{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Market position indicators using CFTC COTR \n",
    "\n",
    "We examine the CFTC **Commitment of Traders Reports (COTR)** \n",
    "for futures and options to derive indicators of market position *among \n",
    "Asset/Money Managers*. Our generalized formulation permits treating \n",
    "asset classes which include: precious metals, US dollar, bonds, and equities. \n",
    "Indicators may be post-processed to obtain further clarity, \n",
    "for example, normalization and smoothing.\n",
    "\n",
    "Detailed explantory notes regarding the raw data can be found here:\n",
    "http://www.cftc.gov/MarketReports/CommitmentsofTraders/ExplanatoryNotes \n",
    "We shall disregard the Legacy format, and focus on the data after 13 June 2006. \n",
    "*Current data is released weekly on Fridays* (for accounting \n",
    "effective through Tuesday).\n",
    "\n",
    "We note the absence of strong linear correlations \n",
    "among our asset class position indicators. \n",
    "\n",
    "Lastly, we compute a dataframe of normalized position indicators \n",
    "which is useful for comparative study across asset classes \n",
    "and the identification of overcrowded trades.\n",
    "\n",
    "*Shortcut to this notebook:* https://git.io/cotr \n",
    "where **Appendix 1 gives an algorithmic summary in a few lines of code.**\n",
    "\n",
    "Appendix 2 visualizes the chronological joint path of the positions indicators for \n",
    "bonds and equities by color heat map -- which could be useful for asset allocation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Dependencies:*\n",
    "\n",
    "- Repository: https://github.com/rsvp/fecon235\n",
    "- Python: matplotlib, pandas\n",
    "     \n",
    "*CHANGE LOG*\n",
    "\n",
    "    2016-01-23  Fix issue #2 by v4 and p6 updates.\n",
    "                   Smooth metals by ema(). Use groupfun() to normalize.\n",
    "                   Add Appendix 1 and 2.\n",
    "    2015-08-31  Simply use fecon.py to generally access various modules.\n",
    "    2015-08-25  Update tpl to v4.15.0812. Add silver COTR,\n",
    "                   and the class of precious metals w4cotr_metals.\n",
    "    2015-08-09  Change of variable names for clarity.\n",
    "    2015-08-07  First version arising as test of the Quandl API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from fecon235.fecon235 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  Python 2.7.11\n",
      " ::  IPython 4.0.0\n",
      " ::  jupyter 1.0.0\n",
      " ::  notebook 4.0.6\n",
      " ::  matplotlib 1.4.3\n",
      " ::  numpy 1.10.1\n",
      " ::  pandas 0.17.1\n",
      " ::  pandas_datareader 0.2.0\n",
      " ::  Repository: fecon235 v4.15.1230 develop\n",
      " ::  Timestamp: 2016-01-25, 02:58:19 UTC\n",
      " ::  $pwd: /media/yaya/virt15h/virt/dbx/Dropbox/ipy/fecon235/nb\n"
     ]
    }
   ],
   "source": [
    "#  PREAMBLE-p6.15.1223 :: Settings and system details\n",
    "from __future__ import absolute_import, print_function\n",
    "system.specs()\n",
    "pwd = system.getpwd()   # present working directory as variable.\n",
    "print(\" ::  $pwd:\", pwd)\n",
    "#  If a module is modified, automatically reload it:\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "#       Use 0 to disable this feature.\n",
    "\n",
    "#  Notebook DISPLAY options:\n",
    "#      Represent pandas DataFrames as text; not HTML representation:\n",
    "import pandas as pd\n",
    "pd.set_option( 'display.notebook_repr_html', False )\n",
    "#  Beware, for MATH display, use %%latex, NOT the following:\n",
    "#                   from IPython.display import Math\n",
    "#                   from IPython.display import Latex\n",
    "from IPython.display import HTML # useful for snippets\n",
    "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
    "from IPython.display import Image \n",
    "#  e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works\n",
    "from IPython.display import YouTubeVideo\n",
    "#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\n",
    "from IPython.core import page\n",
    "get_ipython().set_hook('show_in_pager', page.as_hook(page.display_page), 0)\n",
    "#  Or equivalently in config file: \"InteractiveShell.display_page = True\", \n",
    "#  which will display results in secondary notebook pager frame in a cell.\n",
    "\n",
    "#  Generate PLOTS inside notebook, \"inline\" generates static png:\n",
    "%matplotlib inline   \n",
    "#          \"notebook\" argument allows interactive zoom and resize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COTR example: Gold\n",
    "\n",
    "To get an idea of what's contained in a Commitment of Traders Report, \n",
    "we first look at an example from the commodity futures market. \n",
    "Financial futures have a different format since the notion of a \n",
    "\"Producer\" is not entirely appropriate \n",
    "(though dealers may produce derivatives which may rely on the futures market). \n",
    "\n",
    "The latest data is downloaded via Quandl as a pandas dataframe. \n",
    "The relevant functions are found in the yi_quandl module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            Open Interest  Producer/Merchant/Processor/User Longs  \\\n",
       "Date                                                                \n",
       "2016-01-12         551508                                   39420   \n",
       "\n",
       "            Producer/Merchant/Processor/User Shorts  Swap Dealer Longs  \\\n",
       "Date                                                                     \n",
       "2016-01-12                                    89684              47977   \n",
       "\n",
       "            Swap Dealer Shorts  Swap Dealer Spreads  Money Manager Longs  \\\n",
       "Date                                                                       \n",
       "2016-01-12               44972               118408                85795   \n",
       "\n",
       "            Money Manager Shorts  Money Manager Spreads  \\\n",
       "Date                                                      \n",
       "2016-01-12                 84893                  56205   \n",
       "\n",
       "            Other Reportable Longs  Other Reportable Shorts  \\\n",
       "Date                                                          \n",
       "2016-01-12                   75149                    27804   \n",
       "\n",
       "            Other Reportable Spreads  Total Reportable Longs  \\\n",
       "Date                                                           \n",
       "2016-01-12                     87277                  510231   \n",
       "\n",
       "            Total Reportable Shorts  Non Reportable Longs  \\\n",
       "Date                                                        \n",
       "2016-01-12                   509243                 41277   \n",
       "\n",
       "            Non Reportable Shorts  \n",
       "Date                               \n",
       "2016-01-12                  42265  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  First download latest GOLD reports:\n",
    "cotr = cotr_get( f4xau )\n",
    "#                ^ = 'GC' currently.\n",
    "\n",
    "#  Show column labels and only the last report:\n",
    "tail( cotr, 1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notable points\n",
    "\n",
    "- Number of longs = Number of shorts, by necessity -- and each side equals the *Open Interest*.\n",
    "- Option positions are computed on a futures-equivalent basis using delta factors supplied by the exchanges.\n",
    "- Generally non-directional traders (usually with hedged positions): Producer/User, Swap Dealer, and Dealer.\n",
    "- Generally **directional traders**: *Money Manager* (commodities), Leveraged Funds and *Asset Manager* (financials). Leveraged Funds appear to take very choppy short-term positions, whereas Asset Manager will show a longer-term positional narrative.\n",
    "- Non-directional trading by a Money Manager, Asset Manager, and Leveraged Funds is categorized separately under Spreads.\n",
    "- \"Non reportable\" traders are small players trading contract sizes under CFTC reporting thresholds -- generally, noise traders categorized as **speculators**.\n",
    "- **COTR is released weekly on Friday** (for accounting effective through Tuesday)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing out positions\n",
    "\n",
    "To characterize *informed* market direction we focus on the directional traders \n",
    "who are trading in large size which must be reported to the CFTC. \n",
    "\n",
    "Note that their *Longs* do not necessarily equal their *Shorts*\" since the \n",
    "counter-parties may be other players in the market, \n",
    "e.g. hedged producers, spreaders, or small uninformed traders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "longs  = cotr['Money Manager Longs']\n",
    "shorts = cotr['Money Manager Shorts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# difference in number of contracts:\n",
    "lsdiff = todf( longs - shorts )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ALcAyYLkx5nFv+63AcBFZBlwKWOtkE+rimgfMBa4JZ0KVmrDJ2JU4WaLpqbIUqyx66n0p\nlkqRpVLkgDLFLIwxzxOtWB7L8ZnrgOsitr8EHByxfQeaiht1rtuA2+LI6nB0Z9raoMoNlXVUIK42\nlMNRQey/P4wYAc8/39WSOHoDrjaUo1vT0aGv3khrq4tZOCoTpywi6M5+xXLSWbLcdhv06QO5Mvt6\n4n35z/+ENWvcOItyUCmyVIoc4OazcPQAmpr0fe7c3Mf1NH70I313loWjEnExC0fF8e1vw3XXwR13\nwAUXdLU0nYd4nuMJE2DFiq6VxdE7cDELR7emtVXfm5thz56ulaWzCPZzinFDORzlwimLCLqzX7Gc\ndJYsLS0wdqy6Zaqrdb0rZPnMZ2DLlvzHlUKWbdvUsvjFL9w4i3JQKbJUihzgYhaOHkBrK0yc6Lti\nvvvd3MfH4eabYeHC+Mfv2KGB9gULOicza/NmGDMGLrrIWRaOysTFLBwVx/HHQ3093HcfXH45/P73\n8K1vwZe/XNj5jNGBbpdcAjfckP940Hkl9t5bl5N8LhtLluj7AQdE71+0CD75SX3v0wd273aD8xzl\nx8UsHN2alha1LEAnAfrTn+BXvyr8fC+9pO/2nM89B+edl/34xkb46lf99XffLfzaliOPhOnTM7fv\n3KmWxObNMGyYuqL693fWhaPycMoigu7sVywnnSWLdUMBjBwJhx+uLql16wqTxfbq29r89dmz04PK\nQV54AR5+GPr10/X6+tznjyPLwIHR22+8Eb7/fV9ZQHHKojc+L3GoFFkqRQ5wMQtHDyCsLKqrtWc+\nb15h59uwQd+tsli1SsdyZLMYGhv1/aCD4LTT/M8XQzaF09SkcjU3Q503ddiAAW6shaPycMoiAjth\nSCXQG2UJuqFGjtT3cePSLYsksjQ1waRJfkru6tX6bt1TYayyOPNMjZkErxtFHFms1RC2ZrZsUVdU\n0LIoRln0xuclDpUiS6XIAcllSTRTnsNRbnbs0LEVY8boulUWo0bB+vWFnXPDBpg61VcWq1ap8tm4\nMfr4xkZ49FE49VQNdOdTFnGwSmL9ehg92t/e3q7urvZ2GDJEtw0Y4GIWjsrDWRYRdGe/YjnpDFna\n2mDoUK28uvfe6r8HVRZBd1ASWTZs0HMFlcXUqdl77++8o5YIaMO+bl32+EZcWVpa9JyPPJK+vb1d\nLYudO/3v2r+/jrsohN72vMSlUmSpFDnAxSwc3ZyWFqit1V728uX+9mIsi6Ym37L48Y917EQ2ZbFq\nlW+JAAwerKms7e2FXdvS3AxXXJGZ1RVUFjagvv/+8OlPF3c9h6PUOGURQXf2K5aTzpCltVWVRZiw\nskgiS9CyuOUWHTcxcWK0svjd7+Dss9UVZLHWRTbyySKicZIjj/TjJQC33qrrYWXxq1/5cZOk9Lbn\nJS6VIkulyAHJZXHKwlExLF+uo6yHDs3cV4qYxebN6mL63vc0lTVKWSxcCEcfnXntUsQtxo3zXWEA\nV10Fr72WqSxqazXw3VvqYjm6B05ZRNCd/YrlpJyybN4M06bBF76Q3bIoZJzFmjUab5g8WXvx48ZB\nTU32jKONGzVeEiSfZRFXlpoadTsZA7t2+VZGWFlUVemxQcUSl97yvCSlUmSpFDnAxSwc3RTbGG/d\nml1ZDB6so7mT8PDDcMopvrVy7LH6nk1ZNDXB8OHp2/Ipi1wEs5qqq/W6W7ZobMQGze0obqssQMdc\nNDcXdk2Hoxw4ZRFBd/YrlpNyyrJpk78c5YaqroZrr4W7704myzPPwMknQ9++cPHF8MMf6vaklkUu\nF1guWWzF3P/6L32vrVWL4Z13/GPClgXoPShEWfSW5yUplSJLpcgBLmbh6KZYNxREWxYAU6bAypXJ\nzrt2rT/A7xe/8EdS51IWpbQsWlo0uH799bpu3UvB0eNRyqKuLro0u8PRVThlEUF39iuWk3LHLPbb\nT5ejLAtIL1seV5YNGzItBYhWFtu3ayzBDo6z5Atw55KlpSX9+9TW6liSd99V5QfZlUUhlkVveV6S\nUimyVIocUKaYhYhMFJFnRGSRiCwUkUu87fUiMltElorIkyJSF/jM5SKyTEReF5GTAtsPE5EF3r4b\nAtv7i8gfve0visikwL4LvWssFRGXgd4D2bRJB63165fdshgzRnv+O3fGP29Tkz8KPEiUsrBWhYQK\nNu+zD7z4YmHZWFHKwrqhDj9ct4UH5YGLWTgqj7iWxS7ga8aYA4EjgS+JyAHAZcBsY8y+wFPeOiIy\nHTgbmA6cAvxS5L2/4E3ARcaYacA0ETnF234RsNHb/lPgeu9c9cCVwBHe66qgUioH3dmvWE7KKcvm\nzeoiGjkyu7Lo00ddQmvWxJPFGFUWUZZF//6ZyiLbsYceCqefnn1Oi1yyBAsEgrqhrGVxxBGqmErp\nhuotz0tSKkWWSpEDyhSzMMasNcbM95bbgSXAeOB04HbvsNuBM73lM4C7jTG7jDGNwHJgpoiMBWqM\nMXO94+4IfCZ4rvuAE7zlk4EnjTHNxphmYDaqgBw9iE2btJDeyJHZ3VCgrqi4cYvNm2HQoPRG2BJV\nfykqXmE57zx4/PF41w2SzbJ4913N0nrkkdIGuB2OcpE4ZiEik4FDgX8Ao40x1pu7DrAl0sYBwb/0\nSlS5hLev8rbjva8AMMbsBlpEZHiOc5WN7uxXLCfljlnU18MnPhE9SZBlv/10IFs+WYzRhr86S6nM\nKDdUttHjoKOvly1TOcOEZXnrLb3url253VCTJsEhh0QrixEjCnN79ZbnJSmVIkulyAHJZUlUdVZE\nhqC9/q8aY9ok4Nw1xhgR6dJ5TWfNmsXkyZMBqKurY8aMGe+ZWvbGdLd1SyXIM3/+/LKdf9myFPvt\nB9/5Tu7jjz22gccfhx075uc83xNP6PqmTdH7FyxIeVVn/f1z50JNTfTxc+akGDQImpsbGDYs9/dZ\nsQL27Enxne/AgAEN1NX5+2tqGmhshKqqFC+9BAcd1MDOnbBxY4rXXoOZM/V8O3emmDMnXb4499NS\n7ufhySdT9OvXdc9L0vX583M/L73l/2yXGxsbWbt2LYkwxsR6AX2BJ4BLA9teB8Z4y2OB173ly4DL\nAsc9DswExgBLAtvPBW4KHHOkt1wNbPCWzwFuDnzmV8DZEfIZR/flwx825pln8h/39tvGjB5tzJ49\nuY9bt84YtS+i9y9bZszee6dv+8UvjPnCF7Kfc/p0YxYuzC/jAw/odf/rv4z53OeMuekmf9/3v2/M\nyScbc9BBut7SYsyQIZnnXrtWzzFhQv7r5WL+fGPuuqu4c4TZtElla24u7XkdnY/XbsbSAXGzoQS4\nFVhsjPnfwK4HgQu95QuB+wPbzxGRfiIyBZgGzDXGrAVaRWSmd84LgAciznUWGjAHeBI4SUTqRGQY\ncKKntBw9iHA2UDYmT9ZU1oce0qBzNtrbNb4R5TaCaDdUW5sGoLMxcKCOMM+HLdOxebNfYsQyYoRO\n62oztPr1i3ZD2TkvVq7MXR49H3Pm6L0qJbd7kcVCa3U5uidxYxYfAs4HjhORV7zXKcAPgBNFZClw\nvLeOMWYxcA+wGHgMuNjTYgAXA7cAy4DlxhgbNrwVGC4iy4BL8TKrjDGbgGuBecBc4Bqjge6yETYZ\nu5KwLMU0HMVSzvsSV1kAnH8+nHNOioce0rhAFO3tGiuoy5I3V4iyGDQoep6J8H1paVGlsGmTlvUY\nH4iwjRypwW2bddW3b3S5D/BHm9vpYOMQlqW5Ofs9KpTZs/U92+RR2WTpSipFlkqRA8oUszDGPE92\nxfKRLJ+5DrguYvtLwMER23cAn8pyrtuA2+LI2h15+WX43OeyT/Np2bNHA6dLluicBz2JcM86F1//\nuqbRXn65NqRR81tv2ZI5uC5INmURlTprGTQovmUxebJaFmFlYc9vLYs+ffS1dWvm9//mN+Gmm7QH\nny3wno+WltIri1dfhQMO0FRjR+/BjeCOwAaFOovvfEcVRtQEO+97XwNzvURjO1Pcd7/bebIFKed9\niepZZ6O6Gr7xjQbGjMne6w5OUxpFKd1Q4fvS0qLKYu1a7dkHBwXa5aBS6tdPlVvU909aaiQsS6kt\ni40b9T4ddlh+y6Kz/0e5qBRZKkUOcLWhuhU7dsAJJ+ggM4j2Af/2t3DaaX6lUtAUzp5GEjeUxaah\nRtHerlVqs1FdrS693bv9bYW6ocK0tGgpj8WLtbHv08ffF7YsQJXE9u3RyqKYeTysLKVUFgsWwEEH\n6fdwlkXvwimLCDrLr7h2LTz9NLz5JowdG90o3H9/CmPg5ps1WDpjRvJieqWi3DGLuJaFlcWOho4i\nn2UBOg5jwQJ/PY6yiLIswvfFuqHAr/9kqatT5RG2LILvQZIqi6iYRZLyKPlYvVrHh4wY4WIWhVAp\ncoCbz6JbYRuBtjbtrYXdDcZoY3bHHRrsfO45eP/7k9dH6g4kcUNZck0QFEdZXHmlzppnKVU2VEsL\nTJigy4cckr6vqkqVVNCy6Ns3/T1IpVkW69apTM6y6H04ZRFBZ/kVbSNQUwN77ZXZKKxdC/37N3DK\nKXDZZfCjH2k6qK2P1NmU874ktSwaGhreq+AaRb4AN+hI8eA8GoW6oaJiFjYLa++9M4+fPFl/b4sd\n21oV8W9M2iiXO2axfr0qi+HDXcyiECpFDnAxi26FtSRGjYruQS5d6pft/spX9H3XLu21dpUrqlwU\nErMo1rII14eKa1n85jfwyU9mP6611S/xccwxmftffNGfuyMfgwer4iuUUlsWVlkMG5Z9DIujZ+KU\nRQSd5Ve0ymHUqOislzfegJoalaVPHx1g9ZWvaK/0tNM63xVVrvvS0aENWpQbJpcstbU6v0UwSG3J\nF+CGzIyoOJbFli3wH/8B996bLkuQ1lY9jzHqNgwTLoGei8GD47m+sslSDsti9Gh/PvEksnQllSJL\npcgBLmbRrVi/XnPwR41SH3aUZWF93wBHHaVzOvzmN9prDs621p2xiiJJIwraYF1zDdx4o64/8YQ/\naDGuZZFUWSxcqL9V//7ZB0i2t+c+T5io+TaC1yzUsti5U91mpexU2JjFkCH5lYWjZ+GURQSdGbM4\n91w45xz1AQf956CWxamnZspSWwv77qtZVJ1Jue5LIS6ohoaG9yyHqiptuE45BWxnKamy2LlTLZxc\ncgwcCM8/r0p78GA/lhC+L1u25LdqgnwkclirktSyCMpi58MohxsqjrLozv75clEpcoCLWXQrNmyA\n44/PrixWr063LIJMnaqlsOOSpGREZ9Derq40KCwTCvT+gDbi1ir7+c/1fetW7ZXnIjgBkrUqclk3\nNnX20EPVIrTjXoLs2aPfZ+DA+N9j1qzsZUmKsSyam/W+lkpZGJNMWTh6Fk5ZRNBZfsXgLGr19ZnZ\nJc3NsHRptCx7753Msthvv+IzqEp5X1asgIcf1gYoaSaUleXQQ3V5yxbflz5njp4zjrIIWhb5XFDg\nn+/oo9OVRfC+WKsiiUvtwAOzB4uTBriDsrS0qIurVMpiyxa14gYPjqcsurN/vlxUihzgYhbdiuDE\nOPX1mZZFc3N2V8qUKfEti127VFEsXly4rKXGBqXtmJGkbiiAz3wGvv1tVQzr1+uc1iI6sdC2bfl7\n90mVRUeHvn/wg9ktiziB9SQkdUNZdu/W33zEiNIpCxuvAF+597TxPo7sOGURQWf5FYPKYuhQ7bnZ\nP7Yxuv9f/iValrFj49cMshbLkiXFyVvofZk3TwcUBrG95dWrC3NDWVlsz9taFh/4gBZkjKMs+vfX\naxsTT1mMG6eN76BBmmhg544J3pc44zuSkM0NtWtX7jpVd98Nn/60WhalatCtC8qSz7rozv75clEp\ncoCLWXQrgspCJD13vb1de77Z0klHjfILC+bDBmJff704eQvliCPg2GPTt9lGZvXqwtxQFtuY2l7v\n6NGqHOMoi+pqdavs3h1PWRxxhH/P7f0PFyOME1hPQjbL4hOf0FH/2Vi1yi9iWCrLIqwsBg92cYve\nhFMWEXSGX3HXLm0kgy6L4KhYG8/IJktUqm02mppUGS1dWpzMxdyXsWPT120js2ZNYW4oK4ttTG1D\nVlenCjdOzAJ8V1QcZRFk5Eh47DEtBBm8L6V2Q2WzLB56KNoNZmWxHYRSKws7KRPktyy6s3++XFSK\nHOBiFt2uQFx6AAAgAElEQVQGa1UEA6HBuEUw+B1FXZ02IsERyNloatKBfPnKM5QDG5uYNCl9e7Fu\nKIttTIMji5ub41kWUJyyePNNjY8EKbVlERUbsOM7Ds6YFcbH/tZ2ro89e4qXJRizgHRlcf/9ycfJ\n9GbuvhuefbarpUiGUxYRdIZfsbnZd0FZgnWArLLIJktVVfy6QU1Nmj1l8+4LpZD70tio7+FR1raR\nWbu2MDdUMGaxdatmV02Y4Lvy4ioLmz5biLIAbUCPOabhve2lVhaQWe3WxqqiOhP2vtjnYuhQdWWW\nwrrIFbOYMye7LJVApchi5TjvPDjrrMqQJS5OWXQRwXiFZdw437WQz7KA+BVJS6UsCqGpKTrTa8sW\n/f5tbYVnQ4FvWTQ2aoaYdUOV27Kwjebu3elpr6UOcENm+myzN6lwLheQVRZ1df4838WyaVP6rIRB\nZRHHwnX49O/f/ar2OmURQWf4FYOVSS0TJ2oPGfLHLEB7t/mC3KtWwfXX+8qimDm8C7kvTU062jw8\njqC9nfdmuit0nAVoQ7p5s96HcePUsrDpuAMG5D9PocoiOB/Fgw+m3lsudcwCMi2L1laVO0pZBGMW\nY8boM1Qqy6KtLX1616CyCAf6g7JUApUii5UjqhpxZ+NiFt2EKMsiWE128+b8lkUcZfHaazo+4IIL\n1HUV9acuJ9aqaW1N95tv2aKNWXt78TGL11/XcQ/V1XrP1q7VxjSOD91Wnk2qLPr21WuFraZyuKHC\nlkVrq37fXJbFxo3whz/AiSeWVlkEv1uw6q+zLJJh/9vdqXKvUxYRdFbMIpdl0dSk2VG5ZMk1rajF\nTt06bpwqp2JcUYXcl6YmddnU1KRf21oW7e2F14YCP2ZhZ6YbNkyD5nHLbQwYoC6r557LHTCO4uab\n4bjjYOzYhve2lcuyCCqLlhb9PaOURUNDA7t363Nx9NEqSymVRVChBjsrUcqiUuIEUDmyWDnsb9eV\nZXhczKKbsGqV/uGDBC2Lpqbc1UghXpnoHTv8hrhYZVEIGzeqyyY4huSnP4Vf/lLTaQt1Q1lseuz0\n6fpur5NEWcyZowojau6JXJx9tv5mdnAeaCdg2LBk58nHwIHpFmFra3ZlAb7CsnN/l0tZBGNmnW2x\ndnfa29USLmauks7GKYsIOsO/uXJlZpFAqyyM0R7byJG5ZYmag7qjI70R2bHD990XqywKjVkMH55e\n++qRR/S9GDdUMGYBcPLJ+m6ttSTK4i9/gTPPLCz1c8QIePnl1Hvr9vuWkoED02foa23Vhnr37kwl\nkEqlMoL7pQpwh5XF6NG+soiyLColTgCVI4uVo71df8NCSrmUWpa4xFIWIvJbEVknIgsC264WkZUi\n8or3OjWw73IRWSYir4vISYHth4nIAm/fDYHt/UXkj972F0VkUmDfhSKy1Ht9OtG3q2BWrFC3U5BB\ng/zS11ZZ5CJqUNQf/5j+h+5qy6KpSRvUYKbX/vvr+7BhKv/f/57/u2bDKovjjtN3q3RsxlA++veH\nuXO1+m8hDB+e7grcuLFzlMXQofr7R/VMt29PD+47y6LyaG9XZdsTLYvbgFNC2wzwE2PMod7rMQAR\nmQ6cDUz3PvNLkff6bDcBFxljpgHTRMSe8yJgo7f9p8D13rnqgSuBI7zXVSKSJ+xbPJ3h31y5MlNZ\ngG775S/h5Ze1Ac0lS5RlUV2t77a0RymVRSH3xbqhgvEYm5FVW6tK8cEHdY7xQmTp21etqWDgtaoq\nflqiLcb44Q8nu76lvl7nSbfY71tKwm6olha9d1GdhYaGBrZvT7csSqEsjMmcpyOoLFzMIh4NDQ3s\n2aO/54gRXWtZlCVmYYz5GxAVt48y3M8A7jbG7DLGNALLgZkiMhaoMcbM9Y67AzjTWz4duN1bvg84\nwVs+GXjSGNNsjGkGZpOptLoldhBZmIkT4eqrVQnka3SCMQvr4rHuBmthhpVF3B53qbCWRVBZtLXB\nz36mPn/QuTmCKZlJCbuPxo+P/9njjtPZ9pJkQgUJz0NSDsvCBuEtQctiw4bMMSzlsCy2btXnyHZG\nINqyKCY1u7ewZYt6EYYM6VplkZRiYxZfEZFXReTWQI9/HLAycMxKYHzE9lXedrz3FQDGmN1Ai4gM\nz3GuslJu/+a2bfqQRDUqQQUyYkRuWYYM0Yb3xhv12LY2v1EJZqmUyrIoJmYRVhbjxvmNfCEB4Vyy\nJFEWP/kJXHll8utbhg+Hd97xZeksN5S1LE46KT1n38Yswsqi2JhFVGrx8OHa+di923enBEfqV0qc\nACpHllQq9V56dTETW5VKliRU5z8kKzcB3/WWrwV+jLqTuoxZs2Yx2cuhrKurY8aMGe+ZWvbGVMJ6\nczMMGpTi2Wcz90+c2MA++8AVV6TSSihEne/NN6GtrYGHHgJI8dBDsH277p8/P0UqBTt2NNC/vx6/\neTPU1BQu//z58xMd39EBmzc3eAUSUyxcCC0tDbS1wVtvpTzrpyFt8GHc88+fPz/r/k99CpYv1/OX\n+/ecMqWBlhZd37kTdu1qYMiQ0l5v4EBYtMj/Pi0t0Nio97epKf140F7+tm3+8f36wbx5KfbsKVye\nv/415VkV6fuHDm1g82ZoatL1nTsb6Nu3sOelnOu5npfOXAf1BlRVpWhpga1bO//6qVSKxsZG1gbT\n+OJgjIn1AiYDC/LtAy4DLgvsexyYCYwBlgS2nwvcFDjmSG+5GtjgLZ8D3Bz4zK+As7PIYLoLb7xh\nzD77RO+75x5j/u3f4p1n3jxj3v9+Y6ZONWboUGNefNGY//f/jBkzxphzz9VjvvUtY667Tpd/8hNj\nLrmkePnjsmmTymWMMStXGlNfb4w6Kox54QXdDsZ87nOdJ1OpaWszZuBAXV650pixY0t/jauu0pfl\niCP0/l1/vd6/SZPSj3/kEWNOPdVfP/FEYx5/vDgZXnrJmBkzMrdPmWLM0qXG9O1rzIAB+ps7cvPS\nS8Yceqgxl15qzI9/3LWyeO1mLB1QsBvKi0FY/hWwmVIPAueISD8RmQJMA+YaY9YCrSIy0wt4XwA8\nEPjMhd7yWcBT3vKTwEkiUiciw4ATgScKlblSCJdNCHLWWXD77dH7wtTUqBtg5Uo47DB1+Wzbpi4f\nG+DtymyoYBrp+PHwb/+WLrsl30j1SmbwYB2Zvm1beVxQkBmzWL1a7+d55+l6OLYVjln061f8COvw\n6G1LbS0sW6auxNpaN5I7Dm1t+twUOgtiVxE3dfZu4AVgPxFZISKfAa4XkddE5FXgWOBrAMaYxcA9\nwGLgMeBiT4MBXAzcAiwDlhtjHve23woMF5FlwKWodYIxZhPq4poHzAWuMRroLitBk60ctLZmD6iK\n+IOp8slSU6PZPKNGaayjqUkbiokTOy9mEQ6uBrHBbcsNN8BXv+rLDtrgBZVIMbJ0BSIwZEiKjRv9\nwHOpCcYsOjq06uyYMfqbv/FG+m+aiohZTJjgx4sKxcZJwtTU6HS9EyfqcxaMjVTKbwTFy9LRAV//\nevEB/FQqxdatqijCNb86m6T3JFbMwhhzbsTm3+Y4/jrguojtLwEZRRWMMTuAT2U5121o6m6PIWkd\nomzYnt6UKdoob9jgWxZzvZyzoLKoqyutZWEMHHKIpr4eemjm/nAaqYgffLbf/847SydPV1Ffr6O4\nS/W7hgmmzm7Y4FeShejfNGxZ7LMPLF9enAxhxW+prYVFi/SZa27uuXNyr1yplQeuvrq4zD3wf59B\ng+JPjVwJuBHcEdigULnI1ktLKotVFmec4c9tEbQsjCnvOIsFC/RPtGhR9PFRo5ntjHnFNqrl/o2S\nsP/+Daxcmd1VUyxBy2L16vQyMTYd2vZ4GyLGWXSWsgi7uyrpNypWlmXL9D3u7JS55LAj7LvaDZX0\nnjhl0QWUqgdaVQWf+Qx84Qs6gM/GLOrrdZ+dSa9cMYsnntC8+zfeiN6/bl3myOxx41SebHOLd0cm\nTNDR6eWyLIIxi7CysGMfgjGNKMvizTeLkyFbRYFcbqiehFW2xSoL8Oda6Wo3VFKcsoig3L7WJI1K\nPlluvVV7KCNG6INsH0SrGMoZs5gzBz76UV9ZhHPG//EPDbwHGTu2NL3vSvKH79yZYtUqTYkstxsq\nqgBlcLBlKpXKUBZTpvgzFhZKtsKWtbX6u0dZFpX0G2WT5cIL9TnNR6ksi+Dv093GWThl0QUkcUPF\nZa+9dD5o+yDaUiBRyqIUo2yNgRdfVMvmqafgS19KVwLGaNnvcBmNadPgN78p/vqVxMiRvOeGKpey\nyGZZQGbcIlxIcNAgfQ6KmYd7w4ZoN5T9vlZZdDfLYvHizHnUo2hs1P/PH/6gv0ExVIobKilOWURQ\nbl9rkkYlrix7762ZUfZBrK3NVBb9+xc3AVJQlvnzNWB92mlw001azyrI22+rHOH6V9XV8K//Wtj1\ns8nS1ZxwQtfFLECVhbUsbMwiaFmIFN+LzWVZQLQbqpJ+o2yybN6cf04Y0GP22UcLdf7wh8XJEXRD\ndaVl4WIW3YBcqbOFMnSo9uzefTe7ZQHps5sVw+c/D9/7njZEn/oUfOc76ftXr44ulNgT2Xdf7aGW\nM2ZhFXyUsgjX/AorC8icbS8p2WIWtbXaARk3rjTjOTqbTZviuWaDLsaxY+FrX4PZswu7pk1AcDGL\nHkBnxCziuqGSyLL33ho/GDgwu7IoJghpZWlt1QyYTwcKxn/3u9po2NpA69ZpCeZyUUn+8LfeSiEC\nCxeW1w31i1/AvHm5LYuomAUU7/LINk9HTY3KU13d/cZZdHTofYvTeWpv99O+W1o04G0rFieVw46D\n6Wo3lItZdAPK5a6wBeWsG6q1tbTKwvLSSzBjRnpGkw5O80uml1tZVBIiMHOmxnDKpSy2bIErrtAA\na7hQYr6YBWgv9uKLde6QpBijv2vUgMO6Oo2XgVoWt9xSXGykM2lt1e8W17K4+motPLlxo97jQis4\nV4obKilOWURQbl+rHcFZalkOOkjfc7mhiglCWlnmzYMjjsjcb+Mk4I8yLheV5g8/8kgtA16OTsDI\nkdow2WlpR41K358vZgH6vD37rLrLkrJli54vWFnA0tAAd9yhy5s3w6OPqivUylIpRMliqw/EVRa1\ntaqoi1EWwXEwXe2GcjGLbkB4EplSccgh+p7LDVUKv/KKFeAV900jOBlTb7IsAI48Ut/LYVkMHgxf\n/KJO/Tp2bGajHY5ZhMt92HPs2FFY6meuWEy/fr5F+9xz/vHdAat847qhhgzxB78Wa1lYN5SzLLo5\n5fa1bt2qvYpSyzJjhr4PGBCdDQXFWRZWlvXrM3u3kK4s1q7tPTGLVCrF4YdrzKYcygLg+9/XTJyo\ntM1wzCIqzdU+b6VWFkFmzdJ321OvtN8ozKZN+pvlsyz27NH/0cCBeOX2tcEvZMySjSkNHKj/y927\n0+cA6UzKUhvKUVqSuKGSMH68pvfV1OjriSfUJxv0NZciZhFHWfQ2y2LIEPjv/4ZJk/IfWwhVVX49\nqDDhmMWqVZlxDfu8FaIsbK86HzffrFZnZ8/GWCibN+vo+3yWhfUEiPjKorq6+JiFTWnetq18nYxS\n4iyLCMrta7XTKpZaFhEdadq/vz58zz8P116bWa662JjFhg3RyiIYs2hsLF/DGZSlErCyXHtteWIW\n+QhaFkcf3UBTU2a8qBhlkSQlOKi4KvE3CrJiBey3X34Lob3dv39By6LQmEXQTVjOIHdHR+6EBhez\n6AYkcUMViu2FfvKTmduLjVmsX5+9TlBbm/65WloyUzwd5SEYs1i7Vl1Q1SGfQWe4oawsnTlnSjG8\n8AKcfHI8ZWE7ATbLrLm5cMsiWOixnEHupUvhqKPgz38uzfmcsoignL7WoP+znLLY84dz44uNWezZ\no77ebKUfNmzQ/POpU9V1Ui4q3R/emQQtiwcfTEXOQT54sCqQzrAsgvGTSiEsizF+bbNg1d4owm64\noUM1860QZWHHWdj/ZzmD3LZTmK32lYtZVDhBf2U5+cQnogNnxcYsNm3SBiHccwXtMf3nf6rpazNk\nHOUn2EA3NWXGK0AbJVtQcM+e6DTYbMSNWYA2pBs3xj93V/HWW3oP9ttP33PFEbdsyVQW69cXH7OA\n8loWVlmUql6XsywiCPvytmzRwThRdHQkG4SUNLhdjN83qkEoNmaRzQUFavIee6yavfvuW9g1kshS\nKXS1LCNHqitFG7WG9wbJBRk0SONMI0ZoRtWzz+ogszj0xJjFnDlw9NF+0NpOQxxFlGUxZIhaF7t2\nJZcjOA6mM5RFNhldzKIMpFLwjW9E7zv33OhZ4rKRJLhdDoqNWWTLhAKtQJtKqYVxXcY8iY5y0bcv\nHHigFndcvBimT888ZvBgbeQmTdIqq1ddBddcE+/8SWMW3SEbas4c+NCHdNmOnchGMMAN+h1t1dhC\nXEjBNqAz3FDOsigjYV9eLs3/yCM6Y1xckloWpfb7FhuzyJYJFaQzJjeqZH94V3DYYVqG5cUXUxxw\nQOb+sWO1LMekSdpQrlkT/9yFBrgr4b5YwrK88YZf8WDEiNyus/DMg0Fl0d6eTI7HHkuxcycMG6br\nXWlZuNpQZSDXj7llS7L4Q1dbFsXGLHJZFo6u49hjtezGokXRlsUZZ2gp+UmT4LLLNFMmLklqmdnU\n0krHlu+A/G6onTvTOz/FWBZr1mj1A9tmdKeYhQtwRxD25QWnrIzC9hLikDRtttR+32JjFk8/nT1m\n0ZlUsj+8KzjnHE0FratryPn7JPWxQ2aANxcjR2pGHFTGfbGEZQnGIfK5oXbuTB8QWVvrWxpJlcWI\nEQ1ppXJqa8uXamz/5y5m0YlEKYvNmzWnHTSoF5dyjd6OS6HKwhgtEBfHDeXofETgZz+Dv/4193Hf\n+hb8+te6HFUmf+FCuPHG9G1JOjijRqn1aVNRi53OtVwkURa7dqUri2Isi7ff1qw0y957+1O2lpod\nO/xAfClwyiKCODGLz35W/cCQTFkkdUOVI2ZRSID7xRfhqKNSFeOGqmR/eFeST5bRozUR4bOfTa8Z\nZnn1VXj44fRtUSXPs2HLYqxZA888k+LQQ4ufhrQUhO9LUFl0phvqb39LpSmL/feH119Pdo647Nih\nsaZsncOyxCxE5Lcisk5EFgS21YvIbBFZKiJPikhdYN/lIrJMRF4XkZMC2w8TkQXevhsC2/uLyB+9\n7S+KyKTAvgu9aywVkcB0O52HLWERTJEttMHsrpbFokXaAKxaVRluKEfh9OkDP/1pZkM3b55aAuGq\nsUldp1VVOtZj0ybNjKq0KrTGpGc4jRunz3U2irUstm3Tewv6HwoqiwMOgCVLkn+HOHSVZXEbcEpo\n22XAbGPMvsBT3joiMh04G5jufeaXIu+FgG8CLjLGTAOmiYg950XARm/7T4HrvXPVA1cCR3ivq4JK\nqVyEfXnWpxhsZG1P65JLkgWokloWpfb7FhrgXrIEOjoaeOWVyijjUcn+8K4kriyDBmmWT0eHv+2I\nI3R63HBhvSSWBfgZQn36qCyVUIY7eF927NBBpdZamDJF3UOWcHZj2LI44ABNl4+rLJ56Su/to49C\nW1tDmrKYODH+bH1Jscoi2/+9LDELY8zfgM2hzacDt3vLtwNnestnAHcbY3YZYxqB5cBMERkL1Bhj\n5nrH3RH4TPBc9wEneMsnA08aY5qNMc3AbDKVVtmxyiKoodvb4Ve/0rl4kyiLzqgLlYtCLYvFi1XR\njByplW0d3ZuqKh0YZp9dO9p/0KDilYXlhRf0vdTKYtUqHStSKOFBdlOm6IhuY7ThPvTQ9P9IOMB9\n1FF+0cg4qbP2+7/ySmbMoqpKXYOFlGHJRyXFLEYbY9Z5y+sAW5B6HLAycNxKYHzE9lXedrz3FQDG\nmN1Ai4gMz3GushL25UVZFvaBS5r6VgnjLJLGLIzRAV+HHZbi1FPLX6okDt0pTtCZJJFlyBC/IVu4\nUN+3b89UFkk7OI2NOjr6scdS732+lOyzD5xwQvq2TZvg1FOzfyZ4X8LKoq5OLYemJk3g2LMHVgZa\nnbAbyhLXsrDHLFgAxqQyYpzDhvkTMZWSnTtzK4suqQ1ljDEikqMUV+cwa9YsJnt5aXV1dcyYMeM9\nU8vemKTrc+Y08OijAClSKTjrLN3f2Jji7bdh0CAtORz1eWNgwIAGPvhBf/+WLQ0MGxb/+pZC5Q+v\n9+vXwM6dyT7/8stQXZ3ipJPm84UvFHf9Uq3Pnz+/S69fqeuWOMdXVUF7ewOjR8OXvqT7OzoaaGuD\np5/W/Q1eSe1XXkmxcmU8eSZNggEDUixZMh9oYMuW0n2/Y46x05Lq/9Huf/DBFE89BcY0IJL7eWlv\nB5H0z48cmeLee+GQQ3T9gQc0ON/QoP+Xd95JPz7lDVAdMiS//Fu3wrBhKZ5+2k+KCe4fNkyTAbZs\nKe3zsGwZ1NQ0sHp1+vORSqVobGxkrU3njIsxJtYLmAwsCKy/DozxlscCr3vLlwGXBY57HJgJjAGW\nBLafC9wUOOZIb7ka2OAtnwPcHPjMr4C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SSfGnCE4SswhaFrZUyrp1mR2ufDGL1lb9H/76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\n3+pqeRwOh6MrGDoUTjxRa9iBKgsbp7jwQp0XI0yviVmISB/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      "text/plain": [
       "<matplotlib.figure.Figure at 0xad039f8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( lsdiff )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The preceding chart shows the difference between the number contracts long versus short. \n",
    "It is somewhat useful, but we would prefer a **scale-free [0,1] measure** which \n",
    "reveal position: 0 for bearish, 0.50 for neutral, and 1 for bullish. \n",
    "\n",
    "This will also allow us later to *combine* readings to show position in a class, \n",
    "for example, the US dollar versus various foreign currencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Scale-free measure from zero to 1:\n",
    "z_xau = todf( longs / (longs + shorts ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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emG3mTPHCJ02KaYfyVUKsJhaprpYLfJie+Xe/KzNJqbuUQvCGOzulQ5a9h202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\nmwnEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabf4d9ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Could interpret as prob( bullish Manager ):\n",
    "plot( z_xau )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7o4BRsfbPAoMzXFfLlfvuUwXVM86wLah27qz6wguF6W+7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\nWgn/H27qex1f0E1QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabcabb6c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How does our indicator compare to spot gold prices?\n",
    "xau = get( d4xau )\n",
    "plot( xau['2006-06-12':] )\n",
    "# using the FRED database for Gold London PM fix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GOLD: We can definitely see a \"raging bull\" in action through 2013 -- \n",
    "our position indicator approaches 1.0 frequently! Thereafter, there are \n",
    "four major efforts to re-ignite the bull market through mid-2015, \n",
    "but without much success as the price creeps downward. \n",
    "\n",
    "July 2016 sees a breakdown below the 0.6 positional support, \n",
    "to under the 0.5 neutral mark -- which means more shorts than longs among managers. \n",
    "The Fed raises rates on 2015-12-16 (first time in almost a decade), \n",
    "and the indicator settles down below 0.5 for the start of 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalization to Asset Classes\n",
    "\n",
    "### Following function will compute position for commodities and financials:\n",
    "\n",
    "*It returns our scale-free measure on [0,1] such that comparable contracts \n",
    "can easily be averaged, and thus interpreted.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature: \u001b[0m\u001b[0mcotr_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfutures\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'GC'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mSource:\u001b[0m\n",
       "def cotr_position( futures='GC' ):\n",
       "     '''Extract market position from CFTC Commitment of Traders Report.'''\n",
       "     cotr = cotr_get( futures )\n",
       "     #  Report for both futures and options requested by implicit \"FO\".\n",
       "     #\n",
       "     #  For directionality we use these categories:\n",
       "     try:\n",
       "          longs  = cotr['Asset Manager Longs']\n",
       "          shorts = cotr['Asset Manager Shorts']\n",
       "          #  \"Leveraged Funds\" for FINANCIALS appear short-term, whereas \n",
       "          #  \"Asset Manager\" takes longer term perspective.\n",
       "     except:\n",
       "          longs  = cotr['Money Manager Longs']\n",
       "          shorts = cotr['Money Manager Shorts']\n",
       "          #  \"Money Manager\" for COMMODITIES. \n",
       "          #  The report is structured differently than financials.\n",
       "          #\n",
       "     #                _Scale-free between 0 and 1 indicating bullishness.\n",
       "     return tools.todf( longs / (longs + shorts ))\n",
       "\u001b[1;31mFile:      \u001b[0m~/Dropbox/ipy/fecon235/lib/yi_quandl.py\n",
       "\u001b[1;31mType:      \u001b[0mfunction"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#       ? or ?? is useful to investigate source.\n",
    "cotr_position??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precious metals\n",
    "\n",
    "Let's first take a look at the COTR position for silver (spot symbol XAG). \n",
    "Graphically it is quite similar to gold, so we will also \n",
    "examine their statistical correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tCS/PHLCUpN92nIazU1FdzUTd2cnbyyZzf2UOWLGpZC4Jzv4UoyJMhx+7Mi8p\n4eMRVpC0tY1V5YAzmat2muzZ6nZtFRM6O5nMJ09OPtmgEJFTMh8etkhFkvmkSf4XqiTz6momGLuP\nlE9lLmPp6BhL5vJilaP41Nc7D3MW1yOhfb+oytypVrmdnOLMZtHxzAHeN35497t5NCQ/TJ3KT3Hl\n5Zai52OQjkzm6nw7gpC5m2cO8A0oCpk7jdM6dSpfa042SzqdPlAYLt+lJ+LwqWWtocmTrbpP+Yol\nLhSsZz4ywgpgYMCqCzJnDtsvXupcJXMn9ZBPMpeQF4UKebFKwpo2zdlqKS8HHnss3nhGR3nQgRNO\n4Gk7UdsbP52W6e31zyLxg5dnDlikax97VMVf/gJcfbX/tuRQfeo2ZPxSsXuRuTxeattG3GSurtdO\n5lFGLHIjc6nWVftFJW553nplihULpDKfNMko88QxMsInjdoAWlbG6nHdOuffdHQwMTU3+3vm+bBZ\nZCxy4AMVsn60vJCmT3dvBD3nHO7kEkcsAKfpTZxojd1pV306ytzeqShMLH7KXCKOMsiSzNWblK5n\nDlj/1akBVJ1vRxAyd8szl+sPS+Z79jiTeWkpv6vnneqZF4oyj8Onlso8aulp45lrQFXmkswBHrPR\nPvSXxKZNPKK4EIWrzInYe3Qic9VGcFPmAJdyjXPM0HQauOACazqMMvdTsjrw88wl4qgYOHWquzLX\ntVnq6rLHAy0mZe7kmQPcGKg+rbsp82LvASqVufo0eDAh5565E5k7pc1JrF5tWQVueeb5bABNp9MH\nMh3s5GjPenBS5kR8o7rkkuidRtT9InOHJXQ9823bgH/+01rGS8nqxKKrzNW66mExcSJXz1Rj5u3p\neeZVVXxD6Olh4bF+vWV/lZePHW5OIqxnblfmSXjmAD/VqoNcO3nm9j4GuUacnrlaQiJfscSFgvbM\na2vHknlFhfvOX7MGOP54/mxX5n/8I/du/PSneTpfytzJYgHGkvm0aWPJfHCQT74JE+IdYs5v4Akn\nZT5tGo+5KccuVbNCwsLPMwdYTd9229jvg6KhgUnNrszledbd7W+z1NdbfR/e8x6ro5WX3RQ2myVO\nZW4/3l5oaLBGVpJkPh7UrFHmOYSbzeJF5qtXjyXzVCqFl18GrrwS+PjHrWyYfOWZO2WyAHo2i1Sr\njY3RyVz11+wXt5Nnbic2IYBLL7UuBHseephYdJT5rFnZN72wkGRu98wnTUodmOdns9TUWMdC7QTm\nReaVlbypFTLFAAAgAElEQVTvdIjYLc9cricsmQ8P6x+rOXM4qSCVSh1IqS0rK/565qoyj0LmufTM\nh4e5gT+OWPLSACqzWSTRqRe8HVu3WsWhVGW+aRMXFfrAB3gQgre/PX955k6ZLAA3PqpDeNmV+YoV\n3H08LjJXsX+/tzJ3y5tWj0UcylzXM48D9fXcEKiS5NSp3MDe2OhP5rLmSkMDE8MhmUr+FRX+cQdV\n53Erc3uBMC/Mns1PQyMj3Ng7ZQo3lBa7mpVj2qpDFRY6Nm7kMWa9srl0kTdl3tpqEaAXmasNizNn\nAps3s4/0yisWyV9wAech5yvP3M1mOe444DvfsabtAwfv2MH/p7LSe4T4ILFIdHV5DwnnZLMA2Qo+\nijJ38syDqMcwaGjgm7/6lHT44cC116ZRW8tZUbrKvKvLUqpeHYYkdMlc7henHP4onnkQMq+q4vaF\nP/0pfaDncr6tibg98yj/JZeeuYzzmWeix5IXMl+xglW2VD5uZG7PElmwAPj3v/n7zZuzy7mWl+fP\nM3ezWeyQJV+XL2di6e5mvz0JZe7kmfulJgLJKHN5wspep0mhocEqf2uHJE4/z1wq885O3ofXXMNC\nQYfM1SwYPzj1ro2qzGUaog4OP5wtP5XM82mzxAH5NJrvG1MQyEy8OPqZ5IXMn3kGOP98q8OIG5n3\n9PA8eQEeeSQvN2dOCps3c8qihJfvniSk7+ikzO2QZP6mN/GFd+WVvE9y5ZnnUpnLWNTMglwoc2As\nmadSqQNkHsRm2b+fz1PZg9cLuspc7hcnZZ4rmwVgq6WpKYXdu9n+y7fNEodP3dNj9V2J6plv2ABc\ndlnkkHwh7RW3WjIF7ZlLNaIW0Xcjc7sXLQQT+PbtnJcuu6oD0R5Ro8LNM7ejoSH7gpe1aiorC8cz\nV2+KcXvmuVDmgLMyl+ddkAZQaVWVl/uTedAelE4VKXNlswBcJbGlhZ8MJ00aH8q8u5tvkHEo83/9\ni0ctShqSzOPY93nJMweyh7fSJXOAiwL99a9p7NvHw4Wp68iXZ65rs5SUMFHYIRXh4GB8+bF+nrlb\nDrm6H4vRMwfG5lun02ktm+Xkk3lUJlWZywY1PzK3n8MjI8CvfjV2OS/PPJfKfPp04OGH0wfSEvOt\nzOPwqdVe5VGvI53rOQ5Im8WtAF/Be+aAPpnbx2dsbASefJKHCVM7ceTLZgHc88yd4JaPLoRFIlEx\nMMDtCvZa5erNzstmGRri38fVAzRXnrm8eXl55l7/Z+FCTs20K3OdbBb7Obx1K6fNusFJmeeazDdu\ntMo2F5PP7AaVzKP+F3m84ygz4YWiVeYjIxaByMZPwF1VuynzzZtTB3qF+q0jaaj1LXQwYQLwzncC\nv/ud9Z08cSZOjFYgSPprUlHKNgnA8sxlvWyvBtDBQT5WQgRrVHOKJZeeeVUVby+sZy4RhzKXJGCv\nT56UZz4yEpzMW1pSmDOHp/Nts8TlmdfWRk9NTKVSB45D0mMQ9PbyNeamzAvaM5epeerF4aaqnUiy\nsZHTGu2KPd+eue5jWVMTqyGVcGS+8YwZ8Zw8nZ1jO+GUlmaPwOKnzOPwy4Hceuby6cZJmct9rHNz\namjgp63+flbPYchcPmE5kcrICC9rX2cuPXM5Xq20KvNts8SBOJW5LG2QNJn39PC1WpTK/OyzgY98\nJPv7oJ45kHYk83zVZglqs0ydmu2dS6KZPj3aySP9tR07OCffDlX5+SnzKH65GksuPXMAmD8fOPTQ\nsbEE2W5jI+/D+nq+QYQhc3UgFnssMi1RfXICcm+zAOkDZJ5vZR7VMx8d5Qbompp48swlme/cqfeb\nq692HqvAD729fJ4VpWc+Zw7wm99kf+9G5tu3s1pVIUnQ3pCYL5tl2TLuGq1L5tOmscVkHxAYiE+Z\nb96MA4/PKlTfPJfKPFeeOcBjeTpVDwzyXxoa+NyTTzdhyFxe2E7k7DaCUy7JvK6OM3DGizKX+1SO\n2BSXMt+2TW/53/yGeSAoenv5WBSlMnd6zHUj8+eeA049Nfs7JvFUwSjzpUtTOOUUfTK/8Ubg8svd\nlbmuEnCC9NfkYB52qLnmSSvzfHjmXrEEIfPGRm7AlHZNmAZQqcztlQhTqZTrQNm5JHMA+Pa3U5g3\njz/nuwE0qmcu0xKBePLM+/v5enz+ef/l+/v5eIdJLZY2S1F65rpkPjICrFw5lswliReKZ753L7B0\nqXt5VDuqq/n/yvgbG+OzWSRefTW7d6yE3WYZb565Xxy6aGjgWKXCD0PmfsrcaTi+igrge98Dnn1W\nP1aJMGT++c9bN5XS0uLOM1cL98WlzBcscO9mr0LWW1JLdehC2ixFp8zdTjgnMt+8mfOFnVITgXRB\n2CydnUBnZ3qMFaSDqiqOeerUbJtlx47w8Uh/TddmGY+euVssQT1zwCLzSy+1yiy7QZfMVc/cDnk8\nfv1r/VglwpC56sfmW5lH9cztZB41z3xgADjxRC7O5ueFSwEWRpnH6ZnnVCcFUeadnc5ZCW7KPMoj\nalhs2MDlW+0NWToQgp866ustZX788cCqVXxgdZW+E9rauBKeHUGUeZSBKVQUijIP6pkDFpnr3Kx1\nbRbASqFzi9HpRuyHoLVZ7Mh3A2hUyK78QHzKfNo0zlLbt887Wy0Kmff0sEUbx8AgBWuzqB6YCumZ\n25V5mAEComLDBuCUU1Khf//kk2ytSDKfMoVvYGGHj5P+mhdZSDJ3awCVyjxqhyHVM5cXVrF45jU1\nfJ66DcPmBF1lnkqlXJW5HFE+zCDaYZS56sfmuwE0Ts88jjxzWW9eRyQmqcyL0jO3WyRuhNTczMPI\n2S2CujreMblUF62teqrNC7W12XWtzzgDeOqpaOt023c6qYnjVZnLga11IPPVo5D5a69ZtfvtcPPM\nZYexMCotDJmryLfNEhVqo3JcylySuZ9929rK/BOlATQnnrkQ4gIhxHohxEYhxDUO8xuFEH8XQqwU\nQqwRQixyW5cbmTtloqiPTSqqqoCf/jQ99o+UjB2MN2l0dQHt7WNjCYIvfpHH/5SYNcsaOSkopL/m\nReZq3RUnspYNYQMD48szP+ecYBd4VDLfsYOLwjl55k4DUwDAddcBF12UOzJX/dh8N4BG9czVBvu4\n8sx1lblsLA/TANrZyTZL4nnmQohSADcDuADAsQAuEUIcY1vsUwDWENE8ACkAPxRCOJ5WcdgsXpCD\nCuQK+/c7Py4HwZw52f62OnJ6GAwN8X52U92qzeK0jBC8XE/P+FLmQDBPuakpPJkPDbHNcsghzsfS\nbd9PmMBPZkaZB4dqC8atzP3IfGQk/Bi+HR1srbqReRD4KfPTAWwioi1ENATgHgALbcuMApDFVhsA\ntBOR464MQuZu6hJw95HiLiPrh/37gZNPdo4lLKqqwjfkyhzm2lrnRlmdBlCAj0dUMi80zzwoliwB\nzjxTf3n1HN61i2/QcvByeyxe+z7szTwOz7yYa7PESebSM6+s1CPz0VG2f4Nyjxx8Z+JE930fp2c+\nE8B2Zbol852KmwEcK4TYCWAVgM+6rcytGJAbmTvZLF7INZnby8zGgSjK/LOf5Rxzt5ugemK6qUPA\nUuZxEK88tkTuN/NCxLx5wchRPYd37uRyCm5E4DUMXS7JXIVR5tkIoszDknl/P4suOaRhVPgdfvKZ\nD7AF8wIRvVEI8ToA/xJCnEREY3JLRkYW4frrZ0MIoKmpCfPmzUMqlUJ5OTAwkEY6bd2J1q6VucE8\nLb2jVCqV5SPJ5dPpNIaHgc7OscsnNb1lC7BlC8cY1/orK1kVBPl9Swvwox+lceedK3HqqVejttZ5\n+X37gMFBnm5tTeOll4C3vnXs+srLgRUr0pmTM9z/+clPfoJ58+Zh/vwUhoaA//u/NEpKACGi7Z8w\n027nS5zTmzensXUrAKSwYwdQUZHOFOvKXh4ABgZSeO217PNdzq+qCn78eZ1pPPEEcMEF+vGvXLkS\nV199NQBg5cp0Jp0ymf2je76E/f3atelMW1MKZWXcluW0f3XPl9ZW4KWX+HocGPBefnQU6OpKZ7KR\n9LfX1sajPZWUAG1tVrzpdBpLlixBYBCR6wvAfADLlOlrAVxjW+Y+AG9Qph8C8HqHdZEQ5IihIaKS\nkuzvrr6a6Ec/cl5++fLljt9/8INEv/udNd3T4/z7uHDOOUQ//rFzLGHxq18RXX55sN/cey/RqacS\nVVcvpxtuIDrpJOflLr+c109ENH8+0eOPOy83axbRjTcSvf/9weJQIY/R8DCREHwsqqrCry8K3M6X\nOHH77USLFvHnm28muuIKoiuv5M/2WL75TaKvfc15Pb/9LdEllwTffkUF0cBAsN+o+2XVKqITTgi+\n3bgQ9Rj9+MdEn/kMf37uOaKTT44Wy7x5RC+8QPSBDxD9/vfey3/mM0Qf/zjRsccG287atURHH030\n0ENEb3yjeyxM0+48LV9+NstzAI4UQswWQlQAuBjAUtsy2wCcBwBCiKkAjgKw2Wllbo/Ysp6v+qjh\n1QDq5iOpNsvu3dyl3V5POiy+9z3OC1exfz+wYIFzLGER5jG7o4Mf7fv6Uti5092e0klNBOL1zOUx\nj5odEwVu50ucUG2Wtjbuvez0iJ7K1MouBJtF3S/5tlmiHiPVZsl1nvnoKO+/oFbJvn3c0F5SkgPP\nnLgh8yoADwJ4CcAfiGidEGKxEGJxZrHrAZwlhHgRwP8B+BIROXaAdSNzWWbUXtw/TDaLJPOtW5nQ\nW1qCrcMN117LLxX2cTbjQFgylx0Xdu709sz9UhOBeD1zgE/0vr78ZrIkDfX8bW+3yNzpWMbdADo6\nyqKlJEKvkXw3gEZFvj3zMGQuR1LzGpwiCHwPPxE9QERHEdERRHRD5rvbiOi2zOddRHQ+EZ1IRCcQ\n0e/d1uXV+GXvOOTVAKr6jyoaG7k7fFWVVX1w7VqvfxcM7Ila6OoCXnzROZawCHMxW/mtaezaFb0B\nVCrzOPLM5fr6+vKnzN3OlzhhV+YTJzpnJqXT6dgbQMM2LKv7Jd/KPOoxUp9M4sozD5LNElaZT5jg\nrcyD7Jec9gD1OuEqbB2HwuSZH300cM89vPNlwao1a4LH6QZ7beM48sztCJOaKOuAAN7KvKJCLzWx\nooL/m1+lQF2Ul3Ovx4NRmTsdS7dSCgD3Gg1K5lEzWYD8k3lUxK3M5Q1XpwdoVJslZ8o8Tvgpc12b\nxc1Huugi7kEJAJs2ca7vZkf3PhiIrIMlLzRZJvb8851jCYtoyjzlq8yvuw74xCf8lfn+/c69FHWh\nHqN8K/N8eOZSmTvVM4/bZglL5up+ybfNYj9Gra38lK2LuPPM5TFKUplLjsuJZx43vE44O5nv2xfc\njy4v5xroM2cCq1dzPY44qpF1drJimj6dVRfAZFdXF65iohfcfFYvdHRYfunAgHcDKAD88pe5VeYH\nm2fu1QAKeN9Ic0nmKgpNmX/84zgwcIYOklDmSZO57EQ3rpV5Zyc3XM6d67ysl480cSK/Vq/mrvJx\nkPmePdy1e+JEq7Ld4CBfeHH7sWGV+WGHAbW1HItbN3SVQLzIvLIyujI/mD1zabPIgaHtsRSKMlf3\nS76Vuf0YBW0DsJN5lHrmy5enD9TzT5LMZXmLceeZqxfD009zre+wF39zMz+mHX10PDXOJZlPmmSR\neVJ1RsJczO3tfOObNo2nTznFeTmVQLzUYXU1q33jmetDnr/9/XyjrKvj6p6rV49d1o/M+/qCbXs8\nKvOg7WVxKnPZU72kJHllXlY2zpX5c89xwSE3+PlIclCLo4+OV5lPmmTZLPIRKW4/NiiZDw1xF/5v\nfxv48pc5FvtQexKSnEtKvOuVV1Wx2o9C5gerZ97RwRkKQvD5t25dthWXRJ55XJ55IeWZBy3lEWee\n+ZlnWvXv1aQBN0Ql87g885xqpSA2i9fIHn5obmbfvKkpHjJ/5RW2bIaGLGUehxpyQtBslvXrgUMP\nBV7/eiuzxm3fqWReVubu91dXM5lHsVlUHEyeeVeXNeycqr5VsikUm0VFoY00FIXMoypz9fjoVk00\nnrkClcz7+71HW/HzkSZO5N6fUSoQqlizhod0Uz1zabPk2zNftQo46ST+3Nqa9jwpVDL36t1ZVcX/\nL4oyP1g9866u7Ib755/neTLjKJ1Oe6YmynO2t1d/23F45vm2WezHSP4f3f0QJ5kvX54OROajo7zt\nJJR50XvmfX3hhs6SmDSJBwbQyQy55x7/QZQlmaueeVLlXINms2zcyI/zEl7ZNfIEFcKbqOW+N565\nPuT529mJrCENTzmFn5zUgQu8Og2VlPBgJd/4hv6241Dm+W4AtUMSqLQ1/aDuA2kZhS3lMTwcTJnH\nYbMUnTL3IxqZnO82EouEn490+eXA9dfrKfNf/YrVkxtGR7l62nHHWZ55Wxvw6KN8IOL2Y8vK+CTU\nVRbd3ZYS9ItFEogcgMINksxNnrk+3JQ5wB66JHO/PHMAuPhits90EYdnLgklDlIJA/sxCkrmqjIv\nKYlGkKeemsopmXvdSAs2z9xrp8SpzJubufOQjmUxMOC9zJYtvL7GRkuZX3klcNVVyZCTEMGsFnXs\nQz/okrkkcZNnrg9ZjqK1dSyZNzVl99L1agAFsoWNDuJQ5kIUljqPQuZAtPREu2eeVA/Qolbm+8dU\nOLcQhMx1fSQdy8KPzNesYVUOWJ65HHg3Cc8cCEbm6uDAfrGoZK5js5g8c32Ul3Oxs09+MttmAZjM\nVc/cT5mHIfOotVmA/DaC2mOR57/uuJp2MtdR1G54/PHgnnmYRkyd1MSC9cy97rJxKnMJHZvFi8wH\nBoCHH2a/HLCUuVRZSZFTUspc9cxzqczLy4E777Ty4Mcjmpu5nQbwtlkA79osgF46nAq3EbyCIt+N\noCoGBpgDdMf0jZPMVc+8vn5sxy87kuw0FAQ5JfNvfct9XhAy1/WRdEhxcNB9mT/+Ebjppmwyb2+3\nLswkPHMgWBaOqsx1PXO/x/w4GkDVWLZs4Yvypz8Nv74oyJVn/sAD/NlJmUsBID3zQrBZ7PslLptl\n/35+QokSy8AA9+3weppXESeZn3CC5ZmfdBL3RvdKkpBkHrTBVUeZF6xn/rWvuc8LkpqoC50D6qXM\nOzuBhQuBSy/l6bo6jlGW103KA07aM1eVhxPisFlUrF/P+y6OY1rIOOQQfrfnSKs2C+CdZw7o+bQq\n4urzEJcy37WLs8SiQJJ5PpS5enzKy4HzzweWLXNfPmptlqJU5l5IwjOXZXXtO/nFF63PXmTe1cVp\nf/IkEYJ9cxlneXkyfmyQ9MQgnvm0aYC80XspwzhsFjWWM87gao35Qi48c8Dabz092d+rytwvzxzI\nn2ceVy/Qnh6+doIoVXss+STzZ59NZx2fQw+1UpKdkGR3/iDnbsHkF9jJPA5VKBv6pP/W18cq8ZRT\nrBPNj8zt/uekSdaoPsWmzOvqgJtvZttIR5nH5Zk/9VQ86ykW2FNwa2utzi9E/so8KJl7lWYIgrga\nQHt6mJy6u9lzDoOBASbRfHvmgP/xSLI7fxAUpTIP4iOpB3XRorFFqLzI3GlYOEnkMuakPPMgylyS\nuU4s8oTXaQCNK88838hlLBs2cNqqCtW+OPtsHo3db6CWXJC5k2celzIHrCEcw8QSVJnbraYoZH7U\nUamcknlReuZeUIeNiyubBcgmRpWIpTIfHAT+93+BL3957G+7usYqixNPBC67jD8XgjJXbRYdyIs+\nlz1ADyYceeTY/aaSuRxb0gthbJZCUubyKcRO5v/zP9nXoBf6+wvDMwf0yLy0lDkliLU0bpW5Omxc\nXJ45kE2Mqh0xMmI98u7YwcW07HCyWR56CPjhD/lzUp55kGwW1WbRiUXegJJW5rnyqXWQ71jUKn7/\n+lfa9+YbNDUxbDlmpzzzIDcRN7gp8+9/H1i+XC+WfHrmq1alA5G5HINViHBkPm49c7VxMQ6oB1W9\nQQwOZj/qOhX0cSJzwCLCfCtzOYxdkKeYIMo8X518xhtUZd7X5/8kFTSbJS7PXBVUUeBE5v39XKp5\nwwa9dURNTYxSZC+MZ66WECjRlMhFnWfuBbW4vx85BfGRVGK0k7l6sO0ZCIA7mUsiTCrPXDebRTYU\ny5MnLs+8uppjiDIk3sHqmTtBJfMTTkhpKfNc2Cz2/ZIkmW/axKr15Zf1YolDmYctfz1nzljP3OvG\nIG2WoN3y4/bMC06Zx5XJIqHeoVWbZXAw+w7qROZODaAyVvU9bugq86B+OaCnzKuqjF8eJ1Qy9xqo\nXEJe3PLx3Q9xjXoV9CbiBicyX7+exxgIqsyLxTMPU9xr3OeZ6zR+BvXM5TBcXso8iM1SUsLx5rs2\niz0tUScWHWU+dWr0Th/59qlV5DsWlcyfeCLtm0oqyy3oquSwNot9v6hJCFHgROY7dgBnnw1s364X\ny8AApwHng8zXrfP2zM88E/j0p63pKGQubRbA2W8vas+8vT3enoKNjVbvO1WJ209aN5vFLU+2srIw\nlLlujrmEDpkLAVx4YbD1GrjDns2i8zQlCUTnKTWubJY4lfnkydlk3tfH16JO6uPoKMfR0MDL6/Rw\nzWWe+VNPWUNTynijkDlgqfMoT1gFp8wXLwY+8QnvZYP4SBMnWgW+1APi55kPDPDL7cKrqEi2NosO\nmdvT3HRikY/tSdso+fapVeQ7FpXMDz/c3zMHgmW0hLVZkvLMe3uBGTOyy/7Km5gbmauxSJujpITF\nis5oQ3Hmmc+c6Z5n3trK77JeExAPmbv55kWbZ755Mxdl+tzn4luvOjqQeqJKMpd3c/sJs3o1cMwx\n7o2AlZXJZrPonIg6Oct2CMH/2UuZG8SLoJ45EEwlx5nNEpcyP+SQ7GqD/f3cA1lHmav/R4fMiaxG\nSImkPPNXX+V31eOOU5lHQUGR+f33A+ed55/aE8RH8lLmg4NWhbve3uwD8fTTXFPEDRUVyeaZ6yhz\ne/VD3VjKy5NX5vn2qVXkOxaVzFeu9PfMgWDpiWFtliQ980MPHUvm9fV6Y12qDb+1tc4WqIrRURYp\nqvCKQuavvJLO2p8qmct3dd1xkbnTb4Ocu75kLoS4QAixXgixUQhxjcP8LwghVmReq4UQw0KIJu0I\nMqio4DvsvHlBf+kNO5lXVVkHZ2DAInMhrIZSAHj2WeD0093Xm6Qy102r8itl64ayMqPMcwmVzL2s\nOxVBlXmhZbMcckj2+AV9ffrKXCVzHWVuV+VANDK3D2au7hf5dK/up7Bkrh63OMoPe5K5EKIUwM0A\nLgBwLIBLhBDHqMsQ0Q+I6GQiOhnAtQDSRKQ5PogFeSecMMF/2SA+kmqzDA4Cv/sdt0arZF5SwttV\nT5r2du/BFGQDaD49czuZ68aSC2Web59aRb5jUcl86lR9zzxpmyUpz7yrC5g9e6wyr6lxH2dUjSWo\nMh8ZGfs0H4XMm5tTWUkY+VTmcXrmpwPYRERbiGgIwD0AFnosfymAu7W3riAImQeBXZlXVGQr8ylT\ngFtuYdWgnjR+LeiyATQJhCVzXRjPPLdI2jMvtGyWlhauYaQqc9m+o1P/Jagyd8rHj9ID1J4lpkvm\nQoTLMwfCDTtnhx+ZzwSgZoa2ZL4bAyFEDYDzAfw5TCDyTzVpGDRBPXO1AdRO5lVVwBVXjFUAfmlC\nUpkXq2eeNJnn26dWke9YVDLfuNG/NguQG5slCc98YICzWObOZVK011tyI3M1FlVI6Xrmcdos27en\ns5S52n4Rp82i0wAap2ceZCCkiwA85mWxLFq0CN/4xjfwjW98Az/5yU+yAl2/Pg0gfUCZp9PprPlh\np6dO5Q4L992Xxu7d6QNE9sILaaxcaXUOGBlJ49FHrd/v2ZPG6tXu6+/pSWPz5ujxOU1XVQG7dvkv\nv2pV+kA2SzqdxsqVK7XWX17OpBJXvE7TK1euTHT9xTRdVgZ0d/O0tBv8ft/bm8bTT+utf2gI2LIl\neHz286W1NX2ApML+35YWTkt87LE0amvTB9ITd+5M4+WX0wdubF7ny8gIMDTE86Uy99r+yAgwOpo9\nf/PmNLZuDR4/wDeBDRus6YoKoLOTpwcH+aa0Y4c1f3SUB7QYGkof6Pijs72BgfQBMlf5J51OY9Gi\nRVi0aBGWLFkCbRCR6wvAfADLlOlrAVzjsuxfAXzQY13khWXLuIDkxo2ei4XCRz9K9LWvEZ11FtFj\njxFdfDHR3XcTLVlC9JGP8DILFhAtX279ZsEConTafZ3nn0/0y1/GHysRx3Huuf7L/exnRFdeGXz9\nRx1F9Pe/B/+dQTjs2EE0fTp/fte7iP78Z//fnHce0SWXEI2O+i+7eDHRrbdGi5GI6ItfJPr+96Ot\n4+GH+dohIpo7l+ill4g6O4nOPpvokUeIGhuJOjq817FpE9GcOfx50SKi22/3Xr69nWjChOzv/vAH\nove9L9x/OOsson//25revp1o5kz+fOedRFOmEL3lLdb8OXM45lmziLZt099OeTnRwAB/njaNzxMn\nZLjTk6uJyFeZPwfgSCHEbCFEBYCLASy1LySEaASwAMC9+reRbCTlmQPAWWexj2f3zNU8bfvjnN9Q\nXEn2AK2u9n+0BKJ55qb2Su4QxjNvaQHuvlvP9iikbJZt26yxUCdO5EbQhQuBxx6zPHO/jJYwnnmc\nDaB9fd6eeV1d/A2giXvmRDQM4CoADwJ4CcAfiGidEGKxEGKxsui7ADxIRH1O69GB/FP2kc2doD6u\n6EB60HYyVxs67CeZbgNo0Fh0MGOG92jgEsYz10O+Y1Hrme/cqeeZr1/P77pkXih55q2tXNsHAJqb\nuRF0zx6e9moAVWMJk80Sp2fe1pZ2zWYZGmIyt3vmQasmyuXkTSgOz9z3fk5EDwB4wPbdbbbpOwHc\nqb1VB8gc7yQyRFQyl0Q2OJhd1Mue5+lXse7//T9g1ixgzZr4450xgxtt/ZR3WGXe1JTME5CBM8LU\nZpk+nUfl0SHXOLNZggz15gS1noxU5rImuWwA1VHmkgd088zjVObSF5ewK/P6+uyYwihz+9NULrJZ\ncve6gDoAACAASURBVAYdW0EiaN5wdTVfRGo2y8BANGU+bx7nsCeRw1xayoTe0uK9XNg88wcf5NSx\nJJHv3G4V+Y5FPbdKSlJaPUBbWljhJmmzJJFnPjBgPfVJZS7JvKrKfZxRNRbV4syHMh8ddc8zl8o8\nqs1i5xc3ZV6UtVmOO867k04UuNksqjfmpMyTyiPXwWGHsf/ohTC1WQAzglCuEcYzLynR79JfSLVZ\n1Lomso+HLGOblGced2qi3TNX68tLzzxqaqL9aWpcKfOjjtIf7DWMZ97X5+yZyzuw3cvzawANG4su\nDjsM2LrVe5mwnnkuYGKxoKrRzk49zxzQJ9dCqs1iJ/OdO63rKknPPE6bpbc32zNX68tLmyVXyjzI\nuVswZJ4kdJW5qhjyrcylZ+qFsJ65QW4heweOjOiNASqhS+aFlM0yOGidk83NXAVVorra3WZRoZK5\nTkxOyjxsD1C3MYhlHEnZLONKmQdBUA/UrQFUVeZ2m0VXmSflxzY2+g9mG9YzzwVMLNkoK+PzTYiU\ntooOQuaFUpvFrsxVMi8rc7dZ3GqzyHEOvBCnMud2tNSY7+WxiNNmGbeeeZKwN4BKL1JV5vbHv3wr\n84YG/yGzjDIvHpSV8fGsrdUfKFs21PuhkGqzqGTe3DzWKgyazaJL5nF55na/XEJV5knYLAetMo/i\nmXspc3s2Sz498/p6fzLv7zeeuQ4KIZayMk77KyvTjyVpmyUJz1zNZpk0id/Vc1S3NksQZR5HA+gV\nVzBH9PUBQqTHzJdlqePqNGQ885CoqgK6u63BU6Xi8cpm0Rl3MEkYZT6+IMk8SPZR0jZL2O15QVXm\nMzMl+U4+GVia6Tce1DMvK8uNzfK733Hfjr4+5w510qodGmIBSGTxhVHmERDGM+/psU746mo+aF7Z\nLLo2S1J+rC6ZBx0DNFcwsWRDkvnkyfqx6KYmhrVZkvLMpcCQhLxlC3DRRfw5jGfuR/5OylzemJxG\nvHdCf7/FCZMmpcbMtydRqDeLODoNGc9cE/Lksvcq88pm0bVZkkJDQ/AGUIPChSRznQ5DEsWazaIq\n29JS4LXXrOmgeeZhG0BLSnhbujfD4WHmBHstc4m4ydx+A9ap8+6HoiTzoB6oPNDyIEky98pm0VXm\nSfmxYWyWQvCGJUws2ZANoAMD+rEkbbMknWcOAEuWADfeaE0HzTMP2wAK6FstcuyAvj459kF6zDJq\nEoXa7ia3H9VmcbOTYq3NMp7Q0MDvtbVjlblTd/58KnPdBtAwPUANcg+pzNXOKH5IutNQ2O15wU7m\nH/5w9nwdz1wlurANoEBwMu/tBXbvdq5bpKPMg4w0ZCdznf/ph6JU5mE9UEnmNTXsocehzPPpmbe3\nc/pX0rGEgYklG5LMDztMP5akbZaka7M4IVd55oA+mcsif5LMTz45NWYZtQHUrszDVE3UJXPjmbug\nvp7fJZn390fvzp8Uams5PjcVMzLCw3NNnJjbuAzCQZK5bu9PoPizWZyQhGfupsx1e4GqNsvu3VYJ\nX/u6VGUu45INrEIwmes2uBplnkFYD1RV5nv38p1b3tHVx7/RUT4oTnf7uGLxgxB88+nudp7f0cH/\nRz0hCsEbljCxZKO8nMl87179WIq1NotXo7z9Cdgplnwq89ZWHjLSDtlXRXY8lHGp5XeTUOYmz9wF\nKpm3t3Pyv4R6ksmTSbenXlLwslra2oDJk3Mbj0F4yAbQJPLM+/riyWoqVmUeVwOormdeXp47Mg+C\noiTzqJ55bS0rb9WiUG2WIB2GkvRjvRpB9+yxetjlIpagMLFkQ9osxx6rH4tOd/6uLhYdqjDRRdK1\nWZyg65nnowFU2iwXXJAas4zqmcehzO3tHMYzD4hjjuF3qY7UxkPVZsl3XRYJL2W+Z49R5sWEpDzz\nlhYe8SqOp8hCUeZBu/PH3QDq5JnL1MSklLnOfvFDUZJ5GA90717gyiv5c0kJHxxVmas2S5DGzyT9\nWD+bxa7MC8EbljCxZEOS+fbt+rEEIfMwSLo2ixOS8MzjUubt7bz8ihXpMcu4NYBGIXO1ncN45gEw\nYUL23bumJluZqzZLISlzp16gg4PA//2fUebFhKQ88yhkHmZ7fojbMw9bmwUIrsy3bgWmTHF+yrGn\nJhrPPCbE4YHayVy1WYIo83x45qtWAU88AXzsY7mLJShMLNmQyvy00/Rj0anNEoXM7ftF1kHRTa9z\ngl82SxK1WeJqAH31VbZYnM4XSeayo16uyNx45hqorR1L5oWozJ3IvLOTh9l73etyH5NBOEhijtsz\n3749PmUuRDSFKK8fLyEUNpvF6wajEqoKWbbWD7In+JYtzn45YKUm5prMg6AoyTwOD7SmZmw2Sxhl\nng/PvLOTRyLKZSxBYWLJxpQp/P7yy2nt3+iQ+UsvAUcfHS4mp/0SxTf3s1gA9+78ds9cEp2seeJV\nhMpNmXv101DR38/CrrOTydxpv6jKvLra2k8mz7wA4GSzhElNTBJBydygcDFtGr8HyQf3I3MiYM0a\n4Pjjo8UWZJte0CFzneqAdjHlp1rdGkCbmoB9+7y3BVhkDugp88pKo8xjQxwe6CmnsFUhEbYBNEk/\n1q0B1I3MC8EbljCxZEOSRJBY/Ii1pYVFiT2rSRdOsUTJNffLZAGCe+aAP9G5NYDqknlfn8UFM2e6\ne+ZdXRxLSYkVk7ptk2eeJ9x0E4+AIhG2ATRJuDWAGmVefJDKPIhn3tDA6XIS//xnNhG+/HJ4i8UN\nuVDmQRs0oyjzjg7vbQGstk89ldOXP/EJ52Wqq/nGILORVGUut23yzEMgCQ80bAOo8cydYWLJhlTm\nL7yQ1v7NMccA69fz54EB4Pzzga98xZrf2xuu56dEEp65n42k65nnWplXV3P6ckWFu2fe0eFM5ibP\nvMBg785fCMq8oYGJ247OTj5RDYoHksyD5JkfcgjbbB0dbKkAwIsvWvN1yDMoCsEzD0PmUT1zv+NS\nXc1PSbLKalF65kKIC4QQ64UQG4UQ17gskxJCrBBCrBFOQ1vHjCQ80LDd+ZP0Y+vqnFvjjWceDIUQ\ni+zg9eY3p7R/IwSr83XrOAURyCZaHY/aC3F75lFsFrfaLEDyDaBSmTvFIlFfn22zyP1USHnmnpQl\nhCgFcDOA8wDsAPCsEGIpEa1TlmkCcAuA84moRQgRsjkmvwjbnT9J1NVx3XU7jGdefJgwwX+wEScc\neSTwyiv8+bDDsjvB6JBnUCStzHVHGsqlzaKjzOVYCF42S6GPNHQ6gE1EtIWIhgDcA2ChbZlLAfyZ\niFoAgIjaooXkjyQ80LDZLEn6sbW1zmS+b5/xzIOgUGKprw8eS1MT37y3beNOYirRRrVZ4vbMo2Sz\nRPHM40hNVMncab/Itok4PfNc55nPBLBdmW7JfKfiSADNQojlQojnhBAf0d56AcGezVIIeeZuZL5/\nv1XO12B8o7GRFf327cARR8RrszihED1zv/osfsrcrzyB3WZxgsxC8vPMdUohfOADwA03xK/M/ShL\np0pDOYBTALwZQA2AJ4UQTxHRRvuCixYtwuzZswEATU1NmDdv3gFPSN6BdKZTqVSg5XWmV69OZ9LA\nUhgeBrq60kin41t/mOmREaCnJwUi4JFHrPm9vcCqVWns2jX29xL5iFedlt/lc//J6STOl1xNNzSk\n0NEBrF2bxty5wOCgNX/dOqCmJtr6JeR0RUUKQ0Ph1vfss0Blpffy5eUpDA56ny8jI8Dmzdb1V14O\nPP10Gp2dztsfGQF27XK+XoEU+vv5927x9/cD69alUVbmfr78+99pVFUBVVU8vX17Gvv3A6OjKZSU\n8Pp27+Zpv/3FH9PYsoXjA7hn8M6dPJ1Op7FkyRIAOMCXWiAi1xeA+QCWKdPXArjGtsw1AL6hTP8K\nwPsc1kWFjEcfJTrrLP78z38SnXdefuORqKwk6u3N/m7SJKLW1vzEY5Bb3HIL0RVXEKVSRL/8JdGc\nOUSHHkrU00P0ne8QXXttvNt729uI7rsv3G+XLiV6xzu8l7njDqLLLvNeZvFiop//3JqeP5/o8cfd\nl//hD4k+9znneU1NRHv3em/v5JOJnnvOexkiounTid71Lv78gx/wNteuJTrmGP7uYx8j+p//8V/P\nzJlEANFNN1nf/eUvRAsXOi+f4U5PriYiX5vlOQBHCiFmCyEqAFwMYKltmXsBnC2EKBVC1AA4A8BL\n+reT4LCrijhQiPXMAWerxe2xMOlYgsDE4oygsTQ2yrFDuePR4CD75xs2RLdZnGJJujaLHOTBK5ag\njYNuNgtg1VTxgo5nDrBvHodnLvdR3HnmnjYLEQ0LIa4C8CCAUgC3E9E6IcTizPzbiGi9EGIZgBcB\njAL4JRElSuZJoBDrmQNWeqLaZVun9d1gfEB2HJNkLm/smzczecosi7hQYfPM5fZ0eq7qkLkOucbV\nAAroVU7U8cwB3tdx5JnLfZRrzxxE9ACAB2zf3Wab/gGAH0QLRR+qzxYXCrGeOTBWmQ8NcQqU080m\n6ViCwMTijKCxSGXe0cEdj2R648svR89mcYqlwpZnfvbZPL1mjf/6dJ4U3Mj89NNTaG3l6pJx9QD1\n2p4KuzhyO0b19WOV+dBQ8O78UpGb2iwJoRDrmQNjybyvz6jygwkNDTze68AAl2yWZLFhQ27yzF95\nBVi7Vu+3uspcjuyj4te/Br7wBf7sROZe1o9bD1C5vaBk7gYnMpc3ICAZMg+CoiTzJDzQsN35k/Zj\n7R2HZD3lfMQSBCYWZ4TxzLdu5U5HqgrfvDk3nnmQFFidJwU3cn344fSBXq52cq6t5To0bvCyWXTI\n3G6zuB0jJzLfsQOYMYO/S8JmCXK+FCWZJ4FCrJoI8In8pjcBmzbxtFHmBxcaGqx626Wllp2wa1du\nlHlQMg/bALpzJxMjMPbJ2K2shYSfzeI1dBwRz9exq+rqxnrmO3dy2Vwg/555UZJ5Up65VOaDg/qE\nmbQfKzsh3Horv3sp82L2hpNEMcciyVQWVquo4G79O3cm55knSeZuSnnfvhR27ODz3S6m6uud6/pL\nRFHmcrAJ9WbgdoxmzbIsFZXMgypzJ5vFrWOU8cxDQLVZCilbZPNmfl+aSQjVbXk3GB8oL2cCkRd6\nRYVFKG1tyShzVckGqQEUlsxHRnj8zdFRbuy12yx+ZB6lATTItf71rwOLF/PnKGTupsxNPfOYoNos\nQQ5w0n7sxkw/2pYWJnIvm6WYveEkUeyx7NwJ/Pvf/Lmigo//jBlMgHF75tOns4UjIZX5G97gvz7d\nbBZ7A+iePUB1dRqHHcZWi53MdWyWsKmJTuJI5xhFIXNJ4joZO8YzDwHVZpGPXoWA3/4W+OtfucjS\nyy972ywG4xOlpVaed2VlNpnHfZ4efrj1NAhY5PzEE97eMxBeme/ZwzbSjBl8I7F3GtKxWXKhzFVI\n8t2zxypvrEvmUjSqdpbxzGOEWs1tYKBwPPMPfhB417uAY48F0mlvZV7M3nCSGE+xSGU+fTpf/FGU\nuVMsdjJXCUY+JTqB6wj531ycGkD37AFmz04dyNyytwVEUeZhyFznGEny7e62Om7pkrncp+rNsbra\nOWPHeOYhYFfmheKZS3zoQzxk2Nq1RpkfzJBkLkcuitsznz3b8q8Bi3jmzQNecunX3dvLYujWW/Wq\nJo6OZvvDbW3cw1kSvV3hR20A9XqiCNsGJcl8//5sMverCAlYilxV5hMncoVHnd+7oSjJPOnaLIXk\nmUssXAgcfzx758YzD4bxFEtFBZOPbASNu555TQ2nQXIFPyaspUuBCy9km88JKsn7kbkQYwl2zx5g\ncDB9wE+3e+9RUxODKnOdY1RVxb1xibJrrehYJU7KvKyMrSZ1AG/dWCSKksyTgGqzFKIyBzizYNcu\no8wPZkhlLn3auJU5wDeKPXv4s/Svm5rc1XFLi0WmOvHYG0GlZy6J126zJJmaGFaZNzXx/66v5xsU\nwOtx6t1qhyzJcdZZ2d9PmcI9SsOiKMk8ac88CJnn0o9tagJee8145kExnmKRZC6VedyeOWDVgwGY\neMrLWbG79cJsaQFOPlk/HjvBtrUBr399ytNmCavM/bJZwnrmTU3s76uFzoKQ+QsvcDuYiilTgFWr\nsn1345mHQGUln0Sjo4WrzCWZG2V+8EJms0hlnkTWVVAy376dPXUgHJnLjBCp2O1kXlfnn2eea2Uu\nFbkcTg7QJ3O3rJ8pU4APfxhYvhz48Y+tQbx1UZRknoQHWlLCO7i/vzA9c4AvMi9lPp684TgxnmKx\ne+Zx55kD2WNnBlXmOjcXe0ZLezvQ0pI+QLx2z9xPmUftARrGMy8p4f0UVJn/8598Y1JrmUtIu2b9\neuDznwd+8QvjmYdGTQ0fjEJW5vv369WWNhifyIVnHkSZj45yw6i0DHRS8+wEu38/r9/LZqmv5z4X\nTshVD1A7wpD5Zz7DHaOcyHzrVn7fsIHfdQajVlGUZJ6UByoPRiF75gCnjzlhPHnDcWI8xSLJXPbM\njFKqOahn7jS4+C9+wTHMn8/TXnaIhL0BtLsbOPfclGsDaFkZcOON3HnOCXGnJuoeowkTgtssMlvF\nicyXLAG++lWLzDs7uc67LoqSzJNCGDLPJWSdjCOOyG8cBvmDJHMhuNb4hAnxbyOIzfLoo8CVV1qE\nqJMnbV9Xd7c1JJuTZw7w/3S7URSLMh8d5RGjAOcnqqOOYs9cVeYLFujHU5RknpQHGobMc+2ZA+5k\nPp684TgxnmI54giumghwb80kYlGVuUxNdKspvnKl1fi5ahX3WPaDPW+8uxtYuTLtarMA1vB5TvBq\nAHV7opAIW5sF4BtMEDLv7LRsKCdlDnBbSFubjAN47TW9WIAiJfOkIA9GkO78uYTsMZaEGjMoDlx/\nPXfgSRK6nnlPD/u8Rx/N0yeeqGf72Ml8/36+9qqqrO/t5OxF5jJGJ0yYwEPuuSGqMldtFtnm5gZJ\n0oB7vGpjb08P8N736sdTlGR+sHrmp5zCA1UUQix+MLE4oxhiaWrSI/Nt24BDDgneCKsSlhQob31r\n6kCvSqeMGD8yd4uhudmyNpwQxTOfPDlbWPkpc7V3pxuZl5VZ3DN9OvCd7+jFAmgM6HwwodCzWQ4/\nHHjooXxHYTDe0dg41jOvrBxL5vv2hXtKVJW59MsBvuY6O52Jub7ency9qjUmqcyvvTbbq4+DzAG+\ncVVWZves1UFRKvOD1TP3g4nFGSYWZ7jFohKglzLv7Aw2eIWESub79/N0Os2eeVeXMzHLiopOqY+D\ng+7kKJ8y3FImw+aZA0y6QbJZVDKXOeVOqK/n/V1SYvLMQ0OWoSykeuYGBrnGxIkW8Ugyr6jgxlC1\n2mEUMpeZKbrKvLTUvTHTy2YpK+P1S9vIjjhH7tIhcy9FLtHQEK4vSVGSeZKeeW+v/gCvScYSBiYW\nZ5hYnOHlmXd1MXFLMhdibANfHMpc1gNPpSzP3I2Y6+vZp7fDS5kD3r552NosTlBLgjhBzfzxQkMD\n7+ugsRQlmSeF6mp+vLQP8GpgcDChtJQJvaMje9Qfu9Wyb198NgtgKVs3ITU8zGWg7Srbb4QjL988\nTmUuy/s65bUTcc0VnUwkabMERVFSVpKe+ZYtVuH/fMYSBiYWZ5hYnOEVy8SJnEo3MuJO5p2dVq/k\nIJBkTsS9OqVnLhWyGzHLsryPPpr9vd+IS0GVeZRj5Ga1bNjA+/LEE/3XodosxjMPiepqHjJr2rR8\nR2JgkF9MnMhF3crKrMa6mprs/PCwNotMTdy1C/j5z3n0LAC+ZC6xfHn2tI7NYh/0QSJOZQ64k/nd\ndwPvf7/etlSbJQiKksyT8h1rapjMp0/PfyxhYGJxhonFGV6xTJrEZKuS5NSpwO7dwFVX8UhEUT1z\n2YnmXe/iWKTd4kbMCxYAn/wkcN99fINRh7bzugE0NLiXAojTMwecyZyIi4R9+MPAuecCN9zgvQ5V\nmcfqmQshLhBCrBdCbBRCXOMwPyWE6BRCrMi8vqq99QKDVOZByNzAYDxCKnOVWGfM4Ip/d93FajqK\nZ75/P5P5uecCP/gBfy9J1a3c7SOPAD/5Cedfq8v5eea1te5d+uPuU+K0raef5iecU0/l+V/+svc6\nEvHMhRClAG4GcAGAYwFcIoQ4xmHRR4jo5Mzr28HDCIYkPfPR0WBkXiweaK5hYnFGscQyceJYZT5j\nBmdkdHXxvKjKvL2dnwDssXgNnVZRAUixKhs1/WwWr/osUWqzOMFpvNKHHgIuusg7t1zFvHnA618f\nPBY/ZX46gE1EtIWIhgDcA2Chw3KaYRY2Xvc6fpcnmIHBwQo5uLCdzCW37NrFVousqx4EUr22tTlf\na7Kh0w1/+hNntai9VP2UudvAGn198Spzp/FKe3qC9ZR929uAxYuDb9uPzGcCUAcvasl8p4IAnCWE\nWCWE+IcQwjayXfxIynd84xuBM84ATjop/7GEgYnFGSYWZ3jFUlfHylctnCWVOQA8+SST1jFOz+k+\nkL5yezs/AaixNDb6D3BRXc2NmpLM/ZS5m80yOMj/obk5+/sox8hJmUe5YQSJxa82C2ms4wUAhxBR\nrxDiQgB/AzDXacFFixZhdmZkhaamJsybN+9AsPJxIt/TTz1VWPGYaTOdj+kdO9J49VWgvNyav2sX\nAKTw5jcD996bxnnnASUlwdfPnWvSeOEF4JxzsudPnpxCZ6f/+oaH03j0UR7UYnAQePLJNEpLnZev\nqQE2bUojnc6e/9prwNSpKZSWxrf/6upS2L8/e35fH7B9+9jtu60vnU5jyZIlAHCAL7VARK4vAPMB\nLFOmrwVwjc9vXgXQ7PA9xYXly5fHtq6oMLE4w8TijGKJ5Z57iE44gWjuXOu7jg6iK68keughIoDo\nvvvCb7u6mujd7yb6zW+yY/nUp4gmT/b//WWXEd1xB9HwMJEQRKOj7sv+9rdEl1wy9vvHHyc644yx\n30c5Rp/+NNFNN2V/9x//wbGGwfLlyynDnZ5cTUS+NstzAI4UQswWQlQAuBjAUnUBIcRUIdjaF0Kc\nDkAQkUfRSQMDg0JHfT3bIBWKF93UBNxyizUoxgUXhF9/dTVnpUibReKnP2Uv3g9yNCS13IAb3GyW\nHTuAmXbTOCLcbJY4c9nd4GmzENGwEOIqAA8CKAVwOxGtE0Iszsy/DcD7AHxSCDEMoBeAxlgj0SAf\nTQoBJhZnmFicUSyx1NVxI6fTqFazZ3PudBRUV3Pqo8yGkbGUlOiV0pBd9P0aPwH3Ie/cyDzKMVKL\niEkUimcOInoAwAO2725TPt8C4Bb98AwMDAod9fVM2HblHBdqarhollpCNgiamoBXX/XPMQdyr8y5\nbcFCrpR5UfYAlY0FhQATizNMLM4ollgkydozPeJCdTVXJ5VjaAbdL9Jm8ctkAdzzzHfvdq7DFOUY\nqaMoSfT3hyfzILEUJZkbGBgkC0mySSlzSW5hlbkcDUnHZnHLM1dTI+NC3DZLEBQlmReL75hrmFic\nYWJxhlcsuSbzoPtFds7RUeZuNsvevc7/L6pnHmcDaJBYipLMDQwMkkVNDWeIJEnmQoQnOZXMdRpA\nncg8V8o8is0SBEVJ5sXiO+YaJhZnmFic4RWLEExMSZJ5XZ2VUhh0v8hKiFFslr17ndsEonrmTjaL\n8cwNDAzyhlyQeVgEsVkk2Q8OWt+NjrLnHqRmig7U8VMlcuWZC4qaMKq7ISEoV9syMDCIjlNPBf74\nR6uTUJz4+Me5pO2GDeF+39kJHHIIsGwZ8IUvAE884b38/PnAZz4DXHopT3d0AHPmWPVd4oLM0Onv\nt/Llq6p4e2HVuRACRORbzNAocwMDA0c89VQyRA6wjx1FmdfVsQ/e36834v0NNwDf+pbV2SkJvxzg\n8Utra61h6oj4icBks7igWHzHXMPE4gwTizP8YtEhybCorrYyZnRisaO0lNexb5+/Zw5wDXQhgMcf\n5+n2dvcc+qjHaNo04JVXgF/8gm82lZX6tcyjxFKUZG5gYFDciOqZA871Y9wgBPC+9wH/+AdPu6Ul\nxoGpU4H77weuvz53fjlQpGReLLm6uYaJxRkmFmfkMxY7mYeJRZK57hPEOedkK3M3Mo+6X6ZN43rv\ne/ZE78pv8swNDAwKGlE9c4DTE1tb9cly/nzg+efZw3ZLS4wDU6dyg+zQEJcMyEWOOVCkZF5MvmMu\nYWJxhonFGfmM5b3vBf7rv6LFUl/PxbqamvSWb2jg0ZJeecVbmUfdL8cdZ+W1P/MMMGtW+HUZz9zA\nwKCgMX06cGzEASYlmQfJFZ87l9Mhk1TmF19sfX7gAWtw5qRh8swNDAyKEosWAffdB3zpS/zSwX/9\nF5e+bW0F/vM/gQ99KJnYnnsO+PrXgYceApYsAS65JPy6TJ65gYHBuMaUKWyX6NosAHc0+sMfgOXL\nk1PmAKvx5mb25888M7ntqChKMje+ozNMLM4wsTij2GOZMoXfg9gs55/PnXqA5DxziY0b+T3ImMx2\nBInFd6QhAwMDg0KEHFgiiDI/5hhgxQr2zpPKM5e44w5geDjZbagwnrmBgUFR4sEHeVDpZ54BTjtN\n/3ejo9yDdN8+awzSQoauZ26UuYGBQVFC2ixBlDnABbDGo640nnlEmFicYWJxhonFGbnyzJOKJSmY\nPHMDA4Nxj8mTuRdpMVgluYDxzA0MDIoWHR3xK/NCg65nbsjcwMDAoIAxrjsNFaunlTRMLM4wsTjD\nxOKMYo2lKMncwMDAwCAbxmYxMDAwKGCMa5vFwMDAwCAbvmQuhLhACLFeCLFRCHGNx3KnCSGGhRDv\niTfEsShWTytpmFicYWL5/+2dfayWZR3HP195KQY4BmQoUKcXSMgMtJFLHYVop03NuSVZc5GWLVeW\nq6VNV2YtwGz4VlAtK9ukUVnCGulWSrO1aXEgKQicHkIQZXqYMZOBfPvjug4cTvd54XjO9dzn2e+z\nPdv9XPdznutzfs95fud6ua/rriZcqhmuLr0mc0kjgLuBVmA2cLmkWT28bhnwe2CAty7tPxs3avc3\nzwAAB39JREFUbhzqKvpNuFQTLtWESzXhUs3xuPTVMp8HPGm73fZB4BfAhyte93ngV8Deftf8Gti3\nb1+JavpFuFQTLtWESzXhUs3xuPSVzKcCO7s8fyaXHUHSVFKCX5GLYpYzCIKgMH0l8/4k5tuBG/Kl\nKqLAMEt7e/tQV9FvwqWacKkmXKoJl2qOx6XXSxMlnQXcbLs1P/8qcNj2si6veYqjCXwy8DLwadtr\nur1XtNiDIAgGwGtezi9pJPAv4DxgN/AYcLntLT28/ifAWtv3D8g4CIIgGBC97mdu+5CkzwEPAiOA\nH9veIukz+fwPCjgGQRAEfVBsBWgQBEEwdMQK0CAIgiag1slc0nJJ5zTaA0DSJElfl/QpSSdIulHS\n7yR9R1LxHZUlLZD0PUlrJP1G0lJJby/tkV1aJa2UtDY/VkpqbYRLT0j6WgPqbJV0laSWbuVXFvY4\nQdIiSR/Jxwsl3SXpGkm1zgFDiaTJ3Z5fkeNytaQhvypvsKn1MIukvcAO4CTSgqVVttsa5LIO+Dtw\nIjALeAL4JXA+cLrtqsVUQ+WyFJgC/AG4BHga2AZ8Flhie3VBlzuAGcC9wK5cPA24grTg7NpSLr0h\naaft6QXrWwKcDWwALgLusH1nPtdme25BlxXAG4DRwEvA64EHgAuBPba/UMqlCkl/tL2gAfUe+Rwk\n3QScC9xH+rx22r6uoMulwHrbL0g6CbgNOAP4B/Al28/0+R41T+ZttudKmgl8FFhEmrS9j5TYtxV0\n2WT73fk/9i7bp3Q/V9Bls+3T8vFI4E+235d7CI/afmdBl+22Z1SUC9huu1hvQdJ/ejk9xnaxG5hL\n2gzMtX1Q0gRgFenKsOuADYWT+Wbbp0kaBTwHnGz7QP7babP9roIuT5DWr3Rt+c4kNUZs+/SCLl2T\neRtwru39OU5tnd+xQi5bbM/Kx6uBv5BW1Z8HfNz2+X29x7DoYtneZvuWnKQuA8YA6wprSNJEYDow\nVtJbcuFkYFRhl1clTcrHU8mfo+2Owh4Ar0iaV1E+D/hvYZcOYIbt8d0fwLOFXUbkLTCwvY/U2juR\n1JsbXdjlUPY4CDxu+0B+fgg4XNjlaVKv9jJSz+Ai4Pl8fHFhlzGSzpB0JjDK9n44EqdXC7t0zcVv\ns73c9k7bPyWNTPRJsZbKYGF7E7AJuKFw1UuALaQWxVXAj/Kw2mzgG4V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      "text/plain": [
       "<matplotlib.figure.Figure at 0xad10e68c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Silver position:\n",
    "z_xag = cotr_position( f4xag )\n",
    "plot( z_xag )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Correlation and regression:\n",
    "#  stat2( z_xag['Y'], z_xau['Y'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COTR positions for gold and silver are correlated (approximately 77%), \n",
    "but a linear regression model is not satisfactory. This means they \n",
    "each contain some information that the other lacks. But since the \n",
    "market sentiment regarding both are similar, it is useful to \n",
    "combine their signals as precious metals.\n",
    "\n",
    "#### TECHNIQUE: compute the mean of indicators for an asset class\n",
    "\n",
    "We use the futures and options COTR for contracts on both gold and silver, \n",
    "then average their position indicators. We can run this procedure by \n",
    "retrieval of a variable called w4cotr_metals \n",
    "(where w4 tells us that it's weekly series). \n",
    "See the yi_quandl module for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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urPnWCSuRPN/REa9nvmuXfEZ7Nho3dXXyGe1iPnMm8JWviFCGEfPLL7fGXA+6\nbyXmzNL3YOZMvUA7yxLV+3fuzJxQOii7dskdQhDs58FwY+3avXc2pZIUc52P1NubPqlvPlixAjj5\n5MxY3GhutsZn2bBB/tsbBQ8/XCZEiIo6LmoMcyd2y8PLv66uljuIbGwWu2euGlTjsG28IAKOO05G\nILRz9NEJ7NiR2YXei5tvDp6ZA+m+eUeHCOu4cZmZeSKRyGj8BOQit88+Yo9FIUpmXqresBdr1mQv\n5qV6XEpSzHWceWY8WW0YVq7Ui6Ybzc3Wj1VNNmy3Ho44Qj/xclCSSflRL18uY5U4cVaWuFke1dVy\n56BrIA1LdbXsM4rFEoVnn830ulVbgVtlSRzYfWhV3eTWRV+XmQMyJoxudqQgmMxciEPMS5WSFHOd\nj7R5czzzKIZh5Upg8+bMWNywi/ma1CAJ9oz1/e8HXnst+ryhl16axL/+JV3RdWIeJjPfsMF9jPEg\nOD3zfGTmbixfnsSOHdEaYINit1lUj2Bdo6byzOMWc+OZA/Pny8TeO3dmN/duMikdsIrhgrdXeuY7\nduR28uTvfU9u43ftkuX+fhnNz622V0dLi2RtgNQhd3cDTz1lCUxzs1gEL78cLrbeXhl/o6NDLmpx\nZObr12cn5vZ99vXlLzPXoUoxo5RGBsUu5mqAM7cu+roGUCC/mflwrGa57jr5P3Jk9tn5E0/E02aU\nT0pSzHU+Ui7F/PHHgXvvlR+Bmh9STS5wyimZsbihMvP995cf/J13AjfckC4wxx4b3jf/1a/Eb+/v\nl67ry5fLOCVOwmTm2Yq5s868kJn5KadIF/ZcxmD3zLdulWoYnc3i5pkDIub2Ke7CYDxzOW+fe06G\nMs5GzBOJxJ6G6EJf9PY6z1xNtJArMX/+eeDKK4Hjj5fGxb4+EcywwtDcLPZFQ4PUpz/6qGRU9oy1\nvj787Z3dB16/XrI7nZdfiMy8okLuaLq7C5eZK7sjl2Ju98y3bZNj51Y77maznHIK8Nhj0dp+jGcu\nAt7c7N3bOigyblF2bVj5piTF3Okj9fZa42/ngldfBY45BjjgAOnY88ADMtVZdXU4T0vZLNXVcsKp\nOR/tAmMX3KBYNe5JvPaaNADqhDNoZt7UJA2GcXjmKr7t2wuXmb/+ejIvYq4yc7uYv/028MlPWut5\neebTpwNz5kitdFiiiPlw88y3b49HzJPJ5J4Lc6HFfK/zzFX2kwsx7+4WP/qoo0TMV6yQTgnt7eEz\nzeZmuQV64VImAAAgAElEQVSvqZFsWpUp2gXGLrhBUScekTQA6fxyIHhmrt4fR2au9rt9e+Ey84oK\nsSC2bctPA6gS89NOA158Ebj77vT6cTcxB6JPUhHFZgGA228Hfv/78PsrRlRm3tSUfTGEvTKpVChJ\nMXf6SKqRKW4xX7cOuOoq4OyzpVFl6lQR8yVLLGEI42k1N8t/Jebt7bJsFxjVYBgGJSLTp4s37Cbm\nQTPzA1Mj7cThmav9BpktKFckEgk0NEi5ZT48823bpPftAQeIHafm4VSxuDWAAtG79UfJzBOJBBYu\ntCbCLiTZeuaDgyLADQ1WOWw2sezcKRcF1R+kUOx1nnmuMvOnn5bRDH/0I1meNElKChcvFlsnbJan\nxFGJuerk5MzMw36OnTul0efDH5blq67Srxc0Mz/gAPmvLj7ZUlUl1sGBeR2OLZ3GRrG18umZA/J/\n9GikxpIX3BpAgehiHjUzb2+XzlSljoxVL72pKyvTx8mJws6dUqhQaDEPQ8HEfMcO4A9/iPZep4+U\nKzHv6ACuuEJEHAD23VcyrbWp6TnCeubOzFxhF9UoNktPD3D99cBppyUxOJg+BKydoJm5ajx1zicZ\nBqdnvmhRZs/MfJFMJjFypNzB5dMzV4waZd2FeXnmQH4z82RqwpBimEYuW89cWSyAHAdVQhw1FiXm\nUXvkxkVJeOYvvABcdlk8ApwrMVcNKorWVsnIVQ/DsJl5TY2caDU16T/mbBtA7cOpeo19EiYz37gx\nXAxeVFfLKIKFEnMg/UKaC/zE3J6Z+4l5lAG3wmTmtbVWZr5liwihGvitVIlTzAH5TR1wgMnMPVEH\nR90Gqa7jYbD7SL29wJe/LI9zkZnbxZxIevYdc4wsh/XMieRHnovMvLbWPxa5k5COSn4daMaODReD\nE3ssVVUyEqOu9j0fJBKJPXOx5qoBVHnmfX3i39o9cbuYB/HMOzvDTx4RNjNfu1bG4m9vlxLZIJN/\n5JJsPXN74pWtmCvPfMIEa1yhQlHUnvn73y/d4NVtJ5Dd1e/NN61JHbK9GjvZvj1zVvhJk9LFPCzN\nzfI+u5jHkZm7ZXp2qqqk5+njj+e2a7tuv/vuW9gedep7zLVnvnGjXAjtI1a2tgbPzOvrgYcfDj+G\nTJjM/KSTJIG6//78D4GRK7q6rPH548rM6+rkuwubbBaKvIv55s0i3lu3yoQB73tf+F5Wdh/J3v05\n15k5AHzmM9LQWF4e3jMHZHv2zLyyMr7M3C8WtZ/ubvnxBxmDPSr2WKqrC2uxJJPJvIj59u2SWEye\nnP6aPTNXfqyXzRKFMJl5a6uUTf71r8loO8sB2Xrmu3ZZ53NcnnltbfaVMX5s2uQ91HKY45JFE1d4\n1C3L5s2SmY8dKwcsmy6zbW0ys8i0adkPSu9El5lfeqn8V190WJw2yz775DczB+QiVVWVOd55rqiq\nCje6ZC5oapJKh6DZa1jq6mSWmwULgHPOSX9t1CjpbKbw88zDMjQklVFhxoofPRp45ZV4eksWA/aL\nWWVlPHXmtbXRfo9h2LQpPl8+r5m5OmmUmLe2RhNzu4+0apWUvDU15SczV9TVhffMASszVz/mz3xG\nsiRF1E5DQT1zIPuxyoPgrDMvZFliIpHYc9xzdQFTFtKOHWIp2bGXAirPPE4xHxgInpUrWluBTZsS\ne8pQC022nnmcmbnyzPMh5p2d3r/3ovXMVceJTZvktnPUqOwz81WrZFox50FnlswjG3SZuSJqZn71\n1dJlW/2Yzz4bOOQQ6/UoJ4+XONhRJ3sue0Lq+Na3gEsuyd/+dDQ15fYz24+/U8yd36lfA2hYdu0K\nf8fR2iq/nf33D7+/YsSZmcfhmavfeNjkKgx+Yh6GgmTmN98M/PWv0TNzu4/U1qYX81WrpLE16uxD\njz4qtz9+mXlYr+/446Wqo7JS/pwiHOXk2bZNxCqoZ755c+4maVDYYznkkPiGBogaS1NTbu9G1LYP\nPTRTIO3iEtQzD9NYHCUzHz0aAJIYNy5YIpBr4vDM4xJzu2ee68y8q0u+PzWwly6WoBQkM1eMG5fe\nDToszCLmkydnHnR1W6sGswqLKnd0E4CombmdkSMzM7GwJ8/goFx0Jk70X1dl5u3t0RvaSpVci/mY\nMcDnPy/9J049Nf21qqp0cfG6k6qslHaZMGIeNTMHpM1GnTtR5x8tBvr7c5OZ58NmAeLZR97FXJ2k\nXV2SUQfNzB9/3GrUUD7Sxo0iSvX1mQdd9WpbuTJarJMmyRRkbh5rXZ3sMxuv75VXMqc4C5uZr18v\nWVZlZfBYmHMv5sU2Vva0acD55+duH5WVwE9/ajW0Ol9T5+YJJyTQ3+9+YSGS8enD1DZH9cyBBMaN\nk5EBa2pyK1p+xOGZxyXmyjOvqcmfmLv95ovWM+/okB/Uf/xH+u1kEDE/++xM31X55YC7mEcd7L+/\n37t0T9ks2eA26XKYk+e99zJL4dywi0zQiY2HC2PGAP/5n4XZtz0z7+6Wc8erIVbNBBTUIgxTlqho\naZEY9tnHOpcLKebZErdnrjrVRRn4Lgx+Yh6GvGfmBx4I3HKL9VwYz/yll+S/8pGUXw7En5n7ifnU\nqTI1WNxjQoc9eexi7hfLoYdajcK5zsyH21jZ2WDPzJ98Muk7gFl5efp7/AjTYci+j4aG5J47w1yL\nlh/F5pmrTnW5vsgpnXI79kXrmetK/Wpr5Xmvg6+6/jtP2FWrgClT5LFTzNUVT02c7Majj+p9dT8x\nv+024IwzvLcdhREjpG54aCjY+itXBs/MAWDmTPm/t3nmhcSZmbtVSNkJMxtQlMwckCkLVcnocMjM\n4ypNZLZ+/4W2WcKQVzFXw1TaqasDfv3rzI4WdrZuFV9cZfDKR/KzWSZO9O8QcccdwGuvZT4fdALi\nuL1honAn0Ny5UiETNBbl1e5tnnkhsWfZ06YlAol5mDvWKJk5ANxyS2LPRaDQmXkxeebvf78cl7Ky\nwot50Xrm9qunQjWIetkh7e3SINnfn56x+tks++2XPiaGjsFB/Rfvl5nnkqCNoJ2dwPz5MtZGUEaM\nkL+9zTMvJPbM3Kvvgp0wYh41M7dT6pl5nNUs9kHohpVnTkRziGgZES0nohs1r48koseIaCERLSai\nK922pTvplJiPH+8ew5Yt0oClTnDlI9kz83HjpPedGrq1q0ssGGc5pJPBQf1JHFTMc+HHBj2B1OdX\nZW5BY6mpMZ55PrGLy8sv+3vm6j3Tpwez26KUJgKZY84XUsyLyTN/7rnknrvyXF/kVJlqzj1zIioH\ncAeAOQAOAnApEc10rHYtgMXMPBtAAsCPiUg75ouuhCqImKuu//Yxo4eGZBhP1duuqgo46yzgoYdk\nubMzOzEParPkgqCZud8wtm7U1hrPPJ/YhTKoZ66SkiCiFKU00UmuezrmmjjFfNcu67ef64tcX5+0\nI+ajzvwYAO8ycxszDwC4H8B5jnV2A1A37Y0AtjKztvuBLoNQmYfbbf/ChcBFF8n7lJgnEgls2CDD\nAdiz549+FLj3XpmgNmhmPjCQnc2SCz/WPnmAF84Yg8aSDzEvtE9tp9Cx2MWltTWYZ67O2yCiFDUz\nd445X0x15hs3St+SKOWZ2Yr57NmJNJsll8elt1cu7vnwzCcAsNeDrE09Z+cOAAcR0XoAiwDc4LYx\nnc2iLAK3OfuuvloqMI47Lj0z37EjszH1jDOAV18FLr9cxHz8eNmn15ehy8ztrdmFIGglQ9S7h5oa\n45nnE3sDaFDPXBFkLsvhmJn/9a/St+QnPwk2qmOcYm7/XeXaM+/r8xbzMPgNgRvkujgHwOvMfBIR\n7Q/gWSI6jJkzZjJ8660r8bvfTUEyCTQ1NWH27NlIJBL4zW+A++5LIpm0rkTKK1q7NoF//hNYvjyJ\noSGgpyeBZDKJt99WWX36+g89lMCFFwJvv53EqlVAS0sC27bJMpC5/cFB6ZGnlhOJBAYGAKIk/v73\nzPWdy+o5t9ejLNfUAC+9lER3t/f68+cDVVXW8sKFC/GFL3zBd/uNjcDKlfrjHdfybbfdtuf7zcX2\nwyw7v6t871/EJYm5c2X6vMMO8z9fAFnetct/+7t2AR0d4b9P+/nS2ZnE668D556bu+Nx883Abbcl\ncPjh/ufLkiVJHHgg8OUvJ/Dcc8BXv+q9/Y0bk1i6FDjrLDneO3ZEP79ffDGZ0gSgujpTH+I8Pn19\nMqLnwoVS86/O17vvvhuhYWbXPwDHAnjKtnwTgBsd6/wVwHG25ecAHKXZFh95JPNrr3EGf/oT84c/\nnPn87t3MlZXMvb2yfPLJzM8+yzx37lyeN4/5+OMz38PMPGkSc1kZ85o1zJMnM3/3u/r1mJlnzGD+\nznfSn+vqYq6rc3+Pnblz5wZbMQSnnsr89NP+6z3wAPNHPhI+lvXr5djmklwcl6gUQywVFcz9/czH\nHTeXH3rIf/3OTubx45nb2vzX/f3vmT/2sfAx2Y/LZZcx33tv+G2E4eijmR9+2D8WZubbbmO+/nrm\nn/9crw1Ojj+eed48ebxpE3Nra/Q4f/zjuZxIyOPf/pb5iiuib8uPmhrmyy9n/sUv9K/PnTuXRabd\ndVr9+dks8wFMI6IpRFQJ4GIAjzrWWQ3gVAAgorEADgSgLTR0ux1086W6u2WGeHXLo6bmStjGG9Yx\nZoxYJWPHSiPpN7/p/gF1pYlhLBYrk4qPoDZLVM983LjcT0yRi+MSlWKIRZUnVlQE88wbG+U8GE6e\neV+fVYrnFQtglTFPnBgsrjhtlhkz8uOZM8sxGTkyD545S0PmdQCeBvAWgAeYeSkRXUNE16RW+w6A\nDxDRGwD+BuCrzKxtdnQ76dwOvhrzXGH3zP3EfMwY2VdHh1wQ3A6WzjMvpF8O5N4zN+QfdY6H8cwr\nK/PrmedazHt7re7rfihxDupZO8W8qwuI4lQA+fPM5eIuOpaXOnNmfpKZD2TmA5j5B6nnfsnMv0w9\n3sDMZzDzocx8CDP/0Sv4MJm5m5gnk0lfMVeljg0Nsg23qhadmIcRSbsfGxdqoCU/nBedXMQSFRNL\nOuoc37gxGVjMR4zIbWZuPy756AHqlZk7vyOlFUEvMk4xB4BPfjJanP/6V37qzJXOeDU+hzl389oD\nNKzNEjUzHz06vW69pcVdzFVp4pYt1njOhc7Mg5Ymmsy8dFCZeXe3+4QnuvfkKzOP005gBhYvznze\nS8ydZJOZh5kL1W1b+agzV/1E4rpg5L07fzY2i5rIws8z32cfYIKtgDJIZj5mDPCzn8lzpeKZ9/VF\n88zzgYklnaoq+U77+hKBy0LDZOZRxNx+XCoqgg/u5kdbG3D66ZnPh/XMw2Tm9u78qj1oprN7Y0Cm\nTEnkpTu/SsYqK90/Y5hz1680MVaytVnq66W+HMCeweN1XHVV+hfglZnbbRYVW6Ez3jANoCYzLw0q\nK6Unc3195uQVXu8JkplHtVnsVFQE21cQtm3Ti3Zvb/DMXIlzmMzcnti88YbM2BQF++9/wgRg9epo\n2/FDDbNbXh7PhbSobRbnkLkTJ8qQtn6e+ciRUsmiaGlxH3BrcFAqXgA1L2K4zDxXnnmUzLwYvGGF\niSWdqioZarm6OngsQTPzqDaL/biMGBHftHHbt0uyZd/e4KAIVj48cyB4u5OOt96yPPMpU+TzBOm4\nFBZ10fAS86L1zN0yCOcciYqenvSJh/fbz5o5yEvMnfjZLAsWyGN1QAvtmZvMfPhRWSliHmYi7aAl\ndnFl5nGKOSDtAwqVXYfxzNV44mE9cyB4u5PbttTvqqwMmDVL3waQLUHEPAx5z8zdPHPd1be3N91K\nmTpVhsr188ydKJtl8WLgqafSXxsctMqlojSAFtozt4t5MXjDChNLOlVVwObNwMSJwWPJdQOo0zOP\ny2ZRYm4vQ1Tnc1jPPGgDpC4zjyrmY8cm0nTn4INzJ+Y1NSLmu3fr14mtzjxuiPQtzW5fmFOwx4+X\n253e3nCZeVOTvC+ZBB580HqeOf2KqB4Xg2cepTTRULyozDzMuCy5Lk20k4vMfIdtQI++Pslyw1az\nBB0zZmBAPoMiGzHfuTP999/aan0mHY8/Djz8cPj9KJ0pKyvBzNwte3ATc2dmXlYmU6T99rcyR19Q\nMVczj/f3p/84nCevWi50nXnU0sRi8IYVJpZ06uvFIuzvDx5LrjPzXHnmyl+2Z+Z9fdLnI2g9tTMz\n9xo9UWW19oblqio5LlFEcvnyZNqoon53B5/9LHDBBeH3E6QBtGg9c7cTzs0b1GXfN90E/PCH4TJz\n5bv19XmLuTqgO3YUdrzvqN35DcXLpEkyIbm9Yd6PfGfmcdsszsy8vj64uCoxLy+XP6/YhoYy7/iJ\nRCijZOe9vem/f7+7AzXTV9gLR0l75m4nXEWFXF2dH0iXfX/iEzKSYnd3cDFXX0Z/f/pJYRfz1lZr\nOYyYG89cj4klncmT5bw66aTgseTbM4/bZnF65vX17vtwfkf2unG/zFgn5kD0RtDq6kSozHzMGPn/\nzjvh9qM887KyEvXM3Z7XHTBdLXlZmQjY1q3BZ9lR2/bKzMeMsS4mXV2Fz8yNZz68mDJF/k+eHPw9\n+czM4y5NbG2NJzMH/DNjNzGP6ps7k7kgFxNAGrjDUNKZueqKr0NntbhZKSNGJLFxY/AJFuyZuZeY\n2zPzoNvOhR9bX+99rBTGMw9GMcSiRHzjxmTg94TpNJStZx53Zj5xopWZ33QTsHSpDMfhtg+dZ64S\nlaiZeVQx37gxmVZC6ncxUVl1e3u4/ah+IiXpmXsdWN0X5mwAVdTUyIELWhmgOh709bnbLPbMvNCe\neWNjsNHlTGZeOkyZIoLZ2hr8PWE6DRWTZ97TY02wDgDPPgu89VbpZOY7d0bLzMOKufr9epUmhiGv\nYu6Fm82iy8xHjUoAyJw2zmvbusx8YMDKbEePLh7PvKEh/RbVDeOZB6MYYhk1SmqVTz01eCy5zsxz\n5Zn39cnvSSUkqht/XZ2Ilq4yxa3OHMh/Zj44mAjVALp7tySWW7aE3Y9chL1KE4vWM/ciTGZeXy9f\nnpo/1A97Zu60WdQ2GhqKJzNvaJAfgt9ktiYzLy0OPDDc+vnMzOP0zPv65E5XifnOnSLmqoNMkOw8\njsy8tjZ8l35m+f3bbZYgF5MxY8Jn5qo2viQ9cy903XbdMvNdu2RM6KCz5XiVJra0WDMaqZM5TANo\nLvzYqiq5Wvv1fDOeeTBKNZYw3fnj8MzjslmUmNsHxevstBr7dBcNtzpzIL+ZeV+fzP9rP55BLib7\n7BM+M1cX4ZL0zL0yTV0ZkVtmXl0d3GJR67uVJlZUSHZuHwK00Jk5EMxqcQ60ZRhejBiRv/HM47ZZ\n7Jm5slmqq4MPtWsvTfQbbCtOMd+xIzOB9LuY7N4t/QfCZuZ2m2VYeebOA69GWdOdpJMnB5tHUeFV\nmqi6ANszhjDVLLnyY4M0gjoH2ioGb1hhYtETJpZcD7SVS888bGbu55nnqwG0uxtoaUmPJUhmniub\npSQ9c+eBV1m5zkqpq4uembuJealm5mbUxOFLPmcaCnoX4Aez/M5aWyUZUV3qlWceNDO3lyb6ifLu\n3fox4qNm5s7ffhCbp74+/CQWQWyWMBStmHt119++Pfg8ioB7aaJ9cB6VMagGkEJ65oB/Zj44KCex\nfXChUvWGc02pxpLPOUDjysxVLE1N8jtSDZDZeOb19enD6TrxyszDNoDu2AEMDaXHEqSaZcSI8IKs\nbBav0sQw50teZxryQpeZu4l5dXU4r1i11Pf2ykmiThRnZq7WqajIvjogW/wyc2WxBG0ENpQefpn5\nwIA0ukVtALUTl5irwaNURZb6Tasy4CCZ+dCQiJsS6KhiHqU7f3t7psUaJDOvrAwv5iqZLMlRE71w\nXkW9poWbNSucZ66GC+jslGEA1IVAXRkB6yQLa7EUyjPXlSWWqjeca0o1Fr/M/O67ZVqzqKWJ9lji\nslmU9dfYmJ6ZA1Zpop9nrj6PSlSyycyDijmRdMdvbwdmzkykvRbEM48q5n42S9HOAeqF8yrqlZlf\ndVWw2087VVWZYxLrGkDDNH7mEntmPjgocxoecYT1uvHLhz/OBlBma3AmwGo3am8vnsxcnZc1NRK7\nPSEJOg6J807D7y7VS8yD9KRWrFghdzpq+khFkGqWbG2WYZeZOz1zt8z8rbeSmDgx3PZ1w2HqGkDD\nZua58mPVbSoAzJ8PXHZZ+uu6zLxUveFcU6qx1NWlj9HzxBPA5ZdbyypzHRgI3oHOLZa4xZxIzuG5\nc63XlM3i55nb75iB/GTmgCR77e1AV1cy7flcZuaqmiUOz7yoxTzoELdB0Hnsbpl5oStZgPQf8vbt\nMkuNHZOZD38aGtJFbNmy9LvLXbtkKsWFC7P/rcQt5oDE+u//br0WNDN3zhqUazFX8XR0SGburJTz\nmyBjaChaZq5slmHpmetKE3VE8UCdwjc0FE9mnis/1inm27ale5rGMw9OqcbiFLFVq9Jtl127gBNP\nBA47LPtY4vTM3X63qjTRzzOPKzMP2p1fWSibNomYn3BCIu31sjLv9ovdu6Nl5kFslmFRZx53Zu5s\nIBoc1JcmFnosc4VdzNW8iW+8Yb1uMvPhT319ulfc1ibCs3GjLNt7SWZLLjJzZ7IRNTPPxjMPOskL\nIGLe3p7pmQPevnkubZYwFJWY26+iXpl5HB7owIB7NUuYBtBc+bE6MT/qKEvQjWcenFKNRZeZ9/cD\nM2bIeZptSWIuPXP12E5Qz9xZnZNrm8Uu5lu2ACtWJDPW8RpSIGoDaBCbJVbPnIjmENEyIlpORDdq\nXv8yES1I/b1JRINEFKJwUHBWs8Sdma9enb6sxLxUPHOFuhU2mfnwxy5izFZm3tkp50Qc9eWKuGwW\nVWeusMcXdNRE++8SyL2YK5HeuFEEXVf27DWkQNTMPK/VLERUDuAOAHMAHATgUiKaaV+Hmf+LmQ9n\n5sMB3AQgyczbM7fmTa4987vuAv75T2t5cLB0PHOVmQPWc8YzD06pxlJdbdmBW7ZIgqPshq6u9C7v\n2caSi8wcAM45x3rs1WlIV2eucNpNTuLKzJculf2eeWYiYx2vzDwOmyUfnvkxAN5l5jZmHgBwP4Dz\nPNb/GID7Au/dRq498w9/WGwKxcBAuudYjJm5vSu0QmUoJjMf/hBZWemqVTIynzoXOjvjzcxzJeYP\nPCAllYB3d347zgZQZ1WPk2wbQPv6gPHjgffek6Fsddgz80WLgLfftl7L1mbJl2c+AcAa2/La1HMZ\nEFEtgDMA/DlKIGG688flmff0WPW5KmMI2wCaD898+3bgkUeACy/0zsxL1RvONaUci13MZ8ywRE1l\n5nF65gMD/hOi+OEUc/skMl6ZudMzD2OzZDvQVn+/zFlaVSXT3em+I3sD6G9/C/zf/1mvZWuzxOWZ\n+/UADfPVngPgH14Wy5VXXokpqWnKm5qaMHv27D23EUuXJpFMAgsXJtDTA/z5z0mceSYAyOvqQ6n1\noy6r7f3jH0ksWQLss48sL16cxObNQGNjAo2NYbaHWONTy0uWJFOzfSfQ2QmsWpVETw/Q3S2vL1qU\nREdH+vFZuHBhbPvPdnnhwoUF3X+xLiuCrl9fn0B3N/D888lUkiGvv/RSEitWAEcfHT0e+/kyb14S\nRMDu3QmUl0f/fH19CVRXp78uSUcS8+cD5eUJDA56ny+Dg8DOnaIHiYRM4dbfn8TjjwMf+lDm/oeG\nZPA9tb56fetWoLfXP34ZUTWJUaOAceP06/f3J/HSS8CRR8rvsb3d2t/QkOiHCHLw47V9O1BRIce7\np8faXjKZxN13343QMLPrH4BjATxlW74JwI0u6/4FwCUe22Ivli5lBpiPPJL5y1+Wx7/+tedbIiG5\nh+zva19j/t735Pm//Y355JOZzzqL+bHH4t9vWFatYt53X3l8yCHMCxcyX3st8+23y3O33y7LhuHN\nkUcyv/Ya8zXXMP/4x9b5+6tfMV9/PfNPfxrfviormfv6stvG978vvys7CxZIzAMDzKedxvz0097b\nmDuX+cQT05/7wAeYn3tOv/4jjzCffXbm8x0dzI2N/jE/+STz6aczJxLMN9ygXyeRsPZ//vnMn/+8\n9dpRR8l3BDAPDfnvTzFtGvOyZcxr1zKPG+e+Xko7PbWamX1tlvkAphHRFCKqBHAxgEedKxHRSAAn\nAngk/OVEmDFDfMAtW4CXXpLn3BpA40Cu/tYtYDF65spSUdZPfb23zWIYfqga661bZVAtRdyeORDP\n1HG6thw1A71q7PPzzHUDh73vfcCrr+rXj6MBtLparJZx4/Tr2Lv0d3Wle/FDQ2KVhK1KyetMQ8w8\nCOA6AE8DeAvAA8y8lIiuIaJrbKueD+BpZo4wF7ZFYyMwZYp0Wwby45mrfRTb2Cx2MVdVC3bvUPej\nyVUsUTCx6Akbi/rOu7vl96G85H/8A1izJj7PHIinEVR3XlZWWs8F8cydDaAAcPjhwJtv6vfpJuaV\nlVbVmhdqOOmvfAW49FJ/z7yzM1PMy8vDi3mQapYw54vvqInM/CSAJx3P/dKxfA+AewLv1YPWVum6\nDuQmM3/4YeCGG+RAFnNmXlMjJ8/QkCXmdXXW1FTOel7D8ESV5akZ46uq5Dx9JHUPfOml8e1Ljfuf\nDToxV6MoAsEz8wqHMo0ahVQbUSZuYk4k++3rk2PnFXNVFXDoobK8cmXmOvbMvLMzfQA0NfZ6FDEf\nlqMmKlpbrcdx1pkrzjtPypAGB/WZeVdXuB6g2cTiBZFVWqVup+02y8aNUqqWj1iiYGLREzaWlhYR\nse5uS8ztZJOZO2OJIzPXJRnjx1vVH0HqzHWZ+ciR6SW6dtzEHPAfvhbIvADpviO/zDyKzRKkNDHM\n+VLUYh52yqegKG+wmDNzQMS8pyddzJXNsm5duodqGJ40N1ti3tCQKd6l4JkTAUqTombmTU2Z8xEo\nshVz58ToOpyZeRw2i+q0WDbcRk1U2Ae5SVUxZpCtB6q6LjvrzNUcoWHsnVz6sco3V/MqKjFfvBhY\nu+5XWPEAAB/ESURBVDZTzEvZG84lpRxLS4vYjvbM/PjjgWOOkdfj9MxzZbPYCVpnrsvMcyXmzpi9\nPPO+Pvk92sVc1bnnwmaJ1TPPN62tciUfHNR3BIgDdTvptFm2b5cfTLHMq1lXJ1mAOlGUuB9yiLxu\nMvPhT0uL9Di0e+b77Qccdxzw2mvxZubV1dnfDfuJedAeoLrMPJc2i19lmMrMVQx2zzybBtARI+Ri\nMCwz89ZW+eK8hDxbD1Rl5k6bpaMjvMWSSz+2rk5iUj9Y5xgVdksq17GExcSiJ4pnvmWL1SNaVYao\n8zROz1xZOtngNZ45EG1sFkB+C/39ehsobpvFyzPv6pJj7szMw4o5s3XRGtaeeXNzbvehbiedmXl/\nf3HM/6lwinlrq4wfoSiWOwhD7mhpEUuttlYSnKqq+MTcSXOzu5URlLgyc6eYE7k3gsZts+hQmXlX\nl4zfkm0DqHqP+huWmfmhhwK/+IX3Otl6oG4NoED4zDzXnrldzMeNkyxtzBjgmWfyG0tYTCx6onjm\nq1db56USc1VqF6dnHldmHodn7rRZADkGn/985vgxcdssXp55b698J9naLPa7j7Iy1a83c70w50vR\niXllJXD66bndh1sDKFA8lSxAppjX1MgPbuJE4LTTChubIT+0tEg2qsTbmZnH2Qs4H2IeNTMH5K70\nvvvkT/W3ALIXc6/hthVqCNz+frGBd+2yhDuKzWJvFyCKpxdo0Yl5EOLwzFU3X3XSRM3M8+GZ23+w\nEybop7XKdSxhMbHoCRuLshxV0pFLz7ypKXsx9+vMFtQz12Xmio9/HPjDH6xlt1ETgWBi7hxu280z\nlwG55PPZJ9OJYrM42wXcrJaS9szzQUWFnLT2XmHq5Bk1qjAx6XBm5oB0wHA2fBqGL+pC/s471nIu\nPfNiyMx1DaAAcMIJViXXyy9bz8eRmfvNnWDPzKuqrD4g9v1nI+Zx9AItSTGPo85848Z04VYnQ9iZ\nzvPpmQMi5m6ZeSl7w7mk1GM5/XQrC9wbPHNdaSIAzJsH/O1vwL33xivmO3em2yxunrnKzJWYq0bQ\nbG0WwP29Je2Z54OKikwxVwf2iCMKE5MOnZgffbQ1hoRh7+DBB62JvGtq0sXcy44IS7Fn5oA0/l92\nGbB5c2ZmrCOKzaJDl5krMY/DZoljtqGi6zQUhDg8c6eYqwMbNjPPZ505AHzuc4WJJSwmFj1RYmlo\nsOyFb39bGkWVR1xqdeZuguc3NosdImk7WrcOmD49fpvFzzNXg95lY7M4hy82nnlEdGJeUwOsWGE1\nNBUDOjE37N3st5/UWwPy41eP42DkSKmjjsrgoGSXXncLQQbz8msABaSia+1aeRy3zaLDKzOP0p3f\nORm38cwjUlEBbNqU2ZA4dWr+Y/EirJiXujecK4ZrLNkOd+GMxT6YVBRUpYdXZ7Yg3rCXzaJQmTkQ\nv80S1jOPkpnbJ5MH3G0W45n7MGKEzNxSTJUrOkxmbsgn2Yp5kJ6UQTJztwZQO3Fm5kGqWdR27DZL\nNg2gzsw8jl6gJSnmcXjmQDxinks/trZWeoUF7RhS6t5wrjCx6HHGkq2YB5kwJYhnHjQzz5XNovuO\n1LFxliYyR7NZdJm58cwjUIw15TrsHUUMhlxTTJm5n5iPHSsVLUB2Ys4crAeoMzNXNsvu3WIrERnP\nPBJx1JkDUrNd6Fi8aGmR/8Yzzw4Ti564PfMgYh7UM/ezWVSDJJCdmKsM2f5+3Xfk1gCqLBavz+a1\nX4XxzCOiBrSZPbuwcfihpoUzmbkhH1RViZDqBnzyY/fuYGJeWSlZqRdBMnO7SGcj5kEqWdR2dKWJ\nqsYcMJ55JLL1HZcvl/9ek7zmKxYvmprkf9AfVzH7sYXExKLHGQuRiK1d/Lq6/MsVV60SIQvimdvH\nNHGLJUhmHqeYOxs/vcYzd2bm9n0bz7wAtLTEI+S5Rl3xsx1j2mAIitNq+clPgP/+b+/3qI5Gb7zh\nn+XaxzRxI0gDqD3Dz2agrSB+OaBvAM3GZnF2GjKeeUS+9z2ZV7EYYglC0F55xezHFhITix43b9gu\n5t3d/uefytyffNI/M7eX9LnFEkTMc5mZ646LulPYuVM+Y7Y2i7ooKNyGwDWeuQ9lZf4nSzFhMnND\nvnCKeV9f+lSFOtTsP4sWBbNZ/DLzIGOlBBVzv0bdIPuy77OzMx6bJReZ+V45Nkuc5COWoGK+tx2X\noJhY9HjVUyuCiLnKzNvbg4m5LjO3xxKnmLe0eN+F62wWt++oulo+a1WV7DPbapYgpYlhzpeSFPO9\niXPPlfE4DIZ8EDUzb2wUoQtiswTJzP3GSKqqsjxzLzEfM8aqR9fR3R28/ayqyhJzFWc21SxBShPD\nUJI2S7H7jnHyyCPAbbcVRyxhMLHoKfZYdGLuV83S1WX12YiamdtjCZKZ26tu4hZzt++outqyWeye\neRyZuVtpovHMDQZDJJSVoDSkrw/YsAH41a/c39PZGVzMg2bmcdksDQ3W5O06ombmjY1if9orabLJ\nzIP0jPXDV8yJaA4RLSOi5UR0o8s6CSJaQESLiSiZXUj+FLvvWChMLHpMLHrcPPMXXwSuukqW+/qk\njvwb33DfThyZuT2Wnp5wYu7Mcu0QSXa+ZYv+dZ2Yu31HquNQVRUwaRKwZk18mbmaZN5JbHXmRFQO\n4A4AcwAcBOBSIprpWKcJwM8AnMPMBwP4SOC9GwyGoqK6WkRKWRPKcmlvF+HTYc/M/Wq2a2pkm27+\ncJixUpRn7iXmgEyz6Ga19PQEz8zVhaqqSsZ+J5LG1TiqWdzEPAx+mfkxAN5l5jZmHgBwP4DzHOt8\nDMCfmXktADBze3Yh+VPsvmOhMLHoMbHocfPM16yRRs/e3nT/vK1Nv50wmXlZmazj7AWqYlGZr5tt\noqioEOEfGvIXcy/fPIxnrvbR1CRCPmmS3LXEUWfuJuZxeuYTAKyxLa9NPWdnGoAWIppLRPOJ6PLA\nezcYDEWFEnNABNAu5qtW6d8TxjMH9L75vHnAN78Zru5bNYIGyczD2CxuqAtMc7P833dfucAVS2bu\nV5oYZFSQEQCOAHAKgFoALxPRK8y83LnilVdeiSlTpgAAmpqaMHv27D2ekLoCBVlOJBKh1t+blhWF\njkc9V+jjYc4X72WFWq6uTqTGCU/iiSeAvr6EWgPPPAOcc07m9rq6gI0brff77b+2Fnj++ST22cd6\n/bXXgCVLkrjmGnk9SPzl5UB/fwK7dgGLFycxOKhfv74eWLAgiSlTMl/v7pbXg5wv69cDQAJlZbJc\nUQG0tSVQXi7LGzYAM2YEO/5r1iSxYoVsDwC2b09i4ULgvPNk33fffTcA7NHLQDCz6x+AYwE8ZVu+\nCcCNjnVuBPBt2/JvAHxEsy02GAzFzQ03MIuBwfzoo8wTJsjjadOYb7pJ/57Jk5nffVfW+/3v/fcx\ncybzkiXpzx12GHNZGfNrrzFPnx4s1jFjmDduZD7kEOaFC93X+9KXmP/zP/WvXXgh84MPBtvftGny\nGRW33MJ8zjnMs2bJ8g03MP/kJ8G2dcEF6fu9+GLmP/5Rv25KOz21mpl9bZb5AKYR0RQiqgRwMYBH\nHes8AuB4IionoloA7wPwVvDLSXicWUUhMbHoMbHoKfZYlE2ifOa+PuDEE4Hzz3dvAO3qEh+5ri6Y\nzeLs0t/XByxdmsSRRwL33x+ue30Qm8WrHDKMZ+6stx83TuYhzaXNEuZ88RRzZh4EcB2ApyEC/QAz\nLyWia4jomtQ6ywA8BeANAK8C+DUz51TMDQZDbjj9dPk/c6ZMet7XBzz+ODBjhl7MmUXkGhuDi7lz\nsK3168WHvuMOGaUxyCiGQHAxdyuHBMJ55moMGsW4ccDSpdYkMrloAA2Db3d+Zn4SwJOO537pWP4v\nAP+VXSjBsfuyhcbEosfEoqfYYznpJMnCp061Klqqq0XwVLf+e+4BLrxQOuTs3CkZ5ogRsk6UzHzT\nJmDy5ASOOUaWN2wIFn9lwAbQ2lqrUddJmDrzvr70Kptx4+T47L+/LOciMw9zvpgeoAaDYQ9EwF/+\nImV327fLckWFCJ7KzG+5BZg/Xx6rcVkAybiDZNXOTHnzZmtWLcC9BNKJqjXP1mbxGwdG0dICzJpl\nLY8bJ/+nTpX/hc7MS1LMi913LBQmFj0mFj1esdTVAVu3Wpm2Xcy3brUy3a4u6UADAF/5SrrYeW3b\nmZkPDUksYQaVy5XN4nZcli0DXnjBWh4zRmrMVWYeZKgCQDpMrVplXQyAeDxzM2qiwWDIwCnmDQ0i\nfIODko2vXi3P2zPzywP2MHGK66ZNlu98553AWwFb3OIQ8zA9QEePTl8uLxdBV5n5yJHAe+/5b+df\n/5LPa686zItnXowUu+9YKEwsekwserxiqa/PzMx37LDGBldibs/Mg6LLzI8+WmI56ST5C0JVlXjW\nu3d7zxnqljHv3i3vDzIHqBu/+x1w+OHyuKkps5FUx7x5wKmnpj83YoR+omvjmRsMhqxws1m2bpVl\nZbPYM/Og6DJzu2celMpKucBUVYm3H3R/ip07xeMvy0IFTz3VupCMHBlMzHfssHqRKoxnXgSYWPSY\nWPSUSix1dTK4lmrQVGLe3i7iHWdmvmULsG6deyxu2Gf+8cJNzN3KEqN+RyNHes8KtmuXNSm0s+rH\neOYGgyEn1NWJ8IwaJctKENvbpZHz7bfl+aiZ+bp11nJHR/htqO10dPiLuZvNEqbGPAh+Nst3vyvH\ndHAQaG1Nf62yci/NzEvFd8w3JhY9JhY9XrGocj0lOuXlkqWvXg1MmyYZ8a5dwOuvA9Onh9uvU1w7\nOoDTTnOPxWs7W7em12vrCJuZR/2O/GyW558HFi4Ml5kbz9xgMGSFU8wBqWhpa5OqjtGjxTf/61+l\nA1EYnOK6bZtVzRI2xm3bwtssX/6ydHyKOzP3sll6e4F//hNYssQa5teO8cyLABOLHhOLnlKJRSfm\n9fXAihXSWDl2rFRlTJiQXi8dBHtmPjAgmer8+e6xuFFfH03Mf/xj4ItfjN8zr68X0dZN/9bWBkye\nLHc0mzYFF/PYxmYxGAx7J6pczy7mLS3A4sUi3mPHAgsWiJhH2bYS144Oa7KHsCibxU/MlQ2jSv8a\nG2W/cWfmZWWybd0E2N3dcmdzwAFSRx/UZgm1/+zeXhhKxXfMNyYWPSYWPV6xlJWJ6KoGUEAmY1A9\nF5WYh83KgfTMvKNDyvSiHJegNgsgFwxVI686+bS3x+uZA+5Wi+qc1NCgvwAZz9xgMOSMurr0zHzf\nfeX/PvuImC9caM0wFAaVmff2AhdfnFlzHSa+INUsgMSpBvBSE0EvXRpvZg7IxU0msUinp0fira2V\nzN1k5ilKxXfMNyYWPSYWPX6xOMV88mT5P26c2AXd3dEz8507gTffBBYtkoG2ohwX1Us1iJiPG2eJ\neWenZOdvvx2vZw5Ipc8772Q+r8RctUUYz9xgMOSNL31JxjVX7LuviFBzM/DBD8pzUcRcDYH75puy\nHHSURCfqohA0M1cZc2en1MovWxZ/Zj59OrA8Y8LM9MwcMNUseygV3zHfmFj0mFj0+MVy3XXpYrff\nfiKKRFZtedDhY+2ocV7efBM45xzgvvuie+ZBY1BiPjgopYEzZsigWLr3ZvMdBc3Mc1FnbnqAGgyG\nQBx2GPDMM/KYSGyKAw4Ivx1VwrdkCfCFLwAf+lC0eJQwTprkv+748cAbb4hf3dBgvSdKNY4XBxyA\n1ETN6SgxV2WLJjNPUUq+Yz4xsegxsegJGwtRunhPnx5tkKqyMhG2996TIWSjxAJYdw1BJrAfP16G\nEOjslIoTdSE499zMdbP5jpqb9b1Ao2bmZmwWg8FQ1KjBupqaom9DCaNqmPVC2R9KzD/yEeDAA4NN\ncxc2Jt1cqT090r6gBDsXmTkxc3ZbCLojIs7XvgwGQ3Fz0EFSGrh5c+akD0Hp7JSLweLF/jMc7dol\nIv7ww8D3vie9V3NBT498HudYMJ/9rNhUAwPADTdIjbu9hn/+fOCaa2TiCidEBGb27VZlMnODwZB3\n1CiJYYfPtRMmM6+slOndHnssmC0TlZoaaWAdGkqf/FnZLCr7NnXmKUrZd8wlJhY9JhY9hYylsVHK\n9FRX+yixVFRII2rQ8sJDDgH++Efg6KO918vmuJSViaA7M3M1cXTY0kRTZ24wGIqaxsbs/HLFQQcF\nX/ejH5Ueo0cdlf1+vaivzxw/3d4AWl6eOc2d27RxYTCeucFgyDuf+hTw6quSWeeLwUHg85+XURPV\nDEq5YOpU4NlnxdZRHHcccOutMpTAeedlin1bG3DiidYMToq77gI+9SnjmRsMhiIlrsw8DBUVwM9/\nnvv9qCn27CibpaJC32NVdaRyEibekrRZjO+ox8Six8Sip9CeuV3Mh9Nx0U1Tt2WLVLnU1urLIUeN\nko5Uvb3pz69ZEzyWkhRzg8FQ2hQiM88XTjHfvVvEfMwYEW3drEpEVscmO17T0GVsw3jmBoMh3zz/\nPPDuu8DVVxc6kvg5/3zgiiuACy6Q5fZ26bTU0SHLvb16z/6EE4DvfAdQw7H09soFb9euYJ65b2ZO\nRHOIaBkRLSeiGzWvJ4iok4gWpP6+6bdNg8Gwd3PyycNTyIHMzHzTJhn/XeHW+DpxYnpm3t4erkOV\np5gTUTmAOwDMAXAQgEuJaKZm1ReY+fDU33eD7z4aw8lfixMTix4Tix4Ti55sY3GWJjrF3I0JEywx\n37JFesdWVwePxa+a5RgA7zJzGwAQ0f0AzgOw1LFehBn8DAaDYfjhHJ9l0yaZncmPWbOAa68Vj/2J\nJ2TM+DA9ZD09cyL6CIAzmPkzqeXLALyPma+3rfNBAA8BWAtgHYAvM/Nbmm0Zz9xgMAx7vvUt6Rh0\n882yfNttwMqVwO23+793+XLgmGNkUK5p06T65f7746kzD6K+rwOYxMw7iehMAA8DmK5b8corr8SU\n1MAITU1NmD179p7B19WtjVk2y2bZLJfy8ujRwHPPJZFMyvK2bUBXl7Xs9f4TTkigszOJvr67sWYN\ncNBBUxAYZnb9A3AsgKdsyzcBuNHnPasAtGie57iYO3dubNvKFhOLHhOLHhOLnuEUy//9H/MFF1jL\nX/gC849/HPz9jY3MgPxdffVcTmmnp1Yzs281y3wA04hoChFVArgYwKP2FYhoLBFR6vExEOtmW/DL\nicFgMAwf7JNHA9bsRkFpbrYeq8k7guBbZ56yTm4DUA7gTmb+ARFdAwDM/EsiuhbAvwEYBLATwL8z\n8yua7bDfvgwGg6HUWblSSi/VRNUXXQRceCFwySXB3n/44cDChfJ43jzgxBNjGpuFmZ8E8KTjuV/a\nHv8MwM+ChWkwGAzDG5WZr1wpg27t2GGN3x4Ee2YeZH5TRUl251eNBcWAiUWPiUWPiUXPcIqlpkaG\ns1WjJu7YEd5mqamRLv7LlwePxYyaaDAYDDkkipjPmiVd+UeMCP4+MzaLwWAwxMyuXVIjPjAgGbpz\nfHMvvvIVYNUq4MEHZTnoHKAlabMYDAZDMVNZKeOW9/RINUtYzzzKiJIlKebDyV+LExOLHhOLHhOL\nnrhiaWoCtm8Pb7Ocfjrw4Q+Hj8V45gaDwZADmptlsKzdu/WzC7kRdY5S45kbDAZDDjj+ePG/P/lJ\nYFsW3SiNZ24wGAwFpKlJJmgO45dnQ0mK+XD01+LAxKLHxKLHxKInTs/8qaeAmboZIHIQS0mKucFg\nMBQ7zc0yLvmll+Znf8YzNxgMhhzwqU8Bd90lE1XU1UXfTlDP3Ii5wWAw5ICODqlkGTUqu+0M6wbQ\n4eivxYGJRY+JRY+JRU9csTQ3Zy/kxjM3GAyGvQxjsxgMBkMRM6xtFoPBYDCkU5JiPhz9tTgwsegx\nsegxsegp1VhKUswNBoPBkI7xzA0Gg6GIMZ65wWAw7EWUpJiXqqeVa0wsekwsekwseko1lpIUc4PB\nYDCkYzxzg8FgKGKMZ24wGAx7ESUp5qXqaeUaE4seE4seE4ueUo2lJMXcYDAYDOkYz9xgMBiKGOOZ\nGwwGw16Er5gT0RwiWkZEy4noRo/1jiaiQSK6MN4QMylVTyvXmFj0mFj0mFj0lGosnmJOROUA7gAw\nB8BBAC4loozpSVPr/RDAUwB8bweyZeHChbneRWBMLHpMLHpMLHpMLHrCxOKXmR8D4F1mbmPmAQD3\nAzhPs971AB4EsCXwnrNg+/bt+dhNIEwsekwsekwsekwsesLE4ifmEwCssS2vTT23ByKaABH4X6Se\nMq2cBoPBkGf8xDyIMN8G4GupUhVCHmyWtra2XO8iMCYWPSYWPSYWPSYWPWFi8SxNJKJjAXybmeek\nlm8CsJuZf2hbZyUsAW8FsBPAZ5j5Uce2TMZuMBgMEQhSmugn5hUA3gZwCoD1AF4DcCkzL3VZ/y4A\njzHzQ5EiNhgMBkMkKrxeZOZBIroOwNMAygHcycxLieia1Ou/zEOMBoPBYPAhbz1ADQaDwZA7TA9Q\ng8FgGAYUtZgT0X8T0fGFjgMAiGgUEd1MRFcRURkRfYOIHieiHxFRcwHiOZmIfkZEjxLRX4joViI6\nIN9xpGKZQ0T/S0SPpf7+l4jmFCIWN4joWwXY5xwi+jQRTXE8/6k8x1FGRBcT0UdTj08lov8hos8R\nUVFrQC4holbH8uWp43I1EeW8Ki9uitpmIaItAN4DMAbSYek+Zl5QoFieBPAGgEYAMwG8CeD/AJwG\n4FBm1nWmylUstwLYB8BzAM4HsArAOwD+DcAPmPlPeYzlpwCmAbgXwLrU0xMBXA7pcPb5fMXiBRGt\nYeZJedzfDwAcB+B1AOcA+Ckz3556bQEzH57HWH4BYDSASgBdAKoBPALgbAAbmfmGfMWig4ieZ+aT\nC7DfPd8DEX0TwAkA/gj5vtYw8xfzGMuFAF5g5q1ENAbAfwE4AsASAF9i5rW+2yhyMV/AzIcT0XQA\nlwC4GNJo+0eIsL+Tx1gWMfNhqSv2OmYe73wtj7EsZuaDU48rAMxj5g+k7hD+wcyz8hjLcmaepnme\nACxn5rzdLRDRDo+Xa5jZs8E/5lgWAzicmQeIqAnAfZDKsC8CeD3PYr6YmQ8mohEANgEYx8z9qXNn\nATMfksdY3oT0X7FnvtMhyQgz86F5jMUu5gsAnMDM3anjtED9xvIUy1Jmnpl6/CcAL0N61Z8C4OPM\nfJrfNkriFouZ32HmW1IidRGAmv/f3tmEaFWFcfz3x8YQnZBRyNKpqbDElWRYUGCFWEFJtCho01TQ\nokW0iAxq0RdlUhgFFQXSx0IIij4WkhsVapN9IEhJRWmhTkZOhJTix7/FOaPjNB86jOee9/L84IU7\n5zJzf+8zM897z3PuOQfYWFhDknqAXmCmpEty41ygq7DLMUlz8vF88u/R9mBhD4BDkpaN0r4M+Lew\nyyCw0Hb3yBewr7DLtLwEBrb/It3tnUfqzU0v7HI0exwBttk+nL8+Chwv7PILqVd7J6lncBuwPx+v\nKuwyQ9KVkpYCXbYPwok4HSvsMjwXX2Z7ne3fbL9NqkxMSLE7lanC9nZgO/BY4Us/D3xPuqO4H3gr\nl9UWA08VdnkO+EbSj8AVpPIKuXu2vbBLP/C6pG7Scg+Qyix/53MleQ+4CBgY5dyGwi4/S1pueyuc\nSJz3SXoWOOsri45gQNIs2wdt3zTUKOkC4HBJEdurcknhTeBF2x9LOmp7d0mPzADwUj7+Q9KFtvfm\nG7QjhV22SnqalGe2SLrD9oeSbgBOa4GW2sss3bbH6zoXJXdLlbvOXcASUsllbwMuc4BLSaWMxlcG\nyolhaN2ePbZL3wlXhaQZALb/1zuRtOB0aqBnG0kzgZm29zdw7VnAM6S/4atsz5/gW4qRV4E91/Y/\nBa85HXgcuDc3LSDNpv8UWG371wl/Rs3JHNJIPKnLPp9Ua9sDfNnEtkW5DryMFOgaXK4Ghmr3jbmM\nhaRFtnc27QHhMhZNu0haAlxj+42mHEajybjkMZZzgD/P5P+56mQuaSXwGvATp3bhFwIP2v4sXJp1\nGY/ST5CMR7iMTmUuNX3IdVxcaq+ZvwKssL1reGMefNwILAqXZl0kvTrO6dmlPCBcxqImlwnYRBrz\nKELb4lJ7Mp/GyWeXh7OH8u7hMjr9wCOkgbTh3TwBd4dLuAxnggRaevJdPy2KS+3JfD2wTdIGTpYT\neknPnK8PlypcvgJ22P5i5AlJT4ZLuIygn0oSKC2LS9U1cwBJi0k7GQ0f6PvE9nfh0rxLfvb+UMmR\n/3DpaJfNwBNjJNBdtvsKurQqLtUn8yAI2kNNCbQmpiIuVc8AlTRbaQGpnZIGJR3Ix2vy4zvhEi7h\n0kEutg/UksjbFpeqkznwPmmK9vVAj+0eYGhGVLHFpMIlXMJlaqgpgdKyuFRdZpH0g+3Lz/RcuIRL\nuFTrsom02uc7wO+2rTR7+B7gRtsrC7q0Ki6135nvlvSopPOHGiTNk7QamHB6a7iES7hU59Jn+wXb\nA0OzG23vs70G6Cvs0qq41J7M7wLmkhahGZQ0CGwB5pBWXQuXcAmXznKpKYG2Ky62q36RNoJYAXSP\naL85XMIlXDrLBegB1gI7SfXqwXy8llS3jrhMMi5FAzeJN/gQaUH/j0g7Dt0+7Ny34RIu4dJZLvma\ntSTQVsWlqOwk3twOYFY+7gO+Bh5uItjhEi7hMiUu1STQtsWl9un88sndP3ZJWg58IOliTt12KlzC\nJVw6w+UBYKnT9mx92aPP9suFPaBlcal9AHS/0nrHAOTA30oaoCi2V2C4hEu4TBmnJFBgOXCLpHWU\nT6DtikvJrsQkuh69wLxR2gVcFy7hEi4d57IZWDKirQt4FzgecZl8XKqeNBQEQbuQ1AscsT0wol3A\ntbY/b8asWaYiLpHMgyAIWkDtNfMgCILgNIhkHgRB0AIimQdBELSASOZBEAQt4D8j9qSme95uHgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabd3e0ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  PRECIOUS METALS position:\n",
    "z_metals = get( w4cotr_metals )\n",
    "plot( z_metals )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Positions in the precious metals have recently exhibited \n",
    "swings characteristic of momentum trading. \n",
    "That may be also due to the fact that strategic positions \n",
    "are taken for the long-term in ETFs, for example: \n",
    "GLD in the case of gold and John Paulson, rather than \n",
    "in the futures market to avoid rollover costs and slippage. \n",
    "Counter-tactical positions may be traded against \n",
    "the ETFs in the short-term in the futures/options market, \n",
    "however, those will not be accounted as spreads in the COTR. \n",
    "Thus we must be aware of bias created by trading in related \n",
    "markets which are not under CFTC jurisdiction.\n",
    "\n",
    "Worth noting is the August 2015 dip into net short region. \n",
    "\n",
    "#### TECHNIQUE: damped indicator swings can show trends in the underlying prices\n",
    "\n",
    "We can demonstrate this by applying \n",
    "*exponential moving average*, **ema()**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oVS1p1zvvWH6Ngw6CO+4o/gzO+vLJJ3DccTBrVuFSDKia3/TNN+1HMRtWrYJm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\nlnDyqaV9+/QDskuW2IpUVVXhht599I7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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabd3f02c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_metal_ema = ema(z_metals, 0.05)\n",
    "plot( z_metal_ema )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US Dollar position\n",
    "\n",
    "We use the futures and options COTR for contracts on both the euro and yen, \n",
    "then average their position indicators. To invert direction due to quotation style, \n",
    "we take the complement, i.e. (1-mean), so we still retain the [0,1] range. \n",
    "\n",
    "We can run this procedure by retrieval of a variable called *w4cotr_usd* \n",
