdV3e3XPgZ4AqMAeyLYNYXNZk9VvyP4NPIJAeqnYlSZ3vTn2Ng6fvV2Bmpvy3KA\n4/36ULFYNQ6Hw+FwOKoMMSZX/TCOlitF5ChVXeRXrSNGoKkseZQ6aKzCmJDVFPvY7InIwcB0zHTt\ns6q6QET+Dnxjq+y0+4mYlUCGURobsARVLRKRjRiBLyhuBQ2Hw1Ft+NmtOCIQN0a1gzo+Tt0xAthq\nu53nWygiCRiv29T9OEc60FlEBOiPmTKtUkKMUSBv3MMxK3eMpXR5N+/17fH5PAc4DohRE2ouEMHy\nSwi7N67D4XA4HI4Dms7ABmteNRqYIyI3a+lqGEOARSGEnfIwDRO7LgZjurV0fzpcQQJ547bA2OX5\nahg32f2blDqN/ojxKwjlzfsicAPG6SQgTrPncDiqDXWxwSIeN0a1gzo+TiUBjlV1FWa69hyf8q4Y\nzV+lUdWVwHaMfdwb1WF8H2KMAmn2mmM8in3b52EcUZ7yCraq6lHVxar6Q4jz5mCmhIPihD2Hw+Fw\nOBzhJIG97dE+A47wSbfDBlTeT7wC5F8ha1U9wTR72/0rqup/rGBaUVJCFTphz+FwVBt13M6oTuDG\nqHZQx8cpAcj1Sf8KHCYiY623aVdgYxWcx3uMXVVwrH2ooM3ePpq9/aQ4VKET9hwOh8PhcIST+uwt\n7P0CHI1ZJmwXcDImTNv+4tUO1tTKGV6K8BH2xKx2cQ4BNHv7eY6gOAeNSiDiQrU5HOWhjtsZ1Qnc\nGNUO6vg4JbC3lusvKFnrtSEwBrN4wn6hqgX2/Z1bVt1KHn9ukKIcbNgXEfknxpkC7PJnVURIzZ4T\n9iqIC8rrcDgcDkeVUh8fmz1VVRH5GuOFmqSqH1fhuVqpaqBlWKuTHMzatmDi5a0CXqFqPYLdNK6j\nblLHbVjqBG6MIh83RrWDOj5O/jZ7qOoYoC8+iylUBdUp6IUYoxyglYicidFUTlbVO/YzlIw/TrPn\ncDgcDocj/IhItE/8PC9NCWBHp6p/UfOes9WBV2t5G9CW/QsOHYzI1OyJyMsikiYiv/jkJYnIbBFZ\nLSL/E5EmQdoOF5FVIrJGRG4vq72IDBaRFSKyRES627wmIvJldV+no/qo4zYsdQI3RpGPG6PaQR0a\np59EpK83ISL9gIEYp4xaTbAx8tHgJQGJwPpqOH1IB41wTuNOB4b75U0AZqvqQZglQib4NxKRaOBp\n27YXME4hYS39AAAgAElEQVREDimj/c3AKZhghVfZvDuBB6rsahwOh8PhcATEKlgUsx79BBE5R0Q+\nxMTUexb4M6wdrBl6AD+qqqcajr1/mj0RaVZ1fSlFVb9l31g3o4AZ9vMMjAeOP0dilhjZYKXlt4HR\nZbQvxBhHNgAKRKQb0F5V51fFtTjCQx23YakTuDGKfNwY1Q5q8ziJyMHA4z5Z52De3ccAbYAHq2NF\ni5qmnGO0pJpOv982ewtFZDlGE/d5NQ9IK1VNs5/TgFYB6rRj7zn8zcBRZbSfAryKmTe/CHgMuKMK\n++1wOBwOh8Nip2p/AZTSpc6GA82AxhiFzN+AZFUNOQVZR/gXcDcRLOwdDJwA/B14SkTeBaar6uoq\n6FxQrOt1IMHSP08C5O3VXlVXAIMARGQIxjgySkTeAQqAWwJ56IjIK8AGm8wAlnvn5L0SvEuHN+0l\nUvrj0i5d29KqOjeS+uPSdeL37kTM9OxUzDv0T+BlVf3Sp/5RqjobmB0B/a2JdAKGJVX8PCQDnQms\nHCtBtAKKOhE5HngdMx26HJioqgvKfYB9j9cZ+ERVe9v0KoyUv1VE2gDfqGpPvzZHY9yWh9v0RMCj\nqg+X1V5EBPgCOBd4CpgIdAFOUtU7/c6j6mLqORwOh+MAQUQeAZ5Q1S37eZy+wPsYDV5zoK8apcsB\ni4icBzyJmYGs8hlSERkMfBdMbimPzV5zEblBRH4EbgWuxQzeLcCbVdlZYBZwsf18MfBRgDpLgR4i\n0llE4jBz/7PK2f4i4DNV3YUJ4qh2q19lV+CoMfz/7ToiDzdGkY8bo9pBdY+TiNTHhAY5Zz+P82+M\ntmkDMNtmr9yfY9YWyhijVGBOdQh6lv2exl2A0eaNVtXNPvlLReT5yvZKRN4ChgLNReQvzFz2Q8C7\nIjIe86Ccbeu2BV5U1RGqWiQi1wJfAtHANFX93R42YHt7jPoYAfBEm/Vv4L9APnBeZa/D4XA4HI46\nQDe771XZA4hIDHA95t38PrAGQKs2eHCtxE7Bzq3GU4QU9sqcxhU7n1mlXaoFuGlch8PhcBwoiMgV\nGCXIV2pWr/Av7wss830vWkXMFq+MYNM/YezHZmKmLYNOLTqqDhHpjwnrEvBeB9XsicgnPp/9i1VV\nR1VJDx0Oh8PhcIQFEYnCCGfHY0yf2lgTqSLdOx6c17a+gapm27wU4FIROQgQzIxbqj2eR1W/t/mO\n6qfS07iPhyhzOMKOiCR7vZMckYkbo8jHjVHtoDrGyToyPgOcDuwArsbY7e0CnhORXGCFqs4EDrPN\nRmJi5HkZgHF6bIZZyGAbJvrF2qrsa20gzN+lkOFrggp77svvcDgcDkedZjJG0APjePkrxoO2PnAo\nJi7eKsyUbF/gG4wg97a1zwM4Cfgd4zy5FtijqgtrqP+OUiqn2ROR91T1LPFZu9YHVdU++901h2M/\ncH9IIh83RpGPG6PaQTWN00aM00BfoAmQiRH2wCxCANBTRBYARwOXA+NsvjeuWw+7TbDTtgcsYf4u\nVXoa9wa7H1l1fXE4HI7KIyJNgQE2GKujDESkCTBcVd8us7LjQKQRJjxKF4ywt91ubYDdmNWqJgBv\n2Pr/A+63MWvPx6wGsQyztm0gxZCj5qjc2riqmmr3GwJtVdxJh6PCuPhgkU81jNF0zAvH4YeI9BCR\nS3zSArwCvGVtr/zrJ4jISSKSLCJRIrJZzApJjgikmn7vJgC5GMEOVc3HaPlux9iAtQI+AM4EjsUs\nT7oTE47tYeBGVb1SVVf4OXMckIT5nVQ5Yc+LiAwSkSUiki0ihSLiEZE9Vdc/h8PhKDe9AESkvYgk\niYiKSItwdypCmABMF5HrbPokoLv9HG+1fL4MxXhPHocJmN8OOKsmOuoIP1IaZqMTPlo5G0ZlE2Zq\nNltV81T1A1X9zpZdholNOxf4oWZ77QhBSAeNMoU94GnMwK4G4oHxGJWtwxFWnK1R5LO/Y2Q1Tk19\nspKAjzG/Sck273gRaRegbY6I/G1/zl/L6IeJkzbVvsh7AvOAsUAacKOI/J9P/YmYFYSuxXhhfgTs\nFpGBNdprR7moht+7enb/CsYWz3dt1XnA4RibPn8W2f30AzEGbyjC/E7KClVYHmEPVV0DRKtqsapO\nx3joOBxhRUQSRaR3gPwjRWRyGLrkqHouwkwbISLRQCLwH+ACSoW9t4E1ttyXBOBLERlbM10NHyLS\nADgYmARsBToAg4H1qvo+8CBwD3C5iAwWkW7AEIxGJwlYCJwBPAosDqAFdNQ96gO7VHW5quaq6jZv\ngV0btxXmGdkLO137LEYr7IgQVHV3qPLyCHvZIlIPWCEij4jIzVRzkEQRGS4iq0RkjYjcHqTOVFu+\nQkT6ldVWRB62dWf45F0gIjf4H9tRa3gW+NmbsDZLAjyPebE5wkwV2LDE2+McCjwAZGC0DocC19nt\nTUyMsA5+bfMxL7T37Pe/FXWXnsAaa3P1J/B3zJTselu+2u7XAJcCV2IEu1Ns/marpfGud/61iMz0\nmepzVCMiMqAcdZKr+LT1KfW43QdVLVTVzCBl16hqWhX3p9YTyXbk5RH2LrL1rsU8GO0xxprVgv13\n/jRGe9gLGCcih/jVORXorqo9gCuA50K1FZFEoJ+qHg4UiMhhIpIAXGLrO2onewBEZJSInIyJETUQ\nY3vkqBs8Z/e/Yr7rZ1jNwpcYbdRzqno+Zi3szn5tPUABJgbYqZi1sSMWERm3H/aHiZhAuGDuxWD7\nebvdewPcPowxxbkNM323DXOfUmz5Rox3ZgZGECzR8InIDBHxCoOOKsIK1EtEpLuINKrmczX2SYYU\n9hx1izKFPet52xCIU9XJqnqzqv5ZjX06EvjTev0WYqZoRvvVGQXMsP1bBDQRkdYh2hYDsfZLVR8o\nxBgkT1XVkB4sjohmjd1/DHyBeZ4HAy0xtkiOMFNFNizz7f6fqvqt/TwCGOzz/U3HTEcCJS/QeCBB\nVXthvu9XVEFfqgzrXJIsIvEi0hOjVbutkodrhImRBkbYOw74CmtAb01xWgDLvQ1UdaWqFmGmfTfb\nPI+qnqSqx2MEv3YiMkVEFPPH/8QA1xFfyT47DF6b1FkYJ5uA7M93yZq83IuxyWwkIkMxjhlO2KtC\nItmOPKiwJ4bJIrIDMwWwWkR2iMg91azab4eJ7eNlM/tqaoLVaRsoX1WzgP9iFmhOxWiEjlTVWVXb\ndUcN05hSjQTAQ5iX5UaMN5n3OX5HzLJAjlqCiDQQkWEYrdMwm13yJ1MNvqEeijDhIrwksffant9h\nYodFGscCd2B+m8D8hgXFPs/TxSx67ktjrKYbM3WbhdGCFnorqOoO4A+b3OHT9g9K/zj5sgY4m9Ig\nujOBKBHpZPuSKCKXArkikhSgvaN8zLP7QzACWJViZ7z6A3fbrCcxnrT34oS9A4ZQQZVvwmhJBqrq\negAR6Yqxh7oJ4/VVHZRXI1MhgVNVH8XYqCAiLwJ3ichlmH+qP6vqA/5tzhRxU7wRSBas+h/MbAPX\nxcEnG8303BrMC/3OxvBwgjFEb41Zz/Fs4AsR+Tyc/T5AOQZYUIH6MRgzkScBYmH7VXDEU9ANyLld\nJFnB82ipDZqXPsBBIjIEU7YFwD4DXuJEpANGs1+TtMZMJ+8MUNYVY2t4O8bO9GQRaU/wMArPY2Yr\nNopIqk9+O6DIXu9PGFvGBu1FGp8JHaeW2u6BGZMin3vzNLDV714BvEtpSBaAqZhp30esrfMzGKcO\ngH4i8lvQO+AIRg9K15wF6BpgHLxU9LsExpxpBiY+pZez7X5QIrx5o8hgBc98SD0b+jwOS9P3c2ak\nHURfA/0fhx9zwXMHDIiCqARoWAB5D8PCdPNHDoCroWN3a4axBtY/t7fSBoCuEHs2HPoLbPgMMsZD\nu1Wwqw806wFd8iHvPlg8FlqvhT3fV0CQHQmJx0LvfCiMgehv4Lf/2diDFaQyY1QjhBL2LgJOVFWv\nzQequk5EzsfYdFSXsJfC3obWHbBTDCHqtLd1Ystq6+PMsRp4SFWHi8jLItLdf3r6JxjX0PxIEwdF\nzSG3g3Vv/stMbePSNZ8+CP4eB/8shKTz4cj34I5cqBcPp30Nm16Frh9Dk5kwOwp0N9AWzjkUOp8I\nKwFm23htLl29aZsXW976OVD/e7i1IaxqDBsvg+1nwKRVdvpxHBxaDMXLzXqdJe0nQ2wO0Ar+dQs8\n90977kdMLDlmQ685UHANnLwceggU14PCsvozGP74E1qk2SniytyPu+HxPGjXCT66DN79EP62A9pv\nMl28BGAQLG8Iz/wA446Aa+Ls747/8RZAb4E1UXDmUSDe8n/DCfWg0US4fA+sng3dBbpfA0kdoW1D\nqL8EFvseT0BGwLZPoYXA4b2h70BYuwkWzYZe+RD3G+SnQ0w0ZB4HJxwCy56FJ5pAx3xomgUkwKbG\ncGcfmH8irPQAcyLk+YuU9BuQ3BK2dYK0gyDdW74OehXCJ5tgZH1YWwxd74OhgY63GTpdat5x5T7/\nJui2Gloq3NUQVjeFP5vAlg0wKBN6fQotGsINs6HVCMg6GXa1gKPetQF6K3O9jaHnXGgfBV1fgd8z\nIGeTCeMSNQS2C8T9CqdvgXWnQWpL6LEW+qyC1BGQcigc/6cJFbTX8ftCvzMgfRw07QVRjSDhAtgU\nD/VehsIO0OYJ6HsU9P8QEurDwvL2PwMu+gsyjof1HaFTMVyVCOsGmrWAq+33rip+X9dBryxooRBV\n4k4dAAkWJkdEflXVwypatr+IWVz5D8zUTSqwGBinqr/71DkVuFZVTxWRo4EnVfXocrb9BBNTKA94\nT1VPFJGXgP+o6i8+9VRVnSdaBHKByAvd4YptMP5yOCkOFkRBUT6c1BA+yISRs2DsZFu/FSxqDht/\nLTX2d0Qop8Own2DQRrgfYBmMFWgZB/PzoLcHJAoSOsK7sVC4AcZ3hpe3g2cJJJ4Pb50Kj35mp3TV\n2K4B0ABefwwm3AIPD4MZn5RjJY7ecNWvcI73OJ9D03yIGmNsBMtFPXg/HnbsgYMw2oyoerAjH5oP\nghcSIHeOsTulD1yRANmLSpenKiEVYjvArHfgogvh6fPh8ZfMbxwd4e5rIfckyImGhb3hxzXQLQP6\nxRphLqY7vNDACpEAq6FbLpyaAJ8XQGIxDBaI6wOPeesUATdC36d9bP0uhGPegjuKof4RMCMecpbB\nqHS48AQ4ewFcPgmuvd8K5Ac6aRDTGmavgeE94IuX4PzxkJoFUV3h0eEw82ZYUQxyJHyQDyeH0sIE\n4h9wxPPwmO/z/jk0PQ8eOAK+mAM3jYH7P4Q53vK3oWUvuCAH1tUzYVaitsPbzeGo/sYGulIsh2ui\noF4WLI+DNrHQbCPcCMQ2hGGJMDgWmqrxlo9W0E1w0yjVTbNEOnYyfybe72Te4SX8BJfuhK+bwrBM\nWNYEBudDShy07AfnfQNXNYfTiiBTzHG39oP3y+rvOuiQDoM3wNVnqXoQka/hnETodwSUzAatgORm\n8Fv7UqeniML7nAWTW0I5aISa6qi2aRBrMHwtxttuJfCOqv4uIleKyJW2zn+BdSLyJ/ACJiBo0Lbe\nY4vIaGCJqm5V1QxguYj8DNTzFfQckU13uGojxD+r+nIO/FEMJxeZL3nGLugUBdGNjdE5AP1gflap\nEbQjgsmF+FjzEgAgGjoWQ04RnJwL2zbCrZmwOAX6pEI3D3hSoEcDqD8OtilEfQa3Hwrvfgbf/unj\nodsE1n0A/XOh7bJSb9WQbDXTbIyBE6ZDm4th0ukw88/SgLQkwTMrTUy/fSgCCiFxqg0F1BM+BMiH\n5gAL4G2voAfQErbuNNO++/Af6NEQ/hoL26+AB1+D21MhtgjYBr17Q2YGfOGBvwHkQMsc+HkbvFYE\nmX/CUcvNqhoAZMORRZCVAyfmwSHZsMYDORt97AZjAF9BD+A1WPAfuArgXpj1BcyMgz1N4L3v4aoW\n8NMD8FwXmHg2DJ0IvUKG9q/jbLThg2419pk8ByddD32T4MPt0P9SWNkXco6A7CgomGs8qwFIh+jy\n3Lu/jPNNCUPhzNPg7SRY/w58djdc9W/41rfOGEj3QH4avFgE2YCnwHyHAjrcbIYWv/g5EO0yDo8l\n7ICGxZDtgbxiyMyGZQrFo+CvUaprj1f9v/VwdSFkbIdZu2C2B3JHqW4CyIfNubA+3QQIL2E3xAvE\nxsDrW+DKTHgxBaZuhrtS4BFMjJhZRZCZDl96ID/arPcLQB7ErDSmHvuwB3ruhq/P8tr3GkFicwz0\nXGmn2JfByQJHbIejfNsWgWzzGa9IJpSw10dEMgNtwD6BbKsSVf1cVQ9W1e6qOsXmvaCqL/jUudaW\nH66qP4Vq61P2sar+yyd9m6r2UdULq/N6HFXLZFWdrpovIsnpMF8hvxgyi+DDKOiTB38NhS9+hlOv\nhxsHQ4MCaBbufh+IXG/W2Sw3VtjLA8iGeh4oSDFapKw98PWZqnsy4Oti6JYHHTyQVwTHp8G4Ih87\n3rfglXaQngNjf4EjAIbDp18Ze2O2whGpdkosFArRAItg2B1wbaY1E/nGCmsfQbNd0Gu238sWoAfc\n/HcYFAV559s/H73tslRHw4sHmxUr9uIg2Lxz3xAyACyDbi2sI8V/YAUgS6HRNGgXCzGtoLgRzCiC\n9C3QtAg6FcCWk1Q/3Q7vR8EgoPs66JgKzQSa5cPmOdAuB9YcAxO2wUe74dzdQV74Xq6Bv3Lh+BGw\nsyF4JsKj+cZ2ataT8NhQeGoDnPQeTH4InrnXBHw+IEm1fww+No44rIHBH8LoeKshOs7HNqw+bJ3n\nI+x3gulJMC0GPrvaOFkEJNYqYH6xfzqWwSm3wM1r4dFmUHwv/NHFR6sLkA9xHsgZrfpbEWQrqMcI\nfbHZEFfkJx/shvYeKMiGOG/eZrjmN+i3Ao5bDsfvgPZFsEuhyAOF+bDFA/n4TCGOVc0+Ai4+QfW1\n7fDCOrP8GgBnqXq2mQDgbXcYD3MAUqB7DvyerFp8mmruSNWsEarzz1Tdc5rqMoBRqimp8GwevF0M\nWQqFG6FtEcg66FkcYDGIjdA2CvoU+mnr8iDFAznFVpiNMu2zFRr41vsdBm718fKv6O9dTRJU2FPV\naFVtFGSrqJbZ4agW0mBBMWTvhiVRsCwaNMNMVcT0htwJsOlYOKgFdJwJLfqbgLKOCOM4OL0JPFcA\n9VqDrIBr/oR/FEP2KNWUHFiTZz1JR8PKKGgQBQdvg1e2w/uFsGubtauLh7TekKtQUAzZxXDMTzDy\nThtrrg+8lQDbXgoiVPmhALnQNA8Sr4X7m8Kvv9tzfWu1YOv8NMfPQIc/YeRr8EA05McAl8KEx+CH\njXDyD/DmKrMSyF5cAat2wmEPQdcfoUGejwCbCQ3r+wgGMZCzBRImwsSOsKsQ0o5QLSqAbWkwIAZa\nF9mXWCGkeqAgBzZmQO806L8TPtoKTyh48mEHJmzBnGLIWQ8X/1RqyF+Cr0Ad72PEfzOsFyh+AV4e\nB9vmwgfD4IlkmNoN/vu98TQ9INnuJzjnQKs06D8JHnzSxn5cbRyQaAhbVlmv8T+hXjZ0aAQpxVB/\nURDNFECmFUI+gzbpEJ0NHf4R2MO6BCvsFQIUG9voYjFODbHr4KZfYPgyGPkrDAAohObFkL3BBDQH\nwAN5haa/hwK98uCEKKi/GSbvgJf3wP/STDzHvbHC31mqnrGqeb5Fo1R3ZsL3m42dHwBF0CR/X9v9\nfRihOv801dxNMNEDBTthxG/GPMGjAZyeMqB7MWR5/Jw58mFjMeR4IH4TtCqGPR7IF0j0FYKLoaHH\n3JNI9PTfCye0OWot3phGs0RuGwVp74G0hh+z4NuWcO4vZpqqWQPY1ggSzjLehfwBbx1stUeO6mWq\n3xRgMH6EEZnQbSv0nAy/F8KCbFiXCz8CDFa9u6Syqu4SmRMF8afAXFR1gUjPLGgSB7ui7dgqaA6s\nKYSdMdAkCg5/EC47BbacDs1+MFO0IV+IXnZbzVRH2NMI0n4w0zu/7LCOQ6l+wt7LMOxQePd9mO7V\n7LxcuqYoYDSXDXymrJfB8BbwYyf46ls4aCJMGwJPz7N2R7lQP8HnpRQDuWlQvwAaXQsvF1tNZQFs\njYd+HsgussKhGoEhczf8t4GxnyreDc8Uw85hsGWT9RQ+A7Z9A9/FQEIDOHwX1G8EuYD8AucpFPU3\n8Uv3IgbwwAm+eV+ZuHGcDCMWwqgmcHIipHSA1d/Z7+KBQDrUawjrn4Q734N+K+GIFBh8FWxsYh0h\n8uHM9fBGB9jxEwx8BtZuhMaJsCoF7j4eTt8E3b3HfAi6toLcS2FLHki2fQ5/gbbTQRNgq78mD2Ar\nJLW2Y10AcdZujgz4pAB6FkGuQIwVjBKjjWasHbC0GJL2wIomVvOYZ+ziPNHGaSprG7ydZ7y+PaNU\nfb3lv6roPcuH34pgzA5o1NzM2jTKN57g5eJM1V0/ieSlw+xWcHaBsZUtzoOYeCv0LYehAod7INPj\nJwiepeqZJfKfjnB7OnTJgG/TYUYHuH8tdD8YVm8xdoddMuHrQji2M7w7FZYXQVSMj6dxpOCEPUet\nZ5TqVoCzjKbh4bki0VEQ44GjoyAmG5bfDf83Ci7Kh2a3w98+qsQPkKP68EBMO5jfEIashLyL4XLf\nqR9/jlP9P9+0ncZPmA2XZNjfNYGowXA3qvqxSM8GcMdE+BqgA6xZZ+3xgnEoXJ0OfQfDc9/DPwAO\nhj13wvRr4JnpMDfDvmQ3+8XHS4MuA+DbgyEv2B+LdXBjNHzWHP7aDl2jodcO0CRIXWuFywVwyT9h\n1SPwWx40aOoTviUGcnZCQh60PMy8sOIBCmFrNDTbBu+NtE4So1S3vify93rQ0WO0np7TVVMBvhfZ\nne7VGKrqcXZVocUi/8mAphvgpBjjXJIZZYUTXzZBq47WezIQE+C7iZC4CC7fDT23wMCBUG8FnHkL\nTJpiPQzrKrshPgbyxkPqeEgdBQV7oI1X0NsGiQpFu2H0cKh/L5x8G/QvhAZtYAnAMPhlCozxHnMi\nTGsFi9fBy8/C1YmwRaBwE7T5A7o3L42nCBhbulUwIhE61oNnU+DgKCjwWIHwNNWFwML3RZoKJKjp\nWxFmLz/DTdFQmAGvNbHayG3QXKG40KzcIjmwbIxZhGG/yYZ1zaFVClz+l3GGiimCdRU5xmaYVGSi\naJzqgeJoSNgDDeLts+6BRnmwKh0+Glka57KEUao/LxDZEA+d02HGWaqF/xWZEwsjMcLe4J3w6S54\nrxM8sQp69oRVK+E6D/zetxwOYDVJeZZLczgikmDrECarFudDWjHkeiA7ExY1M//qxp4B982Gi7tV\nfqUCRwUoy4ZlPcQ1g6fyodliuL+hcRjYGUrQC4RAVDGcOgT2jIKda80UrXiPUwBrxcfWqC/8uR16\n3AK955uAxPuQao2zLzcreHhiIPtIyLwcUlrDTx/CYZnQMBryUqHLi9DuA2h+Gxy2HXpfHuAF4otC\nUSG0TIGjs6CLNWxvOBwWroZRrWBRL5j1NQzMgOg8aNQaPD/DLX/AQbGQuwMaeSAuEfBORRVAWh78\nFQNv+t7Hs1QLG8MGD+TnmVU2AHgZCgt9AlZ7yYG1GXBYFLTxQME2mFkE2V4Hjq2QtAxO2wXnejWc\ngTgOdi+EN4+B52fDmEJovALOLITEh0ysvjrNHqgX7aPBfRW+fsoENAZgK1zugQKFosGwsS+8ngut\nFaJ7W2HvBqPlbvmWWR0IgGKIXQ2td8GhqTCgO3yxGgb+DKMfgxd9jp+UDm0bmenI3A0wuhiG5sMg\nj0+/AM6EjJ3wSbFZjSWnGLJWw3WFsCfXhIZZIdB4CzTZCWfsgi83wyNr4cKqEvQAVsBmD2ixCRSe\nsA1ez/MTYMtilGrKGappf8Fd+bBBobjAx7FKIXYnzBqpujjY700q3LcRbjvLBicvgjUx0GEHNFRI\nyIE/zlL1ZMDXOdDJ/t7FYKflAdZDu1/hiKIKxgauapyw56iTHKl63Xq4dgs87zFahwSAx+C7HGi/\nDk4d4zft5Kh5ZkDnPGh6PUxqC4XPwJQz4L2KHicGmhVD1iroCZANZ+FjU3aWaqGCJ89q/c6Gtbuh\n+79h6v0wdCUkZNnfw0Ph6oegawE0mgLjL4atCsMK4TSvNqYVrN8EnTLNNNPPu6DLA3DJufDidLjs\nGHhthNXC/QEHrfH58c+DGDsFVuSBph5oug1mpME7QNPbYUNHmHM4fNsC0nZD0mFw90Y4YbjxbN2d\na22E3jHLa3kE4orNdCvZ8O1muCc5wFKQyarF/8/eeYdHVWZ//PNOegFCEjpBkC4oSBURQUU6QQWs\ngKwusIri2su6COvaUFFxf/aGFcWCgIqIIoiKKEVC75DeJ2UymXp+f7x3cBhTISEB7+d58jy5/c7c\nuTPnnvec7/cg3JwGRxvV3oDFM4xuSH+K4DuB1goivVDigG/zYVkuXOwGlQ4jLLojurTYz0O3PH6E\nD4caWZVoSD4DVgXphoDTmiIjs+ebjgHPZKNhZy+0d0KGF0q94IwD+RneAIiFXV8YEijRhmTPtfoz\nAoAVOu6ArgJBDoibAF9lQd9msGWCUau5B9qnwyQ7dHBDkRHERXnBHgQNbbD5mJMVkUtE3s+EhQJB\nCkJ2QPYhmFkA/54gYrPC99k6QHUVw6eJIlsnHutmc8LMEZF0WFkM+7zgHCHy9fEeI1Fkfxo84oZc\n17FNWaFi6LiWxwSRUl8GHMAJWR6wHdG+0cFiXFcLpHiMhhKvHgoP8QV3+dDLCeduN7rk6woz2DM5\nZanMh3C8SP4okTUeyFIQlQ+RNjhnGtwdC1uXwn1Wo9vyZDETzn0TWjwHbU7mceuKymr2lsCgVrDp\nBgjeDBOCdBBkr+5xCmC9AzJsRseigEMC6ma8UFIIUVtg5BkQqoxgsBAaXARzRxluEDtg4mtwhQsa\n9tKZtPMDj9cMsjN0Z2vv3rDaBgk2iAuB4lzocYbh4AFQAhfbYPgmuHIvnLkTJuyEYcZQWVMLNCqF\nrQy7krIAACAASURBVCNEltggKRm67oNHv4AvmkFehtFQ0R0WdYGMXFjugYSFMC8csgWCPTogKwBd\nb3SFtkYrkwkiNv+i+PLuo1LYkQmL3FCQB98liuSFwBduKE7SjU5eDxSWwJ58QyqjFIIOHCtsXyZe\nCH4MXg3+C9h15UGjy/6oqevtv6wY2ufDlzbYng0rLRAZDpIIj96sZcWOEgvbAR7RgtmeC+G1bXAV\n6B/yv4MVoLHfZ88GbW2QbIEuebAmC95OhgfT4HUX5Fv9ZH/8cenvzOZuKJgjIuNFCkeIOI1zftsD\nxVZ4L1HkeFwmqsRYkYUl8OjhGhiFmSBS6gar28js2SDMAjGeSoK9MvZjy4TXPLour5kyMqMeSAmF\nFlOhvVFSYt1llGIIhNpgl7eSDvfaxgz2TE57HJAioFJgpgcuWABbcuG2ULB+d5L093ZAhBWCXoD5\nN8D7/9QWRn85jkBoc3isM9wGkAw9hsBaJ/S0QCsLnOc+juaZ4SKLj+gauMZJcK+RKTlmiMoDxTY9\n/NI6F7qHQXYkpKTort4+u6HfFC1PwgEY7oKGcdAXGOCTm9gFXbbCgATIzoT+oVAyG34Nh6xcOPsS\neBugtfHD6zt0OiwMgpbFWq+ridLDzHIY7k+Gmb5hIgccdEPjYHQK8lxILYa2HWDlWng3BJQVPrLD\ngabQu5/xY+2B+NIyhmJPhIki3pEiX+yHv10i8hrozGAGPGKDPRZolgLP5OsGkpY50GAHXFkAYzbD\nZaUV1IR7IaStn7TF6UwRxPXXD5tRAhfu1Fk2tRs6KWhfDLsGiTx2RDethLlBfQ7fzAmwBPwZ7gd4\nEF4NgcKvjWvfAZbNhM9sMBkgHOz5EJkE5wHtcuEVNxQ44LeRIqsTRQ6OFvmmt8iUwE5YH4mQlKHF\nyj8IXDZGxO6FUsexFny1wggR5xUi5daDVgevrlEMBtgDlyvtDFPuQ1F5jBRZXagt0QTjwXQ4ZLt1\nGUZYMDQugG8c0HU7nBsMCYZsS43EW3uhfXmagRVhBnsmpyzl1ewFMlHEexjudUCaB4oPGFYzDogb\nX4Uhw4ehQzgsLj6B+2UY3BcPy/3nHfSrITtdCazZ+w0aZsJ5e+CyBHgoD87uD2kC7kLY4IbC4OPU\nRDSGebweKEqFBb0NKzIfNtiQC0OUlpxoNQ5e6Q2fp8KFZ8CqXOieYejnifGjYNHrOvfAsN+hrx2G\neOHcTsaX/BhYEgk3doFtAsFXwVaAbpCXD1FusCgIjoRVXihNh1e8YHeBVcB9mciBRJGjTRcOyPL6\n1b9da8hNnAXbM+AMO+yfKOLN1z61bRfBytdgikV3Bh6XN21l95EvEPWRKJJdDPNyYZkDto8R2ZoP\nK9O0M1FUpg4Qmu2GW3+H2zbDmEDdPoGgTmD3QERtCy53hVvHaQ/0OsEOcaG6Vu5yD5Taoct2ON8G\nQwCyjaBuuojbA4XZfkPiByAhDeIOQqsFfvI1IVAcDGyFUXth/nRY6wVXZ1gyA749CEMLIDQVnh4n\nsr0XTBkjsrXKJy0iI0VWjRRZXdbiA/C3kSKnlEOKoccaCqB0ratj7HGWEbih1Bg50KMQIt4DMPVZ\nnVjwFsOPwTrb38GQfyqihuItO1zmPo4SJDPYM/lLcIVIZh+Yng6vOKDfZhh3rpGFqWwo92vo7YD4\nnnBHaTWKbK+BQfcagWUBtImHLYP8CtIXVWG4qzy+MHTeDkLoBXDlU35q8fWZfAgNNzo3U2BId1ja\nE4YIBGfAUy7I81ZzaMUfAa8L8saIrA8sug6GFcEQY9cm62ELYMsdsLoF/Hg3vOOF4BKIbA9fAlwE\nz4nW53IADd26K+8rl/Y4VQDttfCzo5v262acHla9aCLkHIEZ2+DvAgwWcXjB4YQjefBVOjyYDA8E\nnr8HDiho4cuKtQTXZzDhdVjjghincZyxIsVGtiC0D7R1Qe4YkWoPfx8vI0ScF4u84QsES/Rwb2Ea\nPD0CPk+BaR4oNMRtY1MDPp9eCI7Tum6e5Fp86NkIUXtg1MGTfH+8Cq3u1Z6llhToH6c/FxHo+soW\nbkjIg2V2ODxdOz8B4IbMfOOBA6AIJmTDpEJd4hDlu3dCdfDA2X+UPFgEXGvhrRv1w1N8AbzkExyu\nbsNTZdR0jd7JwKsb9kKN//Ochrbkce7OjnYcOTp6MFHE69Z1uM5ESBZw2WCngMtoNDnhBo10XePr\n9IIrUPS6Msxgz+SUpbKavbI2CIL1CkIs0O5tSGoEuxrDqvPgWt9qbsA/eMqFpn3gjWQ4f7H2kKyU\ntdBwEfznZbg1F4JKoPkamLsWPu5i6KZ9B91mQc+XtY5VlfkCYscY+5gEl/8INy2rQF2/Lgms2SuA\n0CAoHQrPALwAy73gTIPHJ4