dalejung/faber.ipynb

## faber.ipynb
{
 "metadata": {
  "name": "faber.ipynb",
  "notebook_path": "https://gist.github.com/3357531/faber.ipynb"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Faber\n",
      "======\n",
      "Ah, the vaunted faber demo\n",
      "\n",
      "Will be replicating the faber.R quantstrat demo in python while verifying against the R version"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import pandas as pd\n",
      "import trtools.api as trt\n",
      "import trtools.charting.api as charting\n",
      "import numpy as np\n",
      "from pandas.io.data import DataReader\n",
      "import trtools.rpy.api as trpy\n",
      "reload(trpy)\n",
      "import trtools.util.testing as tm\n",
      "r = trpy.robjects.r"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Setup Data\n",
      "===========\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "require(quantstrat)                                                                      \n",
      "                                                                               \n",
      "\"kkj                                                                                                                        \n",
      "# Set initial values                                                                     \n",
      "initDate='1997-12-31'                                                                    \n",
      "initEq=100000                                                                            \n",
      "                                                                                         \n",
      "# Set up instruments with FinancialInstruments package                                   \n",
      "currency(\"USD\")                                                                          \n",
      "symbols = c(\"XLF\", \"XLP\", \"XLE\", \"XLY\", \"XLV\", \"XLI\", \"XLB\", \"XLK\", \"XLU\")               \n",
      "for(symbol in symbols){ # establish tradable instruments                                 \n",
      "    stock(symbol, currency=\"USD\",multiplier=1)                                           \n",
      "}                                                                                        \n",
      "                                                                                         \n",
      "# Load data with quantmod                                                                \n",
      "#getSymbols(symbols, src='yahoo', index.class=c(\"POSIXt\",\"POSIXct\"), from='1998-01-01')  \n",
      "### Download monthly data instead?                                                       \n",
      "### GSPC=to.monthly(GSPC, indexAt='endof')                                               \n",
      "getSymbols(symbols, src='yahoo', index.class=c(\"POSIXt\",\"POSIXct\"), from='1999-01-01')   \n",
      "for(symbol in symbols) {                                                                 \n",
      "    x<-get(symbol)                                                                       \n",
      "    assign(paste(symbol,'DAY', sep=\".\"), x)                                                  \n",
      "\tx<-to.monthly(x,indexAt='lastof',drop.time=TRUE)                                     \n",
      "    indexFormat(x)<-'%Y-%m-%d'                                                           \n",
      "    colnames(x)<-gsub(\"x\",symbol,colnames(x))                                            \n",
      "    assign(symbol,x)                                                                     \n",
      "} "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "pausing 1 second between requests for more than 5 symbols\n",
        "pausing 1 second between requests for more than 5 symbols\n",
        "pausing 1 second between requests for more than 5 symbols\n",
        "pausing 1 second between requests for more than 5 symbols\n",
        "pausing 1 second between requests for more than 5 symbols\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Python data load\n",
      "symbols = [\"XLF\", \"XLP\", \"XLE\", \"XLY\", \"XLV\", \"XLI\", \"XLB\", \"XLK\", \"XLU\"]\n",
      "dfs = {}\n",
      "for s in symbols:\n",
      "    dfs[s] = DataReader(s, start=\"01/01/1999\", data_source=\"yahoo\")\n",
      "panel = pd.Panel.from_dict(dfs)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# groupby into monthly and use cython funcs to aggregate\n",
      "monthly_grouped = panel.downsample('M', axis='major', closed=\"right\", label=\"right\")\n",
      "monthly = monthly_grouped.df_map.agg({'High':'max', 'Low':'min', 'Close':'last', 'Open':'first','Volume':'sum'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data Checking\n",
      "=============\n",
      "Just double checking that we're working with the same data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# verify we got same data\n",
      "for s in symbols:\n",
      "    r_df = r[s+\".DAY\"].to_py()\n",
      "    # one difference is that quantmod returns daily data indexed at 4 pm\n",
      "    r_df.reset_time(0,0)\n",
      "    tm.assert_series_equal(r_df[s+'.Close'], panel[s].Close)    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# verify we got same MONTHLY data\n",
      "for s in symbols:\n",
      "    r_df = r[s].to_py()\n",
      "    # one difference is that quantmod returns daily data indexed at 5 pm\n",
      "    r_df.reset_time(0,0)\n",
      "    tm.assert_series_equal(r_df[s+'.Close'], monthly[s].Close)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print monthly.XLU.head()\n",
      "print r['XLU'].to_py().head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "            Close   High    Low   Open  Volume\n",
        "1999-01-31  29.48  30.73  29.20  30.02  988800\n",
        "1999-02-28  28.67  29.80  28.22  29.66  502900\n",
        "1999-03-31  26.69  29.00  26.69  28.44  412200\n",
        "1999-04-30  29.27  29.83  26.80  26.80  350000\n",
        "1999-05-31  29.61  30.34  29.05  29.36  907900\n",
        "                           XLU.Open  XLU.High  XLU.Low  XLU.Close  XLU.Volume  XLU.Adjusted\n",
        "1999-01-31 05:00:00+00:00     30.02     30.73    29.20      29.48      988800         18.83\n",
        "1999-02-28 05:00:00+00:00     29.66     29.80    28.22      28.67      502900         18.32\n",
        "1999-03-31 05:00:00+00:00     28.44     29.00    26.69      26.69      412200         17.16\n",
        "1999-04-30 04:00:00+00:00     26.80     29.83    26.80      29.27      350000         18.82\n",
        "1999-05-31 04:00:00+00:00     29.36     30.34    29.05      29.61      907900         19.04\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Init Quantstrat/blotter"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "# Initialize portfolio and account                                                       \n",
      "initPortf('faber', symbols=symbols, initDate=initDate)                                   \n",
      "initAcct('faber', portfolios='faber', initDate=initDate, initEq=100000)                  \n",
      "initOrders(portfolio='faber', initDate=initDate) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "R Strategy\n",
      "=========="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "# Initialize a strategy object                                                           \n",
      "stratFaber <- strategy(\"faber\")                                                          \n",
      "                                                                                         \n",