    "(where w4 tells us that it's weekly series)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nmReVTZvsFu2YYyyAB9ks5SKVZx40AtS7Be7XL/0xSxnMHdGnqck88898xjrO\njz4a3nnH/vfKyIL50UuXZn9cVeucX7kyUTMcbKRlqpREP7lWTsx39GfUiGQwL5ev9d//WoF8r9Oz\nudmCZNg9c6/DJ1NrZM89S1cWN8reYykJi5ZsPXOwz9iqVTboplcvC+j+YD58uLWUs51YfOVKG8y2\ndq21zD0tI0dmNw3b8OG5B/NsW+ZheX/A5ZkXlddeS0y6um6dBdQBAxI9++UkG5vF48MP7Ytxyy3w\n4IOpjzl6dOGznTvaLl4wv+MO62/p1cuG08+Z0zKYd+li67O1WmbNSvzvZbOApSSec07m5+fTMs82\nmEeabOaWK8aDCM4BWlur+s9/2v/duqmOGqX673+rVleXX8ujj6qedlrr9evXq/bu3XLdD36geuON\nmY+5YYNqz56qTU3F0ehoO+zYoSpij61bVVetsvWTJ9v8lz/6Ucv9J09WffLJ7I59/fU2J2aHDqq7\nduWnrVMn1ebm7Pbv2dM+61EFNwdoYWzebCPSjjnGlvv3t1vJnj2LPKN2luSSzbJgQXa3q9XVVmB/\n+vTiaHS0HbwCbbt22R2h1xIfMqR1yxxsRqt58+w786MfpT/2/Pk2i0/PnvmNyqyqsjuBFSsy77tj\nh72WXr1yP0/UiGQwL4evNWeOdcZ4ldv69bMPRY8eLYN5OT3zVDaL1wHqaUlXqCiZmpr8qtBlIsre\nYykJi5ZMOpqaUlctbGiwRAA/48aZl/6Xv8Bf/5r+3EuX2qw9nsWSzzXJ1mrZuNGyZJIL1KUiLO8P\nlMAzF5HJIrJARBaKyGUB22tFZJOIzIw/fpKTgpDyzjuJuf/AWuZgH/AdO1rmb5eDVC1z/4TOYNqW\nLs2+oH7Xrlbz3OHws3lzy0wTD+97cOyxLdcffzz885/w97/b5y9dv9LSpXZHGHT8bMk2mLcbv5wM\ng4ZEpAq4AzgBWA68KSJPqOr8pF1fUNXTSqSxFeXIBX37bStG5eF9iLt2teqF27dbp1C58lKzGc5f\nW1vLqlXWEsm2fnK3bqWZPi7K+bqlJCxaMunwRmcm47Vw/VOogd3h/fjH9iPw+ONWDfGww1o/f+dO\ns0cOOsiG72ejJYhsg/mqVYnvbjaE5f2B4ueZTwTeV9XFqrodeBA4PWC/NlePbO5cK0bl4eVsd+li\ngXL79vLqyTabZc2a4IlqU+Fa5o4gUgXziy+21MIgvv99uPJKK437xhvB+9TXW873AQe0vPPNlWyD\n+fvvZx5hvJ9tAAAgAElEQVRR2lbIFMyHAv5Jmuri6/wocLiIzBaRp0RkXDEFBlEOX2vlSvvAeHi/\n7l26WOeN55uHIc8cLKDHYjHWrs0tmJeqZR5l77GUhEVLvp55p06tW+XJnHYaPPJI8DZvbs8JE2yS\n5Gy0BJFtMF+4EEaNyv64YXl/oPi1WbJxht8ChqvqFhH5BPAYEFjtY8qUKdTEJ9Srrq5mwoQJH99K\neMLDsrxyZYx334Xx4215wwbb3rlzLZ07w/TpsRajykqtZ8GCWHxyitbbO3WC556LMW/eLIYOrWXA\ngOyP37VrLevXF1/vrHgycVjez7Ase1RaT6b359VXY/Ef+dyPf9JJcNZZMZ56Ck45peX2Vatq2WOP\nwvU3NMSYPh1WrqxlyJDU+y9cWMsZZ0Tr/YnFYkydOpX6+vpWutIhmqYnT0QmAdeo6uT48uXALlW9\nKc1zFgEHq+r6pPWa7lxhQtWslM2bE97zAw/Al75kHYxDh1rdZX/LvdT86Edm9VzWqgvabodXrrTs\ngN/8xjJxfve77I57551mKf3mN8XV64g2t99uWVF33pnf80ePhiefbJ0i+/Of22f1l78sXOOll1p9\nl7vuSr3PgQfC739v2TNRRURQ1YxWdiabZQYwSkRqRKQzcDbwRNKJBolYtqiITMR+INa3PlR0aGgw\n+8EL5GA2i2dzdO5c/lzzVJ452K2v5+E7z9xRDFatymynpGPw4ODKhp7NUgx+8ANLg3zmmeDtquaZ\n52KzRJm0wVxVdwCXAM8A84CHVHW+iFwoIhfGd/ss8LaIzAJ+BXy+lIKh9L7WunWti1T1758I7p19\nHaCl1uKRyjOHRDCPxWI5B3PnmZeXsGjJpKO+vvBgHtRRumxZ62Ce7zUZMgR++1u48MLg7fX11ljJ\nZu7PQrWUgly1ZKxnrqrTgGlJ6+7y/X8nkOfNWDhZu7Z1MB88OPGh8HeAlotUqYmeHu/HZe1aOPzw\n7I/rWuaOIOrrrcBcvpSjZQ5w1lnw9a+3boCJ2Fyi7aVVDhEdAep1GpSKoJb5kCGJAkH+lnmptXhk\nY7PU1tayeXPL4kWZcHnm5SUsWjLpKNRmGTKkdTDfssVKLifPx1nINenQwXzxmTPNUlm+PFEg7Ac/\nyD2Yh+X9AVfPvCgEBXNIzKxSiZZ5NjYLJGpqZEu3bq5l7mhNKWyWO++EyZNzswGzYeJEuP9+q7f+\nrW/Z9HOedtcyDzmV8Mz9+DtAS6lFNVF35YMPUs9j6PfMt261AJ0tXbs6z7ychEVLOh07d9o0iYXY\nLMOG2TRwy5bBn/5k69580ya6yEVLNlx8sQXz886Dl16Ce++1wL5rF1xxRW7HCsv7A66eeVHYtCl9\np4nfZiklV1xhLYxt22zwQ6oRc/6Wea7B3LXMHcksWWKfu1R3gtlw+OFmffzhD3Drrbau0NZ+Kmpq\nLL32Zz+De+6x+utjx5pv3qEdRbhITuhcal+rsTH9raDfZim2FlUbGXfkkfD005Zq+PbblrebyjP3\nflxqa2tztlm8lrmqTV59wgn5lSVNJsreYykJi5Z0OnKpupmK7t0toF93XcKeTBXMi3FNPDvl1FPt\nzjrVdyUTYXl/wHnmRaGhIX3941K2zN97D772NaittSAOVifaX/QrmWK0zJ94Ak46yUqGOto32dbD\nz8SNN1q5ieZmq15YqpZ5Mn375tagaStEMpiX2tfKFMxLWZvltdfg7LOtvoVXQGvt2taTASTr8Tzz\nfFvm3vRymzblr91PlL3HUhIWLel0FCuYH3igjZgeNcoaJB99FPy9Css1gWhriWQwLzWVbJm//jpM\nmgTHHZdYl6lDthgtcy+Iu5a5Y9UqK1lRLEaOtEbK4MHFsfAcwUQymJfa18qlZV5MLaowbZpZLN50\ndbvtll0w/+gjOOaY2ryzWRoa7ItWrGAeZe+xlIRFSzodmzYlfO5iMGaMTU2YymIJyzWBaGuJZDAv\nNY2NmVvmpcgzf/11C8QHHGCDLlassEC7Zk12LfPmZtOWSw9+p072d+1aG8xRLJvFEV0aGoobzCdM\ngH/9q/BOVUd6IhnMK+2Zl6o2y5tvWqvcuxUdMsSyAhYvpkW53WS8YP6vf8Xy6vjp3TtRM6NYLfMo\ne4+lJCxa0unYtKm4EyBPmGCfz+Sp5rLRUm6irCWSwbzUNDSkHxJfqhGgK1e2nii3uho+/DD7lnku\nFovHgAE2crSYwdwRXYpts4webZ/jVMHcURwiGcxL6Wup5tYyL6aWlSutNe6nd2/7cmUTzCdMqM2r\nZe7l1A8f7jzzUhMWLal0qBa/Zd6xow1ESlVgKyzXBKKtJZLBvJQ0N5vNkW70Wylb5kHBHLIL5rl2\nfnoMGGB2Tv/+rmXe3tm2zXLDCxn9GUQxfxwcwUQymBfb1/JPgJSp8xNKV5slKJh37w59+qTPHfeC\n+UsvxfIO5r162a2w88xLS1i0pNJRbIulEC2VIMpaIhnMi8npp9uAnCVLrEW+bFnrgJpMqfLMg4L5\nU09ZamI6/J55PjbLwIH2Ba6udtks7Z1KBHNHcWjXtVkaG20YO1iFN7BpqMaPT/+8UuSZb99uQ56T\na8JUVWV+rhfM99mnNjQt8yh7j6UkLFpS6cjUX1ROLZUgylradcv8pZdscE7XrjaEGeBXv0pfBwWK\nk2e+eXNLe2f9erNTsgneyXjBfMuW/D3z3r3t4Tzz9o1rmUeXSAbzYvla06dbulT//vDf/1pgb27O\nXJeiUyerOVGIlrPOsiqFHhs2WDDPB8/2mTEjltMsQx4nnmjldsvtmT/5pA2IKjVR9kFLhfPMg4my\nlkgG82LhBfMBAyyYf+EL8OijNhtKOjp2LNwzr6uzu4BvftOWvZZ5Pngt882b87tF7t/fBisVM5hn\noqHBiomdcEJ5zufIjmKnJTrKRySDeTF8rYYGs1YOPdSC2cyZlgd7xhnQo0f65/pb5vlqWbMGnnsO\n7r7b5i3csCH9KM9MerZvh0GDagv6IvbubdfFb//kS6br8sIL9gOyZg28+27h5ytESzkJi5Z0nnm5\nW+ZhuSYQbS2R7ADNh6VL4bOftfonItbhWVNj+bT9+9s+mTo+PQptmataLZTt2+2u4JxzzLfPd25E\nL5g3Nub/gwD2urp1swlx87FrciEWs1b52LFw331WF+bCC0t7TkdmnGceXSLZMs/H15oxw2qfeBM+\nrFmTCJ7r19vfVHNsJuMvOZuPloaGxPOvvhr+8x949tn8bZYePSx9cf78WMG3yMWyWjJdl7lzraDY\nwQfDbbfBN74Bjz9e+Hnz0ZIr99yT+MxUWku+pPPMy22zhOWaQLS1ZAzmIjJZRBaIyEIRuSzNfv8j\nIjtEJGDK1sozZ461PJ96ypb9wby5Obdj+W2WfFi7NvH/CSeYNsi/VX3yyfDPfxanRV0u33z+fGuV\n77uvZeEMGtSyQzjM/PKX1sfSFqmEzeIoDmmDuYhUAXcAk4FxwDkiMjbFfjcBTwMlLz+fq5f03HPw\n059a4Jw3z9b5g/lf/2qDhbLFb7Pk47H5zz18uBUiEsm/Zb7nnub3v/lmYZ45FC89Md11aWqya1BT\nA+PG2brzzrPWusePfgQ33FC4jkxa8mHTJpsCLQxa8iWVjkrYLGG5JhBtLZla5hOB91V1sapuBx4E\nTg/Y71vA34EyJJrlzr332t8vf9kqEELLgDpgAAwblv3x/DZLPqxda1Nq/fCHNmKzSxc7f77BHOCQ\nQyxIFhrMu3SB7343MWVdKXjvPZtKrKrK7iS+9jU4/3x4553EPj//uaVLhpGNG202nraIy2aJLpmC\n+VDA32ati6/7GBEZigX438ZXFSEXIj3pvKRFi1pnY3zwAbz4os0W7o309AfzXOnYMb8883nz4KGH\nrHTA7rvDzTcnto0enb8esA5EyC/P3E9TE7z1lk2MUQjprsv8+S0nKvj97215505L2QTv9RQns6aY\nPuiOHXaNkoP5W2/ZZ6ycWgrB5ZkHE2UtmbJZsvkq/Qr4X1VVERHS2CxTpkyhpqYGgOrqaiZMmPDx\nrYQnvNDlY4+t5Z57YORIWz7mmFrmzoUNG2J89BGsXWtTq82bF2PQIIDcz9epE6xZE8N/rb3t48fX\nMnAgTJ/e+vm//CU8+WQto0bBV79qz/e2X3RRjK5d89MDsHlzDJhFr16FXb/p02s5+mh44okY48fn\n/37MmjUr5fYFC6Bbt5av/4UXYhx+ONx7by1XXgnbttn+ixfXMnJk8T4fhS7vv78tz57dUv/EiTF2\n7gTV9M/3qPTrSfX+NDTU0rt35fVVatmjknpisRhTp06lvr6+la60qGrKBzAJeNq3fDlwWdI+HwKL\n4o9GYBVwWsCxtByA6he/mFhesEB16NDE8qhRqnPmqB51lOrzz+d3jv/8R/WII4K3vfGGaQjiy19W\nPflk1euuU921K79zp+Lll+28K1cWfqxPf1r1b38r7BiPPab60ksJbf7Xe+aZqg8+2Po5r76qesAB\nqjt2qHburHrIIaovvliYjmLz4Yd2nU88seV6UB00qDKaismQIap1dZVW4fATj51pY7WqZrRZZgCj\nRKRGRDoDZwNPJP0Y7KmqI1V1JOabf1NVnwg4Vtl45RW77d25E37zG/jiFxPbTjvNPPQlS7JPRUwm\nXZ55U1Pq573/Pvzv/8JPflL8Wcq9wv/F8DuHDk3YHUF84xtW9zodZ5wBxx9v/59yiuX5X3ONdTS/\n9VbwfJDjx5ufXl9v/QcjR9qAqjCxcaN5/UuWJN5rr39hzz2Ld54tW4p3rFxwnnl0SRvMVXUHcAnw\nDDAPeEhV54vIhSJSsSEeqW49tm2zQNupk+UvP/kk/PnP8K1vJfa5+GIL5qtXW7DIh3R55l4mSFDq\n4gcfwF575XfOTAwZAn365FfPPJlhw+Cqq2D27Jbr58+3gUl33WWvJR277x6juRluv90CxPLlcN11\nVv/m0EODB2j17GmpkbGYeeZDhxYnmOd0q5qBTZssP75LF7j2WlvnZUJl8wOdjZaHH7Y69suWFZYC\nm6uOnTvtO9S9e2nOmYuWShFlLRnzzFV1mqqOUdW9VfWG+Lq7VPWugH3PV9VHclJQRDZutFzt22+3\nEZU33ACf/KQFBY+RI63lsffe+VUohJYdoMl4OeTJuetbttiQ/aFDWz+nGFRVwSOPFKfF36OHBe1b\nbkmsq6+3APzww7b8wx/Cyy+nPoZ3p/Dtb9vfGTNg1y7rgL7+eruGQYwebe/fcccVL5gXk40b7cfu\nkkvs/QQbsFVVVbxa8HfeaX8PPthy2stFU5MF8g6RHEroiOTb5nUaJLNxo7XsTjzRvmxvvGH1P5I5\n/HAbsJIv/pZ5spZUwby+3lrPpfyipLouuXLWWWal+APpY49ZAPZyv6dNS58H3qtXLbfemlh+7TUL\neHvskf6OaMwYK7lw6qnFC+bFui5gAbu62u4iPJtl/Xp7TdkE82y0NDZaZtO6dVaMrRRTFAbpaGrK\nXJeoFBTz/SmUKGuJZDBPhRfMIZHaFhS0zz0XPlPAONV0I0C9YJ7sKa9aZTP6RIEBA+D//s9+DE84\nAZ5+2gpjXXqpedqdO9t+Q4cmBmEls2VLy2v/2mtWlfJvf0t/93DttVba4PDD7T18/XUbG1CMFMVi\nsHGjpe717GlBF3IL5tnQ1GRW1MEHWz7+iSfCwoXFOXam81YimDuKQySDeSovyR/Mvdv8oGB+6qnw\n+c/nf35/B2iyllQt89WrSx/Mi+n39etndkfv3nDHHbB4sVlWBx5oZYMBpk4162Xp0tbPX706Rt++\nCf910SLbd+LE9OcdNMh+QETgsMOsCNm++7YsU5BrYC+2Z15dnbCiwIL5iBFWgjjTYKtstDQ1wec+\nB1OmWIf5iy/aj2kxCdJRqWAeZZ+6lBTdM48S/mBeUwODBxc2qjIV6UaAepMtJLfMV68mntceHc4/\n3yoavviidX7W1NgAnz/8wbxz7/b/lFNaX49t22x0q38g1JFH5nZ+EStE1q2bBcqPPrI+keuuK+hl\nFYS/Ze63WQYMsB8uL8AXQlMTfOpTcNFF8IlP2DVYsqTw42aisbH01TIdpSOSwTyTZw6w335WjbAU\nBNUz/+gjqyeyeLGtT26Zl8NmKYXf16MHTJhgtsmQIVY2YPjwhI11662wdatltyxYkBiC36GDzUfa\nv79dLzDrIFe6drWsji5drMN1w4bc66KUwjNPbpn37WtBPpPVkkmLaqIj0mPkyMTI5WLhPPNgoqwl\nksE8iO3bW5aRFbGMlVIQlGdeV2f1RD74wH5I2kLL3OOwwyx4+ztvvddSU2P51YsWmcf+6KO2fsuW\nRMv8y1+2Fn6qDJZM7Lab/SDMmGEtx9WrC3o5BZGqZZ5tMM9Ec7NdZ69fAiyYe42EUuI882gTyWAe\n5CVNn24tw+98p/TnD5oD1Gul7b67tdyi7pn7qa1tPcjHs09GjrRgPmOGXf+lS6112dhoOe8DBli6\n4XnnFaZhwABrmR9zjJ3re9/L/rnl8Mz79bNHpjlNM2kJCqgjRyYKxBWLVJ55JWyWKPvUpaTdeuYv\nvwynn25WQKkJapl7LbIxY8wa8LfMVW0ATr6DlCrN5MmWmuinSxfrCN1rL3tdV10FN91kLfL16+3H\nbLfd4KtftQ7nQhk4MBHMlyyBBx8s/Jge27ZZAbRUvPii3XVBomXevbu91oULbUTrwIGWeVJo1klQ\nMB861M5bDD8+HY2NrmUeZSIZzIO8pJdeyr2DLV86dbIAcPfdCS0bN1qQ/9SnLNBt2wb332+jUGMx\ny7HOxzPOhVL5fSLBA6yef96+/N7w71277O8HH4CIFSQ76qjCcvo9BgywTKFjjrHl+vrMJQU8Ml2X\n116zO4fkIfQ7d1o+/YknWn/Irl2JlnlVlf1o33abdVJOmmQ/5JnmM82kJSiYd+hgdzfvvZf+2Lng\nPPNgoqwlksHcz/Lllo/7xhvm7ZYDz/v9xS8S6zZutHTHiy+2L/lzz9koya9+1Sa/OOus4tdjCQuf\n+xz88Y/2+saOtYC2227FPYcXyA4+OPHjka5+TC7MmWMd2MkjWufPt0ydI4+0O766ukTLHMySeO01\n20cku2CeiVQBdZ99TE8pcZ55tIlkMPd7Se++a7e5I0eWJg0xCC8oNzQktHgtNrBgfvvt1po7+mhL\n5zv88NLrqpTfN2AAXHCBWTHHHmszBlVVFVfLN79p5QU6dLDBSwceGJzfHkSm6zJnjnXw3n9/y/Vr\n11pH769/bS3jhQtb1vvu0cOmj/PqzIwZk7n1nI9nDvYjuWBB6udddZXVjMkW55kHE2UteeYXhAev\nw6lcFosfzyffudM6OL1g3qVLQtP48VYzJdNgmbbAaadZ6/XRRxPXoFh897uJ/6+80gJrsXKv334b\nfvtby6tfuNC8b7Dh9IcdZu/h0KE2mAkSr837UfeqJXpD8AshVTCvqbFsrVTcfnvh0/25lnm0iWTL\n3O8lrVxpw8S9CnblZPNm0/Kd79jwd6/FZpNMWKvu+OOtA60cZUXD4PeNH2/ZJn37llbLvvtmP44g\n03VZvNhy6ffbr2U+99q1licPwXdWN9wA99yT6E/o0cMCYroRqvl45mB3nf5g3dDQ8sds48aE1mwI\n0vHBB7lNn1gswvC59YiylkgGcz9eNb9cPsjFRDWR9+y1zL0c4YEDzRY44ojKaKsE48ZZYClGKd50\nfOUrZutkSgXMRHOzDUQaONB+fP0DktatS3yuLr7Y9n3mmcT2z37WWvMenTvb+52clpoL6YK5V6UR\n4He/S6RnencDhVTkXLTIMq7K1e/kKD6RDOZ+L2nlyvKkI6bi2WdjHw9O8lrkXlZEsa2GTITB7/Py\nz99+O1bS8/TrZ63p+OxnaUl3XZYvt7EBVVUWzL25PT3rrF+/xL6dO8NJJ6U/l78AV65aIPUcnNXV\nLVvmr7yS6GydO9d+dNJNjJJOx65dZhUNG1b8jutctVSaKGuJZDD3U19vNVgqRXNz4rbaq1VSrOp5\nUaW+3kq3lppx41JXbYRE0bN0LFuWmHHKH8x/9jNLO8z1ji9TMM9EQ0OwJee3WVTh1Vdt5qpZs6x2\ny2GH2XPzwbuTyGZCakd4iWQwT/bMK9kyP/hgmyD6O9+BL33J1hXaEZUvYfH7Bg2Ciy6qLfl5xo5N\nH8wHDIAnnkh/XerqEj6xP5h7doq/ZZ4NmYJ5pvcoXcvcs1k2bLAAPHiw2TyxmPn9uQRzv46tW+3H\nwl8UrZyE5XML0dYSyWDup9It8y1b7MswdmyioFR7b5mXi3HjzGIIwiu3kC4DBKzz0yuX7HnmX/96\nIufce0+zpVQt8+7dbdRxc3PCyx83ztIqP/MZe+zalZ9fv3Vr6fs4HKUnksHc85K2b7dWSqVaFAAv\nvBBr9WW4+GL4/vfLryXKfl8+7L+/pRV6I089Vq1K1IKZPr21loaGhA300kuJkbmDB9uPwx/+YI85\nc2z0Zy74a7YEka9nLmKt802bEoW9/vEPs/YefjgxmCrb1rlfx7Ztif6eStDePrfZ0q4889WrLZDn\nO5dnMWhutpaN/8tw7rktR4c6SkO/fhbgkotQvfYaPPAAHHSQbVu71mqDe7z5pnni3qhP72527Fhr\n8Z59to3c3W+/3D9b/mqK+ZCqZQ4Jq2XdOnvtVVUt9eUSzP24lnnbIJLB3POSVq6srMUCMHZsbWi+\nDFH2+/LloINsBLAfb9j7fvtZdsaSJbX8/vc2LgBslOaaNdaqHzbMWrlg2Uf//ndhnbel8swhkdHi\nBfNk0gXzl1+2sRBBOir9+W2Pn9tsaFeeuTdJciXxPPMwBPP2SKpgPnasbaupsQqLO3faYCZIVDZ8\n663WudkDBxbWQCiVZw6JjJZ16xI/QH56904dzF97rXW5Ao9K2yyO4hDJYO55SWFomc+Y0dozrxRR\n9vvy5cADYebMluvmzrWKlt/+tqUdzpgRY9Ikmxz63XetVg5YXZVif35KlWcOCZvFq58etH39+uDn\nLl1qr33ZstY6Kv35bY+f22woumcuIpNFZIGILBSRywK2ny4is0Vkpoi8KSJlG++4YEFho94KYd48\n81a3bav8l6E947XMVS2D5S9/MZ/8oINsu5fVcs45lrly441mt0ycaMG82Hd2mTpAM5FtyzwomO+x\nR+riY96kIXvs0TrjJbnPxxFN0gZzEakC7gAmA+OAc0QkuTr1v1X1AFU9ELgAuLskSn3U1taybp3N\nDn/BBaU+WzBjx1oLasQI55kHUS4tQ4bYEPq6OhtAc+65NszeC05/+hNs2FDL0KF2Jzd3rtV0OfRQ\ns12K3TLff3+r856qPku66+LVZ08VWP2eeZDNUlOTenq5JUvsbqVbN7tWfh3btjnP3CPKWjK1zCcC\n76vqYlXdDjwInO7fQVU3+xZ7AEmJYqVh0SL78NbUlONswXTrZq2asATz9oiIdXTOm2ct7gMOgF/+\nMrG9Tx8LgkOGmMUwd67tM2aMbS92MP/EJ8zqmD079+euWZN+kFImmyVdMF+61CZOmTixdbVJ9/lt\nG2QK5kOBZb7luvi6FojIGSIyH/gH1jovCTffbFOTxWIxGhsrU3vZz267wTvvOM88iHJq8UaCbtli\nwTm5UFUsFmPIELNVhg2zz81pp9m2Yhdo69DBcr5TTVKR7rosWpR+asFMNkuqYF5fb3bTgAFWt33J\nEpdnnoooa8lUzzxNMU/fTqqPAY+JyFHA/wGBQy2mTJlCTbwpXV1dzYQJEz6+lfCEp1u+6ipobq5l\n+nR49dVY3PvL/vnFXl6/3nKKt22zTrbu3ct7/uTlWbNmVfT8/uVZ8QpY5TjfuHHwj3/EaGiA7t2D\n91+40JYnTbLlDz6IMWYMjBlTfD177AHPPx9j0KDW2z2Cnv/MMzByZOrjr1gBGzeaxfjeezGamlpu\n37QJPviglqYm+zx6z3/6aTjggBgvvGC24JIlsH79LLZsgVNOMZtw7doYsVhlP79hWPaopJ5YLMbU\nqVOpr69vpSstqpryAUwCnvYtXw5cluE5HwB9A9ZroQwfruod5v77VT//+YIPWRB33616/vmqHTuq\nfvRRZbW0Z2Ix1SOOUJ06VfVLX0q9H6hef33p9fz616oXXZT78665RvXHP069fdo01RNPVO3RQ3Xj\nxuB9zj9f9dvfbrnunHPss6qq+sc/qh54oOrkyarHH2/rrr9e9fLLc9frKA/x2Jk2VqtqRptlBjBK\nRGpEpDNwNvCEfwcR2UvE5lwRkYOAzqqaIkGqMPy3xGGwWfr1s6HjqrnX8HAUD89m2bzZapikI1MJ\n22Kwxx75zYK0eHFmm2X1arsTTJXx8pWvWAqmn3nzrFwwWFbPl79sy2+8YR58pW0WR3FIG8xVdQdw\nCfAMMA94SFXni8iFInJhfLczgbdFZCaW+XJ2quPdcENiwEY+eDVYYiHxzPv2hXffjYXCL4do+32F\nMGCAedUffhgczD0tqnDIIaXXM2KEaQnKaEl3XRYvtuemwitd0KdP6snB99nHUna9c6taqVyv5n63\nblbh8+STYxx5pNWuqXSfT3v93GYiVy0Z88xVdZqqjlHVvVX1hvi6u1T1rvj/N6vqvqp6oKoerqqv\npDrWFVfACy/kpK8FXqfPnDmVm3zWT9++1jLP1Bp0lBYRa53PmBGO92LMGGvp/vCHuT1vxYr007b1\n6WN3pOkyXvr1s7tEr5TvypXWUR80EGmvvSzDp9LB3FEcyj4CtJAiRJ6V8Z3v1NLYWPnJZ/v1g6am\nWnbfvbI6PLzOlDBQbi3jxlkBraCZcsqtpVs3mDbN5gdNntYunZYVK0j7WfKCuDcJSir8dd79E1Qn\n6xgyxIJ9pW2W9vy5TUeuWsoazD/96cJGx/k/xPX1lW+Z9+ljf7162I7KMXKkpSaGoWUOVhv9gAPs\nLnLWLPjJT2z0aSoaG80SSfeZ9qokJleJTObww23gElhRsdGjg/fzgrlrmbcNyhrMJ04srGW+fTv8\n9Yvey9cAABGfSURBVK8waFCMDz+sfDDv2hW6dIl9PO1YpYmy31co3uCfdJ55uenVyz7vZ50FTz1l\n4yQefzxYi9cqT+WFe1x1FRx/fPp9TjsNHnnEcsvfeQf23bf1PrF47v3KlVZCoJLfpfb8uU1Hrloy\n5ZkXlZ49U9eOyIbt281q6d0bPvig8sEcTENYgnl7xquxEpaWOSSKbm3aZPNr/upXcMklZgUlT3qR\nyWLxuOqqzPsceqh9Ji+91IL5KacE77f77nbenj2LP3jKUX7K2jLv0aPwlnnnzrD33rWsWVN5zxxg\n6NDa0NgsUfb7CiVdMK/UdenRw0ZrbtxopXVvuAG+971abr+99b7ZBvNsqKqySZ5feSV1y9zvma9d\nW9lg3p4/t+kItWdeaK3njz6ylrmXohiGlvkpp9jwbUdlSWezVIqePS1N0CsGJmL1Ud5+u/W+CxZY\ndkmxGD3a6sPs3Jn6R6JfP8vNX77ctczbAmUP5oW2zDt1gvr6GFDZIlseJ50UC8wWqARR9vsKxQtG\nQYO3KnVdeva0rBJ/uuHSpTHWrGk9icTMmVabvVj07293BkceGezDx2IxOnRIlMStri7euXOlPX9u\n01H0PPNiElTr+b//hW99K7vne8H8/PMt5cq1JhweHeKf5Expe+WkZ0+b9cgfzKuqLI3ynXda7lvs\nYA6W737UUen3qamxFnqHsicpO4pONmP+i/EA9O23VcePb1l34IADrGbGrl2ZaxQccojq669nU83A\n0R55+WXVnTsrrSLBH/5gn+0f/KDl+gsuUP3d7xLLa9eq9uqV3XcgF559VnXFivT7fO1rqmPHFve8\njuJCkWqzFJWglnldnbVWli0Lfo6fjz6yDlCHI4jDDw9XC9Pr00ke1bn//pZ/7lFXZ3ZHprTEXDnh\nhMwzKdXUuDvctkJFO0BVraf/qKPguecSM62kwrNZouxrlRKnJZhKeubQMpjHYjH2269lJ+jy5cXL\nZMkW75qMGZO+hEA5tYSBKGupaAdoY6MNvJk40aZ/yzQYwgvmDkcU8FJnk4PluHHmpXssX165uWw/\n/WmbftERfURTTVZY7BOJqKrSubMF8S5drEzokUfaQKIPPrCBFIsWpT5GTY1VeUtXJtThCAszZ9rE\n0nV1LYP1jh3WiGluNovxmmsshfC66yom1RFiRARVzWjCld1h9Frnt95qczd65Tz79jXLJR3OM3dE\niZ49LVgnzzPasaNt8z7vlWyZO9oOFQnmDQ1WHrSxMTHLeO/etrwrzXTQzjNPj9MSTKW0DB4M555r\nAT1ZS79+NjoUbPRnuYO5e3+CibKWsgfzPn1sdpMOHezhVR6sqrLRe8mDKfw4z9wRJXr0gPvuC97m\nD+aV6AB1tD3K7pkff7wVGzrvPEvHOvRQq/sMNsvKCy+kHtnZvbsV3Q9DTRaHoxBOOQUuughOPdXK\nU7z9dms7xuGAEHvmffrY6M1+/awj02uZgw0pTuebO8/c0VbwWubNzVZVceDASityRJ2yB/O+fRND\n8ffeu+WAhXTBXNWyAJxnnhqnJZgwavGC+YoV1iIv92CnMF6TMBBlLWWtZw4WzF991YL4Nde07Bzq\n0yd1MN+xw/Yt9ig5h6MS+IO5y2RxFIOye+Y33QS33QbHHgv3399ynylT4JhjrJBWMlu22Bdg69ay\nyHU4Sso998Czz9qgnb/+Ff7+90orcoSV0HrmfftaQfygehDV1ZbpEoTzyx1tiU9+Ep5+2krkhmVy\nE0e0qUgHKCRmGvczdGjqglv+tMQo+1qlxGkJJoxaBg2y0aF33mmFtyqlIww4LcGUJM9cRCaLyAIR\nWSgilwVsP1dEZovIHBF5WURSfjy9mWA+8YnW28aNg7lzg5/ncswdbY0TTrAp2yoRzB1tj4yeuYhU\nAe8CJwDLgTeBc1R1vm+fw4B5qrpJRCYD16jqpKTjqKqyejXcfntwHYrFi+GII2wQRTJLllh1xUIm\nhHY4wsTrr1vZ3qYm6Nat0mocYaWYnvlE4H1VXayq24EHgdP9O6jqq6q6Kb74OpCyqObAgakLCu2x\nh40ADcpocZ65o61xyCHW+ekCuaMYZBPMhwJ+J7suvi4VXwGeyktMBxg1Ct5/v/U255lnxmkJJqxa\nqqrgzDMrr6PSOC3BlCLPPOvcRRE5FrgAOCJo+5QpU6iJj9Wvrq5mwoQJ1NbWAgnhNTW1LF4MTU22\n7G1/6aUY27cDtNw/+fnlXvYIg55Zs2ZV/Hp4y7Nmzaro+cO67FFpPe79CV72qKSeWCzG1KlTqa+v\nb6UrHdl45pMwD3xyfPlyYJeq3pS03/7AI8BkVW3VtvY880x8//tWdOjSS1uunz4dfvpTCNEPp8Ph\ncJScYnrmM4BRIlIjIp2Bs4Enkk62BxbIvxgUyHN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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabc2c98c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Dollar position (not price):\n",
    "z_usd = get( w4cotr_usd )\n",
    "plot( z_usd )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the U.S. subprime mortgage crisis expanded worldwide, \n",
    "flight to safe USD makes a peak in our indicator exceeding 0.7, \n",
    "and thereafter, we see an orderly decline through 2013 due to \n",
    "Fed's QE quantitative easing. A bull market develops due to the \n",
    "termination of QE by the Fed, while QE is relentlessly pursued by BoJ, \n",
    "and finally the ECB activates its own QE. \n",
    "The sudden acceleration at the beginning of 2013 was a \n",
    "huge early warning sign of change in market sentiment.\n",
    "\n",
    "Next is the dollar index (against most currencies) used by the \n",
    "Federal Reserve Bank which considers the real trade balance RTB between countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p26owZ04dH38M++4brZ5+/frwyiuw0Ublez9xbicJQU8lP7/VrqeS/0+5tseN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\nCykjIXlX6VoKDeTffmudMWA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Wk5b+InKniDybeN0pIv3j0JINEbk8hnv2F5GTRaRL2v5f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\nNBsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabb7674c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fed US Dollar index, m4 means monthly frequency:\n",
    "usd = get( m4usdrtb )\n",
    "plot( usd['2006-06-01':] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonds position\n",
    "\n",
    "We use the futures and options COTR for contracts on both the \n",
    "eurodollar (strips) and 10-year Treasury bond, then average their position indicators. \n",
    "We can run this procedure by retrieval of a variable called *w4cotr_bonds* \n",
    "(where w4 tells us that it's weekly series). \n",
    "\n",
    "The indicator intends to show market position across the active yield curve, \n",
    "not in terms of rates, but rather prices of fixed-income instruments. \n",
    "This is useful to gauge the effects of Fed policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ofVJ55sOBOao6H0BEngfOABLN73IB8FzO1CUgrL6WN8FEeblN2uBVEAT7MP7p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\nNdbNsE+f4Ekc1q4139+bim3dOtPdLIM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SpyUYpyWYsGgJiw4Ih5a2bb1X1bRtG/XM/TZL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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabd0d08c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Bonds position:\n",
    "z_bonds = get( w4cotr_bonds )\n",
    "plot( z_bonds )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the beginnings of the subprime mortgage crisis to 2012, \n",
    "there is a commonplacent bull market conviction that the \n",
    "Fed wants lower rates across the yield curve: bond rates fall from 5% to 1.5%. \n",
    "Thereafter, the market seeks to front-run the Fed in the event \n",
    "ZIRP Zero Interest Rate Policy is reversed, but is denied on several occassions. \n",
    "\n",
    "Only after 2015 does our indicator spend time below the neutral halfway mark: \n",
    "Fed has ended QE, and the market seeks to determine the time of the first rate hike \n",
    "since the Great Recession. Bottoming rates in 2012 and 2015 technically \n",
    "imply a floor around 1.8% for the 10-y Treasuries. \n",
    "\n",
    "Given favorable economic conditions, esp. unemployment, and the \n",
    "expected hike in September of 2015: a bear market is developing for bond valuations. \n",
    "But surprise: FOMC postpones the rate hike until 2015-12-16, \n",