q4DsOtQ+Hd4z1eYJ2eP4NEcgthazp8aIfDedDqMshNgwdnQnIIFOdA\ns2jIawsrH4FVAh6vrq2LCNZOE+uckJNgfHG3AI+Aq5Px42sU0rMLetphTxZ8kGMM2fcWuf4ykb1D\nRd5LFMlOFPnTMFgiJDsg7SB03goXpEOTyyA3Rp9HuMevxs3oqg33wBlW3TF8fO9Zde+jMhgjYk+F\nBXb4HREZK1J8EGbuhxussNIBrdOhcQ40CNXnHgIQCvnrjAeX6lDVLPsMmKi00HqV7tua4lW4+ElY\n0BTei4aSZpCRD98IOB2QHQRNPLB8gMg9/ts5Id1llJZkQKwLcg/DHXnwzVQIPgSTmsBGX7Dnwwvh\nAq4CiMuEBhaQ0fXUw7Wu8EChWzvLKKBBNrxwvPvyggh4JgSUikyCrwSciIgXXAoie4tMdWv5mGrF\nW7/DoC1+GbzD0DITrrbCSickB8o9VUatBntKqTCl1BqllEUptUIpla+UWhawTjul1C9Kqb1KqUVK\nqTItjJRS1yul9hh/UyrbXik1Xim1TSm1VikVa8xrr5Ra5LdtmLHcDHr/IowQcSbDw4Xwsxv6Xguv\nA/wC0wbDeIDZ0PVueMP3g2KFlp3gYDSkrKlCY0Ur+O8MuD4Gdjqh4atwRgRkdYbS3dD1a1j8KDz8\nA0x5Hp65F/5dndew0sgIuvW5xVm0M8IpUftUqIM95zewdC+MiIY2xZA01vCwnijiPZEsRAa8kQef\nlrf8QpGHrxD5pRj2OwM8aEOgOA3OidAOEo/FQzOL7rDzWCBStCZZYTGsD4JewUBDnVFxj4P0vX52\nTE5olg+rh4ksu1R7eVcNESmBHQ6IFeidCaP8hkCD3McGe6VunamIKIJvqnyMWmKsyAb/jM94kcKJ\nIi6bHuJukwVTUuGGfwCNICQJ+kVB+vpquA+4ge5wUwL8z3e/lscRCN0I158HCwtOIIt+PLghpA18\nZ4fmF+kf57RLYOEBmJEKW0vAOlykKHA7p+787rsFhqXD+Q44nChysAQ2uqFpM3A3hf1TIfZ3GJQP\nUdugl0CEF9wOaJwPreyQUtPZvFOdIlgGdN4GM0oheeyxws/VIggaCLgC3+MxIpt7i0wFyIS3Co2H\ncqMZq1qZPYFzLH4uIvm6yebLISIvl8BOK5xr85OSqYzaDnKuA5brIJd5GAWkATwBPC0iHdHijDcG\nrmAEa7OBfsbfQ0qpRpVsfwva5uVl/sjaPIzhTwggIg60OfRRsUqTU4eq1uwFMlZk2yCRJ9xQNAf2\nXgzPAqyD6eugwXZoDdAKXmwAb+ZB5+GwrwXs/BpGVbTvJRCXBgN3wRVD4SMnNN4FTVtAtg1CHTCy\nAKaMgd4NDa0zqWZH8JtwK0AyhJZAwwhIt1egc1aXBNbslULEDGi0CcZ10B2zrYsCnCVOhFEi3w4X\nqVTMtBAOegNqA0OhsAjaxxmSKYXQR0FYKsz16PoyeygUDIevSyHzFZjbUQ8NuV0Q28F4yjfM4Vu4\nq+jOEUipbtLo5oSsYjh42LCrMrJhR4dqjWAvLAhCvH42atXleO+jqjJWZFsvmOKBwhz4Iga4AaI8\n0O8uiN5XjQzFs9BuO1xpha4/wM1bKnjISYLoUMh/Bz4rgna1XRvojwtCW8F+O1x8D/zqNbI9E0RK\nS2BlPnxR1nY22F8KqRbtP5/gNDprvbB9lXa4sAyG9b21jEpCJiS4dVNTUzvsdUOTEhhgrWeCvvWB\nRJHsHPjCAuGFsO5E9mWHJFsZNbL+99JIkVWJIvuNSS/VjLe8kOGGws1wCegmrBKjJrhEHzthN4za\nDZ22G93wFVHbwd41GB1DIvIdAbU4SikFXAR8bMxaSNmB13BgpYhYRcSKfoodWcn2XvQTcRTgVEoN\nAtLljzffx1LjPE3+YgRBZCbM6GAUwUfDoS+h9X7o0Bi2F0KnYmg7Btb2hEm3wrIsOGdfBU9Tr0Ff\ni9FNejNsVuBOgi6XQ/R+3Q1sN8SenWKI+aoyXAkqosQY9j2k3RMaRkP6drhyYUCmqjwGw/gRMLo6\nx6wp3FoYWCyQsAWmKf1jnVoH57HHA7Fb4c4dhnjy3fDMGhi3AHYaT8yhmbC4IRw5ArOzYdoIESci\nUgo7e2nj+HCj27vRZhi2CcYfgosK4MfjzRxEwvclkJ8Bv9lgi0d3VvYKhi7eYwvKxQZ9POCq9zVU\nItILpv4Ar/eEnzvDMi/YWoIzo5Jgb6nfMO9yvx+1cMj8sYJmnkMQFQzF7fS9Zik9CWVLg2FCFLyz\nHwaFgCscxO1nSwYwWSRltEiZ2edEkS1Z8JDROb6zGN4HXaPpgtIdcOv/weZO+kEx3KV/31QQxBfB\nTxbtT5s8SmRNbb/WU5ESWO3VntnWytcun3Ei2weJPF6NTVzV7cYVIB1eVdq9BrR2XzGADX46Ave7\ntWbnSA9c7Klk/7X24VdKBQHdpeIvvDjAKn98UaVSdv1SS441QU4x1outYPvH0JZYo9Fejg+iM3uB\nbKEMHS2T+s+J1hplwcseKPkP7J4LM6IhOwkStsOVifDu3XDLZHhgth5CsfaBgfGwa1oF2b0k6HMp\n/O9/MKUxdJsMqzfDtU3Bkwcfp8GTPgHV2fDWBJhrhybVOW8PhLcC0iA8BBpdC6EJwAtV6NByA7/B\nFcla+qPWCazZc0N4ke6KswPhWfBOoshJry26BjIVhHig2AWN1sGwkRB2oR6mvXKv0bXrgG+HiHgu\nEzng0xkD3TXrgWg3NC6GLRZo5NWm6OFB0MX3BH48DBHx5MAcB7zv1l27MW7o79I6YUetxXLgU4Fo\ndYK2lzVRs1fVA80RkXaQ0gX2pcC9CrJ3w7C0Y8VujzIGhl8Gi1dDoxBYusbIagPYocV98J84eH7O\nHz+IR0mF6BAjOFbgKaxCBr0/XHfdcYrfuoH1cG1j7XbRMtSwIvPousoqd30mihSUwL5i2DJW5GiC\nZLgu+g8ClGjf50MCF3t1Z6i7GNZ4wVkKh4/n/P8KZECaF1zeWhLzruBe8lLBMO4BSPgdBhUY4v82\nnbFv5tH6fE5jfrDFKOOYKOJNFNnfT2SmT2bK6tfYUxa1+aQTT0ARaQ1TYT2CiKwSkT4iMg6d7fsC\n6KKUWqyUekUpFWGs5wAsSqk6FTw0OfkMF1lTDEmZMGMstA+Hwi0wIB623Q3F82D72/CzB6KzYZGA\n5R+w7HuYdb2hxxZIHrS/EHZfoZ/Aet4EOfGwLw5c2bBotMhvWbrzs3QQBD8AnrYQvcO4ySvDCkEN\ngb8Dh6DB2dDkPChpBURX4X6bDV1LoGVdDPtuhKgsaO4BVxo84YHiQvj6ZJ8HACLigjw3FHghJhLa\nu6FzsrapcgKtLRDhKOc9dUNeMHT06Ozqai+UBEGTPPjG6Po7oczBFSKZE0RsXsgIhpZF8GtvkSn+\nMi2JIltyYbmUYXhfzxGlf3vsjcHugPjby3ng/hZuiID0p2CAGxpEQnIrWKvAEwzFxdA2D7qvMIa6\n/YdqcyAq1MiEKPAUVSHY2wB//wAeOp4XdQ+cHQSOPro0iFDwWWxFu/08javCQJF/jxBZ4j8vHR4V\ncCTB3W6wu3V5QaFLO6rsM1xinO7a/d09pZku4hZtD3iyHzDLrdnLgWgrjPBA5wNwXRL026N1PrMT\nRTIUBB+ACRYIcZUhQl4Ev3m1pE/Tsvbv44SeCKtA4IsLDNBygRillMXIzrWm7CGdVAxNIoMEtKF5\nXmXbK6Ui0cbNw9E6Z5ejrZSuA17zO88/BY9Kqbf4w0PSCmzxRe6+sXlzuk6ne4rIsyeyv89gYSR0\n+B4G9YLYT6HD5fDl13Dty7DkYdgqED0PWvaFyMlQOgdcP8KAWWD3Za5mQc9siLZDqxEw4Q1w5UDq\nZIhYDk99DmMehYEC348Q+bqrUtFTYMZocIyBkjvg4k6Q6r8/+CMz5pu+EFLPRH8o34NnR4GK15mE\n861+ReiB24+CEeHgLNLZjoICaDULelZ2vBOdBhgIqZ/AWYvhwRYQ3EA7BMQDb4oh3VEXn59JkD4F\n9jSDye9CdiycNUp7Kpd8B2094Li3nPObCx2ugpgu8GiiSPbtSrlaQev2sM4Lo++DbolKNTvR810M\n6z1Q+iwwWKkhZSxfUgoHep/A++FfZ3Qy3n8BWQZtnoY+3xo1iCnQPPDz6AWc0LgnvL8Sbo6BHfkw\n0wpBt0PvIJDX4fEQKD4IPSbDwXfh0fkw9SA0/hl6R0Her9C/OVgeg55vwXrf/uHPn9eGsKcQOs2E\nnkFlLC9vegb0Xgj3jYYXmxr1k2lG44kXIl6HVheXcf2q+323CbxecH4JjR6CQUvhkRDYMxLOR6kh\nG6C4FHbUk+/nejndW2RqH6WGoFTXmt6/b17g8ieh58XQ2mefMgt6Kgi9UQ/xn/UOFLwHSxdB23gY\n9S3E5MLhvuiu+++gnRdob9wvAXW2DdpCQgIMpgJUbTXsGMO4KSLSwm/eEOBOERnrN+8j4BMR+VAp\n9RI6oHopYF+NgY1oeQnl+19ErJVtr5R6CNgsIkuVUmvQHXMTgMYiskApFQYcEJFWAccUEalT42KT\nilF+X54nwjKl+rWGf26ELnfAuQ/BKxdDw2BohK65sR6AWxrA4Di4dDpE7YcBm+HWdn5ZlUS4NBmG\nvgGHLBCWDLNbwQPhsDYPGg8UecT/uD8q9e9o6LUY+qbB829UoVHhAehaBC/EAfOgZBRE3ArzlsMF\n38OeXw3twEAawmvF0LYPLDwE5wWBKx3+eWLvXOVcBkOXwZ2jYf4GuGQa/BAL3W8XGVnbx64KnymV\n0AruS4fnz4C5Fvg9D0oaQb9c+HqoSJlOJ0uVapIAC1LgP2NFdn6l1Ijm8Ld9MCkcwvyH306UZUp1\nV7C/tnT0auo+qiqblfrADal94e4tsGgstB4MX74LP/qvtxvCz4IlV8N/34eHR8CTXxkaiD4awwtt\nYHMOnDEClr4BT4RBzvtw081wZyfY9jREPghX/BuuvKCSrFcLeDQDBsyF6bOr0WAzDMZuhfMz4P7/\nQMcn4JXr4fEX4OtNcG06vD5a5IQakZRSQ36DG4MgrBDSLxS57UT2Z1LzlHcvLVXqnFZwU28/b+Nd\n0L0A2pfAkWJd47thiVJt28EzbrAWw7bBIk8vU6pra92gajkXri6ry3qFUmObw/Xnwvjy4pZaG8YV\nbcK9TSnVGUAp9QPaDuYSpVSyUsqnaH4vcIdSai9aX+h1Y/3eSqlXjX3lo+vtfgU2AHONRo1ytzf2\n0RLoKyJLjVnPG/uYDrxnzDsX+LmmX79J7VODP1BWL7iawC8j4LPB2tporQvyXZCfCk+NF8kvgjUK\nYoIhrgA6dYOFOyDi7zowCEqFVgOg2A77PFAUDLvckFuqu+j+9CPjBY8HikPAnmM0a1TGAWjiq1YP\nB3c4KC9k94aY+AqEY0O1uG1QOrSfCNL3JOmOtYB8L4Qvgwfi4fBk2N0ffjsZx64Kl4sk9xOZOU5k\nlwdKvNovM9cOhwp081aZhEKBaI09G+hhXYHSiSKumgz0QHez1qZg8skM9OCoFmIQIuIGWwy4S8po\nevpN1+kVng1pAPcEBIMA2XDzQNjohMjD0LwDLAuFwvGwOBKs/4YjClwx4C2pwjCuF4IVePdXUv8E\n2gKxFTw8Bzrthe69DK3D9lDYCxgK5/8OV7vA+av+3TohROT7QzArDzKLjCFik/pFefdSWdIrLmhQ\nArsuEnlmrMgGY16qUaO3ptDwRi6F3SnwVCo8Xp6cTjF8UwxZFZ1bbXcnvYfRHSsig0SkqYhEikiC\niHxjzD8oIv1FpKOIXOUb1hGRjSIyzbcjEXnTWKej+D1tl7e9sSxNRMb4TX8sIt2Nc8k1ZifyR+Bn\n8hdkrMieIrghFg49CBuD9ZPz14Uw5SDc7FOhHy+Sb4VVF2tvUxS4Z8N5r8MTZ8Fjh6FnD3Dkw/Je\nIpNHiDgzYKEF2ri1gvoxWGHBYXigIYSsgFlVOdcMiFdAEHimwIJLYJUbMmMgNwg6gtYXey8gmPNC\nSBQkp8PgHuANqWKN4IlyBJr4VP9b6q7VkPpaY+aBEoGmbrCdL/LAeP2QWSYjRJzGF7gdYCz8evAk\nZEpPE47KULggsxmokjI+jz9CqyjIuA8OLIfxFxnDo7/D4E1w5TbtO0oc2FwQZYXGDbT8FiFQuAOe\nbAq9rPB1PFCVYM8DoaHa6q7MmlafKwbAK9AxDS54FSZZIaGbdmahP1iDgUzY7oEGufDVnBoaQhsv\nkp8Oh+1l1G6Z1F8sOjg/Jt7yQpRDl7IdZaKIKwXm5MFi30PjRBHvWJGNR91QymCCSGlGJVqbtR3s\nvQ+MNiRS6h3GEO4FwJLK1jWpf9SkPtgQEY9ba1d1KIaNl4kcGCLiGS9yTJBmhWUT4Mgq+G8YFH4B\nMwCyoC9Ad7A6/brhxopsyIVPSv+o/cRvWfHlIsk50Cy0ig0TVmjZHpIS4fNpkN0DUu2wtTGkZ0PT\ngxA6Ev5+G9znv50bwlvC5vZAAqQEB3iV1hZJ0PccWF4ElzwNaSXQt6YMwWsaDxQqiPQaAUNlJMPc\no53EIlJRcFifqW2dvUAMC7pg0I4RzUCVlpHZ2whdW8BOgNFGg0MKNFW6Xq+5G86zQVhzKHFB5EaY\nGgXFn8IdSXBVMUS6wWqFz1oApVXM7IVB3jJ44LkyBNTnwf89YJjQb4EOrWFNJvQqgebnQA5AB3Dc\nC8+2g1X7YeookTI19aqL7zq54EePLmUyqWeUdy+Vldnzal/oPzXujBXZOUGk2t3CXj8NzrKo1S9d\nEXGKyIVSW4WBJ4iIOOrz+ZmcXLxQEAxNbbCtvHWuEMkphp/jwREGhaXQrKkuDeBXeDIcHG7I8N/m\nYpF3RlcwVNYTPowFypOf8McFZzbUw84OD1yqINQGB0vh13jIugku3QXjcqHnAD/9yJ7QaBqknAe/\nNIB9IRBaHZ/f48UOTRMhLBeaO+AsL4SEn2Q3g6qSCS8nww3lmb8HcpnIgdo+p9ORdHgly2iOK4X0\neLDkGvJDGyD6XEMYPwW6doWdaX5aenm6A9ru80DOh4YJYCsxhNCnwE9DoaAzlJZqV4mSCSKlFi1V\nUmlDogdCwows/MfQ33/ZSogx1lEz4dyNMLYrbGwEB5zQuB9Yf4fzN8HU5tBSgXOi30hTTTFV5Oer\nRD6v6f2a1B6G5aIFoAAiUqCpBTp4q9mlXRFSiV5rvXzCNjGpCjVda+SEXA/YHJVopDm0RtuAXoYP\n6p3w4tdweREkFMKv1RW4bQF72sDBBFh+AVxV0boh0CQCin31fgpCdkOxQ0tSZHwPf