      "# Add an indicator                                                                       \n",
      "stratFaber <- add.indicator(strategy = stratFaber, name = \"SMA\", arguments = list(x = quote(Cl(mktdata)), n=10), label=\"SMA10\")                                                                                                                                            \n",
      "# There are two signals:                                                                 \n",
      "# The first is when monthly price crosses over the 10-month SMA                          \n",
      "stratFaber <- add.signal(stratFaber,name=\"sigCrossover\",arguments = list(columns=c(\"Close\",\"SMA10\"),relationship=\"gte\"),label=\"Cl.gt.SMA\")                                        # The second is when the monthly price crosses under the 10-month SMA                    \n",
      "stratFaber <- add.signal(stratFaber,name=\"sigCrossover\",arguments = list(columns=c(\"Close\",\"SMA10\"),relationship=\"lt\"),label=\"Cl.lt.SMA\")                                                                                                                                  \n",
      "# There are two rules:                                                                   \n",
      "# The first is to buy when the price crosses above the SMA                               \n",
      "stratFaber <- add.rule(stratFaber, name='ruleSignal', arguments = list(sigcol=\"Cl.gt.SMA\", sigval=TRUE, orderqty=1000, ordertype='market', orderside='long', pricemethod='market',TxnFees=-5), type='enter', path.dep=TRUE)                                                # The second is to sell when the price crosses below the SMA                             \n",
      "stratFaber <- add.rule(stratFaber, name='ruleSignal', arguments = list(sigcol=\"Cl.lt.SMA\", sigval=TRUE, orderqty='all', ordertype='market', orderside='long', pricemethod='market',TxnFees=-5), type='exit', path.dep=TRUE)                                                                                                                                         \n",
      "# Process the indicators and generate trades                                             \n",
      "start_t<-Sys.time()                                                                      \n",
      "try(applyStrategy(strategy=stratFaber , portfolios='faber', verbose=FALSE))                        \n",
      "end_t<-Sys.time()                                                                        \n",
      "print(\"Strategy Loop:\")                                                                  \n",
      "print(end_t-start_t)   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "[1] \"Strategy Loop:\"\n",
        "Time difference of 7.808313 secs\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How are the indicators/signals processed?\n",
      "========================================="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# last run data is stored in mktdata, which is created throughout applyStrategy, applyIndicator, applySignals\n",
      "mktdata = r['mktdata'].to_py()\n",
      "mktdata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 167 entries, 1999-01-31 05:00:00+00:00 to 2012-11-30 05:00:00+00:00\n",
        "Data columns:\n",
        "XLU.Open        167  non-null values\n",
        "XLU.High        167  non-null values\n",
        "XLU.Low         167  non-null values\n",
        "XLU.Close       167  non-null values\n",
        "XLU.Volume      167  non-null values\n",
        "XLU.Adjusted    167  non-null values\n",
        "SMA10           158  non-null values\n",
        "Cl.gt.SMA       8  non-null values\n",
        "Cl.lt.SMA       9  non-null values\n",
        "dtypes: float64(9)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It appears that the mkdata isn't retained between symbol runs. \n",
      "Looking at it, you can see how indicators and signals are processed. Being path independent, this makes sense\n",
      "For now we'll verify against the last run symbol (XLU), maybe later we'll run each symbol separately to grab it's mktdata"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import trtools.charting.charting as charting\n",
      "# explore the data visually\n",
      "fig = charting.Figure(1)\n",
      "close = mktdata['XLU.Close']\n",
      "fig.candlestick(mktdata)\n",
      "fig.plot('SMA10',mktdata['SMA10'])\n",
      "fig.plot_markers('Cl.gt.SMA', mktdata['Cl.gt.SMA'], yvalues=close)\n",
      "fig.plot_markers('Cl.lt.SMA', mktdata['Cl.lt.SMA'], yvalues=close)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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XtWp5Qb3M0Zy3cadUbm6u1+N9bqB98MEHFBQUsGrVKo4dO4bL5WLixIkkJiay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\nDB08lJmvzYR+MHRw6+9/qqvdMvmvvbbrn48+cluGXH01DBn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JYxI6dOjAKUOHct2MGZw2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CwEBkB8qn4PiIiIiE2tzYl0B01Eml0QpImiooZfYdem1Y02r9ZdNREREZHkaYIWMV/qZYOylhdy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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c1ca510>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's try to replicate indicators and signals in python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XLU = monthly.XLU.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mktdata['Cl.lt.SMA'].dropna() # example of what we're trying to replicate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "1999-11-30 05:00:00+00:00    1\n",
        "2000-06-30 04:00:00+00:00    1\n",
        "2001-06-30 04:00:00+00:00    1\n",
        "2006-03-31 05:00:00+00:00    1\n",
        "2007-07-31 04:00:00+00:00    1\n",
        "2008-01-31 05:00:00+00:00    1\n",
        "2008-07-31 04:00:00+00:00    1\n",
        "2010-05-31 04:00:00+00:00    1\n",
        "2012-11-30 05:00:00+00:00    1\n",
        "Name: Cl.lt.SMA"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import rolling_mean\n",
      "from trtools.core.column_grep import *\n",
      "import operator\n",
      "\n",
      "# sigCrossover-like function. Not 100% sure this is the best way to do it.\n",
      "def crossover(s1, s2, op):\n",
      "    res = op(s1, s2).astype(float)\n",
      "    \n",
      "    # nan < False == False instead of nan. \n",
      "    res[s1.isnull() | s2.isnull()] = None\n",
      "   \t\n",
      "    # replace 0/False with nan\n",
      "    res = np.where(res.diff() == 1, 1, None)\n",
      "    return res\n",
      "\n",
      "SMA10 = lambda df: rolling_mean(Cl(df), 10)\n",
      "\n",
      "XLU['SMA10'] = SMA10(XLU)\n",
      "XLU['Cl.lt.SMA'] = crossover(Cl(XLU), XLU['SMA10'], operator.lt)\n",
      "XLU['Cl.gt.SMA'] = crossover(Cl(XLU), XLU['SMA10'], operator.gt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Not using assert_series_equal because the indexes are still a bit different. Quantmod returns at 4PM, which technical is the\n",
      "# exact moment you can know EOD data. the python stuff returns are time(0,0). I prefer having just the date() and not time()\n",
      "# but neither is more correct.\n",
      "\n",
      "sig = 'Cl.lt.SMA'\n",
      "tm.assert_almost_equal(mktdata[sig], XLU[sig])\n",
      "sig = 'Cl.gt.SMA'\n",
      "tm.assert_almost_equal(mktdata[sig], XLU[sig])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = charting.Figure(2)\n",
      "fig.set_ax(1)\n",
      "# R version\n",
      "close = mktdata['XLU.Close']\n",
      "fig.candlestick(mktdata)\n",
      "fig.plot('SMA10',mktdata['SMA10'])\n",
