twp_201_Day_seasonality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day of week seasonality of SPY\n",
    "\n",
    "It could be very well possible that daily returns of SPY are dependant on the day of the week. There are many potential reasons for that, like some traders would be happy to close their positions before the weekend or be cautious about opening new ones on Monday. \n",
    "\n",
    "Some traders are using seasonality as an extra boost to their performance, [Quantified Strategies](http://www.quantifiedstrategies.com/day-of-week-seasonality-in-sp-500/). \n",
    "\n",
    "Let's do our own analysis and at the same time learn to use some `pandas` functions and how to work with dates and times. Pandas by itself has an [excellent documentation](http://pandas.pydata.org/pandas-docs/stable/index.html), so I will refer to it when necessary.\n",
    "\n",
    "**Reading material** : Chapter 5 of the PDA book. \n",
    "\n",
    "**Note**: you need the [`tradingWithPython`](http://www.tradingwithpython.com/?page_id=504) library to get SPY price data. \n",
    "\n",
    "First, let's load all the scientific libraries and put all the figures in the notebook instead of popup windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "%run common.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas version: 0.21.0\n"
     ]
    }
   ],
   "source": [
    "import tradingWithPython as twp\n",
    "import pandas as pd\n",
    "print('Pandas version:', pd.__version__) # print current pandas version\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data\n",
    "`getHistoricalData` requests data from yahoo finance and returns it as a nice [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) object. DataFrame is a 2-dimensional array, very similar to an excel spreadsheet. It has column names and row labels (index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 4588 days of data\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\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>adj_close</th>\n",
       "      <th>volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-12-31</th>\n",
       "      <td>146.84</td>\n",
       "      <td>147.50</td>\n",
       "      <td>146.25</td>\n",
       "      <td>146.88</td>\n",
       "      <td>104.42</td>\n",
       "      <td>3172700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>148.25</td>\n",
       "      <td>148.25</td>\n",
       "      <td>143.88</td>\n",
       "      <td>145.44</td>\n",
       "      <td>103.39</td>\n",
       "      <td>8164300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>143.53</td>\n",
       "      <td>144.06</td>\n",
       "      <td>139.64</td>\n",
       "      <td>139.75</td>\n",
       "      <td>99.35</td>\n",
       "      <td>8089800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>139.94</td>\n",
       "      <td>141.53</td>\n",
       "      <td>137.25</td>\n",
       "      <td>140.00</td>\n",
       "      <td>99.53</td>\n",
       "      <td>12177900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>139.62</td>\n",
       "      <td>141.50</td>\n",
       "      <td>137.75</td>\n",
       "      <td>137.75</td>\n",
       "      <td>97.93</td>\n",
       "      <td>6227200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>270.90</td>\n",
       "      <td>273.27</td>\n",
       "      <td>270.19</td>\n",
       "      <td>270.43</td>\n",
       "      <td>270.43</td>\n",
       "      <td>78709600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>267.91</td>\n",
       "      <td>268.87</td>\n",
       "      <td>263.36</td>\n",
       "      <td>263.67</td>\n",
       "      <td>263.67</td>\n",
       "      <td>148785900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>264.17</td>\n",
       "      <td>264.54</td>\n",
       "      <td>257.83</td>\n",
       "      <td>258.05</td>\n",
       "      <td>258.05</td>\n",
       "      <td>183534800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>262.13</td>\n",
       "      <td>265.43</td>\n",
       "      <td>259.41</td>\n",
       "      <td>265.11</td>\n",
       "      <td>265.11</td>\n",
       "      <td>141956100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>266.17</td>\n",
       "      <td>266.77</td>\n",
       "      <td>258.84</td>\n",
       "      <td>260.60</td>\n",
       "      <td>260.60</td>\n",
       "      <td>126354700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4588 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              open    high     low   close  adj_close     volume\n",
       "Date                                                            \n",
       "1999-12-31  146.84  147.50  146.25  146.88     104.42    3172700\n",
       "2000-01-03  148.25  148.25  143.88  145.44     103.39    8164300\n",
       "2000-01-04  143.53  144.06  139.64  139.75      99.35    8089800\n",
       "2000-01-05  139.94  141.53  137.25  140.00      99.53   12177900\n",
       "2000-01-06  139.62  141.50  137.75  137.75      97.93    6227200\n",
       "...            ...     ...     ...     ...        ...        ...\n",
       "2018-03-21  270.90  273.27  270.19  270.43     270.43   78709600\n",
       "2018-03-22  267.91  268.87  263.36  263.67     263.67  148785900\n",
       "2018-03-23  264.17  264.54  257.83  258.05     258.05  183534800\n",
       "2018-03-26  262.13  265.43  259.41  265.11     265.11  141956100\n",
       "2018-03-27  266.17  266.77  258.84  260.60     260.60  126354700\n",
       "\n",
       "[4588 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = twp.yahooFinance.getHistoricData('SPY') # get maximum history length from yahoo\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this moment I am only interested in `adj_close` (adjusted close) price, so I'll copy it to a new [Series](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#series) called *price* . Series is a 1-dimensional array (only a single column from DataFrame), but it is still indexed with row labels, which are in our case dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"799.2857278974691\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7910398b70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = data['adj_close'] # get only one column from full data table\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') # nice figures\n",
    "price.plot() # pandas objects have very convenient plot functions.\n",
    "title('SPY price') \n",
    "ylabel('$')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse daily seasonality we need daily returns instead of prices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7910398b70>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret = price.pct_change()  # calculating returns is also easy with pandas. Just call .pct_change() \n",
    "(100*ret).plot() # plot % return\n",
    "ylabel('close-close % change')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have daily returns ready for analysis. Before we go futher, I will insert a short intermezzo on working with dates and times.\n",