2sD3wOsh+lX\nGh1afSFqILS7A75xQVYUuDZrQdZapRmE94Ez8uFsO2xLg5vSjlPaora5TORQTdfbnQqc7Jq9MSI/\n+oak3JDZBxrthqs/hCZXw8wtMGktNCyGJpdCXDZMzoHoJLhbdGbYBXiCoFEuXNUaJrYGzoEPpvmp\nMTgh3GsMdwqIqwrB3nBofL7xu5hpBJAAz8AZz8F5ALnQYAVc2hySHoPvmsB+gM5Q6oFuRbDJAtFS\nw3V1J/s6mVSf8q6RF+w+Pcz9MDwHJnqg0FNJnV118FbgCw5msGdicpRiWHkAbk0UOVLReiHQyAX5\nEyG0EdAfEnvp4KuRwygmrw5eKPofvDQWnjxgDBGVRyREBOuaN68dDrggZ47uhspuozXL4s6A3QDR\ncPAnuBjACsFBYPNCWwdkN4DSTYbhuj8t4b++zEoxWG4JsDurLuG6yN6qtP1X/liR4kSRk+6YYVI/\ncUH6GZDSAfgSOhyGi6Ph0DJo44SY9trndW8anOOBkgL4oQDWOCDToYOsYAXcBe8uhHdt2ivYt+8I\nzx/BntdRhcx5b4icCaWtdAf90WHc+TD1S90MSCo0K4JmF8Ha3mCbDp9fCvN3Qjc35BVrORZXfa1N\nNTn5hIDNAtGHoLVAaCo8UQhTLxep9u9FeXiPlZn8E2awZ3LKUtO1RhNFvFeIZFa2ng3WF8CaUDg8\nEN0Vmw7/ANq6/BwOqkoQNA+CIefBgcKyHWSOEgVhRbA7Cz7Kg/8ehrtAd2ydYQzL9gR3P71udhG0\nKAUVDEEO2GiBhi5Ijgb3CugauP90GJhkGMzfDr3/D54BOOj3I1odnNCgGH6xwlobrDiefZjULie7\nZs+fYEiJhryOsOp9uF/prvjdSXCGC2IagBTCTwrOL4RfLhR5+iJ47Qj80wFzPGC3Q8YFkNUSQvbD\nLbsNNw0nxDgN8Vxj2PdPwd45MP1SSEwyGkQOg4RB0TPwQpER7L0OLVNgSCdYch688gtMyoZefQ3f\n2jvg0GL4xg4DMuBV0a4n3prO7NXldTKpGuVdo1L90OHJ16U3DTyQMkQrltQYZmbPxKSGGSOy+SKR\nV8Mhoy+gIMcFBWnw9PH4obog1wulF0C2A+Lfg2YKVj8DZwQ+qsVAaD78NFLkizEidl8h7+WQHg1F\nMcAQ6HA3fHkXLHRA48MQFg4eO+z3QFEa/OSG2OVwf+C5hEK+ByKsWqYlDiAa3jrzOJwuVkOjGAgK\nht8GizyVKJJR+VYmfyXGiNhLYFs7bfrewANRzeDIrzAsWtfQlrgg2QuOQp9ElohMFPEOEfGkgB0Z\n7QAAIABJREFUwi/5WvIkJBdaOyDDBoN3Qye79ondC0eDvWOGcUtBJcE1q+D2OYYjjh2UAnsPnRWM\n2ADRK6BTLCTthud+hg86GQ8tf/MbMj4EfQthfaLIVi8UGbI8pjyKCaATCV5wKYgRsCUaCgU1iVmz\nZ3LaUtc1LHGQfCbsyoXP8+Hr0SLHpR83BJ7xgj0aesVC6N9hAcAd8NYUP59ONxADIZHw58YArVm2\nfQQQCZkdwTkEBoZBzD6IDAe3G9akwO0zoOAC+CZMWy4dQzjkKHDNhAGpRpax1JBxqW5Dx2twdl9I\nd9fCF5tJzVHX95GAo6NRuxQChV1hfx6cMwI+NWpokz1av/JPWnVjRZ716OHcELfOIm90Q14pjCiA\nd0cYchQCXndAZu83owM+BAo/hX8DKAixQIkHIqPhyGfQJgVatoTtvu1egndfgMlWiN4Kd+6Ac9wQ\nZzNqfd062PNWNqxW7ffJrNmr91R0jQTcQbqhbmt5enkndGwz2DMxqR08kNEDdrpgyyXlOC1UCRFJ\n1cNG7gEQUgpN2xmZNJuf/tg/oWcYqHBDbyyQcNh+Dtg88L2RWSi6ANwbtNadxwsliSLZiEi8Hj5z\nfQmx/vuIhSaD4IsPYPYWmATQGtYFg+23avrp/g5nt4EsbxkagyYmPjxQeg7kh0DhKph8O2w9H166\nE3a5IPkKkcxeItdX0NlqA4I9EOWEPBvsyoeVl4qswmiWMoKvYzJ7O6FhBKTdbmS4M/Xy4GCdYW/d\nGI4kQZsiaNTI8DreAuO6gecmSMmD1m4otEN3INpjZPpiId8N+XLy/VdN6jFFsFHAWVxJA+DxYg7j\nmpy21HUNSwnsM1LzJ9zBOUbkRyt83h4y28CqdfB0e/jCX2z2SxhuFM6VqacUAhmjtWd0moDTAWln\ngn07tIwAdwO/7TxgbQ1HPocOvnkrIeZGiP4HNBcIiUW3zQaBK1TLSsRU5zWlQI90EDPYq9/U9X3k\nBUcjECeMu1Bn9kp/hA8t0NRu6OxVgs0CERY4R6DwIpH/GxJguSng3QhdfvZ7YDkIDUOh6N+wC2Ae\ndI0AhwsKLHD2BVCaDAk2iIkBqxuUBTrkGJ63pdAiD1YpCAvWD035oDU7+4r8o7JGr+pS19fJpHIq\nukYlsN0L9iD4vTaObWb2TExqCTekC7gaVCJmWVUEMq/RQqyP5OvmidBVcPtq7dFLA8jvAltd4Chr\ne6P2z+WEPDvst8GmxuBIhdYWcPsXBIdA05sg/EO45f8g4f8gYRW0ioDiRiBg/KIBw8EzESyHjPMo\ni9XQqDvcpGB1KHyuYHURdG6kh8TKzESamACIfljq5wbLfmi7DW5ClwzE2OFgZds7oUhBRDHsCIdV\nZa3jAfUD3HA+LJsBvaPg7XdhYhTkRIO3FaydD88G6YcTuxfsHUCKIK4Y4ptBfho08YLTC0G/w2AP\ndLTDVhtsM7p9rWUd28QEQKDEDvuHi1Toz3y8qLrsxlVKhSml1iileimlflZKbVNK/a6UutJvnXZK\nqV+UUnuVUouUUmW2xyulrldK7TH+plS2vVJqvHG8tUqpWGNee6XUooDzW6uUMoPeU5C6rmFxQ6YE\nBFEnQkPIC4bQJBjjhosnwlmxwBJouwMi8qFZA3CUp5TuhGwBp4Kc80X+7YJvm0HxQRikDA9XH/nw\nTRc4ZIOE22HBLfD2S3BHIeSFQmEccDf8bxh8NR1Ke4MlrYLM3hwYuh2uBAgzLIA6Q8rFsKU2hGVN\nao66vo9KIcMLrmRoUQBDBFwZOlMW5q6Cm8kEEZsHbPmwtLx7sQCa+b7k34NZJZCQDBc5jEzfOzAf\nsLTSVmT7vWBvAd58aJMPnadARC6MEnC6tDNHTBq8NFZko11n+B3V1desLnV9nUwqp6JrVAK/ZcOz\ntXVsbx1n9q4DlqNN4CeJSHdgBPCsUspn/P4E8LSIdETf2DcG7sQI1mYD/Yy/h5RSjSrZ/hagD9pM\n+Fpj3sPAv3z7FREH8AOGf6+JSXWYIGLrLTK1pvY3RMTjBYcF2rmhqAmEtQE2QLdu8GUJdE+ArInl\nPMGJro9zhRq1QmOgtAvsDoeGUQFNEkHwbkPIbgs/uowgrhTim0CBgu/vg2+7Q1gk2GyQFAL2Aj9d\nvg+hyVUwWMHqKHh7rb7fALgN5v0PprwGb5RU4EZiYgIwGtbYYJsVzlQQVAz7CqGJBcIsVbSu6w3X\njxb5ubzlXiDWaGyygOshmDER5vwd3nODuggKpsNdg2FTCfyYCk82AWWFrk1hT2PoDYR6odQJ3Sza\nQzkLIBSWHoDpNfFemJy+TBRxHbVXrAXqumbvGuBzEdkrIvsBRCQdfZM0MTxzLwI+NtZfSNmB13Bg\npYhYRcSK7rAaWcn2XrT/ZxTgVEoNAtJ95+HHUvxspUxOHU7HGhYBnJCRCnMyIFcBGXAGwB2wIwoK\ny+vkmiBiS4PHRohoMVcRaQSpS+A/vQxLNx9DRDxOyHgaFo+AJ+NgSyGMHwi7HLBpGJQEaekVcUGe\nA6yOP0Z2uRo++gjmAJQY9mfNYH0CfPdf2DUTksMg9smT5MFrcvzU+X0kIjb4PRjOE60HecgOrRUE\nO3SioEr7qGhxX1g2D54eAC9nwbQ5sOcjWHMdDNkOtxyGli/DxlDwBoErRLvaOHrCovthmQeKk+GW\ng3CLDZIVhLkMv+shIp7j8TKtLnV+nUwqpS6vUWWZvUrtY44XpVQQ0F0CdMeUUv2AUBHZr5SKB6zy\nR/o7lbJFZVsCKX7TKcZ6sRVs/xi6fiMVmAwspmwrqi3A+dV8eSYmtcIReCAIbIkieRalXuoFf5tq\naIC5oajE0A0rj7EixxS0u6EwVGfuSgPXtcOBM6HVMljhhq8zoZlLDwXnCbi9UKIgyAuOCEgrgM6B\n++gB70VC8Zu6Tq+VDTpshlFAXBDE/G4KKZtUAQcc8YBNwO2CQwquyIdvampotCvsawoFP8HRMh4b\nhHp0TWlpAUwEnlM6AeIcIeLcoFTeMvguH9rnwppEkTyApUotccHhCSJ/uqdMTOqKOgv2gHgCnsqU\nUi2At4EpZW5RPSp8khORVehhXIwavy+ALkqpO9FDA7eJiF1EHEopi1IqXMyb95TidKxhuVwk2fd/\nQwhrDoebQZcjwFDYUwjrq7M/J2SGQVuvUUfnjw3Wx8D9W6FxMDT3aqmLHC8UGnVTL3tBBH5PgI42\nGPwONJ8MGQAxsHOLYWi/D9raYGwQWByQEqxFaYtTRb44wbfEpJapD/eRCw57wJ4BLzgh2QFpefB+\nDR7CIwEjWTaIcIPVCt82+SMRENrQ6K53QUoRNPVClGHNBkCiyFZqST6jIurDdTKpmLq8Ro5KGjRq\nM9gDPxFWo0ZvOfCAiPjEMXOBGKWUxcjOtcZPldyPVGCI33QCWmIir7LtlVKRwPXooeDlwOXop7jr\nMH6ojPP8U/ColHoLI1WP7rTa4ruYvnStOW1O19b0ndD5Oq3mvzcRWqyBs7vBi9XZ31JY3RDufQOK\nByo1JHD5OkiKgu4roKEFBvTWP7Bpb0PB6xBZILISYJZSkU1h92Mw4XJ4AfBeD59uhJEWkO9hiBNK\nhkN2PixfAO13wmHfL2R9eD/N6fo7PR66x8EHOSK/GMs/ALoL1Mj+l0IrF8g8SAaYBT1DIO5KKM6H\nTzbDTW/BwMlgHyLiUUoNuQEiZ2n9vrBXoe2IMu4fc9qcrstpgyFA23CIpAKU1LyQM8bJBAEpItJC\nKRUKfAUsFZHnAtb7CPhERD5USr2EDqheClinMbAR6AUo3/8iYq1se6XUQ8BmEVmqlFqDbhCZADQW\nkQVKqTDggIi0CjimiEi1HANMTi7K78v3dGSdUnMj4cx10LAxdGoKs4eLLK7ufpYq1QYo9g1D+fO5\nUt0aw8goODsV7nVByXiRP+nifavU1CPQfxYMHwifnAd3XQ4fe7UllAXAA0WlcOQCkYd9253u1+h0\n4K9wjX5W6rEoOEeBxMGbWdBDgasYggaKzNmg1IJQiPGAu7fIDQBfKTWiOVzkhYgM+KCiBpCTwV/h\nOp3q1OU1elyplvdDanlxS61l9kQ/HW1TSnUG+gKDgFil1FRjletFp8PvBRYppf4LbAJeB1BK9Qb+\nISLTRCRfKfUwfxSZzxXdqEF52xv7aAn0FZG5xqznjX3k80cjx7n4PBdNTOoRWfBCYxh7AVxigaT0\nqharB1CRuOs4ke0YVlC9K9iHE1LOgh4l0Ho79L1ed9kG22GXBxy58G44pDkr0OIzMakrQqGFBwot\nEJGpR3kaKAjzwm8AGfBEG3jW5tfI5AGrFxpYoJGjklpZE5O65mrIvb+C5bWW2QMwArtmIvJErR3k\nBFFKPQr8KiKfBcw3M3smdc5PSkVHwJsCzoNwy3iRKklR1DRLlWqSAM9cChflQss18HU0ZPcSmVwX\n52NiUh02KfWuE7LCoIWAMwsWhUITB6SN1PXdfKPUmEL40XePLVOq6xnwmA32DBC5p25fgYlJ5VQU\nt9R2zd77wCql1DypzajyODGGcC/AT3vPxKQ+cb5I8ZdKHXKCq64CPYBEkezvlfppAnR4GVo2hEIr\nTK2r8zExqQ7Z8JEdtsXCeC/MH1GG+PKlIsv9p0NgvxXWWctx5TAxOZWo1czeqYyZ2av//FVqWN5V\nKjEM2kwU+V9Vt5k7d25QTZ9H98cfP6uV0/nPfZDaFTotnz37usq2eeSRRwb/61//WlONw3gfeugh\n80vpJPJXuY9OdczrVP+p62tUUdxiBnvlYAZ79Z+6vrHqI4888kiL1q1b9w8JCQlVStXs59fpDGr6\n4Ye3eMPCcotjYw+VDh26rrJNNmzYcFa/fv12VPUQXq+XwsLC9KysrPUPPfSQ88RO2KQqmPfRqYF5\nneo/dX2NzGDvODCDPZNTjblz58afc845QwcPHpwfGxvrqI1jHJg06Q5LaWnjBrNmPRd34YU5Nb1/\nr9dLUlJS/Pr16/MyMjJWm1k+ExMTk6pRUdxS23ZpJiYmJ4kGDRo06dKli7u2Aj0Ab7Nm+yQoyBE3\ncGCNB3oAFouFHj165DRp0qQJptWaiYmJSY1gBnsmpywBwpJ/eUJCQkKCgoK8AA888EDX2NjY5+Pi\n4hZ069btZt86AwcOvCouLm5B27Zt/2W1WoMAWrZs+d+QkJBlN998cy/fep07d/5naGjoZyNHjhzl\nf4wzZs/+Inr69Fc6n3XWn5b3799/Unh4+OJevXrd4Ju3YMGCnr7/O3bseEdcXNzzcXFxC+bNm9cO\noE+fPlMbNmz4Wnx8/DMXXnjhBN+6oaGhADVed2jyZ8z76NTAvE71n/p8jcxgz8TkNGTQoEEZO3fu\nvD03N3eWzWZr/NRTT7VbuXJlzP79+3vm5ubOatmy5f4ZM2ZcAPDqq6/O79Gjx8f+28+fP//tYcOG\nvRS435BGjTxNLrkkq6zlDz/88PLp06c/Ut45zZ49+/3c3NxbH3/88ScWLFgw1ZgtkyZNeiEnJ+f2\ntWvXflzetiYmJiYmx48Z7JmcspjFyuUzcuTI/GbNmrkBLBaLOyQkxPPhhx92at++/RaAESNGbNy+\nfftZAKNHj/6Ts0ZZ8ypbPmzYMKtS6pgau1mzZm3x/T958uQMgPDwcI/FYjkqffH+++/PaNq06VOP\nPfZY++q9SpOawLyPTg3M61T/qc/XyAz2TExOYx5//PEz7XZ7o9tuu+1Ifn5+dFRUVAlAixYtSux2\ne3RdnNODDz7496uvvvoTgOeff/4Tq9U645FHHnnmqaeemlUX52NiYmJyumMGeyanLPW5PqI+sG7d\nugZPPfXUrIULF84DiImJsdlstkiA9PT0yIiIiOKq7mvVqlWN4uPjn23SpMn86pxDfHz8K/Hx8c+s\nXLkyBmDw4METmjdvfmjevHnbAQYMGFAMMG3atNTq7Nek5jDvo1MD8zrVf+rzNarVYE8pFaaUWqOU\naqOU2qiU2qyU2q6Uus1vnXZKqV+UUnuVUouUUiHl7Ot6pdQe429KZdsrpcYb3rxrlVKxxrz2SqlF\nAee3VillBr2nJj0rX+WvidVqDZowYcK/brrppheHDRtmBbj22mt3HThwoCfAihUrenfr1s1f/66s\ndv2j84YOHVqQk5Pzz+zs7Dsq2iaw7f+6665bmZOTc/uwYcOs06dP73Pw4MGzfvnll3d9y5OSkiIA\nVq9e3UhEzIaMusG8j04NzOtU/6m316i27dKuA5YD6cB5IuJSSkUB25VSn4hICvAE8LSIfKSUehG4\nETim8NsI1mbzh1f7RqXU5yJSUMH2twB9gPHAtcD/gIfxs0YTEYdS6gfgMuDT2nkLTGqRmLo+gfrK\nDTfcMDgvL6/ziy+++I8XX3yR6dOnv/Loo4/ubNeu3e9xcXELGjRokPnll18uBujateuthw8fPm/v\n3r0DDhw4sHTFihVf9O/ff9KOHTsuAejXr1/chg0b3vHff//+/a/bsWPHUP/lI0eOHPXzzz+Pczqd\nDbp06RK9a9euBaWlpUeHit9///1ZISEhxfHx8c/ExsYe2bNnzzPjx4+/KTc3t52IqKlTp758Mt8j\nk6OY99GpgXmd6j/19hrVdrB3DTBTRFx+8yIAF1BiKPxfBFxtLFsIzCEg2AOGAytFxAqglPoGGKmU\n+rCC7b1ona4owKmUGgSki8j+gH0vBe7CDPb+RF2rgZscP59++ul3wHeB83/66adFwCL/eTt37nwe\neN5/npF9e5dy+OWXX94D3vOf99VXX30JfOmb9pddASguLp5CAHv27KnWsLDJXwfz+8fkRDA/P8dS\na8OXSqkgoLuI7DGmWyultgJHgGdEJA+IA6wi4jU2SwValbG7lkCK33SKsV5sBds/hjawHo3+cXsQ\nndkLZAtw/nG9yNOfIXV9ApXQtq5PwKR8tm/f3rOwsLB5XZ+HSaW0resTKIchdX0C9Yy2dX0CpxhD\n6uCYbevgmFWi1uzSlFLNgB9EpFPA/BbAGmAUYAV+FpGOxrIE4EsROTtgmzuBcBF5xJh+EChBZ/LW\nV2H7Kej06gbgTiAfuE1E7MbydKCdiJT6bWPaNJmcUrRo0YLu3bvjdJ76lrIej4cNGzacFq/FxMTE\n5GRRnl1abQ/jllXAnW7UyfVAD53GKKUsRnauNTo7F0gqx0bpCeghqrzKtldKRQLXo4eClwOXAxPR\n9YSv+Z3nMcGd6Ytrcqrx/PPPnzN8+PAOnTp1qlAjb9++fWEjR46cuXfv3vkdO3a8Iy8vrx0g9957\n7zP33HPPQQC3201sbOxrAwcO/NQYnj3Kueeee+ORI0d6At7HH3983rRp01Lj4+OfAXC5XFHR0dGZ\nqamp/+7Vq9cNBw8e7DNu3Li333rrrfXDhw8f3a1btyPz589PSktLCxk0aNA/9+/f/2RZ57h8+fJm\nGzdu/OKhhx6qcsewiYmJiUnZ1GYXag4QDaCUaqWUijD+bwwMBJJEpxVXo4Mv0EHZkjL29TUwTCkV\nY2x/KfB1Fbe/G3hORNzoekHQgV2kcT5hgEdEas1P1MSkPnHLLbcMHThw4E9QrqsFf/vb384PDw/P\nD8xwb9iwITo1NfWs3NzcW2+88cZXnn322XEAOTk5t+fk5Nzes2fPlT169PgJICsr64zk5ORbvvvu\nu0szMzOD9+/f33X+/PlJAC1btnRFRkYWvfrqq2WVbZiYmJiY1CC1FuyJiAfYppTqDHQF1iultqAz\nco/6avmAe4E7lFJ7gcbA6wBKqd5KqVeNfeWj6+1+RQ/FzvU1a5S3vbGPlkBfEVlqzHre2Md0/igu\nPxf4uaZfv4lJfSUpKan//fffvwXKd7VYs2bNJX369PkuMMPdpEkTJ0BpaanKyspqEB0dXeC/fMeO\nHeffd999P4IuhUhNTQ0NDg52XXPNNWOvueaaZf7r9uvXb9M777wzoHZepYmJiYmJj9oexn0PuExE\nnkAP2/4JETkI9C9j/kZgmt/0m8CbVd3eWJYGjPGb/hgI9N9MJKCr0MTkdMbhcDTs3Llzqf+8f/3r\nX9N8rhbTp0/v06VLly0Wi8Xr8XiO0b5r166dMyEhISkuLu4dEVGLFy++1bfMEE6WCy+8sBCgd+/e\n6y699NJ7rrvuuo8/+eSTEbt27crv1KnTqOnTpy+56667Dvbq1St97dq1F5yEl2xiYmLyl6a2xYTf\nB0YbEiv1DmMI9wLKHjo2MflLMHjw4AktWrQ46HO1WLFixai33nprBWXU3C5cuLB5RkZGB5vNNume\ne+6ZO2vWrBt9y55++umB3bt3X+ebXrJkyaojR47854cffuh+ww03LNm0aVO/TZs2PffKK69cDuD1\neuvl94KJiYnJ6UatBnsi4hSRC6W2Wn5PEBFx1OfzMzGpDcLCwop27NgRATqLF+hqUVBQkNCrV6//\nrl27duK6desmvPjii619yzIzMyNCQ0NtAO3bty90OBxHRZO3bt16wcyZM9f5HyspKSkiOzu7xX33\n3ff/7J1neFTV1oDfFWroofeAgFQREBBQIIooSBdQLFiuih27osgFK+pVsV2viviBiqJX8IqggNKx\nIRhA6SBNAqGEAGmkre/HPgOHYWbSM5Ow3+c5T7L32e2cfWbOmrXXXuuvlJSUCidOnBCPo+Xo6Og6\nderU2V3Q12uxWCxnOwW9jGuxWEKMNm3a/PLSSy+1mzZt2s9eUS32bNmy5bWjR4/eDtC/f/8rMjMz\nw+66666/b7/99k4ZGRlhH3744a+TJ08+Ua1atTdUtcR99933FsCaNWvKpaamlh82bNhBd1+33Xbb\nVaNHj54FULNmzV1NmzZ9a+DAgR8D/Pbbb+1Hjx79jff4LBaLxZK/FJifPYvFUrjkxvVKYY3Nm9jY\n2JLdunV7aPv27S/7Om9dr1gsFkv+UdA2exaLJcRo2rTpiWAKegC1atVK9yfoWSwWiyV/scKexVJM\nSE1NTS8umx7S0tIAMrIqZ7FYLJasscKexVJMSEhIiNu2bVvZhISEIm2Lu2XLlojDhw8fBayjc4vF\nYskHrM2exVKMmDhxYqPIyMjO4eHhEhZW9H7LpaenExcXFx8TE7Ns/PjxycEej8VisRQHrLBnseQA\nJ+zf48AOVZ1WAO1fBPwHOBdYD9ymqtE+yt0MfOijiUbly5fXpKSkr1T13BIlSvzfuHHjngB44YUX\n7s3IyGg+bty4+3zUK1QmT57ccO/evRvCwsLm/fOf/xzmOpUJnBg/fnyOv5hEpBnwPnAeUBr4BbhT\nVf9yzmfr3jplxwB3AzWBGOA1VX1bROph/HI2Byar6sNO+QeBFqp6R07HnVNEpBHwFzBXVQcUdH/Z\nISf3NtQRkUeAl4FbfH3GszrvKhfweXTKlAXWAs2Af6vqGZ/NQO2EwvNoKSKoqj3sYQ/XAZQOcK46\nRiBZXAD9lgX2A9uBO4G/nf/DfJRtBFztHNcDKRihpCTwglNvvDPWJs64dwDVgn1/XePPBGbnY5s9\nMbGy7wbecNpflIt729Kpuw24C9jjpOuHwr3Nr3sX6DkvqOe2KBzAI879vTE357PzPLrKvAAkOOfe\nzMVzHfTn0R5F4yh66zwWSz4jIo1EJFNEfhSRH4DdIvIPEdksIglOfnun+Crnb0+nzvh8HEpfjCbp\nHVV9FxPnuTEQ5V1QVXeq6heq+gXGtq008KGqpgPlMC/fxU7xipjY0m+q6uF8HC8i8oWIpIlINSf9\nsnNf2jnp20Vkq3Mff3U0QAXFT6p6iaq+o6r3A0eAVs65bN9bIBFIB/ZiYnnHYoTpFAr33tYUkYUi\nclxEjorILyJS3VWkiojMFpFjIjLdqVNDRKKdOsdFZJmItHLO+XrOI5285SIyV0TiReQjESnt1PH3\nOXCTk3ub23vhGfsKEfnBueZnReQp5/9o51oCXf/dTht3iEhlEYkRkT9EpLSIPCIih0RkPUaD5t1/\nwPN+CPQ8IiJtgQcwglpu2ym059FStLHCnsVyiq7Ab8A44APMr+PnML+Uv3HC6z3hlN0AjAD+m4/9\nN3b+7vX629hHWTd3YHauvu+kPwPaA0sw1yNAD+DN/Bqoi0+AEsAQJz0M2KCqa0TkUuA9jLD0INAQ\nmC0iVQtgHKhqmud/EekIRADLnKxs31tV3Y3RUF0EbATaAaNU9RCFe2+vBy4BXgEeBqI5/Tu7G/Aj\nsAm4VkQuxjwHM4H7gRcxMclf92rX85w/5cq7ECPYLgRuAO4QkSj8fw7c5Pa5zQ2dgDnAYWAsRus1\nBXOdDxDg+lX1Hcw1voi5rurATRhN7ssYzfgkoJe7QxE5P9B5V7lwEanuHOUDPY8iEuaM4W1O/YD0\nSRbPdWE+j5aiTLBVi/awR7APTi2LrXLS/3LS7iMD89L3LOMuKoBxPOS0PcJJ3+Gkbw1Qp4lT5huv\n/FqYF2NpzAv8SuAezJLPL0DLfBpzKeAQMB+4wBnLGOfcK066l5N+zkn3pQCWcV1jaoF5MW8HauX0\n3mKE0uOYl/AAYDVwFKhXyPe2nzPG5cBE4BKv53WFkx7jpK8H6mIEwAzXsxvj6zn3ylvqpM9x0jMx\nAo7Pz0Fen9s8fEaXOenpTvoSINL5/yugjr/rd+pFOnObCTzr5N3vpG9x0s/gWqbN6ryr7QmuPj/M\n4nm8FfMj6Dxn3jKBj4DqOXmuC/N5tEfRPqxmz2I5RYxX+iHgMqA30AfYCRTkjiaP4XYD5289d76I\nlBWRUl51PAbY/3Fnqmqsqv6G+eI/gfnl/zpwI0YrOS4/BqxG6/BfzEt3FOb+TM+PtnODs2S3BLPk\neqmqxjqncnJvuwDlgVmq+g1GiKjo5BfmvZ3r9DkPuBhYKCJurZInUkq687ckMBqjuZsEXI7RspX1\natr7OQejEYJTmkP3c+7rc+Am4L3NZ+Kdvx5t11FOjb0ERjALdP1VAI9msq5X22Fef73J6vw0zH26\nDCMoB3oe6wM1MJszPnbybsDY4J3xWQ/QTqE9j5aiTZH2x2WxFBBzMMtm12FeJnWBG1TBQk1PAAAg\nAElEQVS1pfMFnAk0E5HrMNqV3fnU73fAAeAuEUnA/PrfgfnyBkjC7HQ8D8Cxq7oZ2KWq33o35px/\nAbPEWgLzUrwGo6Hcmk9jBiPc3QHcBixX1T1O/lyMoPC0iDR1ricOo22onI/9AyAiDTC2S1UxL/uu\nItJVVWeQs3u72ckbKSKxGM2LAltcfRX4vRWRYZhlyK2Yl/ZFGM3V9gDVPEJaVcwSZz1OCUiB6Ors\nMu3mpJcAf2A2I5zxOfCqm9W9LSyEANfvfHanOWP9H3C3iHzJKXu3B0SkBHCLV7tZnQdAVXdgrhun\nv0DP4xeY+wvQBqMV/I5TP9pOPo9ZtOPpq7A+65aiSrBVi/awR7APfCwpYmx5NmC+dHcCH7jOTcQs\nBWVgXoSDnLJ/A+86ZW4GjmGWVfYDbX3l+RhLd2Ad5hf6aqCD61wmsM6VHuGM4Uk/1/UoMMmVHoN5\n8W3CaykuH+7hDmcst3nl34YRkhKAX4Fu3vc8v+4fZkOAZ6nx5LJjLu/t3ZjduMnO37sK+95ilrvX\nO89gHPApRkt12vOKEcgyMJqcus59TsDYhK0D4gI85568JZgfOfGY5cRSWX0OsvvcFsRnFPg/55o7\neD1Lga7/GafOYIx2byNmp3VlzI+7gxhN23TP/XT1H/C8nzEHfB5d5XritRvX/Txmp53CeB7tUbQP\n62fPYskjIjIIeAmogHnZ1MDYer3v/L8c86LeB0x256nqi8EYcyhh71/wkFM+++ao6sDgjsZisRQU\nQbXZE5ESzjb5b5x0VRH5XkS2iMgCEanip14fEdnkuHR43JXvs76IXCQia0XkN2c5CRGpIiLzC+M6\nLcWeUcBSjM0NGHsvgMOqehSjFSmDWVpx55Uu7IGGKPb+WSwWSwES7A0a92OWCDzqxTHA96p6Lmap\nZox3Bcdm4m2MoXArjMuBllnUfwizJPIAxqUCGLcDz+f3BVnOSr7EODcezemG7er66z6gYDd6FDXs\n/QsSavw1hlmtnsVSvAnaMq6I1AemYgSuh1R1gIhsAnqqaqyI1AaWqGoLr3pdgfGq2sdJjwFQ1Rf9\n1ReRGZgdSR5Hn1OA51V1RKFcrMVisVgsFkuQCOZu3EkYo9JKrrxaempLeSzGf5A39TBGtR7+xjgE\nDVR/IsboOAljxPwKxiGnxWKxWCwWS7EmKMu4ItIfOKAmULb4KqNG5ehL7eidJ77Kueur6lpV7aqq\nvTBOaGOAMBH5XEQ+FpGaub8ai8VisVgsltAlWJq9bsBAEbkS40qgkoh8DMSKSG1V3S8idTD+kLzZ\nyynnnWCcU3rC8wSsLyKC0eiNAN7CuCxojLEVesqrrLUJslgsFovFUmRQVZ8KtKD7fsH4GPrG+f9l\n4HHn/zHAiz7Kl8Q4FW2E2Y23BiccTFb1MT6jRjv/z8IIit2B13z0o4Vw7RMK6R4Xq36K4zUVt36K\n4zUV5r0rrtdW3PopjtdU3Popjtfkr59AckuoRNDwaNFeBL4QkVsxDjyvBhCRusBkVe2nqukici8m\nFmcJYIqqbgxU32mjHEbY6+1kvQZ8i3ECel3BXVpAlth+ck2jQupnie0n1zQqpH6WFLN+gtFno0Lq\nZ0kx66cw+2pUSP0sKWb9FGZfjQqpnyU5rWCdKvtBRFT9qUMtQUdEpqrqzcEeh8U/do6KDnauQh87\nR6FPsOcokNwSbD97FktumRrsAViyZGqwB2DJNlODPQBLlkwN9gAsWTI12APwh9Xs+cFq9iwWi8Vi\nsRQVAsktoWKzV2Swu3SLNlaALzxEJEpVlwR7HJassXMV+tg5Cn1CeY6ssJcLrDa0aGI871gsFovF\ncnZhl3H94E8d6uQHY0iWPCIiVrNnsVgslmKJ3aBhsVgsFovFcpZihT2LxVIgiEhUsMdgyR52rkIf\nO0ehTyjPkRX2ihGNGjVi4cKF2So7YcIERo4cWcAjslgsFovFEmyssFeM8GxAyI4gZzcrWAqaUN2V\nZjkTO1ehj52j0CeU58gKexaLxWKxWCzFmKAJeyJSVkR+FZE1IrJBRCY6+VVF5HsR2SIiC0Skip/6\nfURkk4hsFZHHXfk+64vIRSKyVkR+E5GmTl4VEZlfGNdbWKSkpDBx4kQ+//xzKlasSPv27QHYsWMH\nPXv2pFKlSlx++eUcOnQoyCO1FHdC2X7Fcjp2rkIfO0eFg4iUkFwufYXyHAVN2FPVFOASVW0HtAUu\nEZGLgTHA96p6LrDQSZ+GiJQA3gb6AK2Aa0WkpXPaX/2HgL7AA8CdTt5TwPMFcHlBo2zZsjz55