{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of using the model module\n",
    "This notebook shows how to use the components defined in the model. The model module is meant as a light-weight trade model which allows flexible portfolio construction and valuation. The portfolio then handles the position management (long/short), and p&l accounting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tia.analysis.model as model\n",
    "import pandas as pd\n",
    "\n",
    "from tia.util.fmt import new_dynamic_formatter, DynamicColumnFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "try:\n",
    "    plt.style.use('fivethirtyeight')\n",
    "    plt.rcParams['lines.linewidth'] = 1.4\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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"
     ]
    },
    {
     "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj close</th>\n",
       "      <th>dvds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-20</th>\n",
       "      <td>41.73</td>\n",
       "      <td>43.17</td>\n",
       "      <td>41.68</td>\n",
       "      <td>42.91</td>\n",
       "      <td>45738800</td>\n",
       "      <td>42.91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-21</th>\n",
       "      <td>43.00</td>\n",
       "      <td>43.15</td>\n",
       "      <td>42.53</td>\n",
       "      <td>42.64</td>\n",
       "      <td>25932200</td>\n",
       "      <td>42.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-22</th>\n",
       "      <td>42.67</td>\n",
       "      <td>43.13</td>\n",
       "      <td>42.55</td>\n",
       "      <td>42.99</td>\n",
       "      <td>24856600</td>\n",
       "      <td>42.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-23</th>\n",
       "      <td>42.89</td>\n",
       "      <td>43.61</td>\n",
       "      <td>42.80</td>\n",
       "      <td>43.34</td>\n",
       "      <td>45029600</td>\n",
       "      <td>43.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-24</th>\n",
       "      <td>45.66</td>\n",
       "      <td>48.14</td>\n",
       "      <td>45.65</td>\n",
       "      <td>47.87</td>\n",
       "      <td>127995200</td>\n",
       "      <td>47.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close     volume  adj close  dvds\n",
       "Date                                                              \n",
       "2015-04-20  41.73  43.17  41.68  42.91   45738800      42.91   NaN\n",
       "2015-04-21  43.00  43.15  42.53  42.64   25932200      42.64   NaN\n",
       "2015-04-22  42.67  43.13  42.55  42.99   24856600      42.99   NaN\n",
       "2015-04-23  42.89  43.61  42.80  43.34   45029600      43.34   NaN\n",
       "2015-04-24  45.66  48.14  45.65  47.87  127995200      47.87   NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load microsoft - note it retrieves the dividends too\n",
    "msft = model.load_yahoo_stock('MSFT', start='1/1/2010')\n",
    "msft.pxs.frame.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc84d668>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2lv3797N8+XKOHTt2S9c4YsQI/P39yc7Odri2R2XXsmnTJlasWMGWLVtYv349ISEhPPHE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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc824240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 3))\n",
    "msft.pxs.dvds.resample('Q', how='sum').to_period().plot(kind='bar', ax=axes[0], title='msft dividends')\n",
    "msft.pxs.close.plot(ax=axes[1], title='msft close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Trade(108, qty=1.0, px=46.45, ts=2014-12-31 00:00:00)>,\n",
       " <Trade(109, qty=1.0, px=46.45, ts=2014-12-31 00:00:00)>,\n",
       " <Trade(110, qty=-1.0, px=47.59, ts=2015-01-08 00:00:00)>,\n",
       " <Trade(111, qty=-1.0, px=47.59, ts=2015-01-08 00:00:00)>,\n",
       " <Trade(112, qty=1.0, px=43.86, ts=2015-02-20 00:00:00)>,\n",
       " <Trade(113, qty=1.0, px=43.86, ts=2015-02-20 00:00:00)>,\n",
       " <Trade(114, qty=-1.0, px=42.03, ts=2015-03-10 00:00:00)>,\n",
       " <Trade(115, qty=-1.0, px=42.03, ts=2015-03-10 00:00:00)>,\n",
       " <Trade(116, qty=1.0, px=41.62, ts=2015-04-17 00:00:00)>,\n",
       " <Trade(117, qty=1.0, px=41.62, ts=2015-04-17 00:00:00)>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# Create a signal for when the 10x20 moving average (buy/sell single contract)\n",
    "# - you can create your own simulation and build trades (using model.Trade or\n",
    "#   model.TradeBlotter)\n",
    "from tia.analysis.ta import cross_signal, sma, Signal\n",
    "ma10, ma20 = sma(msft.pxs.close, 10), sma(msft.pxs.close, 20)\n",
    "sig = cross_signal(ma10, ma20)\n",
    "trades = Signal(sig).close_to_close(msft.pxs.close)\n",
    "# show last 10 trades\n",
    "trades[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Build the portfolio\n",
    "port = model.SingleAssetPortfolio(msft, trades)"
   ]
  },
  {
   "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>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>1389</th>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>1</td>\n",
       "      <td>59</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>-41.62</td>\n",
       "      <td>42.91</td>\n",
       "      <td>-36.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.51</td>\n",
       "      <td>1.29</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1390</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>1</td>\n",
       "      <td>59</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>-41.62</td>\n",
       "      <td>42.64</td>\n",
       "      <td>-36.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.51</td>\n",
       "      <td>1.02</td>\n",
       "      <td>6.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>1</td>\n",
       "      <td>59</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>-41.62</td>\n",
       "      <td>42.99</td>\n",
       "      <td>-36.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.51</td>\n",
       "      <td>1.37</td>\n",
       "      <td>6.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392</th>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>1</td>\n",
       "      <td>59</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>-41.62</td>\n",
       "      <td>43.34</td>\n",
       "      <td>-36.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.51</td>\n",
       "      <td>1.72</td>\n",
       "      <td>7.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>1</td>\n",
       "      <td>59</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>-41.62</td>\n",
       "      <td>47.87</td>\n",
       "      <td>-36.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.51</td>\n",
       "      <td>6.25</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  pos  pid  tid  txn_qty  txn_px  txn_fees  txn_premium  \\\n",
       "1389 2015-04-20    1   59    0      NaN     NaN         0            0   \n",
       "1390 2015-04-21    1   59    0      NaN     NaN         0            0   \n",
       "1391 2015-04-22    1   59    0      NaN     NaN         0            0   \n",
       "1392 2015-04-23    1   59    0      NaN     NaN         0            0   \n",
       "1393 2015-04-24    1   59    0      NaN     NaN         0            0   \n",
       "\n",
       "      txn_intent  txn_action  close  open_val  mkt_val  total_val  dvds  fees  \\\n",
       "1389           0           0  42.91    -41.62    42.91     -36.55  0.44     0   \n",
       "1390           0           0  42.64    -41.62    42.64     -36.55  0.44     0   \n",
       "1391           0           0  42.99    -41.62    42.99     -36.55  0.44     0   \n",
       "1392           0           0  43.34    -41.62    43.34     -36.55  0.44     0   \n",
       "1393           0           0  47.87    -41.62    47.87     -36.55  0.44     0   \n",
       "\n",
       "      rpl_gross   rpl   upl     pl  \n",
       "1389       5.07  5.51  1.29   6.80  \n",
       "1390       5.07  5.51  1.02   6.53  \n",
       "1391       5.07  5.51  1.37   6.88  \n",
       "1392       5.07  5.51  1.72   7.23  \n",
       "1393       5.07  5.51  6.25  11.76  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the ltd transaction level pl frame\n",
    "port.pl.ltd_txn_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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the daily pl frame (rolled up by day)\n",
    "port.pl.dly_frame.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>56</th>\n",
       "      <td>Short</td>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>2015-02-20</td>\n",
       "      <td>-1</td>\n",
       "      <td>47.6</td>\n",
       "      <td>43.9</td>\n",
       "      <td>47.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.2%</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Long</td>\n",
       "      <td>2015-02-20</td>\n",
       "      <td>2015-03-10</td>\n",
       "      <td>1</td>\n",
       "      <td>43.9</td>\n",
       "      <td>42</td>\n",
       "      <td>-43.9</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-4.2%</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Short</td>\n",
       "      <td>2015-03-10</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>-1</td>\n",
       "      <td>42</td>\n",
       "      <td>41.6</td>\n",
       "      <td>42</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1%</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Long</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>1</td>\n",
       "      <td>41.6</td>\n",
       "      <td>nan</td>\n",
       "      <td>-41.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>15%</td>\n",
       "      <td>6</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    pl  \\\n",
       "pid                                                                             \n",
       "56   Short 2015-01-08 2015-02-20       -1    47.6     43.9         47.6   3.4   \n",
       "57    Long 2015-02-20 2015-03-10        1    43.9       42        -43.9  -1.8   \n",
       "58   Short 2015-03-10 2015-04-17       -1      42     41.6           42   0.4   \n",
       "59    Long 2015-04-17 2015-04-24        1    41.6      nan        -41.6   6.3   \n",
       "\n",
       "       ret duration ntxns   state  \n",
       "pid                                \n",
       "56    7.2%       30     2  Closed  \n",
       "57   -4.2%       13     2  Closed  \n",
       "58      1%       28     2  Closed  \n",
       "59     15%        6     1    Open  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the positions\n",
    "F = new_dynamic_formatter(method='col', pcts=1, parens=0, trunc_dot_zeros=1)\n",
    "F(port.positions.frame.tail(4))"
   ]
  },
  {
   "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>dly</th>\n",
       "      <th>mthly</th>\n",
       "      <th>wkly</th>\n",
       "      <th>yrly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>avg</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.042</td>\n",
       "      <td>1.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474</td>\n",
       "      <td>2.195</td>\n",
       "      <td>1.094</td>\n",
       "      <td>5.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd</th>\n",
       "      <td>(14.200)</td>\n",
       "      <td>(12.410)</td>\n",
       "      <td>(13.080)</td>\n",
       "      <td>(7.870)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd dt</th>\n",
       "      <td>08-Nov-11</td>\n",
       "      <td>31-Jan-13</td>\n",
       "      <td>13-Nov-11</td>\n",
       "      <td>31-Dec-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dd avg</th>\n",
       "      <td>(2.296)</td>\n",
       "      <td>(8.320)</td>\n",
       "      <td>(3.830)</td>\n",
       "      <td>(7.870)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>1,336</td>\n",
       "      <td>64</td>\n",
       "      <td>277</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                dly      mthly       wkly       yrly\n",
       "avg           0.009      0.184      0.042      1.960\n",
       "std           0.474      2.195      1.094      5.609\n",
       "maxdd      (14.200)   (12.410)   (13.080)    (7.870)\n",
       "maxdd dt  08-Nov-11  31-Jan-13  13-Nov-11  31-Dec-12\n",
       "dd avg      (2.296)    (8.320)    (3.830)    (7.870)\n",