    "\n",
    "## Working with dates and times \n",
    "[ref]: # (datetime - to and from strings)\n",
    "Some very good examples of converting dates and times are found on the [PEAC Python](http://pleac.sourceforge.net/pleac_python/datesandtimes.html) site. Nevertheless I will sum up a couple of frequently used tricks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime as dt # shorthand notation for datetime module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will get todays date as standard `date` type and convert it to `string`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-02-21\n",
      "<class 'datetime.date'>\n",
      "<class 'str'>\n",
      "Today is  2018.02.21\n"
     ]
    }
   ],
   "source": [
    "today = dt.date.today() # get todays date\n",
    "print(today) # print it\n",
    "print(type(today)) # get the type, note that it is of datetime.date type, so not a string (even if it prints on the previous line)\n",
    "s = today.strftime('%Y.%m.%d') # this is how to conert date type to a string: use .strftime()\n",
    "print(type(s))\n",
    "print('Today is ' , s) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use almost any type of [format](http://www.tutorialspoint.com/python/time_strftime.htm) to convert date to a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-Feb-21\n",
      "21/February/2018 (Wed)\n"
     ]
    }
   ],
   "source": [
    "print(today.strftime('%Y-%b-%d')) \n",
    "print(today.strftime('%d/%B/%Y (%a)'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting back is easy with `strptime()` funcion, **but**: you must use `datetime` class of the `datetime` module instead of `date` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'datetime.datetime'> 2012-08-03 21:59:59\n",
      "<class 'datetime.date'> 2012-08-03\n"
     ]
    }
   ],
   "source": [
    "dateAndTime = dt.datetime.strptime('20120803  21:59:59',\"%Y%m%d %H:%M:%S\") # convert string to a datetime class\n",
    "print(type(dateAndTime), dateAndTime) # yes, it is datetime...\n",
    "onlyDate = dateAndTime.date() # use .date() to convert to date class\n",
    "print(type(onlyDate), onlyDate) # and now it is only date!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another helpful function of the `date` class is`.isoweekday()` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(today.isoweekday()) # return current day number (1=Monday, 2=Tuesday etc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse weekday seasonality\n",
    "to do this, I will write a function that splits a series to a 5-column table, each column for each business day of the week. There are definately other ways to achieve the same result, but this is wat seemed logical to me. Don't expect to understand this code right away, take the time to read the comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def splitToWeekDays(s):\n",
    "    ''' \n",
    "    return a dataframe where each weekday has a separate column \n",
    "    Parameters:\n",
    "    ------------\n",
    "    s - Series , with dates as index\n",
    "    \n",
    "    Returns:\n",
    "    ----------\n",
    "    dataFrame with business days as columns\n",
    "\n",
    "    '''\n",
    "    df = pd.DataFrame(index=s.index) # create empty DataFrame \n",
    "    df['data'] = s # add column named data, containing values from s\n",
    "    df['weekday'] = 0 # add column named weekday, init with zeros\n",
    "    \n",
    "    \n",
    "    for date in df.index: # for each date \n",
    "        dayNr = date.date().isoweekday() # get weekday number\n",
    "        df.at[date,'weekday'] =dayNr # set day number for this date in 'weekday column'\n",
    "    \n",
    "    out = pd.DataFrame(index=df.index,columns = list(range(1,6))) # create empty DataFrame with 5 columns for each bus. day. \n",
    "                                                         # this 'out' DataFrame will be the result of this function\n",
    "    \n",
    "    for iDay in range(1,6): # now copy data from df to out\n",
    "        out[iDay] = df[df['weekday']==iDay]['data'] # get selection, copy to 'out' dataframe\n",
    "\n",
    "    out = out.fillna(value=0) # replace missing values with zeros\n",
    "    out.columns = ['Mo','Tu','We','Th','Fr'] # replace number with day names\n",
    "    return out "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the function can be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mo</th>\n",
       "      <th>Tu</th>\n",
       "      <th>We</th>\n",
       "      <th>Th</th>\n",
       "      <th>Fr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.97e-02</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>-5.42e-03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.50e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.49e-03</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>1.35e-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.93e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-6.26e-03</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mo        Tu        We    Th        Fr\n",
       "Date                                                \n",
       "2018-02-06  0.00  1.97e-02  0.00e+00  0.00  0.00e+00\n",
       "2018-02-07  0.00  0.00e+00 -5.42e-03  0.00  0.00e+00\n",
       "2018-02-08  0.00  0.00e+00  0.00e+00 -0.04  0.00e+00\n",
       "2018-02-09  0.00  0.00e+00  0.00e+00  0.00  1.50e-02\n",
       "2018-02-12  0.01  0.00e+00  0.00e+00  0.00  0.00e+00\n",
       "2018-02-13  0.00  2.49e-03  0.00e+00  0.00  0.00e+00\n",
       "2018-02-14  0.00  0.00e+00  1.35e-02  0.00  0.00e+00\n",
       "2018-02-15  0.00  0.00e+00  0.00e+00  0.01  0.00e+00\n",
       "2018-02-16  0.00  0.00e+00  0.00e+00  0.00  2.93e-04\n",
       "2018-02-20  0.00 -6.26e-03  0.00e+00  0.00  0.00e+00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayRet = splitToWeekDays(ret) # split series to daily returns\n",
    "dayRet.tail(10) # print last 10 entries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we would have 5 long portfolios trading `$100` for each day of the week, this is how they would perform (frictionless of