    "and our indicator gets less bearish. Compared globally, US bonds yields \n",
    "are very attractive, and the USD is very strong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LzY969gHni3PdWOxuLBKP1JW6JhMmWD7wSmqxQbTTb1tjjcxx0eXQ4ijkhWKx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\nBsG8C+/Mtxv6l1/C4MGWUiEd776buHz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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabba2c0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bondrate = get( d4bond10 )\n",
    "#  10-y Treasury rate INVERTED in lieu of price:\n",
    "plot( -bondrate['2006-06-01':] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equities position\n",
    "\n",
    "We use the futures and options COTR for contracts on both \n",
    "the *S&P 500 and and its e-mini version*, then average their position indicators. \n",
    "We can run this procedure by retrieval of a variable called *w4cotr_equities* \n",
    "(where w4 tells us that it's weekly series). \n",
    "\n",
    "It is worth noting that our position indicator is extremely bullish \n",
    "going into the Great Recession, however, even during the worst sell-offs \n",
    "in equities the indicator never goes into bear territory. \n",
    "We suspect this is because many asset managers are constrained to going net long. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pt64OWrSoYflyaNnSyqdPr2HQIJg8OUb//nDNNTUsXAi1tbVs3gzbt+dPTyku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\nuhu4DJgEzAYeVtU5IjJWRMYGqn4FmKS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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabb588ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Equities position:\n",
    "z_eq = get( w4cotr_equities )\n",
    "plot( z_eq  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TECHNIQUE: normalize market indicator\n",
    "\n",
    "This basically translates the [0, 1] indicator into statistical terms. \n",
    "A normalized series has 0 as its mean, and its units are stated \n",
    "as standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tOqPjRJmyMuLlHLfKJz17xuPJqrDbbtVs3py/8y9caDHrVKQLpwQx8aBj8+WX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\n5vUhY1Q16wcwHHgD2DbNPpovxo4dm7d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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabf224cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  So let's normalize the equities indicator:\n",
    "plot(normalize( z_eq ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2015-08-08: Given our data, the neutral mark is around 0.77 for equities position. \n",
    "Normalized data shows we are currently over 1 standard deviation into bear territory, \n",
    "even though the market seems to make advances upward every day.\n",
    "\n",
    "2016-01-18: Since the last Fed rate hike, equities indicator has gone under -2 std \n",
    "which is the most bearish reading thus far -- *including the Great Recession!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  S&P 500 prepend successfully goes back to 1957.\n"
     ]
    },
    {
     "data": {
      "image/png": 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PB9vhn6/v2rGfdP7Jdxz+OKCfd6FdgUpV/QZ4GjhRRCpFZEec62eyqtYCy0Sk\nj4gIcIp3DsMwSphVq4L1xDDLTp0Kq8Won0zCMscAk4BdReQLEfkNMALYyQvVHAOcCqCq03HunenA\nC8C5oSb7ucC9wCxgdmKETj7I5lEnn0RFB5iWVJiW5KTTouqyYIKbzSpxikJ/YpN86yg0paylXh++\nqg5KseuUFPWHAcOSlL8P7JWVuhJg3ToXU3zUUcVWYhiF45tvgk5agGbNghG0Pv/8J/zxj4XVZaTH\ncuk0ktGj4eSTYdo02GOPYqsxjPyzaBF06BBftvvuMH16cfQYlksnZ4wb5zqf1q1Lvv+009xyyhSY\nO7dgsgyjaJyS5Nm+T5/C6zCyp6wNfi58bf5kDksSh555bNjgljU1sOOO+dORK0xLckxLcpJpqawM\n1ufNc8vWrQuvo1iUspayNvgNYdw4OOuswJD7owTPOadu3XCr/6ab8q/NMKLAZ5/B2287P37nzi5p\n2i9/WWxVRiaYDz+BHj2cewbg1VddLm+A//s/1wnlo+pGEP7zn/Doo3D88UG5YZQjs2a5SU3uv9/1\nWSX68Y3ikakP3+aJDzFkSGDswX25wT2udu4cX3fRouAPwJ/FB9zUbm3a5FenYRSDoUPhiSecS2eL\nLYqtxmgIZe3Syca/tXo13HdffNkjj7jlihVw6aXx+/yW/F57QUVFUL7nnm755Zdwzz3Z68g3piU5\npiU5vpYXXnDGHuBHP4rPb19IHVGglLWUtcHPlA0bYLPNsjtmzRo3T+fEifFf/s8/d8uTT3Z9AYZR\n6ixfHj/OxJ5gSxfz4QOTJsGBB7r1UaPg1FODfa+9BvPnw7PPwsMPB+UzZwYz/Lz+ussQ6KMa5BHZ\nsKHugBTDKCU6dHAuzL33ho8+goEDLb991LA4/AxZuzYYAt6ihWuZh6muhk03hYUL4/N8r1nj6gP0\n6gVjxsCf/wzHeFn+/bwizZrBM8/k9S0YRl5ZtMgtBwxwy0K7c4zcUdYGPxP/1ty5gT9+9OjAqD/0\nENzgTdxYWQlffBEcc8klbvDJxo1uu3VrOPFEZ+RbtnRlflgnwDvv1K+jUJSy/zGfmJbkPPNMDHBz\n0/pjUooRnROle1LKWpr8f/WvfuWW550XxBInepUqKoIkUVOmpI65b9HCtfTPO8/FKvukGrRlGFFn\n8mTYYQfo2dM1hubMsXDMUqbJ+/DbtIGjj4YHHkjta3/lFejf362LxP8hhNefew5+/nMXk//oo0F5\nVZUZfaNpOSSYAAAgAElEQVT02LgRfvxj97r11mKrMdJhPvwMWbHCddKm61gNu2fatg3WX301vt6m\nm7plYt6dbbZpnEbDyBXz5sGDD2ZW98orXQt/UKp8uUbJUdYGP+zfevtt1/oOs3y587kffnj68yxf\nHqz7rh2oO/jEN/iJEQyLFsWICqXsf8wnTUXLE0+4/qcTT4RWrVxQQSqGDQOIRSIxWlP5fLLF4vCT\nMGMGHHCAc7mEU7hOmwbbbVd/2OTAgcnLwyNsIT6WP5xbZM2a7PQaRr7wY+gfecTNVuUHHqTiuOPy\nr8koHE3Ch+9H3nTt6iZkOPPM+PJMLikJ3rG333YpYcPlixcHrf6TT4bnn3c58idPhu+/b9x7MIxc\ncOutcO217rvqk/j9f/55N8Zkxx3d5D4771xYjUb2WC4dj7BB7twZ1q93A6l+//vGnXf//euWhVv8\nn3ziQjmbNXNuow0b0j8+G0YhuOgi91QbJvG7OXq0W65bF582xCh9ys6ls2yZe61cCd26xeL2+Qb/\n0Ufdq7IyfhLmdPjZMOvjgQfcctEi5+Jp0QIqKmJZt/BXrsyufqaUsv8xnzQFLevXu+Uuu8Q3hG69\nFd57L9j2x5IsXgzvvZcfLdnSFD6fhpBzH76IjBCRhd6E5Yn7/iAiG0WkfajsMhGZJSIzROSIUHkv\nEZnq7bstK5VZsP32LpKmQweX/sBHFTbf3LVmfL/l2rXBl7s+xo4NQjPTuYCS+TybN4fZszO7DrgY\n/tatXQI2w0jEN9zZsnq1Wz78sPsNPPOMC7n84x+hd++g3n/+45arVsVPdmKUAaqa9gUcDPQEpiaU\ndwLGA58B7b2y7kANUAF0AWYT9BNMBnp7688DRya5ljaGjz5SdeY4eF1/veqdd7r9f/iD6o03ql53\nnds3aFB25z/iCHdcfYBqp07x26C6YkX9x77+elB/ypTs9Bnlz+DB7ruxcWPmx2zY4I555BHVrbeO\n3/fb3wbft7VrVXv3jv/9ZHMdo3h4trNee15vC19V3wSSDRv6G3BJQtlAYIyqrlPVuZ7B7yMi2wJt\nVHWyV28UcEx9186G+fNdcqdEhg4NZqtavNhNRfjee86H7/sqM6W+iIYwybJvfvVV/cf17RusL1uW\n+fWM8mfsWBg50q1/+61r6dfW1n/ctGluecIJQV4cn3Cep1tucQEG4FIgQ91gBaO0aZAPX0QGAvNV\n9aOEXR2B+aHt+cB2ScoXeOU5YeFC6NTJrV90kXsUXbYMXnopFlfvvvtcjpynnoLdd8/+yzxsWPCD\nS8eECUEufUcsRc26+DNsgXMP5foHV8r+x3xSClp+85tg/csvXYfqttvWf74jjki97/334aWXoHt3\nuOwyV+ZnxEynpdBERQeUtpaso3REZDPgT0B4uFLOzNLgwYPp0qULAFVVVfTo0YNqL/ew/+YSt2fM\nqPaOjtG7N7Rs6bY//riGioqgfv/+MV5+GaCaTp1Sny/V9sqVMXbYwR2frn7//vHbm27qwjIfeyzG\nww9DTU3y40eNinmjd932woUx731lpi+T7ZqamkYdn8vtmpqaol4/qts+iftXrXLbPXtW8+abEDQk\nUp9vzRpYtqyaBQvgggtiHH103fpdulSzZg1UVcVo0QL22qua3XaD7t2j9X2JyrZPMfXEYjFGjhxJ\nbW1tHV1pycTvg/PHT/XW9wIW4nz3nwHrgLlAB2AoMDR03HigD7AN8EmofBBwd5LrZOW3euAB1b59\nVXv2DHyO6XjjjaDeu+9mdalGceGF7pp+30Eqv2jYd9q+fWbvyWg6gOrNN6uef378dyUdJ5zg/PLp\nmDfP9TltsYXqm2/mTq9ROMiVDz/JH8RUVe2gqjuq6o44V82+qroQeBo4UUQqRWRHoCswWVVrgWUi\n0kdEBDgFaPQUCqNGuclHPvwwsxGB228frPs+ykLw97+7KAg/pnnJEje8/cMPk9d/7z04+ODC6TOi\nz4YNzr33+98H0Tb1sXy5cy36fvlUtGjhxox8+y306NF4rUZ0ySQscwwwCdhVRL4Qkd8kVPkhSFFV\npwNjgenAC8C53r8PwLnAvcAsYLaqjm+s+O++C9YfeaRuCoPERx1/IvLf/raws1DFYu5R2de7bJlL\nYOXcS64z+Yor3Pphh7k/I38GrnxoiQqmJTmJWmbMcKG9/kxqrVrF1w8n9wM3C1t1tQtDzoR27YL1\n1q3TaykWUdEBpa2lXh++qqbNlaeqOyVsDwOGJan3Ps4dlBMuuihouey2mzPglfXEDDdr5loxiTlw\nCkFlpetcBjdkHYJInmOOwfPJBonXLr7YdbZZpsJosWyZ+xy7di3cNf0Z2XxDn9hYGT/epULwefJJ\n9+SbKZWV8JOfwIsvNk6nUQJk4vcp1IssHNYDB6oecIDzy0+blvFhReOnP607RsAfH3DttUHZ+vXB\nMXPnurKePYuj2ajLFVcUtg/o2WeD78ZOO7myyy932998o/rjH6uedFJQ/+WXVXfeOf57lsl4joMO\nsv6iUoZ8+fCjwKOPutDKQYOcr3uPPYqtqH78+W/DrFsHn3/u8o77hHOauIig1L5+o/D4KS+SpQzu\n2BH++9/cXWvJkviU3n4L//LLXdbXLbaA//3PhRr79O/vysIkG5+SyL771s2xY5QfJWnwZ8xwy/rc\nHVHxtcVisaTupvXrXRx0obVEhahq2bgR7rgjvo/I59tvg/XrroMzzgi2v/rKzfs6dGhutFx0UXy5\nP/lOy5ZuHAm4/PbJEvlly623uslRUmkpNlHRAaWtpeSyZa5eDVdd5X4MiROQRJnmCXd6iy3g66/j\nO8zS4XfYGfln1iw3L/HuuwcD4fzRqqNGBfX8J7N77w36Xp5/3r2GD2+8Dj8X1CefuIibjh3r1unS\nBebOdesNzbED7rtl2VybAJn4fQr1IgMn4pQpztd4xRUN83UVi5NPjvernnpqXZ9+srf/xBOufOXK\nwmtuqnTpEnwen37qfOWguuWWbnnxxXU/szvuiC9bt65xGtatU913X9V33klfb8MG1RYtVLt1U73o\nouD6xx2neu+9qvPnN06HURqQoQ+/pFr4GzbAPvu49T//ubhassV36fTrBxde6FLUhluLQ4bEp1Xw\n+cUv3PLxx13svpFf1q4NWszg3DMDBrj1b75x4yluvNG1tsMulxUr4s+zcmX8/MfZcvDB8MEHUFWV\nvt4mm7g5kz/91L18hg+3iUuMupSMD3/9etexBC5VQSZx9FHxtcViMY491q2/8oozIL4P1qd3b/j1\nr1Of49RTc6clKkRRi++68XnySZgzBw491G37ocC+797v6EycvyDxDyAbXn45xjvvuPX6DD4k9737\nHf6NJSqfUVR0QGlrKRmDP2CAS+r04x87n2apUd8PcMiQ1Puuu8614sJkknnTyJ4bbqj7pHXddTA1\nYTaI1q3hjTeCfpUVK4IGCTRuAht/vMauuzZszMhVV9XtMzIMoDR8+HvsEfgmb7+98f6uqOC/px//\nOH298eNdLn4fP7/5s8+q3n9/fjU2Jdatc/d1771VH388+HwqK1U//FB1xAjVyy4L6i9erNq2rVs/\n80zVu+5S/dGPVJs3V/3gg4brmDxZtVevzOuH+w4KmSPKiA6Uiw9/6VL4+ONg+7DDiqclX5x9dvr9\nfrZNn88+c0s/RjtX7p6mzKxZrkUNrpV/5JEup9F++zm/fqdOdfPMtG3rXI2jR8M997jpLd97z6U1\nWJJsBokM6d07+wi0TTZx4aS5cuUY5UnkXTr+hA3jx7sfU6LvOx1R8bXVp8MfOp+KTTeFt992hgfg\nzDPzp6WQREnLOefEfljfdFO3DCfYS2aAN9nEuW5OPtlt+3lo9trLfV7peOutdHtjWXW4Ll0Kd97p\n1utLL5ItUfmMoqIDSltL5Az+nDkuigXg+uvh/POhZ0+X66OQGS4LSX3x9Ztu6kblPvaY68RzOfMD\n5szJn7amwMaNrjPdx59MxydTf7g/EnavvZJ3pPqsXu3+5FetqrvvxhvdMv0fQjxt2wbXTkysZhhx\nZOL3KdQL+MEX+fLLgV9yyy3z4vYqOpWV7v0tWZK+3urVyWP2/ddTTxVGb7ni58e55hrVrbaKn68A\nVNu1S33seecFn8Ps2a5sxAjV005LfczixcExq1bF7zv66IblTpozx823YDRNKPVcOv37B+vffFM8\nHfmkttZFd9QXeue7GFLhT0dnNAxv4i2GDHEuxMQnrnT3338ahSALamWleyJLRbg/JvzdHjkSnn7a\nPdVmy447uhw7hpGOyBn8ww+HDh2C7Y8/znzCh0Si4mtLpaNdu4Y/gvfoET+BdeLk1NlqKQZR0DJ6\nNDz7LFx8cayOK8cnWeI7n513dvmQNm4MxoZMmhSf0CyRsCunc2d47jn3J+PPWTt/fiyr95BPovAZ\nQXR0QGlriZzBnzAhPg66e/f6W7hNgdtvj99esyb+jzG8bmTOX/8KW28dDKxK5K23nEFOx777xj8V\n1PcnPnhw/PaYMfHbNuuUkS/EuX+igYgoKIMHu8dbcJ5Owxn48B/f66/DIYfEGxq7V9nxxReuhf3R\nR66jNVdMnQonnVR3sJZPuk76K6+Ev/wld1qMpoGIoKr1pleMXAsfghGLYf9oUyfRrXDIIcXRUU74\nU17m0tiDm7vYD6HNlsaE3BpGfUTO4Ku6R95LLw1C1BpKVHxtudbx+98H6/68uFB3btNCaGkMxdAS\nnve4TRsX+ptrLfV12iYSdgG1bGmfUTKiogNKW0smk5iPEJGFIjI1VHaTiHwiIlNE5AkRaRvad5mI\nzBKRGSJyRKi8l4hM9fbdlu6abdq4bH8VFVm9l7LHz51/yy1BWXhu1cbkb2kKfPedc4vNnu06a5cv\nj//zzBWVlUELf/Xq+Nb+3XfH123Xzs1t3Lev227ZMvd6DOMH6ovbBA4GegJTQ2WHA5t468OB4d56\nd6AGqAC6ALMJ+gkmA7299eeBI5NcqxAhqyXLrrtq0pz5Iq78v/8tvKZ88/33ql27unkBGsusWe4+\ndeigeuWVbo6CfLBwoYvnV1XdZhvVY49V3W8/F99/3HFBDP7f/ubGYrRrF5Rt2JAfTUZ5Q67i8FX1\nTWBJQtkEVd3obb4LbO+tDwTGqOo6VZ3rGfw+IrIt0EZVveSyjAKOyfRPyXDEYnXnK4Wgs7YcRyL3\n6uXy3Bx/fOPP5ee3OeAAFwufa9+9j+/DX77chc7OmOHSgnz2GUyZ4upMmeLy6VdUOF0HH+zKM0n7\nbRgNJRdfryG4FjtAR2B+aN98YLsk5Qu88rwSFV9brnRsuy3stFM0tOSCTLT4ifM2bGh8Wmzf4E+Z\n4qYhDEc95dqH/913sPnmbnv6dLesqXF/XhBMLO5PW/jYYy5hW661NJaoaImKDihtLY3KlikilwNr\nVTXNMJPsGDx4MF26dAGgqqqKHj16UF1dDQRvLtPtGm8IZUOPz9W2T/7OX+1fgVgsff2ampqi349M\nP5/x4+Pf39ixMfr2bfj1Jk6MseWWMGeO2/788/rvV0O2Dzyw2tMdr3/y5Bj9+kGPHkF957Ovpk0b\nuPRSp8en2J9P1L4vUdn2KaaeWCzGyJEjqa2traMrLZn4fXD++KkJZYOBt4BNQ2VDgaGh7fFAH2Ab\n4JNQ+SDg7iTXyaOXq3zZaafAB1xOHHlkfM6gl19u+LliMdWtt1b95S+D811zTe60htm4MXnOo3vu\nce/pueeCukcf7faF8/cYRraQz1w6InIkcDEwUFVDmUF4GjhRRCpFZEegKzBZVWuBZSLSR0QEOAUY\n15BrG3XxfdHHHVdcHbnm3Xfd8oUX3DIcUpkNy5a5HPWLFsExoZ6j+rKUNpRU51292rmV/GgrcCkZ\n8qnFMMJkEpY5BpgEdBORL0RkCHA70BqYICIfisidAKo6HRgLTAdeAM71/n0AzgXuBWYBs1V1fM7f\nTQJZPerkkULpaNOm/jpRuSeQXsvOOzvjWFPjfNvHHAPDhrkJR7LlNi8I+Kc/dfMGjxoF8+fDn/6U\nmZZcccEFrhM3bPCTDdAqlc+okERFB5S2lnp9+Ko6KEnxiDT1hwHDkpS/D+QpLqJp4/+l3ncfjEj5\nyZQOU6cGOf67dXPLFi1cXpsFC7Kf1cnvKO3e3bWkTzkld1qzZd68+g2+YeSLyOXSiZKeUmHgQJdW\nF5yLoNTdA3//uxsQ9fXXsOWWrqxfP3jtNRfa6PXpZ8T69S708Z//dGkLCjWYL91nsGZNMDPVkCHu\nj9q+9kZjKOlcOkZ2/O1vQRx3JukVosK6dc6wf/FFfPnbb8PvfhcYewhSCmeTKlsVHn3UrZ9zTnFG\nbldW1p2vwDf2AHfdBQsXFlaT0XQpa4MfFV9bvnXsvHPQGVlfDpeo3BOAysoYf/873HxzUOYb6dNP\nj6/rt5izMfhvvumyVkL9A5rydV823xzGjUs9b3GLFi49cyG0NISoaImKDihtLWVt8JsSvkErFZ9w\n+I8pbIwffNAt99knvr7/5PLUU5lfIzxrWrHw39vy5cXVYRhQ5gbfH7BQbAqhI1ODH5V74hLAVQPx\nCeAWLEie0Mw3+PPn193n849/xOeg9/9UPvusfj35ui/+k0kmEVT51tIQoqIlKjqgtLU0aqStER18\nw5JNWt5i8ckncNllbv288+JDLZctSz7Hr2/ww/5v1eB9T5vmwh798rDrJ5tO3lzTs6dbPv00tG9f\nPB2GAWXewo+Kr60QOjJt4Rfrnqg6w756dXhimxitWsWndZ4yBfbYo+7xvsH3JwA/7rj4qQCHD3fL\nn/3MLT/8MLhuJuT6vuy3n4u+GecNL2zXDrbLMHtUVL63EB0tUdEBpa3FWvhlgm/wo9rC79YtiIcH\n57bZf3/XMvdH0J56qktq9u9/1z3+1FPhkksCg//CC/GTgY8e7Zb+fXjqqeLG20+eXLfM5mY2io3F\n4ZcJK1Y4P/G0aclbyMVg0SJo1swZutat4/c9+KAb9TpsGFx+OXTqFIRnpvoKPPkkHHusW2/Rwv1R\nqMK33wYhnIcdBmPHwhZbuJG1zz+f/FzFYM89XfZP+4obuSbTOHxr4ZcJrVu73PHZhC3mmw4d3PKI\nI+ru69XLLSdNcsvEWPxkhFvI/lPB2rWBsX/mGRgwwLlSILuO0kIwbpzF3BvFxXz4BaBQOlq3di39\nKGgJ8/nnbjlggBvxWlsLu+3mtPhzyoJ7GkgXvpgseVptbbDu55a/8kq3vOmmzDUW4r7ssgsceGA0\ntGRKVLRERQeUthZr4ZcRbdpEJ97bzwIJrgNzyBC4+OK69cKzTm3YUNf1EybZ08tbbwXr4SiYXr2g\nc+fM9RpGU8B8+GXEoEFw9NFuWSwWLIAbb3QG97TTnBumTx/X4XrUUcmPCeedSffxP/54+hTQGzc6\nH//06W706ptvNuw9GEapYbl0miCtWxe/hb/99m4A1Esvubj477+H1193naip8PPdn3VW+nMfc0zQ\nLwD+4K0AEefOmTkTvvqqYfoNo5wpa4MfFV9bIfPh12fw863lgAPccvRo56f3STaYytfi+7UHDEh/\n7mbN4n32fqfsGWcE/n0/dXKyyd7TEZXvCpiWZERFB5S2lrI2+E0NP1SxmITdM+HWeNu2qY9p0wZe\nfNGFVGaCP9rWj9rp0SMoS9cHYBhNHfPhlxFXXukMnx+lUmj+9z8XieLzxhtwyCFu/fvv3R9SLvj6\nazfA7K234Fe/gv/8x3UK+1RWurJiDrwyjEJicfhNkGbNGjYFYK6YOdO1up991mWq3GYb2H13lzsn\nV8YeYKut3NJv4SeOYC2VjKGGUWjK2qUTFV9boXQ0b17/BCj50KIKffu6SJwBA5yhB+dPf/PN1IOq\nGqtls83cMhcpC6LyXQHTkoyo6IDS1pLJJOYjRGShiEwNlbUXkQkiMlNEXhKRqtC+y0RklojMEJEj\nQuW9RGSqt++2rFQaGVGsFv68ec59M22ai8bZemvXSVtZ6ba33z4/1/XTKidOIGIYRnLq9eGLyMHA\nCmCUqu7lld0IfKOqN4rIpUA7VR0qIt2Bh4D9gO2Al4GuqqoiMhk4T1Uni8jzwD9UdXzCtcyH3whu\nuskN3Q/PIJVvNm6EO+5w6Y5XroQ//jG7Ea6NZdo0Nzl5fTNaGUY5k7M4fFV9E1iSUHw0cL+3fj/g\nTbDHQGCMqq5T1bnAbKCPiGwLtFFVP4fgqNAxRo5o3jzIJjlpUmHy6hx1FJx/fpDi+NRT83/NMHvu\nacbeMDKloT+VDqrqp4FaCPgBeB2B8JxE83Et/cTyBV55XomKr61QOpo1c63t//7XxbbfdVf+tbz4\nolv6RjfdAKt8a2kMpiU5UdESFR1Q2loaHaXjuWty5ocZPHgwXbwpiqqqqujRo8cP03j5by7T7Zqa\nmqzq52vbJ9/XmzPHbc+a5bY//TRGLBZfv6amJmfXe+01//1V06IFrF4dY8oU6Ngxs+Oj8vlEbdsn\nCnpy+X0pl22fYuqJxWKMHDmS2traOrrSkVEcvoh0AZ4J+fBnANWqWuu5a15T1d1EZCiAqg736o0H\nrgbmeXV298oHAX1V9eyE65gPvxH8619w9tku9v2NN1xOm2QJy3LFF1+4ycaXLHHupJUr46cgNAyj\nMOQ7l87TwGne+mnAuFD5iSJSKSI7Al2ByapaCywTkT4iIsApoWOMHNHce1574w23lHo//sbRubMz\n9vvs4yZdMWNvGNEmk7DMMcAkoJuIfCEivwGGA4eLyEygn7eNqk4HxgLTgReAc0NN9nOBe4FZwOzE\nCJ18kM2jTj4plI5mzeK3N0ny6eZay667uhGv4TTFmRKVzwdMSyqioiUqOqC0tdTrw1fVVMl2+6eo\nPwwYlqT8fWCvukcYuaJ5wqfZqlV+r9e5s8uKme/rGIaRGyyXThnx8MPxufAffzyYAzYfiMDixdCu\nXf6uYRhG/Vg+/CZIos++vjQL2RKO658zxy033zy31zAMI3+UtcGPiq+tUDoSH46SpVloqBYRl7vG\nj7t/9lk3OXliv0E2ROXzAdOSiqhoiYoOKG0tZW3wmxqJBj9ZC/+NN+C77xp+jW+/dctp0/KXI8cw\njPxgPvwyYvRoOPnk+LLw7Zw7F3bcMfP4/MWLXb75CROCiJ+bb3YzWT31FPzoR3DmmTmTbxhGA7F8\n+E2QjRvT7/fz3XTrltn5pk6FV16BpUvdTFLnnOOSo4H7I/DTExuGURqUtUsnKr62QulIZ/Cff97N\nFAWxjAdkDR3qlh9+CO3buwlOfBYubLzBj8rnA6YlFVHREhUdUNpaytrgNzWSGfJZs1zn6s9+BsO8\n0RGXX57Z+d55xy0POww+/zzefZMLg28YRmExH34Z8eSTdePuJ0+G3r3devv2zi9/6aUwfHj956uq\niu/gVY3/U3n7bdh//8brNgyjcVgcfhNkwAB49VW3fvjhbnnoocF+P1d+pvPLDhyYfv92eU9wbRhG\nLilrgx8VX1uhdDRvHhh4/0HJ76gFWLUKIMbSpZmdb0lo2ptf/MItZ8yA22936/7ctQ0lKp8PmJZU\nREVLVHRAaWspa4Nv1GWrreCrrzKru3ixC+M84wyXtgFchM+MGW69oiI/Gg3DyA/mwy9DROCgg2Di\nxLr7WrSANWvc8ic/cXH1/mTgPhs2QCwG/fu7Cco7d667/9NP3VyyhmEUH/PhN3FWrIjf9jNpjh7t\nlmvWwNNPw7XX1j329dedsYe6xh5cOgUz9oZRepS1wY+Kr60YOpYvj99+7z1n4Fu2jNGmTVCeLN/O\nz3+eX20+Ufl8wLSkIipaoqIDSltLWRv8pkyiId9kExfF07x5/J9BMj98OCumYRjlg/nwy5AZM5yP\n/vHHg5w5X33lomo2bozPcHn66XDvvcH266+DN2cye+zhkqQZhhFtzIffhNltNxddE85V74dQJk57\nOGJEsL5+PRxzTLCdzL9vGEbp0mCDLyIXicg0EZkqIg+JSAsRaS8iE0Rkpoi8JCJVofqXicgsEZkh\nIkfkRn56ouJrK5aOZLnqE7Wowtq1bn3ECH6I0T/gAOjTJ7/6ovL5gGlJRVS0REUHlLaWBhl8EdkO\n+B3QS1X3ApoBJwJDgQmquivwireNiHQHTgC6A0cCd4qIPV3kmWSTmAN06uSWfhqGWbPc8qyzgjpv\nvgkdO+ZPm2EYhadBPnzP4L8N7AMsB54E/gHcDvRV1YUisg0QU9XdROQyYKOq3uAdPx64RlXfSTiv\n+fBzyL/+BWef7dbDt7VbN5f5UhV69XL1fvSj+Dw59jEYRumQVx++qi4AbgE+B74ElqrqBKCDqi70\nqi0EOnjrHYH5oVPMBywTS57xo20SjXc4jXLLlnWjcvxYfcMwyouGunTaAUcDXXDGvLWIxM215DXV\n07UT896GjIqvrVg6/GRpYWKxGOPGuZG0EG/w/cFW7doVRF5kPh8wLamIipao6IDS1tLQGa/6A5+p\n6rcAIvIE8GOgVkS2UdVaEdkWWOTVXwB0Ch2/vVdWh8GDB9OlSxcAqqqq6NGjB9VenKD/5jLdrqmp\nyap+vrZ9Cn392bP96wf7a2pquPDCYHvlSli9uhpVePllV3/jxsLoi8rnE7VtnyjoqampKfr9iNq2\nTzH1xGIxRo4cSW1tbR1d6WioD783MALYD/geGAlMBnYAvlXVG0RkKFClqkO9TtuHgN44V87LwC6J\nDnvz4eeWv/4VrrgivT9+0CA3svb444O0yVOmwN57F0ajYRiNJ69z2qrqZBF5DPgAWO8t7wHaAGNF\n5HRgLvArr/50ERkLTPfqn2uWPf9kcof9SVF8t86IEWbsDaNcaXBopKpeo6q7q+peqnqaqq5T1cWq\n2l9Vd1XVI1R1aaj+MFXdRVV3U9UXcyM/Pdk86uSTqOiAulo2boTzzw/8/Rs2FE9LMTEtyYmKlqjo\ngNLWYrHwTRzfjeO38I86qnhaDMPIL5ZLp4y59lq46qr0rp3rroMrr3SzWP3udxZ/bxiliOXSMTj4\n4JoQICsAAAo0SURBVPrnnfVTJY8bF+TMNwyjPClrgx8VX1uxdFRXw/z58WWJWnbZxS0326zwydKi\n8vmAaUlFVLRERQeUtpayNvhG/fzsZ87oL1zojL5hGOWL+fAN+vd3CdSuvNJNWG4YRmlhPnwjY9q0\ncS38li2LrcQwjHxS1gY/Kr62qOiA5Fq++cZNal5ogx/1+1IsTEtdoqIDSltLWRt8IzMmTnTLTTct\nrg7DMPKL+fCNH/Lgv/IK9OtXXC2GYWSP+fCNjPEnSbEWvmGUN2Vt8KPia4uKDkiu5fjj3bLQBj/q\n96VYmJa6REUHlLaWsjb4RmbsvrtbmjfNMMob8+EbADzxhMuLX1lZbCWGYWRLpj58M/iGYRgljnXa\nEh1fW1R0gGlJhWlJTlS0REUHlLaWsjb4hmEYRoC5dAzDMEocc+kYhmEYcTTK4ItIlYg8JiKfiMh0\nEekjIu1FZIKIzBSRl0SkKlT/MhGZJSIzROSIxstPT1R8bVHRAaYlFaYlOVHREhUdUNpaGtvCvw14\nXlV3B/YGZgBDgQmquivwireNiHQHTgC6A0cCd4pIXp8wampq8nn6jImKDjAtqTAtyYmKlqjogNLW\n0mCDKyJtgYNVdQSAqq5X1e+Ao4H7vWr3A8d46wOBMaq6TlXnArOB3g29fiYsXbo0n6fPmKjoANOS\nCtOSnKhoiYoOKG0tjWlh7wh8LSL3icgHIvJvEWkFdFDVhV6dhUAHb70jEJ5wbz5Qz4yrhmEYRq5o\njMFvDuwL3Kmq+wIr8dw3Pl7ITbqwm7yG5MydOzefp8+YqOgA05IK05KcqGiJig4obS0NDssUkW2A\nt1V1R2/7IOAyYCfgUFWtFZFtgddUdTcRGQqgqsO9+uOBq1X13dA5LSbTMAyjAeQ9tYKIvAGcoaoz\nReQawJ8G+1tVvcEz8lWqOtTrtH0I57ffDngZ2MUC7w3DMApD80Ye/ztgtIhUAv8DfgM0A8aKyOnA\nXOBXAKo6XUTGAtOB9cC5ZuwNwzAKR6RG2hqGYRj5w0baGoZhNBHKwuCLyN+9TuNi69hCRK4WkTNE\nZBMRuVxEnhORm0SkXRH09BORO0TkaRF5UkSGi8guhdbhaTlSRO4WkWe8190icmQxtKRCRK4qwjWP\nFJHTRaRLQvmQAuvYREROEJHjvfX+InK7iJyb7wGSUUVEtkzYPsW7J2eKSL0dpFGkLFw6IvI1MA/Y\nGngYN8DrwyLoeAH4CNgc2B2YCjwKHA7sraoDC6hlOLANbrTzMcBnwEzgHOB6VR1bQC23AV2BUcAC\nr3h74BRgtqqeXygt6RCRL1S1UwGvdz1wIPABMAC4TVX/4e37UFV7FlDLXcBWQCWwDNgUeAr4OVCr\nqhcUSksSba+qar8iXPeHz0BErgAOxgWeDAC+UNWLCqjlWOB1Vf1WRLYGbsaFxX8M/EFV56c9gX+e\nMjH4H6pqTxHZFTgRl8KhOe7DGaOqMwukY4qq7uP9+y9Q1Y6J+wqhw7veNFXd01tvDryhqgd4TxoT\nVXWPAmqZpapdk5QLMEtVC/bUISLL0+xuqaqNDWTIRss0oKeqrvNyTo0BPgUuAj4osMGfpqp7ikgF\nbsDktqq6xvvufKiqexVIx1Tc+JxwC3pXXGNFVXXvQujwtIQN/oe4zAIrvHv0of/7KpCWT7wUNnjB\nL28DjwGHAb9W1cMzOU9ZPaqp6kxV/YtnzH4FtAReKKAEEZH2QCeglYj4YxS2BCoKqANgg4hs4a1v\nh/dZq+qSAusA+F5EkqXR6A2sLrCWJUBXVW2T+AK+KrCWZqq6DkBVl+JajpvjngoLPdnkek/HOuA9\nVV3jba8HNhZQx2e4J+Nf4Z4uBgCLvPWjC6gDoKWI7CsivYAKVV0BP9yjDQXWErbVO6vq31X1C1Ud\nifNsZETBWjOFRlWnAFNIGP2bZ64HPsG1Tk4H/u25+roDfy6gDoBhwAciMgvohnPl4D0OTimwlsHA\nXSLShiC9xvY418HgAmt5AOgM1CbZN6bAWuaISF9VfR1+MK5DROQ64NgCa6kVkdaqukJVf+IXeoMn\n1xRKhKoe7bkv7gFuVtWnRGS9qs4rlIYQtcAt3vrXItJRVb/0GnDrCqzldRH5C87GxETkWFV9QkQO\nBTJOqFMuLp02qpruUb1geI/A4j2mVwA9cO6dL4ugZQvcyOdZXguyqHjGw8+ftEBVC92ijhQi0hJA\nVes85YjI9pn6ZfOJlx+rlaouKvB1WwPX4r6/P1LVyOTdEpFmQAtVXVXAa1YCl+PGOoFrMK0CngEu\nVdXPMzpPORh8cFEGBKN4Fdc5OLnQg7s8v3Rv3AdSNB0hLX1wiesoppZUiMhuqjqj2DrAtKSimFpE\npAewv6reXYzrp6LI96QK5535NtvfclkYfHGTqdyJS7kcdhl0xY3ofbEp6YialnQUOjImHaYlOVHR\nErE/wUjcE8juvpSLD/8fQH91efZ/wOs0fQHYrYnpiJQWEbk9ze6qNPtyjmlJTpS0pOElXP9LQSiR\newJZ3JdyMfjNCOK7wyygsO8xKjqipmUw8Edc51/4kVKAk0yLafnhYumNbKEHLw4mAvcEcndfysXg\njwDeE5ExBO6LTriY/BFNUEfUtPwXmKaqbyXuEJdl1bSYFp/BRMTIEp17Ajm6L2Xhw4cf5swdSHwH\n5dOqOr0p6oiSFm9swveFjGowLaWpRUReA65IYWTnqmqXAmqJxD3xtOTkvpSNwTcMo/SJkpGNErm6\nL2Ux0lZEqsQlBpshIktEZLG3PtwLYWpSOkyLaSlVLaq6OCrGPir3BHJ3X8rC4ANjcUPmq4H2qtoe\n8EegFSxJWIR0mBbTUpJaomRkicg9gdzdl7Jw6YjITFXdNdt95arDtJiWUtUiIi/hMrzeDyxUVRU3\nQvs0oJ+qHlEIHZ6WSNwT73o5uS/l0sKfJyKXiEgHv0BEthGRS4GMhhyXmQ7TYlpKVUsXVb1BVWv9\nUaSq+pWqDge6FFAHROeeQI7uS7kY/BOALXEJhpaIyBIgBmyBN6duE9NhWkxLqWqJkpGNyj2BHN2X\nsnDpAIjI7rg8Ou+GE6mJyJGqOr6p6TAtpqUUtYiLRhmKS4XsG7eFwNPAcFVdXAgdIT1Fvyfe9XJz\nX1S15F/A+biJI8bhZr46JrTvw6amw7SYlhLXsjvQH2iTUH5kU70nubovBRWcxxsxDWjtrXcB3gcu\nLPQHExUdpsW0lKqWKBnZqNyTXN6XckmtIBrMRjNXRPoCj4vIDhA3VVpT0WFaTEupajkT6KVuKsEu\nnoYuqnprATX4ROWeQI7uS7l02i4SlzcbAO9D+jmuc6Vgc2BGSIdpMS2lqiXOyAJ9gZ+KyN8pvJGN\nyj2BXN2XQj6W5PFxpxOwTZJyAQ5qajpMi2kpVS3Aa0CPhLIKYBSwsSnek1zel7KJ0jEMo/QRkU7A\nOlWtTSgX4EBVnVgcZcUlV/fFDL5hGEYToVx8+IZhGEY9mME3DMNoIpjBNwzDaCKYwTcMw2gi/D+e\nQeYIn4FkLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabc3fc6c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  SPX price data showing the post-2009 equities bull market:\n",
    "spx = get( d4spx )\n",
    "plot( spx['2006-06-12':] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation among position indicators\n",
    "\n",
    "Note that this is quite different than the usual look at price correlations -- \n",
    "it would be more akin to seeing how informed trading correlates across asset classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# class consists of precious metals, US dollar, bonds, equities:\n",
    "z_class = [ z_metals, z_usd, z_bonds, z_eq ]\n",
    "\n",
    "z_data = paste( z_class )\n",
    "z_data.columns = ['z_metals', 'z_usd', 'z_bonds', 'z_eq']\n",
    "#  Created \"group\" dataframe z_data with above named columns.\n",
    "#  Please see fecon235 module for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         z_metals       z_usd     z_bonds        z_eq\n",
      "count  500.000000  500.000000  500.000000  500.000000\n",
      "mean     0.793928    0.426817    0.625487    0.760595\n",
      "std      0.138830    0.126658    0.121728    0.079848\n",
      "min      0.458814    0.115009    0.318970    0.572008\n",
      "25%      0.690709    0.334414    0.553198    0.706483\n",
      "50%      0.828849    0.397585    0.634678    0.737262\n",
      "75%      0.914398    0.534973    0.735099    0.807362\n",
      "max      0.986663    0.766730    0.828573    0.932568\n",
      "\n",
      " ::  Index on min:\n",
      "z_metals   2015-07-21\n",
      "z_usd      2006-11-28\n",
      "z_bonds    2015-02-10\n",
      "z_eq       2016-01-12\n",
      "dtype: datetime64[ns]\n",
      "\n",
      " ::  Index on max:\n",
      "z_metals   2009-09-15\n",
      "z_usd      2008-11-04\n",
      "z_bonds    2010-02-02\n",
      "z_eq       2007-05-29\n",
      "dtype: datetime64[ns]\n",
      "\n",
      " ::  Head:\n",
      "            z_metals     z_usd   z_bonds      z_eq\n",
      "Date                                              \n",
      "2006-06-13  0.780357  0.281513  0.647033  0.822280\n",
      "2006-06-20  0.753881  0.278856  0.666125  0.831975\n",
      "2006-06-27  0.744485  0.286700  0.658137  0.828952\n",
      "2006-07-03  0.814733  0.287490  0.661316  0.835962\n",
      "2006-07-11  0.844632  0.238682  0.670296  0.842244\n",
      "2006-07-18  0.823423  0.208211  0.680698  0.830463\n",
      "2006-07-25  0.830726  0.212226  0.662294  0.845535\n",
      "\n",
      " ::  Tail:\n",
      "            z_metals     z_usd   z_bonds      z_eq\n",
      "Date                                              \n",
      "2015-12-01  0.505108  0.634222  0.415267  0.605873\n",
      "2015-12-08  0.503900  0.640700  0.427966  0.600320\n",
      "2015-12-15  0.476669  0.634973  0.458157  0.602185\n",
      "2015-12-22  0.505865  0.634860  0.485277  0.595232\n",
      "2015-12-29  0.483986  0.629510  0.491514  0.605502\n",
      "2016-01-05  0.500215  0.623186  0.496034  0.577636\n",
      "2016-01-12  0.522803  0.610352  0.501229  0.572008\n",
      "\n",
      " ::  Correlation matrix:\n",
      "          z_metals     z_usd   z_bonds      z_eq\n",
      "z_metals  1.000000 -0.557377  0.603395  0.267038\n",
      "z_usd    -0.557377  1.000000 -0.397219 -0.349376\n",
      "z_bonds   0.603395 -0.397219  1.000000  0.278966\n",
      "z_eq      0.267038 -0.349376  0.278966  1.000000\n"
     ]
    }
   ],
   "source": [
    "stats( z_data )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***No strong correlations among position indicators.*** \n",
    "\n",
    "As expected z_metals and z_usd are negatively correlated, \n",
    "but precious metals sentiment is most correlated to bonds. \n",
    "We have closely examined the relationship between gold and interest rates \n",
    "in another notebook, and indeed, *real rates* are very significant. \n",
    "Our bond indicator, however, only relates inversely to nominal rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Normalized position relative to history\n",
    "\n",
    "We saw that our position indicator may not entirely span [0,1] as expected \n",
    "due to institutional reasons (see the equities case above). \n",
    "Thus a *position indicator reading of 0.50 does not strictly \n",
    "imply neutrality with respect to positions*.\n",
    "\n",
    "As a remedy, we can normalize an indicator, *relative to its history*, \n",
    "and then look **comparatively across asset classes**, especially recent values.\n",
    "\n",
    "Be aware though that as history changes, a normalized indicator \n",
    "for a given date will generally *not* stay fixed (unlike the [0,1] indicator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Normalize indicators:\n",
    "z_nor = groupfun( normalize, z_data )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            z_metals     z_usd   z_bonds      z_eq\n",
       "Date                                              \n",
       "2015-07-28 -2.397536  1.738209 -2.035013 -0.980016\n",
       "2015-08-04 -2.360331  1.830168 -1.749320 -1.073864\n",
       "2015-08-11 -2.108856  1.699333 -1.741836 -1.084636\n",
       "2015-08-18 -1.965351  1.466904 -1.686287 -1.078823\n",
       "2015-08-25 -1.664973  1.086153 -1.747761 -1.538751\n",
       "2015-09-01 -1.611363  1.362334 -1.661385 -1.618071\n",
       "2015-09-08 -1.688794  1.524941 -1.574311 -1.614662\n",
       "2015-09-15 -2.003987  1.544770 -1.449483 -1.749689\n",
       "2015-09-22 -1.555358  1.717556 -1.607234 -1.859989\n",
       "2015-09-29 -1.511154  1.593426 -1.519983 -1.948061\n",
       "2015-10-06 -0.998605  1.451820 -1.575402 -1.692089\n",
       "2015-10-13 -0.285950  1.422868 -1.669291 -1.666700\n",
       "2015-10-20  0.117193  1.280852 -1.807818 -1.669211\n",
       "2015-10-27  0.342607  1.615705 -2.052048 -1.615690\n",
       "2015-11-03 -0.080483  1.637337 -2.080569 -1.806226\n",
       "2015-11-10 -1.297465  1.616776 -2.099652 -1.997783\n",
       "2015-11-17 -1.957013  1.724855 -1.935206 -2.170417\n",
       "2015-12-01 -2.080381  1.637519 -1.726965 -1.937705\n",
       "2015-12-08 -2.089085  1.688665 -1.622646 -2.007246\n",
       "2015-12-15 -2.285228  1.643445 -1.374622 -1.983894\n",
       "2015-12-22 -2.074928  1.642558 -1.151831 -2.070973\n",
       "2015-12-29 -2.232522  1.600314 -1.100589 -1.942351\n",
       "2016-01-05 -2.115624  1.550388 -1.063456 -2.291344\n",
       "2016-01-12 -1.952927  1.449060 -1.020780 -2.361818"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Compare recent normalized indicators:\n",
    "tail( z_nor, 24 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "2015-12-16: This is the day of first Fed rate hike in almost a decade. \n",
    "It is very instructive to compare the readings for the previous day \n",
    "to the most recent output.\n",
    "\n",
    "2016-01-18: Both z_metals and z_eq are two standard deviations \n",
    "into a bear market -- although their position indicators \n",
    "are in neutral territory. This is a great example of \n",
    "the utility of normalized position indicators.\n",
    "\n",
    "*Normalized positions can useful in the identification of \"overcrowded\" trades.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closing remarks\n",
    "\n",
    "In general, we hope that the position indicators are *not* correlated \n",
    "too closely with the underlying prices because we are seeking information \n",
    "which is not derivable from observable prices. \n",
    "It is most interesting when market positions *diverge* \n",
    "from identified patterns in price charts.\n",
    "\n",
    "**Normalized position indicators are useful to examine \n",
    "asset classes since the units are comparable across \n",
    "their COTR histories.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix 1: COTR at the command line\n",
    "\n",
    "We can algorithmically summarize this notebook for quick computation \n",
    "by the function **groupcotr()**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bonds': 'w4cotr_bonds',\n",
       " 'Equities': 'w4cotr_equities',\n",
       " 'Metals': 'w4cotr_metals',\n",
       " 'USD': 'w4cotr_usd'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Asset classes are specified as a \"group\" in a fecon235 dictionary:\n",
    "cotr4w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature: \u001b[0m\u001b[0mgroupcotr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'Equities'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'w4cotr_equities'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'USD'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'w4cotr_usd'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Metals'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'w4cotr_metals'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Bonds'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'w4cotr_bonds'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mSource:\u001b[0m\n",
       "def groupcotr( group=cotr4w, alpha=0 ): \n",
       "    '''Compute latest normalized CFTC COTR position indicators.\n",
       "       Optionally specify alpha for Exponential Moving Average\n",
       "       which is a smoothing parameter: 0 < alpha < 1 (try 0.26)\n",
       "       COTR is the Commitment of Traders Report from US gov agency.\n",
       "    '''\n",
       "    #  For detailed derivation, see qdl-COTR-positions.ipynb\n",
       "    positions = groupget( group )\n",
       "    norpositions = groupfun( normalize, positions )\n",
       "    #  alpha default should skip SMOOTHING operation...\n",
       "    if alpha:\n",
       "        return groupfun( ema, norpositions, alpha )\n",
       "    else:\n",
       "        return norpositions\n",
       "\u001b[1;31mFile:      \u001b[0m~/Dropbox/ipy/fecon235/fecon235.py\n",
       "\u001b[1;31mType:      \u001b[0mfunction"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Source code for retrieval of COTR [0,1] position indicators\n",
    "#  (exactly like z_data, except column names are nicer),  \n",
    "#  followed by operator to normalize the position indicators:\n",
    "groupcotr??\n",
    "\n",
    "#  This encapsulates the TECHNIQUES in this notebook,\n",
    "#  including the option to apply smoothing.\n",
    "\n",
    "#  group* functions are defined in the fecon235 module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  MAIN in action!\n",
    "norpos = groupcotr( cotr4w, alpha=0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               Bonds  Equities    Metals       USD\n",
       "Date                                              \n",
       "2015-12-29 -1.100589 -1.942351 -2.232522  1.600314\n",
       "2016-01-05 -1.063456 -2.291344 -2.115624  1.550388\n",
       "2016-01-12 -1.020780 -2.361818 -1.952927  1.449060"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Most recent results as a test exhibit:\n",
    "tail( norpos, 3 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Thus in a few lines of fecon235 code, we can comparatively observe how \n",
    "various asset classes are positioned in the futures and options market by informed traders.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix 2: Joint path of Bonds and Equities\n",
    "\n",
    "We found earlier that the linear correlation between Bonds and Equities \n",
    "is about +28% with respect to their position indicators. \n",
    "A scatter plot is useful to discern non-linearities, \n",
    "but even better would be one with a ***heat map which reveals \n",
    "their evolution chronologically.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  To broadly visualize a decade of data, \n",
    "#  we resample the weekly normalized position indicators\n",
    "#  into MONTHLY frequency (median method).\n",
    "norpos_month = groupfun( monthly, norpos )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabe7548c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot from 2006 to present:\n",
    "scatter( norpos_month, col=[0, 1] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using a color heat map, from blue to green to red, \n",
    "we can visualize the joint path of Bonds and Equities \n",
    "chronologically since 2006.\n",
    "\n",
    "The col argument tells us that the x-axis is for Bonds, \n",
    "and the y-axis is for Equities. The *narrative* starts \n",
    "just prior to the Great Recession, when they both \n",
    "indicate bullish positions (Quandrand I in blue). \n",
    "Due to the recovery in employment, the Great Recession \n",
    "era is said to have concluded, but in 2016 \n",
    "we find ourselves in joint bearish territory \n",
    "(Quandrant III in red).\n",
    "\n",
    "From 2009 through 2015 (horizontal drift to the east from green to red), \n",
    "Equities rose in price while its normalized position indicator fluctuated around -0.5. \n",
    "During that epoch, the market increasing became more \n",
    "bearish with respect to Bonds (from +1.3 to -2.2). \n",
    "This coincides with ZIRP and numerous QE Fed policies\n",
    "(Zero Interest Rate Policy and Quantitative Easing).\n",
    "\n",
    "The most recent all-time peak for the S&P 500 is 2130.82 \n",
    "on 2015-05-21. Months later, on 2015-12-16 the Fed had its \n",
    "first rate hike in almost a decade (thus terminating ZIRP). \n",
    "The scatter plot illustrates the break from the foregoing \n",
    "horizontal epoch (dark red points, Equities <= -2.0).\n",
    "\n",
    "It is important to note that *the narrative above is \n",
    "not derived from price action, but rather the number of \n",
    "contracts positioned in the futures and options markets* \n",
    "by informed traders: Asset/Money Managers. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
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 },
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