       "cnt           1,336         64        277          6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at basic p/l stats\n",
    "PLFMT = new_dynamic_formatter(method='row', precision=3, pcts=1, trunc_dot_zeros=1)\n",
    "\n",
    "tmp = pd.DataFrame({'dly': port.pl.dly_details.summary, \n",
    "              'wkly': port.pl.weekly_details.summary, \n",
    "              'mthly': port.pl.monthly_details.summary, \n",
    "              'yrly': port.pl.annual_details.summary})\n",
    "PLFMT(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FMT = new_dynamic_formatter(method='row', precision=2, pcts=1, trunc_dot_zeros=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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/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>dly</th>\n",
       "      <th>mthly</th>\n",
       "      <th>wkly</th>\n",
       "      <th>yrly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ltd</th>\n",
       "      <td>8.52%</td>\n",
       "      <td>8.52%</td>\n",
       "      <td>8.52%</td>\n",
       "      <td>8.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltd ann</th>\n",
       "      <td>1.55%</td>\n",
       "      <td>1.54%</td>\n",
       "      <td>1.55%</td>\n",
       "      <td>1.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.34%</td>\n",
       "      <td>0.08%</td>\n",
       "      <td>2.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean ann</th>\n",
       "      <td>4.02%</td>\n",
       "      <td>4.07%</td>\n",
       "      <td>4.12%</td>\n",
       "      <td>2.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.40%</td>\n",
       "      <td>6.56%</td>\n",
       "      <td>3.17%</td>\n",
       "      <td>17.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std ann</th>\n",
       "      <td>22.27%</td>\n",
       "      <td>22.73%</td>\n",
       "      <td>22.89%</td>\n",
       "      <td>17.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sharpe ann</th>\n",
       "      <td>6.98%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>6.76%</td>\n",
       "      <td>7.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sortino</th>\n",
       "      <td>7%</td>\n",
       "      <td>6.76%</td>\n",
       "      <td>6.89%</td>\n",
       "      <td>9.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd</th>\n",
       "      <td>(44.86%)</td>\n",
       "      <td>(41.89%)</td>\n",
       "      <td>(42.80%)</td>\n",
       "      <td>(32.14%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd dt</th>\n",
       "      <td>08-Nov-11</td>\n",
       "      <td>31-Jan-13</td>\n",
       "      <td>13-Nov-11</td>\n",
       "      <td>31-Dec-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dd avg</th>\n",
       "      <td>(6.62%)</td>\n",
       "      <td>(41.89%)</td>\n",
       "      <td>(12.75%)</td>\n",
       "      <td>(32.14%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>1,336</td>\n",
       "      <td>64</td>\n",
       "      <td>277</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  dly      mthly       wkly       yrly\n",
       "ltd             8.52%      8.52%      8.52%      8.52%\n",
       "ltd ann         1.55%      1.54%      1.55%      1.37%\n",
       "mean            0.02%      0.34%      0.08%      2.68%\n",
       "mean ann        4.02%      4.07%      4.12%      2.68%\n",
       "std             1.40%      6.56%      3.17%     17.50%\n",
       "std ann        22.27%     22.73%     22.89%     17.50%\n",
       "sharpe ann      6.98%      6.80%      6.76%      7.84%\n",
       "sortino            7%      6.76%      6.89%      9.28%\n",
       "maxdd        (44.86%)   (41.89%)   (42.80%)   (32.14%)\n",
       "maxdd dt    08-Nov-11  31-Jan-13  13-Nov-11  31-Dec-12\n",
       "dd avg        (6.62%)   (41.89%)   (12.75%)   (32.14%)\n",
       "cnt             1,336         64        277          6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at basic ret stats (By default return on initial invest of each position)\n",
    "tmp = pd.DataFrame({'dly': port.performance.dly_details.summary, \n",
    "              'wkly': port.performance.weekly_details.summary, \n",
    "              'mthly': port.performance.monthly_details.summary, \n",
    "              'yrly': port.performance.annual_details.summary})\n",
    "FMT(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win_pct</th>\n",
       "      <td>44.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_avg</th>\n",
       "      <td>0.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_std</th>\n",
       "      <td>6.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_min</th>\n",
       "      <td>(9.55%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_max</th>\n",
       "      <td>27.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_avg</th>\n",
       "      <td>19.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_std</th>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_min</th>\n",
       "      <td>(2.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pl_max</th>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_avg</th>\n",
       "      <td>23.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_std</th>\n",
       "      <td>17.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consecutive_win_cnt_max</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consecutive_loss_cnt_max</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<tia.analysis.model.pos.PositionsStats at 0xcbae9e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the position stats\n",
    "port.positions.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>51</th>\n",
       "      <td>Long</td>\n",
       "      <td>2014-08-19</td>\n",
       "      <td>2014-10-02</td>\n",
       "      <td>1</td>\n",
       "      <td>45.33</td>\n",
       "      <td>45.76</td>\n",
       "      <td>-45.33</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.015663</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Long</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2014-11-28</td>\n",
       "      <td>1</td>\n",
       "      <td>46.05</td>\n",
       "      <td>47.81</td>\n",
       "      <td>-46.05</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.044951</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Long</td>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>46.45</td>\n",
       "      <td>47.59</td>\n",
       "      <td>-46.45</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.024543</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Long</td>\n",
       "      <td>2015-02-20</td>\n",
       "      <td>2015-03-10</td>\n",
       "      <td>1</td>\n",
       "      <td>43.86</td>\n",
       "      <td>42.03</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-0.041724</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Long</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>1</td>\n",
       "      <td>41.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-41.62</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.150168</td>\n",
       "      <td>6</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",
       "51   Long 2014-08-19 2014-10-02         1    45.33     45.76        -45.33   \n",
       "53   Long 2014-10-30 2014-11-28         1    46.05     47.81        -46.05   \n",
       "55   Long 2014-12-31 2015-01-08         1    46.45     47.59        -46.45   \n",
       "57   Long 2015-02-20 2015-03-10         1    43.86     42.03        -43.86   \n",
       "59   Long 2015-04-17 2015-04-24         1    41.62       NaN        -41.62   \n",
       "\n",
       "       pl       ret  duration  ntxns   state  \n",
       "pid                                           \n",
       "51   0.71  0.015663        32      2  Closed  \n",
       "53   2.07  0.044951        21      2  Closed  \n",
       "55   1.14  0.024543         6      2  Closed  \n",
       "57  -1.83 -0.041724        13      2  Closed  \n",
       "59   6.25  0.150168         6      1    Open  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Easy to get portfolio subsets such as long/short, winner/losers\n",
    "port.long.positions.frame.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get a specific position\n",
    "pos = port.long.positions[57]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>1291</th>\n",
       "      <td>2015-02-20</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>43.86</td>\n",
       "      <td>0</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43.86</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>43.86</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>2015-02-23</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.15</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>44.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>2015-02-24</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.09</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>44.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.99</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>43.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>2015-02-26</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.06</td>\n",
       "      <td>-43.86</td>\n",
       "      <td>44.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  pos  pid  tid  txn_qty  txn_px  txn_fees  txn_premium  \\\n",
       "1291 2015-02-20    1   57  113        1   43.86         0       -43.86   \n",
       "1292 2015-02-23    1   57    0      NaN     NaN         0         0.00   \n",
       "1293 2015-02-24    1   57    0      NaN     NaN         0         0.00   \n",
       "1294 2015-02-25    1   57    0      NaN     NaN         0         0.00   \n",
       "1295 2015-02-26    1   57    0      NaN     NaN         0         0.00   \n",
       "\n",
       "      txn_intent  txn_action  close  open_val  mkt_val  total_val  dvds  fees  \\\n",
       "1291           1           1  43.86    -43.86    43.86     -43.86     0     0   \n",
       "1292           0           0  44.15    -43.86    44.15       0.00     0     0   \n",
       "1293           0           0  44.09    -43.86    44.09       0.00     0     0   \n",
       "1294           0           0  43.99    -43.86    43.99       0.00     0     0   \n",
       "1295           0           0  44.06    -43.86    44.06       0.00     0     0   \n",
       "\n",
       "      rpl_gross  rpl   upl    pl  \n",
       "1291          0    0  0.00  0.00  \n",
       "1292          0    0  0.29  0.29  \n",
       "1293          0    0 -0.06 -0.06  \n",
       "1294          0    0 -0.10 -0.10  \n",
       "1295          0    0  0.07  0.07  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get txn level detail associated with the position\n",
    "pos.pl.txn_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Report Provided (can add own splits but Long/Short, Win/Loss included)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>1.54%</td>\n",
       "      <td>30.89%</td>\n",
       "      <td>(22.42%)</td>\n",
       "      <td>7.72%</td>\n",
       "      <td>21.84%</td>\n",
       "      <td>(11.59%)</td>\n",
       "      <td>(5.73%)</td>\n",
       "      <td>7.42%</td>\n",
       "      <td>(12.25%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mret avg</th>\n",
       "      <td>0.34%</td>\n",
       "      <td>2.38%</td>\n",
       "      <td>(2.04%)</td>\n",
       "      <td>0.75%</td>\n",
       "      <td>1.74%</td>\n",
       "      <td>(0.99%)</td>\n",
       "      <td>(0.41%)</td>\n",
       "      <td>0.63%</td>\n",
       "      <td>(1.04%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mret avg ann</th>\n",
       "      <td>4.07%</td>\n",
       "      <td>28.56%</td>\n",
       "      <td>(24.44%)</td>\n",
       "      <td>8.96%</td>\n",
       "      <td>20.90%</td>\n",
       "      <td>(11.93%)</td>\n",
       "      <td>(4.93%)</td>\n",
       "      <td>7.60%</td>\n",
       "      <td>(12.53%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mret std ann</th>\n",
       "      <td>22.73%</td>\n",
       "      <td>16.85%</td>\n",
       "      <td>11.44%</td>\n",
       "      <td>17.64%</td>\n",
       "      <td>14.44%</td>\n",
       "      <td>8.01%</td>\n",
       "      <td>14.02%</td>\n",
       "      <td>9.48%</td>\n",
       "      <td>9.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sharpe ann</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.83</td>\n",