course):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc9dfb3e4a8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EsHTpUr/LMfBojOmtt97ixRdf5MyZM1RXV7v9TxAEQVDVWdSe/uhYNTP/qWuS\neWHRCEIC1Z/aQA+T9wSVc2j/8ssv59Zbb3U71lOKovDhhx/2uMaMs6CazWbj0KFDF9UWX/Cop799\n+3b+9re/YTKZ+ro9giAIfqG8qZW/byvhzgkmprQN5ZtbHQyPMmJsq6kfYdQTYdQToFODvUEngn53\nOAO8Xq9Hr1fLDx85coSJEyf2uBpsQ0MDAC0tLT16fnZ2NpGRkdhsNlpbW3v0Gr7k0V8tPDzcbbme\nIAjCQJdfZ+VktYWvcmq1Y02tDsICO/6sLp0Ux+WDQxkeJYrLdEdnQb+2tpaysrIev2ZeXp52Oysr\nS7tdVVVFaWnpeZ/b0tKibTVvMBg4duyYx+/rcDiw2XxfRtmjnv7ixYt54YUXuPXWWzskUMTHx/dJ\nwwRBEPoze9vWd65bXZ+usTIpIaTDuVOTwtwS+4SOcnNz2bt3LzfffDNhYerf6syZMwAYDAZ0Oh0R\nERE0NDT0KHgqikJ2djbZ2dnase3btxMVFUVpaSn79+8H1JGE2bNnc/r0aYKDgxk5ciSSJJGdnc2G\nDRsAmDx5MtnZ2TQ0NCDLskejDqtXr6a4uJi0tDQyMjK0ixhv8yjo//Of/wTQ/iiuRO19QRAGIkfb\n8nHngP2W/AYsdhlTiP9ldPuSzWbjwIED7N+/n9bWVrKyspgzZw55eXlUVlYCkJKSAsDChQtZuXJl\nj5buHTlyhMzMTO3+vHnz2LRpE2vWrHE778SJE5w4cUK7f9ttt3Ho0CFOnTqlHRs9ejRms5nCwkJe\neuklZs+ezeWXX37e9y8uLgYgJyeH9PR0kpKSuv0ZeoNH304R2AVBENzZXTe5B2rbNtK5Y4LIffLU\nyZMnKSws5OjRo9qxQ4cOkZaWxtdff60dc24n7uwddyeZr7a2lk8++URbeTZo0CDCw8NJT09nz549\nNDU1aef+5Cc/4b333nN7/meffabdnjJlCuPGjUOSJG00AmDbtm2MGzeuy23Pa2rUjZLi4uKorKz0\nab0BcUkqCILQA84gX25Wh5odbcP8gQZReMcTzsqundm0aZPbfUlSx1MCAgIA+O6770hNTdWO5+bm\nsnXrVqZPn056err2PEVRWL9+vRbwBw8ezA9/+EPt8ZkzZ7J+/Xouu+wyZs+ejSRJ3HjjjXz99dck\nJydTWFjo1o4hQ4YQHR0NQGpqKomJiRw/fpydO3disVg6Dfrr16/n+PHjAAwfPpzKykq3KSFv8yjo\nP/XUU9rB6N5mAAAgAElEQVQf91xPP/10rzZIEAShv2tqdfD+QXXoubRRDfrOarFiVd6F1dbWsmXL\nFrdjCxYswGAw8PXXX2vL4hITE5kxY4Z2TkREBDqdDlmWaWpqwmAwYDQaycvLw2w2s2nTJkaPHk1g\nYCCgZuo7k/5Gjx7doYrsmDFjGDJkCGFhYVqMmzZtGiNGjNDOOXXqFBUVFdjtdgYPHqwdd/b2o6LU\njZE6G30oKirSAj6oQX/v3r39P+jPmzfP7X5dXR3ff/89V111VZ80ShAEoT+raWkfnnWuwnP29HVd\ndJAGOrvdzokTJ9Dr9axfv77D4ykpKW7B8IorrnAL+E7XXnst69ev58yZM2zevFnbD8appaVFC/rO\nYfS5c+cyceLEDq8lSRLh4ecvljRq1ChGjRrV5ePOKYfOhuydOQmgFrJzXli47ifgbR4F/blz53Y4\nNmPGDF555RVuv/323m6TIAhCv+Zom89PDA+kpLGVzXn1Wt19sRS/c8XFxR2G7QFGjhzJyJEjteB5\n2WWXceDAgS5L3Doz5Tdv3gygBfy0tDRycnLcgq/z9oUC+8VwXmDk5OS4rWaTZVnr5T/44IPo9Xrt\nIsCXPf0eTz7FxMS41UQWBEEYKJxJfEaDGuH/Z0cpG8/UA3Q5FTrQdbXMbtGiRYwZM6bD8a7+jq5D\n7K5SU1MB2Lt3r3bMue6+L5fHJSYmAh0DeUlJiTZN4Xx/5wVLTk6Oz3YM9Kinf+7VWWtrK7t372b0\n6NF90ihBEIT+zJm4L8rqekZRFLce+NVXX83u3buZNWtWh3MvdNHUWa378ePHaxVjc3NzmT9/Pjqd\njrq6OkDtpPYVnU5HSEiIFsS3bt3KgQMHtMddEwedn+306dPY7XafrNX3KOhv3brV7b7RaCQtLY1F\nixb1SaMEQRD6M2dPX2pbpR8bYqC6WWz72hlFUXj//fepr1dHQu6++24iIiKYPHlyr72HxWIhIiKC\n2bNns23bNk6fPk1qaioOh4PQ0FC35XV9Qa/Xc/ToUVpaWjh9+rTbYyEh7cWaXC9onCsRvM2joP+H\nP/yhr9shCILgN45VNLvdvyE1ikNlzTRYROA/l8Ph0AJ+XFzcBefXnb1fT6ZJnMvrnL3/UaNGsW3b\nNsxmM4DXetOuPXiASZMmMXr0aEpLS91GJlw/U0/3DrhYHq/TLy0tZefOndTU1BATE8PMmTO7nFsR\nBEG4lFWZ1eCeGBHIiaoWDJLEn65NxnfpWf2Xc4381Vdf7VHvfuzYsTQ3N2tz9OczdOhQlixZQmxs\nLIC2R8yZM2dwOBzk5OT06dC+k7PXnpSUhNFoZNq0aYSEhHSIkb4K9K48Cvrbtm3j9ddf5/LLLycu\nLo6zZ8+yevVqfvGLXzB79uy+bqMgCEK/YnXIDAoNYHRsEJvO1BMXGoAkSYgZ/o6c5Wu7qlZ3rqio\nKK699lqPztXpdG4Z83q9npiYGIqKiigqKgLwyu6wt956K9nZ2YwfP57g4OAuzwsNDWXYsGGMHDmy\nz9vUFY+C/sqVK3nyyScZN26cduz48eNazWFBEISBQFYUvj5RS2ZeA4F6iRtSo7hscCjxYb6Zn+0L\nziz03lqF4Mza74tA11kbly1bxo4dO8jKymLo0KEd6sz0hZCQEKZOnXrB8/R6PTfffHOft+d8PAr6\nLS0tHTL1U1NTtWEbQRAEb/r+TD1HK5p5cHqCV4vhfHioik+PqevCpySqVdwSwgO99v59zWq18vrr\nrzNq1CgWLlzYK6/prIjX1br7nrjhhhvOG3+uvPJKrrzyyl57v0uJRxMMixcv5qOPPqK1tRVQl+yt\nXLmSxYsX92njBEEQOvP8zlI2nK6nwerdtc4nKtsT+H57VaJX39sbnPvDO0vP9gbn6/Rm/YLRo0d3\nWmFPuDCPLr3WrVtHXV0da9euJSwsTNuVKCoqinXr1mnnvfrqq33TSkEQhE44ZO+mzrmOKlyKRXgO\nHTqk3V65ciVLly7VkuR6yuFwdFp8R/ANj4L+Qw891NftEARB8Mgb+8q12w4vlzCXFYXYEAN/nJfs\n3Tf2ksbGRrf7OTk5FzVM3tjYiMViOW9y20DjsCs0N8uEhemQfFCz2aOg75rAJwiC4Ctb8xtYk1Or\n3Xd4uYa5Q4Gk8ECGRBq9+r7eUFJSAkB8fDyxsbFkZ2dfdKlY57r1zqroDVT7dzVTVmzjhlsjCfBB\nOohHQd/hcLB9+3by8vI6JE/cd999fdIwQRD8k11WeHt/BTOGhDExIbTXXtchK5yttwJw1+Q43j9Y\n6fXhfVlR/HoXPYvFwkcffcTIkSNJSUkhKipKq1Z39OhRAK655hoGDRrEqVOnLmo3uObmZj7//HNA\nXb8uqKwW9W/qgwq8gIdB/8UXX+Ts2bNMnjyZyMjIvm6TIAh+RFEUXt2WT3qsjrFxIRQ3tLImp5bd\nhY28dWvXW5J2x8dHq1h5uAqHAgYdJISrS+TsXg/6oPfjbfT27dtHY2MjBw8e5ODBg8TFxbFgwQIi\nIyO1XC3nHL5z33pQk/G2b9/O1KlTMRqNDBo06ILv1dLSot3uzcx9f1VdaedIVjON9TLRJj06H+3b\n4NF/iYMHD/Lqq6+KeRlBEDrYcLqeD7LKGBkTxP8sGK71vl33nL9Ya3PrcLTF92FRQQS0Bd59JWaG\nR3tW9OViFdRZOVltYXJCyIVP7qfOnbOvrKzk/fffZ/bs2TgcDpKTk7Wytc568gaDAbPZTGFhIYWF\nhQDce++9bjXlO+M6SuCLjWV8qbVVRq+X0LsE9uoKO4316t/E3OjlZBQXHi3ZGzJkiHYVKAiC4Op0\njTrl5yxQ4+x8ywqcaXtMURTeO1DBprbtZ7ur1SVjb1JCCOnxasBpsXnvx/OtLDWB8FSNf9YnURSF\nkydPdvpYTU1Nhzr1ZrMZRVE4cOAAubm5bucfPHjwgu/nmg/gaTW+S0FutoV/fdHA2k/rOX1C/a7Y\nWhVyjrZ/b+ISfDfy4XH2/muvvcakSZM6DO/PmTOnTxomCIJ/+D5PDeSGtt63a3LdF8dreGxWIo2t\nMp9n1wAwL6X7U4Q2R/trXjY4lJAAPYF6yatz+uZW9QLDavfPCvvOIOw6bJ+ens7Ro0fJzs4G3BPu\nRo4c2WHHuIiICBoaGty2ye2KsxLfLbfc4rMd5bytptJOzpH24J59yMKQ4YFUV7T/veYtCico2Hc1\n+D0K+pmZmZw4cQKz2UxgYHu6oSRJIugLwgBn1Ouw2B3a/LrsEoidJV2f2nj2ot6jtS3oL0qL1pID\n9ZLk1ez90ED1hzolxn8y9+vq6pBlmZiYGC3Qp6Wlcfr0aa666irGjx9PbW0txcXFAERHR2vPdW6d\n/uqrr2Kz2Zg5cybTpk3jzTffvGBW/+nTp1mzZg0wcObzy0tt7Nmi7u43coyR0yfUpNN1XzZo50yd\nFUJomG+nOjz6r7F27Vr++te/MmTIkL5ujyAIfsY5wO7sdbt2vo0GNVBWte01HxPc/QDg7OXPSA7j\nF1NdNlfRebc4z6BQtbf6+Gz/yER37mMPkJycrF2AmUwmrrvuOu28H/zgB7z44osAJCZ2rDJ49dVX\nk5+fz+WXXw6oIwVHjhxhzJgxXe602tCgBroFCxa4bYhzqaqutGsBf+ykIEaNCWLMhCAO72uhqcFB\nS4tMZJSehCTfj3h49C8wKirKKzsVCYLgX8qbWmlsK4Xr7Om79r6D24K+rW1OvqbFzgcHK1k2Oc7j\n97C2PXdcnHvimF4nYffSlP6mM/WsP11PbIiBuFDf/3B7wtl7B7QEPOi4vaskSSQmJlJSUtLpMPz4\n8eMZP368dv+yyy5j27ZtVFdXdxn0naMKU6ZM0S4A/NXR/c2UFNq44qpQomLcQ2ZpUSsnjlhoalA/\nrynewKgxav6CTicx+Yr+l/Tp0cTCokWLeOGFF8jNzaW8vNztf4IgDFxPbWwPJg4t6Lc/bpcVFEXB\nalcYF6eu/jndzUQ459B+4DlLnHRAhdmGxS7z2bFqyptae/AJPPOPnaUANFi8W+v/YrgumXPV2Z7u\nc+fOZdq0aR71yp0ldc+3ht/5WH/YP/5i1NXYyTvZitWicGhvx7/nmRyrFvBBHdbv7zzq6b/11lsA\nZGVldXhs1apVvdsiQRD8RlmTTbt9oqqFzLx6cqvbg/q3J+sYER2EAkxJCkMB9peaz1vkpqjeyjOZ\nRdx9+SBmJodzqFQdNg04d12zJHGg1My+4ibeP1jJ2Xorj1zZt5vg2LxcF+BiuCbbzZ49G7PZzIED\nBzo912QyeTya6wzkznn9pqYmDAaDW4b+pRL0G+ocbrfLS2yER+gICdPjcCjUVDkIi9Ax94ZwWlsV\njMb+/3k9CvoisAuCcCEWu8KKHaXMGhoOQIBOwiYrvLJH3Vo1yCAR1Dbc32h1EBnU/vMjKwp2WSFQ\nr6Og3kpZk42Np+uYmRxOdVs+wKRzqvvNHR7BF8dr2FqgDh/X90EvXFEUNuf7z/C0oijU1tYSGRnJ\n7t27teNhYWGkpqai0+kuel97ZyCXZRlZlnn77beJiYlh2bJl2jmXQtC3tMha737spCCOH7KwZ6sZ\ngwEW3BaFrVW9AExMDkCSJIxG/yja1K2smqqqKmpqaoiJiRFz/IIgoJPcE/dAHdKPCTYQEqCjqKF9\nyN2o1zEzOZwDpWbKm2xuQf8fO0rJzG/gpcUjaLW3JwTWttj530OVAJhC3H+ubhwTzRfHa7SldH3R\nBy9rsrFiR2kfvHLfKCkp4bPPPiMtLU2bS7/rrruIjIxEkiRmzZp10e/hXMu/fft2tm/fDqjr/F3J\nsuzXAR+gpbl92D5ltJHgEB37dzbjHEDJPqReEIRH+FfhIY+Cfm1tLc8//zy5ubmEh4fT2NjI6NGj\nefjhh4mJienrNgqC0E+lxgahlySiw4LZnqf+8DtkBUmCMXHBbkFfr5PQtSX5Pf6vAkbFBPHs/GEE\n6CV2FamV4jLzGrTcgKwSM3d/fkp7/rlb2Qbq1aDiLNCjKAr7ips4XqlOM/zmqiTSTMHkVrUQbtQz\nOLzr3U0Uux0lcy3S5OlIpvZ5bW8W/+kNznn8nJwcAG644QaioqJ69T08CeaHDx/WVgv4K7lt4GjG\n3FB0OomkoYHU1zjIP6UuxasqV6N/fKJ/JHY6eRT033zzTYYNG8aTTz5JUFCQtmnDm2++yW9/+9u+\nbqMgCP2UrEBogE4rzANqIp8OcB5JMwVTb7GTGhvklsR3qsbC7StzeHpeMsmRRk5WW/j0WHWn7xPb\nyVI/Z2Kfs0LeobJmDpU1t79+tYXYEAOP/6uAuBAD/+xiHwB523qU99Qla8qqf6L71e+RJk0D2pMI\nR8UE+UUlvnPXz/fFMmtJkggLC6OpqYlZs2ZRVlamJXXX19ezevVqbDab3+/TUlOtBnXXvRYkHcgy\nVFXYsVoURqQGojf4x7C+k0fjLzk5Odx1111aokZQUBDLli3rUJpREISBRVZAkqCkoT0gqj19CWfH\n/NqUSF6/eSTJkUatZr6rFTtKOFntHlAnnVPf/s1bOs5Dd0js68T2AnUEobLZzmPf5mtLB50U2aEF\nfO0zffuJdvtwuZpE+KOJ/jGd6Zq8FxERccH6+D117bXXMmLECMaPH09ISAhNTU0UFBTw3nvvUV+v\nVmi84oor+uS9vcFuVzhxWP1O6l2uNyUJFAV2fq+WpQ8I9K+ADx729ENDQykqKmL48OHasZKSkl77\nQlVVVfHyyy9TV1eHJElkZGSwcOFCmpqaWLFiBZWVlcTFxfHII49o20AKguB7SlsWvsXWHmwcioJO\ngkij+vPiutQuNFCd/xwXF0x2pToUXeeSgDc8ysjDMweTEhNEs83BI2vzKWuydbqznU6SeHjmYG05\n3bnssuK2C9+pGgu1LQ4GhbX3dZTVH7g/KTAQms3a3Sqz+rlGxPTv2vEWiwVZlrVhfejbTW6GDRvG\nsGHDABg8eDBHjhzRKvANGzaMm2++uc/euy8oikJTg0xouA5ZVji2X/1uRpv0RES1/x1dN9BJGhZA\n4tCup4z6K4+C/k033cQzzzzDvHnziIuLo7KykszMTJYsWdIrjdDr9fz4xz8mJSWFlpYWnnjiCSZO\nnEhmZiYTJkzglltuYfXq1axevdotQ1QQBN85XtHMmVorEUEGt+DqkNUe0S3jYhgSGcjM5HDtsQnx\nIfz5uqEE6iX+47sCt9dbsWA4KS7BNSRAzx+vTab5PPPqztK4nVl1pIqbxrrnHO0raWLh6PZSs3tP\nVpA74nruzPuXOh2RNhHyT6IcyYJRY7HJMnEhBqKC1B9+56ZC/c2qVauor6932/LWWzvbjRkzhv37\n91NVVQX4534slWV2dm8xk5YehGVoM2fz1FyUKTND3XJJRow2EhWrJzbOgM5Pt1j2aHg/IyODRx55\nhMbGRrKysmhsbOThhx8mIyOjVxoRHR1NSkoKAMHBwSQlJVFTU8PevXu1L9CcOXPYu3dvr7zfQOHv\niTRC//b6PnUet6DOyp2Xt5emdSgKEhAWqGfuiEitFC+o86PjB4UwMiaIn01pD1D/lh7rFvCd4sMC\nGXGerXPDAjsGtk/vSCPYoMNsk6lpW+7n/H2uMrfXFZCbGvjzkBv5dNi15A67DABp0GBorEd+4WmU\nb1bS6lAI0OvQSRKv35TC364f5sFfpm+YzWbWrVunZcw7NTQ0aEPqFRUV2vFx48Z5rW2uFxi9nTjo\nDVar+lvZUOfA0qKOPF2zIJzgEPcQaTBIxMUH+G3Ah24s2UtPTyc9Pb0v2wKoX9q8vDxGjRpFfX29\ntgFEdHS035dz9KYjR46wefNm7rzzTmJjY33dHOES5FwqJ8sKt01KRGltYcWOUmRF6ZBpfy6dJHHT\nmBjeylKD1M1jerYKKPycoJ8QFkCAXuKhmQn8bWsJVc02DDqJz+5MY8mqXLfiOoVHjmm3Kxbfw1i5\nBIamoOzYCC3NyPu2sW3ilQyPUqusJZwn+78v1NbWEhgYSGioWp8gPz+fEydOAGqhtKlTp2I2mzl+\n/Hinz588ebLX2urM6HfdsMdf5OVaKcxXe/alRTaaGmoBCPSTdffd5VHQf+6551i0aBFjx47Vjh0/\nfpy1a9fy2GOP9VpjLBYLy5cv5+677+5WvsCGDRvYsGEDAM8++6zf1BAwGAx91taCggJkWaaxsZG0\ntLRee92+bHNf8Lf2gv+0OcCQD9iQUdscGREBlIKkJ8CgdOszDE/q2aYsMbEKD18Nh0oayDxVTWCA\n+rcbbg0ASsgqMROolzCZTBgNpzAEBmntKv78M0CtKa8flEjcxCkAKO+uoWLJNTisaiCICDZ6/b+H\nw+HghRdeIDo6mkceeQSA9evXu52zb9++Tp+7YMEChgwZ4tU2O5O8U1NT3d63P3+X62pb2bi2lIY6\nm9vxxgYbBoPE4MS4C1689hfd2cnQozOzs7N59NFH3Y6NHj2av//9791r2XnY7XaWL1/OVVddxfTp\n0wGIjIyktraW6Ohoamtr3fZ6dpWRkeE21eCcW+rvTCZTn7XV+WX96quvSE5O7rX5vb5sc1/wt/aC\nH7VZUXv6NoeC3W6nqVHNlLfa7Ogkz/4dTksKIybYcFGfd16ykaq6th9n2UFVVRWhiktioaxQVVWF\nDoUjxbVUVVWh5OViPnEMhqlBv7K2gaqq9vl6afESrN99BcCUlnwqt1YijZ3U4zZ2l9WqrgWvra1l\n06ZNHDx4kLq6OkAttuPcPc/VPffcQ2NjIwkJCUiS5NXvkLMCX2trq9v79tfvssOusPaz+i4fn3lN\nKNXVnS8f7Y9MJpPbtvfn49GcfkBAABaL+5Iai8XSa4FEURRee+01kpKSWLx4sXZ86tSpbN68GYDN\nmzczbdq0Xnm//kjelYmStf3CJ3rIdVctMS1y6VNKzqLkHvVqHodzbX5sW6U8Z80WT4b3nX43dwi/\nnJ5w0W2pblGDfFKEOhQfFWRQEwOjjTx1TTIAZpus1QmQn/9vrDXtwahDsmBoOCfD1TXuAXsykf/n\n9xfdRk8pikJlZaV2PzMzUwv40dHRREVFaZvegDqMf9999xEWFsbgwYN90judPHkygwcPZsSIEV5/\n754oK3bv3U++IoT5N7d3Ks/dTe9S4tEnmzRpEm+88Qa/+MUvCAkJobm5mbfeeqvX5oxycnLYsmUL\nQ4cO5fHHHwfgzjvv5JZbbmHFihVs2rQJk8nUYbThUqK89T8ogP7Nr3r0/IqKCjZu3EhaWhpDhw6l\ntbW9Etr5dsPq7xwOB7Isd7rlp6BSZBn5D78CQPfoM+ClHmlieCAFdVYenqlur6prK8djtSt0kl/X\np5xr9m9IbU8iS4kJYsXC9iB03ago1ubUothsKM1NfD7pDu2xlnP26JWiTTw3Xl0pFGbvfLe6vrJp\n0yaOHTvW6WPOqbr58+czb948KioqSEhI8HnJW9clfP7AGfTHTgriVLaV8EgdgUaJhCEBDEmOAPxn\nN8Xu8ijo33XXXbz44ovcc889WiWmyZMn89BDD/VKI8aMGcPHH3/c6WNPPfVUr7xHf6bInn/BcnNz\nMZvNXHbZZW7Hy8rKqKyspLa2VhuBmThxIocPH/broP/FF19QXl7OL3/5S7+ZX/O6vPYiWUpVOVia\nwSEjhfZ9TYvkyEDSTMFux6pb7EQEeTfq3zYuloSwACbEt+cCKXm5yK/9Fd39T8DwUYQF6FAAR2Ee\nL6f9kP3RoxkaGUhZk41vcmr5+dT2vAJ50hU0nzhFSmMR0yuPqq8nO5B0ff+5OhtWHjduHIsWLdKG\n/UGdx01M7NtdBS8lNZV2FAWiYvSUFNoIC9cxakwQo8a0rw6ZNisUkym6X05J9BaPgn5YWBhPPvkk\ndXV1VFVVYTKZ/HJZRr+Vd1K76Xj+D+h+9TskQ8eerdVq5bvvvgNg+PDhbpmyzvKbOp2OM2fOAGoJ\nTn8P+iUlJQDYbDaP56wGnKb26Rvli/9F2fg1NDWif+7dPn3bc7fHTYps/++TV2vt7Cl9JjrYwOI0\n9xUASs4RqKlEfv2v0NyEYcgcSLqGF77Yy+bB6lTh8wtH8Nt1BZxxKbFrlxVW7FKXI84tyyJAabso\nt1ohuG8q3DlZLBbKysoYMWIECxcupLm5GZ1OR2hoKOHh4W5BX/Dcgd1mivLV3v3gZPW3NTLGvzbK\n6S3dGhOKiori6NGjIuD3tjqXHaqOHYDazhNIduzYod0+e/as22PO8puSJGnz+c4hcX8J+oqi0NiW\nDOZwOPjqq/apDn9KqvE2xdI2/BwcCo31UFwA9TU4fv8A8p4tffa+stK+/h3otMSuL8hffoi88k1w\n1qGvroCWZgJb1NKpmxPULP2Xb5+AXicxKSEUhwJrctSlWnuLmtjWVr53mLkMQtuKC7lU6uuJhoYG\nysrKzpt34cy/iYuLQ6/XEx4eri3ZE3qmtKhVC/gApYXqbdOgS3fe/ny6PRH0xRdf9EU7BjSl9Zyr\nd5f5eO0cReHIkSPafdeNNU6fPs3OnTuB9qzfcePGafN8BQXulc/6o+3bt/Piiy/yzjvvUFRURFNT\nE/n5+drjR48e5dixY35ZcKiuro7t27dfdC/N4XDw/vvva8mtmragr/vjS+CaXFtWjPL1yot6z84U\n1lv5xZen2Vvc5Bb0Df0g6Cu2VpRvVqFs/Fod8XCKS2DObTdod2cNDWdykvuGMG/sK8chKzy7tRiA\n38yIY9LP70W64+fqa5882qM2tba2cvToUd59910+/vjj817AOoeVk5KSujxH6J6T2eq/uyuuCiUh\nqX0EVefB3g29SbFacLz0J5TsA15933N1O+j7449uv3dO0Jdf/GOHU5w94ORkNRPZ2XtXFIW1a9cC\n7ltejhs3TivJ6Szo0V/V1NSQlZWl3f/8889577333M45fvw4Gzdu7DDC4Q8++OADsrKyKCoq6vFr\nKIpCfn4+dXV1HDp0yP3BU23FWYKCkaZd7f5YWc/fsyuF9VbKm9TekuvwvusGOP8xyzdzzcqBXe13\nGtuXZEk3/Yio8elMTAhhRLSRW8e1TwW4lhDenN8+VTI4OhRp9HikCerIAA3uS7yU2mqUipLztsds\nNvPaa6+xadMm7djatWs7/R2VZVmrN9JXG+UMNFarTH2tg8ShAcQnBjBxajDDRwWSMCSAGJOXe/pF\n+XBoD/LHb3v3fc/R7aB/1VVX9UU7BjbrOVt2Vld0+FGw2dQfWWdpTWdP32w2oygKycnJTJw4UTs/\nMDAQo1EtKtLQ0MDKlSspLi7uww/RMzk5OXz00UcAbhs6OV177bVu97/88kttnt/bFIcDpTCv2xe+\nzgs01x3QuqugoEDb0ORcNuewc2AQ0rIHkG680+1xpZNh6ZZmmczvGig523FU6ULkLj6+a09/9rDw\nzk/qRYqioBTlt09vgJrECO1z74YAdK9+hm7GXCRJ4plrh/L8whGkxrYnH8a4bNvr3LwnY2QkI6LV\n5X+EhIHBgLKzPXADyH97Avm/7j9vG12XzjrV1dVRWFjIrl273L4TzttpaWmiiuZ52O0KVotnU5Yt\nTep54RHqCJgxSMeEKSFMmxVKSKh3Vzwom9r+/Rb7duS125/65z//eV+0Y2A7d3gfwNyoBpkDu1Cs\nanIPoCWzOX8gnAElLS3NbY2s87ybbroJvV5PRUVFl0HDlyorK1EUhVmzZnHjjTeydOlStyJM48eP\nd9tEBGDdunXebiYAyvffIP/xYTXvogfO3esc1MStoqIiFEXh9OnTrFmzhjfeeIMvv/zS7XkbN24E\n2osuOWusFxYW8roSwYm0KUg6HZIxCN1Nd6L702tIt/9UfYGm9h7qvu1mMr9t4GS2hcZ6mTO53Z9y\ncA3680e15/e4zul7Y6WFsvlb5Kf/HeWL/20/6Jwai4lT2zH96k6TYl0tHB3VHuDb3DouRvsMkiRB\nfBIU5WsXfPL2DVClJvsplWVdvva5ozvx8eoKgfXr17Nnzx639fjO74fzHKFzuzKbWPdlAw7HhS++\nnX7AMzwAACAASURBVDspR8f6LmlPKT6L49dLUfZsvvDJXuDR+EZTUxNfffUVBQUFHYr0PP30033S\nsIFCaTajfPlhxwdOHEaRZZQ3n0O67SdU1ai9NWcP4PDhw8yaNUsL+jqdjvDw9t6V83ZYWBgPPvgg\nn376KSUlJTQ2Nrqd52vOrPwpU9Qh1NjYWKZMmcL333+vnXPDDTdw9OhRGhsbOXnyZKfBs6/Je7ag\nrHoLAKXgFFL65Sh2O8r6L6EoD+mq+TA6Hek866WdNQegfSrm008/paamhvDwcG0Kx6miQh3xWbVq\nFQBBKFyTNpJvT5zm8OHDZGRksG3bNgCqAt2XzUnxiZA4FAVQco8hDVKH20uL1BGjxgY1OLa2dn+6\nzuES9RNcdp3z+px+W3EdpdZleZXzAjoyWu1RhXVexdNVgF7HH65J5u7PT2nHhkS4XwRIk6ajFBfA\n6eMwahzKuy9oj8nP/Sf6v3Y+ZOv6XY2OjmbSpEmsW7cOs1n99+yaZOs811u74/mr2mr172RrVdAH\nu3/nFEXh+CELUbF6EpMDUdq+q97aIEcxNyK//P+g+Cy6X/4nUlo6Ss5hMDcizVsMioyS+a1X2tIV\nj4L+P/7xD+x2OzNnzhTLpnpbF3OCSkkh8tcfkRU3gsHbN9McEEZw1CAiIiKIjo7WfiBcg0hwsPrD\nP3To0A49rfHjx1NSUqJNE/QXJ06c6PCdcn6OyEg10SoqKorZs2cDao3vkydP4g1KqxUp0IjSakV5\n87n246s/QI42wbED2tW7smcLjJmI7tdPI7n8aDc3N2u3jx49yrZt27Db7VxzzTUYDAZqatSVG+cG\nfIAtW7Zo/x3joqK4ftcaIo9sJHTGzR3+Oxo7q709fJT6//W1XX5Gc2P3V3bILtMb4we1zz13tud9\nn3IGVNfpi1YrSBJSfCJK9kGIG+zRS0UHG1i1ZDQVZhv2TnqQ0jULUNZ+jJL5LdKoc3avq6lCsbSg\n7N6MNH0OUlD7BdjJkyeJjY3l9hCQ0qdQgHtAdw36zlEBEfQ7l3/SSmND+0VUUX4ro8a678C4e4uZ\nyrK2nRVnS+zdpn43vFG7SGk2I/96qXZf/udyGJkGWTvU7+SSn6Gs+QQUxWs1HzrjUdDPzc3ln//8\np6iK1hea2n7sY+KgphLprl9R/OkHFJ0tIS7cxO6EkYTaLJgDggiV1S9zQkKC9gPhGvSNRiM/+9nP\nMBqNHd7GuSFDf0vEdDgcHX7kUlJSWLJkiRb0XRkMBq/09OVN36B89Ib6I75YrdwmLbgNwiJQPnkH\n5Z3ntXOlm5eqozUnDiPffyukpKG761dIScP47LPPtPNch3I3bdrEqFFqUE4PNlBQ30hjYDA6ncQ8\nycIGh9Etd+GO6Zcjb/0UgNBg9cJn9uzZyC3qRYXcyRC2FB4JgYFdLjVLHh5IYX4rSjfK5oL78L5P\nE/YdbfPhOUdQKsuQ4hLA1gqBRqQlP0e67hYweT5UHmTQMTSy478dADk8Cos+gOAuooeydR3Kx2+B\nIiPNXQioWfuyLKNXFAxrP4ZDu4h44HfuH8HhwGq1UlFRwa5dahKiyNzv3JH97pUROxver61uz5Fw\nBnwAvcELX9TjB9tvR8ZAXbUa8AHp8iuRdHoU52+dwwH9OegPHTqU6upqEhIuvka24E45ma3ecPYO\ngkL4YuRUkCE5tq1meIB6NRunU7/kkiRpwd75/84f7a7W9DqHk/t6zb6iKOzatYvBgwd3mpgH6g/d\nF198QXNzM7Isu9URd7a1q3lNnU6HzWajurq615OdlKP7kTPXovv541qVO2X3ZpS24knSyHFIk6Yh\nV5SibP4OQkLRPfUCUmwccnwiyvYNcOIwnMlB/uw99P/+FLW1HXvZkydP5uDBg5w6dQqj0cicg+uh\ntZVqYyjB9rY56XFztPONeh3yc/+p3R8eoKMCyNnwLY7qCjCGss+icLnFou12pgkOhRb1x6+qwn10\nICSsLYApQDd+E12Dvk+rJLpc/Cm7M5EW36H27g0B6mhLXO/9Xq1Zs4b8cXP4qcNG2MHdHU9w1tpo\naQ9M2dnqv+2JUW3/JsuKiI+P54EHHqCkpIQvv/wSh8PBG2+84XYxHhbW95UU/Y3d1jHAd3btLzvA\nYABnfmRYhI6RaUbCI/u+q6+0rRbRPfceNNQhv/z/kK5ZiDR5BkS3/Vbp20Kuww4Bvhk19yjop6en\n8+c//5m5c+d2KMwzb968PmnYgFGr9v6kWRkon7yNJaL971sY7h7UTOVqJrBer9eCt3Pt94Vqb/dl\n0D9x4gR79+7lxhtvpKSkhL179wIwd+5ctxUFoF4U5ObmuvViuzOClJiYSFZWFmVlZYSGhnYMch6q\nra3l2LFjTJs2DaPRiOJwIP/jv9U2fvgKyq5MCAtXR2KcUzBt0w7S0geQbr1LXSLXduWum3YVTLtK\nfZ1nfwOVpSiWFq1s9cKFC9m7dy9TpkwhNTWVs2fPUlNTo47AtCWfxVrb5nl1epIcVoZUFjG6sRzJ\n+es2Oh1yjzJl3UfsmZDBjsIy+P/snXecVPW5/9/fM2177x12aQtIRywUCaIi9hRMoil649WY5Kb8\nck27iUlMTLlJvJoeY0s0tlhiRYqIKII06X2BZRe2t5nZaef7++M758ycndndWVjQIJ/Xixc7Z06f\nc77P93mez/N5XJFJXn19vRk9MJGcgv+9zax/rZuOtsgoWTHSiWGvdQk2QDYeQe7YQnDa+YRcyWaa\npS/0D0q0KBRUuXtfL7h7wstCQ5rAJAKjXBLgAa+DTz3zKH3lyWSjwdKP3JtNmxThs8YXOTcZDOBw\nOMySPJ/PFxN9e7919D9IaKz38+4aT9zvmhoD1E6KPKOd7SF0HXIL7GaI/7x5aSQlnwaDX7cX+dyj\n6kNqOiIzG9vdf4ld0UjDnUQlz8kiIaO/a9cucnNzLeIwBs4a/ZOD3PwO1IxDXHwV4rz5+EP9G2UN\nidR1hBDmQGFI7vY3QJvbnkKjv3XrVtrb29m4cSPbtkUETN577z3T6BsRgK6uLnbv3m3ZfijEQoPJ\nv3z5clasWMHtt98+oLe5dOlSgsEgM2fOtCxft24du3fvpqiwkGp/D50P3UeS0HBIHfm2IhGK629B\nJKei/1+YrJqkBmohBPSjay9sNsSIUciVL6H/v89in3E5o0aNoqamxmKQL774Yg4dOsRYh4S1Eaa+\ndtcfKBgznuva29Gff9QU1xEz56D9xzeUsMc/H4k5LvTz2wqNxrzpFoOfk2/jnKku9u9WE8aujhDd\nnUFKfvNN/L5e/rL9AHa7nS984Qtx+3S7+3ake78QCikxopS0iBRxTxdi6vnDepg33rCqGtb3+i1G\n/62iGoIt3cwBOB4piw0Gg6SkpGD3ecypgFzxImLh1SaPZfXq1THHi/c8y442el59Gnn+ApW2+ZDA\nIJ7GQ6/XOlnq6lTPeHGZg+ZjQTQbOF2noYokGED/+bdUaqmiGjFQb3sjvH+8AZmSNiDx91QhIaP/\n/e9//1Sfx4cSek8XeD2QlqFe9PQMggM0ehASCPjRNC0mrD9Y6sUw+n2rL4YDRk4+2uBHnxsoPQEj\nAgCRPttCiFjvdABEpy+klIRCobiGac+ePaxYscLsNhht9Ds6OsyJR2D9alreeo3HRp9HebWdqyaO\nVfl6VxLazDnWGvecPPPPYDBIKBRi27ZtjB071nJeYvYlyJUvQa8XPRiM67kVOO3kh9zIx+4PbyRA\nSsgpiEQPrvwkoU3vQP1BKFCENFE7BVvtFPg/xR6/NNDOK45s835EI+CXUDuNYJ+y4OQUTTGMewqg\negmblzfSI9PpLZhHm3eHeX3Bpc9iX/TRmHN/Iyxgc+f88pjv7llURQKVVMODUFCFS1PSkGtfRy5e\nogR50obXKBoT6/6wKb8KgDmNu5F1+/B6vTQ3NxMMBhk/fjzs3QC5BUoOuKkB/e+/J93jxqYlmZG6\n8vLyuDX9BuSGNbifegheehrtlm8iaoenw+kHHW0t8fk7WTk23D2RyafHHeLIAXUvC0scXHqtE5vt\nNLH2uzoh4Edc+xnEpdcOvG44vK/f/U20ex6DlNMvsZywJFFPTw8bNmygra2NnJwcpk2bdjb3dJLo\nXf0aANqFC81lBtmrIDuLBXPn8uizES9QF4CvF03T8Pv97Ny5k1AolFCI21jnnXfeITs7m9bWVqqr\nq4flOuIRB0Hp5RsksWgRkry8PD75yU/idrux2WxDDmeWlZWZRMZ4Rn/fvn1mYyIDwWAQu91OY2Mj\nTz75ZGT5nu1sz1HEqaMBHe38+cjCEmWAgW37D7BtzPlclSJJSctg7969vPrqq+i6Tm1tLTt27KC3\nt5cLLrjA3KcoH4H2pe/R9sdf0tXTQ0k8z+2pBxTjH6BsBNo37oJeT6yXYAxaWfH5C9Vf+Q4fb2nh\niSeeiPH0lz7XSapYgD1b1ZO7kgR2EWRcw1LY+i6ibIE63642SE+nKW8S7uN15vahd1ZBlNHv6urC\n4/GQ7rKRmWRjcnHsgFWVfWLplhNC2NMX8xcjH/gN+l3h1tsFw8s9ikccFdMvRL77ZuzKgQArVqxg\n//79ANh2v4dctxLKRkBppeKChDFv3tUsb1Wh/9GjRw9o9M1wsKcH/d4fov3675YqgTMVAX/kma4e\n4yIYlBw64Cc5VaOnO/K7bHrHQ1uzUfIIDsdpKtHr7kTuUc6OKC4dnONii3q/36dKuITZ+z/96U8p\nLS0lLy+PjRs38uCDD/Ktb32L0aNHn+pzPGMR2BVOl4yPzNoN1byFly8mJyeHwsJCjh8Pi4AIAb5e\nRowYwcaNG9mzZw9paWkJlfjk5eWhaRpNTU08/PDDACxatGhIXnY8HDt2zBzgDER7LYZR9kf1E7j0\nUqWBfqKNRC6++GJWrlxJXV1dzIB85MgRU5b43HPPpauri507d7J06VIqKioscqgAK3MiPcDz8pQn\nL6ojxMK1a9fidabwanE514Q/G8bVIGpt2LCBxsZGFi5caAoLBatrWVuk7q3dbldeeHsrIhwtkA1h\nOeGcfLQvfU+1wY2TMtCu/QxyzzbE7Isty417Z7Pbzb91XWfp0qXs2rWL5ORk8lMX023LINWuohUL\nr8ok9KvvwU4l46uFu8d1p6t70J49FlvHq+Yx1qQXcfCV9eg55dw6s4innnqKnp4eGLOYsoz3v3RX\nhj197fz5hF5+Eo6pd0ecM3OQLRNHKBSylF0aENfeiOz10HT+JfC2imBJgKYGy/tQvCMsL52UjMjO\nVbX+YYzCz/Lw3xkZGSxZsqT/ypSA8mLF9V9APvYn6OqIkH/PZEhIz9DIyrUzZmISNptgwtRkdr7X\niwzPB6SUpsEHsJ8Ggy91HfnYn5CvvxRZWFQ2+IbGWD31/EFFo04VEjL6Dz74IDfffLPFm3nrrbd4\n4IEH+OlPf3rKTu5Mhty3g943lkJeYcyPn5aWRk6O0gY3CGDbtm0jy+dBHtxDsZQUejppaWpKuJmO\nEIJrr72Wp556ylx29OjRkzL6O3bsMLXCo7Fo0SLeeOMNMxJhs9lMYztnzhzz2k4U6enpVFdXU1dX\nR09PD36/n97eXoqKiky2/JIlSygoKMBut7Nz50727dvHvn1KfGXEiBEcPHgwZr9NTU38XzhsXlNT\ng8fjwRtmYx85cmRAGd2GhgbWrl3LwoULqaurUx0CM1UFwnnnnQcb30L/w88gPROqRikd7jET0b72\nwwHrdcX4KYjxU2KW33DDDZF1wt7F8ePHzT4LXq+XVrmB/Izz0YWN0rJwNCVKyKageROM+5xlv/rY\nSRDub7DLmQ573mZ5bjq3zixSBh9UWRqnptxo165dSCkZN27c4CsbOX1Au/nryKXPwpiJiPTBBXkS\nhaGjYFRcmMjMxvaVH9ATpRnR4Uxhc36F+Xnh4a1U9qjmOtqV10NuAXJ9JIdve/0lzlnyZXoDQbOn\nRjzIQMAkiYm8QjW5cHcDiWkQnCrIjW+jP/h/iAs+gvaJm4e0rTH5FNd+Blm3F+26zyAKItcTCkp6\nvTq6hIISh4Wwp2kCTZjBOEuYf9w5SadHDfLN15TBz8xBzLtMjeOJGP3wpE4440dHTwcSMvqNjY1q\n4IrCrFmz+POf/3xKTurDAMPT0z5l1e7Ww0Q9A+np6cyfP59ZhTm4frMM+fffKz3xvBrcXmvd6mAo\nKSmhoKDAlHA9kXr37u5ujh49SmlpqWXCMWbMGHbv3s3MmTNxuVwUFRWxc+dOvF4vR44coampCU3T\nmDRp0pCPGQ9GaumNN94wKwFuu+02k1dgTCzS09PJzs42JwOpqalcfvnl3HfffZb9VVRUWJr5GBOE\naBw4cID29nZqa2uZO3cuq1atMr19UAbLbrdbpFeL3B0kOewR1bjuTtj6LhSWot34xRMW6IgWNDLS\nI305Fe7eA7js2STbihGb1iCnzrXUBjuCsfX7en916FF8ASklgkjKZDhhSCwnZvSDZrhUVNYg/uMb\nw3ouXq/X7AsRM1ENT9Sj36FHx0QIhP+5bTk2KdH++2dQPhJhpMCKy6ExEsafk+ZAO3dBv+cgvR6z\nfBRQk0ZA/80P1ISx8uQidScDfekz4HUjlz2PXlmDNmteYtu9s8qMNsl/qsZaMjMLrv2MmbLYvM5D\nQ7gFbrxHUmhgZLOOHFRRxFlzU8kvOvXes/T5TP187ad/RpyIfk1m9jCfVeJIKJlaVFRk6eUO8Pbb\nb5/ViB4CpJRmmFBfvVRJumoajJscs168mWpy7SRVsuVxQ1cHImoQvq51f8z6/SGaKT9UJn9DQwO/\n+MUvePrpp+nu7kbXddLT05k7dy5z5szhtttuY9asWUCEQ3DgwAEz3HnDDTcM2yy8rKzMPCcD69ev\nN42TYYxsNhs33HADCxaogfWcc85B0zSuuOIKZqRHZttjxoyJOYbD4cDpdJpNjozqlaqqKhwOBwsW\nLGD+/PmUlpZSUqJkbrdt20ZHR4e5D7seUmHnPgRK7Xu/MaVxTxZ976nLnkd6qpG28KFrdrRejyo9\nNIx0fhGaHsuMDvVTPVLXEdHoF1KSdWQtf/zjH98XSWQTB/dYWwkPM6Jz7NFCUVKzmaxr4x2a2BJZ\nt7a2Fsf3foP45H/CyNERgw/mYC9mh3k83vjlaAb0u76O/uv/AcAxbpJihy9eAp4eZP373DK74bDS\ngQAlT5wg5F/+N3bZypdMaWPZ0UrT0cj7Em/MMJYFg5J9u9SzeVoMflsL+lc/hVzxgvLuh2jwxZRZ\niCuWIBZedYrOcHAkNE3/7Gc/y913383LL79MXl4ezc3NNDY2cscdd5zq8ztjsHz5cnbs2MHChQsZ\nvXcH2Gyk3/QVPH0GrdbW1n4fclE1KkIaiaoHzm08pLThN6xBu/kbCT+IQx2wo8ObhtqYy+WK670b\nzX8CgYDJlI9upHOyiOYxJCUl0dvby44dO3A4HIwaNSpm/TFjxpCammoa5xEjRlD4u7W05I5gwidu\noKCggKysLMrLyzlw4ACTJk1i+vTpgDLkBmGvoKDAkhKZMGECEyZMQEpJZ2enyZcwMKrzuGpv6+sF\nux3t5w+qSVs/5McTQbS3XZKzmOSkHBZdl8m9996LLjX8zgw0GUT/8dfM9bTbv4v+o/8i2duMN1k1\np5EyxJEj1tbFAaGepb3HI017JBJXz3FCxFdUHA4k1CPC642ZTA0HpJQEAgGTDHrjjTda9En0KDKW\n2STH28lWVIg+OTkZUT4CUT6CvtA++jnk8n8hrrkBuXpppCtgf4gqAcz48nfpsNlg/uXIF/4BPV39\nOglSSuRffw2llWiXXoesP6jKBT9+UwwBUOr6kEvHpN8HXg/i6k8j330T+frLyOtvGXQ/0R0RxU1f\nRVSOQv/5HepaNqwBQP/dTxEV/wUOFc1zOGOvz3jkDu3zgYS8wtPTJle+87oqzSsuR3zkiiFvL5JT\nEFd+cvhPbAhI6JceM2YM9957L5dccgkjR47k0ksv5d57743rHZ1FLLq7u80w8Jo1a5BeN+QWkLLw\n6ph1W1pazDxyDKLKO7QoT98mpdKG3/i2mn0PgBkzZjBhwgSSk5PRdZ1jx47x7rvvJiTPG10lIKVE\nStkv894wBO+8o9TLpk6dmrCXH7rr6+j/fHjwFcPIycnhnHPOIRQK9VvCZ7PZqKysNIWApK8XV0cr\nizLsjBgxgtTUVG688UYuuugibrrpJtPgR19La2trv+FsIQRZWVnccsstfPrTnzaX2/UQsrMd+eZS\nQCDSMxClFXH3caJwOBxcddVVlOZehcuRQ2qapiaJCGR4cpjsjeTyxc1fR5RUQFYuMzb9wlze7VX5\n6ezsSOjRHzb6q7ZGOsmlN0Qmf6dK1nmwNtAyGFDcgmnDW5MP8Oijj/KHP/zB/BxTpRT1zBscCkfU\nBHqg/iSishrt8/9lNgKSTz9kIfdFI0a0xwgJO9V7KJ96QEn/9t1u01rkS0+qMsanH0Lu3or+3GNK\nKni9VRdAX7sS/Zar0ePsZyDINWEKYnauyTkxPHV9xQvo696Iv2F4fBJXXI826yJEcRnal1Ukg8oa\ndc0H90QS9kBVTez9LC5Xz2XzccWzqRh56omlUkrkbhXt0777K7S5l57yY54KJDy9S0tLY86cOVx1\n1VXMmTPnbLneEBAtxWqz2VReN6X/+9dXltaAqBqlavqnX0hbUtQEIMrrl++8PuC5FBQUMH/+fJKT\nk9m7dy8vvvgib731Fp2dnZb1gsEgXV1d/e5H1/UY/oHlXPssN8L+g0H2eqFuL/JlRTiUwSD6g/cg\nd28ldN+P0aPYsnPmzAEU695ut9Pb20t3d3dinmc4pygmTh9kRWs+12D49weXy0VOTg6TJ6u0jSsU\nRP7jz0rZL3jqmh1VVlbitKsQ9Ky56tkSmoZBcc7qiqSAxEx137QbbydlzEiKSmycMz2ZDre6J5dc\ncgm33HILY9KSSNU9pEmdyb5IY51Uz3HzbyPyNByI13Gu33X/8LPwyQzfOLRt2zaWLVtGa2uruezG\nG2+MmejphZG0jDHpTb38OnNZIhVN0e+H/qvvIeOl2oyKl9opaN+/By05/BtETSrksueR3ZH3VPZ0\nof/uJ8hn/xbZ/z13wmal6y+3vqv+DxtVo8e7fO05pC/xqIkxzogp5yGu/QwkpyIP70d/a4VitUc1\nqLIgrJwYTU4VI0bDuElw9JC5nSOoIiCllY64tfbGMkOe93S0zpXP/l211T5nxvtKxDtZJBQTaWpq\n4rHHHqOuri5G3OX3v//9KTmxMwnGC5adna