JOM\nGDGC48ePEx0dDcB1111Hp06dOHz4MOPGjWPatGl2KddisVgsltOZAdwb7EHkN0FdxlXVJOff0kAJ\n4AgwEJjm5E8DBvuo2hnYpqo7VTUNMzmDnHP+6qcB5Z0jVUSaAPVVdVn+XZFBRPLlyAtuX4C7d+9m\n1apVPPvss5QqVYru3bszYMAA6xzaUqCEsv2K5XTsXIU+do4KHhFphZElrs9N/VCeo6AKeyISJiJr\ngFhgsaquB2qpaqxTJBao5aNqPWCPK/23k0eA+hOBj4DHgX8DzwFj8+ta3Khqvhz5RUxMDBEREYSH\nh5/Mi4yMzLf2LRaLxWIpBjyKkRWaiUj9YA8mPwm2Zi/TWcatD/QQkUu8zivgS+rxzhNf5dz1VXWt\nqnZV1V5AEyAGCBORz0XkYxGpmfcrCg28tYJ16tThyJEjJCUlnczbtWuXXca1FCihbL9iOR07V6GP\nnaOCxRHuBgFvAN8AV+Wijah8Hla+ERK7cVX1KDAXuACIFZHaACJSBzjgo8peoIErXd/JI6v6juHl\nWIxmbzzwCDAZGO3diYhMFZEJzvFAKE+km1q1arFz586T2sHIyEg6duzI+PHjSUtLY8WKFcyZMyfI\nowwuIhLlnk+bzv800C6UxmPT/tNAu1Aaj03bz1MQ7u/LwFRVjQM2A//wKv+ViNwTKuN1HRPEyCpT\nCUDQYuOKSHUgXVXjRSQcmA88DVwBHFbVl0RkDFBFVcd41S2JmYxeGA3dSuBaVd0oIi8Hqi8iNwGV\nVfVNEZmFEfIaA0NU9SFXuSIXG7dx48ZMmTKFdu3aMWjQINavX88555zDqlWr2LFjBzfddBPR0dF0\n7dqVFi1aEB8fz0cffRTsYRcaYmPjWiwWi8UHYkzK7lDVX0WkLLAfaK6qsSIyGPgEWKOqFwd1oAHw\nJ7dAcIW98zAbKMKc42NV/ZeIVAW+ABoCO4GrHYGwLjBZVfs59fsCr2M2dkxR1ZOuW3zVd86VA+YA\nvVU1Q8zu33eAE8B1qrrVNb4iJ+xZAmOFPYvFYrF4IyIRwG6gqrPpExH5BKgKvAu8B9wAfAVEquoR\nV90WQIqq7izscXsTksJeqGOFveKHFfYKFxGJCuXdaZZT2LkKfewcFRwi0h+4X1V7u/IqAjcBo4Cv\nVXWciHyLWer9wlFATQSGA4tUtX+w5yiQsBcSNnsWi8VisVgsQaIHcJobNlU9rqpvq2pbVR3nZH8H\n9BURAT4FjmE2fF4kzl6BUMVq9vxgNXvFD6vZs1gsluKNiJRR1RM5rPMLMCYrrZyY6FvLgQcxARsu\ncEzCPgTWq+qrTrmgCApWs2exWCwWi6VYIyJlgA0i0i8HdSoA5wG/ZlVWVbcBCRg7vvtUNcM5NQ24\nWUyotWnA+672/+FsHA0qVtizWCwFgttVgCW0sXMV+tg5yha3AxHO3+zSBYhW1eRslv8amKOqy115\nyzHRuVYCkcBQEakrJrTrE0CzHIynQLDCnsVisVgsliKN423jSUyI1CjJfqCEM+z1suAJ4EZ3hqpm\nYrR5YUA/jD3f3UB/oC5QOQftFwjWZs8P1mav+GFt9iwWi6V4IiKPAZ1UdbizlLpGVSdlUac0sAUY\noaq/5LF/ARO5S0TOBVYA2zFav8tUtUNe2vfqy6ddorXZs1gsFovFUiwRkcqYaFj/dLKmArd4BLAA\n/APYlFdBD4yQ59EEqeoWjA1gXWAKUCWv7XsQkSEYu8QcKS6ssFfMufnmmxk3blzWBYt4n5bQw9oY\nFR3sXIU+do4C8hDwnapudNJLgYrACH8VnCgZYzklIOYZrzl6FLgFOEw+CXtOtLFJQE2gdU7qlsyP\nAVhCFxEhhz8AimSfFovFYjn7cEKv3gt08uSpaqaIDAf+KyKdgMdUNd2r6m2Ypd6VBTEuVd0EbBKR\nUkClfHLH8ijwG0aAvAL4M7sVrWbvLMDX85We7v3cF3yflrML6+2/6GDnKvSxc+SXx4EvVPUvd6aq\nrgIuAKKAgT7qXYUJl5pv+JojJ/xaClAhL207TpvvxyxXzwP65KR+0IQ9EWkgIotFZL2I/Ckio538\nqiLyvYhsEZEFIuJT/SkifURkk4hsFZHHXfk+64vIRSKyVkR+cxwjIiJVRGR+YVxvYREdHU2HDh2o\nVKkSI0aMICUlBYAlS5ZQv359Xn75ZerUqcOtt95KfHw8/fv3p2bNmlStWpUBAwawd+9eABYvXkzb\ntm1Pttu7d286d+58Mt29e3dmz54dsE8PkydPplmzZlSrVo1Bgwaxb98+AMaPH8/o0aMBSEtLo3z5\n8jz22GMAJCcnU7ZsWeLj49m5cydhYWF89NFHREZGUqNGDV544YWT7a9cuZKOHTtSuXJlateuzcMP\nP5zft9VisVgsIYYT5uwG4Flf51U1DvgfLq2fi9bAuoIb3WnEk/el3J7AclXdBSwCujg7kLOHqgbl\nAGoD7Zz/KwCbgZbAyxiVKxiJ/UUfdUsA24BGQClgDdDSOeezPjATYyx5EfCKk/cK0MPP+DRAfkhy\n4sQJbdiwob7++uuanp6uX375pZYqVUrHjRunS5Ys0ZIlS+qYMWM0NTVVk5OT9fDhwzpr1ixNTk7W\n4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mz2VPXmfB+KxWKxWCyWkEBEugDzMJsHmopxXTbZdV6Aiqp6zE/9Wpil2xigy9myESNE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\nY+wZDAaDwWAw5GL+H8Vm4hA+N8vcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc9a6470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ability to show different return frequencies\n",
    "fig, axes = plt.subplots(4, 1, figsize=(10, 12))\n",
    "ax = port.performance.dly_details.plot_ltd(ax=axes[0])\n",
    "port.performance.weekly_details.plot_ltd(ax=axes[1])\n",
    "port.performance.monthly_details.plot_ltd(ax=axes[2])\n",
    "port.performance.quarterly_details.plot_ltd(ax=axes[3])\n",
    "\n",
    "axes[0].set_ylabel('Daily'), axes[1].set_ylabel('Weekly')\n",
    "axes[2].set_ylabel('Monthly'), axes[3].set_ylabel('Quarterly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc9afe48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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uv7fthFt6thYYAduX41ziAKcDHUX4TRrL3WghZlGXARZbTI/JKD0mo9S0QD41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\n0Wg0mgBGG2qNRqPRaAKY/w+fFzP3iWMvTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdadf2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \n",
    "# Show the Return on a dollar\n",
    "#\n",
    "port.performance.dly_details.plot_ret_on_dollar(label='LongShort')\n",
    "port.long.performance.dly_details.plot_ret_on_dollar(label='Long')\n",
    "port.short.performance.dly_details.plot_ret_on_dollar(label='Short')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>ltd ann</th>\n",
       "      <th>mret avg</th>\n",
       "      <th>mret std ann</th>\n",
       "      <th>sharpe ann</th>\n",
       "      <th>sortino</th>\n",
       "      <th>maxdd</th>\n",
       "      <th>maxdd dt</th>\n",
       "      <th>avg dd</th>\n",
       "      <th>best month</th>\n",
       "      <th>worst month</th>\n",
       "      <th>nmonths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>(0.6%)</td>\n",
       "      <td>(0%)</td>\n",
       "      <td>0.6%</td>\n",
       "      <td>(1)</td>\n",
       "      <td>(0.3)</td>\n",
       "      <td>(0.8%)</td>\n",
       "      <td>30-Dec-10</td>\n",
       "      <td>(0.8%)</td>\n",
       "      <td>0</td>\n",
       "      <td>(0.6%)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>(17%)</td>\n",
       "      <td>(1.5%)</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>(1.2)</td>\n",
       "      <td>(1.1)</td>\n",
       "      <td>(24.9%)</td>\n",
       "      <td>25-Nov-11</td>\n",
       "      <td>(9.3%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(6.6%)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>25.8%</td>\n",
       "      <td>2.1%</td>\n",
       "      <td>20.3%</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>(12.6%)</td>\n",
       "      <td>01-Jun-12</td>\n",
       "      <td>(2.1%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(8.1%)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>(5.4%)</td>\n",
       "      <td>(0.3%)</td>\n",
       "      <td>21%</td>\n",
       "      <td>(0.3)</td>\n",
       "      <td>(0.3)</td>\n",
       "      <td>(22.2%)</td>\n",
       "      <td>06-Sep-13</td>\n",
       "      <td>(8.5%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(12.1%)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>26.7%</td>\n",
       "      <td>2%</td>\n",
       "      <td>9%</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>(9.7%)</td>\n",
       "      <td>16-Oct-14</td>\n",
       "      <td>(2.5%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(2.7%)</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>(42%)</td>\n",
       "      <td>(3.9%)</td>\n",
       "      <td>42.1%</td>\n",
       "      <td>(1)</td>\n",
       "      <td>(0.9)</td>\n",
       "      <td>(18.5%)</td>\n",
       "      <td>24-Apr-15</td>\n",
       "      <td>(10.4%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(15.8%)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-2012</th>\n",
       "      <td>2.2%</td>\n",
       "      <td>0.3%</td>\n",
       "      <td>18.5%</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(24.9%)</td>\n",
       "      <td>25-Nov-11</td>\n",
       "      <td>(7.8%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(8.1%)</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>past 2yrs</th>\n",
       "      <td>(0%)</td>\n",
       "      <td>0.2%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>(0)</td>\n",
       "      <td>(0)</td>\n",
       "      <td>(22.2%)</td>\n",
       "      <td>06-Sep-13</td>\n",
       "      <td>(3.9%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(15.8%)</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltd</th>\n",