      "fig.plot_markers('Cl.gt.SMA', mktdata['Cl.gt.SMA'], yvalues=close)\n",
      "fig.plot_markers('Cl.lt.SMA', mktdata['Cl.lt.SMA'], yvalues=close)\n",
      "\n",
      "# Python version\n",
      "fig.set_ax(2)\n",
      "fig.candlestick(XLU)\n",
      "fig.plot('SMA10', XLU['SMA10'])\n",
      "fig.plot_markers('Cl.gt.SMA', XLU['Cl.gt.SMA'], yvalues=XLU['Close'])\n",
      "fig.plot_markers('Cl.lt.SMA', XLU['Cl.lt.SMA'], yvalues=XLU['Close'])\n",
      "\n",
      "#hooray looks same."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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P4I03PDtWb59rvekF/WmuDL1KFRsTbk7wayGjrIcecrtPUBpoqho9FhayIz09\nIMeXJT8/n+nTp7Nw4ULuvvtu6tSpQ82aNRk8eDBz586t0OAqS69evQDo3LkzJpOJ9957z2mfXbt2\ncf/991Pvr2Dzdu3acc899zjsM3r0aNLS0kr/n5aWxpgxY5zG8f7779O6dWumTJlCamqqqjFKJBKJ\nROKOjRthwgT45BPo3LmqR6N96tcXv+tZs4RBLJFIAocvFRuVPGOKJfovXXJ7rqA00NQ2ejx/+HBA\nji/Ljh07uHDhAkOHDnW7b0Vk/+Wt+/7777FarYwYMcJpnx49epCYmEhKSgoHDhxQPM+QIUPIzs7G\nYrFw5swZtm3bxpAhQ5z2S01NJS4ujtjYWH755Re++eYbn8avFr3FQ4P+NEu92kdvmrWi15xsxjDU\n4DgZMBhgxgzxswzlc7JKwnfchfH8/DOMGgXvvw/duvll2Kp7lAX7NQJJmzaQni6qO+7dq+4YrXyu\n1aI3vaA/zcGuV8lTr2TwRao4V1AaaGobPdZp2TIgx5fl9OnTNG7cWDHHy98sWLCAUaNG8frrrxMV\nFcX//d//kVluuSwsLIy77rqLVatWsXr1aoYMGUJYWJjDPocPHyYrK4sRI0ZgMpkYOHCgg9dNIpFI\nJNrDWmCFszhOBux2IpcudWioZU42M2PrDAdDzpJgYWn80grDeM6cgdhYeOEFuOUW/407UMU6tEbP\nnjBvnqjqePJkVY9GItEPVbHAE5QGmqpGjyEhxAwfHpDjy9KoUSNOnTpFcXGx2319JSwsjISEBHbt\n2sXp06e59957GTFiBGfPni3dx2AwMGbMGFJTU1m2bJlieOOyZcvo0KEDbdu2BWDEiBGsWLGCSypc\nqr6it3ho0J9mqVf76E2z1vUqGkBnnTdVZChdugRxcTB4MDz4oN+GVmlU1xy08jzwgOgMcMcdYHUT\nhaX1z3V59KYX9Ke5qvQqPhsV+igqhTMqFRjJVXHNoDTQ1Da37PFXXpe/jy9LTEwMtWvX5sMPP1R8\n35Mqjp5gMplISEjg3Llz/Pbbbw7v3XLLLZw4cYLff/+dnj17Oh2blpbGgQMHaNasGc2aNePpp5/m\n1KlTrF27NiBjlUgkEknl420lRkuChfjO8R4lvU+eLH6++KJXl5SUw5cV+eeeE43A77kHCgr8NyaJ\nJJhwer6ZzWT27Qtms+L+lU3kokVgcf8MXfTAIq8KjARlHzS1jR5dlav19fiy1KtXj5kzZ/LYY49R\nq1Yt+vc0ZdzoAAAgAElEQVTvT0hICJ999hlZWVkelcwNDw/n4MGDtGnTRvH9559/nttvv51OnTpR\nXFzMq6++SoMGDWjXrp3Tvh9//LGicbhjxw5+/fVXvvvuO5o0aQKIypHPPPMMaWlpxMbGqh6vNwR7\nfHAg0JtmqVf76E1zddXri1fIEwNhyRJYuxa+/NLzXlzBgtp7XFmhTL6EdRoMsHChMNAefFD0olPK\nwqiun2tv0Zte0LZmp+eb1UrYXz/9hS89z5SOK2l0X1gIeXlw6BDUrDmWF1+E2rXBaBSvCwwBPqrw\n/EH7qFXT6DGQx5dl0qRJXHnllcyaNYtRo0ZhMpno1q0biYmJrF+/3sFQMplMZGZm0rNnTwwGg8N7\nSUlJxMfHc/78ed555x2io6OJiorip59+okWLFtSoUYNx48Zx+PBhatWqRefOnfn0009Lx1r2XO3b\nt3cYY8l7aWlp3H333URFRTm8/9RTT9GrVy/Onj1L/fr1VWuXSCQSSfXAFGrCitWv/Xq2bYOEBMjO\nhgYN/HbaSkftJKy65MPVqgUrV0L//jB1Kvz731U9IonEf5iTzVi3WpmXPM+v5e2drrHBypPHn/Tq\nGna7MMD27RPFk/btg6ysv/HuuyJHNDwcWraEVq3EvwsLwWYTr+8YhzsDDXsl4upylTwMiRt8uR9b\ntmzx30CqCXrTLPVqH71pro56TXNM9tBbQ522T58+XdXxzz77rNt9du2y25s0sdszMz0dXfAR9PcY\n7NO9+O49fdpuv/Zau33ePOf3gl6zn9GbXrtdu5pJwk4f7CSV+ZsAe7ywi9wev3TpUrfbTHNMdvpg\nN80xOV67/HXtdntxsd3+0092+/LldvukSXZ7nz52e716dnvz5nb7rbfa7Y8+are/+qrdvn693f7r\nr3Z7QUHF44sHt3PtoPWgSSQSiUQiUcZaYIUi748fNGhQhe/v3i0KgrzzDgwc6P11JIGlYUPRG+3m\nm6FFC1Do4COR6I7y4ZFK3jJLgoWki0kkJSQ57BvWKIyQ0MuFBnfvhmeeER6ymBi4/noRVdC1K/yV\nSRQQvCoScuHCBbp3706XLl1o3749CQkJgAjha9GiBV27dqVr165OJeIl2kfL8dCu0JtmqVf76E2z\n3vRCxZr37IFBg+D110GhzWa1RMv3uFUr+PhjePRR2LHj8nYta1ZCb3qhempWKm6ktuBRpJfXLGk9\noqYB9fn081gSLBw7JnI8Bw6EYcPg119hzRoRUjxggG/GWaSKfbzyoIWFhbFlyxaMRiOXLl3i5ptv\nZtu2bRgMBiZNmsSkSZM8Ol+DBg0CVg1R4jkNqnOigUQikUi85qefxORj3jxQ0YlGEiR06QIpKWIi\nuX27aGwtkQQjSsWNfCl45EuhD6WiQGfOwKuvwoIF8NBDwnNWr57Xw/Mar8vslxSuKCgooKioqHRS\nby/Xk0sNf/zxB3a7XZOvLVu2VPkYPH398ccf3n4sdNeTA/SnWerVPnrTrCW9aqsQKmnev18UnXjh\nBbj/fv+Oq6rR0j12xeDB8K9/iZ9nzuhDc1n0phe0odmcbGbG1hkO/cNKCh2p6R/mi3FXYtjZ7cL7\nPHYstG4tvGVffy2ehf4yzmw2G5szM5k9YQKfq9jfawOtuLiYLl26EB4eTt++fUurBi5YsIDOnTsz\nfvx4hwbLJSQlJZW+tPDBkkgkEokkWPB2JXnPHujXD2bOhDFj/DsmSeXx2GMiPHX4cFE1TiIJdpTC\nDy0JFqb3nu5VdUVPDL4zZ0TLis6dxXMvKgoOHBCtK/zZceO5uDievPpqUgcPxrZkCVepOMZg98bl\nVYb8/HwGDhzICy+8QPv27Ut7b02bNo3jx4+zZMmSyxczGLzysEkkEolEohecQnbMZlIKCxl7/nzp\nJsMMA2SBfYvv36lffgmxsfDKK9rznFUbDAaSgCQ/zJGKikSoY6NGooedzCCRBAslxTpMA0ylxlfJ\ns4w+YJ9++fNf4swppeRvBITLy8X5lLaVPd+5cyJnc8UK2LpVLGj87W/Qt69yP0FfsdlszI+KIrGc\np89AxVGHPg+lXr16DB48mF27dtG0adPS3l8TJkwgJyfH19NLJBKJRKIbzMlmHl7+sONGq5XcCxcC\ncr3Nm+Guu+Ddd6VxphVq1hSTz6+/hlWrqno0Ej1TvviHJ8U6nDCZHH+6QMn7duYM7N3bnlGjICIC\nUlNFxdMjR2D1arj11sAYZwA7s7OJycvz+DivhnPq1KnS8MXz58+zceNGunbtyokTJ0r3+fDDD+nY\nsaM3p9cUWgnjVFthRyt6PUFvmqVe7aM3zcGk11pgpaCoIODXycrK4qOP4L774L33RN6Slgmme1wZ\nXHEFjBuXxbRpUBD4j1NQoLd7DMGv2Zf8MCcsFnLj48Fy2fCyJFhYGr/UKRSyuLgGX38Ns2aJFhSt\nWsHu3V3o2VOEMK5bB6NHg9lc/iL+Z0d6Oj3KxRtnqTjOqyqOx48fJz4+nuLiYoqLixk9ejS33nor\nY8aM4bvvvsNgMNC6dWsWLVrkzeklQYjSH5kvlXMkEolEUnVkZopV5LVroVu3qh6NJBB06QJXXy3C\nHB95pKpHI5EoYwo1YcXqlB+mhkuXoEuXsSxdKqotlrwOHEjkvfdE+OK0adCrF8ydu4JHH03yvwA3\nnD98GKMXx3lloHXs2JFvvvnGaXtaWpo3p9M01bFHhRNmM1itou5ymZULJaNNE3o9RG+apV7tozfN\nVanX24UuU6iJizUvenzc+fPw5JOwdWsfNm+G667z+BTVEr19pkFoNplEfmF8PBi9mSVWI/R6j/1J\nVSy8u2oYrVSRtnnztqxdKyoufvEFfPWVaNB+ww1w7bUwahS0awfXXFOTOnVwe77KoE7LltjAwUjr\no+K4AEVcSqor5mQzhqEGh+o3EolEEuyoDcMONrwNAbIkWEi4OcGjY37+GaKj4dw5kZ+kF+NMz9xw\nA/TsCa+9VtUjkVQH1D6PKuN5W2IoFhXBhg3wwAPwyiuTeekl8f7kyZCbCz/+CMuWQWKiqF7asSNO\nxlnZ81U2McOHszMkxOPjpIEWYII9Prg81gIrnC2XwGmxwPTpDt4zzGaYMcMpgHfq1KmVNNLgobrd\nY1+RerVPddTsS65D0Os1mSA01GmzJyvCy5bBLbcI79l//gNff53lv/FVA4L+HgeAEs3PPw8vvywK\nJWgZPd9jf6BUnh6UjbHyz1vFxX0X80S1HDgA//gHXHWVML6io+E///mCzZvFZ/r226FhQ69OXan0\n6NWLHRERDtuyVBwnDTSJd1itjj//omyhGIlEIgkEiqu3ycmVk/HtR8zJZpK3JTtsM4WaCK1Zzhiz\nWCDB2VumZkX4+HGRDD9rFmzaBA89JMuu64127WDoUPj3v6t6JJJgRrHCotlM7rhxbp+tiov7LuaJ\n7vjlFxGSe9NNoiLppk0ilPHJJ6FBA49OFRQYjUYuRkczKzKSTSEh2FQeJw20AKO3mOiqivGtSvR2\nj6Ve7RPMms3JZsaljnMOwy4o8HgiUIJfK415gFLFRkuChUUP+F5g6/x5mDMHOnSAZs1g1y7o1Ony\n+8F8jwOB3vSCo+bnnoO334Zjx6puPIFG7/c4IHhpZAGKZfFdNYwGOHgQxo2DmBhR3ObAAXjhBcdQ\n7Op6j2euXs2kvXsxZGQwf/x4Puvf3+0x0kCTBDXVNa9EIpEEBp/66Ligqgw0V/iSK2G3i74+110n\n8sxycoTnxE3rIEmQ4q/vwBYt4MEHRWiYRKIapd5jCqGLioaXQnqMUo+y77+HDz8cSvfuohz+gQNi\nQaF+/cBIqiqMRiP9Bg0icfFiZm3Y4HZ/aaAFGK3ERKv1jPky0VET5xyMaOUeq0Xq1T560+yqjUh1\n46uvRJ7ZCy9ASgq8/75YiVZCb/e4Wuo1m8l9+GH3+7mgvOapU0XPu19+8XFcQUq1vMc+EnDNSjUI\nFLxqSoZXRdjt8NlnMHCgKIXfpMn/OHAAkpIqNsy0co/V6JAGmkQVTiu6Kju6e0J1MMYkEok+qE7P\no7w8GDMGhgwRIUK7dkE1jQTSL0qFYKxWv3aZbtQIHn9chL5K9I1SUY+Kwg8d8GH+V1gI33/fia5d\n4amn4L774Lff4IEHjlbL/LJAIg20AFNd42XdorSqgv9z0GZsnYFhRnBntGv2HrtA6tU+waS5MjxZ\ngcydtdlsZG7MZMLkCQwYN4AJkyeQuTETm01tqnhF54aZM0VuWYsWokHr+PEisd4dwXSPK4Og1+ui\nEIwvKGl+6in46CNRnlxrBP09DgDealYq6mFJsLA0fql7L5iL+Z+7Q15+WXj0v/22K3PmwA8/iAWl\n2rXVh3Vr5R6r0SENNIn/8KGkqqvyrtN7T8c+3e6wrTqGHkkkEu+oTp6s8sQ9GkfU6ChiV8ayJGwJ\nGyM3siRsCbErY4kaHQVbvDtvcbEold+uHezdK3LN5syReWYS9zRoAA8/LMJgJfpB7bxJraGkdlEr\nP78eU6ZA69bCs//BBxAfn8odd0ANaYFUiPz1BBitxMuqwmol96+fZVHzYPAk8T/34YeDqpy2ru4x\nUq8e0LJmX3JdPVkcstls5JzMIbdTLoWtCqGkT2kIFLYqJLdTLtQEilSfEoCdO0X56VdegZUrRUEQ\nbxyAWr7HSuhNL7jWPHEirFkDR49W7ngCjbzHrvH3QldFhty5c6Lv4m23waJFD3PxolhEWrkSunXz\n7bpauccyB03ilsrwRvn7wWApaEKm9SYOHRKJphKJRBsE0/MoeVuyk0ffk2dZ9vZs8urlVbxTC0Bl\nMcr8/HqMGgXDh8Ojj8KXX8LNN6sejqQaUFltapo0ERUdZV80ib+4eBEOHmzDgw+KcOuVK0XPxUmT\n5vHqq94tIukdaaAFmGCPl/W38RTp17MJ7HYRxjN7Ntx4I7zFbv7NFGJiRBf53r3hiSdg1Soo8nA1\n2h8E+z32N1Kv9qkqzeWfR4YZBqc8VFOoCeqrSGT3AKWJcUFRgU+l/NPXp1PYvLDina6CGgWuv4ZP\nnRK9q8RK9GNcfTX8/LMoCOJreJDePtfVQa8v7RWUqEjz5MmwfDmcOOHXS1Yp1eEeK+HLwlRlaHa1\ncHDwILzxBtx5pzD6s7L60r49/PgjrF0LcXFwzTUt/DqW6nqPyyNz0CQV4irvK5jYu7c9bdvCHXfA\n77+LFb9nuJLN3MqxY7B/v+iXcepUDq+/DtdfD1u8zOuQSCRVh9LzyD7d7pSHakmwYP/Qrrqcs7eE\n1gx1NgKTk1WHV+/8YeflsEZXhEBko0iHTTYbLF0qyk9ffTVs2iQ8ZqdPhzJzJtStq16DROKKK6+E\nBx6Al16q6pFI/N2eyN+RCGPHjqW4GPbsgUWLxAJRmzbCg79rF4weLSoxjh+/hMmToVkzx2Ml3iEN\ntAATzPGyAWn4qnI/dw8QqxXGjoXNm29lyRJRcerVV6FvX6jJpdL9mjSBW2+Fdu3W8vnnMG2aCN0Y\nOrTyer0E8z0OBFKv9qkKzYF4Hqll9seznRaqEm5OcDYCCwqccmxdEVoYCm4caBRCRIMIAI4cEX2q\nWrUS/csmTIBjx0SO2bBhUKeOWjXq0NvnWm96wb