       "      <td>(1.96)</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.51</td>\n",
       "      <td>(1.45)</td>\n",
       "      <td>(0.41)</td>\n",
       "      <td>0.78</td>\n",
       "      <td>(1.29)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sortino</th>\n",
       "      <td>0.07</td>\n",
       "      <td>2.26</td>\n",
       "      <td>(1.18)</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.48</td>\n",
       "      <td>(0.76)</td>\n",
       "      <td>(0.34)</td>\n",
       "      <td>0.69</td>\n",
       "      <td>(0.70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd</th>\n",
       "      <td>(44.86%)</td>\n",
       "      <td>(16.68%)</td>\n",
       "      <td>(75.06%)</td>\n",
       "      <td>(26.22%)</td>\n",
       "      <td>(12.82%)</td>\n",
       "      <td>(49.92%)</td>\n",
       "      <td>(43.77%)</td>\n",
       "      <td>(8.81%)</td>\n",
       "      <td>(51.61%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdd dt</th>\n",
       "      <td>08-Nov-11</td>\n",
       "      <td>08-Nov-11</td>\n",
       "      <td>10-Mar-15</td>\n",
       "      <td>01-Nov-11</td>\n",
       "      <td>08-Aug-11</td>\n",
       "      <td>10-Mar-15</td>\n",
       "      <td>04-Dec-14</td>\n",
       "      <td>24-Mar-15</td>\n",
       "      <td>08-Oct-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg dd</th>\n",
       "      <td>(6.62%)</td>\n",
       "      <td>(2.79%)</td>\n",
       "      <td>(25.27%)</td>\n",
       "      <td>(3.77%)</td>\n",
       "      <td>(2.45%)</td>\n",
       "      <td>(16.89%)</td>\n",
       "      <td>(17.85%)</td>\n",
       "      <td>(3.45%)</td>\n",
       "      <td>(51.61%)</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>59</td>\n",
       "      <td>26</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win cnt</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lose cnt</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winpct</th>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret avg</th>\n",
       "      <td>0.36%</td>\n",
       "      <td>5.88%</td>\n",
       "      <td>(3.99%)</td>\n",
       "      <td>1.65%</td>\n",
       "      <td>7.57%</td>\n",
       "      <td>(4.26%)</td>\n",
       "      <td>(0.97%)</td>\n",
       "      <td>3.59%</td>\n",
       "      <td>(3.76%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret std</th>\n",
       "      <td>6.99%</td>\n",
       "      <td>6.98%</td>\n",
       "      <td>2.49%</td>\n",
       "      <td>8.55%</td>\n",
       "      <td>8.43%</td>\n",
       "      <td>2.32%</td>\n",
       "      <td>4.69%</td>\n",
       "      <td>3.53%</td>\n",
       "      <td>2.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret min</th>\n",
       "      <td>(9.55%)</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>(9.55%)</td>\n",
       "      <td>(9.55%)</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>(9.55%)</td>\n",
       "      <td>(8.24%)</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>(8.24%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret max</th>\n",
       "      <td>27.24%</td>\n",
       "      <td>27.24%</td>\n",
       "      <td>(0.22%)</td>\n",
       "      <td>27.24%</td>\n",
       "      <td>27.24%</td>\n",
       "      <td>(1.06%)</td>\n",
       "      <td>10.63%</td>\n",
       "      <td>10.63%</td>\n",
       "      <td>(0.22%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dur avg</th>\n",
       "      <td>23.03</td>\n",
       "      <td>33.38</td>\n",
       "      <td>14.88</td>\n",
       "      <td>26.47</td>\n",
       "      <td>38.33</td>\n",
       "      <td>14.60</td>\n",
       "      <td>19.48</td>\n",
       "      <td>26.64</td>\n",
       "      <td>15.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dur max</th>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>37</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lvl1                     All                             long             \\\n",
       "lvl2                     All     winner      loser        All     winner   \n",
       "port ltd ann           1.54%     30.89%   (22.42%)      7.72%     21.84%   \n",
       "     mret avg          0.34%      2.38%    (2.04%)      0.75%      1.74%   \n",
       "     mret avg ann      4.07%     28.56%   (24.44%)      8.96%     20.90%   \n",
       "     mret std ann     22.73%     16.85%     11.44%     17.64%     14.44%   \n",
       "     sharpe ann         0.07       1.83     (1.96)       0.44       1.51   \n",
       "     sortino            0.07       2.26     (1.18)       0.49       1.48   \n",
       "     maxdd          (44.86%)   (16.68%)   (75.06%)   (26.22%)   (12.82%)   \n",
       "     maxdd dt      08-Nov-11  08-Nov-11  10-Mar-15  01-Nov-11  08-Aug-11   \n",
       "     avg dd          (6.62%)    (2.79%)   (25.27%)    (3.77%)    (2.45%)   \n",
       "     nmonths              64         64         64         64         64   \n",
       "pos  cnt                  59         26         33         30         15   \n",
       "     win cnt              26         26          0         15         15   \n",
       "     lose cnt             33          0         33         15          0   \n",
       "     winpct             0.44          1          0       0.50          1   \n",
       "     ret avg           0.36%      5.88%    (3.99%)      1.65%      7.57%   \n",
       "     ret std           6.99%      6.98%      2.49%      8.55%      8.43%   \n",
       "     ret min         (9.55%)      0.07%    (9.55%)    (9.55%)      0.12%   \n",
       "     ret max          27.24%     27.24%    (0.22%)     27.24%     27.24%   \n",
       "     dur avg           23.03      33.38      14.88      26.47      38.33   \n",
       "     dur max             104        104         37        104        104   \n",
       "\n",
       "lvl1                              short                        \n",
       "lvl2                   loser        All     winner      loser  \n",
       "port ltd ann        (11.59%)    (5.73%)      7.42%   (12.25%)  \n",
       "     mret avg        (0.99%)    (0.41%)      0.63%    (1.04%)  \n",
       "     mret avg ann   (11.93%)    (4.93%)      7.60%   (12.53%)  \n",
       "     mret std ann      8.01%     14.02%      9.48%      9.52%  \n",
       "     sharpe ann       (1.45)     (0.41)       0.78     (1.29)  \n",
       "     sortino          (0.76)     (0.34)       0.69     (0.70)  \n",
       "     maxdd          (49.92%)   (43.77%)    (8.81%)   (51.61%)  \n",
       "     maxdd dt      10-Mar-15  04-Dec-14  24-Mar-15  08-Oct-14  \n",
       "     avg dd         (16.89%)   (17.85%)    (3.45%)   (51.61%)  \n",
       "     nmonths              64         64         64         64  \n",
       "pos  cnt                  15         29         11         18  \n",
       "     win cnt               0         11         11          0  \n",
       "     lose cnt             15         18          0         18  \n",
       "     winpct                0       0.38          1          0  \n",
       "     ret avg         (4.26%)    (0.97%)      3.59%    (3.76%)  \n",
       "     ret std           2.32%      4.69%      3.53%      2.68%  \n",
       "     ret min         (9.55%)    (8.24%)      0.07%    (8.24%)  \n",
       "     ret max         (1.06%)     10.63%     10.63%    (0.22%)  \n",
       "     dur avg           14.60      19.48      26.64      15.11  \n",
       "     dur max              37         43         43         28  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = model.PortfolioSummary()\n",
    "# defaults included are long/short, win/loss but can provide a method to split as like\n",
    "summary.include_long_short().include_win_loss()\n",
    "\n",
    "# a few simple stats methods provided but easy to create own\n",
    "analyze_fct = model.PortfolioSummary.analyze_returns\n",
    "rpt = summary(port, analyze_fct)\n",
    "FMT(rpt.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jlxahdOru26YlJIdvwJKEZ9gYjDs2EzXj8gZ3I4SS4JTAtPZLzJ+90yh9uy69HtdlNzZK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dAAAC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\ng3BKAoFAIOg0/D+rYqV52dvlgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9e1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = pd.DataFrame({'Long': port.long.pl.ltd_dly, 'Short': port.short.pl.ltd_dly, 'All': port.pl.ltd_dly})\n",
    "_ = tmp.plot(title='P&L')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xde21128>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ODqZXr17KNUaNGsXLL7+sfB4+fDgxMTFMmTKF8+fPs3TpUn788UfGjRtXWt0WCAQCwT1M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\nR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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd7f3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show Daily Drawdowns\n",
    "port.pl.dly_details.plot_ltd()\n",
    "plt.title('P&L Drawdown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xe1bd5c0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e7PrT7t8lDHKaQIeZObYWQUFB/PTTTyxdupSioiJCQkIYM2YMzzzzDJ9++qmH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QGUTsgb96x0TkWZNhvD3TQU7sWWcR/gkFNXqXAvGz9+POPHj7f52erVq62uSZJEXFxcmd/Zo0cP\nTpyoRMgmQZ3mjU6OvHI8C43eGFTbYIC4TA1TOzQk+qbRAD5XLGO8vVxiXkAjnO6U72FSH3jCXUnM\nH/cnD0fTC5kfUHaZ/381Hzu5RKiXHUfSCjiabtzvDXu8IY+7Kmnj9PDyiYZ4qvkmKZ/Bre25rblv\nkNs4KbhyS8vMxx3rlbOVpHbHUJSJQVfw4Kg7+kIkhQP6vN8wFFxHn5uErKFPhX5PVWQMsSd36YTM\n5QkMRVnInTpUtvq1mvozBRPUOpo3VPDWE41456fb5GsN3C7SozdAq4a2u+3sLo70aKrmyh2bH9dL\nAhqriPlDw9OuhRzJUuOgkCjUGcr0NF0WaxQwu0jPyv8a9/yecFcypHXlY6mWl8fdVHw/oDFKmcSN\ne8HTG9vJeKyR0Tja/YknVl5eHn/88QdFRcaJ1c2bNx9QovrR5zujzfdDcSkWmdq2g6IJ7U0l+ruP\nQVE24IriShKyhqWfFTfo9WgKHgdAeTkBTdo+9IVZ3Ja6okr5A4PDOGgA0m9pVdmkGoVCocDV1RUn\nJyfrz6qhPoJyotVqycjIoLCwsNQ8fZmZmaU6LtUFdHe0+OnySUq6Q65Wj5/uLvo/bjPEsZD4nPvL\nrSN8G9CiIJekpLqtiVwux9HREVfX8h0LGNTKntaOChrfuUqIrwdvR99i8+U8JrZvaHHfoav5rPlf\nLkuevB/txWQYAfp52/OoUN47P9mkgZz/F+KKs1qioVJGn+Z2NKhkWMDbt29z/fp12rZta97eycvL\nw8GhamPPVjUGXRv0Ps7InP6CpLR+gBdHn+uAQZuPzM4dfW4KMkdfpDIMqqEgA72X0dFH5toefbNC\ndHInlA0aV/jYSW2lqKiIpKQkioqKaNzY0m1KGMcailarJT4+nsceewwHB4dSN/a9vLxq/AD/M3gW\n6XFqoaGVowKlTMKumYYubiqCO0lk3NVxvUBHWycldsXehOqyJlqtluvXr3P16lW8vb0feH9DpYzg\ne2/TXRsb92q/vHKX20V6gjzU9PJUk5ar5f17b4tTT2Ujk+4nIV7UtRFPNau+INq+xfYXTfWvKAaD\ngdTUVLp27WoxjmQyGTJZDXc2kexArkDS3kGmftA+rw6DXIbcvincvYpMMiCV1j6DAd3dFGQyCWQq\nZGhAJqFReSC3L/sNtS5hZ2eHn58fsbGxODk5oVLd72PCONZQEhMT6dSpk00npuJoNBqL/9C6hqsK\nFHkSyQXGPUWFUoW9WoVCJtFcBdbh7uu2JiqVCh8fH5KTk/njjz9wdy9/zsriDi77UgrYl1KAv5vS\nKtSebyMFCwIa8UeBHn/32q+jRqOhUaNGVZ6+65EgyZDsGmPIv4ZBbm+dzsSEXouhKOdeGcn4x1B6\nwAyDoUQQBZ1xqdlQwbOYdYUmTZqQm5trsSIjjGMNRS6XP9Aw1geKPwtMMUNthZSrbzRr1oyEhIQK\nGUcwhtx740y22UknNlODXIKRjzVgW+JdAJ5rYUfzhgqaNyzrm2oPOp3OImRlbUNSuWLINzrYlBuD\nAX1eCjJJZn0UQ5KbY6BKajcMRVkYCjON1+qrZ6pCYd6LNlHD1xTqL7VylvsIkHE/i3x9RqlUVjow\nf8nlSZ0BBre25wl3owFxVAqBbdGmTRuysrKsri9atIiPP/7Y6npkZCSdO3cmICCA5557jqtXr5o/\n27x5M35+fvj5+bFlyxbz9dWrV9O+fXvUarX5tySlI3K3IORuQVz4TUbDFn9l76nfzdfkbkHmXJYJ\nV1Lo1asXzq37ELnma/S5v6G/8ysp8Wf427P98Q8IpktANz7510KQJKQGLcBgwFCUjWRnjFX83Xff\n0bFjR/z8/Pjggw+s2vXxxx9b1K8kpbXtt99+o0ePHvj5+TF69Gg0Gk2NLG9CGMc6THUNZoD4+Hh6\n9eqFo6Ojzd8qz30TJ06kefPmTHzm/pkrWwtFpQ3m6OhogoODCQwMpHv37vz0008261Ddg7GqBnN5\nGerTgO1PW+4redjL+bC7Mx8HOxNaBdFn6iKlTVhLu96lSxfOnj3L+fPnGTJkCBEREQBkZWWxZMkS\nzpw5w5kzZ3jvvffIyTEuiQYHB3Po0CFatmxZ8kfQ6fXMfestnn32WaODnmn5VJLMsU1dXRsRGRlJ\neNi9NFJyO+Rugdh5dOXDyDXE/fw/Tuxfw9qN/yY+8XeLQOGSXWN0Oh0zZ85k//79xMXFsX37di5d\numS+JzU1laioKOv63cNW227dMh6tmjt3LjNnzuR///sfLi4ubNy4scaVL44wjnWY6hzMbm5uxkEa\nHl5mHcu6b9y4cezbt4+ysk/ZGswJCQkARERE8M477xAdHc2CBQvM7SlOdQ/GqhzM5UUll2jSQM7+\nvsYl2VaOxqU0mSTRxV1l9hatqyQnJ9OxY0emTZtGhw4dePnll/n+++8JCQnBz8/PPInKzMykX79+\n+Pv7M3nyZAuP8aVLl9KhQwdCQ0O5fPmyzd8JCQkxb40EBgaSnp4OwOHDh3n66adxdnbG2dmZPn36\n8N133wHg7+9fquH59NNPGTJkiO2l9HuxURu7uxEQEIDSzhgDV2bfFCQZTT2b4e/fBWQKGjo04C9t\nWnLtptEJy+SZKslUnD9/Hl9fX1q1aoVSqWT48OHs27fP/DP//Oc/WbJkSana2mrboUOHMBgMnDhx\nghdffBGAsWPHsnfv3hpXvjjCONYCTIN5woQJVoO5a9euNXIwN27c2DhIH7DXU9Z9PXv2xMXFpVQf\nBDC+HZYczAcPHgSgadOmZkOTk5NDs2bNrMpX92CsysFcURoqZRzu35i1T9UPt/3iJCUlMX36dC5e\nvMjly5fZuXMnJ06cYNmyZbz/vjHT/eLFi+nZsyexsbEMGjTIvJISExPDzp07OX/+PHv37uX8+fMP\n3Ab54osveO45Ywqra9eu4eXlZf7My8uL33//vczy6enp7Nu3j0mTJgG2J7gyx8eQFcuOIXPwRrKz\nTgqRclNL3MUrBAU9abyv4WPI3bqa62aKeFaybnv37qV58+Z06tSp1HqW1rasrCycnZ3N3sHNmjUz\nP1/Onz/P5MmTq618aQjjWEtISkoiPDzcajAvWrSoRg7mh4FaJtHNwzJfXHp6utVgvnbtGgDvvfce\ns2fPxtfXl4iICBYvtk46W5cGc2VQyaU/lXqqttKqVSvat2+PJEn4+fnRu3dvADp06EBKSgoAp0+f\nZtSoUQD07dsXFxcXDAYDp0+f5oUXXsDOzg5HR0cGDBhQ6jlkgC+//JILFy4wa9asStd31qxZvPfe\ne0iShMFgsPl7ktoNSV72edTc3FxGvTKLf32wlEamYOKSZHbEKe25kJ+fz7Jly5g/f775WllttqjX\nA541AQEBrFmzptrKl4YwjrWEVq1a0aFDB6vB7OfnVyMHc1XT3llJR1clJVf8yur4kyZN4qOPPuLX\nX3/lgw8+4LXXXivXb9XWwSwoP2r1/UmWTCYzH/2RyWRotfePOdg0QvcMVFn3mIiKimLZsmV8++23\n5tWRZs2akZZ2P+pMWlqaxQTJFhcuXGDMmDG0bduWXbt2ERYWxt69e1mzZg2BgYEEBQWZJ4WlodFo\nGDFiBKNGjeKFoWNAkkhNTSUwMJDAwEDWr1+Pp6enzbr9+uuvpKSk0LVrV9q2bUtaWhrdunWzSjdo\nq23NmjXD1dWVnJwc9Hqj10B6errNNld3+eII41hLqG2DuTQqMpiL46KWoZZbD2YvLy+bgwGMS66m\ndGovvvgi0dHRVt9b3YOxKgezoGrp1asXX3/9NQCHDh0iOzsbSZLo2bMne/fupaCggDt37nDgwAGb\nE5oLFy4wffp0du3aZbFP+Mwzz3DkyBFycnLIzs4mKiqKZ555xqp88XGakJDA5cuXuXz5MkOGDOGT\nTz5h4MCBTJ48mejoaM6dO4enp6fNsqZ/v/baa7Rv356wsDDz9RYtWhAdHU10dDQTJ06kS5cuJCYm\nkpycTFFRETt37mTAgAF07NiRtLQ0cx2aN2/O2bNn8fDwsPid0tomSRIhISF88803AGzZsoWBAwda\ntbm6yxdHGMc6RE0azKVdK+9gLo2SgzkgIMBqMPft2xcAX19fTp48CRhzhNrK3Vfdg7EqB7Og/JTs\n/8X/bfr7vHnzOH36NP7+/uzZs8e8t96lSxeGDRtGQEAAAwcOJDAw0OZvzJ07l7y8PF566SUCAwPN\n+8cuLi7MnTuX4OBgevTowVtvvYWzszFs36pVq/Dx8SE9PZ2AgACmTJlSoXZdv34dHx8fVq5cydKl\nS/H19SU3N5czZ87w1Vdfcfz4cfPk0uQ3UByFQkFkZCT9+/enc+fODBs2jPbt2z9QPxNltW3JkiWs\nWLECPz8/srOzzakOY2JizNsM1VG+NKScnJyKJ3gTPHRMEXLA6JAzZMgQYmJiAJgwYQL9+/dn8ODB\nXLp0idGjRxMTE0NWVhZjx44lPT2d7t27ExUVxX/+8x9cXV15//332bJlCx4eHnh7e9OlSxdmzpxp\n8Zt9+/bll19+oUkT4ya+t7c3//73vwHYtGkTy5YtA2DOnDm8/PLLgHEwf/TRR9y4cYPGjRvTt29f\nPvvsM65fv05wcDC3b99GJpPh6OhIXFwcDRtaniwv674xY8Zw6tQpMjMz8fDwYMGCBYwbN85Kq0OH\nDjFr1iz0ej1///vfmT59Og4ODpw/f56wsDAKCwuxt7dn5cqVdOnSxap8aW377bffGDNmDNnZ2fj7\n+7Np0yaUSiUxMTGsW7fOvDT6qMuDcTIRFxdH27ZtH9iXoG4mxS4v+fn55OTk4Ovra3G9NsRWfdTU\nV00yMjLIz8+3iK9aYeP4+eefs3LlSjIyMmjXrh1Lly6le/fuZZZZvXo1Gzdu5OrVq7i4uDBy5EgW\nLFhg/vz06dO89dZbJCQk0LRpU2bMmPFAq17XKW4cy6K+duayqA+aCONYfnQ6HSkpKXTs2NHien3o\nJxWlvmqSkpJi9skwUaFl1W+//ZaIiAj+8Y9/cOrUKYKCghg2bJjFnklJ5s6dy4YNG1i0aBHnzp1j\n586d9OjRw/x5cnIyw4cPp1u3bpw6dYo33niDN998s8rd1msb5V1mFNRP8vPzLfahBaUjl8spKioy\nn80VCIqTn59PRkaG1aSgQm+Offr04fHHHycyMtJ8LSAggEGDBlm4+Jq4cuUKwcHBnDlzptRZ64IF\nCzhw4IBF9JKwsDDi4+M5fLhy2azrAlevXsXR0fGBThj1daZXFnVdE71eT2xsLN7e3jRoUL4ci/X5\nzRGMk80rV64gl8uRJAlJksjNzbVa5q/v1CdNDAYDer0ejUZD27ZtrdLclTvweFFREXFxcRaeTgC9\ne/fm7NmzNsscPHiQVq1acfjwYYYOHYrBYKBHjx68++67ZoePc+fOERoaavWd27ZtQ6fT1dm8fA/C\n29ub5ORkMjMzUalUpW6A13VDUBnqsiY6nY68vDxatmxZbsMoMDqQmJagTR7AiYmJpUajqa/UN01M\nEyVblNs4ZmZmotPprFx33d3drc66mEhOTiY1NZXdu3ebnQ/efvttXnrpJY4cOQIYs3GX/M7GjRuj\n1WrNjhj1lVatWmEwGNBqtWKZVQAYlwgrM2Gsz2+NJTEFVijvfm19Qmhyn4easkqv11NYWMjatWvx\n8fEBYO3atXTt2pWYmBieeOKJh/nzdQJJkmp1uh2BQCCojZTbIcfNzQ25XG71lnjz5k2z639JmjRp\ngkKhMBtGAB8fH+RyOampqQB4eHjY/E6FQoGbW/3JSC0QCASCmkO5jaNKpcLf359jx45ZXD927BhP\nPvmkzTLdu3dHq9WSnJxsvpacnIxOp8Pb2xuAoKAgm9/5xBNP1Nv9RoFAIBBULxU6yjFt2jS++uor\nNm/eTEJCArNnzyYjI8N8JnHhwoUMGjTIfP9f//pXOnfuzLRp0/j555+Ji4tj2rRpBAYGmg9jv/LK\nK1y7do2IiAgSEhLYvHkz27ZtY/r06VXYTIFAIBAIyk+F9hwHDx5MVlYWH374IVCEStEAAAz4SURB\nVDdu3MDPz48dO3aYsyLcuHHD4i1RkiS2b9/O7Nmz6d+/P3Z2doSGhlrkA2vZsiU7duwwn4f09PRk\n+fLlPP/881XTQoFAIBAIKogIHycQCAQCQQlqTODxmzdvcuvWLfORBdNZpPqMSYPiWTfqOzk5ORQW\nFpr/LfqJMS7kzZs3KSgoAIQmSUlJREVFieNPJbh06RJvv/02iYmJ1V2VWkG1G0etVsv06dMJDQ1l\n0KBBTJw4kczMTPNZpPqIVqslLCyMiRMnAtRrLUxoNBpmzZrF0KFDefHFF5k3bx46na5ea6PRaJg5\ncyb9+vVjxIgRDB06lLt379ZrTf773/8SEBDA+PHjLbZ46jMajYapU6cSHBxMYWEhLVq0qO4q1Qqq\ndRRptVqmTp1KQkICa9asYciQIVy8eJExY8aQkJBQnVWrNn766ScGDRrEoUOH+Pbbbzl16pRVzsb6\nxtGjR+nWrRuXLl3ijTfeoH379uzdu5d58+YB9TMO7Z49ewgMDOTKlSv861//Yty4caSmptoM41gf\nMPWBwsJCevfujVqtZuXKlfWybxRn8+bN+Pj4kJiYyOnTp1m+fLk5Jm991+ZBVJtxNBgMXLt2jejo\naF599VV69uxJWFgYu3btIjExkc8//7zUyDt1mXPnzuHt7c369esZOHAgb7zxBmDMs1YfO/Pt27fZ\ntWsXoaGh7N69m379+vH+++8zfvx4oqOjuXPnTqnhn+oyp06dYtiwYezZs4eQkBDGjRtHcHAw9vb2\n1V21asHUB+Li4nB1dWXdunVs3ryZs2fP1stxY2Lr1q00adKEr7/+mg4dOhAbG8vhw4e5cuWKeRle\nYJtqM46SJJGdnc21a9cICAgAjLM+T09PZs2aRVRUFD/88EN1Ve+RY9onGjRoEGFhYYSEhDBlyhTS\n09NZt24dYIyrWd8wGAx0796dsWPHolKpMBgMyOVy8vPzkSQJR0fHerXHZuoDs2bNYty4cSgURofz\nq1ev8vPPP+Pi4sLx48ersYbVg8kAyuVyWrZsSUhICAEBAXzwwQdIksTt27eruYaPFo1GA8DixYvR\naDR89tlnjBw5knHjxjF//nz69evHtGnTqrmWNZtHZhwPHjxoda1NmzZ4eHiwY8cOY2Xu7ZVMnjwZ\nBwcHvv/+ewvni7pGcU1Mbffy8jJn3u7QoQPjx4/n3XffRavVolAo6rwhMGlietg5OTkxatQoOnfu\nDNw3DllZWeaMJXX9zbF4PzEFxvD09DQfoVqzZg2dO3fG0dGRqKgoRowYwbvvvkteXl611PdhY+tZ\nYuoDP//8M/n5+QCsW7eOY8eOMWTIEAYNGlTnt2qK66JUKjEYDAQFBdGzZ09WrlyJk5MTW7Zs4fPP\nPycyMpIDBw6wfPnyaqxxzeahG8fvvvuODh06MHr0aHMKquLLHC+88AI7d+4kIyMDpVJp7tiTJk1i\n//79dXJJxJYmtnB0dGTUqFE4Ozvz5ptvAnV3n6CkJiUNnqndpknEhQsXLPKC1kUq0k8OHDjAwYMH\nOXDgAKtWrWL16tXcvHnzEdb24VOWHqb+8fvvvzNgwAAATp48iVqt5uTJk4SFhfGXv/zlkdf5UVCa\nLqaJ5MKFC3n99deZN28enTp1omPHjvTv35+IiAjWrl1rfssUWPJQjeOPP/7I+vXref755+nfvz/v\nvPMORUVF5gefvb09ffr0wdnZ2RwYwM7ODoAWLVqgUqnqnNtxaZqUxDTYfX19mTFjBhs3biQ5ORm5\nXM7Jkye5devWo676Q6M8mpj6jEwmIzU1laSkJLNxlCSJ+Pj4R17vh0l5NDGtIowaNYrg4GDzxKFT\np04UFBTw66+/PvJ6PywepIdJC0dHR7Zv305oaCiLFi0iPDwcBwcHUlJSqqvqD5WydDGtNLm7uxMe\nHm5eaTDh5eWFVqvl8uXL1VH1Go98zpw571T1l+r1eiRJoqioCK1Wy6hRo+jVqxeffPIJ9vb2BAUF\nme9p2rQpGo2GTz/9lDZt2tCyZUuUSiU7duwgLy+PGTNm1Ills/JoUhxTm+VyOU2bNiU2NpYvv/yS\nLVu28MUXXzBixAicnZ2roylVRkU1MbF7927i4+OZM2cOsbGxjB49mlWrVvHyyy/XeoeUimhi6iMl\nx8emTZuQJIkZM2agUqkeaf2rmvLqIZPJyM3NZcOGDZw8eZK//e1vrF27lr59+yJJEgsXLmT48OG4\nuLhUc4uqhvLqYuobtvrBhg0bcHd3Z9KkSY+07rWFKjWOsbGx2NnZmR9Qrq6uBAQE4OrqipubGwUF\nBXzyySeMHDnS7EihVCpp164dRUVFLF68mOPHj/P999+zdetWpk6dSkBAAAaDodYayIpoUloG7rS0\nNHbt2sXFixcZMGAAu3fvrtUZSyqriakfbNu2DYVCQXR0NK+//jq9e/dm//79tTqD+Z/tJ6mpqWRl\nZTF//nx27tzJlClTzI5utZGK6qHT6bCzs8PHx4dXX32V0aNH06hRIwD8/f1p1KgRzzzzTK19jpj4\ns/0kJSWF7Oxs5s+fz5EjRwgPDxc5HEuhSsLH7dmzh7lz56JUKtFqtYwcOZIJEybQpEkT8wxHkiQy\nMjJ49tlnCQkJITIy0sro7d69m0uXLpGRkcGUKVNq9X9aZTUpyY8//sjEiRNp2rQp69evp3Xr1tXQ\nmqqhKjTR6/V07tyZtLQ0evTowYcffki7du2qqUV/nqrQ5PLly3zxxRfs2bOH1q1b8/HHH9fa5MaV\n1UOv19sMflCbJ9bFqap+sm7dOnbv3k27du2IjIzkscceq6YW1Xz+9JtjTEwMs2fP5rXXXmPmzJl4\neXmxYsUKsrOzCQoKokGDBuaO6+DggLOzM0uXLuXZZ5/F09MTjUZDXl4earWadu3a0atXL5577rla\n/Wb0ZzQxLTPn5+ejUqlQKBT07t2biIiIWr0kVBWaFBQUYDAYzBGE5s2bh7u7e3U3rdL8WU2KiorQ\n6XS4ubmhUqkYMmQIb775Zq0dO1XxLCkoKDAf+TEZjNpOVfQTrVaLu7s7KpWKoUOHMmfOHFxdXau7\naTWaShtH02zl4MGDHD9+nJUrV+Lt7U1AQABqtZqoqCju3LlDjx49LDqpr68vZ8+e5dSpU7Ro0YLw\n8HDs7Ozw8/Or9R25KjVRq9W0b9+eRo0amY8s1EaqUhNTTtHu3btbJNCubVSlJgqFgo4dO9KqVSsr\nh4vaQlX3ET8/vzoRQu9h9BMfHx9zLl1B2VS6B5k639WrV/Hx8bHojGPHjqVz584cPnyYS5cuIUmS\n2a3Y3t6eV155hWPHjtG/f39UKhUDBw6sE525qjWpC8meq1qTukBVa1LbJ5XiWWIb0U+ql3L3oqNH\njzJr1ixWrFhhEbnmySef5KeffuLGjRuA8WxNw4YNGTBgAAaDgaNHjwJGr8vCwkLWr1/PhAkT6N69\nOz/88APbt2+vtR6GQhNrhCbWCE0sEXrYRuhSs3igcbx+/TojRoxg0qRJ5Obm8s033zB8+HCOHj2K\nwWCgd+/etGzZkhUrVgD3XYf79OmDTCYjKSnJ/F23bt0iMTGRVatWcfDgQfz8/B5Ssx4uQhNrhCbW\nCE0sEXrYRuhSMynTWzU/P5/w8HAKCwuZP3++2VOyb9++eHh4sGnTJnQ6HTt27GDq1Kns27ePnj17\nmstPmDCB69evs3///offkkeE0MQaoYk1QhNLhB62EbrUXMp8c7S3t0etVjNy5Ehat25tjrzQt29f\nLl++jF6vRy6XM3jwYPr37094eDgnTpxAr9dz48YNkpKSGD58+CNpyKNCaGKN0MQaoYklQg/bCF1q\nLg8852gKeA2Y3YWnTZuGRqNh3bp1Zpfp/Px8hg0bxqVLl+jUqRPx8fF4eXmxcePGOpdcU2hijdDE\nGqGJJUIP2whdaiaKB95w7z/NYDBYeE8NHjzYfF2v12Nvb8+GDRv45ZdfiImJYdSoUQwbNuwhVr36\nEJpYIzSxRmhiidDDNkKXmskDjaMJ0yZwcnIy8fHxdOzYETC6G+fn52Nvb4+HhwceHh6EhoY+nNrW\nMIQm1ghNrBGaWCL0sI3QpWZR7qMcpiwRZ8+etQhsu2zZMsaPH2/hMVVfEJpYIzSxRmhiidDDNkKX\nmkWF3xzPnz/PwIEDOXr0KDNmzKCwsJDVq1fX6ogllUVoYo3QxBqhiSVCD9sIXWoWFQo8XlhYSLdu\n3UhOTkalUjFnzhzCw8MfZv1qPEITa4Qm1ghNLBF62EboUnMo95sjgFqtxtvbm969e7NkyRLUavXD\nqletQWhijdDEGqGJJUIP2whdag4VTlml0+nqRMzPqkRoYo3QxBqhiSVCD9sIXWoGVZLPUSAQCASC\nukTdCF8vEAgEAkEVIoyjQCAQCAQlEMZRIBAIBIISCOMoEAgEAkEJhHEUCAQCgaAEwjgKBAKBQFAC\nYRwFAoFAICjB/wFahuBdOyFoHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf5bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "port.performance.dly_details.plot_ret_on_dollar(label='All')\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', prop={'size':12})\n",
    "plt.title('Return on $1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11062630>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rs27dOkAbN/e9997j5s2b2NjY5HfoBAKBABB7jmUSPz8/FAoFq1evJjMzk127\ndnH69GnAtPVocnIyNjY2ODg4EB8fz+LFi43KW7RowbVr1zhz5gxNmzaldu3a3L59m1OnTkmCq0WL\nFmavCdCnTx++//57zp8/T3p6Op9++inNmjWTVpetW7fmp59+onbt2lhYWBAQEMB3331H1apVKV++\nfL7u++eff5ZSjzk6OiKTyXIEwRcIBALI25JevDnKIBYWFmzatIkffviB6tWrs2PHDiM3m+wrx9Gj\nR5OamkqNGjV4/fXXJUtTPba2tvj6+lK7dm3J7aNFixZ4e3tLkYosLS3NXhOgbdu2fPTRRwwePJja\ntWtz69Yt1q9fL5U3b96ctLQ0WrVqBUCtWrWk4PSG5OY3uXfvXlq2bEnlypWZPXs2ISEhWFlZFWYI\nBQJBGScv4ShcOQSC54AwyTePGBvziLExT1GPTV4+3mLlKBAIBIKXDqFWFQgEAkGZQ6VSmQwTml/y\niqMthKNAIBAIXjg2bdpE//79pQAiBUUIR4FAIBCUOfQZlfQJLwqKUKsKBAKBoMzh4uICkMO/Or+I\nlaNAIBAIyhx64Xb//v1nOt8cIkLOcyK7Htza2horKysaNGggHUtMTOT8+fMAuLq6kpaWRqNGjUy2\nd/HiRWQyGXXr1n1+nS4gKSkp2NralnQ3BALBS4g+cUFhV455qVWFcHxO9O/fn5kzZ9KhQwdat26N\no6Mj/v7+/Pzzz1KdK1eu0K1bNwDeeOMN0tLS+O9//2uyvQEDBhAVFUVCQkKx9D8/3L17V/hkCQSC\nEqWwK0ex51gCnD17luTkZFxdXaXEyomJiZTLFmfV8I/z+PHjHOWG6OOSpqamPoceCwQCwYuJ2HN8\ngZg8eTJyuZx27doZ5SHs06eP2XOSkpJyFY567t69WyR9FAgEgrJATEwMaWlpBT5PrBxLgJs3bzJz\n5kw8PDwk4VivXr0c6ZMM/zhnz57Fzc0tz7Zv375dtJ0VCASCF5wHDx4U+JxSt3Jct24dDRs2xMPD\ng3bt2nHs2LFc61+8eJGuXbtSsWJF6tatS3BwcI46YWFhtG3bFg8PD3x9fQkNDTUq379/P02bNsXb\n25uRI0eSmZkplT158oSmTZty6dKlZ763GTNmsHfvXhISEvD29paOX7p0iQMHDuQImp39u+E55hDC\nUSAQCIxJSkoq8DmlSjhu27aNWbNmMW3aNA4fPkyLFi3o27cvd+7cMVk/KSmJXr164eHhwf79+wkK\nCmL58uUDFRbVAAAgAElEQVSsWLFCqhMZGUm/fv3w9/fn8OHDTJkyhQ8//JBdu3YB2gEYPnw4w4YN\n448//uDs2bNs2LBBOv+zzz6jd+/e1K5d+5nuLTk5mTVr1tC7d2/AONGvh4cHFhYWOc5p1qwZQ4YM\nkb4PHDjQbPtnzpwBhFpVIBAIAHx9ffHz8wOgTZs2BV44lCrh+M033zBo0CAGDx6Mj48PwcHBuLu7\nG6UuMmTLli2kpaWxatUqateuTY8ePZg4cSIrV66U6oSGhlKpUiUWL16Mj48PgwcPZsCAAZIAjYuL\n49GjRwwfPpzatWvTpUsXrly5AsCpU6c4cOAA06dPf+Z7u3r1qtF3Q+FoDrlczrx586TvpgSonmrV\nqtGoUSPu3btX+E4KBAJBGSEjI0NKlg7Qrl27Ap1favYcMzIyOHfuHO3btzc63qFDB06cOGHynPDw\ncFq2bGmUk69Dhw5ER0dL1pvh4eEm2zxz5gxqtRoXFxc8PDzYu3cvKSkpHD16lPr166NSqZg4cSJf\nffVVrkIpvxiqZRUKBZUqVcrXefos9bnlKdTj6ekpVo4CgUAAZGZm4uHhwY4dO4D8bUsZUmqCAMTF\nxaFWq3MYnbi4uBATE2PynJiYGCpXrmx0zNXVVSrz9vYmNjY2R5uurq6oVCri4uJwc3MjNDSU2bNn\nM3PmTF5//XUGDRrE119/TbNmzXBxcaFLly7ExMTQt29fZs6cafYeltasabZM8/Ahc4HvgddtbLAN\nCUEWHw+ZmaSPH48sPh6LP/5A9vAhaDRkduxIVsOGWOhmL3a2tiiOHUP+zz9kvvUW8lu3kF+7huqN\nN6RreHp4cOjwYSgl7hwWKlWp6UtpQ4yNecTYmEeMjXmyj41apUIJtPPzo5aPD80aNy7Q2GWlpORa\nXqqDAORnNZUf/P392bdvn/T9xo0bbNy4kYMHD9KjRw+GDx9Oz5496dChA02aNOH111832Y51w4Zm\nr5F65w5cuUIzoH/9+sQmJmL18CEWKSnE//ADFnFxWCUkIFepwNIS9c8/k3jrlnR+mzp1SAsJAVtb\nEjQanA8dQmZnR6xaDbpxcEhI4HZUFNG7dhXZ2DwL1sB9XYQfgTFibMwjxsY8YmzMk31sMh8/JvXG\nDe7/8gvWKhUPL13i/i+/5Lu9mIcP8X7lFbPlxSYcK1SogEKhyLFKjI2Nxd3d3eQ5bm5uJuvry3Kr\no1QqqVChgsl2J02axLx585DJZJw7d47evXtjY2ND586dOXTokFnhOGrbtlzvcVWtWrg+eECL5s1J\nHzMGq+XLkVlaYpOUhMbeHpmzM7KEBJDLISUFe3d3kMu5GBKCu7Mz9r/8gsbGBhsXF5ROTmBjg6WL\nC1haAlC3WjVS0tOxd3DAyd4+174UB3fv3sXT07Oku1EqEWNjHjE25hFjY57sYyOXy3EoVw7PihVx\ndnBAI5PhabAHmRcZeSwwim3P0dLSEl9fX/bv3290fP/+/ZLFUXZatGjBsWPHSE9PN6pfqVIlSb/c\nokULk202adIEhUKRo81NmzZhb29Pjx49JJ2z3rUjPT09Tz10brw/bBhzunfXCjM7OzJ79UIjlyN/\n8AB5fDwaGxsy3nkHjZ0dsidPUEREAODt5oYVgH7vMysLlLp5iy4tC4CrzmfyYSHMlgUCgaAskaXR\nIJdrRZidtTXJBVRHlypr1bFjx/LDDz+wceNGLl++zIwZM4iJiWHo0KEAzJs3jx49ekj1+/Tpg42N\nDWPGjOHff/9l165dLFu2jDFjxkh1hg4dSnR0NLNmzeLy5cts3LiRH3/8kXHjxuW4fmxsLMHBwSxZ\nsgQAJycn6tSpw/Llyzl37hy//PIL/v7+hb4/h4wMHJ2d0ZQrBzIZWTVragWlhQWo1WBhgaZqVdBo\n0JQrh9zAwlX2+DEahUKrQs3I0ApHmcxIOFrrVpAZBn6aAoFA8DKSpdEg163+7G1seFLAKDm526oW\ns3Ds1asXQUFBfPHFFwQGBhIeHs7mzZslo5sHDx4QGRkp1XdwcGD79u1ER0fTvn17ZsyYwbhx4xg7\ndqxUp0qVKmzevJmjR48SGBjIl19+SXBwMG+++WaO68+aNYvx48cbmf+uXLmSX3/9le7du0v/Co1M\nhsbZGZW+DaWSzO7d0SiVkJ4uqUczBw0CKyuwskJx5Ii2bloa6ISjLCkJjUKBRiZDplZLzeuFY1pG\nRuH7KBAIBGWArKwsFAYrx2P//EO/+fP54++/831+bhS7Qc6wYcMYNmyYyTJD/0U9devW5bfffsu1\nzdatW3Pw4ME8r71u3bocx3x9fTl69Gie5+aHrJo1kcXEkFW9unRMU7myVuBlZEjCUePqirpBAxTh\n4cjv3EENyNLTtXuRWVnIEhO1K0e1Gvnly6h1q1kbIRwFAoEAAHVWlqRWraKzW9kTHs6e8HD+O3cu\nrzdrluv5SXlYq4rYqkVIZp8+ZIwaZXRMY2GhVY8qlWgMgpBrvL216lb9pnBmpvazXI78xg3tKlKh\nQG7g12glhKNAIBAAxmrVyb1789Xo0VJZ73nzSDahZjVcLT5MTMy1fSEcixKF4qlRjR57e0kwqpo3\nlw5rFAppJak9oNEKR0tLNM7O2s82Ntr/ExNBrcZa1/bwL780usTaX38lthTleRQIBILnTVZWliQc\nZTIZb7dvz7sdO9JM555xI1sqq/BLl3Ds0YNLugAyfT/9NNf2hXB83igUqF57jYzBg8EgJVVWgwZo\nKlZEo3c30Wi0alWFQjLGUbVqhcbaGos//kBx5gyOutyQhoIwLimJKatXM9GESlogEAjKKhqNBpn8\nqQizt7Fh5YQJfDNhAgCPs6lNw3VRzE5lC/VpDiEciwF1QIDWStUQmYwsLy+tUATJmjV97FjtalG3\nipSlp4OVFbKkJGytrY2aePT4MZ11EX0eCfcOgUDwEmGoVjXEXheS83E2146/Tp8GIMPAAyA3hHAs\nQTRKpdanEbTuHQoFODg83YfUu3YoFGh0gnFcz55Y6dSrIb/9xiVdJHpbg/izAoFAUNbJMjDIMaSc\nrS2Qc+W4V5fZKCk5GUDSxJlDCMeSRKHQrhhBKyT1f2j9ylH3XaNUotEJv9peXqRnZpKemUmULloQ\nPH0gBFruP3rEiSLI0SkQCEon5laOTjqhN2bZMmm1eNHARfB+fDwajYYneQQNEMKxJLG11VqpajTa\nf/qIPvo/uFyu/WewwnRxdAS0e40bfv9daiq/qoKXheZjxvBaEaQiEwgEpRNDgxxD9HGnUzMy6DV3\nLpeiovhd5/uokMuJiY/nSWoq6tIUIUdgjMbaWvuH1AtIvXCUy7W+kbGxaCwtQamUggG46oTjsX/+\nMWrLlNnyy0yCTnUiEAjKDhqNhu1hYWRlZRmFj8vOkE6dpM8xCQlExcRQr2pV3mzZkvvx8SQ8eZLn\ntYRwLEmsrLR+jVFRptWqGRladw65XAojp185Xrh5E4BvP/yQVvXq5akiEAgEghed38LDGbx4MT8f\nOGBWrQoYxVm9ER1N1IMHeLu54WRvz6GICP7S7T/mhhCOJYlCgUahQBEerhWOhitHIMvbGxQKsqpU\nkaxa9ZvIX2zZAsArlStTx9ubk5cvMyc0tPjvoRRiGHtWb7AkEAhefB48egRAii5JhLmV4/V796TP\nIXv2EJeUhIujI54617kJK1bkeS0hHEsQjX4/USYzysSh6twZVceOZDVqRPrUqdpA5jr9eDmdmbIe\nSwsLzt24AcCyPFJqvSxcuXNH+vxTtowtAoHgxSVNN/GdtHIlaoPYqtmp6uEhfT57/TqJyclYKpWM\n7dFDMtjJCyEcSxKF4un+okoluWuoGzdG3by55OuIhYW052iZLQKPpVJJvM7HsY4ujRdA/JMn/HLs\nWDHdSOmipc4JGCBTGCoJBGWGVIP0hYDZpO8rxo83+h6XlISlUkk5W1v2BAXl61pCOJY0+qg4KpXW\netUUlpZP/SGzF1lYMEqXgcRQxfD+558zcOFCVAZZPV5GhKGSQFB2yO7Yb27PsZytLQ4G79PElBSU\num2r+tWq5etaQjiWNDonf5laLfkyZscoWEA2LJVKRr35JuN79iTNYFb1z61bgFY3/zKzV+fnJBAI\nXnxux8QYfTe35wjwZ3AwS3SJIDQaDRbKgiWhEsKxJNEHG1cokMXHgzlduKWl1t3DBPqHw9rSklSD\nbB334uIASHmJV06junUj8sEDYckrEJQR/rl1i/defx1nXZxqcytHgLpVqtC8Vi3pu6WBcJzcuzfv\nvf56rtcSwrEk0UXB0VhZobG1RaNz08iBhYU2ko7GOHf1zLffxtneHgAbKytJEF42sNCMionh4Llz\nz6f/pZw2DRsCL/cEQSAoK6jUaq7cuUPdKlWw0KlIc1s5Zsdw5fjpkCE59iWzU+zJjgVP0VSqpE16\nfOIEWFuj1r3Mc9SrUAGZUoksMdEoJ+RHgwZJn10cHUlITiZTpWLiN99Ix1/VRYl5/Msvz+kuShca\ngwmEnU5NnfySq5YFgrJAVGwsGSoVdby9idc58euFpDl8a9SQPivzqJudPMVuYmIir7zyCpEGsenK\nEunp6dSrV4+IiIjiv7hcjqpjx6dxVM3oxLM8PbWWrDp9+7iePfF0cTGq42oQVs7GxN6lJtuqs6yh\nUqsp9+abhP7vf4DWWs1aNw7Lt28vya4JBC88cUlJHD5/vkT7cE2Xn7G2lxc1KlYEoGPTprmeI5PJ\naKAzwMmhQcrjnZincFy+fDnt2rWjqi7l0owZM2jfvj3u7u40NLPSuXjxIl27dqVixYrUrVuX4ODg\nvC5Deno606dPp0aNGnh6ejJgwADuGThyAiQkJPDBBx/g7e2Nt7c3I0eOJNEgm3N8fDz9+/encuXK\nBAYG5hB4H330EfPnzzc6ZmVlxejRo/nss8/y7ONzQal86tJhDktLNK6uyHQuG0HDhnEpm8O/PjhA\nYnKyFGzXkOwm0GWN+MePAaS8lhZKJSqdG8fu48dLrF8CwYtOemYmVQcNouvs2SQWY1jGm/fvG7li\nxSQkoJDLcXd25o/gYC5v2ICTblspNzrrksw39vExLngW4ZiRkcG3337LIAP1nUajYeDAgQwYMMCk\nj0lSUhK9evXCw8OD/fv3ExQUxPLly1mRR0SCWbNmsXv3btavX89vv/3G48eP6d+/P1kGVprDhw/n\nwoULbNu2jf/+979EREQwcuRIqfyLL74gJSWFQ4cOERAQwMSJE6Wys2fP8tdffzFjxowc1+7bty/7\n9+8vmdWxPrmxpWXu9Swscv1j6nOYXb17F4BK+iTKOsq6ajE+W6xEhVxOmwYNABjRtWtJdKlIyMrK\n4sutW40SXAsExYnh5DKmmJ7DrKwsGo4YwYy1a6Vjjx4/xtXJCblcjrO9fY53nDk+fvddknbt4g0/\nP+OCZxGOBw4cIC0tjcDAQOlYcHAwI0aMoEaNGiZVdVu2bCEtLY1Vq1ZRu3ZtevTowcSJE1mZS6b6\nxMRENm3axPz582nbti2NGjVizZo1XLx4kQMHDgBw+fJl9u7dy9KlS2nWrBnNmzfnq6++4vfff+f6\n9esAXL16ld69e1O9enXee+89rly5AoBKpWLixIksWbIESxNCyN3dnSZNmrCthCLMZHl5kdmlS651\nNAoFslyiyOuF44AFCwAY3LGjUXlyGbfYzB5I+EF8PHK5nPI6q7YXlfM3bzL3229ZsnVrSXdF8JKi\nj+MMMNNAWD1P9FmGDhpo/5LT06X3nPZAMso//9TGoM4Dc8ECciNX4Xj06FEaNmxYoIbDw8Np2bIl\nVgb7Xh06dCA6OpqoqCiT55w9e5bMzEw6dOggHfP09KRWrVqEh4dL7drb29OiRQupjp+fH3Z2dpw4\ncQKA+vXrc/DgQVQqFXv37qV+/foAfPPNNzRq1IiAgACz/W7SpAlHjhzJ930WJZmDB5NVu3bulRSK\nXGc6DtkCCLz3+uuETp/OZ0OHAmXf3zG7cHxXNzmwUCrJfIEDIehn6gX10RIIioq4pCRpj+9JMVl+\n64Wj4T5hpkolJXoHkEdGIktKQp5t+y3fPMvK8fr161SuXLlA14uJicHNzc3omKurq1Rm7hyFQkH5\n8uVznKc/JyYmhgrZltEymQwXFxepzqRJk1AqlTRu3JjffvuN5cuXExkZyfr16/m///s/pk+fTuPG\njenfvz8PHjwwasvT05Nr164V6F6LFbk81z+mu7MzM/r3B6BGpUpUdnWlT2Ag7Ro1Asp+pBi9WnVc\nz57smj9fcnGxUChe6BByabpZcVYeuecEgufFo8ePqezqyjuvvVZsPsP65AFpBv7dmSqVka8itrba\n7abC7oPmIRxznY4+efIkh6DLi8IsX81RUAtLBwcH1mZb9vfq1YtPPvmE7du3c+XKFU6ePMmiRYuY\nMWMGGzZsMDo3SWfwYo6rV68WqD9FiUtcHJZxcTzW7SmawrdKFQDUajV3dfUe6wyWou7coVI+Nq8L\nyt1c+lOcRN65g0IuZ2ynTshkMqlfMiA+IaFE+lkU17yrs9D7evt2xhjkqHvRKS3PTWmktI1NXEIC\nlkolXs7O/BwVRWRUVJ4uFM/Kg/h4QDspvHv3Lo8eP+ZBQgLovgNYPnxIubQ00u7cIdnAxS2/yDIz\ncc+lPFfh6ODgwGOdFWB+cXNzy7FCjI2NlcrMnaNWq3n06JHR6jE2NpbWrVtLdeJ0UV/0aDQaHj58\naLbdH3/8EUtLS3r16sU777xD9+7dUSqV9O7dmzfeeMOoblJSEo7mnPB1+GS3dipGLDw8kKWm4uDp\nabZOkm6FZGlhgaeunlwXzNymXDnpWFFx9+7dIm+z0CiVONrZ5dB02FhbY2VjU+z9LKqxsTKImnTw\n8mUGGmw9vKiUquemlFEax0ahVOJQrhytGzUiaMsWUjUaqhZRH7OyskhKSclhdZqpE74KhQJPT08a\n9OpFpkpFmwYNpPGxOHQIjYsLdlZWOBWmPxkZ5LbhkqtatXr16twxSP+TH1q0aMGxY8dIN9jj2r9/\nP5UqVcLbIGuEIb6+vlhYWLBv3z7p2N27d7ly5Qp+OgujFi1a8OTJE2kPErT7kMnJyVIdQx4+fEhQ\nUBBffvkloBWkGToVVUZGBups+1C3b9+mZs2aBbrX4kQeGYk8jxmlPqSSoRrRViccU9LSyqyvo1qt\n5mBEhKRKNcRCqXyh1aqGhlQjv/qqBHsieFnJVKuxUCioq3Pnu1BIq/5MlSrH9sD2I0fwGjCAX3V2\nI3r0e44ytD7M+t+wkWZSH3ozHwY5JnmWPceWLVty/vx5o5fqjRs3iIiIIDo6mszMTM6fP09ERASZ\nOt1wnz59sLGxYcyYMfz777/s2rWLZcuWMWbMGKmNU6dO0bx5c07r/PEcHR159913mTt3LgcPHuTc\nuXOMHDmS+vXr065dOwBq1arFa6+9xqRJkzh58iTh4eFMnjyZzp07U8MgCoKe2bNnM3bsWGmW4e/v\nz48//sjly5dZtWoVLVu2NKp/+vRpWrVqletglSgaDVhbQy6GNe7OzgD416kjHbPTCcfhS5bg8tZb\nz7ePJcSXW7dy+Px5quuMBgyxUCheWIOc9Xv2MCskhJr5nBWX1cmPIP8kp6Wxfs+eHJP/ZyFTpUKp\nVOLq6IiDrS3BP//MQwP/8vxSvlcvBi9ebHQs8v594Gnydj36PUeZTCbFiYanCRUk5HLtO7Ewz/6z\nCMd27dphZWXFwYMHpWMTJkygbdu2rFq1igcPHhAYGEi7du24r7tJBwcHtm/fTnR0NO3bt2fGjBmM\nGzeOsWPHSm2kpKRw/fp1Ug1mxUFBQbzxxhsMHTqULl26UK5cOX766SejmcLatWupX78+vXv3pk+f\nPjRo0IA1a9bk6PfevXu5ceOGkQ/kiBEjqFWrFh07duTmzZssNvgjxcTEcPbsWd4q7cJDqUSWzSrT\nEJlMxuUNG/h63Linp+jUE+qsLGk2Vta4du8er1SuzE9z5uQos7a0fGEDIGz44w8AWhgETzbHw8RE\nHLp3500TYyB4edhx5AgTV65kexFa3qt0K0c9l27fZua6dYVqa+fRo0bf03VCMEa3x6hn75kzADjZ\n2/PIwBbEyCpdF1lMlpWV66KhsOS652hpacmQIUP4/vvvpRXc7t2782y0bt26/Pbbb2bL27Rpw6NH\nj3JcKzg4ONdoOk5OTvznP//J8/qvvvoqr776qtExa2vrHMY6en7++Wfat28vRQEqrWgsLJA9eYIm\nF+fXvBxjM1WqMucW8INOHZ89ETRAeQcHKXrOi4bewriWl1eedaN1v6cD586RlpGBhUJBWmampDkQ\nvByk61SMhVnZmcPQSjQpJQV4akWdX8xZW/+wdy+gTZDw/d69DHr1VS7cvMkcXQQwL1dXHhn8fuXZ\n1ar6fLjp6VrNWkF41vBx48eP5+DBg2U6turq1auZU8pn3Jk9emgj6eSycswPxRn+qTjIS5VYvlw5\n4vKwQi4tpKans8VAS9NWF55xeNeuzB86FEUuIQYNV8dpGRlMXrUKj759n19nBaWSRJ3wyu73+yxk\nqtU5JtTWeUX0MtMvQ9bv2cMtA+PN/x46BGD0e81QqYyFo8FvQFO+PJpy5dDIZCiPHSv46vFZhaOj\noyNXrlwp9auqwmJlZcXFixfNxoktLWTVrYumYkUw8ZDlxf8WLWK4LgLPi6piNEe6mTyXehxtbaXZ\nbmlndkgI73/xBdd1Ts2WFhbUqFQJB1tb7K2tycrlx2w4k1+5axehv/8OaGfs09as4c9Tp55v5wWl\nAn0y4KIM+pGpUknbM/otm+hsmr/89stwgvf1jh0ABOrevY9122x6G4HKLi5k6oSj/rwuujipejSV\nKoGVFbKsLJR//VWgPuWFyOf4AqGpUAFZIV70revVo5cuOlBqYS27Sil6oRCqS82VHXsbmxcm2fGR\nixcB7Ytn459/kpSSgo1uhq5UKNBoNGzWhVM0JC4piX8Nok8F/fij9Hn0smWs2b2btz755Ln2XVA6\nuKmz/cjIY9JYEAy3YoZ26sTwLl0KrI2ZHRICaG0f9FgqlXzwxhuETpsGQDPd3vpj3TvOt2ZNUtLS\nuBEdTcXy5Tm8eDFrp041alfj4EBmv35obGy0AQEKwrOuHAWlB42TE7JCzgj1apA//v67KLtU4uiF\no6k0XQD2trYvjHDUx438+cABxn79Nd/9+afkiqOfuQ9bsiTHeU1GjWKaCcM0eLofKyj7DF60SMrI\ns/HPP9lZREY56ZmZRmHbnOztuXLnTq77jtvDwoy0VIbCVG98k5KWhp21NW7OzjSsXp1jFy+y78wZ\nbsfGYm1pSWCDBly8dYtthw/TpkEDXB0djfqBRgNyOVl16qBxdycrW4S1PBHCseygcXKCtDQoRCgx\nvfCYFRLCycuXi7prJYb+B2ptZtZob23Nk1Lu4/kgPp6srCxJdbTHwJdXCoNnsOczaeVK6X7W/vqr\n0Z6MOcoZBmwWlDkW//STkYXq49RU3lm0qFBt7QkPx613b1RqNRmZmcQkJEhuYqCdxGWqVLxnxnjy\n/qNHDF68mCmrVwNaP+QLkZFM1HkDnNQlhHiiE44A7X19OXX1Kj0+/pjzN25Q19ubXgEBqNRqoh89\nor2vr+nO6gx0NDY2yIrYGl8IxxcIjaMjMrkcCrESMtxAP3DuXFF265lZv2cPETduFOpc/ezU2tzK\n0cYGjUZTagOvZ6pU1Bw8mPmbNkn38sDArN3RIEKOnpA9ezj+778A0gsoL5LT00v1BOF5o1arWfTj\nj/maSLyIfPb990XSjkajod/8+aRmZHDi0iUCJk0CoL4uYTBokxq4OTlxxUyAGH1w8p91WwB6F7Ia\nlSoBWuGrUqt5nJJCOV3ChFcbN5bO/+nAAepVq4aHwUrQpHDMykKjt161toaCqpLFyrHsoLG0RCOX\nQyFmSC4GofHO6VJ8lQQajYaw8+ell1RicjITV67k/S++KFR7CTrrWycTQgSeqipLq2pV/yLZfuSI\nSUvi93WGVHd0IRg/GTwY0PqaFcTROysr66XOCenUsycLfviBYYV8zko7LfLK6pNPDK2lO8+cyb9R\nUbSqVw8/g/ZdHB0Z26NHDt9EgK2HDvGlLr1apkrFtsOHJQMbe90q8ds//uDy7dtkqFTU08WDbtOg\nAa83bSq1Uz+bAaiHOZWpfuVoZVVwjZoQjmUIneqwMOoDQ+Fx9J9/iqxLBeVGdDRdZs+WQqHpheTl\n27cL5XKhFyjmMoKXduGoDw93/d49Ih88kBI0V3BwYM/ChbSuVw+AIZ06MWvAACa+9RY+np6cvHw5\nxyro/c6dzV5HqVCw9fDh53QXpRvDvTH9nlxZw3Dya/hbvxEdzScbN3ItH2md4pKSTO5pmwq/6GRn\nR1JKSo4J2tDPP+e7P/+Uvp+5fl0633Br4LQuA5KvLmSnUqFgw4cfSuV1dKFGvXQZnUyi0UjCEUtL\n5DExyMPDkWXLuJTr+bkghOOLhIWF9mEoRGgomUzGkWXLWDpmDLEJCVKKp+JGL8z0L6kkg9XSUZ21\nZl4cioggcPJkdhw5IvlzmVI/grFwfJKaWmRGCkXFBp3LhZ7VOjXWivHjCdAJStAKy9kDB6JUKHB3\ndiY1Pd3I0XvDhx+ybOxY/jN5Ms2zRdSpWL48zWvV4oROFfuycefhw5LuwnPHMAbvfF0OV4BGH3zA\nki1beDcoyOy5sQkJHDx3zsh467UmTdjy8cd0bt6cue++m+McB93v7bHBdVUm3ksajUY6rjSIsnP6\nyhV8PD2NfrflDHLS6oX9iRUruPXDD2b7Lq0cHR3RODqiiIlBeeIEsnxMBvJaZAjh+AKhUSq14ZIK\nuQpqWL069XTqittmcms+b/TJS/U/mMcGrin34uL45dgxAMLOn2f38eMm2xi9bBlnrl3j3UWLSExO\nRqlQmI0EoxeO73/xBW2nTOGdRYu4W4peloYBlzv4+uLt5kbizp108/c3e46NLiTeQ91Ke1S3bnTW\n+RY3koAAACAASURBVH8N6NBBCh6gT1D7++LFVCxfXkqc/LJw9OJFfgsP52Ap22MvajQaDQcjIqTv\n1Tw8GNWtm1Gd3IKFzw4JoducOXyrC1f4+6JFbJ83j87Nm7Pl449pq8sJa4heqBkGG7hkIpn9sm3b\nuKi7toVSKT2bp69epamJLEf6bQM3XQqqcra2lNclVMiBwcpR3bIlqrfeQmNnh6ZcOZRHjmiNF7PV\nlxm892Qm1MKGCOH4IqETjgqdAAFArUa5cycyXd6/vKiiS+8VVVLC0cAw5mFiInEGqsFpa9YwcOFC\nYhMT6TJ7NgMWLMhx/uBFi4z6HpOQgJO9vdk8ovp9jit37kgGBKXJ1/NNgwD4+pm1PJdIOABWlpak\nZ2ZKK8eZAwYYTQ70QvCnOXN4/MsvVPPwwMXR8YWJFFQUaDQaOs2cSf/585m0cqVRWbk33yxT6tXs\nKzYLpRKrbBFsXHJJx6cPpHH59m0+/+ADWulU+blR1cMDQApYAXDKIN+toUDbFhYGaP0aOzZtir2N\nDVfv3qWObr/RkMm9e3PuP/8xso41i6FaVSZD3agRqjfeQN2yJRp7e2TZ9vDlUVEoDx1CpvMFVeQR\nGEMIxxcJW1uwtNQKSQCNBnlUFDKZDHk+VZLuzs5YKpUlIhwzMjP5w+CBHPbFF4xeuhQw3lswjIV6\n+Px5ozayB1S+ER1tVqUKT1eO2ftRGpi6erXksD+8Sxc+NwiUnxvWlpakZWTwMDERhVyeI1XXR4MG\nETJ1KrUNUsTZWltLq/aXgRzZG7KhD2xdFtDv6dlaWbFp5kxa1q3LKwZ5Taf27cvDxEQpA4Yhdx8+\nNNpiqWtCYJmisosLAPcNVl9nrl3jlcqVCZ0+nd8WLpSOH9Ktai0UCmysrEhJT+dxaqrJ361cLjeZ\nXcckOj9Hg5NRN22KumlTrdDM9juXJSSApeVToZlHjGkhHF8k5HKwsdEG2kU7E1KcOSOtKPPXhBwv\nV9ccwjEjM5NPNm6Ugl0/D5Zs3coag8D1+86elfYsDPfXDKPAzFm/HtCmpTK0pNNz/d49k3kc9ZgS\njnmFnCsOlm3bxn9+/VX6/tWYMfl+KVhZWJCWkUFsYiLlHRxyrDQrVahAP12iAD12VlYkl1J3ludB\n9mDzy8aO5fVmzaTvzzuTfXGid5VYOXEiPVq3RiaTMbBDB3x1qfz0FqENRozIcW794cONXLtM/V5M\nYW1piaVSaWRhHXHjBo1r1qRPYCD1qlbl63HjKF+unBS1R6lUYmVhIQUhz++1csWExkhjaal9H2b7\nncv/+Udr1arfa8zjnSmE4wuGqkMHNAqF1mw5NhaNra12L9KMn58pTGWq2BMezpItW1iXSzaVZ8Uw\nL5sh59eupaq7u/R98c8/Pz3n0SMep6Qw99tvJXePtVOmSOXX7t41bamakQGJiUZGAHpKg3DUZx2o\n4ubG6Xz6KupxLleO2IQEHiYm4pqLusyQl23lmF11/n7nzvx37lzpe1EG5i4omSoVn2/eXGQajEwT\nBi9KhYLDS5fy+JdfqGLw2wKIjouT3Hqyq2Tzm8VFJpPhZG9v9B55lJQk7RWCNtRc37Ztpe8WCoUU\nDhGKQDgaqlUNsbUFhQLFhQvI9DFgnzzRLiosLbV7kRqN9r2ZC0I4vmBo7Oy0gQDS0lBcvw52dtqV\nYwEcvO2srXOsEPVn56WOKiwajUayzNSrZEAbyLiqhwevNmmS45w+gYHEJiTw4/79RscrODjwjy5W\nozn1jPL337HQGRhkx5yhT3HiX6cObwUEcCEkBJ98JjPW06h6dW7FxLD2t9+MXka5YWdtTUp6utnU\nQWUNw71tKxPRk0pygrT10CE+/e47lm3bViTt6dWqlmZe9ob7d9fv3eOVIUOo/u67fGciULehxWhe\n1KtalaP//INKrSb455+5GxeX47doKJhtrKxQGTx/RRK1yZRwlMu16tPHj1Hu2wdJScijotCULw8K\nhTY+tUqFaSsFg2aevXeC4kRjZ4dGLkf+4AHY22sfDguLAgUGsDOxitCr5p5XLE5DoXvwyy/xr1MH\n0M4uQfvCz067Ro1QZ2UxNdvKqoKDA15ubpJFp6mVoyw52Wwg4qJ6KT0LKrVaiptaUJq+8or0uZrO\nMCIvbHWahdJkjPQ8MfRtNJxAHPzySzzKly/RxN96wf3ppk2SJWdhyMjMJCMz06QfoSGGDvTvGoSU\nm7BihfS5qrs7EWvX5pkP1pCOTZtyKCKCDtOmMX/TJtIyMnII17YG2Y5srKwkFS8UcuWoViM/f177\nvjO3cgTUjRtrf/8WFlphmJyMxtMTdb162u/p6c+uVk1MTOSVV14p0/kc69WrR4SBKXRpRmNnp1UZ\nXLyIRiYj8+23yfLxQfbkCeQzV6OdLt6oIWkGM23DvbCi4jddvNAerVrh5uzMrwsWcH/LFqk8e344\nF0dHsz9UvVBprHMgNqlW1QvGUhoyLUOlKvS+Vw2DvUmzkUOyoR+zl0W1ahj0enjXrtLnJj4+1PX2\nLlGjLEN/3sImJb714AEV3nqLRh98wDld6EVzK0crCwt2zZ8PwPmbN/+fve+Oj6LO33+mbc2mJ6TT\niyHUk6Z09KRrCBgRPEVBFFDQk+MQOf3p3WEvoKByx1fuVLABgoeidEQUCNJLCiUhCSmkZ7NtZn5/\nfGYms5ttqbQ8r5cvw5aZ2d2ZeX/e7/fzfh7lcXVJdcm0aX4vtGT86e67ARAijgzXMn/PDh0U9R6D\nVoteHTsq6jcNCY50djaYjAzQFy7UJeSoQJnNRFKO40AVFYEymyFGRBB7q+pq0n9sbHBcsWIFhg8f\njnbt2uHEiRN47LHHkJSUhOjoaPTr1w/Lly+vo9l46tQpjB07FtHR0UhMTMTrHgRq1bBarVi4cCE6\nduyI2NhYTJ06FXkug5xlZWV4/PHHkZCQgISEBMyePRvlqpOrtLQUqampiIuLw9ChQ+sEvCVLluAV\n6SSRodVq8eSTT+Lvf/+7z2O8LqDXkxOipgagKIgBARA5DpQoktkePxAeFITLRUVOv9spVWbnmqk1\nFqIo4uX//hcA8OnixQCIV6Frf+PUv/+tqGRQFKVY2LhC7lvIpbFo1wBhtZLgyLKgr8NFncVmc7IB\nqi9omlZKsVH+UN5R20tqTsLV9YSisjIEG43IWbcOz6SkOD3Hsew1Lat+qSKWNdRr9H7pPna5uBjT\nJGaou/66DLlS4w6fP/88Hhgxot7H4K6d4drfBKD4kMrViwTpNQ3KHO12cl1fugTKS+ZIFRVB1Goh\narVgcnJAORwQdToI7dtDDAwEc/584zJHm82GtWvXYtq0aQCAY8eOISIiAh999BF+/fVXLF68GG+8\n8QbekaTAAKCiogLJycmIiorCrl27sGzZMqxYsQLvq1J4d1i8eDG+++47rFmzBlu3bkVlZSVSU1Od\neiQzZ87EyZMnsWHDBnzzzTc4fvw4Zqvo72+++SbMZjP27t2LwYMHY/78+cpzR48exfbt27Fo0aI6\n+54yZQp27dp1Y2THGg05IbRa0mBmWdBFRRADAggJRYYo1h2ClXBnUhJyioqchIN/OXVKkWwCSOC5\n+y9/wdmcnEYfsr+r44TISOXicvC8kwxWt/h45W85OAbKosUu/Upm/36IHEf6Cw1cmTcXLhcVISIl\nBWeysxscHAHgx9dew9gBAzBpyBC/Xq+UVW8Rxurl4mLERUS4nYGlaRrnXeaCLxUU4EALyCq6BkN3\nerorNm2CacIEj9v4KS3NLTdA48XPUK/Vug1Gf3voIdyjYvHWF8dXr8b6F17AE+PH49jHH7vVeG0v\nXdOyM9Cq+fPxz8ceq7uo9QcOh/N17SE42kePrh3V4HlyL2QYCN26wTF0KOk/+giOXq/O3bt3w2Kx\nYOjQoQCA6dOnOz3ftm1bHDt2DJs3b8azEoPwq6++gsViwapVq6DVatGtWzdkZGRg5cqVmCe5SLui\nvLwcn376KVauXIlhErvpo48+Qo8ePbB7926MHDkS586dw44dO7Bt2zbcLv2Y77zzDsaMGYOsrCx0\n7NgRGRkZSElJQYcOHfDwww9j7dq10vfpwPz58/HWW29B41K+A4A2bdqgb9++2LBhg/I5rltQVG2f\nkWUhMgyENm3AFBWRMoIE5tgx0OnpsE+ZUucEGtKjBziWxaSXXsIpidhy4sIF/CU1FSa9Hs99/DEu\nXLmCX8+cQb85c7D5lVc8W8b4AVkc/D9uFiaukMsyFdXVyk0tqV077Hv3XYTcdx+AWgeOORMn4s6k\nJHSS1P5l0KWlEE0motjv0lvq26kTLNcwaziqEn2nPVzY/iA8KAhfvPCC369XMsdbJDhWmM0e9XZl\nSzC1T2HSzJkAgMotW5r0OMqrq2G12aDXamEyGJxMqQH3rNnlGzcCIE4ijJts0JNxdVfVbKM7qMuo\n782di5iwMEVZqaFoHxWF9lFRGDdggMfXvDd3LmaMHq1ktqEmE56SruV6w+GAyLKgWJZUzjxcQ2K7\ndiT4Sf9RNpsyAscPGwZ23z6fPA2vofOXX35Bz549PaqPACRTDFGVdg4ePIhBgwZBqxotGDlyJPLz\n85HtRl4IIFmd3W7HyJEjlcdiY2PRtWtXHJRO5IMHDyIgIAD9+/dXXjNgwAAYjUb8JklwJSUlYc+e\nPXA4HNixYweSkpIAAB988AF69eqFwYMHe/wcffv2xf7rTHfTI2hayYzAsnCMGQOhSxen/hp18SLJ\nLt2cACEBAXhuyhRkFxbCKjX0qy0WhJpMMBkMEATBqQc5cenSRh3uPqm87a7k4gp5NSk7hh9Yvhxb\nly0DyzBK9iNnjhzL4nYVOUWBfFFoNKAkNwsZQQEB17SkphYLv+ivQHITQKsqRa/ftauOpuvNgh1H\njqDnrFm4WlHhk3npTu6sqdF79mx0/NOfEJOaijOXLiFb+s3fmzsXnMucoAxZJN/dIs5bWdzX510g\n+SlWbtmCR0ePbnRg9Bcmg0ER1G8s5F6i6EX4Q4Z90iTw/fqRACqKZAROhnyP8AKvz2ZlZSHOy2rk\n6NGjWLduHR599FHlscLCQkRKEmUyIiT1k0IPqiyFhYVgGAahLml2RESE8p7CwkKEuRA0KIpCeHi4\n8poFCxaAZVn06dMHW7duxYoVK3Dx4kWsWbMGS5cuxcKFC9GnTx+kpqaiwOXGFBsbi0xVY/m6Bk2T\nbFD+gTkOQkSEk2ULJZURPHmcyWr4VysqlEH8IKNRucAKVDqcaq+1+mLtjz9iviTf5Y8klGtpKKl9\ne2XI/+f33sPiqVN9lyOl70dITARlt0O9xWCjsY6DeUv6HKpto9wpljQXZDNoq82GWW+/jad8tDn8\nwab9+5tt9KehmPXOO7hw5Qoyc3OVsrsr1i1ZAgD41/ffA3B2bKmPDZg/ULcUNh84oMzqzrjnHnSI\njq6TOZqtVuV45BJ454cfxmqJJKc+Z6JDQ1GxeTO+WLoUP772ms9jWTJtWpNnxi0NqqICfN++hHsB\neA1wQteu4Hv3JuVV+X4pgR8wALwbzVg1vN5lqqqq6gQ6GRkZGUhNTcWcOXMwQVUf95Zl1hf1vWkF\nBgZi9erVTo8lJyfjpZdewsaNG5Geno5Dhw7h1VdfxaJFi/DJJ584vbfCh/Zkhko78FoitqwMtCii\nZPBgVEs9wYDCQoSWlqI0NxdcQQGCeB5CVRWqTp0CYzajRgqGMhzSBZh+/nwtwcVsVgZjf5FUM9q1\naQNzTQ1yc3PdHounx2Ws/eEHAEB8eDhgtfp8vbdtGwA8PGyYz22EV1fDIYooMpnQpqoKWgDyEqF9\nRAT+99tvuHz5MiiKQpXFgnuWLsUTY8fioQaQEupz/Om5uXjpP/9BeGAgiisq0CU6ul7fR2NQKt2k\nc1U3V3/2LV+Drtf1iUuX8NCrr+IPnTrhs+eeq/fxNNfnlhcfOUVFoAXB7X76SP3rT7Ztw1+Tk/Ef\n1fjSmcxMr4pLjYHakDgvLw8GjkN+UZHTMX4t6ZACwMXsbFhDQ3GlpASL/vUvDOjQAXNWrQLHshAF\nAY+MGoW8vDz0kshZLXUuXTPwPCqLilBksyG4qgqaykoU5OSgxguxhy0rQ6zFAqqqCsX5+aiS7+ES\nv6Ku9Lnqvd6OJTAwEJVunLPT09MxYcIETJ48GX/729+cnouMjKyTIRZJpS1PgTYyMhI8z6OkpMQp\neywqKsKdd96pvOaqi8KKKIooLi72uN1169ZBo9EgOTkZ06dPx8SJE8GyLFJSUjBu3Din11ZUVCDI\nh9pIZzcq8tcC2rAwgOehS05WHqMtFmgOHYIhMhLsuXNAZCTA8wg8fx7QaGB3GTQvkoJjYHCwUmbs\n0aUL2kdFoevXX+PtTZsASILFNI1YN4Pqubm5To/bHQ6UVVUhQpors9hsuCqtjL/9+9/dbsMTTHp9\nvV4PAFR2NsSoKHBGI/ikJOg7d4Y2KAjBAKJiY/HOk0/iakUFbA4HDEFBCDWZsGLTJlytrMTOEyfw\nV5eeemPg+t0AwK8S5f6fjz2GEb17I8Rkcjug3hwwSOe2UXXjn7F8OVY/+2ydcrfd4cDvmZno360b\nprz8MvYcO4bCb75xes1rUl+szGyu9+/k7rtpCrhmfVERER73M7pfPxw8exaf/fwz8lTZnTYgoFmO\nzRWxsbGICA2FVRCc9lemKrPSOh2M0u9mdziw5cgRHLtwAd++/DJGNqKac6Oi4ORJmEJDoe3fH5oT\nJ4DQUGi7dYMgyeR5gvbHH0GJInRt24Kvxz3ca1m1Q4cOuKxiNALA2bNnMX78eCQnJ+MfblwT+vfv\njwMHDsCq6lnt2rULMTExSFCxIdXo3bs3OI7DTtUKLjc3F+np6RggNXr79++PqqoqpQcJkD5kdXW1\n8ho1iouLsWzZMrz99tsASCC1SeU0m81W50LKyclBJ5fs6nqFbcYM2CdOdH7QaCRlA6uV2LKwLCHt\nBAaS8qrL55VJLWarVXGZjw0Ph0Gnw10q9md8RIRbs1MA+CEtDc+oHA+mLVuGDpL3G8/ziEhJwfn8\nfCyfN69eKjCHPvgAaatW+f16AIDdDjYtDez27UShX/oMIschc/Zs/P7hhxjeq5cyF1ggyUrJZUG9\nG6JWUyO3uBgcy+KBESMQFRraYoERqC2rqv33Dpw+jf9z03t8++uvMWrhQuz8/Xf8cOiQW+EARipR\n0RTVomVpbzjlUuL1psDSOTYWJZWVeOXTT/HZjh3K480tK9cxJgYHpZJ2sNFYZ3/qDH3Ys88ifupU\n5d9ZeXm4o3v3axIY6RMnwO7Y0SAv2aYCU1VFSIgREXDcfTf4wYMhuBEPcYUQHw9Rp3NqO/kDr8Fx\n0KBBOHHihHLynzlzBhMmTMCQIUPw7LPPoqCgQPlPxuTJk6HX6zFnzhycOXMGmzdvxnvvvYc5c+Yo\nr0lLS0O/fv1wRLKNCQoKwkMPPYQXX3wRe/bswbFjxzB79mwkJSVhuCSg3LVrV9x1111YsGABDh06\nhIMHD+KZZ57B6NGj0dHNyuH555/H3LlzlVXZwIEDsW7dOpw7dw6rVq3CIJVVEAAcOXIEd9xxR72+\nvGsFMSoKgkp5AgCZ6aEo0muUlCFAUUSE1w1rUya3/J6ZiZyiIpj0emVuSb6pfPTMMwgxmTwSWBas\nXo1/ff89LDYbVm7erLAAAeD1L79U/u5bz0VHt4QERNdDqQMAYLVC1OlAVVaShQHDQAwPB1gWgkqu\nTib85EvBUSZENKdyjNliQUFpKdLS09GrY8d6tx6owsJG35S0HIdAgwGvrV/v9Li7EqIsSn+/aibY\n9RzILiwERVHIyM3FoXPnGnVsTYU7VaNbQK0hrzuoZ2ztDodyPbgz7G3UMbnYP43u10+xagoyGlFU\nVuZk+/SBFwGOK6Wlfs+1NiWooiIwly6Re0tLzcmKIrivvgKtWvDQFgsh4nAc+IED4Rg71uMohxqO\ncePgGD8evMs90xe8Bsfhw4dDq9VijzS0umnTJhQXF2PDhg3o2rUrunXrhm7duuE21YBpYGAgNm7c\niPz8fIwYMQKLFi3CvHnzMHfuXOU1ZrMZWVlZqFGtYpctW4Zx48ZhxowZGDNmDEwmE9avX+90I1m9\nejWSkpKQkpKCyZMno0ePHvjoo4/qHPeOHTtw/vx5pxnIWbNmoWvXrrj77rtx4cIFvKZqYBcWFuLo\n0aOYJLG5bkhotQBNg/3xR0Cng+OOOxTGJmi6TnCUM6Xn//1v7Dl2DOFBQcp3LTNFg4xGaDnOY3CU\nFV4iUlKwyKXXe0HV2+qqmlFsLlCyHJTRCFGvJ/JxAQEQdTqIKm85+eYiB0fZQd1TdtwUiLr/fnT6\n05+w7+TJ+s92VVSA3bcP9NmzjToGmqbx3P33I8eFvSsHEPVvLAcK9WNnXLKyS4WFeGz0aMRHROAT\nDxq21xo9vWQVeq2zUL8s6SY0QxacMmQIvpLaT+qKlV6rRVZ+PnrPno1MP5zrC0pL/fM5bGJQV64Q\ngwOGAd1SCyFBIDPcJ0/WHofDQUTF6wkxLAz8wIFEbrMe8Npz1Gg0eOSRR/DZZ59h+PDhWLx4MRZL\nCifekJiYiK1e3B2GDBmCElktXbWv119/3auaTnBwMD7++GOf+x81ahRGjRrl9JhOp6tD1pHxxRdf\nYMSIEWgnyRrdiBC1WnLiWCwQTSYIPXrAIQUJ5scfQdntUF/2anWLnUePOgkAyAP38RER0HCcR6mt\ndm3aIMPDRV1aWYm7+vbF8rlz60jDNQusVkUxCCwLXh5Glh1MJBh0OgQaDLginX9ymbE5tTblyktR\nWRm2qI2q/QBVVUVGUpogs3WXwddYrTh54QIGPf00/vbQQ0gdPhy/qQLxwvvvxxtffonfs7IUhjMA\n5BQWom1UFO7s3t1JTOJ6gjuneRkh0oLp9i5dcDg9XXlcbGJhdjvPQ8NxiqaoWvVJPZZRVlXlU9Ku\nsLQUkdcgOMJqhRgSAqqiAnRBAVpEul4Q6izqKSlgthR8ysc99dRT2LNnz42hHtMAWK1WfPjhh3ih\nHgPV1yVMJjjuvJOUV8PCIIaGgu/fH3xiotuyqnpkgqFpp57blGHDcHjlSvTs0KFO5lhUVoYFK1ei\n2mJBgsqgWI3IlBT8lJaGiOBgxHsgSzU1KCk48oMGwTZ1Kum1AuRicrnhBRmNSlCUb1DNpbXp6oLx\npp+Gxgp4ntwkGigzpoa7ObjswkIclLKBl//7X3R/7DEnrczxAweiY0wMTquuf0EQYLZaEWgwQMNx\nqK6pwZPvvYej13gUSp6lmztxIp4YP96rYkyAVFZ9+I9/dHq8qTNHh6ShGx8ZifOffor7VRZOi1X9\nxBF//jNyJcLhhwsWuN1WWXW1k3B3S4GyWCCGhxMT4Zbqk8vnvfr6EQQy391C8Bkcg4KCkJ6efkNn\nVd6g1Wpx6tQp9KxnPfp6hNClC8mU1LM/cs/Rzc1fVqngBUEZEgcIKUAuhboGx3W7duHf33+Pf3//\nvUciRo3NBl4QENpMlHi3kPRUHaNGEXUMGS6ZI0A0HeVZMnVZtaisDO2mTUNGE1LieZd9zxwzpl7v\npxwOUs5qgmNSZ/Bn1qzBo6NHY+PPP3vU9/znY4+hb+fOiAkLQ4Hk+L7lwAHFi1Kn0UDLcTh3+TI+\n3b4d8z/4oNHH2BiUV1fjkXvuwauzZuENH4uQe++4Ayvnz8dDd92FrvHxiJX60k0dHO08r8zlRqha\nFwCZ+5VnLgEoAgE92rd3u60Qkwl3/+EPTXp8foHnAZ0OQnQ0uc5aQkSD5537ibm50Obnt1xwRqtl\n1c0FnY4EA9VJJXroOQLk5ifDE0tPw3FKybG8uhonpHGEf23dip0+nExCVL2+ZofVCtFgqNugdxMc\neUHAR999hxWbNuGSRD6x2u347rffcLWiAj+lpTXZYal7mT3at3crB+Z9A3ZyQzAawbmQaeoLNTs2\nLiICI/v0Qd7Vqx7FCOTFU2RwMIqkcYcH//lPrJDGfHQcBw3HKZ/xWkvT5ZeU+G25pOE4PHTXXWAY\nBodXrsQPy5YBqJvpNxZmq7VOf1ONcQMGYFBiIgAofUeZ2f3Q3Xfj1ZkzsUU2RRBFr+LizQmRYSBG\nRxOXC5eWWKNQXg76yJG6AVcQnK5l7sABYqvXGhxb0RCIJhMpI7rJHOm8PNBehJVdNR9laFUOBnct\nXIj1u3fDoNUqhBuGpnF5/Xr8bfr0Oi4bLRYc8/LAZGYC7vbnxgha7pE9L+nKBhuNsDkcOCcJKtTH\n084XZHPXNc89h1+WL6/fmysqwBw+TOQCtVon7dyGwHV0RNax/ff33yujGTLUcl+RwcEoVCn7KNuT\nMkcZlU1Q+m0obHY7isrK/LbwcoXsZ9rUmWO1xaKUcN2BoijMl4iAaRkZCDOZoNdqUbF5M1Y+/TTm\n3nuvYlHml+yhKBLX+yYEJfX/xPBwgONIFaMBiwj61CnARfCdzssDXVgI5tAh533KmSNFEYasVkvY\n6H7a8jUFWoPjzQSOg2PcONjVor5SsKQvXQLjhfHo2nuRoZUyA0EQFIcOeaV7W3w8SjdtQpDRiIWp\nqVizcKHTe1uEiAOAvnwZYmws7G4+g8iy5ELzgrDAQNgdDqWc2pTaq/JoQEMcONgDBwCjEUJSkiKa\n7BF2O5idO72yWrUuv0eEygSYFwQEGY34x6OPYu8772CrZIMEkCCafvmyk8waQDJHdXB0F0BbCrKj\nRkMXNrIIfJNnjhYLjD6smbpIEp3//ekn5TxRl19lRrE/c7FUfj64H34A1dS6vQxDxqN0OtCZmWBU\nnpR+b+LcOXC7dzs/6HAQwpnrwqqqivgt0jSomhqSubJs02atPtBy1J9WtAh41wFhafUlBgYS0orM\nApPw1H33YUiPHhijEnRXQyY1LN+0CZHBwdBpNHhlxgysmDcPdrPZ6SKOkVbtHMvC7nA0ynWiPmAu\nXoQYHe3ca5QREAD40DDtHBeHfSdOKJlPU9o6ydR918zMLzgcEPV6CJGRoH0EV6qqCnRNDXD2E66A\nPwAAIABJREFULITOnWv7zKqSns7l5toxOhqDk5KQkZuL56ZMwRMebJK6SkzmCS+8AKNOp5CYtBzn\n9Bs7eB6VZrNPAezmwHiJUBcmE7HqiebIHEVRRJXFAqOXsioAdFCZDN/pxndRnjv2RjCSQVVXA3o9\nqPJyiH4I/fsFQVAWZ5TNRgRHpB603xBFtws8+sIFIDAQVFUVqJwciPHxZHzp118hRkRA1OsVJrpr\ny6i50RocbwVI9law24nno6rMo+47uoNcKl0qkTDenD1bIQy4ajn27tQJWf/5D/RaLXYdO4axHgJu\nk0IUSQDwZF1jNIL2kQkOSkzEjt9/VxirrsLkjYGcOfrVK7JYQGdlQZAYxkJEBOjqasISdDjAffed\n5/fyPESaBiWpITFpaaBzcmC//37lJQaXmzRN0/he6rV5gzzmox55AIjaC8Mw+MfnnyuPVdXUXJPg\neFtCAs5kZzuNJNUH8tnTVMExLT0dHMtCEATEemB1y2AYBt//858w6HQIcRMAaZrGo6NHY6rKtcgj\nbDaij9zU/V/5/GUYErDq2/tTWUbJoM6cAcUwEGkaokYDJiMDjvh4kkUajYqyl2yiILZwcGwtq94C\nsD32GByDB3sk5niDq+xbuA/92ciQEJgMBkwcNKhlyAMS5Vv0dEPW6eqoy+x44w3l72cnT0ZwQADs\nDgfSJFHipvR7rE9wZA4dIr1TWQCfZSGGhREHAvl793TzdjiUKgG7Zw/oixdJv9lmA/vTT6CPHas/\nGUiCq4rO2r/8Bb8sX47osDClxC7jWtmBJURGYnS/fnX63v5CyRzdlFUtNhv+unp1vXqqw//8Z0Wx\nZ6CbbNAVg3v0QN/OnT1mh+/NnevXdlBdTQJXUyr9qDJH+5Qp5LyqZ/ClioqU0qwM5tIliIGBRLmG\n42pnTAWBBMzw8FrTdo4DD8DREgtuCa3B8RaA2KYNhPbtia9ZPS+apPbt8apkBAsA4Q0sWzUbpGDv\nuOcet0+Lbjwt+3frhmCpj/NMSgo0LiVLSxOuumVCjj/BkaqsJD2d0lKwP/0EqrSU3IgA8ttRFFjJ\n5cTpfRcugDl+XAmOCmmB48B9/TUoq5UEXZAZup2SbZK/CA8KcprJu6N7d6dxA7Wl2bUKjnaHo87v\nWB/I5WF340l7jh/HB5s3Kyzd+iK0hYhpVGkp6OJi0qezWJpu5EIUSf8PgNCpE/h+/YhaTT3AnD5N\n9E05rpbMw7Lg+/eHY8yYWsIPatWuhOhoUFYrqJISiFFRyJ01C7xqTrS50RocbxVIWqvctm311kcc\n0bu38nenFnAsqBdkVpunMo8Hw+dtr72Gh//4RwQZjXWDYxPdVNLS09Fz1iwA/lm5iQYDocpnZICy\nWkEXFtZmxNL8KmWzObMRKyvB/v47uRnKzugc52SILQYHK33maaNGoZ9KpcVfyMExJiysDiN03ZIl\n+OQvfwFw7YKj1W5vlJC7/Pu4K6vKwS2zHrOm8vbmuhoENBfKy8Hu3EnOD4oCnZ0NbvPmhm2rpgZU\nVhb5WxRJSVRF5hI1GtIj9CT6YLeDW78elKycJIqkfx4XB1GrrSXVOBzkHGVZCLfdBiYzE8yuXUB5\nOcSwMAjduwM8D7qgoFbxqgXRGhxvFWg0pEwXGgqqnga17SXCwMN//KMyLN2UoIqLARcmpN9wOEgg\n8JCZiRoNKFGskzEntm2L9596ChRFKaWsAL0e8RERTZI5fvj995igUl3yWJITRbBbtoDKyyOUeY4D\nffUqmU9l2VrHc5qGKP2b+/FHQLKS47ZuJc9JrwfHkZ4QwygC7AAUn86GgqIofLF0KX549dU6z+m1\nWnSKiQFw7YKjzeHwyQim8vM9Vk7ksqq7zFF+7LzLGIInCIIAURSxfN48vCotjpob3NatEAMDa/vv\nDFPvFoqyrS1bwB49CpjNoE+fBkXTEFWydULHjhDDw8F4uo9YrYBeD+boUekNAiiHg5gLcxxh0koB\nUyaM8d26QQwOBl1VBSYzE2J8PIR27QCDgZDSunRp0GdpDFoJObcIRJ1OccSmS0pQn+KqXqvF/vfe\nU26AjQV19SqZydRoiNXU3r0QQkPBSw4s9dqW3U6yJU+9Jp2OZE12u8dxCPmmOrJ3b1wsKGgSh453\nv/0WAJkRfOPxxz2Tk+x2UDwP5sgR8n1oNOSGwXFk9SyVL6mKCvJ4QABEQQBdXAwhIEBZ9CikK6A2\nKKqCIwXUyQDqC28EKzlru5ZlVW+ZI/fVVwBNg4+Lg+DG4s7bKIfcN/YnOJ7Pz8dcaZ61JQf2RaMR\n0Okgms11XO/rAyo/Xzn/uC1bAI0GQqdOTsFJjI2FY+hQsO4IYtLcoqjRgKqpAVVQADE0lFQ1JHME\n5uRJCAkJoESR9NQhqXvpdBDtdlAMAyEqqjbIa7WEeevGW7g50Zo53iowGMjNkmVBlZV5JnZ4QM8O\nHWBo5BA6AFIG/PlnML/9Rv5ttQJaLeiSkobZ4Uj9CdHDLJkor6S93LTlm1hS+/bQclyTslULy8ow\nacgQz2QYu538JtXVAM9DiIsjAZ3jYJ88GUKvXgAAvnt38LffDuvTT0OMjASKishnZxiSNWq14Lt3\nrxWBkP+TfjPRYAAlqRs1B+QZSmszWn95gtliwdGsLOzxpNjE8+R7MhjAeBC78DbKIQfMKj/Oz0+2\nbcPPkpME28AA1RCIISG1wURmk9I0qJIS0GlpgMrQ2SNqasDu3g0xMJC42bRpA9FoJBmp62cxGEjm\n57KY4PbvByOXZLVaIhggZ+vyYo1hlAAqSDOe0GphfeYZsijUaiFK1SoxMFBZ1Lc0WoPjrQKKgu3R\nR2EfM4Y006+R1Bdls0HUaEBLQ8qU1QpRctOgJCk3X6AzM8GtXw/6xAnSY6Fpz1Y2Gk2tz2VlJejf\nf69zQQ9KTMSi1FTMHDsWBq22SeccfYE5eZKUPE0mwGSC/d57IQYFwTZ1Kum5yAgMJP51LAsxOBgU\nz4OqqVFsyewpKXDcdx/skyZBiI9XbmaiXg8hOhpCp06gJWHr5oBM1JKFAGqsViS/+CLSXMY/mgNX\npJk7j5md5GwCivLo6uAtc5T1cf2xNVNbgrVk5kgXF0t/0LCPGKH0mdkdO8BcvgxWsh10B6qsDPTp\n06DPnydZHsdB6NED9gcfBP+HP8DhpqIj6nSkr6pedDocZDGm1ZLrjuPIfUaWgpPZqjQNqrgYYmws\nCeQydDryn6qdYJs5E1YPQuzNjdbgeAtBjIoi/7EsMQW+FrDba29Sokj+LWU5dGEhmN27AUEAnZYG\nZt++Om+nSkuJpJpeDyYjg1x8Go1nQo6kN8vu2AHu++/B5OTUcbgINBjwwvTpiAgKglElSt4YhJpM\nCAkIwJ633/b+wpoaciPR6UiPJTAQ1kWL3AsayNBoyI3IYoHIsnCMHw9BMvwWbruNDE9rNOATEyEk\nJcF+//0Qg4KaxPbKE+TZxqWffAIA2Lh/P7YfOYLhf/6zR4H6pkKJdC5PdDEwp48fB33kCLj//Y/c\nqGU2b3Z2nd6jHBzP5uQoIusy1AbI3j4Lz/M4rPI7bLHgqD4mhgGCgsD360c+r8EA0WTy+tvTx4+D\nycoCc/o0YDDANns2WWR17AjHffdBlLM79S7lvrYqm6ZKSiBKmR+0WpIhFhSAvnxZCY58z55kHre8\nHIIbv037/ffXksgAcv1eg7lZoDU43nIQQ0JIGa+J9Rf9hkSgAceBunyZ9NLkUkt6OujSUsBsBpOZ\nCbqwsA6pgN2+nfQm5IuzqsrzjCMk4XV51Sr35byMs+g4DjtlIkEjUFVTg79OnYq+XjwFUVEBOi9P\nYdsK/vZ0NRpCcpAyR757d6d+qhgZCRgMcNxzD+zJyeTmotc37eybBxSUluJyURHW79qlPFbUTLJy\nPM/jSkkJiqWS4Wtq8ovVCub8eTAXLxKVJOlmDY0G7O+/15HZk8uqb3z5JTr96U+osVoVJSC1s8or\nn37q8XgOnjuHiwUF6Cj9jg2dK6031D6HDAMhNJQEKIaBaDRCaNvWY9sBAMnUTCbAYIB90iSSPfqA\nGB5OFtmqci1VWEiG93U6MrvIMESZS6oIiTRNeocMQ4wC3OxH6NgR1sWLnZSdrhVag+OtBq2W1PHd\nZI4Ki6yZQOXlgd2/nzTn9Xqwv/xC5vNYlkieSUGSKi1VLiy5N0kVFZG+CcuSAC8FOqqiwrvDt0QC\nUMqMkISUPWDDzz+DFwSUN0Lg2Gq3w+ZweFWKoUpLwf30ExnupyjYHn8c/JAhfm1fZFnyGWpqSNBz\nIdnwffrA+pe/OJGURI2m2YPjPEnTd8HKlTivMsH++H//c8q+mgov/uc/6Pzww0opVy0dR5WXkwDB\nsrVkNK0WokZDHnfpQbtKHUZOnoyoKVMA1M6qAsB/t2/3eDw/HDqEUJMJf5RspVosc5SOz3HnnSSw\nBAWRCkJQEAmWPXqAEgQw+/fXeSt9+DDo7GzyPXEcMQv3BwYDuVZVLQiqpARC+/awPvccmV2Ueoz0\n5ctkTpJhIHIcKEEgZJwW0l5uKHwGx/LycnTp0uWmNjvu3r07jvuwX7qZIIaGkqxDBaqgAOy+fURZ\npZlAZ2fXskcZBggMhBgWBntyMllRchxEoxHssWPkwtLrSYbL82B//hncnj0ATcMxbBi5mKWVqzLu\n4A5yM19m8PkQQng6ORkAsT9qKGR/xEAvq3V2+3aIoaEQTSbYJ0wgA9D+EjjkQGe1koWO2x2w7t/T\njFj22GNIiIxEfkkJSquqMG3UKADAa1984TXjaij2HDsGACgoKYFRp3O2hpLNciXWrvXPf4Z13jyl\nR+takaA9fPeiKCr6uABQ4WbRVG2x4PfMTJy+dAm3d+miCO571NMVRdCXLjXdQlQ6PlFlLC6Gh8O6\naBHsDzwAITyciEvIGsN2u7I4YLKza91sOM5/5xeZBKcOjjYbOY8lmzzr4sXK+IYTg1q+DpuC4NeM\n8Hk1rlixAsOHD0e7du1QXFyMSZMm4bbbbkObNm2QlJSEhQsXotyFCXXq1CmMHTsW0dHRSExMxOuv\nv+7zQKxWKxYuXIiOHTsiNjYWU6dORZ5q9QkAZWVlePzxx5GQkICEhATMnj3bad+lpaVITU1FXFwc\nhg4dWifgLVmyBK+88orTY1qtFk8++ST+Lnum3QpwMxhPSRJNlA+R7gZDFEFfuECYaAEBZBWp1YIf\nOBBCjx7gO3QgK3yaJoocGg2hd1utoDMyyHMOB3lfTAxs06aRzNFm8x4c5YxADpAUVTuc7AaPSEo7\nVxoRHOWZRm+ZoxgUBDEmBtZnn4WgElnwB0JUFKiaGjAZGSQ78AdycGzm/t/Y/v1RXlWFCrMZ/VWD\n29sOH27yfcnlzryrV+sKjktyeqI0CgCNBpCZjwxTx6nFk0h+jdXqRNIxuyFrvfP11xj6zDP44dAh\nBAcEKKISnvqTVE4OmMOHiYVTU0BNeFFDo4HQoQM5RyT2KqqqwG3YAG7DBlAXLpDvSM7gpOzRX4hR\nUaSiIwigT54EzGZynapfExmpBEt5waaIU1wHpVNv8BocbTYb1q5di2nTpgEgNfQJEyZg/fr1SEtL\nwwcffIA9e/bg6aefVt5TUVGB5ORkREVFYdeuXVi2bBlWrFiB999/3+uBLF68GN999x3WrFmDrVu3\norKyEqmpqU4n5syZM3Hy5Els2LAB33zzDY4fP47ZKsfvN998E2azGXv37sXgwYMxX9I2BICjR49i\n+/btWLRoUZ19T5kyBbt27bpps+M6cKM3Kkp9wObKHKmSErJC1Whgf/RR0ozXaOCQbKbE2NjaAWaK\nIqMJnTqBoihijyP1DkVJR1Vs2xZCjx7kPT4sgdSBEQwDRkWacEWUNOzcmOAoZxeBPjJaITra97G7\ngZiQQG5CgYGK5JbP92g0ZNaxgYPh/iLEZFIMpANVi4Msl4VuU0AOjvklJXVkDSlZa1ajccqubQ89\nRHrULqV1tYLRe3PmYJkkyF9eXe1UVu0aH1/nONQjHoEGAxLbtgXgeXFESSM4zOnTpIXQUJjN5D85\nOHoSQZB9XhmGsFYNBoihoWDT0pTsWgwOhm3GjFoNXz8gJCaCrqkBt349mKwsIrbhEhyFdu1IcJaE\nKsTISMBorF20XMfwGhx3794Ni8WCoUOHAgBCQkIwY8YM9OrVC3FxcRg2bBgeffRR/CbPrAH46quv\nYLFYsGrVKnTr1g333nsv5s+fj5UrV3rcT3l5OT799FO88sorGDZsGHr16oWPPvoIp06dwm7J/+vc\nuXPYsWMH3n33Xdx+++3o168f3nnnHWzbtg1Z0lxNRkYGUlJS0KFDBzz88MNIl2jkDocD8+fPx1tv\nvQWNmzp3mzZt0LdvX2zYsKF+394NCpHj6qyc5RELcByYX39t8n3Sx45BNBjgGDkSYmgo7FOmkL6Y\nDI4jF45UBhWDgiCGhJAxB3lGU+4dSr+hEBOjmKD6BT9sb0wGAwL0+kaVVSulkrXJW+DzYOHjD8Tg\nYHLTd7nxe4VMTGrm4Bis6lmpiSzN0X+Tt19aVVU3EMnBUauFQ+VmISYkEEd71/NfdU48OGoUkiTx\nBbPVqpRV5913n8KMVeOqqnoVFBCA5MGD8dv772OQB6FwOiODnMdGY4NHqqgrV8B99x24zZvBnDih\nLBzdgmHIooDjQFks5HqRzk1REpawjx9Pvpd6gO/dG6JeT8g5Oh1ZtLr8DmKbNrWjRdI1LkZFKT3g\n6xleg+Mvv/yCnj17etSFzM/Px5YtWzBixAjlsYMHD2LQoEHQqj74yJEjkZ+fj2wPA7hHjx6F3W7H\nSNVJHBsbi65du+LgwYPKdgMCAtBfpdIxYMAAGI1GJTgnJSVhz549cDgc2LFjB5KSkgAAH3zwAXr1\n6oXBgwd7/Kx9+/bFfjcN65sRYmgoUe9X3SCo4mJy8gYFkcFdSNleU5TheB50RQURGpZJJ3I/SAXb\n7NmwTZ1KLnKKghgTQ0o9MtNQflwq/VCVlaRM6yt7knsc8urax0BxsNGIzQcOeH1Nj5kz8foXX7h9\nTh4FcOtgIoqgzpxRBvgbBIoCP2hQ7YLCD4hyaauZg2OQKluOCgnB9LvuAkDYu0X+DKLXA3JwrKqp\nqauOU10NMSgI1r/+FYKrLqda/NoNdBqN0jf8z08/KaXUqJAQlFRUOFWzKs1mp3Ml0GAARVFIbNvW\nfR9TFEGZzWS4nuOcCC0KampIT9ITRJEQ2/R6QOolioDX80mMjSUZo9TSAEDUaYxGcs25yYh9Qj7/\nDAayWIuMJIHQzfGqF7V8t27k9Q2omrQkvAbHrKwsxLmZcXnssccQExODxMRE6PV6vPvuu8pzhYWF\niHS5YCMkP7NCD0PehYWFYBgGoS7U3oiICOU9hYWFCHNx+aYoCuHh4cprFixYAJZl0adPH2zduhUr\nVqzAxYsXsWbNGixduhQLFy5Enz59kJqaigIXp+zY2FhkehLSvckgM0PpI0eUx6jSUvD9+pE+H8MA\n1dVgd+4Eu3Nn4wUDpH6m4G2sASD7NRhIX9DhqJVF02gIaUcuu0o3Qr5PH4gJCb63C5ALWSbl+Oir\nXC4uxuFz5/D5zp1u+0aXCgpwsaDAI8nkUkEBjDqdWzcGqrAQ7LlzhBDViGzKMXIk7A8+CKFTJ//e\noNEQpm4zy7vJmaNRp8OwXr2wav58fPb88wCA0X/9K77cvRs95s5tkn3JQaq6pkZR6JFBWSxOeqBO\nzxUUEHKK6relDx/GHADJd94JoNb78u2vv8aLa9cCANqEhIAXBEx5+WVM++c/AQDf7NsHs9Wq/NZB\n3krpALEjkxVsKMrttcXu3Qvm4EGPC1N2yxbi26jTkWtCo6m9djyBomr3K5df9XpY584li9AGwvbE\nE7AuWADbrFmwzZtH+rou4AcOrJU5BMAPGkTIUdd5cPSqrVpVVVUn0AHAsmXLsHjxYmRkZODll1/G\nE088gf+TzHD9cR/wF/UdHg4MDMTq1audHktOTsZLL72EjRs3Ij09HYcOHcKrr76KRYsW4RNpYFl+\nb4Xso+cBGZLf3w0PUURYRAQCzp1DiaRhGHr1KopqaiAGByOyuhrl2dkItdkgFBeD37gRZR7GDFwN\nj92BKy5GQGUl8mNiYPfxHVI2GxLMZjjsduRmZMDUqROsMTGwMQxiLRbQDgdyLl6svcAHDwZ8CKnH\nV1aCdjgg2mwwR0RA73CgzMtxL0lNxT+++AKz33kHs995BwBw9sMPled/PnECAMkw3X3+MxcuIDYs\nrA6hDAA0ubkIlGS3SjMzUdaQFbsafi7o2JISxFRWojI3F8jLAwQBDpfFZlOgUsoOx/frp3w3ZqkU\nmX75Mv752Wew8zzOX7zYKBcNoFaqrry6GrzdjtzcXNBmM3SXLoGxWGDVanHVzfkWe+kStKKIqxkZ\nEKRgFnbiBBaMHg3LuHHIzc1FuaoXWFJZCYqiwEn3ox/T0gCQc//C5csw6fVYOGkSFq9di6rKSqdz\nIuD4cWgKCiBqtSgbMABcaSmMZjPsHAe2qgrWvDzUuATUytxcUByHqzk5dRdQgoCwigpQogiHxQJB\nEMAIAgS7HZeLiwEvKkhhDANDdTUK77kH1rg4slAsLCT/NQU8Xds0Ddx7b91z1SVB8W8XTXsP7uxl\nYe01OAYGBqLSTY09MjISkZGR6NSpE0JCQjBmzBi8/PLLiI+PR2RkZJ0MsUiSVHIXaOXHeZ5HSUmJ\nU/ZYVFSEO6WVXGRkJK66/PCiKKK4uNjjdtetWweNRoPk5GRMnz4dEydOBMuySElJwbhx45xeW1FR\ngSAfzWhvX+SNBloUoblyBfrwcIBhwAUGQtelC8Cy0AQFwRAcDCYkhDTzq6pgdGNVlZubi1gfFlbU\n5ctgs7IgRkRA1727x9W8GlppjtHQuTOg+s7pxx8Hc/AgOnfrVi+tRW1QECmjWSwIaNcOdEmJ8+cR\nRaftPTpuHP7hUjJtExWl9M1sknZmjc2GmJiYOgvCq9XViA0Lc/vd0OXloENCAJqGXqNBRAudU1Rp\nKTRBQTBGRBBSBk0T49rGbDMvj5TpVNnCECnjWjBlivL5I1Q3QVYKiMagILTx41zwun/pey+uqEBw\nYCBiY2PBfvcdKIcDol4Pvm1bhLr5fpmxY8Hu2AF9VRX4Ll0AngcXFgZTSAjsbdsCFAWLy29q0Gpx\nm6RCJOOr337Dz2fPItRkwsNjx6La4cDs++5TSrIAwO3bR+ZwNRoEWK1gLlyAGBoK64svQvP++xB5\nHoLqPMnNziYjQAEB0EVGAjodmH37CLElPh4oLwcXGkpaC3o9rE89BebwYYjBwejsy7niiSdAVVYi\nQark3WjIyMho0Xuw17Jqhw4dcNkL7R2A0qyW/9+/f38cOHAAVlW5YNeuXYiJiUFCQoLbbfTu3Rsc\nx2Hnzp3KY7m5uUhPT8cASUG/f//+qKqqUnqQAOlDVldXK69Ro7i4GMuWLcPbknyXKIqwSStNm83m\nNLsEADk5Oejkb4nqJoCo1xMZJ6uVuDVIItWiVguRpkFLCxoxNLRRvQH63DlCIDEa/QqM5E3uT0uh\nc2fYp02rvwix3GvUaIjav81WW7KyWMB9+y0olWJKZEgIRrqMV6jZqwXS31a7HdtVpWmAzDhm5eUh\nzk1WRpWWgjl7lnwfLOt3v7ApIMqEJmkcxu+Srt3utvRHFRaCPXQIjJRFyYgND0flli0KoQUAhvTo\nofytzvYaA6vd7kT4+U36/WRiGVVV5ZHwwQ8fTkSxpUyGysio7clKn7VdmzZ4UMWB0Gu1dRixS//v\n/3Dw7FnklZTAZDBg0QMPOAVGBSwLUaMBc/QoUaKRSGGi0VinzK3LzlYG7GVOAF1QQHqM1dVgDx4k\n16NOB8eIEUBAAPjhw/0bB9LpIN6ggfFawGtwHDRoEE6cOKGUN7dt24bPP/8cp0+fxqVLl7Bt2zY8\n++yzGDBgANpJWpCTJ0+GXq/HnDlzcObMGWzevBnvvfce5syZo2w3LS0N/fr1wxHpxhIUFISHHnoI\nL774Ivbs2YNjx45h9uzZSEpKwnBJ9LZr16646667sGDBAhw6dAgHDx7EM888g9GjR6Ojy4oOAJ5/\n/nnMnTtXWb0OHDgQ69atw7lz57Bq1SoMctFhPHLkCO64446GfYs3ImT2Is+DqqoiIxKBgRBjYyF2\n7Ag6PZ1QvPV6z6QcV5/EmhpQqlIiVVoKuqwM0GhgrY+vHcM06UUsRkRA6NgRtkceIQIIgHITpDMz\nIQYEKCQkGZ8vWYKfVPO5R1QlobyrV5Uek8xqffvrr2GaMAGxqam4cOWK2/YCVV4O0WSCY/RoWJ96\nShljaRHIJCZ5OJ6iiCGtSihbgdVKSEOiCPann8BJ9ltqUNnZRLPTD6cKDcdhr1SevihlkRWe/C39\ngCiKCJ80yYlR3DYykpyLKlazNxaz0K0bGafYu5c4wkgavPJ5wTAMPnrmGfSS9D8ZmnZi4qrhS5Bc\nlEcpZOa1HEADAshCTQXKZiMLVBAxcerqVXJcokhYriwL28MPw/r886SX14pmg9fgOHz4cGi1WuyR\nFN11Oh0++eQTjBkzBgMHDsSSJUswduxYfKEqQQUGBmLjxo3Iz8/HiBEjsGjRIsybNw9zVU14s9mM\nrKws1KhUWpYtW4Zx48ZhxowZGDNmDEwmE9avX+90k1m9ejWSkpKQkpKCyZMno0ePHvjoo4/qHPeO\nHTtw/vx5pxnIWbNmoWvXrrj77rtx4cIFvPbaa8pzhYWFOHr0KCZNmlSf7+7Ghhwc7XayymYYMo/E\nssTMNCCAsNtCQjzKrQX8/ju4r79W/s1t3QpWYu5RZWVgd+wA9HrY77sPkIWE/YB1/nzY/vSnxn0+\nFWyzZhHx7bZtyQKAYYhFFAjjVU0WkGHU6TBQRcX/7cwZ5e9D587hrr59AQD5UqlfJm3IGNGzZ90D\nKSsDDAYIXbvWzp61FCSWLlVaWksG0etBu3GyYI4fB3vmDOjDh8m54SYbouRABPjFgE2evrH6AAAg\nAElEQVRyEVK/Z9EifONGWN4fPOTGcPlfzz1HnOnVCixeRgXEsDDibVpRQZihNE0qKS7BSpbEKygt\nhYbj3I6k/OPRR70fsOSPCJ2OaIfOm0eOwWCoM1LFVlYqvw/z669gd+9WvmequhpCp04QpTnKVjQv\nvF6dGo0GjzzyCD777DMMHz4cw4YNw7Bhw3xuNDExEVu3bvX4/JAhQ1DiMkem0Wjw+uuve1XTCQ4O\nxscff+xz/6NGjcIoSbpKhk6nq0PWkfHFF19gxIgRSvZ7K0CUh4J37iQu3LKKBiRBAI0G9vvuA52T\n45H2rs3PByIjQV+4QDRQVZJQVGamcgPyW1BbRjOq8Cv+cGYzEBamuIJQHm7wDE2DFwSczckBABSV\nl+PkxYuYd999+HLPHvz9s8/wB5deT7DRiEGq8QH61CmSVZvNxD3jGnjTgWEgtG1LLLJ0OjKwLeuO\nukKawWSysyFGRpKFhNns9LtQZjOEtm1Bnz8P6vx5iD76XZzLQsDmcOCR119Hip96sjLsDge+/eUX\n5d9fLF2KMf36gaIosP/7H6k4yIPtbtjCykeUApCo0zlnvy5VkhFSuTJCWtzJGrGvzZqFRatXo0f7\n9orkoDsI0dFASAjZriyvJhNw9HpQJSVgv/0WjvHjAYaBtrSU9HAlAQNoNGQxxzCgbDYI/s61tqLR\n8Ckf99RTT2HPnj03rXqM1WrFhx9+iBdeeOFaH0rLQqaAa7Vgzp51plXLdG9JuNlTWVWUTHmZtDSS\nCckrdkg+jWFh4AcPdkvvvmYwGgGNRskclcDvQXf0wmefYWTv3jBLN9DT0nWgDojJL76o/P33GTOQ\ns3690zaYjAww+fmgi4rclzFbAhQFfujQWtq/pFgCALBYiD1YeTnJAu322r6kXk/m8VxZkBYLhPbt\niUqKJx9FFyyeOrXeh/3f7dthmjABdocDPx4+jOj77wcAfPPii6jcsgVj+/evrS7JYz4MU2sB5gly\nGVUWspfnYF3O9TYhIXhh2jT8z0VeUu5J+7Q3E0UIMTGw33tvrcehDIuF+JiKYu1MsXT8oCiIwcFk\nESvrktpsXgN+K5oWPoNjUFAQ0tPTb9qsSqvV4tSpU+jprgx2M0N2q9Dra1epEvjbb4djxAjCkGMY\n58zRbgd9+jTA83AEBpKLXaslvUmpjMXs3w+qogJCXBwhDVyLTMkTKIqoeEi9JaWs5WEBEBIQgLiI\nCCU45kpBIs6D8MAdaoNiebtShiIajW698VoKotFIqgI0TfRoaZoMnOflgaqqAvfTT+C+/BJ0bi7p\ne+n1ioWRU3AURVJ+NJnIzZ3nibamDxs0uWf37QsvICQgAHqNBnk+DJhXbd4MgPR0U/7f/4NVIrC0\nbdOm7ueTiE5Cx45E6MKLw4Ss8esYOtRZGMLNebDogQdwm0spU5aRq/N7u4CSsnAxOhr28eOd+oR8\n795kscayxKO0pgYCRZHH5XlceaFK06Dsdr/spFrRNGi1rLqFwScmKtmDk32MRgP+jjtqxYJVwZG+\ncAFMZiYoed5IqyXBRlayAUBfvQrKpQx3XUGvr+2Tyf8XRcBiAfvdd3Vu8gatVlFJKauqgoZlEaDX\n4z5pzAgABt52Gy59/jn6de3qvC9Z91LKsu0TJzbbx/IJOQjQNOwPPAC+Tx8yDC8bRuv1pAQoBU6h\nb18I7dtDDA8nvycAXL5MAqEs6s2yoMrKiHeiiknuDrPGjsWxjz9G17g4bH/jDdTYbG6FFHKLi2Ga\nMAGHzp3DaWmG9e+ffaY8P3noUHRyV6rXaMD37An7/ffDptJVdgehc2fwQ4aQ81yv99sRpWzTJlz6\n/HNQFIWMtWux3JeggSAomaDQu7fTIlSMjYXjnntI79FiIYpPDAO+T59aNrEqcxRDQuqq/bSi2dAa\nHG9hOO67j9wsjcbaMqMr1ObAFRWEpRgUBCYrC1rZqFimwcvi4FLpzpsJ8TWFwwEmPR3MgQO1N32e\nB52dDcpur8NcFUQRZ3NyYLXbUW2xwCj16f77179iQUoKOkRHY+2iRW4VcZQZSoqqVR+6VpCdGRgG\nQpcuELp2JaMElZXO2a1UTbBPmkQeDwxUHN+5X34Bk5VVW8KUyu6iRuOTucqxLDpI+p2dJRb5p9u3\nY8maNcpohyiKmC4Rbhb/+9/gBQEhUgYYExaG8m+/xf8tXOjeSNhmU0hlPqsVOh3RXOU4ReHFXVnV\nFQzDKL9zVGgoNL6EDATBLaFJBt+nDyn/MgwxDpaY2mJICPj+/eG46y7YHnyQVGhkxahWtAhakC7X\niusSNE1KY550L1WZI/fDD4BWCyEuDpTVCkdAAPjERDjGjwedng7m99/BHDtGyquCcN1mjlR1NdGk\nLChQSoxUZSVhk2q1dZwFoqWZxc927MDloiIYVM+/8sgjeOWRRzzvTPru7PfeCyExsck/S30gyqLr\nsrZmQABEhgGTlUUkxGR5PY2GZC8y1CMbciYj/S2qstH66LaqWejLN27E8o0bMXfiRNz1hz/gsOSa\nIjOE97/3Hv67fTtmjx/v0XcRdjsZ/m9If5thakW7m9rSy+HwbgNFUeCHDAFz5gyYEydgDwyEluNg\nUzkdQWaw+uv32YomQWtwvNUh3dzsHsZYRIYhc4EWi2JtI/TsCaqkBMUaDbRSmVC47TZAoyEOARIh\nornNdRsFOVBQFCET5eWBqq4mNzKX475T6ivN/+ADALVZj1+QS9LXw41NbfwM4ikpE25EgwH8oEEQ\nOncm/UgVQUvU60HxPKjcXLKY0GrJPB7HgVKJulMORx21IW/IXrcOs956S/F6/GDzZhSWlTm9Jjo0\nFPGRkXj+wQe9bos+ehQiy/rvb+mK5uqL87xP9wmhTRvyndI0BHcZsU5HFiLXsupwC6I1ON7isD32\nGFlte1LBkcgA3P/+B1Gng2PIEMJAZRiYXXQOhZgYOAYPJl5xv/wCQaWSct2B40g5kGUhxMaCOXOG\nLAIkYQQ1XFVPAuqjGCQHi+vhxiYzVWUEBMA+fTrJbkJDyeiNGwjt20PkOLAHDpC+slZLRNy1WtKf\n1OlqVYisVr8d3kMCAnBnUpKTEfI3P/+MP0+Zgre++goA/LYOYzIzIcbENDw4NnXmKAiAwwFKFH1b\nqhmNEDp2BJ2bi9LBg1En95UYrH4J7LeiydAaHG9xiG5Yf07Pm0yEMACAEgTClvN0o9fr4ZA0a3kX\nBaLrCXzfvmAOHCBlQJOJEE4EAbh6ldD6XYKj3iU4GnxkAk643jJHl9/OnwWMKKu66PWgBAH2wYOJ\n60VUFPjISLDbtpHxjwb4RQ7v1QvRoaFKEBQEAXf16YNnUlLQbcYMJwk3bxBiY4nQRD3EJpzgYZQD\nAFGnuXCB9Ab9/B25TZvIoiMoyC87KLskemHzIKxtfeqp67ZNcbPiOrhiW3E9Q4yPh9C9O0SGIWUd\nL/T4GwWOe+4hmTLLwjpnTm0vTr75uNzguyUkoIcqiHjse7mDzFa9HoIjyPiAffz4+r0pIICUW6Xy\nq9CjB/kOpYxYbNOmtmRbz1J6n06dkL52rdOCo2t8PIKMRuSsW4c3VSpXvlBfs14neBnloAoKwKSl\nEUlFf0HT5FrRav3XFPYGWZO1FS2G6+OKbcX1C5qGY9gwkjmYTBBukjkr20MPEdKJ0Vg7dC2XB93c\n4B9QGXrvkyyrfIEqKgK3fTshrVwncIwfD0ElBO4XaBr2qVPJjb5NmzqzdrapU8lIAuBRatAXCr7+\nGvfcfjsAIEwi1bAM478FniA0rifnbT+SaH0dIQQf2xNNplb90xsY189V24rrFmJoKKDRwDFkCNDQ\nns51BjEmBo577yX/kPqqYlAQWfG7CY5q0Wl/MsfA334Da7US5w2NhvTobmRQFGzTp7sfQtfrIcTF\nNShzVOOLF17AldLS+mXm8uFJw/YNhqzsJAiokztKyjWUD79XBRYLIbmNHeufW0Yrrku0BsdW+AbD\nwPrMMz5ZdzcsGAYixxFSxJUrhDrvggCJVDG6Xz+8/cQTPjfJlZQAYWEQTSbYJTeQGx2iN5WsBpZV\n1WAYBrEelId8gSoubvB+yQa8LF5EkYiS++EkQuXkgD1yRHEGacWNi9ZfrxX+oR4qIjcaRKORzG92\n6UKG2d2QSm6XlG/+kpqKeH98GCURBFlj9rqS0GsGiLLLi8rVgsrNJUSd5kZNDdEI9rPc7RY+CDl+\nb6aggMjYSfq1rbhx0Zo5tuKWhxgTA+vCheQGe/SoW8ZlQmQkKrds8Xubgk4HGI3gfWhv3jQICAAY\nBnR2Nvj4eMUMGTU1pF/ZjKAsFq8qNP5tRFq8OBwk+1WXaOsRHOmKClJGb80cb3i0/nqtaAWglIxF\nrZaY3zYSFEXBMXIkHNdSS7UlIc050gUFgNUKOiODeILqdHUMfZsMkg0ULBaIFAXbjBkN35YUHJm0\nNHDffEMCpFwilhnHDAP6/HnPmygtJaVXlUxfK25ctAbHVrRCBebMGcUEusHgecKe9HMY/maB0Lkz\n0d09dIgECY0Gok4H5vjxZlFLYk6cALdpE6iaGkKoakxfV87ypHlO7osvSJAElMxR1GpBHzvmcRNU\ndjbRRO3Ro7XneBOg9ddrRStUcNxxB0SW9SzE7g/kgfhbLDjaH3gAYlgYCVZ2O/hevQCOA33pEujf\nf2/y/VG5uSRLk7RyG1VaVcvqGY3Eg1SrJYFRzhw1GmLV5QqzGXRaGqiyMggdOhAVoetotrUVDYPP\nX6+8vBxdunS5qc2Ou3fvjuPHj1/rQ2nFdQC+Vy9SDvODmegRNhtEilIsvG4lCImJoCoqQPE8hPh4\noglqMPhtiFxvUBRQU9MwwXHX7QAkwGo0tWM9sl4s4FH8mz57FsyVK6CLioijiaw328CZz1ZcH/AZ\nHFesWIHhw4fXMTu+evUqbrvtNoSEhKC0tNTpuVOnTmHs2LGIjo5GYmIiXn/9dZ8HYrVasXDhQnTs\n2BGxsbGYOnUq8vLynF5TVlaGxx9/HAkJCUhISMDs2bNRrnKTKC0tRWpqKuLi4jB06NA6AW/JkiV4\n5ZVXnB7TarV48skn8XcXp+9W3KIwmYhHYSMyR8pmIzfH+miw3iSQg4OoMukFyzZYHMD7zohuLWU2\nQ3RnF9aQTRqNsD34IOypqeTYrVYyQwlJY9bdnK8gkM/NMGTmMzERYnQ0hIiIJjmmVlwbeA2ONpsN\na9euxbRp0+o8N2fOHPTs2bOOgkVFRQWSk5MRFRWFXbt2YdmyZVixYgXef/99rweyePFifPfdd1iz\nZg22bt2KyspKpKamQlBdVDNnzsTJkyexYcMGfPPNNzh+/Dhmq+Sl3nzzTZjNZuzduxeDBw/GfJXh\n6dGjR7F9+3YsWrSozr6nTJmCXbt23bTZcSvqAYYhWYgPV3sqIwNUUZH7J61WEiBusbIqIOmwyh6f\ngKIuo5Qnm3RnIkDTxJOyKdiqUiAX27YlwZ2iQFVVKWVVIS6OEIBc2Kt0aSnJKjmOeGKGh8P2+OMN\n13ltxXUBr8Fx9+7dsFgsGDp0qNPjq1atgtVqxdy5cyG6nChfffUVLBYLVq1ahW7duuHee+/F/Pnz\nsXLlSo/7KS8vx6effopXXnkFw4YNQ69evfDRRx/h1KlT2L17NwDg3Llz2LFjB959913cfvvt6Nev\nH9555x1s27YNWVlZAICMjAykpKSgQ4cOePjhh5EuaSE6HA7Mnz8fb731FjRuLqI2bdqgb9++2LBh\ng+9vrBU3PSirFUxWlme5MIsF7KlTYPfudf98ZSW5uTZRNnNDQc6gOA5Cly6wPf44HMOHAxwH5rff\nQJWWwnD6dNPsSwqOaIrgCJDAKGd7HAcRALtvH+jMTLI7kwmUKILKzAQl3XOo7GxQVmutyfctWC24\nWeE1OP7yyy91ssNjx45h+fLl+PDDD93qHh48eBCDBg2CVtVvGTlyJPLz85Gdne12P0ePHoXdbsdI\nlQJ/bGwsunbtioMHDyrbDQgIQP/+/ZXXDBgwAEajEb/99hsAICkpCXv27IHD4cCOHTuQlJQEAPjg\ngw/Qq1cvDB482ONn7du3L/bv3+/t62jFLQLHoEEQAwLA7toF7vPPwbksmqiSElJG86CZSlVVwR4S\ncmsSMqTsSSbjiCEhxEpKowF98SKY/fsRUFDQNOxV2Q6M55tEv9aenAzbAw+Qf8j2Xno9qRDQNJFO\npGmwR44QFRyQ31rU6+G4+27yeVvHN24aeL16s7KyEBcXp/y7uroaM2fOxOuvv46oqCi37yksLESk\ni4JIhLQaKyws9PgehmEQ6kLFjoiIUN5TWFiIMBe/OYqiEB4errxmwYIFYFkWffr0wdatW7FixQpc\nvHgRa9aswdKlS7Fw4UL06dMHqampKCgocNpWbGwsMqUVYitubfC3304cOvR6Mh7gIotGmc3kZumh\nVEhVV8N+i5bUhPBwQKsl36EEkaYJ81MKHALDNM3soyBApCjCjm2CoCQkJiquM6IUGJXSuEYDMSCA\nfBbJy5LZtw/MmTNkfCMpCfzgwWQbrbgp4HW5VVVV5RToFi1ahIEDB2LChAlOr1OXVv1W0fcDriVb\nXwgMDMTq1audHktOTsZLL72EjRs3Ij09HYcOHcKrr76KRYsW4ZNPPnF6b4UPYeEMD15rtzJu1u8k\nKCYGptOnQdtsYKxWlGZmgpdunIZLl6CprATjcKAyLQ22mBiA58GVlIA2mxFw4QLsw4fftN+NT0ye\nDFRUkP8AQBAQyfMw2GwQamrA6HS4cukS+EaUndniYgSWlAABAWB4HuaDB3FFkvhrEogiTO3bI+jI\nEXDV1bBWV+PKlSuIq6oCXV0NkaZB5+RAZFnYc3KQm5MDxMcDly41ete37HnjB5r6u+nsxUDaa3AM\nDAxEpUqEee/evcjNzcW6desA1AavLl26YMGCBXjhhRcQGRlZJ0MskogLrhmljMjISPA8j5KSEqfs\nsaioCHfeeafymqsuPSBRFFFcXOxxu+vWrYNGo0FycjKmT5+OiRMngmVZpKSkYJxkyiujoqICQT4c\nJ7x9kbciMjIybt7vpFMnwGyG9q23ALsdOpqGGBsLAGCys4HYWFA5OTCEhkKIjQX31VeEhGM0AlFR\nuEpRN+930xA8/TS0b7wBqrQUlTSNGI2GsDkb0CukSkrAZmYCAQGkR8jz+P/tnXlclNX+x9+zMMOi\nLGq4YKhoiELiEi65gJiQ4dXrFletn5milZqS+1pevfEqsxTKtTTMstQ0UylRQMRuKmnkmhvhnmjK\nJjD7749xniuyqAkMDOf9es3rJc825/l45vk+55zvogoJoXZ56+3tjZ1cjiI1FUdXV5x8fVG7uSFT\nKMzfa0l04OqKYzl9t03/ph6TytamzGlVLy8vLl++LP29detWfvrpJ/bv38/+/fuJjo4GYOfOnZLX\naMeOHfn555/RaDTSeUlJSTRq1AhPT88Sv6dt27bY2dmRmJgobbty5QpnzpyhU6dO0nXz8vKkNUgw\nr0PeuXNHOuZebt68SVRUFB9++CFgNqTau1M5Wq0Ww31rHpcuXaJFixZlySGoSchk5lqPSiWo1cgt\nVR8MBmS3bmHw9oa7D04KC83TrGq12SHD0ZE7bdpYt/1VDVdXc6C+gwMmuRzFkSMod+9+9OuYTChT\nUjC5umKqWxeThwf655/H8Oyz5d9mQN+jByYnJ3MyesuUqlKJoX17yfFI99JLFfLdAutSpnHs0qUL\nx44dk0aIzZs3x8fHR/pYjJ23t7e0rjh48GAcHBx44403OHXqFN9//z1Lly7ljTfekK57+PBhAgIC\nOHJ3UdvFxYWXX36Zt99+m+TkZH777TfGjh2Ln58fQUFBALRs2ZLnnnuOSZMmkZqayqFDh4iMjOT5\n55+nefPmxdo+a9Ysxo0bh8fdt/3OnTuzYcMGTp8+zfLly+nSpUuR448cOcKzFfQDE1RfdEOGmB+C\nd1+sZLdvg1yOsXlzyYDabdliLgTs4IDR0xPtSy9hFF6LxTD6+IBSiVGhMHt+PkR9RFlWFvJDh8zr\nuzk5yNPSQC7H0L072jFjzEWrK7CgsKlBA7C3Rx8ScrdBZq9Ug4+POeynbl2bKEcmKE6ZxjEoKAi1\nWk1ycnKpx9y/xujs7MzWrVu5du0aPXv2ZPr06YwfP55x48ZJx+Tn53P+/HkKCgqkbVFRUYSFhTFy\n5Ej69OlD7dq1+frrr4tcf/Xq1fj5+TFo0CAGDx7M008/zcqVK4u1KSEhgfT09CIxkBEREbRs2ZLe\nvXvzxx9/8N5770n7MjMzSUtLY+DAgWXJIaiBGJ96CkObNsjuzjTI8vKkhyJKpTlkw9HRHN+nUmHo\n1g1TkyZWbnXVxOjhgcnJidudO/9vVH7PDFNJKA4cQHHtGvLz57HbsQPFlSuY7Oww+PlhatjQHF9Y\nkcjlaGbNwtChg/lvOzuwt8dkyf5jI8W/BcWRZWVllen1snDhQi5cuFDM0cWWiImJISUlhY0bN1q7\nKdWKmrI+oty9G0VKCvrevZGfPo382jU0c+eiWrsW2YULZq9JT090ffpgatQIZLIao80jodEgv3KF\n0wYD3k5OqFaswOToiOGuX4FEXh52cXHog4NR7t2Lyc0N2Y0bmNzcwMkJY6NG5qlMa4RNaDTIbt/G\n1KABsitXzC9J5ZjsQfSb0qlsbR4YHDRhwgQCAgLIyMgolkLOFtBoNKxYsUJyMhIIiqHXm9PJ6XSg\n05mDvQHtyJEoUlKQnz+PbtCgmhn0/yio1Ri9vODsWWnUJ792DQ4exNi4sfnFwmjEbudOTK6uKOPj\nzWuL9eqBXo+pYUO0r79u3fhRtdo81QqSg5bANnmgcXRxcZEyzdgiarWaEydOWLsZgiqM8e6DXLln\nj9kj9Z6cmYbu3TF0727F1lVT7jq2mOrWRX71qtlIFhRg6NABk6ur2blJpQJHR7QREcjPncP41FM1\nM7GCwCqIniYQPACTuzsmOztkWq3ZIacGVtuoCDQzZqDv1QuTk5M5n629PfKLFzF6e5unTJ2czKEx\najVGX9/ySREnEDwkwjgKBA+DJcOLTGZOHSd4fGQys1FUqcwjQnt7ZAUFGNq1QxcWhkmtFp6gAqvx\n+AkJBQIbx1S/vtlDValEJpdXvIdkDcLYujVaFxdU69ebU7XpdBg9PcHFBZ2z8+PXaRQI/ibCOAps\nhry8vCIlzsoTRWAgiv/+1+yZePMm2gfE6Gm12gemI7wfmUxGrVq1yjUFY5VHocDk6Ylm/HjUn3xi\nHkHedWwyNmtm5cYJajLCOApsguzsbGrXro1dBY3q5I0aoWzUCDAX9NW5uZV5fH5+Pm4POOZ+9Ho9\nOTk5ODs71ywDCeDsjGbqVCnJgkBgbYRxFFR7DAYDdnZ2FWYYAfNUqlyOoVkzc+qwCkCpVGJvb49O\npyux7qjNc289RYHAygjjKKj2GI1GFBUcEG708MDQogWGdu0q1GtSoVCg1+sr7PoCgeDhEPMXApsi\nNTWV5557jpCQEGbMmCFtX7JkCSEhIYwePVoyPuHh4Tz55JPs3btXOi4yMpJmzZoRGxtb9MIKBYZO\nnYicPr3Y/kWLFuHt7c2CBQtKbNPJkyfp3bs3vXv3lmJqDx48SK9evQgODmbNmjXldPcCgaC8EMZR\nYFM0adKEuLg44uPjuXnzJidPnuTGjRukpKQQHx+Pr68vO3bsACA6OrpIQnyAGTNmsHDhwlKvX9L+\nV155hc8++6zUcxYuXEhsbCzr1q2TDOjSpUtZt24dCQkJfPHFF3/3dgUCQQUhjKPApnB3d5fW65RK\nJXK5nF9//ZXud7PY9OzZk4MHDwJQv379YueXtO1B+5944okyHWiysrJo1KgRDRs2JDs7G4A6deqQ\nnZ1NYWEhTk5OD3dzAoGg0hDGUWCTHD9+nL/++gsfHx+ys7NxvhsvV7t2bclAVRb3hpdYyr+99tpr\nDBgwgGeeeYZ//etfldoegUDwYIRxFNgct27dYsqUKSxbtgwwl1GzxBzm5ubi8ghlhm7cuEGfPn0I\nCwt7pDa88sorhIWFkZmZWWRUKb8bpjB37lySkpJIS0vjyy+/pLCw8JGuLxAIKhbhrSqwKfR6PRER\nEfznP/+RCnC3b9+e1atXM2nSJJKSkujUqZN0vGUkdy/3bnviiSf44YcfSt1f2rbPP/9cKrTt5ubG\n1atXkclk0gg2Pz8fZ2dn7OzskMvl6HQ67Mux9JFAIHg8hHEU2BRbt27lyJEjzJ07F4D58+cTEBBA\n165dCQkJwdPTkwkTJgAwdepUdu3axQ8//MCoUaN45ZVXWLRoEZs2bcJkMnH9+nWmTZtW5Pol7Y+N\njeWzzz7j9u3bZGdn88EHHxQ5Z/bs2YwYMQKZTMaHH34ImL1i//GPfyCXywkNDaW2KHclEFQpHljs\nWCAojapSmFWn02E0GnF0dLR2UySuXLkijRwfBY1Gg16vt+lRZFXpN1URoU3pVLY2D1xzzM7Oxtvb\nm4yMjEpoTuWj0Wjw9fXl6NGj1m6KQCAQCKoIDzSOMTExBAUF0bRpU8C8fnL/5/PPPy9yzokTJ3jh\nhRdo2LAhrVu35v33339gQzQaDVOnTqV58+Z4eHgwdOhQrl69WuSYrKwsxowZg6enJ56enowdO7aI\n5+Ht27cJDw+ncePG9OjRo5jBmz17drFAbbVazeuvv15mbJtAIBAIahZlGketVktsbCzDhw8vsj06\nOpozZ85In3td0XNychgwYAANGjQgKSmJqKgoYmJi+Pjjj8tsyMyZM9mxYwdr1qwhLi6O3NxcwsPD\ni7jBjx49muPHj7Nlyxa+/fZbjh49ytixY6X9H3zwAfn5+ezbt49u3boxceJEaV9aWhp79uxh+vTp\nxb57yJAhJCUl2ezoWGAmPz+fN998U/q7oKCA5s2bk5ycLG0zmUw8++yzxTPkAP/+97/p3bs3oaGh\nnD9/HoCLFy8yZMgQwsLCpGD+BQsWEB4ezo8//gjA2rVr+emnnwDzS+D9iQcEAlB0EPsAABKeSURB\nVEHVo0zjuHfvXgoLC+nRo0eR7c7OzjzxxBPS5971kU2bNlFYWMjy5cvx8fGhf//+TJw4UXKrL4ns\n7GzWr1/PggULCAwMxN/fn5UrV3LixAkptdfp06dJSEhgyZIlPPPMMwQEBPDRRx+xa9cu6UF19uxZ\nBg0ahJeXFyNGjODMmTOA2YNx4sSJLF68uMSEzvXr16d9+/Zs2bLl4VQTVEs2btzICy+8IP0dGxuL\nn59fkWPi4uJKDOrPysoiNTWV3bt3M3/+fFavXg2YDeHy5cvZuXMnL7/8MmDuq1999RXffPMNWq2W\nw4cP07VrV8A8U+Hm5ib1WYFAUDUp0zj+97//pU2bNsUeFDNnzqR58+YEBwezdu3aIm7shw4dokuX\nLqjVamlbcHAw165d4+LFiyV+T1paGjqdjuDgYGmbh4cHLVu25NChQ9J1a9WqRceOHaVjOnXqhJOT\nk5TxxM/Pj+TkZPR6PQkJCdKD75NPPsHf359u3bqVeq/t27eX3u4Ftkl8fLyUKUer1fLLL7/QuXPn\nIv138+bNDBo0qFhohqU/G41GsrKyqFu3Lnq9ngsXLjBx4kQGDBjAuXPnAHNdRo1Gg0qlYu3atYwc\nObLItQIDA4uFhwgEgqpFmcbx/PnzNG7cuMi2WbNmsXbtWrZt28bAgQOZM2cOixcvlvZnZmbi7u5e\n5BxLvFlmZmaJ35OZmYlCoaBOnTrFzrOck5mZSd26dYvsl8lk1KtXTzpm0qRJKJVK2rVrR1xcHDEx\nMWRkZLBmzRrmzp3L1KlTadeuHeHh4Vy/fr3ItTw8PKSHm+DhqG5edbdu3ZJStX355ZfFMtMkJCTQ\nvXv3Eit8ODg40LlzZ9q3b8+0adMYMWKElLs1OjqaqKgoKXykb9++LFy4kFGjRnHy5EkuXLjApEmT\nOHnyJABNmzaVZjVqItWt31QmQpvSqWxtyjSOeXl51KpVq8i2qVOn0qlTJ/z8/Bg/fjwzZswgOjpa\n2l+eRVpLCrYuC2dnZ1avXs2xY8fYsWMH3t7eREZG8s4777B161bOnDlDamoqTz/9dLG1x3uzqAhs\nG8vMwnPPPVekj61bt47hw4eX2O8yMjI4duwYaWlpxMbG8s477+Di4kLLli2pW7cuPj4+3Lp1CzBX\n+4iNjeXAgQNERESwe/duFi9ezMqVK4FH79cCgaDyKTMJgLOzM7m5uWVeoF27duTm5nLz5k3q1auH\nu7t7sRHijRs3AIqNKC24u7tjMBi4detWkdHjjRs3pLUad3d3/vrrryLnmUwmbt68Wep1N2zYgEql\nYsCAAbz00kv069cPpVLJoEGDiqUDy8nJeaS0YoLqh5ubG3l5eeTk5HD58mUGDhxIeno6u3fvpm3b\ntpw7d07ykrY45ljeVu/cuSNlt6lTpw65ubk4ODhQq1YtCgoKuH37dpFA/tzcXDIyMvDz8yM7Oxuj\n0Sh5VmdkZODt7V35AggEgoemTOPo5eUlreeVxrFjx3BwcJAMS8eOHXnnnXfQaDTSOk1SUhKNGjXC\n09OzxGu0bdsWOzs7EhMTGTx4MGAOoj5z5oyU6qtjx47k5eVx6NAhad3x0KFD3Llzp0g6MAs3b94k\nKipKWtsxmUxotVrAvN5kMBiKHH/p0iVatGhR5r0KqjchISGkpKTQp08fydErKiqKZ599FldXV2nN\n+csvv8RgMPDUU0+xZ88eDAYDoaGh2NvbExoail6vZ9GiRYB5JuWf//wner2+yPLCihUreO211wDw\n8fEhJCREyrazb98+Xn311Uq8c4FA8KiUmSEnPj6eUaNGcfHiRWQyGT/88AOZmZkEBATg4OBASkoK\nc+bMYfjw4URFRQHmEVhAQADdunVjypQpnD17lvHjxzN9+nTGjRsHwOHDh3nttddYuXIl7du3B2Dy\n5Mn8+OOPLFu2DFdXV2bPnk1OTg7JycnSVO2QIUO4cuUKS5cuxWQyMWnSJJo0acKGDRuKtX3MmDF0\n6NBBCvWIiYlh06ZNrF69mo8++oisrCy+/vpr6fjevXvTp08f3nrrrXKSVlBZPGyGnPz8/GLLAJWN\nVqtl4sSJLF++vMT9NSFDjkBQHSjTOGq1Wlq3bs2nn35KUFAQCQkJzJ8/nz/++AOj0UjTpk35v//7\nPyIiIqRqA2CufD5lyhSOHDmCm5sbI0eOLJKjMiUlhf79+7N9+3Zp2lSr1TJnzhw2b95MYWEhgYGB\nLF68mEaNGknnZWVlMW3aNCl+rE+fPixatEia7rKQkJBAVFQUe/bskbYVFhYyYcIEdu3aRatWrVi1\nahVNmjQBzM4+vr6+pKamSskOBNUHvV6PXq+3ibqIhYWFGAwGYRwFAivzwNyqCxcu5MKFC1Jcly0S\nExNDSkoKGzdutHZTBH8Dk8lETk5OMW/n6khWVhaOjo4leswKBILK44HGMTs7m4CAAOLj421yVKXR\naGjfvj0bNmygTZs21m5OlcFoNCKXy9Hr9SiVVb94i06nIz8/Hzs7u3L1mC6JvLw8VCqVlFDCotXj\nYDKZ0Ol0qNXqaj1qtNSvrF27Nvb29uWijS2Qnp7OH3/8QXBwcIX3z+rIqVOn+OqrrxgxYkSV8f0Q\nVTkERdDr9bz11lvcuXOHzz77rNo93IxGY4WFSuh0OmbPns3Ro0ext7fH39+fefPmldsorzqPFnU6\nHVOnTmX//v04Ozvj6OjIxo0bq1SlFGtx7NgxevTogYuLC0lJSTRr1szaTaoy6HQ6Jk6cyIYNG4iI\niGDBggVFEshYk+rz1BNUOL/88gv9+/fnxx9/ZMuWLaSkpEijx+qCXC5HoVCU+yc5OZmuXbty6tQp\nIiMj8fHx4fvvv+ftt99GoVCUy/dWV7Zt20ZAQABnz55l8eLFjBgxgkuXLjFv3jxrN82qWF7SNBoN\nwcHBqNVqoqOjRZzrXdatW4eXlxfnzp1j//79vP/++5JhrAoaVf35MkGlcejQITw9PZk2bRpr1qzh\nrbfeIjU1FaVSiclkqrHTQTk5OWzdupWePXvy7rvvolKpCA0NpXHjxmzfvp3c3NwaXaw4JSWFIUOG\nMH36dJRKJYGBgRw4cAAHBwdrN82qWH4vv/32G3Xq1GHVqlUMHDiQ8PBwOnXqVGN/TxbWr19P/fr1\n+frrr6lTpw5paWlkZmbSrFkzGjdubPX+I4yjQJo67d+/Pz179qRVq1ao1WoGDhzIqlWrGDNmDAaD\noVqsPVYEJpOJLl264Ovri0qlwmQyoVAoKCgokNbXqtv0c3lgMBhQKBRMnjy5SP+4ePEiR48eZdCg\nQezdu5egoCDrNtRKWF4oFQoFTZo0ITAwkA4dOrBo0SK+/fZbcnJyinna1wR0Oh12dnYsXLiQiIgI\nli9fzvHjxzl58iQODg789ddfdO/enTVr1li1nTXr1yyQiIuLk/5teah7eHjQqlUrAHx9fRk1ahQL\nFiyQnHLuLR9my1i0sUztuLi4MGzYMPz9/QGkBBK3bt3Cw8MDKN+0iVWZe/uNZSq4YcOGUg7mFStW\n4O/vT+3atUlISCA8PJwFCxZw584dq7S3srhXFwuWPnH06FEKCgoAWLVqFUlJSQwcOJD+/ftz+vTp\nSm2ntbhXHzs7O0wmEx07dqRbt25ER0fj4uLCF198waeffsqSJUvYuXPnQ9UBrkiEcaxh7Nq1C19f\nX4YPH058fHypx9WuXZthw4bh6uoqxahWhXWAiuR+be43eJb7t7xM/Prrr1Kcrq3zKP1m586dxMXF\nsXPnTj7++GOWLVsmpZC0NcrSxdJfrl69St++fQFzdiS1Ws2+fft48803admyZaW3uTIpTR/LC+b8\n+fOZMGECc+bMoU2bNvj5+REWFsbMmTNZuXIlOp3OWk0X3qo1iZ9//pnFixfTokULLl++THp6Onv3\n7i1W49IyHaTValm3bh1Tp07l119/pWnTpuzbtw9/f3+by0P7sNpYuHTpEkFBQWzfvp3WrVsD8Pvv\nv+Pj41OZza4UHkYby7Ty/WvTp0+fpnPnzmzevJlevXpZo/kVxoN0sUw7R0RE4OTkxG+//calS5cY\nM2YMn3zyCZGRkUyaNMnKd1FxPEgfS5+5c+dOsQQemzZtYsqUKcTFxeHr62uN5os1x5qApRO6u7sT\nFBRE3759pfqZq1atYvz48UWOtzzcVCoVL7zwAlu3bmXYsGGoVCouXLhAUlKSzRjHR9XGQmJiIvXr\n16d169akpaUxefJk0tPTOXz4sE0kI4BH08Yymr5/tL1jxw4CAwPp3Llzpba9InlYXRQKBXl5eVy5\ncoUTJ07w4osvsn79ejw8PFCpVMyfP59+/frh5eVl5TsqXx5WH0ufKSmz1YEDB+jRo4fVDCOAYsaM\nGe9Y7dsFFUpaWhr29vaS11edOnXo0KEDderUoW7duhQWFhITE8PQoUOLlSazcPnyZbZu3crx48fp\n27cv3333XbG6mtWRv6uNZWS0YcMGlEolqampTJgwgeDgYHbs2FGqjtWJx+03ly5d4tatW8ybN49N\nmzbx+uuv06FDh8q+jXLnUXWxpAH08vLi1VdfZfjw4ZIDTtu2bXF2diYkJMRm1qsft99cuHCB27dv\nM2/ePPbs2UNkZKRVq9eIaVUbZNu2bcyaNQs7Ozv0ej1Dhw5l9OjR1K9fH6PRiEwmQyaTkZmZSWho\nKIGBgSxZsqTYdX7++WciIiJo0KABq1evtong5fLQxmg04u/vz+XLl+natSsffPCBTUynloc2Z86c\n4fPPP2fbtm00a9aMjz76qNoX8P27upTmwWxrYVHl1W9WrVrFd999h4+PD0uWLLF6phwxcrQxjhw5\nwvTp0xkzZgyTJk3Cw8ODpUuXcvv2bTp27Iijo6P0o3VycsLV1ZWoqChCQ0Np0KABOp2OgoICVCoV\nSqWS4OBgZs6ciZubm7Vv7bEpD20KCwsxmUzo9XrefPNN5syZQ7169ax9a4/N42pjKQNXt25dVCoV\nAwcOZNq0adV+luFxdGnYsKHUZywhQBZDYSuUR7/R6/XUq1cPlUrF4MGDmTFjRpVYmhDG0UawvKHF\nxcWxd+9eoqOj8fT0pEOHDqjVahISEsjNzaVr165FfqDNmzfn4MGDpKSk8OSTTxIZGYlaraZVq1Y4\nOztLoQrVmfLURqVS0bZtW7p06WITa0XlqY1SqcTPz4+mTZtKoR3VlfLuM61bt7apONiK6DdeXl6l\n1vy1Brbzv1XDsfzwLl68iJeXV5Ef4ssvv4y/vz/x8fGcOnUKmUwmuVI7ODgwcuRIkpKSCAsLQ6VS\n0a9fv2qdzux+ylsbW6K8tbGVUVF562JLhhFqRr+xrf+xGkRiYiKTJ09m6dKlUgV7gE6dOvHLL79w\n/fp1wOxOXqtWLfr27YvJZCIxMREwe9JpNBpWr17N6NGj6dKlCz/99BPffPON1dM2PS5Cm9IR2pSM\n0KVsaqI+wjhWM/7880/Cw8MZO3YseXl5bN68mRdffJHExERMJhPBwcE0adKEpUuXAv9zre/Vqxdy\nuZz09HTpWtnZ2Zw7d46PP/6YuLg4KV6vuiK0KR2hTckIXcqmJusjvFWrEQUFBURGRqLRaJg3b57k\nPdqnTx/c3d2JjY3FYDCwceNG3njjDbZv3063bt2k80ePHs2ff/7Jjh07rHULFYbQpnSENiUjdCmb\nmq6PGDlWIxwcHFCr1QwdOpRmzZqh1WoBc2c9c+YMRqMRhULBgAEDCAsLIzIykuTkZIxGI9evXyc9\nPZ0XX3zRyndRMQhtSkdoUzJCl7Kp6fqIkWM1w5IEHP6XiWLcuHHodDpWrVoluYsXFBQwZMgQTp06\nRZs2bfj999/x8PBg7dq1PPnkk1a+i4pBaFM6QpuSEbqUTU3WR6SPq2ZYOqrJZCriMTZgwABpu9Fo\nxMHBgTVr1nDixAmOHDnCsGHDGDJkiNXaXRkIbUpHaFMyQpeyqcn6CONYTbEsfGdkZPD777/j5+cH\nmF2sCwoKcHBwwN3dHXd3d3r27GnNplY6QpvSEdqUjNClbGqiPmLNsZpiKYdz8OBBHBwc6NixIwDv\nvfceo0aNKuIlVtMQ2pSO0KZkhC5lUxP1ESPHaorlTe7w4cP069ePxMREJk6ciEajYdmyZTaRveXv\nIrQpHaFNyQhdyqYm6iMccqoxGo2Gzp07k5GRgUqlYsaMGURGRlq7WVUCoU3pCG1KRuhSNjVNHzFy\nrMao1Wo8PT0JDg7m3XffRa1WW7tJVQahTekIbUpG6FI2NU0fMXKs5liqjQuKI7QpHaFNyQhdyqYm\n6SMccqo5NaWj/h2ENqUjtCkZoUvZ1CR9hHEUCAQCgeA+hHEUCAQCgeA+hHEUCAQCgeA+hHEUCAQC\ngeA+hHEUCAQCgeA+hHEUCAQCgeA+hHEUCAQCgeA+/h/ILDbEl7JzvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf546f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the LTD Returns\n",
    "port.performance.dly_details.plot_ltd()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf31ce80>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WFhbiww8/xPTp09G7d28AwIABA5CZmYmLFy+KN48NHDiwQe0yadIkpKeni+2iUCiQl5en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\nH/lJyuz8YRuEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1207f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the Monthly Return Histogram\n",
    "port.performance.monthly_details.plot_hist(figsize=(7, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x128064a8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T05P//ve/rFq1ivj4eGrUqMGCBQvYvn27xo+1SqXS6nmqW7cuGzZswNvbm23btinT4Fy+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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1260d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See the range of returns for the positions\n",
    "fig, ax = plt.subplots(1, 1, figsize=(9, 3))\n",
    "port.positions.plot_ret_range(ax=ax)\n",
    "plt.title('Position Return Range')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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