       "      <td>0.7%</td>\n",
       "      <td>0.2%</td>\n",
       "      <td>17.9%</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.9%)</td>\n",
       "      <td>25-Nov-11</td>\n",
       "      <td>(5%)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(15.8%)</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ltd ann mret avg mret std ann sharpe ann sortino    maxdd  \\\n",
       "2010       (0.6%)     (0%)         0.6%        (1)   (0.3)   (0.8%)   \n",
       "2011        (17%)   (1.5%)        14.8%      (1.2)   (1.1)  (24.9%)   \n",
       "2012        25.8%     2.1%        20.3%        1.3     1.8  (12.6%)   \n",
       "2013       (5.4%)   (0.3%)          21%      (0.3)   (0.3)  (22.2%)   \n",
       "2014        26.7%       2%           9%          3     3.5   (9.7%)   \n",
       "2015        (42%)   (3.9%)        42.1%        (1)   (0.9)  (18.5%)   \n",
       "2011-2012    2.2%     0.3%        18.5%        0.1     0.1  (24.9%)   \n",
       "past 2yrs    (0%)     0.2%        21.4%        (0)     (0)  (22.2%)   \n",
       "ltd          0.7%     0.2%        17.9%          0       0  (24.9%)   \n",
       "\n",
       "            maxdd dt   avg dd best month worst month nmonths  \n",
       "2010       30-Dec-10   (0.8%)          0      (0.6%)      12  \n",
       "2011       25-Nov-11   (9.3%)        0.1      (6.6%)      12  \n",
       "2012       01-Jun-12   (2.1%)        0.1      (8.1%)      12  \n",
       "2013       06-Sep-13   (8.5%)        0.1     (12.1%)      12  \n",
       "2014       16-Oct-14   (2.5%)        0.1      (2.7%)      12  \n",
       "2015       24-Apr-15  (10.4%)        0.1     (15.8%)       4  \n",
       "2011-2012  25-Nov-11   (7.8%)        0.1      (8.1%)      24  \n",
       "past 2yrs  06-Sep-13   (3.9%)        0.1     (15.8%)      28  \n",
       "ltd        25-Nov-11     (5%)        0.1     (15.8%)      64  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show a return report\n",
    "F = new_dynamic_formatter(method='col', pcts=1, trunc_dot_zeros=1)\n",
    "# Ability to added custom groupps\n",
    "F(port.performance.report_by_year(prior_n_yrs=2, ranges=[(2011, 2012)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>lvl1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">All</th>\n",
       "      <th colspan=\"3\" halign=\"left\">long</th>\n",
       "      <th colspan=\"3\" halign=\"left\">short</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>lvl2</th>\n",
       "      <th>All</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>All</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>All</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">port</th>\n",
       "      <th>ltd ann</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(0.1)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(0)</td>\n",
       "      <td>(0.1)</td>\n",
       "      <td>0</td>\n",
       "      <td>(0.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mret avg</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(0)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(0)</td>\n",
       "      <td>(0)</td>\n",
       "      <td>0</td>\n",
       "      <td>(0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mret avg ann</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(0.1)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(0)</td>\n",
       "      <td>(0.1)</td>\n",
       "      <td>0</td>\n",
       "      <td>(0.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mret std ann</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sharpe ann</th>\n",
       "      <td>3.9%</td>\n",
       "      <td>66%</td>\n",
       "      <td>(65.5%)</td>\n",
       "      <td>49.4%</td>\n",
       "      <td>66%</td>\n",
       "      <td>(77.2%)</td>\n",
       "      <td>(51%)</td>\n",
       "      <td>nan</td>\n",
       "      <td>(51%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sortino</th>\n",
       "      <td>3.4%</td>\n",
       "      <td>53.1%</td>\n",
       "      <td>(33.9%)</td>\n",
       "      <td>45.2%</td>\n",
       "      <td>53.1%</td>\n",
       "      <td>(21.2%)</td>\n",
       "      <td>(22.6%)</td>\n",
       "      <td>nan</td>\n",
       "      <td>(22.6%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd</th>\n",
       "      <td>(24.9%)</td>\n",
       "      <td>(17.4%)</td>\n",
       "      <td>(36.3%)</td>\n",
       "      <td>(19.2%)</td>\n",
       "      <td>(17.4%)</td>\n",
       "      <td>(13.6%)</td>\n",
       "      <td>(30.8%)</td>\n",
       "      <td>nan</td>\n",
       "      <td>(30.8%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd dt</th>\n",
       "      <td>2011-11-25 00:00:00</td>\n",
       "      <td>2015-01-30 00:00:00</td>\n",
       "      <td>2015-04-24 00:00:00</td>\n",
       "      <td>2011-11-25 00:00:00</td>\n",
       "      <td>2015-01-30 00:00:00</td>\n",
       "      <td>2011-03-17 00:00:00</td>\n",
       "      <td>2015-04-24 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg dd</th>\n",
       "      <td>(5%)</td>\n",
       "      <td>(3.2%)</td>\n",
       "      <td>(10%)</td>\n",
       "      <td>(3.4%)</td>\n",
       "      <td>(3.2%)</td>\n",
       "      <td>(4.4%)</td>\n",
       "      <td>(8.4%)</td>\n",
       "      <td>nan</td>\n",
       "      <td>(8.4%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nmonths</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">pos</th>\n",
       "      <th>cnt</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win cnt</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lose cnt</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winpct</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret avg</th>\n",
       "      <td>2.1%</td>\n",
       "      <td>26.4%</td>\n",
       "      <td>(10%)</td>\n",
       "      <td>14.3%</td>\n",
       "      <td>26.4%</td>\n",
       "      <td>(9.8%)</td>\n",
       "      <td>(10.1%)</td>\n",
       "      <td>nan</td>\n",
       "      <td>(10.1%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret std</th>\n",
       "      <td>20.5%</td>\n",
       "      <td>17.7%</td>\n",
       "      <td>2.2%</td>\n",
       "      <td>24.4%</td>\n",
       "      <td>17.7%</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.7%</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret min</th>\n",
       "      <td>(12.1%)</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>(12.1%)</td>\n",
       "      <td>(9.8%)</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>(9.8%)</td>\n",
       "      <td>(12.1%)</td>\n",
       "      <td>nan</td>\n",
       "      <td>(12.1%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret max</th>\n",
       "      <td>38.9%</td>\n",
       "      <td>38.9%</td>\n",
       "      <td>(7%)</td>\n",
       "      <td>38.9%</td>\n",
       "      <td>38.9%</td>\n",
       "      <td>(9.8%)</td>\n",
       "      <td>(7%)</td>\n",
       "      <td>nan</td>\n",
       "      <td>(7%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dur avg</th>\n",
       "      <td>182.3</td>\n",
       "      <td>356.5</td>\n",
       "      <td>95.2</td>\n",
       "      <td>264.3</td>\n",
       "      <td>356.5</td>\n",
       "      <td>80</td>\n",
       "      <td>100.3</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dur max</th>\n",
       "      <td>467</td>\n",
       "      <td>467</td>\n",