1K1/bvgtpYRraB/urexfgp2H79N2TjEdxPPgPAorrN\n1pUSNCzGQBavxSfAyy+/zMGDB7n55ptNydCWKOEgXdcH9PSjUVRUlBDhS1+zHPlknxalTQ3INcuR\ne7ZD8zHklnUQbmgyefJkJk2ahBCCjRs3mpv0ZUJLPQTHG6CozBxspdEIY/LgkxFDBRAw0wODYdas\nWVRWVlLy4+XmMu2L30lo2xNBa5PyeKpqnKbsqBAakrCIU81Y2LAPMf3CSJ/4cZNwjJvEjPA+JCEy\nUmpJTcnF5bKR5HJCTy8TdT2sDBWL0MO/g18MzUvsD9HE0IEqJeSB3bBlnfowDIOvruscOXIkppyz\npKQkRnYcsHTZk1JSWlqKfeJ02K7In4nKSosbb0c+/hdVftfVAVl9yIJ+NdaKc2YgyiKpgr4Tav1r\nn0a7/buISTNhf5Ta5ejxiBlzFPnXwKa16O+sQj54D2LhNXC0LvJddye4+tdZkPUHkY1HoW6PGsMm\nTkeEJyJi4nTkulWWRlTRoXxQaQRT3TK1T+rG6YJgALl+NRKBJ6UQl0MyaUYK8WDM6402u6dDhEeu\nVc+H9tHPnvJjnUok3Fq3sLCQG2+8sV+DdBb9w/D07Xa7GboXufkx6xmTg0HD4A4nmtTRhUam35oT\nlCtfQs6+JG4+MRqjR482WfzGsevq6nC73dTW1polbb29vabRP3YsEuLVdZ1AIDCg0a+qqqKuri4h\ngy+lRD7y20jntPAyo2c6zcdit+loVW1jK2sYOXKk2Vu+pqYG2dWOfPUZfOfNQx7Yg3zkd1BZEy55\nkqr7VXKq0tcfBNG/R6Id0JxOJ5WVlYSKy6DxCGLxEsTkcxPadqjo9eq8tTLsQUUNfjabwOtrgHRo\nmXcpvisXU1JSwo4dOxBCWBjyO3fuRNeDCGFjxYvdXHJ1Blp2CbR2UeDeT2OgH9JYd0f85QnC7Xbz\n5ptvMmrUKEu74FWrVpGZmRm3aZO+VE14GTEaMWHaCR/b6NJ25MgRnnvuuZjva8dOJRiU2Pt0aGt2\nR945KSU2m80SXRpMEtuANnshMjUN/fd3I5c+AzW1Srd9zAS1gtESOd6YWz1WkXrDlQCyuREB6H/6\nmbmKGDcJMfYc+k7XDCKdfCksUjVmIuzeCp4eIJacLVuOI595JCImZew/Ss9C+qOInjPnqHX37YSy\nqAm4pyfyd4ZVNVE4XeZ5Bm1q4pGeGsJmiz8W9s3zD4earezuhL07CIweB2nWyZ6UEjo7EPMXI4r7\nL6/8d0BCRr++vp4f/ehHZxtBDBH19fX885//ND8bIeijqdmUxanpNIz+oPfZ4WTO0d3szSokwx8b\nktPv/ia236oXWgYDyGcegfRMtEsjIci+3ojH41G15VgNW98IgCGf+9xzzxEIBAbMXxqM54S64TUf\ng1BQDRg7t0B3J/LVfyKffihmVdnZjsjMRv/rb2DnFrQ77yM5Vw1W+fn5cOQg+j/+BHt30LH02ciG\nh/YhD0VK8bT//GbCPIORI0dijxLCSRTi/PnIpx9CzLlkSNsNBdEdyFLTIs+OlJJAqBNdD/Bq+D58\n+ctfNrUVoo3+a6+9Ft5I/d5+n8SdUwLswhNs7PfYJ5vRr6+vZ/fu3ezevZuLL1YCRMYz9vzzz3Pr\nrbfGes6N9VA7BdtX7zzh4x5vDPCvx/cz9bwUS/Ry1sw5HKurRMog+7c7CHi9Md5ma3fEeBkp93NO\ntAAAIABJREFUruzsbKZPn05VVdXQKlTSlXGRrz2HfO05EALtfx9BbngT+Q/VsEXkxDoI2n8r465/\nIcwCb21WKY8oESxx7jzVbrigBJoaEHMuQb6hxJfE/MWKgQ6Iqechd29F7t6GqIhV6dT/9jsV1p56\nvmoXe2A3YsGViAVRDPSw0ddu+zZUViPXvYH+9IP0JidBdTgyEpa0Fpdcg+ib3kyO3GMp1DNckOWj\nPwghSEnV8LjDrcX7mRwkAtndif4/XzT7N7ShcvaWckifVzkkOQOn9/4dkJAVHzdunNll6iwSx7p1\n6yyfjdDwxvzKSOlUFIbi6Y9vP8rVBzfiWKJEMcSsiyLfRxlq+dJTyKXPKkKPHsmT9g1TR5MHDT1w\niORWjXMzSuUMryxaaa8vZs6cyaWXXsqFF1448PWA+cKJc+eife4r6phxDL66pieRgXCaBJCrXsEp\nQ3zqU5/iqquuUm1o9+6wbjRuEoyMEE+1/74bMUCKpS8WL15sKgkOBdql16H99klE9vC2AY5GdPo7\nJy/yXBlVFYeaHzOXrVq1Ku4+jMqK0mI1EdB1CNmUx6rL/n/jkzX60WF8Y4I5Y8YMc1lfrons9UDD\nYUT+yZUL94Sbs2x828OGtREjnppSghACTVMTjV5v5F1aeDisoCmsEytN09A0jfPPPz/h9I+JqhrE\nvMsQC69GXP8FkBL52rPIv/8BbJqKEMXR2RdCqPP8gdKbkMueV+z3shGIj31eMctzVVpK+9bP0b77\naxXOB8Sij6Fd/wW0b/xE6fiHeS3yifvjcwsO7oHcAmy33oH2tR+h/b+fqCqAqAiEqAobyJy8iGR0\nfR2dd0dVeIU9fTFqfOz1hHkm4qLLkbVT1d9y4JJiPdzjefR4V0w0ZiiQm9+JNGwKjwn6j7+G7PUi\nd25BBgKwQ/FdzBLFf2Mk5Onn5+dz1113MXPmzJgc1yc+8YlTcmJnAvr2Uh89ejTH6+vxeLtP2ugb\nELVTkEVliNkLkWtVdzhLnjNKgQ2vx8yl9f0do0P30YIzoVCIlpYW0yjk5OSQkpISV5q0L5KTkxOX\naTZCf31zfeMmIWZdZM0VrnhB5XWNCcmKF5DNx8i5Ygn6HVECLXYHjtHjCV6wIKKVnl+EuPbGuN7T\nqcKpJv34eiODY7S094gRI3j33Xct627ZssX8u6GhwTRSDoeDkSNHMnFKPutWu9FDErstAQGRk9Rd\n2LMnIjxjGP1osuSjjz7KwoULGTNmjHovDoeb35SPPKnjGrng8IHNP5OTMoCI5+9Kihj4UZ3HOdCR\nT0tmRtSm/ZNZE4FwOBGfuhUA6XErElyYxKrd9HXE1PMG2hyKIlE5MWse2k1fi1lFpGWY1QLaLx8y\nBX7MNAIgFn0c+dITKsw/LlKCK4MB8LhNr164kpReSN9jLP6ESl9VVKv7kZNnpuaMskK5XbUajkdi\nFmMmot31R8gtQGzdDrsHN/pjJiRxcK+fEaNO7v2S74UbgRWUYPvqnSS99izuJ/6K/qWwbcvKRUxX\narRi0vBJPL9fSMjT9/l8TJs2jVAoRGtrq+XfWfSPvgxkTdMQAlqS0+mVsQNF3855/SJqwiCKSrH9\n6HeI0eMRN94ePnAUCSqKkavf+RWVByc2hRCd34/G6tWrefTRR02lNE3TWLJkifm9waA/GUgp0f8U\nLh1LSTMHKABROxkRZkuLj9+EdsfPVSiwbq/1Ore+i/6TiMEXl12H9vUfk3PX79BmzkFoGtrMOWg3\nf/20GvzTgej2o66oVqKDpSKivWhd15XHGk5NNx4NoMvBh4eT9fSjI0VGN7ukpCRLVGXp0qUmiVRu\nfBtQRuJEoOuSdes2cehIOEokddrdm8zv+9oZqUeuUFz3GWxJyfgDVt7JcAlOiZRUxOUfjyzILeh/\nZWObKC6BOO+iAdYMr5OZHbeWXkxTkwvZ1mz9Ys929X9GLKHRsr3DiRgx2rwX2qe/GPmysw15aB/y\nrTBRckR8R0AUFCNsNmT4moQ+cAVHxUgXcy9Jx+k68bSzbD4Gm1VZsfYdVTmQsug660odrchlKvU5\n2H34d0BCnv5tt912qs/jjEN7e3tMpYMQgtK8XI4cO06fVtCmXK2x3kDo73tt9kL0tmbkC48jm48h\n8ousDNr2FhUSn3ZBzD4CgQAulwu73U53d7e53Gj2Yyyz2WxmuWZKSopZmnZS8LpVFCK3AApLLN6j\nmDgdUVqJ9rP71Yxb09C+9xv0b38BisvRvvpD9Pt/pTwUgPIRaP/9M+WRfAiwbZOXwwf82B1w8RWZ\nlmYj0eSyrKxsOjqskae9e/eaeX3lsWpk56ht9u30sTrUyaBq4idh8KSUHD9+nPT0dLq7u032vt1u\nN9NIBoLBIPr6N5HL/wXpmYiixAiVfbFlfSdr31G16lVlIYS9lZAeeU93bFF/F5c7aDwSIMrmo116\nHUJLx7NrF+3t7WRnZ5uTpeGCdvWnkbMvgcbDUDHEaMbIk9BNMULy4UmY1EPQ1oL+sEofiNGxIfmB\nICZOQ3zyP5GP/gH9z/8LRmlnTe3A/ebBlIoW8jSoPfaocU275Zsmz0BkZEH5CGg4rCIjx+rR77sL\nMWP2adH1P9U4PTJGH0L0zUWCMtbZqYqwEorSQA8EApY+BicziIiqUUhAHtyjSDw+L9SMQ8yYrUKH\nW9YjpqlQ1ac//WmOHTvGsmXLzDa3NpvNwqI2YExgDEPy+c9/fvgGu3C3Qe2jn431QsI5tGjvXOQX\nqV7UTifC7kD76GfR7/q62sc37/7QGHyAg3sU/6K0whnTXcyi1FdSwrx5c3n22Qixsbe3l0MdPr76\nrz3M6+xkZ28qf3hiDzfbVWvasVoKPQwMmbjURwwMhUfD6IOKTjgcDhwOh9nfACC4/AXkLlWWKRaf\neEpx586IXkFdvapdt9vtVJVPp6tNPTclFQ6mnZfKype7Yjz/8vJydu3axSOPPMK8efOG1dM3IHLz\nIU51T78YPUF58Enxy9sSgpGCChPy5AuPI//1D3U+M2YPylhvawnideuUVkZSj0FHCpvP+TKaHmC8\now5noAcRR5/f79PZ8LYHr0fn3DmpkX4KfW6+7GyH1DTki09CahragitP9GqjD67+j0orCiHQvv1L\ncPeo1EhNLbbf/P3kj/UBwVmjf4pghPaXLFnCP/6hXh7WvYHdpghChlndtWsXO3dadatPahAJ5+nk\nO6uQM2ar8NyEqYog89ifVFlKGDk5OWZKwePxkJ6ejs1mi1sjbQzKhqEfTnEm2Rxmh8drHpISP0Qt\nopeXVqqa4drJH4oe4wAtxwO8/bpiQxeVOZg4Lfa6XS4XF198Mb29vdTU1FgmomVlZfT29vLP7a2U\n96oIk1ezGo184cCZNh1X9zs0ivjiJ3GyVAnDSCnNmzePRx9VXeSKiyOd1iZNmkROTg7PPPMMoQ1r\noKcNMecStPmLgROTAO5wvwfAzBkXsW694sB8/vOfZ98OnYMe5eVOnaXug6ap1Em0YY/mwqxbtw6P\nx/O+9yCx/b+fnPxOwlU4cudmQru2wLaI7oW4/gsDbtrr1VmzXE0PC0sc2B2ChiN+NhydDOEMRYq3\nibETkhDTY0m961a7TY5FR1uITMMham0CVPRCBgPo37lFFeiHqwDk9AsRfbUNhgrD6Pfh3Qi7431t\ninMqcdbonyQ6OztJTk6OKV1zbwrnif78S7IrJtLe3o727N9wpmdB2URe2bEPX9k6M1cejYQMqqbF\nFSYRSSkq5/3eerNRh0jPUoPWmInQRzAjLy8PTdPQdZ1gMIjT6TRD+vFwKnTW6e5SXcvyogbPidNV\nN7oEvHbhcGIzpDw/BOj16qbBBxg1ztXvRDG6LC86bZOcnEx9fT22lqcwirQaklTI/JlgC9fYFZku\nM7WWi7qW8VcRP5cpT2KCGgwGyc3NtdS1970Oo1wvZCxPUyQ0t9vN/fffz7Rp0ywtvweDrvtxOJKY\ndd5E5s6bTmtrK0lJSei6IqbmFdjNc0hK1ujq0OlsD5GVo4bKaJ0Sg8yan//vzxERmk1xhbZviv0u\nPTPOFhFsfDvyLHZ1hhACNrxlJfqGiqrQFs+Nu3009UkPSbYdzQEkwhcZq+Ta1y38JABajsUKGiUA\nGQzAkTooq4wY/Q+R/szZwvtBoAU6SGt+Hpu/JeY7r9fLQw89xIsvvhjz3dv1ig3vqj/A4lkzuaws\nn7Sgj8J2ZVDdfj8vvPCCuX5hYSHFxcUmi3rQ87rzPrTv/jrud+LceeqPsNSsSZxJSo4x+hCpp6+t\nrbWEhA0N9tTUVFOgJ1G1saFAvrdeiZJEDfjarXeg/epvZ0QObbhhtBI1YBikwWBEaRwOh0VfH6C7\n8nz8mhr42ohEeloI4AwbZYctTsOkE/x9/H4/+/fvx263W9JEfZs3GZPMfZmFqqvZWEXg6+lRnmV0\nO2EpJS+88EJMqaz5vS6R6JQUqYhSenq6OcE2RGCiG7fUjFUTztbmyP3oe98gwTbA/w6Ip4JYODh3\norU5YrXXLO/hzWWxSSEtXPHg9+sE/NawvR5FnDi0309zU/jz1vXof/oF0utBPnRv7IF7+pcJHwjy\nmb+h/+TrqpS5H0//TMYJe/o7duygoqLijNfgd3l2kdL5Nimdb9NU81PLdwbzOLoFJ0DP8WP4NDsp\noQApQT+aHiCzqxkJ9DdElpWVMWvWrIRLgEQccR/zuys+gdy1RbV0BWXsAZGUjPTFGn3DexFCMHLk\nSNPTNyYAdrudiy66iLa2NsaOHTugJv8Jwe+LMR7C4bSUJp5FBJ3tJ0ZwMn7PwsJCioqKzOX1uZP4\n+VWqVrvVE+Dzz0Ty3hUVThyX3MSlf3mYHSWXcLjlyZM48wjuv19J9yYnJ1uMft86d0PYSU9Nx/b9\nu83lRgoqettAIMCBAwc4cOAAU6dOjVGCDARDSBnAZosd9kaPTyKv0E5BUeS7rDCpMTq13PfdnDNn\nTsIKfB94jB4PB3Yr4182ArHooxb533hoaepfLjkaR7WRjAdWvNiNpsHCq5SjEQpJerp08grttBwP\nWkophdSR61cj5l4Wd5/6ypfQRk1A9NN/QW5Zh/6vf6B97POW8kSjjDm6P4HRxOjDgBM2+nfeeadZ\nWnP99dcP5zl9sBB+46MJS4cPHzaZu/Gw8jWlenV+flhta8ObyK6B5UozMjJi5DxPFCIjGzHnUrMD\nlzAGpeQUpV/f67Xkvquqqsw6/ehB1Bg0q6qqqKiooKKiYkAFvhOGx51QudFZQEdr0FKiNxRHOzc3\nl8svv5zCwkLa2trM5e6USFolN8VBtIz5mOJkRJaT4jHV7PbEGjfZ7zQ2PjZu3EhOTo5JFq2pqTGf\nuXjEUCEEud5ugg7rUGWU8EVvE/0+rlq1io985COWbV57TaXSHPbYaJXDKSgssS43dh0ISHZt9ZKZ\nbaO4zElSUgq94Za4pyTd9T5B+0YUN0DXLeWA8bBprZv6Q7Gk39JKB0fDyy9eXMybK4/jdeuseLGL\ngF958Q1H/PR06aYUclq6RkufrKLLpzgo+i+/bS4Tiz4GWTnIR/8IOzarNsVXxtoffdlzyMfvN7e3\n/fl58zvpdVtXrp1yxubv4+GEjf7jjz9Oa2urRcjlzER4IAmrcLndbp599lny8vK45JKItOrDDz/M\nlVdeycGDBznYph7WpInTYNVzyJUvWfY4vquJ7RmK4TJz5kyqq6sTk6odCqK95Mxw3qtIMXD1//ok\n2p2/VZMAT4/Fe4n396kMsUtdVz3FB+g6eBYRdHdZJ5oG6SwRCCGorlYZ/I6OyCRU6/P7/vLSKjat\ncuPy20zxuVS7n4Urv8Afa61ErKHk9P1+v9kfwUBhYSEOh4PLLrvMFIAy9y0l8pmHscsQB+3pJqHO\n6/WarP60tDRCoRDvvPOOJUpg9I6IRnub4jRUVSZWfiY0gRCqfNHA4o85OHfaYlateQI4uUqbDxos\n7/kgBr+nO2Qa/MISO03HglRVO8nMtlNS4eDoITUGllWmMmlGMts3ei3PrpHzz8hU9692cjKaJjiw\nx0dppYOJ01KwFV+MfCQi3oTDiVj0MYQrCZlbgH7vj/oN8RsG3/ys65HKoBarJol289c/VGnEkyLy\n5ebmMnv27OE6lw8kkroNYot6KAwPpaWlxSK+09HRweOPP26RsE3NyVE5sePhMHt6JnR3clFRNvNv\n+hIpKSkW+dthheGR5xZA1Sh1BTNnI//xJwiFkEufQe7fBUcPwRe+BxDTNe90vAhyzTLVO7sflv5Z\nWPHeu2qwvPjKDFxJ4oR/o2gPta/hqs5JoiEtQEdbKNLIxGaLXzcdCsV0U+sPfSNjM2fONCe7o0aN\nit3g8H7ky0+jVStZXr/fj8vlivQJQGlFrF69mvfee88ycfZ4PPj9fktkKhgMkuwspWJE4hPMPi3t\n0SWkpkRIjXrIxqa1bmpqk0jPOHO8/oEgpaQtzHOYfkEKxWXOmNJFV5Iwn538QgfzLnPw+itddHda\nn4GuTp30TA2bTTB+SjKlFQ5SM2w4HAJZM84Uf9K++G0YP1Wl/VCdB8ktUA5D3/MzuAmjx0fEhXo9\nyORUJRB0rB4x7QLEJdcoBcEzKFqTCBIy+sFgkNdffz1ua93bb7/9lJzYBwVCV0ZeijCLOMrQr1+/\n3rJutMFffHATBWVfgi98A/1HX1X7uuJ65KN/UN3ehCA1NfXUGX0jhFlVE/HYo1i4sr1VGXywjGwy\n6u/oXP+pgGxtQhriH6UVp+QYZxI8bh1dh6RkYbbPHQyBkGTd0W4mFaWS5owMbtFGf6BBz/ztbXaL\n0ffLVJzCrTQhnn8UbvvvQc+lr9EflLAaTomNmnEujdt3oes6fr+fw4cPA6rlsZTSnIgb6aiCggKa\nmprMahRQ72pXdytOey5O59Cf5+QUgdcjkbpFtReXvYy6QwFcSRq1k8+Q3P4g2LLOy5E6xWfKyAwL\n6fQZI+YviiV9Tj8/lZUvd8csj+6Yl5Vrj/7C/DNuC+zkFEXya22GtHRTn0O+rZT/xIw5EG4trH/l\nk4rQfFBFDsR1n1E6Jh9CJGT077vvPg4dOsS0adNMpveHD8oYRhv9/fv3W9ZITU1FSsmIziYqdZ96\nESqqlT62zY6YeymiYiRUxnayGnaEJSxjulkZ2BqlyR7uTS4DfvQ2le8qKy1l4sSJHDhwwFI7PZyQ\n+6L0Capr+1/xQ46GI36aGoKkh0OhE6clHtLf1NjDz1c3cMXYbG6eFsnd5+XlMXv2bF465kQboIjH\nHMttmkUspdmZT2nAjRRKCCoR9JWlHowfIt3hBi3hwby7u5snnngCXdeZPHkyjY2N6LpuTiYMAqpR\nYbJt2zaSkpKYMGECu3cbfeZldL+cQVFYauf40SApaTa8niChkOTQ/sjkXtPsQGJktjMBoaA0Df4F\nH0kjNT3+hLGvUBRAWoaN2Rensfo19bsuuCKDjragOXGIwWBE3uQU2PwO+uZ3YOJ0s2zX7CR4znTk\nnkh1h2nwF17zoTX4kKDR37JlC/fdd9+Q24qeWVBGPzoX2hdutzKYtoAfMT7SwU37zv8CQuWUonpQ\nn0qIcZORU89HJKJadWA3FI8Cv4+cl/8BI6YysaKMyspKbr/99lOXt9yn+CDi0ussHbvOQqG+y8ey\nfZ3k73ei65AfZpZn5yYejnSHy6Oa3VbClaZpTJo8mR88uptRcegk6Rk2OtpCke5lNruFshcIC6i8\nUTKWq/2x6pPx0NfTH5QEFyZcaWGjb4pcobgAx44dU3n/PjH4rKwsjh49ytq1a811s7NzaGtrIy9j\n1pDIj2PGJ+Fy+UlO0WhtCvLGa904nYKc9BnkFQiT2T9cwbBAQOLr1Unrx5i+3zh6WBn82slJlo6O\niSIrx87EacmkZdhITtFIThnAsA9i9LWF16Ab3TS3vovcthG5YxPU7VUqgjn5kJGtRm67HXH1p0GX\naJddN9Buz3gk9Kvl5eXFlWb9cEBa/m9s7L+3uIFeb5C/pE7hY94g2cn2k5PHPEGIzGxst94x+IpA\nlk/lxXKddip62vjPrctxzFMNOE4pUcnjhqxctOs+c+qOEUZIl3T6QuQkD/7I9/hCuAMhCtPe35LB\nb75yCHdAN2Vxm48FSUoRls5vg6E3qKzS2iOxtdOv7VPGOsURu7/ayUkUlTnIyQsbnz51zF02FfFr\nSUqDPe8ldi59UoODPluH9gHEyCrfdttt2O12tm7dypEjR6ipiSg5jh07lvLycrZv324u83i8dLaH\ncNgyKa8oGlK6KjPbzqQZdur2Ke++1yNxZAgyU8aRnW7nQFgGeSjRg/7Q2hTkrZXqd3K6BBcuSCM1\nbWDjr+uSun1+Cortp3yi4PfpbFmv0pGVI098kl5Vk+C24d9dzIwv6sN4a2ts/Z4fRD4YXRjHTUL7\n3q8hO29QkaEPCxIy+nPmzOEXv/gFl112WUxL1gkTYtssnlkIt7vVVQhPCIHdbjfrhMvLy7G5klld\n30t5r8o1/nXkdfhI5sV/7uMvV1eTnzr8gjbDiZHdzfzH9pU42/YCYEMit6xTZJlTCHn0kGrBeRrw\n0KYmntvVzoPX1pA9iOH/zrLD1HX4eGrJaBy294+d7Q6Euy46wWhpb9OG5lK+tKe93++6/Srcfsec\nWAEWp0ujqDSK1Bnu6WDA70xh4uHD7MkutnY7HAB9GfUDGX0ZDCDXrYa0dGxRglDXX3+9mbs30gV9\nJxN9qwBWLH8DGUpHCI0pQ6h2sOwzKgRtaMk0NUauu+88IuBXYfDSCseAkzRPT4i9O30cPmAVXPL7\nJCte7Gb2xWn9ii811vt5d42asB87auf8i05tBYwhClVW6Ygbvh9uCLsd7Z5HwRWfKyGiIwGGQe/u\nVCz/S65W6xgp1rMwkZDRf+WVVwB47LHHLMuFENx3333Df1YfQAhC2L2HTIb79ddfj8/no6ysjPou\nH489s9U0+j575CHd2OBmQXXmkAfrUwXtqz9Ebt2AXPacZblTD0WIfQCBANLrgaYGRFgTX27fRPeB\nncia8YhxVuW0oUIGAup4w5ju8Id03qjrIifZztSSNA53+HjgvX3MKnby3C5l/Fo8gQGNvi4ldR1h\nry4ocbxPUdYdTRFWsrBDkkMjM8tGxcjEow9SShq61UAdz5vXw2FxRyLRnD6evkxKQZOS0BDq9KO1\nAWAQT7+9FQJ+xPzFFs88WvK2traW48ePmyJZLpeLqqoqi/CQ0+nE7ekAOkhNycPpPLFJXE6+nfIR\nTpqPBWIY/fGwZb2HxvoAvV6d0eOTsNsFvl6dni6d3ILI83fogN80+PlFdvIK7Ox8LzKJ2bW1l1lz\n4xvztpYIR8LrHrj3/MnC49bNDoS1U04fYbFfTlIfaF/8DowYjXzyr4ixk5Ss8FnERUJG/9577z2j\n6lGHBknInoUt2EFa22tIWYNNE5QkHeevdcmMCHWx7biHgKYG43a7Enn4yswC7lnXxO/WHWNDQw/f\nnjtok9LTAlE7GUrKY4y++f21NyLXr0Y2NSC/vAQA7RcPIrJy0Jc+i2fHJsgvQvvS96Cw5IReLnl4\nf6Si4SQnDwY2NvTw9PZWtjWp8OM9i6p4Ylsraw53c7glwkVZeaCTmpykfkO8+9siA+73Vxzhwop0\nrh0/zBoKCeDBTaqvuQOB7oH0Yo0ZFw6NU9PQHSAc3Sekx1oqY1FC89E+pDtdaGhIs05fBvzxtrLA\n8PQ1TWPKlCkWHfsYhJn7YswEc+ypqqqyrGJ4/IFAgIKCApYsWWJ+V15eTjAY5CPzL+Zvf38YgMqq\nwfvTDwQhVKFLPKPfd5m7Wxnk/bt87N/lY/T4JDragjQ1Bjl3Tiq5+XY8bp2OthA2O1x0WQbJKeo6\nK6qdHN7vp26/3xSvAeho9/Pmsm5y8u1UVjs5sNvH6UBLU4C3V0YEbVwn0b9+uKF9827VsGvEKISm\nIT5x8/t9Sh94DGr0dV3nhhtu4MEHHzwluusfdAgJgaRKbD0dIENIKSnL9JF9/O9Ml+Xc8eZ8taLm\nZEJTE/eNUcpy55SmA0oE4p36SD7VH9LZ1ODmnKL3kRQ5EEEmt0AJ5Rj96QF6OlVjC0PCt/kY+v98\nEXHVJxGLl8TfzwCwMGpzTr5Zidsf4s6V9ZZlP369nmaPCr9ubIwMWC/u6eDCygxqC+KHeR8OG1tQ\nE4D9bb2n1ej/cOURNjTsAmBcfrJZD52ZHR1elrR6guSl2AfMTxv5/OJ0B83u2BC8QYBLzOhbDbQu\nQZMSPZzMDh4+CJnx75OUkubmZkKhELW1tSxYsGDQw8n3wtUl6ZmIMJ2oL9vfGI/a29tjOt1dc801\nbFrrZs2yADlp00CEWLBgzqDHHQim0Y8zgZJSheqTUjQ0TdDba11nz/ZeUtPUvaqv87PuTbdJAszO\ntZkGX12nRs24JJqPBwkFw1VDQcm/HleRxPbWEPt3WQ2+x62z6pUuSqucZs+AoSAYlHg9eozWgNej\nWwx+ScUHywaIUbWIUWcrf4aCQadsmqZRUlJi6dD14YIEIfAnVwOKKVyTq4xfsctKjirydnBtTRqf\nnZJPXoqDiYWxhuXNQ9385I2jPLkttoHPaYO9f6MvyqpUKUw0DC2BviIsbUO/Bunutqhlib7HOgG8\nURerymUYfPM4UX+/sjd+BUaPP8R7x2PFPhq6BvdihwNuf4gNDZEBdmZpGlfYlSHNzrXhC+rct7aR\nax7dzc3P7mdjg7u/XQGR8H2SXTP/tn6v7ktCxLY+0rXK6OtIIdCBrvt+gtRjxXuee+45fve735nK\nnWPGjBn0ULLXg3z1afWhYiQlJSWMHTuWadOmWdYrL4/0eE9JSaGtOUjDET+6LgkGpakYl5k6nunT\nT56fEs/TLyhWflNne4jlL3azfZOXnq4Qfp8kJ99qQN09ysofPRwwDX5hSf+5eE2LaAJ0dsTvtzD3\nkkgf+K5OnZ1beuOuNxBCQcnLT3fy+svdeNyR40gpTbY+wMKrMoakAHkWH0wkFKe58MIs4Rj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K7m8U07OdDqpDhztWn0V9dVM6bwIA4h+Pi0Cu60bSNHexfhUW+ZLa3Mcg5CO8SGBjfnVkYMa2Vu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9dfaf9748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(100*dayRet).cumsum().plot(grid=True) #note the .cumsum() instead of .cumprod(), I assume the portfolio is rebalanced each time.\n",
    "ylabel('% return, non-compunded')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daily average returns look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc9de1aa470>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgOHchW5gtTpx4oQcDofWrFlT6FZWnV/+8pfL7t+6det57gT4983Pz+uJJ55Q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\nADDc/wMbjEGLKPT1EwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9de1af6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(100*dayRet).mean().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...There is only a very tiny bias to the upside, with Tuesday scoring the best and Thursday being least favourable\n",