3mKVPg3Xfhf/+rnPH4E6U5hJbusVojq7xmc7KZ\nh5e7752npqS+zSZyFe+5Bxo3hrvvhi++EL0WP/lEPJ+WLhWeskaNVA3XZ7Ryj9XoqBX4YUiqE6ZQ\nE1asfgsfMiebsW61Mi95nsNkp6IVox07xMpe374we/Y6evV6AJvNxs7sbHakp7MPmA3EZGbSo1cv\njEYjAAaDyM+4806RQN+jhzDWnntOrjpLJMFGSkpKwFdX1V7jUvElJ8PQbhcLQ0eOiD5jx47BBl7i\nV5oQMh7CwsSrdm1hPJnN4lWvnng1rXMLtfL2cCnStZVW62gI/bv/kxEjhKdszBjx/LvmGh+FSyQq\naNECRoyA+fOrX2+06lzdVQ1+1Wc2iySxpKTSTZYEC0kXk0hKSHLYtaAANmwQOWSffgrR0XD//SKU\n8cor/TOcysq1rO5IAy3AVLd4WVd/tGqJLPd/xVVxs1msQs+b59hHw14TshK5eyG89ZbwgsEDPBcX\nR+2cHGLy8phYWIgRsAE7Y2OZHxHBxehoalx3XelpwsLESvTYsWKFsEMHePNNuP12ryS5pbrdY1+R\nerVPZWiujAmW6mvUFz/sdvj2W0hPh0WLnuDtt0UoT0SEeF3BCW7ley7dNIYLFyh9nT8v8jHy88Uj\n7ePvt1L0+72ERGVApOsx1Pg5gvW/9OK++4QnozLL5Ovtc603vaBO89SporLe5Mkip7s6E+z3WGnB\nyJxs5uK2iySRVGajs0GlhDnZzMWjF0khpXRbyRzOU06fFvOk118XC0T33y8M96ZNPT6VW3xZmAv2\ne6wWNTqkgSapfEpChMqGCp1pDd8uh8bn+PZbaN5cbLbZbNTOySGx3ETLCPQrLKRfbi6zgPMKS85X\nXglpabBxI/z979C9u3jghIcHRJVEIvEBf3vvPaHW+S4YNsVzdaoovjF8ONxzz/ssWvQ3zp+/7L1v\nzBKOAjER95d675UmXYYZfSALhl53Lzm7Ia9eHoURf5XaL4SQvBAi8iOIvj6a1QuNla5XIimhdWsR\nujZ/Pjz/fFWPRtsoLRhZC6zOrTZUhlErHquExeLS2Pv1V3Hv//MfsSi+aRNERam6vCTA6CIHrTKS\nKF2hlXhZteSW+7+7OGe7XRhRvPMlNHkPRg8sNc4AdmZnE5NXcanqmLw8jh865LS95B737y861rdo\nAR07iphpf5bn19s9lnq1T6A1KxUEsSRYmN57uvfFP0JDnV1QSkU9/tpmtcLixRATAyHfz2LyzU/z\n4Ydw4IBo4tu8+TGm3xfH/KgoiI1l4pIlrAQmAsTGMj8qiufi4ir00q1euJq9y/eSMTKD8RfG02ZT\nG8ZfGE/GyAz2Lt/L6oWrvdPqB/T2udabXlCvedo0WLiweuailSWY7nEg5pjlz2kKNVHL4uxnURNC\nePw4fPDBMKKjxWNz715YsiT4jbNguse+IPug/YXSF6jW45crA6eHQMnkqMwkSXHS9df7Z+pexX33\nicqMxvFD4KpXMNV2TBbbkZ5Oj8KKM+1jCgvJ//FHp+1l77HRKK6zfr0oNjJsmChhLZFIKp+AFARJ\nSHAMmVbg4kX4pbg/4y+8TsuWIsciMRGGD09nzhzo3FnksgI0a9as1Hvf76/QarjsvU/MzSU0J4c5\nW+c4FRgxhZoIrRkq9jcaGdR/EItfWsy0B6ex+KXFDOo/qDR3ViKpaiIj4b77YO7cqh6JD5jNZPbt\nq7rKaqBRmmMq9VYs+6yoCKXiH5YEC6M6jnLat6IQwuJieOcd8axr2VJUX5wzx7HyoiQ40IWBVpVo\nJV5WCaeHgMVC5PTpTpOk8oZc/hELX9/5EZ0bHObKK+Grr+Dca18orp6fP3wYd9MYIxCan69qzF27\nigav11wjHlAbNqg6rEK0fI+VkHq1j2Y0JyRw6lcLaWkibDE8HLKbLeTa2WP46Sf48ENRVKhNm1ZO\nh/5fq1aqvPf8UehkaFoSLCTcnOC0fzCVnNbMPVaJ3vSCZ5oTE0V0ybFjgRtPQLFaCfvrZ2Vgs9nI\n3JjJhMkT6DCwAxMmTyBzYyY2m83lMUq9FS0JFhY9sMjt9awFVgqKCpy2e3KP9+0TxdfeeQc++ww+\n+aRTpea9+gOt/B3rsg9aZYUuStQzduxYioqE9+r++0UZ6YMHryE1VXizKiofXadlS1w/7gQ2oCA3\nV/XKWe3a8OKLkJoK48fDpEki0V8ikVQtSqE53lT8KigQpepXrBjJ1VfDf/8rDLH9++HBB9/lH/9w\nrEimdA213vuW/6tRJXlzEok/ad5cVD2eNauqR+Kequ7hGvdoHFGjo4hdGcuSsCXsvWkvS8KWELsy\nlqjRUcQ9Gqd4nGJvRdQt3LjytKk59sQJ2LKlLz17ipL5O3ZAp05uD5NUMZoz0IItdLE6xsv6UgK1\n/O//f/+DGTOEK/1f/xKNDQ8ehHvvXUPfvu7PFzN8ODtDKu72uiMkhHolcUkecNtt8N13cOgQ3Hij\n6HzvTW5adbzHviD1ap+q0qw02fDE67Rnj1hwadECFiyAqKi9f+VaiKquriqSKT3z1Hrvb6wTGfCm\n2YFAb59rvekFzzU/+6zou/fbb4EZj79wFSKd6+frKC3422w2ck7mkNspl8JWhZeb0YdAYatCcjvl\nknNCOfRZsbeiSlx55V3d4+JiUfBjxAi47jr488+6fPMNPPkk1Kzp1RCCAq38HcsctBJmzLicWFCC\nUvK4BPBPGM6BA/Doo9C2LRw9KrxnX30Fjz3mWUPDHr16sSMiosJ9dkRE0OzZZ53zT1Tc40aNREnt\npCT4xz/gppuE69+fRUQkEkngsNtFpda0tDEMHCg88l98AVlZ0LnzbrxN9VLtva9Xz7sLSCRBRuPG\n4jt65syqHknlo2SMKS34Z2/PJq9exaHPefXyKLYUO21XvfhtMomiRx4ef+4cZGfD7NnQrp1YrOrX\nTyxCP/HED7Rsqe7ykuBAcwaaUhIm06c7z7gLCiolVlkr8bJqCQvrzrBhwtBp1Ah++knEO3fo4N35\njEYjF6OjmRUZyaaQkNIJkw3YFBLCrMhICqKjCVHysqm8xwaDcPt//z088YQwLPv2hc8/VzdGvd1j\nqVf7VAfNRUWwZg3ccAM8/TR06rSb334TkxNPGz0r6VXtvW/fXvG9YG/GWh3usT/Rm17wTvOkSfDJ\nJ/Dzz/4fT6CJVLmfWmNMaZE3fX06hc0rDn0ujCikU71OTt4y1YvfFguRi5zz0soff+wYHD7ch7//\nXeTXN20qFpp//x2WLRMRQo88IiQEU/6rL2jl7zggOWgXLlyge/fudOnShfbt25OQIFyuf/zxB/37\n96dt27YMGDCAs2fPejxgf6CUhOkrVVmmv7pw9qwwbpYvH02fPpCbK3qq+KPz/MzVq5m0dy+GjAzm\njx/P/W3aMH/8eAwZGUzau5eZq9WXqq7ovtWsCSNHwo8/inCoMWNgwABRVEQikQQPmzfDtdfCK68I\n7/cPP0CXLruVFp2dUGs4qfbet3IuMALamRBJqi/ezFPq1xdGmpseydUatRUWlRZ5d/6w83JYoytC\nIP+SusJlrlB6fly4IKIFJk8WOWQdOkBGhghhfPNN0Wz6yy9Fbn+PHs6BY5LqhccGWlhYGFu2bOG7\n777j+++/Z8uWLWzbto0XXniB/v37s3//fm699VZeeOGFQIzXgcoyknwp06+VeFlXFBfDu++KydKl\nS3Dnnc/w5JNwxRX+vY7RaKTfoEEkLl5Mu9GjSVy8mH6DPC9VrXTfyn+OatUSBtq+fcKzNnw43HUX\nfPut8jm1fo/LI/VqH79qNptJMRj8ElJutwujbNQokWO2fTvExorm0kooGWNKEx8lvT5576sBevtc\na0mv2rmPkmY1xz75JGzd6vo7L5D4Mq/L9eG6ahf3QwtDoWIHGhRCvVq+hz7b7SIK6ZVX4PbboUkT\nERBmMsHbbwtP2eOPZ/HUU8IgCwvz+ZJBj1b+jgOWg1YyKS4oKKCoqIgGDRqQkZFBfHw8APHx8fz3\nv//15tQeEWwFQfTGvn0ilPHtt0U/oTffhMjIBlU9LI9x9TkKDYWHHxb5dP37w+DBwmDbt69yxyeR\nVBfMyWYMQw2OK9FWq5g4lVmJNoWaoL7rBvZKnD8vvNqpqaIK2aBB7leIffVilffe/6t/fyfvfbCH\nMkq0hVI/LE9QM2+64goRKjx+vHAiBQq1oYa+GG3+Xshvf1V7Qo5VvCgTkhdC324qqqCVobhY5Ipl\nZsL8+aKiZqtWMHCgaCI9fjwcPizya6dPFwZZLece1RIN4dXtLS4u5vrrr+fgwYM88sgjREVFcfLk\nScLDwwEIDw/n5MmTiscm1a4tGooiYjArI57Uly9Qc7KZi9sukkRSmY1m0fFURQxAZehLSUmp9HCa\n48fFg+OZZ0RScckKti+eU7X3yacJUXIyzJvntqFtWcLCxIrihAli1b5nT9HUc/p0saKllZhotUi9\n2ket5vLPHmuBFc6Wq65mMgnjrFwDe084fBiGDhWJ79u343XhD1dUpLfEe99v0CDF96trKKPePtda\n0WstsEKR83aleYAv35Xjxonqp7NnizprgUDtInv5/UyhJqz1rU4LPJE+XEMtrVq0IuLnCHIr8NdF\n5Efw/BvPO2yz2+GXX4RX8tgxOHlSvE6cEPOp/ftFeOl114nXjTeKfLJrr614IUorn2u1VGe9WVlZ\nHnkAvTLQatSowXfffUd+fj4DBw5ky5YtDu8bDAYMLj5RSQUFDoaNWuPCFyNE7XHmZDOFXxdyPv18\n6TbFh6GLwhNVYShB5XsSz50TIX/jx4u8M3+h9nen+nccGiqanpWloMDrJUGjUZQhHj9eVLm67joR\nC/7UUxX3cpNItIqqZ4/FIp75Xia1bN0qFkQmTxa5MTKvQiIph9lM7sWLIjbfTxgMosBXly6ih+CN\nN/p2Pm/nR+ZkM9atVuYlzytd2AlEW4vQmqHUDq3tdr+QkBCiw6Nht6jWWBjxV6n9QuE5i8iPIPrK\naOx2I598InLCcnJEFWuTSRQ1atECwsNFMaPwcJGr37atLCyudco7pWa4WfnwqYpjvXr1GDx4MF9/\n/TXh4eGcOHECgOPHj9PUVcOZspjN5I4bp+pTqTqp0wesBVYunHbsWFy2OaDNZmNzZiazgX3A7AkT\n2Jx5uXO80hi1Ei9bQlGRKKTRsaPoa1aeYNIbuWiRR54ytTRuDK+9JkINcnKgdesssrP9fpmgJZju\ncWWgN73gfe6KP7Hb4fXX4d57IS1NeOsDZZzJe6x9NK3XalVcePR18bZZM1FwIj5ehBhXhM1mI3Nj\nJhMmT2DAuAFMmDyBzI0Vz4/U4KrnmRJqr6A0d1z0wCLVht/qhavZu3wvGSMzGH9hPKQDO2DZ4Aye\nHSaTTnAAACAASURBVLCXC3mradZMhCoaDGIh+6efRAjjBx+I+UNioljsvfNO6NbNe+NM059rBbSi\nV40Ojz1op06dolatWtSvX5/z58+zceNGpk+fTmxsLKmpqTz77LOkpqZy9913K5+gTJhLqSfKy3L3\nBUUFFJR7KKldBVGLJcFC0sUknouLo3ZODjF5eUxENCi1LVnCzrQ05kdEcDE6mhrXXee36/oLm81G\n9vZs0tenc/j0YVo2asnwgcPp1bOXV+d75hn48094773gX8kOtDezbVvxsJ07V0wiJ04UIQmuChZI\nJNWZyvTUX7gg2l3s2iXyzdq0qbRLSyRBjSnUxMWaFyvtenFx4ntu2jR46SUX+zwaR87JHOFRal4o\nYg0LIW1lGhFvRRAdHs11TQM8PzKZuLJcKDVms5hflktrKDt3tNvhm2/g55/H0rs3GAw2Cv7MJvR/\n6RjoxADq03xkJt1v6cW11xo5c6Y+FgscPmzkom0Q7ZsPgjNdwNqJv43vRf/+Yj6QkgINql9Kvk/I\nfFz/4rGBdvz4ceLj4ykuLqa4uJjRo0dz66230rVrV+69916WLFlCZGQka9asUT6BHz0aSsZYws0J\nJCUk+e0aAG+8MZYR+X1ZWJjrsN0I9CsspF9uLokX4IjxFj75RKyS5OaK14kTffjb3+CBBzw3aHwN\nmXT30OQ04MGYXntNlHjdvl2xhyJQdfHBVflgePbZPowcKb7IPv9cFDHwpBl3daM6x4B7g970gvea\nTaEmrDjnhnjC0aOiGE/LlsJLXbeu16dSjbzH2kcreksWjd1iNhN58aKwFHzAYICFC0VZ9yFD4JZb\nHN+32WzknMwht1Ou4xshUNiqUORq7YZrGjg2J1QKXXRlUKnCYsEpA96VE8AO5HVjyhRITxeLqiNG\nwLWGOJrsy6HP//K4qahQLMQD21bvYG1GBCvqRPN9wdssXiyeT1df/dfiUZ0D0ONjTrzdq9LTHYLp\nc10ZKT7BpNcXAtIHrWPHjnzzzTelZfb/8Y9/ANCwYUM+++wz9u/fz4YNG6hfv77HA64IJZd0ws0J\nTi5p1RP10FDHlZYK6NtzDsPsFXeOv+VkHus+asAbb8CePWKCfs89MGWKWHUaPFgkurujbJjA8+8+\n7xQmoJayD83CVoWX+3aUPDQ75UJNFJONlVi9WniKPv1UJLIGGx7lpZW/7wrNKD3lqqtErsy114oY\nc9k7TaJ1zMlmZmyd4fBctiRYmN57utc5Ijk5EB0tCoKsWVM5xplEoklchD16Q+PG8NZbIsXtzz8d\n38venk1evYrnR7mmXOasnePwrFAMXfQxqsqJku/6v34ePSq+7g1f78PwwUpCQoR38MABmDbNRstD\nOcw6kcttfxlnIBbiBxQX8sq5XAbWzeHxx//NuXMiZPGTT8TCNS1eg2s2yFx0iV8JymAspVwHpR4V\nanvcKJKQ4LRCUzbfrCxFh3dx06WKG1/0shfSM/Jl1q0T5eanThWJ7SZTFrt2icp/N9wgVqKKi5XP\nEfdoHFGjo4hdGcuSsCX8euuvLAlbQuzKWKJGRxH3aJw6bcC0pGluH5q0AKwVl7q228UDbfJkYZy5\ns3+DPj5Y4b4rNaP0hBLNISHCGH/1VdGbKTERPLSrqwVBf4/9jN70grNmJWPM32zeLBay3npLPD89\njTjwxYsu77H20bRek8k5rMVkItePddhjY6FPH/j738W8oIT09ekiQqcirgLKRWWWzDt88baXx+ke\nWyxcSnyeVW9bGDQIOncWEU5fbGxL0f+uYfZsUQTFYICd2dnE5FU8Z4rJy+P4oUN+G68/0PTnWgGt\n6A1YH7RAozbXwd/uVEuChYSbE5y2h+bn466qsxEI/e47x41mM5kDBxISIibr2dmwfDn07g0bNoiC\nGyWo8XjlnMhh0aJFqrRs2bVF1UOza4OuLle7S4pCpafDzp3iQSZxz5AhopTuwYOimEpmZlWPSCLx\nDaXVbl+9ZWX56COxoPXee2Ii6A3Vtdy9ROISJcNLCYultH2Rw7ZRo/w6nAULRITQ669f3nb49OHL\n8xVXhECbxm0cnhX+fH4ocfSoKGT2yitP8+67Yi5z9KhYAOrRw3kBaEd6Oj0KK54zxRQWkv/jjwEZ\nr0RSnqo10Mq5n4OVgnr1cOcIsQEFZZeVAKxWwsqEGFx3nchRio8XBlurVuKZ+vPP6sIE8url8fkX\nn6sac/6lfFUPzYIQ5RCIU6dEc+Y//xSGZUSEqstqJj7Yk4p1SgsKzZvDqlXwxhuiT1xcnOh9ogW0\nco/Voje94JtmJU9WRd6t5ctFQ/hPPxUr9FWBvMfap1rqVTK8PMDfudlGowgJnDULtm0T21o2aglu\n1oIphHq16vl1LEr07t2HbdvE922nTuLXN3bsUjZsEAtAFYUgnj98WN1CfH6+P4fsM9Xyc+0DWtEb\nkBw0v2KxiG6/ASiF7k/qtW/PzpCKrZ0dgJrHT82aouHxV1/B+vXCi9a3L4x5zH2YQGFEIT8eUbd6\nU69WPVUPzR4dezht/uknscJ0881iRfuKK1RdUlN4UrGuon0HDRIrjv/3f3DttRdYsMDRcyqRaA0l\nT5Yr79bChSKccdMm3/ssSSQSN5jNouu0DznXbdqIuiMli47DBw4n5FjF86OQvBDaX9Xeabs/Dcis\nLFHAZNw4MXfJzRX5YY0bn1Z1fJ2WLdUtxOfmyoZlkkohKEMc/VG0wZ80a9WKHW5cSDuAZgrbcys4\nJioK/v1vOHIEWrRTFybw7f++VZUH0v6q9qoemsMHDnfY9v77IgRz2jSYM8fzkvHBHh8ciGqP7oy5\nOnXEiuPo0Yt5/33o3h2+/trvw6g0gv0e+xu96TUnmzHcYnB4zvg7X8RuF2tzL78sPPRRUX45rdfo\n7R6D/jTrTS8ofDf5qQjH7bfDI4+IyocH9x8hIr/i+VFEfgS33HSL03anhRuFqCp3z56dO0W0z/jx\n0KtXFj//LPqOlUwh1X7nxwwf7n4hPiSEesXF/iti4gf09rnWit5qkYOm+MfjY9EGr6/rgpCQEC5G\nRzMrMpJNISGlqyw2YFNICLMiIynAvX3lilq14Pp26sIEKCoXOO1iRaxVi1aqHpol/dAuXRIVJydP\nhnXrRBhmIKmqsvi+5qn40qy3SZNTbNkivjwGD4annoI33/yPT+ORSDxBzefXWmCFP53zzZbGL/VL\nvsj58yLcaMMGUUZf9jiTSKof//ynqO6YmtqZ6PBoIndHEpIbcnkeUwghuSFE7o4k+spoHn74Yfcn\ntViIXLrUIarKVa7agQNw113CSBwxQqSKDBggopTKovY7v0evXu4X4iMiaBYS4pSWE1oz1K/FTiQS\nCAIDraoSuz297szVq5m0dy+GjAzmjx/Pv/r3Z/748RgyMpi0dy8zXSTzRqo8v5owAQ6FEPLbhwz8\nxsLixcLz5mpFLCQkRNVD02g08vvvYgXq++9FY9gbblA5aAXUxgdX14R+JW+ZGmPTnGwmeVsyBoMw\nfvfuhXPn4Nln72L1aseqWMGOVmLA1aIlvUqfX0WjTaGVhtLfrKcLLcePCw99rVqwZQuEh3t0eMDQ\n0j1Wi940600vBHYhtEYNSEuDgwevJtK0mp1v7iVjZIYIJ0oHdkDGyAz2Lt/L6oWrVZ9XzXNm+XK4\n6SbxLDlwAP72N1FF2Zd7bDQa3S/ER0cT8s9/OqXlKLV8qiz09rnWil41OvxXg1UHGI1G+g0aRL9B\ng5zftFggKcnrc/fq2YuItyJEU0cXRJ6L4K6H9nLDDUP49FMbUydl05JhNOYHHuUcsRmZ9LqtF8Yr\nrwSrldUmE7YTJ8jens3tL9wOvwNNIWNqBr169qJOHSOZmeLhNnasCDkqv/qkW5KTnZplmpPNXNx2\nkSSSLu9nNotyl27uvbXA6tBzrlEjWLzGTPH5G5gzZwtvvy0qZLV3DtOXSAKKJ/mW5fFkoeXbb0WF\n04cfFqvvnpbRl0gkwUW9ehAfn8qxY5Po2tXIjBmDqNu3Pn9uOovpNhOD+ivMlbyg5Dnz55/w+OMi\nrPGzz0TZfH8yc/VqbDYbO7OzmZ+ezp4tW+jQty8xw4czqVcvjEaj4oJWVUUESbRNlXvQgg1PK5C5\n2y9X5XWNRqMqj1fDhgUcXBtH56+iWH0hlm18wAYO8BLHYEgszzaO4j5rOKdoi91qxWg0Mqj/IEy3\nmeAaMN1m4pabBpGWZqRDB/jHP0Tftpkz/WOcaSU+WCnM1lpgpaCoXNVLq5Vcb5uBWq20vJTF11/D\n3XeL1cApU4IqvF0RzdxjlWhZr1J/M1OoCc76tz9RejoMHCjWPBITg8840/I9doXeNOtNL/i2+KJE\neePEnGxm/rfP8FF7MxkZwrPVatUZRkUsw5JgobAQzp4VxUQOHIDDh8V6pjfs3g3duolnx9dfKxtn\n/rjHJQvxiYsX0270aBIXL6bfoEEYjaLGoydFkCoDvX2utaJXjQ7pQSuHL398igmv58877ZeSkqJ4\nztULxeqNK4+X0WgkMTERY04OieUevEZgEIUMOp/L0zTmXbaxnHCu7QHt2sHUdhbajoKcHFHe/+ab\nRS+TPn2Cb7JU2USGhYn4iCqiVi2Rl3bvvcJAa98e3n5bJGJLJIHEVX+zsfvG8v/s3Xl4VOXdxvHv\ngAmLzLBKQBDjUq2spkUEFQhYFkUBBaSoEFmstb5WRKtE1AyKBt4qFFHrhpKAIIoVqS8EUTZFlFIF\nRRHqEkFErGyJBEiAef84JmaSSXJmS2bOc3+uK1dgcpbn5oRkfuc8y5z0OWEfv6gI7r4bFi+2xraG\n031aRGpW2YKv9M+Pzp2tmRSXLIGxY/uTkAAnTlizQBd/HD0Ke/ZAgwbQooXVxblFi/J/rlXLKupK\nf7zzDsyYAddfX/25RWqCCrRoyssjuWzXN4+H3OIVoAMofuLFe8BqoAd+3QR2f/MN11ex2v2VCQfJ\nu+ZB/vdv89i2zRo8u22b1V/87LOtIi1aA/PjsX/wDQGK6GAkR6YZJCVBVpb1S27UKLj6apg6FerW\njdAJIiQer3E4TMsLkemys2uXddOhcWNrbGuTJuG3K1pMvMamZTYtL1R/ZpfL6sb84YdPcM89GSQm\nlr8BfOIE7N9vFWrff+//sXWr9fn4cWst0VNPhXPOsW4k/+1v0KZN5efXNXY+p+TVGLRYFGb/tYOf\nfWZrtfsnP/uMZs2sWZYuvjisU5opMRHq1Al594qektqRmgqbNlljAy+8EObPr/lpyCX+eTI95K/J\nZ3rm9JIB7e5EN/nkR3wGsrfegpEj4c9/tp6gBbtch4iEr6a63rlcvgp/fdaqZY3BbtpUY65FKqNf\nm1EW6T7giQcP2lrtvmGo46LC5JT+wclPP11upiZ3opvE2uVn6swt+4LHQ26ZKYXdiW7qNrX3KGzO\nnDk0aWItEv7nP1tj0558MnZmenTKNbbLKXkr6s4YaArrUH9u+XzW/DqjRsGLL0J6enwUZ065xsEw\nLbNpecG8zKblBfMyOyVvXKyDJha762vVadXK1mr3Z3XtGm6TjBbozmNeeh7pl6T7v+h2W4PISsvP\ntyYZKbPv4UX2ulIWvzl2uazFN9etg6eftt7sisSqggIYMQJeew3+9S/o3bumWyRiDs0kKOIsKtCi\nzO4PTbt3rEfdc4+t1e67DR1q63iR5pT+wbbl5ZE8aZL/axWsiReqc8+Ft9+23vjOmhWxw4bMtGts\nWl4IPvPOndC9uzXXzpo1UMV6rzFH19j5nJ430E3FcDLbuWlc3DU6VhZpdvo1DsS0zE7JaydHTBZo\nyXXrllupvW7TujHzQyDSihcxtsPuavdde/SIRNMkFHl5EX/c1awZLF8O06ZZXR9FIiXQTaRgxq68\n9541VvL3v7cmIqpXL3JtE5GaUfamcaAlOSrqIq2neSLhi8kC7YbDh8uN/zm86HCNrdQeDjtPxgKt\nr1XRnSm7q90Xr9lR3ZzSP7giAde6i/A4w4rPDf/3f3DLLdZMjzXF6de4rHjNa7fbdKBizE5mn8/q\nejt4MDz3nLWmYrwu2RGv1zgcpmU2LS9ENnOgMawQ/g2eSNI1dj6n5NU6aHEsLz0P71Ev3nRvua+V\nXe3+8I4d1GvTxm+1e4mOiP7icbuDXrWzUydYuBCGD4cVK6Bjx8g1R5yl7I2DSM7YePAg3HijtXzH\nO+9Y3XBFJD6FM+twTS7SLOJkKtCiLFr9ZYtXu+/dv3/VG1cjp/QPDkbImfPyoOw6ecWPIMq8XvoX\naK9e8NhjMGAArFplrW1XnUy7xvGYN9CU+pXd9CmrsswbNljdGS+7zOrSGGvr9IUiHq9xuEzLbFpe\nsJ+5unqBRJuusfM5JW/cjkFzEt1dcr6IXmOfDzIy/F/zeMgdPRo8v/T9Hz7cquEuvth6kiZSWkXd\nkcJx4gQ8+ihccQX89a/wxBPOKM5EpGqxNiGIiNOpQIuycPrLxuNAW6f0Dw5G1DMXL25eZpHzsWOt\nCUNGjYJHHqm+ddJMu8ZOymv3Z0rZzP/9L1x5JSxaZD1BGzIk8m2rSU66xnaZltlJeUP9fxyOiiYE\niSVOusZ2mZbZKXm1Dlqc09O3+FVdxXWPHvDBB7BgAYwcCYftLbUmhgrlZ8rq1ZCSAh06wNq11mQ1\nIlJzov3eINCMjSJSvVSgRZlT+svaZVpeCJy5OovrNm2siRp8PrjkEvjuu+iez7RrbFpesDIfP251\nox0xAmbPhqlTrXXOnMjUa2wS0/JC6Jmj0UW6OkT6GsdDLybTvq+dkldj0EQMUb8+zJsHAwdakzcc\nPFjTLZLqZHdKfbu+/RYuvdQq/D/8EPr1i+jhRSRGBPO0LB4KlkhSLyapSSEVaDt37qRXr160a9eO\n9u3b89hjjwHg9Xpp3bo1KSkppKSkkJOTE9HGxiOn9Je1y7S8EPlxhqH+EnS54P77oXt3a4xQYWHV\n+4TCtGscD3kjNQvbwYMwaRK0bbua3/0O3nwTWraMyKFjWjxc40gzLbNpeSFAZrfb/zPBPS2L9YJF\n19j5nJI3amPQEhISmDFjBp9++invv/8+TzzxBFu3bsXlcjFhwgQ++ugjPvroI/rH2BTwscqd6Cax\ndmJNN0NqQKBfeOH8EnS5YOZM6/fvmDHVN3GIxLcjR6wZGn/1K9i9G559Fu69F2rXrumWiUjE5OVZ\nswTnVT7Rh2ZsFKl5Ia2D1qJFC1q0aAFAgwYNOO+889i1axcAvireEXpLre+UmprqmP6kFbGTr3h9\nIidw+vUMJNYy164N8+dbXdTuuQcyMyN7/FjLG22xlNeT6aHo30UcXlRqNpjitfMmTy6pyN2JbvIb\nVb0o9dGjMHcuPPCANRHIqlXQrh1AalTaH6ti6RpXF9Mym5YX7GUOtIB9MGsmxhJdY+eL57yrV68O\n6glg2AtV5+bm8tFHH9G1a1fWrVvHrFmzyM7OpnPnzjz66KM0atTIb3tv2YV5BTCvb7dEV716sGSJ\ntU7aaafBn/5U0y2SSMgvzIe9ZV70+azZPEr9bK1qKuz8fHjmGZgxA9q3t2YBvfjiiDdXRGJM2fca\nFRVjek8iElllH0pNnjy50u3DmiTkp59+YujQocycOZMGDRpw88038/XXX7Np0yZatmzJHXfcEc7h\nHcFutRzrfbvtckr/4GDEauZmzWDZMpgyBd54I3LHjdW80RJM3khP1mGX3TdT//0v3HcfnHkm/Otf\n8M9/Qk5O+eJM19j5TMtsWl4InNnue414fE+ia+x8Tskb1XXQioqKGDJkCNdffz2DBw8GoHnz5rhc\nLlwuF+PGjWPDhg2hHl5EIuDMM+Ef/7DGo33xRU23xvkiNVlHsKp6M5WbC7feCueeaxVp69fDSy9Z\n3RpFREQktoRUoPl8PsaOHUvbtm0ZP358yeu7d+8u+fNrr71Ghw4dwm9hnIvn/rKhMC0vxH7mrl2t\n4UlXXQWHDoV/vFjPG2m283o8AQf81dRTNYAtW6wFzH/7Wzj5ZPj0U3jqKTj77Mr30zV2PtMym5YX\nzMtsWl4wL7NT8kZtHbR169Yxb948Vq1aVTKl/rJly7j77rvp2LEjnTp1Ys2aNcyYMSOUwzub2w2J\nmrFRghBgamQ8Hua4XFZRYMMf/2i9Sb/xRs3sGAmeTA/1htazsaGH3JtuCvk8oRR3hYXw6qvWenh9\n+liTfnz5pbXQtAlT5ouIiMS7kAq0Sy65hBMnTrBp06aSKfUvu+wysrOz+fjjj9m8eTOLFy8mKSkp\n0u2NO+X6meblQXp6jbSlOjilf3Awop450NTI+fnk/vzZDpcL/v53+Pxzaxr+cJh2jSdOnFjutfzC\nfI7sPeL/YqD/2/n5IS9I58n0cNM8/+KusiU5PvkEbr8dWreGxx+Ha6+Fr76CiROhzFxNVTLtGpuW\nF8zLbFpeMC+zaXnBvMxOyRvVMWgiEl/q1bPGo02dCmvX1nRr4sf3339f7rVorF1Y9mlZfmE+hcf9\ni7u89Dyevv7pkr8fPGh1WbzgAhgwwHrI+v771nT5I0da11xERETiiwq0KHNKf1m7TMsL8ZU5ORmy\ns+H3v4efly4MWjzljYQF3y7Ak+nflTQvPY/0SyL7JLzsBCMVFYFpaTfwzjtwww3W9Xz7bXjwQfj6\na2s9szPPDL8tpl1j0/KCeZlNywvmZTYtL5iX2Sl5ozYGTUTiV9++MH489O8P+/bVdGtiX+HxQmv9\nMRvsTndf9mmZJ9ND5rv+E4yULQL37IG//hV+/Wu46Sbo2BG2b4dXXrGuZe3atk4tIiIiMU4FWpQ5\npb+sXablhfjM/Je/QL9+cPnl8NNPwe0bj3ntCjgpxwH7+9tZO8g12cXorNG2jnfihIulS2HIEGuK\n/K1b4YUXrNkYJ0yAU06x37ZgOPkaB2JaXjAvs2l5wbzMpuUF8zI7Ja/GoMUou3fZRYpF+nvG5bKe\nxrRvD4MHw9GjET183Lpp3k3lujOeVOsk3InuCvYIni/DR0bPDL/XSj8tO3jQmoVx7FiYMWMCXq9V\nTO/YAc8/DxddZF0/ERERcSYVaFEWqJ+pnbvs8cop/YODUR2Zo/E943LB009D48YwYgQcO2ZvPydf\n40DdGSddOYm89Lxy29oqmm0uq7FjB2zdOoDUVGsWxueeg/PPh3vvfZMNG+APf7C9okJEOPkaB2Ja\nXjAvc7zmDefmXLxmDpVpecG8zE7JayfHSdFvhojEqtq1Yd48GDjQWiNt9myopds2fip6g2SraM7L\nA6834Jf27oVFi+DFF60ui1dffQGjRkGvXlC/fvFWQ0JosYg4hZNv6IpIxfRWLMqc0l/WLtPyQvxn\nrlPHmn5/2zZr8pCqFrKujryhLNAcLZHsXlpYaP1bL1gwgjPPhJUr4c474bvv4NlnranyfynOak68\nf08Hy7S8YF5m0/KCeZlNywvmZXZKXo1BEzFMqIXNySfD0qWwfj3ccUfVRVq0lZ1yPp4lJyezebNV\n/LZubS0Uft55W/n2W1i40Hp6WadOTbdSREREYoW6OEaZU/rL2