       "      <td>133</td>\n",
       "      <td>467</td>\n",
       "      <td>467</td>\n",
       "      <td>80</td>\n",
       "      <td>133</td>\n",
       "      <td>nan</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lvl1                               All                       \\\n",
       "lvl2                               All               winner   \n",
       "port ltd ann                         0                  0.1   \n",
       "     mret avg                        0                    0   \n",
       "     mret avg ann                    0                  0.1   \n",
       "     mret std ann                  0.2                  0.1   \n",
       "     sharpe ann                   3.9%                  66%   \n",
       "     sortino                      3.4%                53.1%   \n",
       "     maxdd                     (24.9%)              (17.4%)   \n",
       "     maxdd dt      2011-11-25 00:00:00  2015-01-30 00:00:00   \n",
       "     avg dd                       (5%)               (3.2%)   \n",
       "     nmonths                        64                   64   \n",
       "pos  cnt                             6                    2   \n",
       "     win cnt                         2                    2   \n",
       "     lose cnt                        4                    0   \n",
       "     winpct                        0.3                    1   \n",
       "     ret avg                      2.1%                26.4%   \n",
       "     ret std                     20.5%                17.7%   \n",
       "     ret min                   (12.1%)                13.9%   \n",
       "     ret max                     38.9%                38.9%   \n",
       "     dur avg                     182.3                356.5   \n",
       "     dur max                       467                  467   \n",
       "\n",
       "lvl1                                                   long  \\\n",
       "lvl2                             loser                  All   \n",
       "port ltd ann                     (0.1)                  0.1   \n",
       "     mret avg                      (0)                    0   \n",
       "     mret avg ann                (0.1)                  0.1   \n",
       "     mret std ann                  0.1                  0.1   \n",
       "     sharpe ann                (65.5%)                49.4%   \n",
       "     sortino                   (33.9%)                45.2%   \n",
       "     maxdd                     (36.3%)              (19.2%)   \n",
       "     maxdd dt      2015-04-24 00:00:00  2011-11-25 00:00:00   \n",
       "     avg dd                      (10%)               (3.4%)   \n",
       "     nmonths                        64                   64   \n",
       "pos  cnt                             4                    3   \n",
       "     win cnt                         0                    2   \n",
       "     lose cnt                        4                    1   \n",
       "     winpct                          0                  0.7   \n",
       "     ret avg                     (10%)                14.3%   \n",
       "     ret std                      2.2%                24.4%   \n",
       "     ret min                   (12.1%)               (9.8%)   \n",
       "     ret max                      (7%)                38.9%   \n",
       "     dur avg                      95.2                264.3   \n",
       "     dur max                       133                  467   \n",
       "\n",
       "lvl1                                                         \\\n",
       "lvl2                            winner                loser   \n",
       "port ltd ann                       0.1                  (0)   \n",
       "     mret avg                        0                  (0)   \n",
       "     mret avg ann                  0.1                  (0)   \n",
       "     mret std ann                  0.1                    0   \n",
       "     sharpe ann                    66%              (77.2%)   \n",
       "     sortino                     53.1%              (21.2%)   \n",
       "     maxdd                     (17.4%)              (13.6%)   \n",
       "     maxdd dt      2015-01-30 00:00:00  2011-03-17 00:00:00   \n",
       "     avg dd                     (3.2%)               (4.4%)   \n",
       "     nmonths                        64                   64   \n",
       "pos  cnt                             2                    1   \n",
       "     win cnt                         2                    0   \n",
       "     lose cnt                        0                    1   \n",
       "     winpct                          1                    0   \n",
       "     ret avg                     26.4%               (9.8%)   \n",
       "     ret std                     17.7%                  nan   \n",
       "     ret min                     13.9%               (9.8%)   \n",
       "     ret max                     38.9%               (9.8%)   \n",
       "     dur avg                     356.5                   80   \n",
       "     dur max                       467                   80   \n",
       "\n",
       "lvl1                             short                              \n",
       "lvl2                               All winner                loser  \n",
       "port ltd ann                     (0.1)      0                (0.1)  \n",
       "     mret avg                      (0)      0                  (0)  \n",
       "     mret avg ann                (0.1)      0                (0.1)  \n",
       "     mret std ann                  0.1      0                  0.1  \n",
       "     sharpe ann                  (51%)    nan                (51%)  \n",
       "     sortino                   (22.6%)    nan              (22.6%)  \n",
       "     maxdd                     (30.8%)    nan              (30.8%)  \n",
       "     maxdd dt      2015-04-24 00:00:00    NaN  2015-04-24 00:00:00  \n",
       "     avg dd                     (8.4%)    nan               (8.4%)  \n",
       "     nmonths                        64     64                   64  \n",
       "pos  cnt                             3      0                    3  \n",
       "     win cnt                         0      0                    0  \n",
       "     lose cnt                        3      0                    3  \n",
       "     winpct                          0    nan                    0  \n",
       "     ret avg                   (10.1%)    nan              (10.1%)  \n",
       "     ret std                      2.7%    nan                 2.7%  \n",
       "     ret min                   (12.1%)    nan              (12.1%)  \n",
       "     ret max                      (7%)    nan                 (7%)  \n",
       "     dur avg                     100.3    nan                100.3  \n",
       "     dur max                       133    nan                  133  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build a custom report which shows return information and position level information in single frame\n",
    "RF = new_dynamic_formatter('row', pcts=1, trunc_dot_zeros=1)\n",
    "\n",
    "summary = PortfolioSummary()\n",
    "summary.include_long_short().include_win_loss()\n",
    "RF(summary(port).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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