    "    But how stable has this behavior been over time? We'll now compare two halves of this dataset. Here I use `.ix[col,row]` indexing to select data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc9dc8ee6d8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WrdP999+v+++/X+vWrVNTU5MkadWqVbr55pu1fPly7d+/X5WVlZKk0aNHa+nS\npXrkkUd03nnnaf369cG0CQCA8YIK/PLyck2cOFGSNHHiRJWXl3eqqays1OjRo+VwOORwODR69GhV\nVlaqsbFRR48eVWpqqmw2myZMmBA4/tJLL1V0dLQkKTU1VT6fL5g2AQAwXlCBf+jQITmdTkmS0+nU\n4cOHO9X4fD653e7Atsvlks/n67Tf7XafNNhLSkqUnp4eTJsAABjvtPfwFy9erIMHD3baP2PGjG/9\nQ202myzLOm1dUVGRoqOjdeWVV56ypri4WMXFxZKkvLw8eTyeb91XONjt9ojrOdIwx6HHHIcec/w1\nVeFu4MydC6/daQN/0aJFp3xs4MCBamxslNPpVGNjowYMGNCpxuVy6YMPPghs+3w+XXLJJXK73Wpo\naAjsb2hokMvlCmyXlpbq7bff1r333iubzXbKHrKzs5WdnR3YjrRPCvPp5tBjjkOPOQ495viEuHA3\n8C2E6rVLSEjocm1QS/per1cbN26UJG3cuFGXXXZZp5r09HRt3bpVTU1Nampq0tatW5Weni6n06m+\nfftq165dsixLmzZtktfrlfTlff8XXnhB8+fPV+/evYNpEQAAKMiv5U2bNk35+fkqKSmRx+PRvHnz\nJEl79uzRq6++qltuuUUOh0PXXnut7rnnHknS9OnTA1/du+mmm/TYY4+ppaVF6enpGjNmjCSpsLBQ\nbW1tWrx4sSQpJSVFP//5z4NpFQAAo9msrtxMjyA1NTXhbuGMsEwXesxx6DHHocccnxBXdU+4Wzhj\ndckPhGTcblvSBwAAkYHABwDAAAQ+AAAGIPABADAAgQ8AgAEIfAAADEDgAwBgAAIfAAADEPgAABiA\nwAcAwAAEPgAABiDwAQAwAIEPAIABCHwAAAxA4AMAYAACHwAAAxD4AAAYgMAHAMAABD4AAAYg8AEA\nMACBDwCAAQh8AAAMQOADAGAAAh8AAAMQ+AAAGIDABwDAAAQ+AAAGIPABADAAgQ8AgAHswRzc1NSk\n/Px8HThwQIMHD9bcuXPlcDg61ZWWlqqoqEiSdM011ygrK0uStHfvXq1cuVItLS0aM2aMcnJyZLPZ\nAsf9z//8j5599lk9+eSTGjBgQDCtAgBgtKCu8Dds2KBRo0Zp+fLlGjVqlDZs2NCppqmpSevWrdP9\n99+v+++/X+vWrVNTU5MkadWqVbr55pu1fPly7d+/X5WVlYHj6uvr9f7778vj8QTTIgAAUJCBX15e\nrokTJ0oPcuCOAAALxUlEQVSSJk6cqPLy8k41lZWVGj16tBwOhxwOh0aPHq3Kyko1Njbq6NGjSk1N\nlc1m04QJEzoc//TTT2vmzJkdrvgBAMC3E9SS/qFDh+R0OiVJTqdThw8f7lTj8/nkdrsD2y6XSz6f\nr9N+t9stn88nSaqoqJDL5dKFF1542h6Ki4tVXFwsScrLy4u4FQG73R5xPUca5jj0mOPQY46/pirc\nDZy5c+G1O23gL168WAcPHuy0f8aMGd/6h9psNlmWddLHjh8/rqKiIv3mN7/p0ljZ2dnKzs4ObNfX\n13/rvsLB4/FEXM+RhjkOPeY49JjjE+LC3cC3EKrXLiEhocu1pw38RYsWnfKxgQMHqrGxUU6nU42N\njSf9YJ3L5dIHH3wQ2Pb5fLrkkkvkdrvV0NAQ2N/Q0CCXy6XPP/9cdXV1uuuuuwL758+frwceeECD\nBg3q8hMDAAAnBHUP3+v1auPGjZKkjRs36rLLLutUk56erq1bt6qpqUlNTU3aunWr0tPT5XQ61bdv\nX+3atUuWZWnTpk3yer0aNmyYnnzySa1cuVIrV66U2+3Wgw8+SNgDABCEoO7hT5s2Tfn5+SopKZHH\n49G8efMkSXv27NGrr76qW265RQ6HQ9dee63uueceSdL06dMDX9276aab9Nhjj6mlpUXp6ekaM2ZM\nkE8HAACcjM061c30CFVTUxPuFs4I9+VCjzkOPeY49JjjE+Kq7gl3C2esLvmBkIx7Jvfw+U17AAAY\ngMAHAMAABD4AAAYg8AEAMACBDwCAAQh8AAAMQOADAGAAAh8AAAMQ+AAAGIDABwDAAAQ+AAAGIPAB\nADAAgQ8AgAEIfAAADEDgAwBgAAIfAAADEPgAABiAwAcAwAAEPgAABiDwAQAwAIEPAIABCHwAAAxA\n4AMAYAACHwAAAxD4AAAYgMAHAMAABD4AAAYg8AEAMIA9mIObmpqUn5+vAwcOaPDgwZo7d64cDken\nutLSUhUVFUmSrrnmGmVlZUmS9u7dq5UrV6qlpUVjxoxRTk6ObDabJOnll1/WX//6V0VHR2vs2LG6\n/vrrg2kVAACjBRX4GzZs0KhRozRt2jRt2LBBGzZs6BTMTU1NWrdunfLy8iRJd999t7xerxwOh1at\nWqWbb75ZKSkpeuCBB1RZWakxY8Zo27Ztqqio0COPPKKYmBgdOnQomDYBADBeUEv65eXlmjhxoiRp\n4sSJKi8v71RTWVmp0aNHy+FwyOFwaPTo0aqsrFRjY6OOHj2q1NRU2Ww2TZgwIXD83/72N1199dWK\niYmRJA0cODCYNgEAMF5QV/iHDh2S0+mUJDmdTh0+fLhTjc/nk9vtDmy7XC75fL5O+91ut3w+nySp\ntrZWO3bs0J/+9CfFxMToJz/5iZKTk4NpFQAAo5028BcvXqyDBw922j9jxoxv/UNtNpssyzrl436/\nX01NTVqyZIn27Nmj/Px8FRQUBO7vf11xcbGKi4slSXl5efJ4PN+6r3Cw2+0R13OkYY5DjzkOPeb4\na6rC3cCZOxdeu9MG/qJFi0752MCBA9XY2Cin06nGxkYNGDCgU43L5dIHH3wQ2Pb5fLrkkkvkdrvV\n0NAQ2N/Q0CCXyxU45vLLL5fNZlNycrKioqJ05MiRk46fnZ2t7OzswHZ9ff3pntI5xePxRFzPkYY5\nDj3mOPSY4xPiwt3AtxCq1y4hIaHLtUHdw/d6vdq4caMkaePGjbrssss61aSnp2vr1q1qampSU1OT\ntm7dqvT0dDmdTvXt21e7du2SZVnatGmTvF6vJOmyyy7Ttm3bJEk1NTVqa2tT//79g2kVAACjBXUP\nf9q0acrPz1dJSYk8Ho/mzZsnSdqzZ49effVV3XLLLXI4HLr22mt1zz33SJKmT58e+OreTTfdpMce\ne0wtLS1KT0/XmDFjJEmTJ0/WY489pjvvvFN2u1233XbbSZfzAQBA19isb7qZHoFqamrC3cIZYZku\n9Jjj0GOOQ485PiGu6p5wt3DG6pIfCMm43bakDwAAIgOBDwCAAQh8AAAMQOADAGAAAh8AAAMQ+AAA\nGIDABwDAAAQ+AAAGIPABADAAgQ8AgAEIfAAADEDgAwBggKD+Wh4AAN0tVH+IRurZf6SIK3wAAAxA\n4AMAYAACHwAAAxD4AAAYgMAHAMAABD4AAAYg8AEAMACBDwCAAQh8AAAMQOADAGAAAh8AAAMQ+AAA\nGIDABwDAAAQ+AAAGsFmWZYW7CQAAEFpc4YfZ3XffHe4WejzmOPSY49BjjrtHT55nAh8AAAMQ+AAA\nGIDAD7Ps7Oxwt9DjMcehxxyHHnPcPXryPPOhPQAADMAVPgAABiDwAQAwAIEPAIAB7OFuwGRHjx6V\nzWZTnz59wt1Kj1JUVHTS/ddcc003d9Kz7dixQ7W1tZo0aZIOHz6sY8eOKS4uLtxt9Ritra166623\nVFdXJ7/fH9g/ffr0MHbV8/j9fi1ZskSLFi0KdyshR+CHwaeffqqCggI1NTXJsiwNGDBAt912m4YN\nGxbu1nqEqKgTC1etra165513lJiYGMaOep7//u//1p49ewKB39bWphUrVmjx4sXhbq3HeOihhxQb\nG6vhw4crJiYm3O30WFFRUerVq5eam5sVGxsb7nZCisAPgyeeeEI//elP9Q//8A+SpO3bt+uJJ57Q\n7373uzB31jNMmzatw/bVV1+thx9+OEzd9Ez/93//p4ceekjz58+XJLlcLh09ejTMXfUsPp9PCxcu\nDHcbRoiJidGdd96p0aNHq3fv3oH9s2bNCmNXZx+BHwbHjx8PhL0kjRw5UsePHw9jRz1bW1ubPv/8\n83C30aPY7XbZbDbZbDZJ0rFjx8LcUc+TmpqqTz/9lJW/bjB27FiNHTs23G2EHIEfBnFxcVq3bp0m\nTJggSXr99dc1ePDgMHcV+drb2xUdHa1f//rXgSDy+/1qbGzUD3/4wzB317OMHz9eTzzxhL744gsV\nFxfrtdde05QpU8LdVo9w5513ymazqb29XaWlpYqLi1NMTIwsy5LNZtMjjzwS7hZ7jPr6enk8HmVl\nZYW7lW7BL94Jg6amJj3//PPauXOnLMvSxRdfrH/7t3+Tw+EId2sRbf78+XrwwQe1f//+wL7o6GgN\nGjSIe6Bnyf/+7/8qLS1NF110kbZv366tW7fKsiylp6dr9OjR4W6vR8jJydFDDz10yse5ODh7vvpv\nhiQ98sgj+tWvfhXmjkKLK/wwcDgcPe7e0Lngq/eu8fHxYe6k52poaNBTTz2lzz77TBdccIFSU1M1\nYsQIDR8+PNyt9RhxcXGEejf5+vVuXV1dGDvpHgR+N/rqneSpfPUBKHw7hw8f1osvvnjKx6+66qpu\n7KZn+ulPfyrpy89F7NmzRzt37lRJSYn+8Ic/KDY2Vvn5+WHuMPIdOnSI87ibfHXr7//93z0Vgd+N\ndu3aJY/HoyuuuELJycnhbqfH8fv9OnbsmLhLFXotLS06evSompubdfToUTmdTj5cdpZwHnefjz/+\nWDfccIMsy1JLS4tuuOEGSQp8XuLpp58Oc4dnF4HfjVatWqX33ntPb7zxht544w2NHTtWV1xxBd8R\nP0ucTie/lCTE/vCHP6i6ulp9+vRRSkqK0tLSdNVVV/H5k7OI87j7rF27NtwtdCsCvxtFRUUpPT1d\n6enpam1t1ebNm3Xfffdp+vTp+v73vx/u9iIeV0ShV19fr9bWVsXHx8vlcsntdqtfv37hbqtH4TxG\nqPAp/W721W9+27x5sw4cOKBx48Zp8uTJcrlc4W4t4jU1NXGl2Q0sy9K+ffu0a9cu7dy5U/v27ZPD\n4VBqaqr+/d//PdztRTzOY4QKgd+NCgoKtG/fPo0ZM0aZmZnc80REa2ho0M6dO7Vz50698847OnLk\niJ566qlwtwXgFAj8bvSjH/0o8Gsbv/6J0J76ARH0PC+99FLgyj46OlppaWlKTU1VWlqahg0b1uHv\nGAA4txD4ALrs6aefVlpamtLS0uR0OsPdDoAzQOADAGAA1t8AADAAgQ8AgAEIfAAADEDgAwBgAAIf\nAAAD/H+2b8h++pr8BAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9de1f4278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J2rNnj1avXu3bvrm5WVdddZVmzpypF198UW+88YaGDh0qSZozZ46mTJlyrPYX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\nA9f1DZfvwnrWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9de1f4668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... sorry for 1-liners...\n",
    "(100*dayRet.iloc[:2500,:]).mean().plot(kind='bar') # .ix[:2500,:] indexing selects first 2500 days of data\n",
    "title('First 2500 days')\n",
    "figure()\n",
    "(100*dayRet.iloc[2500:,:]).mean().plot(kind='bar') # .ix[2500:,:] indexing selects data after 2500 days\n",
    "title('Last %i days' % len(dayRet.iloc[2500:,:]))\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the charts above it seems that the daily seasonality got stronger over the years.\n",
    "And finally the data for year to date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc9dc8445f8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9dc94cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(100*dayRet.iloc[-250:,:]).mean().plot(kind='bar')\n",
    "title('YTD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuesday is now lees significant compared to other days"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
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   "toc_position": {},
   "toc_section_display": "block",
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}