mVaXoitzOEUNo0awZtvwqWXwt13\nw7RpgSejiKW81SGYvIcOWVPfb99uPZHctg02b76BvDxIS7MK4LPOAq93E2734Og1Oky6xs5nWmbT\n8oJ5mU3LC+ZldkperYMmIkFp3BhWrLAKtUmTqudJWk11Z4zUebdvh0cftcaOJSVZhdgrr1gzY/bt\na828+PXX1mLSZ50VkVOKiESMZpYWiT0q0KLMKf1l7TItLzgvc9Om8NZb8MYbgee3iHTeck/9PB6Y\nPLnctIXhFFSB9rX7tDFQ3u+/t54ynnuuVZht3251Df3hB/j4Y2vyj4cesoq1Ll3ibxFpp31PV8W0\nvGBeZtPygv3MTpmIRNfY+ZySV2PQRCQkzZpZRdqiRdZ6abEgnO6bAfedPLlcH87E2omVrnlWUABT\npkC7dtYTsvnz4dtvreUKrrgiNib4EBERkfjm8vmqbzoAl8tFNZ5OxLlcLryAF/z6IXq9XrwVTOse\nim+/hQsvtGYYvPzyiB22hCfTQ/6b+bj7uv3WGpszZ47/XV2PB29+Pl6325q6vhLl9gVcvVyQai0S\nXSzQv1WgfQGOH4e5c+Hee+HiiyEzE84802ZIm20UERERM1RVE+kJmohUqHVrazzVDTdYE15EWvGi\n0GUXhy5XvOTn+3+uxE3zbsKT6d89MqNnhl9xVpFARdOmTdC5s7V49KJF1syL4RRnFZ1HREREBFSg\nRZ1T+svaZVpecH7miy6ynhgNHAgHDsR+3sLjheUKvlAcP25170xNXc2ECfDOO9C1awQaGAdi/RpH\nmml5wbzMpuUF8zKblhfMy+yUvBqDJmIQT6aHyWsm+z898niY43KVm3AjWGPHWjMSXncdnDgRZkNr\nQLCzlO3YYS038MYb1viykSMDLzkgIiIiEmkagyYSjwKMQXNNdsFq/MdaVTBWLRRFRVaR1rWr9UQt\nEgK2OeCG9nMEGm8WSEXj9ebPtxaVvuMOuPPO+JuBUURERGJbVTWRFqoWEVsSEqzxaBdcAB07wogR\nNd2i8JR9qvbTT3DLLbBhA+TkwG9+UzPtEhEREbOpi2OUOaW/rF2m5QWzMjdrBvfeu5rbboO1a2u6\nNVBvaL1yE4LYVXqijg8/tAqyhATYuNG/ODPp+hYzLbNpecG8zKblBfMym5YXzMvslLwagyYiEXfW\nWfDiizBsGGzdWrNtObL3SLkJQeo2rVvpWmal+Xwwcyb07w8PPGDN1HjyydFoqYiIiIg9GoMmEo8C\njMkKuKZYBMeglZWVBV4vrF8PLVpUvF1BQQFr161l0fJF7Ni7gzZN2zC031B6XNyDk/96clhj0Or8\nrg51Lq3jt4aaXZ99BnfdBT/8AC+9FP7U+SIiIiJ2aB00ESdyu/0/A3npeWT0zAipWAnGnDlzAEhL\ng9GjYcAAa/xWIMP/NJx2I9sxcMFAZtedzYrkFcyuO5uBCwbSbmQ7WGXzpAHyAqRfkh5UXp8PgBS4\nbwAAIABJREFUli+3npj17g3dusG776o4ExERkdgRdIG2c+dOevXqRbt27Wjfvj2PPfYYAPv27aNP\nnz6cc8459O3blwMHDkS8sfHIKf1l7TItL9RQ5rw8yMiwPlez0nnvuw/OPx+GD4djx/y3KygoYMOe\nDeR2zKXo9CJI+PkLCVB0ehG5HXOhNnDcxknz8kh+4YWQ8x46ZHVf7NAB/vIXq725uTBpEiQmVr6v\nvqedz7S8YF5m0/KCeZlNywvmZXZK3qiMQUtISGDGjBl8+umnvP/++zzxxBNs3bqVqVOn0qdPH7Zv\n386ll17K1KlTQ2mziMQRlwueespa1PkPf/BfI23turXsarir8gO0BmyuKV16Ug87fD5rcekxY6B1\na1iyBP72N9i82XryV7duUIcTERERqRZBF2gtWrTg/PPPB6BBgwacd9557Nq1iyVLlpCWlgZAWloa\nixcvjmxL41RqampNN6FamZYXzMtcdnr6hARYtAj+8x/4059+GSK2aPkiik4tqvxgpwFHI9cWgF27\nYMoU+NWv4I9/hLZtrfFmS5bA734X/ILTpl1fMC+zaXnBvMym5QXzMpuWF8zL7JS8dnKEtQ5abm4u\nH330ERdeeCF79uwhKSkJgKSkJPbs2RNwn9ILw6ampjrmH1vEZA0awNKl0K8f3HorzJoFO/bugOQq\ndkwAamN71sWyip+qnTgBK1ZYT/PWrLG6MC5YAJ07B1+QiYiIiETS6tWrg+qiGfIkIT/99BNDhgxh\n5syZuMsM3He5XLgqeFfk9XpLPkwozpzSX9Yu0/KCeZlzc3MDvu52w7Jl8K9/wYQJcFrTNlDFAzSK\nIOWUlJAnNtm9G6ZNs56WpafD5ZfDjh3w979bC2pHojgz7fqCeZlNywvmZTYtL5iX2bS8YF7meM6b\nmpoaVP0TUoFWVFTEkCFDGDlyJIMHDwasp2bff/89ALt376Z58+ahHFpE4ljDhtYsiWvXwsGdQ0n4\nLqHS7RN2JdD2tLZBnaOgAObPt2ZibNsWtm2znpb9+99w443W0zwRERGReBV0gebz+Rg7dixt27Zl\n/PjxJa8PHDiQrKwsALKyskoKN9OZ8JSwNNPyQmxlDjQmq7rP0aiR1d1w+9Ye1PmyVaXbtjrYiu4X\nda/ynHv3wj/+YU3u0aoVzJ1rTfO/axc8/zx06RK9royxdH2ri2mZTcsL5mU2LS+Yl9m0vGBeZqfk\njcoTtHXr1jFv3jxWrVpFSkoKKSkp5OTkMHHiRFasWME555zDypUrmThxYihtFpEwBDvTYWnF65tV\n+prHA5MnW58r0aQJrF5dn6SjXaj1RjK1vkr4pbtjESTkJpC8OZkuLbpw0003lex34gTs22c9Ffvn\nP62ukikpcMYZ8Oyz0LGjNeHHsmUwYgTUrx9yXBEREZGYFHSBdskll3DixAk2bdrERx99xEcffUT/\n/v1p0qQJb731Ftu3b+fNN9+kUaNG0Whv3Inn/rKhMC0vOCdzoLFl5V7Lzyf3589VadIEvnh/If9+\n9lMu/GoJPHsNzG8B62oxs9sSMgZ+ytmNFzJwILRvD0lJUKcOnHUWXHklPPYYNG0KTzxhPUFbtgxu\nvx1atoxA2CA45foGw7TMpuUF8zKblhfMy2xaXjAvs1Py2skR1iyOIhKf5syZU+XTNk+mh/w1+UzP\nnB7yJB4A559fn/fe6Y9rzEOw6HX48nxm7k6kfXtr4ei0NDj7bGjeHJo1s6btFxERETGVy+crXrWo\nGk7mclGNpxNxtOLZgCrlcuEFvPDLAmUeD978fLxuN+T9UniVPZ5rsgtWA6ngy/BVfDybXJNdsAro\nkYDvgcKg9hURERFxiqpqopCn2ReROFXcPbFUN0VPpofJaybjyfxlbFnx2mRVrlHm8TDH5So3Li3Q\nmDZcQO2q5t4XERERMZcKtChzSn9Zu0zLCzGeuXiNQnflRVZ+Yb7fZ4C89Dwyemb4d290u60xaKWP\nV8G4tIrWS4s3MX19o8S0zKblBfMym5YXzMtsWl4wL7NT8trJoQJNxMny8iAjw68rY9jHS0sL6Xi2\nn8iJiIiIGEwFWpQ5Zc0Gu0zLC+ZltrPWWqAuk3npebyQ9kJYE47UBNOuL5iX2bS8YF5m0/KCeZlN\nywvmZXZK3qisgyYicS5At8dgnm7ZKdACdZmE8NZpExERETGBCrQoc0p/WbtMywtxmDlAt8eA480q\nYKdAc5K4u74RYFpm0/KCeZlNywvmZTYtL5iX2Sl5NQZNRMISajGm8WYiIiIioVGBFmVO6S9rl2l5\nwdmZA3VJLJc3QJfJYJ7IxTonX9+KmJbZtLxgXmbT8oJ5mU3LC+ZldkpejUETkeiL9EyRIiIiIgZT\ngRZlTukva5dpeSH2M0d6zJjdvE4Zqxbr1zcaTMtsWl4wL7NpecG8zKblBfMyOyWvxqCJiO2ZEyNd\nUGnGRhEREZHguXw+n6/aTuZyUY2nE3E0r9eL1+ut9n2r43giIiIiTlVVTaQnaCIiIiIiIjFCBVqU\nOaW/rF2m5QXzMiuv85mW2bS8YF5m0/KCeZlNywvmZXZKXo1BE5Fq4ZQJQURERERqmsagicSpWBqD\nJiIiIiL2aAyaiEPpqZWIiIiI86hAizKn9Je1y7S8UHOZa2oae9OusWl5wbzMpuUF8zKblhfMy2xa\nXjAvs1PyagyaiIiIiIhIHNEYNBEDaQyaiIiISM3QGDQREREREZE4oQItypzSX9Yu0/KCeZmV1/lM\ny2xaXjAvs2l5wbzMpuUF8zI7JW/UxqCNGTOGpKQkOnToUPKa1+uldevWpKSkkJKSQk5OTiiHFhER\nERERMVZIY9DeeecdGjRowKhRo/jkk08AmDx5Mm63mwkTJlR8Mo1BE4kJGoMmIiIiUjOiMgate/fu\nNG7cuNzrKr5ERERERERCF9ExaLNmzaJTp06MHTuWAwcOBNym+M691+t1TF/SypiQsTTT8oJ5mZXX\n+UzLbFpeMC+zaXnBvMym5QXzMsdz3tWrV5fUP3bWsT0pUie++eabuf/++wG47777uOOOO5g9e3a5\n7dStSqTmJScn13QTRERERIyQmppKamoqYBVrWVlZlW4f8jpoubm5XHnllSVj0Ox8TWPQRERERETE\nZNW2Dtru3btL/vzaa6/5zfAoIiIiIiIiVQupQBsxYgQXXXQR27Zt47TTTuP555/n7rvvpmPHjnTq\n1Ik1a9YwY8aMSLc1LsVzf9lQmJYXzMusvM5nWmbT8oJ5mU3LC+ZlNi0vmJfZKXnt5AhpDNqCBQvK\nvTZmzJhQDiUiIiIiIiI/C3kMWkgn0xg0ERERERExWLWNQRMREREREZHwqECLMqf0l7XLtLxgXmbl\ndT7TMpuWF8zLbFpeMC+zaXnBvMxOyWsnhwo0ERERERGRGKExaCIiIiIiItVEY9BERERERETihAq0\nKHNKf1m7TMsL5mVWXuczLbNpecG8zKblBfMym5YXzMvslLwagyYiIiIiIhJHNAZNRERERESkmmgM\nmoiIiIiISJxQgRZlTukva5dpecG8zMrrfKZlNi0vmJfZtLxgXmbT8oJ5mZ2SV2PQRERERERE4ojG\noImIiIiIiFQTjUETERERERGJEyrQoswp/WXtMi0vmJdZeZ3PtMym5QXzMpuWF8zLbFpeMC+zU/Jq\nDJqIiIiIiEgc0Rg0ERERERGRaqIxaCIiIiIiInFCBVqUOaW/rF2m5QXzMiuv85mW2bS8YF5m0/KC\neZlNywvmZXZKXo1BExERERERiSMagyYiIiIiIlJNNAZNREREREQkTqhAizKn9Je1y7S8YF5m5XU+\n0zKblhfMy2xaXjAvs2l5wbzMTsmrMWgxYNOmTTXdhGplWl4wL7PyOp9pmU3LC+ZlNi0vmJfZtLxg\nXman5LWTI6QCbcyYMSQlJdGhQ4eS1/bt20efPn0455xz6Nu3LwcOHAjl0I5j2r+DaXnBvMzK63ym\nZTYtL5iX2bS8YF5m0/KCeZmdktdOjpAKtNGjR5OTk+P32tSpU+nTpw/bt2/n0ksvZerUqaEcWkRE\nRERExFghFWjdu3encePGfq8tWbKEtLQ0ANLS0li8eHH4rXOA3Nzcmm5CtTItL5iXWXmdz7TMpuUF\n8zKblhfMy2xaXjAvs1Py2skR8jT7ubm5XHnllXzyyScANG7cmP379wPg8/lo0qRJyd9LTuZyhXIq\nERERERERx6isBDspGid0uVwBizGtgSYiIiIiIlKxiM3imJSUxPfffw/A7t27ad68eaQOLSIiIiIi\nYoSIFWgDBw4kKysLgKysLAYPHhypQ4uIiIiIiBghpDFoI0aMYM2aNfz4448kJSXxwAMPMGjQIK65\n5hp27NhBcnIyL7/8Mo0aNYpGm0VERERERBwp5ElCREREREREJLIi1sVRREREREREwqMCTURERERE\nJEaoQBMREREREYkRKtBERERERERihAo0ERERERGRGKECTUREREREJEaoQBMREREREYkRKtBERERE\nRERihAo0ERERERGRGKECTUREREREJEaoQBMREREREYkRKtBERERERERihAo0ERERERGRGKECTURE\nREREJEaoQBMREREREYkRKtBERERERERihAo0ERERERGRGKECTUREREREJEaoQBMREREREYkRKtBE\nRERERERihAo0ERERERGRGKECTUREREREJEaoQBMREREREYkRKtBERERERERihAo0ERERERGRGKEC\nTUREREREJEaoQBMREREREYkRKtBERERERERihAo0ERERERGRGKECTUREREREJEaoQBMREREREYkR\nKtBERERERERihAo0ERERERGRGKECTUREREREJEaoQBMREREREYkRKtBERERERERihAo0ERERERGR\nGBFygXb8+HFSUlK48sorAfB6vbRu3ZqUlBRSUlLIycmJWCNFRERERERMcFKoO86cOZO2bduSn58P\ngMvlYsKECUyYMCFijRMRERERETFJSAXat99+y9KlS5k0aRLTp08HwOfz4fP5Kt3P5XKFcjoRERER\nERHHqKxuCqmL4+23385f//pXatX6ZXeXy8WsWbPo1KkTY8eO5cCBAxU2xqSPtLS0Gm+D8iqz8iqv\nMiuvyZlNy2tiZtPympjZKXnT0tKqrLWCLtDeeOMNmjdvTkpKCj7fL5XfzTffzNdff82mTZto2bIl\nd9xxR7CHFhERERERMVrQBdp7773HkiVLOOOMMxgxYgQrV65k1KhRNG/eHJfLhcvlYty4cWzYsCEa\n7Y07ycnJNd2EamVaXjAvs/I6n2mZTcsLzs48Z86ccq85OW9FTMtsWl4wL7NT8trJEXSB9vDDD7Nz\n506+/vprXnrpJXr37k12dja7d+8u2ea1116jQ4cOwR7akVJTU2u6CdXKtLxgXmbldT7TMpuWF5yd\nOTc3t9xrTs5bEdMym5YXzMvslLx2coQ8iyNY48mKJ/6466672Lx5My6XizPOOIOnn346nEOLiIiI\niIgYJ6wCLTU1taQKnDt3bsjHadKkCfv37w+nKRJBjRs3Zt++fTXdDBERkYjIyclxzN13EXE+l6/0\nTB/RPpnLRaDTVfS61AxdDxERiVderxev11vlayIiNaWq99ohTbMvIiIiIiIikacCTSJq9erVNd2E\namdaZuV1PtMym5YXzMscaOIQpzPtGpuWF8zL7JS8dnKoQBMREREREYkRGoMm5eh6iIhIXPJ48Obn\n43W7IS+v5GWNQRORWKIxaCIiImKG/Hz/zyIicUgFmg3vvvsuF110EY0aNaJp06ZccsklbNy4kTlz\n5lCrVi0mTJjgt/3rr79OrVq1GD16tN/rP/30Ew0aNODyyy8vd47HH3+czp07U7du3XL7Abz99tv8\n+te/5uSTT6Z3797s2LEjsiEjxCn9g4NhWmbldT7TMpuWFyKfec6cORHdLtI0Bs35TMsL5mV2Sl6N\nQYuAvLw8rrjiCm677Tb279/Prl27yMjIoE6dOrhcLs466yxeeeUVjh8/XrJPVlYW55xzTski3sVe\nffVV2rRpw+rVq9mzZ4/f11q1asV9993HmDFjyrXhxx9/ZMiQITz00EPs37+fzp07M3z48OgEFhER\nCZLdAijShVJNFXwiItGkAq0K27dvx+VyMXz4cFwuF3Xr1qVPnz506NABgBYtWtChQweWL18OwL59\n+1i/fj0DBw4s17c0KyuLcePGcfHFFzNv3jy/r1111VUMGjSIpk2blmvDP/7xD9q3b8+QIUNITEzE\n6/WyefNmtm/fHqXUoTNxIVDTMiuv85mW2bS84IzMnkwPo7NG48n0VLltcnJy9BsUY5xwjYNhWl4w\nL7NT8trJoQKtCueeey61a9fmhhtuICcnh/3795d8rbgAGzlyJNnZ2QC89NJLDBo0iDp16vgd55tv\nvmHt2rVcc801XHPNNSXblxVowOCnn35Kp06dSv5ev359zj77bLZs2RJ2PhERkXhQ9mlZfmG+32cR\nEaeImwLN5Qr/IxRut5t3330Xl8vFjTfeSPPmzRk0aBA//PBDyTZXXXUVq1evJi8vj7lz55KWllbu\nOHPnzqVLly60bt2aq6++ms8++4xNmzYFyFm+oYcOHcLj8b9D6PF4+Omnn0ILFUVO6R8cDNMyK6/z\nmZbZtLwQXuaa6FYYzNOyQDQGzflMywvmZXZKXkeNQfP5wv8I1a9//WteeOEFdu7cyZYtW/juu+8Y\nP358STFVt25dBgwYwIMPPsi+ffvo1q1buSdh2dnZDBs2DICmTZuSmppKVlZWgJzlG9qgQQPySk0X\nDHDw4EHcbnfooUREREJQE8WO7adlbjfJP38WEYlXcVOgxYpzzz2XtLS0ct0LR40axfTp07n++uvL\n7fPee+/xxRdfMGXKFFq2bEnLli1Zv3498+fP95tcBAI/QWvXrh2bN28u+fuhQ4f48ssvadeuXYRS\nRY5T+gcHw7TMyut8pmU2LS84OHNeHjf4fH5roAEkL1gAntCevsUrx17jCpiWF8zL7JS8GoMWAdu2\nbWP69Ons2rULgJ07d7JgwQK6devmt13Pnj156623uPXWW8sdIysri759+7J161Y2b97M5s2b2bJl\nC4cPH2bZsmUAHD9+nCNHjnDs2DGOHz/O0aNHS4q3q666ii1btvCPf/yDI0eOMHnyZM4//3zOOeec\nKKcXEREJTUzNsFhYGNG10WIqm4g4jgq0Krjdbj744AMuvPBCGjRoQLdu3ejYsSOPPvoo4P/Eq1ev\nXjRq1KjkdZfLxdGjR3nllVe49dZbad68eclHcnKy3+QiDz74IPXr12fatGnMmzePevXq8dBDDwHQ\nrFkzXn31VSZNmkSTJk3YuHEjL730UjX/S9jjlP7BwTAts/I6n2mZTcsLkc3syfQwec1k//FhHg+5\no0dH9amVO9Ht97kyuRE+dzyMaTPt+9q0vGBeZqfktZPjpOg3I76deuqpLFy4MODX0tLSAk4IAlbB\nVWzfvn0Bt3niiSdK/uz1evF6vRW249JLL2Xr1q02WiwiIhIdnkwP+WvymZ45nbz0vIo3LH5aFcGn\nVmXlpefhPerFm+6N2jmCMWfOHG644YaaboaIOICeoElEOaV/cDBMy6y8zmdaZtPyQuiZA03WkZee\nR0bPjMoLthqWXA3niLWnaqZ9X5uWF8zL7JS8GoMmIiIiUh0yMwN26dR4NREJlgo0iSin9A8OhmmZ\nldf5TMtsWl4wL3NugNfCKZwy380svyZbBROR1NSTNdOusWl5wbzMTsnrqHXQRERExAzV8dQpnMKp\n8Hhh1WuyiYiESAWaRJRT+gcHw7TMyut8pmU2LS/EfuZIP3VKDmPfeO2iGOvXONJMywvmZXZKXo1B\nExERkZgWTgGUnJwcsXZUJNYm/xAR51OBJhHllP7BwTAts/I6n2mZTcsLkc8cTqEUTgFUdlr7Y8dg\n7ly49lr44x9h4kSYOhWWcyVv0xufz95xCwoKyFmRw7g7xzF35VzG3TmOnBU5FBQUVLxTYiK4q16T\nrbqY9n1tWl4wL7NT8kZ1HbTjx4/TuXNnWrduzT//+U/27dvH8OHD+eabb0hOTubll18uWbRZRERE\nnCsS638VFBSwdt1aFi1fxKoNq/j2p28Z2m8oPS7uUeW+RUXw4ovw0EPQsiXccAMcPQoHDsD+/bCP\nXzGeG6jfFaZMgd/9ruJjDf/TcDbs2cCuhrsoOrUILoWvir4ie0E2rZ5qRZekLoF3TE+HStYzFRGx\ny+Xz2b2f5G/69On8+9//Jj8/nyVLlnDXXXfRrFkz7rrrLqZNm8b+/fuZOnWq/8lcLgKdrqLXS/+w\n3rF3B22atin5YV2/fv0q2xju/nZ4vV6+/PJL5s6dG5HjxYKKroeIiDhXoIWWy77mmuyC1UAq+DIq\n+T3hcuEFvEDpx1ZerxdvmSLG6/Wy9Yet/kVRAlAECd8l0OpgK3L35oKr/HmPHYOsLHj4YTj9dMjI\ngJ49yzfH63JxPy5eeekEGRmQlARnnvk8L7wwxm+7goIC2o1sR27H3AqjJW9OJvfHXLjUasuuXbBi\nBTz66CcUFnagXTvo1s36+O1lzZlWeBDv0aMV/1uJiHGqeq8dUhfHb7/9lqVLlzJu3LiSgy9ZsoS0\ntDQA0tLSWLx4cSiHLjH8T8NpN7IdAxcMZHbd2axIXsHsurMZuGAg7Ua2Y/ifhkd1/7Lmz59P586d\ncbvdnHrqqVx++eWsW7cOl8sVcsbU1FRmz55d6TYPP/wwZ555Jm63m9NOO43f//73fvvXqlWLjz/+\n2G+fq666ilq1arF27Vq/1+fMmUOtWrV4+eWXQ26ziIg4U6CuhtUx/qqoqIgNezaQ2zGXotN/Ls4A\nEqDo9CKrWKoNHPff74cfoE8fyM6GOXNg5crAxVmxWvgYPhy2bIExY2Dx4qvo1w82bPhlm7Xr1rKr\n4a5K27ur4S74bxtY/ijt2kGnTrBsGZxxxle8/DIMGwY7dsD48dD0p1yeL3ybMr+ORUQqFVKBdvvt\nt/PXv/6VWrV+2X3Pnj0kJSUBkJSUxJ49ewLuW3z3zOv1VtgHs6CgoMof1hu+31BhX/Bw9y9r+vTp\n3H777dx777388MMP7Ny5k1tuuYUlS5bY2r8iVRV3WVlZzJs3j7fffpv8/Hw2btzI70r1y3C5XJx7\n7rlkZ2eXvLZ3717Wr19P8+bNAx6vQ4cOfttHmlP6BwfDtMzK63ymZTYtL8RW5m++/abKoojWQD64\nE60xXhs3wgUXwMUXW4VZ9+7ldykoKGBlTg4PjRvHO8BDwMqcHAoLC0hLg//5n8e5+moYMgQGDYLN\nm2HR8kXWE7xKFLUqgh8uIrFuIVlZsGcPLFwIv/nNR3TqBCNGwKxZVhv/yyl0YRbXXWcVhT/+GNI/\nUUhi6RpXB9PygnmZ4znv6tWrS+ofO13Cgy7Q3njjDZo3b05KSkqFj+ZcLleFxUfpAq2iaSbt3sFa\nuy7wLalw9y/t4MGDZGRk8OSTTzJ48GDq1atH7dq1GTBgANOmTav08eSJEye44447OOWUUzjzzDN5\n/PHHqVWrFsePH2fSpEm88847/M///A9ut5s///nP5fbfuHEj/fr144wzzgCswnfcuHF+21x77bUs\nXLiwpB0LFizg6quvJiEhwW+7b775hnXr1vHCCy+wYsWKCgtoERGRihQXSMWfg+LxwOTJ1udSPtv5\nWZVFEadBSuMU8tLzyMqCyy6DGTOs8WS1a5ff/P7hw5nRrh0MHMjts2dzH3A7wMCBzGjXjvuHD6d2\n7ePcdBP85z/Quzf07w9vrNrxy03diiTAGW3/xdGVE+ncOfD5i51MAe15mc8+s2K3a2c97dMoAhGz\npKamRrdAe++991iyZAlnnHEGI0aMYOXKlYwcOZKkpCS+//57AHbv3h3wCY5ddu9gLVq+KCr7l7Z+\n/XqOHDnCVVddVeW2ZT3zzDPk5OSwefNmPvzwQxYvXlxSvD700EN0796dJ554gvz8fB577LFy+3ft\n2pXs7GweeeQRNm7cyPHjx8ttc+qpp9K2bVuWL18OwNy5cxk1alS57bKzs+nZsye/+c1v6Ny5My++\n+GLQeexwyhoVwTAts/I6n2mZTcsL9jJ7Mj1MXjMZT+YvBVVeeh4vpL1AXnpe8CfNz/f//LODxw7a\nKooOFB3k1lutiUBWr4arrw68aUFBAXU2bGBSbi69i4qoD6QC9YHeRUVMys0lccMGioqs9wl168Jt\nt8EXX8BpTdtAFbUiRdAowVPu5cpmsnS74W9/g6VL4fHH4dJLIS+Ef8JgmPZ9bVpeMC+zU/JGZR20\nhx9+mJ07d/L111/z0ksv0bt3b+bOncvAgQPJysoCrK50gwcPDrrBxXbstXcHa8feHVHZv7S9e/fS\nrFkzv+6cdr388suMHz+eU089lUaNGpGenl7uiVtlT+Cuu+46Zs2axfLly0lNTSUpKYn//d//Lbfd\nqFGjyM7O5vPPP+fAgQN07dq13DbZ2dkMGzYMgGHDhkW1m6OIiMS//MJ8v8/FbM3YWDzdvI1p5xue\n1NBWUbTn6wv4+mtrzFi7dhVv+v7atXTbVXkvmm67drH7m2/8Xjv5ZHjwL0NJ+K7yNxAJuxLo1blX\nudft/Lv89rfwwQdw9tlw/fVw4kSVu4iIgcJeB624K+PEiRNZsWIF55xzDitXrmTixIkhH7ONzTtY\nbZq2icr+pTVt2pQff/yREyH8FN29ezennXZayd9bt25dbpuqxqFde+21rFixgoMHD/LUU09x3333\nsWLFCr/9r776alauXMkTTzwR8OnZunXryM3N5eqfbzcOHTqUTz75hM2bNwedqSrx3D84VKZlVl7n\nMy2zaXmhGjLn5VlTKtp4TNT2tLZVFkXkJnBOiw4sWQJVreCzftEiuhb5vwlYXWabbkVFHFywoFx3\ny2+++oZWB1tVevxWB1vxoPfByhtRidq1rado+/dHd1Z+076vTcsL5mV2Sl47OcIq0HpltAU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HDxxRfjcrn8vub1eklLS+Pw4cM8++yzdOnShXbt2rF161Zat25NrVq1GD16NDt27OCkk06iU6dO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c147e90>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rpy2.robjects import r\n",
      "%R orderbook = getOrderBook('faber')\n",
      "ob = r['orderbook'].to_py()\n",
      "ob.faber.XLF.stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "1997-12-31 05:00:00+00:00  Order.Qty                    0\n",
        "                           Order.Price                 NA\n",
        "                           Order.Type                init\n",
        "                           Order.Side                long\n",
        "                           Order.Threshold              0\n",
        "                           Order.Status            closed\n",
        "                           Order.StatusTime    1997-12-31\n",
        "                           Prefer                        \n",
        "                           Order.Set                     \n",
        "                           Txn.Fees                     0\n",
        "                           Rule                          \n",
        "1999-11-30 05:00:00+00:00  Order.Qty                  all\n",
        "                           Order.Price              24.44\n",
        "                           Order.Type              market\n",
        "                           Order.Side                long\n",
        "...\n",
        "2011-06-30 04:00:00+00:00  Prefer                                 \n",
        "                           Order.Set                            NA\n",
        "                           Txn.Fees                             -5\n",
        "                           Rule                    ruleSignal.rule\n",
        "2012-02-29 05:00:00+00:00  Order.Qty                          1000\n",
        "                           Order.Price                       14.76\n",
        "                           Order.Type                       market\n",
        "                           Order.Side                         long\n",
        "                           Order.Threshold                      NA\n",
        "                           Order.Status                     closed\n",
        "                           Order.StatusTime    2012-03-31 00:00:00\n",
        "                           Prefer                                 \n",
        "                           Order.Set                            NA\n",
        "                           Txn.Fees                             -5\n",
        "                           Rule                    ruleSignal.rule\n",
        "Length: 253"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "start_t<-Sys.time()                                                                      \n",
      "updatePortf(Portfolio='faber',Dates=paste('::',as.Date(Sys.time()),sep=''))              \n",
      "end_t<-Sys.time()                                                                        \n",
      "print(\"trade blotter portfolio update:\")                                                 \n",
      "print(end_t-start_t)   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "[1] \"trade blotter portfolio update:\"\n",
        "Time difference of 0.4212649 secs\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "OrderBook\n",
      "==========\n",
      "\n",
      "Following is the beginnings of replicating the order stuff. Unlike the stuff above, it's being done outside of notebook. Maybe it'd be better to just link to a github url"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numbers\n",
      "\n",
      "import pandas as pd\n",
      "\n",
      "ORDER_TYPES = ['market', 'limit', 'stoplimit', 'stoptrailing', 'iceberg']\n",
      "ORDER_SIDES = ['long', 'short']\n",
      "ORDER_STATUS = ['open', 'closed', 'canceled', 'replaced', 'rejected']\n",
      "\n",
      "def verify_order(qty, price, side, ordertype, status, **kwargs):\n",
      "    if not valid_num(qty) and qty != 'all':\n",
      "        raise Exception(\"qty must numeric\")\n",
      "\n",
      "    # 0 is valid price\n",
      "    if not valid_num(price) and price != 0:\n",
      "        raise Exception(\"price must be valid number\")\n",
      "\n",
      "    if side.lower() not in ORDER_SIDES:\n",
      "        raise Exception(\"side must be long or short\")\n",
      "\n",
      "    if ordertype.lower() not in ORDER_TYPES:\n",
      "        raise Exception(\"{0} not valid order type\".format(ordertype))\n",
      "\n",
      "    if status.lower() not in ORDER_STATUS:\n",
      "        raise Exception(\"{0} not valid status\".format(status))\n",
      "\n",
      "def valid_num(val):\n",
      "    try: \n",
      "        # test 0\n",
      "        return abs(val) > 0\n",
      "    except: \n",
      "        # test np.nan and None\n",
      "        return False\n",
      "\n",
      "def apply_threshold(threshold, price, ordertype, tmult):\n",
      "    if ordertype not in ['limit', 'stoplimit', 'stoptrailing', 'iceberg']:\n",
      "        raise Exception(\"Threshold cannot be placed on this order\")\n",
      "    if tmult:\n",
      "        if threshold < 1:\n",
      "            threshold = price * threshold\n",
      "        else:\n",
      "            threshold = price - (price * threshold)\n",
      "        tmult = False\n",
      "    price = price + threshold\n",
      "    return price\n",
      "\n",
      "def process_order(symbol, timestamp, qty, price, ordertype, side,\n",
      "              threshold=None, orderset='', status='open', statustimestamp='',\n",
      "              prefer=None, delay=0.0, tmult=False, replace=True, ret=False, \n",
      "              txnFees=0, label='', *args, **kwargs):\n",
      "    \"\"\"\n",
      "        Model after the first portion of addOrder in quantstrat. \n",
      "\n",
      "        Split up the addOrder into order creation and orderbook creation\n",
      "\n",
      "        Note: the quantstrat addorder allows multiple orders to be passed. \n",
      "        That would come into play when using ordersets and replace. \n",
      "        \n",
      "        If we added that functionality, we'd probably need an order class\n",
      "        so we can pass in a list of orders. \n",
      "\n",
      "        The way it's done in quantstrat is clumsy in Python since everything isn't a vector. \n",
      "    \"\"\"\n",
      "    verify_order(**locals())\n",
      "\n",
      "    if threshold:\n",
      "        price = apply_threshold(threshold, price, ordertype, tmult)\n",
      "\n",
      "    # Not sure if timestamp can ever be None? ignoring that path in the quatnstrat code\n",
      "    timespan = (None, timestamp)\n",
      "\n",
      "    # Not sure why we're able to pass this in since we clear it out here\n",
      "    statustimestamp = None\n",
      "\n",
      "    # there is logic that implies that we can pass in multiple orders with one call. \n",
      "    # we will ignore that for now\n",
      "\n",
      "    # add delay note: timestamp will be datetime\n",
      "    timestamp = timestamp + datetime.timedelta(seconds=delay)\n",
      "\n",
      "    # I should deviate from R here and make order a class but meh\n",
      "    order = (symbol, timestamp, qty, price, ordertype, side, threshold, status, \n",
      "                 statustimestamp, prefer, orderset, txnFees, label)\n",
      "\n",
      "    return order\n",
      "\n",
      "class OrderBook(object):\n",
      "    columns = [\"symbol\", \"timestamp\", \"Order.Qty\",\"Order.Price\",\"Order.Type\",\n",
      "               \"Order.Side\", \"Order.Threshold\",\"Order.Status\",\"Order.StatusTime\",\n",
      "               \"Prefer\", \"Order.Set\", \"Txn.Fees\", \"Rule\"]\n",
      "\n",
      "    def __init__(self):\n",
      "        self._orders = []\n",
      "        self._cached_df = None\n",
      "\n",
      "    @property\n",
      "    def orders(self):\n",
      "        if self._cached_df is None:\n",
      "            df = self.to_dataframe()\n",
      "            self._cached_df = df\n",
      "        return self._cached_df\n",
      "\n",
      "    def to_dataframe(self):\n",
      "        df = pd.DataFrame(self._orders, columns=self.columns)\n",
      "        return df\n",
      "\n",
      "\n",
      "    def add_order(self, symbol, timestamp, qty, price, ordertype, side,\n",
      "                  threshold=None, orderset='', status='open', statustimestamp='',\n",
      "                  prefer=None, delay=0.0, tmult=False, replace=True, ret=False, \n",
      "                  txnFees=0, label='', *args, **kwargs):\n",
      "\n",
      "        order = process_order(**locals())\n",
      "        if ret:\n",
      "            return order\n",
      "\n",
      "        # add order\n",
      "        self._add_row(*order)\n",
      "\n",
      "        # update orders\n",
      "        if replace:\n",
      "            pass\n",
      "\n",
      "    def _add_row(self, *args, **kwargs):\n",
      "        self._orders.append(args)\n",
      "\n",
      "    def update_orders(self, portfolio, symbol, timespan, ordertype=None, side=None,\n",
      "                      qtysign=None, orderset=None, oldstatus=\"open\", newstatus=None,\n",
      "                      statustimestamp=None):\n",
      "        if oldstatus not in ORDER_STATUS:\n",
      "            raise Exception(\"invalid status\")\n",
      "        if newstatus not in ORDER_STATUS:\n",
      "            raise Exception(\"invalid status\")\n",
      "        if qtysign not in [-1, 0, 1]:\n",
      "            raise Exception(\"invalid qtysign\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r['.strategy'].keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "('order_book.faber',)"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

## xts_extras.ipynb

      
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